Hybrid modeling of Parkinsons disease integrating zebrafish neurobiology with in silico predictive analytics

Hybrid modeling of Parkinsons disease integrating zebrafish neurobiology with in silico predictive analytics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Hybrid modeling of Parkinsons disease integrating zebrafish neurobiology with in silico predictive analytics Mwafaq Kmail, Jaya Kumar, Teoh Seong Lin, Wael Mohamed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8426422/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Parkinson’s Disease (PD) remains a major neurodegenerative disorder lacking disease-modifying therapies. Traditional single model approaches often fail to capture the complex molecular, environmental, and genetic interactions that drive disease heterogeneity. This review highlights the emerging paradigm of hybrid modeling, combining zebrafish (Danio rerio) experimentation with in silico computational and AI-driven pipelines to advance PD research. Zebrafish provide a powerful in vivo system to study dopaminergic neurodegeneration, mitochondrial dysfunction, oxidative stress, and behavioral phenotypes with high translational value. In parallel, computational neuroscience and systems biology tools, including network pharmacology, molecular docking, virtual screening, transcriptomic profiling, and machine-learning–based predictive models, enable rapid hypothesis generation and therapeutic discovery. By integrating these two modalities, hybrid platforms offer a multiscale understanding of PD pathogenesis and allow efficient identification of biomarkers, drug candidates, and gene–environment interactions. This review synthesizes current evidence, methodological advances, challenges, and future directions for establishing zebrafish– in–silico hybrid pipelines as next-generation tools for PD precision research. Parkinson’s disease neurodegeneration in silico zebrafish hybrid modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder characterized by both motor and non-motor symptoms, affecting patients ' muscle control and overall quality of life ( 1 , 2 ). PD results from the gradual loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) and the accumulation of misfolded α‑synuclein in the nigrostriatal system ( 3 ). PD is the most common movement disorder, and after Alzheimer’s is the second most prevalent neurodegenerative disease ( 4 ). In the previous few years, the prevalence of PD has increased significantly worldwide, and this increase is expected to continue. According to the Global Burden of Disease Study, PD in 1990there affected 2.5 million patients, 6.1 million in 2016, and by 2050, this number is projected to reach up to 25.2 million. This increase results from population ageing and global growth, underscoring the urgent need for effective therapies and preventive strategies ( 4 , 5 ). The etiology of PD is unknown, but it is known as a multifactorial disorder influenced by genetic and environmental factors ( 6 ). While monogenic forms are rare, genetic risk factors are identified in 5–10% of cases, often with a hereditary predisposition ( 7 ). Pesticides, herbicides, and industrial chemicals are all examples of environmental factors that increase the risk of PD ( 4 , 7 ). Age is the strongest risk factor, with a median onset around 60 years and incidence peaking in those aged 70–79. Prevalence varies across regions, being higher in Europe, North and South America than in Africa, Asia, and the Middle East ( 6 ). PD is biologically complex, encompassing both monogenic and sporadic forms and involving multiple interacting pathways, including mitochondrial dysfunction, oxidative stress (OS), impaired proteostasis, neuroinflammation, and synaptic failure. This complexity leads to variable symptoms and disease progression, indicating that single-pathway or single-cell models cannot fully capture the mechanisms of PD or predict patient responses to treatment ( 8 ). Animal models for preclinical research should be well-characterized, manageable, and translationally relevant. Suitable species share physiological, anatomical, and genetic similarities with humans. Common examples include roundworms, fruit flies, zebrafish, rodents, and non-human primates ( 1 ). While small models like yeast, worms, and fruit flies can express human PD genes to study protein roles, they cannot fully replicate protein interactions, neuronal loss, or clinical symptoms ( 9 ). Rodents are widely used in research because of their availability and genetic tractability, whereas larger animals face ethical and financial constraints. In vitro systems allow controlled mechanistic studies but lack whole-organism context, including neural circuitry, pharmacokinetics, and immune-vascular interactions ( 1 ). Hybrid modeling is a new approach to PD research, which combines biological studies with computational and AI-driven analysis. The identification of early biomarkers and the creation of more effective therapies are supported by the integration of in vivo research with in silico methods, such as AI analysis of omics data, which improves knowledge of the cellular mechanisms behind illness start and progression. The complex and multifactorial nature of PD makes it extremely challenging to develop a single model that captures all key neuropathological features. Studying α-SYN aggregation and its clearance in vivo remains crucial, yet suitable models for this are lacking ( 3 ). In many recent PD studies, wet-lab experiments are increasingly used with in silico approaches to enhance mechanistic understanding and accelerate discovery. For example, network-based analyses and docking workflows can screen large chemical libraries against PD-related proteins, with promising compounds subsequently validated in cellular or animal models, creating a feedback loop that continuously refines both experimental and computational predictions ( 10 ). In a recent study using an ex vivo mouse brain model, metabolomic analysis revealed energy-related abnormalities, which were further examined with an in silico kinetic model that simulated mitochondrial dysfunction, predicted ATP loss, and identified stress-response pathways not detectable through experiments alone ( 11 ). Similarly, in vivo and in silico approaches are used to model PD and investigate underlying cellular changes. Omics data from these models can be analyzed using genome-scale metabolic models and AI tools, linking molecular alterations to disease outcomes ( 12 ). In this context, the zebrafish model is particularly valuable because of its unique advantages, such as optical transparency, a vertebrate central nervous system with conserved composition and organization, and ease of genetic manipulation, making it an ideal platform for investigating PD mechanisms. Zebrafish are a strong model for behavioral neuroscience research because they are like humans exhibit a range of cognitive processes, including learning, memory, fear, anxiety, perception, social interactions, and sleep patterns ( 1 , 11 ). Zebrafish help in combining in silico and phenotypic drug screening, which leads to accelerated discovery at lower cost. Molecules can first be prioritized computationally against a target, then tested in zebrafish disease models for efficacy and toxicity. This approach allows rapid evaluation of many compounds before costly preclinical phases ( 13 ). This review presents the current landscape of zebrafish and in silico modeling in PD research, emphasizing the advantages, challenges, and future directions of hybrid approaches for precision medicine applications. 2. Pathophysiology of Parkinson’s Disease PD is characterized by progressive degeneration of dopaminergic neurons in the SNpc ( 14 ). This leads to striatal dopamine exhaustion and disruption of basal ganglia circuitry that leads to cardinal motor symptoms of bradykinesia, rigidity, tremor, and postural instability. In parallel, widespread extranigral pathology and non‑dopaminergic involvement underlie the prominent non‑motor features that begin years before motor onset ( 15 ). Figure 1 shows the difference between the healthy and PD substantia nigra. The key neurotransmitter dopamine (C₈H₁₁NO₂) is mainly produced in the substantia nigra, with additional synthesis in the ventral tegmental area and hypothalamus. Within the dopaminergic system, there are four main pathways nigrostriatal, mesocortical, mesolimbic, and tuberoinfundibular. Dopamine is important because of its function to control movement, reward, motivation, and several cognitive and hormonal functions. For motor activity, it is produced in the substantia nigra and in the ventral tegmental area for reward signaling and acts as a precursor for norepinephrine and epinephrine. Dopamine levels rise in response to pleasurable stimuli or certain drugs, and balanced signaling is essential for coordinated motor control ( 15 ). The five subtypes of dopamine receptors, D1, D2, D3, D4, and D5, belong to the G-protein–coupled receptor superfamily. The two main families of these receptors are the D1-like family includes (D1 and D5), and the D2-like family includes (D2, D3, and D4). While D2, D3, and D4 receptors are structurally connected, D1 and D5 receptors have similar structural characteristics and chemical sensitivities. Dopamine receptors communicate via transduction routes mediated by Gi or Gs ( 15 , 16 ). The prodromal stage of PD can begin 12–14 years before diagnosis. Studies suggest that pathology may first appear in the peripheral autonomic system or the olfactory bulb before spreading through the brainstem and reaching the substantia nigra. This early progression leads to non-motor symptoms such as hyposmia, constipation, and rapid eye movement sleep disturbances that precede motor signs ( 6 ). However, the precise pathogenesis of PD is still unclear, but evidence indicates that the oxidation of endogenous dopamine can trigger OS in dopaminergic neurons ( 17 ). Early symptoms include mild tremors, stiffness, slow movements, soft speech, reduced facial expression, fatigue, irritability, or cognitive slowing. These symptoms develop slowly, making detection difficult ( 18 ). Multiple mechanisms lead to Neuronal degeneration in PD, such as α-synuclein aggregation, OS, mitochondrial dysfunction, and apoptosis ( 19 ). Toxic α-synuclein affects synapses, disrupts calcium balance and mitochondria, and may spread extracellularly, contributing to disease progression. It is also linked to genetic forms of PD (e.g., GBA, LRRK2) through effects on autophagy and lysosomal function, making its regulation a potential therapeutic target ( 20 ). In PD, alpha-synuclein accumulates to form Lewy bodies, while disruption of neuronal function results from mitochondrial dysfunction, OS, and impaired protein clearance. These changes, along with neuroinflammation, lead to the loss of dopamine-producing neurons ( 21 ). Recent studies showed that neuron-derived exosomal α-synuclein in plasma correlates with motor dysfunction, highlighting its potential as a non-invasive biomarker for early PD detection ( 22 ).When reactive oxygen species (ROS) exceed the cell’s antioxidant defenses, this leads to OS, resulting in damage to proteins, lipids, and enzymes, and ultimately causing neuronal death, particularly in dopaminergic neurons ( 19 ). However, ROS play vital physiological roles in cell signaling, immune responses, and apoptosis; excessive levels can result from environmental stressors or xenobiotics, leading to exacerbation of neuronal damage. Antioxidants, such as vitamin E, flavonoids, and polyphenols, may help reduce OS. Additionally, dysfunction of the autophagy–lysosomal pathway (ALP) impairs α-synuclein degradation and promotes its release through exosomes, potentially accelerating disease progression ( 22 ). Early interventions such as antioxidant therapy, dopaminergic precursors, or strategies to reduce α-synuclein toxicity have the potential to slow PD progression and provide neuroprotection. Understanding these mechanisms and early pathological changes is important for the development of biomarkers and preventive strategies capable of detecting and mitigating PD before the onset of motor symptoms ( 23 ). When two mitochondria merge to form a single elongated organelle, allowing the exchange of proteins and mitochondrial DNA (mtDNA) and promoting the renewal of mitochondrial components, this process is known as mitochondrial fusion. This fusion is important because it is primarily regulated by mitofusins located on the outer mitochondrial membrane and optic atrophy protein 1 on the inner membrane. In contrast, the division of one mitochondrion into two smaller ones, known as mitochondrial fission, involves. This process is primarily controlled by the dynamin-related GTPase Drp1 and the mitochondrial fission protein Fis1. Fission supports mitochondrial quality control by facilitating organelle transport and the removal of damaged mitochondria ( 24 ). α-Synuclein controls mitochondrial fusion–fission, transport, and mitophagy, processes important for neurons' polarized structure (25). Several PD-linked genes, including PINK1, Parkin, DJ-1, LRRK2, and VPS35, disrupt mitochondrial homeostasis, increase OS, and contribute to Lewy body formation, all of this along with impaired autophagy, inflammation, and pharmacologically induced mitochondrial dysfunction (e.g., by MPTP, rotenone, or paraquat) promotes α-synuclein accumulation, highlighting mitochondrial correction as a promising therapeutic strategy for PD ( 24 , 26 ). The process of PD starts when DAMPs released from damaged neurons or toxins like MPTP, rotenone, 6-OHDA, and misfolded α-synuclein activate microglia, which increase ROS and nitric oxide, causing OS and further neuronal damage ( 27 ). ROS and DAMPs trigger the NLRP3 inflammasome, generating inflammatory markers such as IL-1β and other cytokines, while NF-κB amplifies pro-inflammatory gene expression ( 28 ). Chronic stress shifts microglia from anti-inflammatory M2 to pro-inflammatory M1, leading to an increase the inflammation. The adaptive immune system (CD4 + T cells, complement) and peripheral inflammation (gut-derived LPS, systemic TNF) further exacerbate CNS damage. This cumulative response leads to dopaminergic neuron death, α-synuclein aggregation, and mitochondrial dysfunction, driving PD motor symptoms ( 27 , 28 ). Figure 2 shows the inflammatory pathway in the substantia nigra. 3. Zebrafish as a Model for Parkinson’s Disease Zebrafish have emerged as a strong model for studying PD because of their genetic and neuroanatomical similarity to humans, as well as their display of human-like behaviors including learning, memory, and locomotion. The optical transparency of embryos and larvae enables high-resolution in vivo imaging, making zebrafish a versatile platform for preclinical and translational PD research ( 1 ). Figure 3 summarizes the advantages of zebrafish. Moreover, zebrafish offer flexibility in modelling different aspects of PD pathogenesis; both neurotoxin-induced and genetic models have been established to induce PD ( 1 , 29 ). Each replicating a different part of dopaminergic degeneration and selected non-motor symptoms ( 30 ) ( Table 1 ) . Among the toxin-based models, 6-hydroxydopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), and rotenone are the most widely employed ( 31 , 32 ). Transgenic or knock-in models carrying PD-related genes such as synuclein alpha gene (SNCA), leucine-rich repeat kinase 2 (LRRK2), PTEN-induced kinase 1 (PINK1), Parkin RBR E3 ubiquitin protein ligase (PARK2), and glucosylceramidase beta 1 (GBA1) produce a gradual PD-like change ( 33 ). Table 1 Overview of Experimental PD Induction Methods in Zebrafish Model Type Agent / Gene Mechanism of Action Phenotypes in Zebrafish Advantages Limitations Citation Toxin-based 6-OHDA Target dopamine neuron DA neuron loss; reduced locomotion Rapid and reproducible Requires injection; lacks progressive pathology ( 1 ) MPTP Converted to MPP⁺ targeting complex I inhibitor Mitochondrial dysfunction & motor deficits Connect to strong mitochondria Single MAO differs from mammals, and MPTP effects are transient, limiting progressive PD modeling. ( 1 , 34 ) Rotenone Converted to MPP⁺ targeting complex I inhibitor Motor impairment; DA neuron degeneration Mimic environmental PD toxicants High variability; systemic toxicity ( 35 , 36 ) Genetic model SNCA (α-synuclein) Protein aggregation and Lewy body formation α-syn aggregation; dopaminergic dysfunction Models synucleinopathy Overexpression artifacts, no direct SNCA ortholog, expression differences ( 37 ) LRRK2 Kinase dysregulation; mitochondrial stress Subtle motor deficits; altered mitochondrial dynamics Models’ common familial PD mutation; potential for neuron loss and behavioral defects Phenotype inconsistent; normal function unclear; reliability as a PD model uncertain ( 38 ) PINK1 Defective mitophagy and mitochondrial Mitochondrial defects; DA neuron vulnerability Strong mechanistic link to PD Morpholino PINK1 knockdown is transient and sometimes off-target, giving inconsistent results. ( 39 ) Parkin (PARK2) Impaired ubiquitin-mediated mitochondrial turnover Mitochondrial dysfunction; mild motor changes Conserved pathway No observable behavioral deficits, limiting the model’s ability to fully recapitulate PD symptoms ( 40 ) GBA1 Lysosomal dysfunction and impaired lipid metabolism Lipid buildup; α-syn accumulation; neurobehavioral changes Strong link to sporadic PD biological differences from humans and technical limitations in modeling a chronic, complex neurodegenerative disease ( 41 ) ( 41 ) 3.1.MPTP MPTP is a lipophilic protoxin that easily crosses the blood–brain barrier (BBB) and enters brain cells, MPTP underlies most animal models of PD. In vivo , MPTP is metabolized by monoamine oxidase B (MAO-B) into MPP⁺, which selectively induces the loss of dopaminergic neurons in the substantia nigra in mammals ( 1 , 42 ). MPTP, when it enters the brain, is initially stored in acidic organelles, mainly lysosomes, of astrocytes because of its amphiphilic nature. Then MPP⁺ is then released into the extracellular space and via the dopamine transporter (DAT) is taken up by dopaminergic nerve terminals. However, the exact mechanisms of MPP⁺-induced cell death are not fully understood; several key toxic events have been well studied in detail ( 43 ). The mechanism through which MPTP induces PD is illustrated in Fig. 4 . According to the exposure pattern, dopaminergic neurons may die through apoptosis or necrosis. Although MPTP/MPP⁺ is removed from the brain within 12 hours, and ATP returns to its normal level after 24 hours, degeneration of dopaminergic neurons occurs over a much longer period; this means MPTP triggers additional harmful processes beyond the initial energy loss, indicating additional toxic cascades ( 42 , 44 ). In zebrafish, acute MPTP exposure decreases brain dopamine and leads to motor and sensorimotor impairments; however, it still cannot fully mimic human PD pathology because it does not trigger dopaminergic neuron loss or α-synuclein pathology ( 34 ). 3.2. 6-OHDA 6‑OHDA, a hydroxylated derivative of dopamine and norepinephrine, is selectively taken up by catecholaminergic neurons via dopamine and noradrenaline transporters ( 45 ). Once it is inside the neuron, it induces rapid neuronal cell death through different mechanisms, including OS, mitochondrial dysfunction, and the generation of ROS ( 46 ). Unlike the slow neurodegeneration seen in PD, 6-OHDA does not form Lewy bodies, causing rapid neuron degeneration ( 47 ). 6‑OHDA does not cross the BBB, so it is delivered by stereotaxic intracerebral injection, especially into the medial forebrain bundle, substantia nigra pars compacta (SNpc), or striatum to lesion the nigrostriatal pathway. Unilateral lesions are typically used to induce asymmetric dopaminergic loss, leading to characteristic amphetamine‑ or apomorphine‑induced rotational behavior that correlates with the extent of dopamine depletion ( 45 , 48 ). Patients with PD usually experience neuropsychiatric symptoms, including anxiety and depression. Recent in vivo studies in mice showed that 6-OHDA injected into the striatum causes both motor and non-motor PD symptoms, including anxiety and depression. Unilateral 6-OHDA injection selectively degenerates SNc dopamine neurons while sparing VTA neurons ( 47 ). Andrea Slézia et al. showed that unilateral striatal injection of 6-OHDA in mice induces progressive degeneration of SNc dopaminergic neurons, and one and two weeks post-lesion, there was significant cell loss ( 49 ). 3.3. Rotenone Rotenone is a highly lipophilic compound derived from Lonchocarpus and Derris species. Rotenone easily crosses the BBB and directly impairs dopaminergic neurons, contributing to progressive neurodegeneration because of its lipophilicity. Its primary inhabitant mitochondrial Complex I, leading to blocking electron transfer to ubiquinone and reducing ATP synthesis. ( 35 , 50 ). Rotenone not only damages mitochondria but also, by binding tubulin, disrupts microtubule assembly, causing mitotic arrest and inhibiting cell proliferation ( 51 ). A recent study showed that rotenone induces mitochondrial dysfunction and mitophagy in BmN cells via the PINK1/Parkin pathway ( 50 ). Rotenone through Complex I inhibition triggers mitophagy by reducing mitochondrial membrane potential. Loss of membrane potential allows PINK1 accumulation on the outer mitochondrial membrane, where it activates MFN2 and recruits Parkin( 50 , 52 , 53 ). 3.4. Genetic model Gene-editing methods, such as transgene insertion, gene disruption, gene inactivation, expression reduction, and mutant gene silencing, enable researchers to modify gene function in controlled ways ( 54 ). These methods are usually used to model PD by overexpressing dominant mutant genes such as α-synuclein and LRRK2 or by knocking out/knocking down recessive genes like Parkin, DJ-1, and PINK1 ( 55 ). 3.4.1. SNCA SNCA (synuclein alpha gene) is located on chromosome 4q22.1, encodes the 140–amino acid presynaptic protein α-synuclein (α-SYN) from its last five exons, and is the first gene linked to familial PD ( 56 ). α-SYN is located at presynaptic terminals, its function is to regulate neurotransmitter release, and when it accumulates and aggregates leads to neurodegenerative disorders called synucleinopathies, including PD, dementia with Lewy bodies, and multiple system atrophy ( 3 , 57 ). The toxicity of α-SYN also leads to increased amyloid-β and tau aggregation, which highlights its central role in neurodegeneration ( 57 ). α-SYN can disrupt autophagy at multiple stages, including autophagosome formation and lysosomal fusion, and alterations in chaperone-mediated autophagy (CMA) and proteasomal pathways have been observed in PD brains, correlating with α-SYN accumulation ( 3 ). Variation in this gene is considered a risk factor for idiopathic PD and may modestly influence age at onset as well as the variability of motor and non-motor symptoms, including cognitive decline, sleep disturbances, psychiatric features, and hyposmia ( 58 ). Experimental SNCA models, such as transgenic animals have been developed to mimic key features in human PD pathology, including Lewy body-like inclusions, dopaminergic neuron loss, and motor deficits, and show their ability to be valuable tools to study disease mechanisms and evaluate therapies, including gene therapy and neuroprotective strategies ( 59 , 60 ). 3.4.2. GBA1 Glucocerebrosidase (GBA1), located on chromosome 1q21, encodes the lysosomal enzyme β-glucocerebrosidase (GCase), which hydrolyzes glucosylceramide into glucose and ceramide, and mutations in GBA1 form the most important risk factor for PD ( 61 ). Around 5% of PD cases result from GBA1 mutation, and both heterozygous and homozygous mutations increase the risk of PD by 20–30-fold ( 62 ).PD patients with GBA1 mutations show different symptoms, but compared with non-GBA1 PD, patients with GBA1 mutations generally develop the disease at a younger age ( 63 ). In research models, no single model reproduces all disease features because GBA1 dysfunction affects PD models differently. Therefore in vivo model is necessary to select the appropriate model for studying specific mechanisms or therapeutic strategies ( 64 ). In animal models, GBA1mutation often fails to mimic human PD because the mutations often show weak pathology in animals ( 65 ). Experimental GBA1 models show mixed results. Such as increased α-synuclein accumulation and mild motor deficits, but largely intact nigrostriatal neurons in mice with reduced GCase activity (D409V) and α-synuclein overexpression display. Furthermore, α-synuclein fibril injection into D409V mice does not significantly worsen pathology. These results suggest that while GBA1 mutation promotes α-synuclein accumulation, additional factors are often required to mimic human PD ( 63 ). 3.4.3. Parkin The PARK2 gene, located on chromosome 6q26, contains 12 exons and encodes parkin , an RBR-type E3 ubiquitin ligase. The main function of parkin is to control mitochondrial quality and also acts as a transcriptional repressor of p53 ( 66 ). Mutations in the parkin gene lead to autosomal recessive PD (AR-PD). Parkin mutations represent the primary genetic basis of AR-PD. Parkin contains two RING finger domains, with an in-between RING (IBR) domain positioned between them. Ubiquitin is a small, 76–amino acid protein generated from several precursor proteins encoded in the human genome. During the ubiquitination process, ubiquitin is covalently attached to lysine residues on target proteins, marking them for specific cellular outcomes ( 67 ). More than 100 parkin mutations have been identified, representing about half of familial PD cases and at least 20% of sporadic cases with young-onset PD ( 56 ).When it is activated by PINK1, parkin marks damaged mitochondrial proteins with ubiquitin, improving the removal of dysfunctional mitochondria through autophagy. Lysosome pathway mutations in parkin cause autosomal recessive early-onset PD. A recent in vitro study using patient-derived dopaminergic neurons shows that parkin has a PINK1-independent synaptic role. Mutant parkin led to increased oxidized dopamine because it disrupts synaptic vesicle recycling. When PINK1 and parkin mutations are combined lead to higher dopamine oxidation and earlier disease onset, indicating that partial loss of parkin worsens pathology ( 68 ). Table 2 shows different animal models used to induce PD by parkin protein knockout or knockdown, highlighting their effectiveness. Table 2 Animal Models for Inducing PD via Parkin Loss-of-Function Model Effectiveness for PD Induction Key Phenotypes Advantages Disadvantages Citation Mouse Parkin KO Moderate (mild spontaneous; strong with toxins) No major neuron loss Established genetics, affordable, good for circuit studies Lacks robust degeneration, short lifespan, and limits aging effects ( 69 ) Pig Parkin KO Low-moderate (biochemical more than degenerative change) Mitochondrial defects with small motor issues Large brain size, closer to human physiology Expensive, low throughput, ethical concerns ( 70 ) Primate Parkin KO High (age-dependent full pathology) Nigral loss, α-syn pathology, motor deficits High translational relevance, complex behaviors Very costly, long timelines, and welfare issues ( 70 ) Zebrafish Parkin KD/KO Moderate (early dopaminergic vulnerability) Around 20% loss of diencephalic DA neurons, MPP + sensitivity, and motor dysfunction Transparent embryos for live imaging and high-throughput screening, Simpler brain, immature immunity, basic behaviors ( 71 ) 3.4.4. PINK1 PINK1 (PTEN-induced kinase 1), encoded by PARK6 on chromosome 1p36, is a mitochondrial serine/threonine kinase. It maintains mitochondrial quality in dopaminergic neurons by detecting damaged mitochondria. Loss-of-function PINK1 mutations cause autosomal recessive early-onset PD, with nigrostriatal degeneration, motor deficits, slower progression, and variable Lewy-like pathology ( 67 , 72 ). Misfolded mitochondrial protein (dOTC) expression in dopaminergic neurons causes neurodegeneration and motor deficits, which are worsened by PINK1 knockout and lead to loss of L-DOPA responsiveness, as shown in a recent in vivo study ( 73 ). However, PD models typically induce pathology through PINK1 knockout, knockdown, or patient-mutation knock-in, mimicking recessive human variants to study mitophagy defects, OS, and dopamine dysregulation. These methods reveal early changes but often require aging or additional stressors for robust nigral loss in rodents ( 74 ). 3.4.5. LRRK2 LRRK2 (leucine-rich repeat kinase 2) is a large multidomain protein with kinase and GTPase activities that help to regulate vesicular trafficking, autophagy, and inflammation ( 75 ). Pathogenic mutations in LRRK2, such as G2019S, R1441C/G/H, Y1699C, I2020T, and N1437H, form the most common genetic cause of familial PD. Around 10%–15% of cases result from mutations in LRRK2, and are also found in 1%–2% of sporadic cases in Western populations ( 76 ). These mutations lead to late-onset nigrostriatal degeneration, α-synuclein aggregation, and immune dysregulation in dopaminergic neurons due to increased kinase activity and impair normal GTPase and kinase function ( 77 ). In recent study by Qing Xu et al. showed that age-dependent motor deficits developed in transgenic mice expressing mutant LRRK2, leading to early nigrostriatal axonopathy, hyperphosphorylated tau, and impaired dopamine transmission, which are the main features of PD ( 78 ). Although various LRRK2 knock-in and transgenic models exist, knock-in mice generally do not show PD-like behavioural or pathological changes. The motor deficits and pathological phenotypes of PD only reliably reproduce in transgenic mice with LRRK2 overexpression or additional exogenous stress ( 76 ). 3.5. Key Readouts in Zebrafish PD Studies Zebrafish are used to study human diseases, especially neuropsychiatric and neurodegenerative disorders, because of their high anatomical and physiological similarities to the human brain ( 79 ). As a vertebrate model, zebrafish have many advantages for PD research, such as high genetic conservation, optical transparency for real-time neural imaging, rapid development, and high fecundity, leading to efficient, high-throughput studies of PD pathogenesis and therapeutic interventions ( 1 ).PD research using zebrafish, many readouts are routinely used to characterize pathology and assess potential treatments ( 71 ). For example, dopaminergic neuron counts, mitochondrial activity, ROS measurements, transcriptomic profiling, and locomotor/behavioural phenotyping. Together, these measures provide a comprehensive, high-resolution overview of PD-related changes in a vertebrate system. Zebrafish lack a clearly defined midbrain area; their tyrosine hydroxylase (TH)-positive neurons are widely accepted as functional equivalents of mammalian DA neurons ( 80 ). According to recent study shows region-specific DA neuron distribution in zebrafish, using TH immunofluorescence, DC2 and DC4 in the ventral diencephalon, as well as neurons in the pretectum and telencephalon (Vd/Vv), are predominantly dopaminergic, whereas DC1, DC3, DC5, DC6, the locus coeruleus (LC), and raphe nuclei (Ra) are largely non-dopaminergic or noradrenergic ( 80 ). 4. In Silico Approaches in Parkinson’s Disease Research There is advanced predictive power, rapid screening capacity, and mechanistic insights that surpass many traditional experimental models when used in silico model for PD drug discovery ( 81 ). In silico approaches are now important experimental and clinical studies because they enable large-scale data integration, mechanistic disease modelling, and rapid virtual drug discovery, leading to provide powerful computational support that accelerates and improves PD research ( 82 ). 4.1. Computational modelling techniques Biophysical computational models work with experimental data and theoretical frameworks, providing powerful analytical tools for investigating neurological diseases. Computational models using medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) usually represent striatal activity. In PD, loss of dopaminergic input from the SNc leads to reduced direct-pathway (D1 MSN) activity and improved indirect-pathway (D2 MSN) signalling, producing excessive thalamic inhibition. Computational program used to mimic neurodynamic models in PD by altering MSN connectivity, inhibitory balance, and dopamine-dependent parameters ( 83 ). Bayesian and machine learning (ML) predictive computer models in PD research refer to probabilistic and data-driven models that predict disease risk, progression, or prodromal markers by analyzing complex interdependencies among clinical, genetic, and environmental variables ( 84 , 85 ). Evolutionary algorithms (EAs) offer a strong ML approach for analysing motor dysfunction in PD. EAs can automatically identify movement features that differ in PD patients from healthy controls by iteratively evolving a population of candidate classifiers. Using simple sensor data collected during clinical tasks like finger tapping or free movement, this enables EA-based models to achieve high diagnostic accuracy. This EA framework can be applied to animal models, where movement data from video tracking can be classified to detect PD-related genetic mutations. This cross-species applicability provides an objective and scalable computational tool for assessing motor impairment and evaluating the effects of existing or novel PD therapies ( 86 ). Traditional model interactions between PD risk and prodromal markers treat the marker independently; Bayesian networks overcome these limitations by combining prior knowledge with longitudinal TREND study data. This approach identifies key marker interdependencies, predicts PD risk probabilistically, and generates realistic synthetic patient profiles, providing a more accurate and comprehensive framework for understanding and predicting prodromal PD ( 85 ).The prediction of protein–protein interaction sites, including antigen–antibody interfaces, has been enhanced by recent deep learning approaches, such as graph convolutional networks with attention mechanisms. Combining these predicted binding regions into docking workflows improves accuracy and reduces false positives, highlighting their potential application to PD-related targets such as α-synuclein and LRRK2 in structure-based drug discovery ( 87 ). Drug discovery is traditionally slow, but advances in computing have greatly accelerated this process by streamlining hit identification and hit-to-lead optimization, making drug development more efficient ( 88 ). CADD improves the hit rate of new drug candidates by using targeted computational searches that outperform traditional high-throughput screening and combinatorial chemistry. Overall, CADD approaches fall into two major categories: structure-based and ligand-based ( 88 , 89 ). It helps explain the molecular basis of activity and predicts improved derivatives. CADD in drug discovery is mainly used to: ( 1 ) filter large compound libraries into smaller sets of predicted actives, ( 2 ) guide lead optimization by improving affinity and ADMET properties, and ( 3 ) design new compounds through fragment-based or stepwise modifications ( 89 ). Common in silico methods in PD drug research include virtual high-throughput screening (vHTS), molecular docking, quantitative structure–activity relationship (QSAR) models, pharmacophore modelling, molecular dynamics (MD) simulations, and ADME / toxicity prediction ( 88 ). In silico virtual screening identifies neuroprotective candidates for PD by scanning large chemical libraries against key protein targets. Studies screened the ZINC database for structural analogs of known neuroprotectants, decreasing candidates from 50 to 7 through toxicity, carcinogenicity, and docking filters, yielding molecules like SS2 with strong DJ-1 binding. This approach accelerates the discovery of non-toxic agents capable of modulating neurodegeneration ( 90 ). 4.2. Virtual Screening & Molecular Docking Against PD Targets LRRK2 The kinase domain of LRRK2 is composed of 14 secondary structural elements, made up from nine α-helices, three β-sheets, and two intrinsically disordered regions ( 91 ). LRRK2 is a key target in PD, as its mutations contribute to dopaminergic neuron damage. Three promising candidates, including CRA_1801 identified in a recent study with predicted high potency (pIC₅₀ > 7). When applying machine-learning (ensemble) QSAR modelling on pIC₅₀ data for LRRK2 inhibitors and screening existing drugs from DrugBank. Molecular docking was then performed to predict how these compounds bind to the LRRK2 kinase domain, revealing key interactions and helping prioritize them for further validation with molecular dynamics simulations ( 92 ). α-synuclein Previous studies used high-throughput docking to identify α-synuclein fibril-binding compounds ( 93 ). A main event in PD is the aggregation of α-synuclein into fibrils. Using 43 diverse ligands, a ligand-based pharmacophore model identified critical features for inhibition: two hydrogen-bond acceptors, one hydrophobic region, and two aromatic rings. Based on this, a 3D-QSAR model (R² = 0.920, Q² = 0.752) was developed to predict activity, guiding the design of novel indolinone derivatives. In vitro testing with the thioflavin-T assay confirmed inhibitory activity, with the best compound achieving ~ 45% inhibition, demonstrating the models’ reliability for designing α-synuclein aggregation inhibitors ( 94 ).To identify new candidate compounds, large databases of known α-synuclein inhibitors were explored to explore chemical space via QSAR modelling and virtually screened natural products, such as those from the LOTUS database. In a separate study, 875 phytochemicals were assessed using molecular docking and molecular dynamics simulations, revealing compounds like Crebanine with favourable binding energies and stable interactions over ~ 40–60 ns, suggesting their potential for further experimental validation ( 95 ). According to these findings that shows combining in-silico docking, pharmacophore/QSAR modelling, and MD simulations is a good strategy to identify both synthetic and natural small molecules that may inhibit α-synuclein aggregation ( 96 ). Such approaches offer a promising avenue for developing disease-modifying therapies for PD. MAO-B MAO‑B is a main therapeutic target in PD, as its inhibition slows dopamine degradation, thereby enhancing dopaminergic transmission, leading to a reduction in PD symptoms ( 97 ). Many studies have used virtual screening, molecular docking, and MD simulations to identify novel MAO‑B inhibitors, exploring both synthetic scaffolds and natural compounds. MAO-B inhibitors have been discovered by docking FDA-approved drugs and new compounds into the enzyme’s active site, followed by validation using MD simulations and free energy calculations ( 98 ). For example, ten indanone derivatives evaluated, ligands L3 and L5 exhibited the strongest binding affinities (− 8.809 and − 9.276 kcal/mol, respectively) with stable interactions, and ADME predictions indicated good oral bioavailability and gastrointestinal absorption, highlighting them as promising drug candidates to inhibit MAO-B ( 97 ). In a multi-stage in-silico workflow combining 3D-pharmacophore modelling, 2D-QSAR, ADMET filtering, docking, MD simulations, and MM/PBSA binding free energy calculations was applied to four chemical databases (ZINC, DrugBank, TCM, and UNPD) for selective MAO-B inhibition. From this screening, 22 top candidates were identified. Among these, four compounds, ZINC21285023, ZINC79651118, ZINC58283019, and UNPD89644 (crotafuran E), displayed stable binding, better interactions with key residues such as Cys172 and Tyr435, and performance comparable to or better than the reference drug safinamide, making them strong leads for further experimental validation ( 99 ). QSAR Models, Pharmacophore Modelling & Machine-Learning Integration Quantitative structure–activity relationship (QSAR) modelling is a main ligand-based approach in drug design, complementing methods like molecular docking and virtual screening by enabling the prediction of a compound’s biological activity from its chemical features without the need for synthesis ( 100 ). Studies in silico for neurodegeneration follow a sequential workflow that combines ligand-based approaches (such as QSAR and pharmacophore modelling) with structure-based techniques (like docking and molecular dynamics) ( 88 ). For α-synuclein, a ligand-based pharmacophore and 3D-QSAR model built from known inhibitors enabled the design of new indolinone derivatives, several of which showed validated anti-aggregation activity in vitro ( 94 ). For MAO-B, QSAR + docking has been used to propose bioisosteres of known inhibitors (e.g., Rasagiline), improving the pool of candidate molecules ( 101 ). Machine-learning predictive models with QSAR have been used to estimate docking scores or potency, enabling much faster screening; furthermore MAO-inhibitor study showed that ML models could predict docking scores thousands of times faster than standard docking with little loss in accuracy ( 102 ). Omics-Driven Computational Discovery Omics technologies, including genomics, proteomics, transcriptomics, and metabolomics, enable the study of high-throughput analysis of biological processes by combining multiple omics, which provides deeper insights into disease mechanisms and normal physiology across different molecular levels ( 103 ). Metabolomics, combined with other omics and clinical data, can enable early PD detection and provide system-level insights for therapy ( 104 ). RNA-seq and single-cell RNA-seq and other transcriptomic studies of PD-relevant tissues reveal differentially expressed genes and cell-type–specific changes. For example, CSF RNA profiling identified protein-coding and non-coding transcripts altered in PD, showing potential minimally invasive biomarkers. Another study utilizing blood transcriptome data derived a two-gene prognostic signature associated with motor progression and linked with peripheral immune cell alterations (increase in neutrophils, decrease in CD4 + T-cells), offering a potential blood-based predictor of disease trajectory ( 105 ). However, transcriptomic studies still have some limitations, for example, it is difficult to separate genuine disease-driven transcriptional changes from shifts in cellular populations because bulk post-mortem brain analyses in PD can be confounded by changes in cell-type composition, making ( 106 ). This has spurred the adoption of more refined single-cell and cell-type deconvolution methods, as well as network-based and multi-layer computational analyses. A recent integrative snRNA-seq study across neurodegenerative diseases (including PD) detected both shared and disease-specific transcriptional changes at single-cell resolution, identifying novel regulators (e.g., stress-response genes) and offering deeper insight into cell-type–specific pathology in PD ( 107 ). By combining differential expression analysis, network modelling (e.g., weighted gene co-expression network analysis, WGCNA), hub-gene detection, and machine-learning-based–based prioritization, this led to the successful identification of potential biomarkers and therapeutic targets using comprehensive bioinformatics pipelines. When single-cell transcriptomic data from PD patients were used to reveal that oligodendrocyte precursor cells (OPCs) play a more sensitive role than mature oligodendrocytes in PD–PD-associated transcriptomic changes ( 108 ).Nevertheless, there are challenges to this computational omics paradigm. Bulk RNA-seq analyses can be confounded by cell-type composition changes, reducing the specificity of DEGs unless carefully corrected ( 106 ). Also, overlap between transcriptomic and proteomic data remains limited, emphasizing that RNA expression does not always predict protein abundance or functional change ( 109 ). 5. The Hybrid Model: Integrating Zebrafish with In Silico Tools Combining in silico methods with in vivo zebrafish models helps in improving therapeutic discovery by allowing each approach to overcome the other’s limitations, resulting in a more precise, efficient, and biologically meaningful hybrid research strategy ( 110 ). Why Hybrid Modelling Works Zebrafish have many advantages, such as being genetically tractable, an optically transparent vertebrate model with strong human homology. Because of this, it offers rapid, cost-effective disease modelling and high-throughput drug discovery across cardiovascular, neurological, metabolic, and cancer research ( 111 ). Researchers can observe developmental processes, organogenesis, and cellular dynamics in real time with high-resolution phenotypic readouts at the whole-organism level, because zebrafish embryos are externally fertilized and transparent, enabling ( 112 ). Moreover, zebrafish have high fecundity, rapid development, and low maintenance cost compared with mammalian models; because of these advantages, hundreds of embryos can be produced, facilitating large-scale experiments with robust statistical power ( 113 ). Zebrafish is a strong whole-organism model for studying development, disease phenotypes, and drug responses in a system that closely reflects human biology. That is because zebrafish and humans share many disease pathways; drugs often act on similar pathways in both species ( 114 ). In silico methods allow researchers to rapidly screen large libraries of compounds or genetic perturbations, prioritize promising candidates, and generate hypotheses by using computational modelling, virtual screening, molecular docking, and mechanistic modelling. For example, molecular docking studies have been used to predict interactions of therapeutic peptides before in vivo testing ( 115 ). In silico reduces the need for extensive early-stage animal experiments because it enables simulation of complex biological or behavioural processes. For example, computational models have been developed to simulate three-dimensional swimming behaviour of zebrafish, replicating observed dynamics and allowing virtual experiments to test hypotheses on behaviour ( 116 ). In silico tools save time and resources, enabling more focused, efficient downstream in vivo studies by reducing the pool of candidates (compounds, targets, pathways). This greatly reduces the workload and increases throughput compared to purely empirical screening ( 117 ). Both computational and zebrafish model work in a continuous loop; computational predictions can be validated in zebrafish, and the empirical data from zebrafish can, in turn, refine computational models. This loop improves both predictive power and biological relevance. For example, in studies of immune or complement-system inhibitors, in silico molecular docking on zebrafish ortholog proteins predicted effective binding, which can then be followed up with in vivo functional assays in zebrafish ( 115 ). Furthermore, by using zebrafish phenotypic readouts (e.g., development, organ toxicity, and behaviour), researchers can validate computationally generated hypotheses about drug effects or genetic perturbations, combining the scalability of in silico approaches with the realism of a living organism ( 114 ). Many in silico hypotheses can be triaged rapidly, the most promising ones validated in zebrafish, and only the top candidates move on to more complex mammalian or clinical stages, reducing time, cost, and animal use, so this bidirectional process provides a powerful, ethically and economically advantageous pipeline ( 133 ) Hybrid Workflows: In silico ↔ Zebrafish Integration The hybrid workflows typically proceed in two complementary directions: ( 1 ) from in silico → zebrafish (prediction followed by in vivo validation) ( 118 ), and ( 2 ) from zebrafish → in silico (behavioural or molecular readouts from zebrafish plugged into AI/data pipelines for deeper analysis) ( 116 ). These hybrids overcome the limitations of purely computational or purely experimental approaches. In silico → Zebrafish: From Prediction to Validation In the in silico → zebrafish workflow, computational methods include molecular docking, QSAR, and AI-based predictive tools that are used to identify the best molecules or targets relevant to PD, including LRRK2, α-synuclein, and MAO-B. Then, by assessing PD-relevant phenotypes, including gene expression, locomotor behaviour, dopaminergic neuron integrity, and stress-related responses, zebrafish are used to validate these predictions. This approach helps to narrow candidates with favourable ADMET properties and brain penetration ( 110 , 118 ). This approach has been demonstrated effectively. An example for this approach, a study to predict telomerase-binding compounds that used virtual screening of a polyphenolic library using ligand- and structure-based docking, followed by validation in zebrafish models of premature aging and chronic inflammation, confirming biologically relevant anti-aging and anti-inflammatory effects ( 110 ). Zebrafish → In silico: Phenotypes to Data, then to Insights Experimental data from zebrafish are used to improve predictions, uncover mechanisms, and guide further drug discovery from a computer model. AI or QSAR models are used to analyse behavioural tracking, neuronal imaging, and gene expression data from zebrafish PD models to identify drug effects, predict affected pathways, and uncover new PD-relevant correlations ( 108 , 119 ). An example of this approach, a study on zebrafish for behavioural and gene expression changes that used the bioactive peptide xenin, extracted from a marine sponge. Results from molecular docking and modelling suggested xenin could stabilize the PINK1-ubiquitin complex and improve Parkin production, connecting in vivo observations to mechanistic predictions ( 120 ). Such zebrafish → in silico approaches have many advantages, such as improving throughput, and allowing quantitative comparison across treatments or genotypes, by providing objective, scalable, and reproducible phenotyping, reducing observer bias ( 121 ). Additionally, zebrafish-derived data can also be used to train machine-learning models to predict outcomes in other settings by clustering phenotypic “barcodes” and inferring possible modes of action, even for previously uncharacterized compounds ( 122 ). Demonstrated Achievements in Hybrid PD Modeling Hybrid workflows that integrate zebrafish models with in silico tools have shown their ability to accelerate drug discovery, mechanistic analysis, and the overall understanding of PD ( 110 ). Many examples show the practical applications and successes of these combined approaches. One successful application for AI is using it for drug repurposing. For example, zebrafish larvae exposed to large libraries of FDA-approved drugs generated behavioural signatures that helped identify compounds with neuroprotective or neuroactive potential ( 123 ). Combining zebrafish screening with AI significantly enhances drug discovery, lowers costs, and prioritizes safe candidates for follow-up. A study accurately predicting neuroactive molecules across diverse structures, with 58% validated in human protein assays, using deep metric learning on zebrafish behavioural data from 650 CNS-active compounds ( 124 ). A valuable basis for computational modeling is offered by zebrafish transcriptomics. Gene-expression profiles from zebrafish exposed to different chemical classes were used to construct co-expression networks, revealing transcriptional modules associated with neurobehavioral and toxic responses ( 125 ). A study using a zebrafish microarray data to develop tissue- and transcription-factor-specific gene, several of which were validated across independent datasets, showing both the reproducibility and mechanistic insight offered by zebrafish-derived molecular signatures ( 126 ). Hybrid approaches have also been used to study gene–environment interaction (G×E). Zebrafish are increasingly used to examine how environmental pollutants can affect neurodevelopmental and neurodegenerative pathways, providing experimental data that improve computational simulations of G×E mechanisms ( 127 ). Finally, combining zebrafish behavioral, developmental, and transcriptomic readouts with in silico toxicokinetic and toxicodynamic models, such as stress-responsive gene network models, leads to more accurate prediction of chemical risk, dose–response behavior, and long-term neurodegenerative trajectories ( 128 ). 6. Opportunities for Advancing Parkinson’s Disease Research Through Emerging Experimental and Computational Strategies Both machine learning and in silico tools play an important role in predictive toxicology, with models like QSAR and deep learning improving toxicity forecasting and biomarker discovery. There is improvement in the translation of preclinical findings this due to the use of micro-physiological systems and PBPK modeling, which further offer human-relevant drug response predictions ( 129 ). Precision Toxicology: Integrating Environmental Exposures with Genetic Background For clarifying disease origins, it is important to understand how genetic and environmental factors interact across human development ( 130 ). In PD, G×E interactions play a major role in disease onset and progression ( 131 ). Environmental contributors, including pesticides, industrial chemicals, and heavy metals, have long been associated with elevated PD risk. However, no single pollutant has been confirmed as a primary cause ( 132 ). Occupational exposures to metals and solvents have also been explored for their potential involvement ( 131 , 133 ). Combining controlled genetic variation in model organisms with high-resolution computational analyses, this will enable precision toxicology to study these complex G×E interactions ( 132 ). For example, transcriptomic studies in zebrafish exposed to different toxicants have shown that different chemical classes lead to different gene-expression patterns and co-expression networks, indicating that pollutants leave distinct molecular signatures. Integrating these signatures with genetic variation data can help target biological pathways that confer vulnerability to PD ( 126 ). For example, animal models that use genetic susceptibility with environmental toxins such as rotenone or MPTP can mimic PD features. These models show that SNCA mutations amplify pesticide-induced α-synuclein pathology, while LRRK2 variants heighten sensitivity to metal exposures, offering mechanistic links between sporadic and familial PD and enabling targeted intervention testing ( 134 ). Computational methods have advantages as they lead to precision toxicology. Polygenic risk scores and epigenetic modeling predict how environmental exposures influence gene regulation in PD-relevant cell types ( 135 ). Combining multi-omics datasets with exposure histories supports personalized risk assessment, informs drug repurposing strategies, and accelerates biomarker discovery for early diagnosis and therapeutic monitoring ( 136 ). Rapid Drug Discovery Enabled by Zebrafish and Computational/AI Tools Zebrafish have formed a powerful vertebrate platform for high-throughput screening of neuroprotective or neurorestorative compounds relevant to PD ( 137 ). For example, a recent study targeting the renin-angiotensin-aldosterone system (RAAS), using a zebrafish model of dopaminergic-neuron ablation, screened over 1,400 bioactive compounds and identified several candidate neuroprotective agents, showing the potential for rapid preclinical drug discovery ( 138 ). Zebrafish assays are highly scalable and compatible with automated imaging and behavioral tracking, allowing for combination with computational drug-matching algorithms, phenotypic clustering, and machine-learning frameworks for hit prioritization. Larval zebrafish offer behavioral data that can be used as input for deep learning or neural-network classification pipelines to discriminate genotypes or treatment effects, streamlining candidate selection ( 139 ). AI applications in zebrafish research help in high image recognition and automated analysis, improving behavioral, genetic, and neural assessments. This led to enhanced identification of gene–function relationships, disease modeling, and therapeutic development. The advance now enables automated tracking, image recognition, and large-scale data processing, offering objective, reproducible, and high-throughput analysis of the large datasets generated in experimental zebrafish studies ( 140 ). AI in new research is increasingly used to analyze the behavior of many individual zebrafish, ranging from a few to hundreds, while also detecting the effects of chemical exposures and their interactions. Both conventional behavioral analyses and AI-based approaches were used to assess cognitive and locomotor effects in adult zebrafish that were treated with the neurotoxin MPTP to model PD. Using the Y-maze test, zebrafish exposed to MPTP exhibited impaired spatial working memory, indicating cognitive deficits. AI analysis further revealed a distinct swimming-pattern cluster specific to the high-dose group, demonstrating that MPTP produced unique, quantifiable behavioral changes detectable independently of human scoring ( 141 ). All these findings together highlight how AI enhances the sensitivity, objectivity, and throughput of behavioral phenotyping in experimental neurotoxicology, making it a powerful complement to zebrafish-based drug discovery and disease modeling. Multi-Ancestry Genetic Insights with Zebrafish Functional Validation Zebrafish have genetic similarity to humans, easily manipulable gene expression, and relevance to human pathology; because of that, they are a powerful model for human disease research ( 137 ). Zebrafish allow functional validation of candidate genes and variants identified in large-scale genetic studies, including rare or non-coding variants linked to PD, due to their sequenced genome, high homology, and conserved synteny with humans. The embryos’ sensitivity to drugs, combined with CRISPR/Cas9 accessibility, enables researchers to assess neurodevelopment, neurodegeneration, and behavior, making zebrafish ideal for testing disease mechanisms and potential therapies. It is important to include diverse ancestries, functional follow-up in systems capable of modeling broad genetic variation. Zebrafish provide a platform, helping ensure that findings are globally relevant and reducing translational bias toward populations. Beyond neurodegeneration, zebrafish have successfully modeled numerous human neurogenetic disorders, further highlighting their utility in neurogenetics and functional genomics ( 142 ). In PD, mutations in 15 genes have been linked to monogenic forms, yet these account for only ~ 30% of monogenic cases and 3–5% of genetically complex cases ( 137 ). Among these, LRRK2 variants are the most common heritable cause, with the p.G2019S mutation contributing to ~ 1% of sporadic cases and 4% of familial cases ( 143 ). Zebrafish models provide an important bridge between genetic discovery and functional validation, enabling mechanistic insights that span both common and rare genetic contributors to PD. Climate Change–Related Neurotoxic Exposures and PD Risk Climate change is a major challenge to nervous system health through both gradual environmental changes and acute pollution events ( 144 ). Among the environmental change factors, neurotoxic pollutants, including certain pesticides, industrial solvents such as trichloroethylene (TCE), and airborne particulate matter, are well-established contributors to PD. Studies show that many of these agents lead to mitochondrial dysfunction, induce OS, and gain access to the body through occupational and environmental exposure pathways ( 145 ). As illustrated in Fig. 5 , environmental pollution contributes to PD pathogenesis through interconnected mechanisms. Air pollution, particularly fine particulate matter (PM2.5), has been highlighted as a major concern. PM2.5 can enter the central nervous system via the lungs and bloodstream, where it causes inflammation, OS, and DNA damage, increasing the risk of neurodegenerative diseases and stroke ( 146 ). Studying these environmental contributors is not easy because of long latency periods and the difficulty in reconstructing lifetime exposure histories. In PD specifically, disease onset often predates clinical diagnosis by many years or even decades ( 147 ). Making early environmental contributions difficult to trace. Modeling long-term pollution effects is still complex, because risk depends on dose, duration, and timing of exposure. In this context, adverse outcome pathways (AOPs) provide a mechanistic framework linking early molecular biomarkers to later disease outcomes, enabling more systematic study of environmental drivers of PD ( 148 ). Mechanistically, many PD-associated toxicants, including pesticides and industrial solvents, impair mitochondrial function, increasing OS within dopaminergic neurons. TCE and other mitochondrial toxicants also interact with genetic risk factors, such as inhibition of LRRK2 (a major PD gene) reduces ROS production and mitigates toxicant-induced cellular damage in vitro and in vivo ( 149 ). This leads to enhanced gene–environment synergy, where variation in genes affecting mitochondrial quality control, autophagy, and proteostasis amplifies the neurodegenerative impact of environmental insults. A recent conceptual framework integrates these ideas, proposing that genetic mutations compromise mitochondrial maintenance while environmental toxicants further damage mitochondrial networks, together accelerating dopaminergic neuron loss and PD onset ( 150 ). To understand the complexity of these interactions, hybrid approaches combining high-throughput animal models with computational toxicology, network biology, and systems-biology tools are increasingly used. An example of this, network analysis used to map how diverse environmental contaminants target the main hub proteins in the human interactome, revealing biological pathways through which exposures may influence neurodegenerative disease risk ( 151 ). These integrative strategies help clarify how dose, timing, and mixtures of exposures interact with individual genetic backgrounds. Modeling PD Progression, Longevity, and Neuroprotection PD models focus on acute neuronal loss rather than long-term disease progression, resilience, or recovery, which form a major limitation. Adult zebrafish are an excellent system to study not just degeneration but also spontaneous repair, neuroprotection, and resilience because they can regenerate dopaminergic neurons after neurotoxic injury ( 152 ). Furthermore, after dopaminergic neuron degeneration (e.g., via 6-hydroxydopamine, 6-OHDA) in adult zebrafish, regeneration occurs within weeks, along with behavioral recovery ( 153 ). These features make it easy to study factors that affect disease progression, neuronal vulnerability, aging, neuroprotection, and potential longevity-related pathways, especially when integrated with computational models of gene networks, stress response, and aging dynamics( 154 ). 7. Limitations and Challenges Several limitations still exist with full translational impact in PD research, even with rapid advances in zebrafish models, computational tools, and AI-enhanced analytics ( 155 ). Variability in zebrafish behavioral assays remains a major challenge in neurotoxicology and PD research ( 156 ). Behavioral outputs such as locomotion, habituation, and learning are highly sensitive to many factors, including age, sex, tank geometry, illumination, water chemistry, and handling, leading to substantial within- and between-laboratory variability ( 157 ). Even in adult zebrafish, longitudinal behavioral studies reveal significant intra-individual fluctuations over time, reducing statistical robustness and making interpretation of subtle neurobehavioral phenotypes challenging ( 158 ). Another challenge lies in the lack of standardized computational pipelines for analyzing the increasingly large datasets produced by high-throughput imaging and behavioral tracking ( 159 ). It is difficult to reproduce findings or compare results across laboratories because of variation between choices in data preprocessing, feature extraction, normalization, and analysis. Reviews emphasize that inconsistent metadata reporting, such as developmental stage, exposure conditions, or behavioral endpoints, remains a major barrier to integration of zebrafish datasets into broader toxicological frameworks ( 160 ).AI and deep learning help to decrease the bias in behavioral analysis. However, their effectiveness is limited by the need for large, diverse, and well-annotated datasets. Although recent work shows that machine-vision and pose-estimation tools can classify complex zebrafish behaviors and detect treatment-related differences, their performance is often limited by small sample sizes, limited phenotypic variability, and inconsistent labeling common in academic datasets ( 161 ). Even though zebrafish share major neurotransmitter systems and core molecular pathways with mammals, their brain organization, immune responses, metabolism, and lifespan differ substantially ( 29 ). These differences limit mimicking certain behavioral or neurodegenerative phenotypes in human disease. Zebrafish models typically capture acute or sub-acute exposures, whereas human PD often develops after decades of low-dose environmental exposure, an aspect difficult to replicate in short-lived species ( 137 ). Collectively, these limitations show the need for harmonized experimental protocols, robust computational standards, larger and better-annotated datasets, and cross-model validation strategies to fully realize the translational potential of zebrafish and AI-driven approaches in advancing PD research. 8. Future Directions Future research is expected to focus on technologies that enhance the precision, scalability, and translational relevance of PD models. Automated detection of subtle motor, cognitive, and sensorimotor impairments that are impossible to identify manually, AI-driven zebrafish phenotyping will enable to identification it, especially with high-resolution, video-based behavioral tracking, which will enable earlier and more sensitive identification of neurotoxic effects ( 162 ). A recent study demonstrated that an AI-based neural-network system could reliably identify behavioral patterns in adult zebrafish treated with psychoactive drugs, underscoring the feasibility of AI-driven movement-pattern classification in CNS drug and disease research ( 121 ). According on these advances, we propose a hybrid zebrafish–AI framework that integrates in vivo neurobiological modeling with in silico predictive analytics to support early disease detection, mechanistic insight, and therapeutic evaluation in PD ( Table 3 ) . Digital twins a virtual patient replicas built from molecular, behavioral, and environmental data, forming a powerful tool in personalized medicine, enabling simulation of disease progression and prediction of individual treatment responses ( 163 ). This concept parallels recent advances in other fields where wearable-based digital phenotyping has been integrated with genomic data and AI to predict psychiatric and neurological disorders ( 164 ). Climate change is increasingly identified as a global health threat with unclear impacts on brain disorders ( 165 ), it is important to include this change, like temperature shifts, pollutant patterns, and extreme weather, into neurodegeneration models to clarify population-level PD risk, using the zebrafish model, which offers a scalable platform for studying environmental neurotoxicity under changing climate conditions. Drosophila melanogaster is usually used as a model for neurodegeneration research because of its highly conserved dopaminergic circuitry, rapid generation time, and ease of genetic manipulation ( 166 ). Multi-model hybrid platforms offer a strong strategy for enhancing mechanistic discovery in PD by integrating the strengths of multiple experimental systems. Combining zebrafish, invertebrate models (e.g., Drosophila), mammalian systems, and advanced in silico tools will further increase mechanistic discovery by capturing conserved pathways while allowing high-throughput hypothesis testing. Reviews of zebrafish neurological disease models highlight their flexibility in genetic manipulation, neuroanatomical imaging, and compatibility with chemical screens, making them well-suited for integration into hybrid pipelines ( 167 ). Multi-ancestry genomic datasets, such as the Global Parkinson’s Genetics Program (GP2), identify ancestry-specific risk variants and their interactions with exposures, which help to understand how genetic diversity affects susceptibility to environmental neurotoxins ( 130 ). The use of federated, multi-ancestry genomic datasets, such as those generated by the GP2, leads to enhanced genetic analyses, improved identification of ancestry-specific risk variants, and a deeper understanding of how genomic diversity shapes susceptibility to environmental neurotoxins. GP2’s global scale and multi-ancestry design make it a powerful resource for linking genetic variation to disease risk across populations ( 168 ). Together, these directions point toward an integrated, data-driven ecosystem for PD research that connects molecular biology, computational modeling, environmental science, and global population genetics. Table 3 Proposed hybrid zebrafish–AI workflow for PD modeling and translational research Specific Aim Scientific Question Experimental (In Vivo) Workflow In Silico / AI Workflow Key Outputs Aim 1: Establish graded zebrafish models of Parkinson’s disease Can zebrafish models recapitulate early and progressive PD phenotypes? • Induce PD using MPTP/rotenone (larvae & adults) • Include low, medium, and high doses to model prodromal → advanced PD • Optional genetic models ( pink1 , park2 , gba1 ) • Label datasets by exposure level and disease stage • Create baseline phenotypic clusters • Validated PD severity spectrum • Reference dataset for AI training Aim 2: Capture high-resolution behavioral phenotypes Can subtle, early behavioral changes be detected before overt motor deficits? • Continuous video-based tracking (30–60 fps) • Longitudinal monitoring (days–weeks) • Quantify locomotion, turning, freezing, startle response, circadian activity • Extract time-series behavioral features • Generate digital behavioral biomarkers • High-dimensional behavioral dataset • Early PD behavioral signatures Aim 3: Anchor AI predictions to neurobiological pathology Do AI-detected phenotypes correlate with dopaminergic neurodegeneration? • TH + neuron quantification • Dopamine measurement • Oxidative stress & mitochondrial markers • Regression models linking behavior to neuronal loss • Feature importance analysis (SHAP) •Biologically validated AI outputs • Interpretable biomarkers Aim 4: Develop AI models for early PD detection and staging Can AI detect PD earlier than conventional assays? • Use blinded biological samples • Include early-stage and sub-threshold phenotypes • Train ML/DL models (RF, XGBoost, CNN-LSTM) • Binary & multiclass classification (control/early/late PD) • Early-detection AI model • PD staging algorithm Aim 5: Validate the hybrid model using therapeutic perturbation Can AI detect treatment response earlier and more sensitively than manual scoring? • Treat PD zebrafish with L-DOPA, MAO-B inhibitors, or neuroprotective compounds • Assess behavioral and molecular rescue • Predict treatment response trajectories • Compare AI sensitivity vs traditional endpoints • AI-based treatment response markers • Translational relevance Aim 6: Establish an iterative hybrid biology–AI framework Can biological insight continuously refine AI performance? • Use AI-identified features to refine assays • Design targeted follow-up experiments • Retrain models with refined datasets • Improve prediction accuracy and explainability • Scalable hybrid PD modeling platform 9. Conclusion Combining zebrafish biology with advanced computational and AI-driven technologies is reshaping the landscape of PD research. Researchers can explore disease mechanisms with high resolution and scale by integrating high-throughput in vivo models with advanced behavioral analytics, digital simulations, and multi-model hybrid platforms. These approaches not only increase the quality of detection of subtle neurotoxic effects and gene–environment interactions but also enhance the predictive power and translational relevance of experimental results. Currently, climate-related environmental risks, complex genetic architectures, and long preclinical disease phases form a challenge to traditional research frameworks. Here is hybrid models offer a path toward more precise, integrative, and human-relevant insights. Combining zebrafish systems alongside invertebrate, mammalian, and in silico tools will accelerate mechanistic discoveries and support the development of more effective strategies for early detection, intervention, and therapeutic innovation in PD. Declarations Data Availability Statement All data analyzed or generated during this study are included in this published article and its supplementary information files. No new datasets were generated or analyzed beyond those already available in the public domain. Ethical Approval This article does not contain any studies involving human participants or animals performed by any of the authors. Therefore, ethical approval was not required. Consent to Participate Not applicable. This study did not involve human participants. Consent to Publish Not applicable. No individual person’s data are included in this manuscript. Author Contribution MK and JK conceptualized the review framework and conducted the literature survey on zebrafish models of Parkinson’s disease. TS contributed to the in silico and computational neuroscience components, including AI-driven predictive analytics, systems biology approaches, and data integration strategies. WM conceived and supervised the overall study, integrated the experimental and computational perspectives, and led the writing and critical revision of the manuscript. All authors contributed to manuscript drafting, reviewed the final version, and approved it for submission. Acknowledgement NA References Razali K, Othman N, Mohd Nasir MH, Doolaanea AA, Kumar J, Ibrahim WN, et al. The Promise of the Zebrafish Model for Parkinson’s Disease: Today’s Science and Tomorrow’s Treatment. Front Genet [Internet]. 2021 Apr 15 [cited 2025 Nov 27];12. Available from: https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.655550/full DeMaagd G, Philip A. Parkinson’s Disease and Its Management. P T. 2015 Aug;40(8):504–32. Lopez A, Gorb A, Palha N, Fleming A, Rubinsztein DC. A New Zebrafish Model to Measure Neuronal α-Synuclein Clearance In Vivo. Genes (Basel). 2022 May 12;13(5):868. Luo Y, Qiao L, Li M, Wen X, Zhang W, Li X. Global, regional, national epidemiology and trends of Parkinson’s disease from 1990 to 2021: findings from the Global Burden of Disease Study 2021. Front Aging Neurosci [Internet]. 2025 Jan 10 [cited 2025 Nov 27];16. Available from: https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2024.1498756/full Su D, Cui Y, He C, Yin P, Bai R, Zhu J, et al. Projections for prevalence of Parkinson’s disease and its driving factors in 195 countries and territories to 2050: modelling study of Global Burden of Disease Study 2021. BMJ. 2025 Mar 5;388:e080952. Kouli A, Torsney KM, Kuan WL. Parkinson’s Disease: Etiology, Neuropathology, and Pathogenesis. In: Stoker TB, Greenland JC, editors. Parkinson’s Disease: Pathogenesis and Clinical Aspects [Internet]. Brisbane (AU): Codon Publications; 2018 [cited 2025 Nov 27]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK536722/ Tysnes OB, Storstein A. Epidemiology of Parkinson’s disease. J Neural Transm. 2017 Aug 1;124(8):901–5. Subramanian G, Fanai HL, Chand J, Ahmad SF, Attia SM, Emran TB. System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson’s disease: a network pharmacology and in-silico evaluation. Sci Rep. 2024 Oct 8;14(1):23414. Potashkin JA, Blume SR, Runkle NK. Limitations of Animal Models of Parkinson′s Disease. Parkinson’s Disease. 2011;2011(1):658083. Laub V, Devraj K, Elias L, Schulte D. Bioinformatics for wet-lab scientists: practical application in sequencing analysis. BMC Genomics. 2023 July 7;24:382. Poliquin PO, Chen J, Cloutier M, Trudeau LÉ, Jolicoeur M. Metabolomics and In-Silico Analysis Reveal Critical Energy Deregulations in Animal Models of Parkinson’s Disease. PLoS One. 2013 July 23;8(7):e69146. Cesur MF, Strazdins R, Sarkar S, Çakır T. In vivo and in silico models of Drosophila for Parkinson’s disease. FEBS J. 2025 Sept;292(18):4753–73. Cornet C, Di Donato V, Terriente J. Combining Zebrafish and CRISPR/Cas9: Toward a More Efficient Drug Discovery Pipeline. Front Pharmacol. 2018 July 3;9:703. Moustafa AA, Chakravarthy S, Phillips JR, Gupta A, Keri S, Polner B, et al. Motor symptoms in Parkinson’s disease: A unified framework. Neuroscience & Biobehavioral Reviews. 2016 Sept 1;68:727–40. Latif S, Jahangeer M, Maknoon Razia D, Ashiq M, Ghaffar A, Akram M, et al. Dopamine in Parkinson’s disease. Clinica Chimica Acta. 2021 Nov 1;522:114–26. Gurevich EV, Gainetdinov RR, Gurevich VV. G protein-coupled receptor kinases as regulators of dopamine receptor functions. Pharmacol Res. 2016 Sept;111:1–16. Zhou ZD, Yi LX, Wang DQ, Lim TM, Tan EK. Role of dopamine in the pathophysiology of Parkinson’s disease. Transl Neurodegener. 2023 Sept 18;12(1):44. Maiti P, Manna J, Dunbar GL. Current understanding of the molecular mechanisms in Parkinson’s disease: Targets for potential treatments. Transl Neurodegener. 2017 Oct 25;6(1):28. Dong-Chen X, Yong C, Yang X, Chen-Yu S, Li-Hua P. Signaling pathways in Parkinson’s disease: molecular mechanisms and therapeutic interventions. Sig Transduct Target Ther. 2023 Feb 21;8(1):73. Vekrellis K, Xilouri M, Emmanouilidou E, Rideout HJ, Stefanis L. Pathological roles of α-synuclein in neurological disorders. The Lancet Neurology. 2011 Nov 1;10(11):1015–25. Zaman V, Shields DC, Shams R, Drasites KP, Matzelle D, Haque A, et al. Cellular and molecular pathophysiology in the progression of Parkinson’s disease. Metab Brain Dis. 2021 June;36(5):815–27. Sepúlveda D, Cisternas-Olmedo M, Arcos J, Nassif M, Vidal RL. Contribution of Autophagy-Lysosomal Pathway in the Exosomal Secretion of Alpha-Synuclein and Its Impact in the Progression of Parkinson’s Disease. Front Mol Neurosci [Internet]. 2022 Feb 17 [cited 2025 Nov 29];15. Available from: https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2022.805087/full Pizzino G, Irrera N, Cucinotta M, Pallio G, Mannino F, Arcoraci V, et al. Oxidative Stress: Harms and Benefits for Human Health. Oxid Med Cell Longev. 2017;2017:8416763. Pozo Devoto VM, Falzone TL. Mitochondrial dynamics in Parkinson’s disease: a role for α-synuclein? Dis Model Mech. 2017 Sept 1;10(9):1075–87. Adebayo M, Singh S, Singh AP, Dasgupta S. Mitochondrial Fusion and Fission: The fine-tune balance for cellular homeostasis. FASEB J. 2021 June;35(6):e21620. Li W, Fu Y, Halliday GM, Sue CM. PARK Genes Link Mitochondrial Dysfunction and Alpha-Synuclein Pathology in Sporadic Parkinson’s Disease. Front Cell Dev Biol [Internet]. 2021 July 6 [cited 2025 Nov 29];9. Available from: https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.612476/full Grotemeyer A, McFleder RL, Wu J, Wischhusen J, Ip CW. Neuroinflammation in Parkinson’s Disease – Putative Pathomechanisms and Targets for Disease-Modification. Front Immunol [Internet]. 2022 May 18 [cited 2025 Nov 29];13. Available from: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.878771/full Li H, Pan H, Xiao C, Li H, Long L, Wang X, et al. IL-1β-mediated inflammatory responses in intervertebral disc degeneration: Mechanisms, signaling pathways, and therapeutic potential. Heliyon. 2023 Sept 7;9(9):e19951. Pansera L, Mhalhel K, Cavallaro M, Aragona M, Laurà R, Levanti M, et al. Zebrafish as an Integrative Model for Central Nervous System Research: Current Advances and Translational Perspectives. Life. 2025 Nov 14;15(11):1751. Kin K, Yasuhara T, Kameda M, Date I. Animal Models for Parkinson’s Disease Research: Trends in the 2000s. Int J Mol Sci. 2019 Oct 30;20(21):5402. Schober A. Classic toxin-induced animal models of Parkinson’s disease: 6-OHDA and MPTP. Cell Tissue Res. 2004 Oct;318(1):215–24. The rotenone model of Parkinson’s disease: genes, environment and mitochondria. Parkinsonism & Related Disorders. 2003 Aug 1;9:59–64. Konnova EA, Swanberg M. Animal Models of Parkinson’s Disease. In: Stoker TB, Greenland JC, editors. Parkinson’s Disease: Pathogenesis and Clinical Aspects [Internet]. Brisbane (AU): Codon Publications; 2018 [cited 2025 Nov 30]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK536725/ Tagkalidou N, Stevanović M, Romero-Alfano I, Elizalde-Velázquez GA, Herrera-Vázquez SE, Prats E, et al. Motor and Non-Motor Effects of Acute MPTP in Adult Zebrafish: Insights into Parkinson’s Disease. International Journal of Molecular Sciences. 2025 Jan;26(4):1674. Subhan I, Siddique YH. Effect of Rotenone on the Neurodegeneration among Different Models. Curr Drug Targets. 2024;25(8):530–42. Kalyn M, Hua K, Mohd Noor S, Wong CED, Ekker M. Comprehensive Analysis of Neurotoxin-Induced Ablation of Dopaminergic Neurons in Zebrafish Larvae. Biomedicines. 2019 Dec 28;8(1):1. Noor SM, Norazit A. Fishing for synucleinopathy models. Fisheries and Aquatic Sciences. 2022;25(3):117–39. Ren G, Xin S, Li S, Zhong H, Lin S. Disruption of LRRK2 Does Not Cause Specific Loss of Dopaminergic Neurons in Zebrafish. PLOS ONE. 2011 June 16;6(6):e20630. Flinn LJ, Keatinge M, Bretaud S, Mortiboys H, Matsui H, De Felice E, et al. TigarB causes mitochondrial dysfunction and neuronal loss in PINK1 deficiency. Ann Neurol. 2014 Jan 21;74(6):837–47. Flinn L, Bretaud S, Lo C, Ingham PW, Bandmann O. Zebrafish as a new animal model for movement disorders. Journal of Neurochemistry. 2008;106(5):1991–7. Keatinge M, Bui H, Menke A, Chen YC, Sokol AM, Bai Q, et al. Glucocerebrosidase 1 deficient Danio rerio mirror key pathological aspects of human Gaucher disease and provide evidence of early microglial activation preceding alpha-synuclein-independent neuronal cell death. Hum Mol Genet. 2015 Dec 1;24(23):6640–52. Meredith GE, Rademacher DJ. MPTP Mouse Models of Parkinson’s Disease: An Update. J Parkinsons Dis. 2011 Jan 1;1(1):19–33. Sian J, Youdim MBH, Riederer P, Gerlach M. MPTP-Induced Parkinsonian Syndrome. In: Basic Neurochemistry: Molecular, Cellular and Medical Aspects 6th edition [Internet]. Lippincott-Raven; 1999 [cited 2025 Nov 30]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK27974/ Bradbury AJ, Costall B, Domeney AM, Testa B, Jenner PG, Marsden CD, et al. The toxic actions of MPTP and its metabolite MPP+ are not mimicked by analogues of MPTP lacking an N-methyl moiety. Neurosci Lett. 1985 Oct 24;61(1–2):121–6. BREESE GR, TRAYLOR TD. EFFECT OF 6-HYDROXYDOPAMINE ON BRAIN NOREPINEPHRINE AND DOPAMINE: EVIDENCE FOR SELECTIVE DEGENERATION OF CATECHOLAMINE NEURONS. J Pharmacol Exp Ther. 1970 Sept;174(3):413–20. Varešlija D, Tipton KF, Davey GP, McDonald AG. 6-Hydroxydopamine: a far from simple neurotoxin. J Neural Transm (Vienna). 2020 Feb;127(2):213–30. Masini D, Plewnia C, Bertho M, Scalbert N, Caggiano V, Fisone G. A Guide to the Generation of a 6-Hydroxydopamine Mouse Model of Parkinson’s Disease for the Study of Non-Motor Symptoms. Biomedicines. 2021 June;9(6):598. Lau K, Kotzur R, Richter F. Blood–brain barrier alterations and their impact on Parkinson’s disease pathogenesis and therapy. Transl Neurodegener. 2024 July 29;13(1):37. Slézia A, Hegedüs P, Rusina E, Lengyel K, Solari N, Kaszas A, et al. Behavioral, neural and ultrastructural alterations in a graded-dose 6-OHDA mouse model of early-stage Parkinson’s disease. Sci Rep. 2023 Nov 9;13(1):19478. Zhang H, Yang J, Guo Y, Lü P, Gong X, Chen K, et al. Rotenone-induced PINK1/Parkin-mediated mitophagy: establishing a silkworm model for Parkinson’s disease potential. Front Mol Neurosci [Internet]. 2024 Apr 19 [cited 2025 Dec 1];17. Available from: https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2024.1359294/full Heinz S, Freyberger A, Lawrenz B, Schladt L, Schmuck G, Ellinger-Ziegelbauer H. Mechanistic Investigations of the Mitochondrial Complex I Inhibitor Rotenone in the Context of Pharmacological and Safety Evaluation. Sci Rep. 2017 Apr 4;7:45465. Bhatia D, Chung KP, Nakahira K, Patino E, Rice MC, Torres LK, et al. Mitophagy-dependent macrophage reprogramming protects against kidney fibrosis. JCI Insight. 2019 Dec 5;4(23):e132826. Barazzuol L, Giamogante F, Brini M, Calì T. PINK1/Parkin Mediated Mitophagy, Ca2+ Signalling, and ER–Mitochondria Contacts in Parkinson’s Disease. International Journal of Molecular Sciences. 2020 Jan;21(5):1772. Peng Y, Jiang D yi, Yao S yu, Zhang X, Kazuo S, Liu J, et al. Gene-modified animal models of Parkinson’s disease. Experimental Neurology. 2025 Aug 1;390:115287. Dawson TM, Ko HS, Dawson VL. Genetic Animal Models of Parkinson’s Disease. Neuron. 2010 June 10;66(5):646–61. Zhang C, Chen S, Li X, Xu Q, Lin Y, Lin F, et al. Progress in Parkinson’s disease animal models of genetic defects: Characteristics and application. Biomedicine & Pharmacotherapy. 2022 Nov;155:113768. Wong YC, Krainc D. α-synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies. Nat Med. 2017 Feb;23(2):1–13. Pedersen CC, Lange J, Førland MGG, Macleod AD, Alves G, Maple-Grødem J. A systematic review of associations between common SNCA variants and clinical heterogeneity in Parkinson’s disease. NPJ Parkinsons Dis. 2021 July 1;7:54. Piper DA, Sastre D, Schüle B. Advancing Stem Cell Models of Alpha-Synuclein Gene Regulation in Neurodegenerative Disease. Front Neurosci [Internet]. 2018 Apr 9 [cited 2025 Dec 2];12. Available from: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00199/full Szunyogh S, Carroll E, Wade-Martins R. Recent developments in gene therapy for Parkinson’s disease. Molecular Therapy. 2025 May 7;33(5):2052–64. Huh YE, Usnich T, Scherzer CR, Klein C, Chung SJ. GBA1 Variants and Parkinson’s Disease: Paving the Way for Targeted Therapy. J Mov Disord. 2023 Sept;16(3):261–78. Smith L, Schapira AHV. GBA Variants and Parkinson Disease: Mechanisms and Treatments. Cells. 2022 Apr 8;11(8):1261. Polinski NK, Martinez TN, Ramboz S, Sasner M, Herberth M, Switzer R, et al. The GBA1 D409V mutation exacerbates synuclein pathology to differing extents in two alpha-synuclein models. Dis Model Mech. 2022 May 25;15(6):dmm049192. Farfel-Becker T, Do J, Tayebi N, Sidransky E. Can GBA1-Associated Parkinson Disease Be Modeled in the Mouse? Trends Neurosci. 2019 Sept;42(9):631–43. Migdalska-Richards A, Wegrzynowicz M, Rusconi R, Deangeli G, Di Monte DA, Spillantini MG, et al. The L444P Gba1 mutation enhances alpha-synuclein induced loss of nigral dopaminergic neurons in mice. Brain. 2017 Oct 1;140(10):2706–21. Ahmad A, Nkosi D, Iqbal MA. PARK2 Microdeletion or Duplications Have Been Implicated in Different Neurological Disorders Including Early Onset Parkinson Disease. Genes (Basel). 2023 Feb 27;14(3):600. Dawson TM, Dawson VL. The Role of Parkin in Familial and Sporadic Parkinson’s Disease. Mov Disord. 2010;25(0 1):S32–9. Song P, Peng W, Sauve V, Fakih R, Xie Z, Ysselstein D, et al. Parkinson’s disease-linked parkin mutation disrupts recycling of synaptic vesicles in human dopaminergic neurons. Neuron. 2023 Dec;111(23):3775-3788.e7. Noda S, Sato S, Fukuda T, Tada N, Uchiyama Y, Tanaka K, et al. Loss of Parkin contributes to mitochondrial turnover and dopaminergic neuronal loss in aged mice. Neurobiology of Disease. 2020 Mar 1;136:104717. Han R, Wang Q, Xiong X, Chen X, Tu Z, Li B, et al. Deficiency of parkin causes neurodegeneration and accumulation of pathological α -synuclein in monkey models. J Clin Invest [Internet]. 2024 Oct 15 [cited 2025 Dec 3];134(20). Available from: https://www.jci.org/articles/view/179633 Doyle JM, Croll RP. A Critical Review of Zebrafish Models of Parkinson’s Disease. Front Pharmacol. 2022 Mar 15;13:835827. Wang Z, Chan SW, Zhao H, Miu KK, Chan WY. Outlook of PINK1/Parkin signaling in molecular etiology of Parkinson’s disease, with insights into Pink1 knockout models. Zool Res. 2023 May 18;44(3):559–76. Moisoi N, Fedele V, Edwards J, Martins LM. Loss of PINK1 enhances neurodegeneration in a mouse model of Parkinson’s disease triggered by mitochondrial stress. Neuropharmacology. 2014 Feb;77:350–7. Ren X, Butterfield DA. Fidelity of the PINK1 knockout rat to oxidative stress and other characteristics of Parkinson disease. Free Radical Biology and Medicine. 2021 Feb 1;163:88–101. Zhu C, Herbst S, Lewis PA. Leucine-rich repeat kinase 2 at a glance. J Cell Sci. 2023 Sept 12;136(17):jcs259724. Gan LH, Sun YM, Zhou XY, Qi ZY, Liu FT, Tang YL, et al. The homozygous LRRK2.p.N1437D point mutation mouse is a novel model of parkinsonism. npj Parkinsons Dis. 2025 Mar 28;11(1):61. Domenicale C, Magnabosco S, Morari M. Modeling Parkinson’s disease in LRRK2 rodents. Neuronal Signal. 2023 Aug 16;7(3):NS20220040. Xu Q, Shenoy S, Li C. Mouse models for LRRK2 Parkinson’s disease. Parkinsonism & Related Disorders. 2012 Jan 1;18:S186–9. Fontana BD, Mezzomo NJ, Kalueff AV, Rosemberg DB. The developing utility of zebrafish models of neurological and neuropsychiatric disorders: A critical review. Experimental Neurology. 2018 Jan 1;299:157–71. Du Y, Guo Q, Shan M, Wu Y, Huang S, Zhao H, et al. Spatial and Temporal Distribution of Dopaminergic Neurons during Development in Zebrafish. Front Neuroanat. 2016 Nov 28;10:115. Himwaba M, Jarnda KV, Dweh MT, Liu X, Xiang Y, Zou X, et al. The role of in silico and in vitro models in Parkinson’s disease: Drug discovery and therapy applications. Ageing Research Reviews. 2026 Jan 1;113:102953. Schmidt BJ, Papin JA, Musante CJ. Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today. 2013 Feb;18(0):116–27. Yu Y, Wang X, Wang Q, Wang Q. A review of computational modeling and deep brain stimulation: applications to Parkinson’s disease. Appl Math Mech. 2020;41(12):1747–68. Zhang J, Zhou W, Yu H, Wang T, Wang X, Liu L, et al. Prediction of Parkinson’s Disease Using Machine Learning Methods. Biomolecules. 2023 Dec 8;13(12):1761. Sood M, Suenkel U, von Thaler AK, Zacharias HU, Brockmann K, Eschweiler GW, et al. Bayesian network modeling of risk and prodromal markers of Parkinson’s disease. PLoS One. 2023 Feb 24;18(2):e0280609. Smith SL, Lones MA, Bedder M, Alty JE, Cosgrove J, Maguire RJ, et al. Computational approaches for understanding the diagnosis and treatment of Parkinson’s disease. IET Systems Biology. 2015 Dec;9(6):226–33. Sunny S, Prakash PB, Gopakumar G, Jayaraj PB. DeepBindPPI: Protein–Protein Binding Site Prediction Using Attention Based Graph Convolutional Network. Protein J. 2023 May 18;1–12. Makhouri FR, Ghasemi JB. In Silico Studies in Drug Research Against Neurodegenerative Diseases. Curr Neuropharmacol. 2018 July;16(6):664–725. Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational Methods in Drug Discovery. Pharmacol Rev. 2014 Jan;66(1):334–95. Sharan S, Kumar P, Ambasta RK. Discovery of Novel Compounds Targeting DJ-1 as Neuroprotectants for Parkinson’s Disease by Virtual Screening and In Silico Method. Curr Comput Aided Drug Des. 2021;17(3):351–9. Wei C, Heh CH, Lit LC, Chin SP. Comparative Molecular Dynamics Reveals How LRRK2 Inhibitors Distinguish G2019S from Wild-Type. Neurochem Res. 2025;50(4):259. García MC, Cuesta SA, Mora JR, Paz JL, Marrero-Ponce Y, Alexis F, et al. Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease. Sci Rep. 2025 Feb 3;15(1):4085. Chia S, Faidon Brotzakis Z, Horne RI, Possenti A, Mannini B, Cataldi R, et al. Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates. Mol Pharmaceutics. 2023 Jan 2;20(1):183–93. Yang J, Hu J, Zhang G, Qin L, Wen H, Tang Y. Pharmacophore modeling and 3D-QSAR study for the design of novel α-synuclein aggregation inhibitors. J Mol Model. 2021 Aug 25;27(9):260. Boulaamane Y, Jangid K, Britel MR, Maurady A. Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations. Mol Divers. 2024 Aug;28(4):2495–511. Gupta G, Joshi D, Narayan G, Sharma S. Exploration of novel phytochemicals as α-synuclein aggregation inhibitors in the context of Parkinson’s disease therapy: an in-silico approach. In Silico Pharmacol. 2025;13(1):37. Mettai M, Daoud I, Melkemi N. In Silico Approaches for the Study of New Anti-Parkinson’s Agents. Chemistry Proceedings. 2023 Nov 1;14(1):55. Khan A, Kaushik AC, Shujait Ali S, Ahmad N, Wei DQ. Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson’s disease. RSC Advances. 2019;9(18):10326–39. Thai KM, Pham DT, Ngo TM, Nguyen HT, Nguyen PV, Pham TQ, et al. Targeting MAO-B selectivity: computational screening, docking, and molecular dynamics insights. SAR QSAR Environ Res. 2025 July;36(7):583–619. Adeniji SE, Uba S, Uzairu A. QSAR Modeling and Molecular Docking Analysis of Some Active Compounds against Mycobacterium tuberculosis Receptor (Mtb CYP121). Journal of Pathogens. 2018;2018(1):1018694. Speck-Planche A, Kleandrova VV. QSAR and molecular docking techniques for the discovery of potent monoamine oxidase B inhibitors: computer-aided generation of new rasagiline bioisosteres. Curr Top Med Chem. 2012;12(16):1734–47. Cieślak M, Danel T, Krzysztyńska-Kuleta O, Kalinowska-Tłuścik J. Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors. Sci Rep. 2024 Apr 8;14(1):8228. Razali K, Algantri K, Loh SP, Cheng SH, Mohamed W. Integrating nutriepigenomics in Parkinson’s disease management: New promising strategy in the omics era. IBRO Neurosci Rep. 2022 Oct 12;13:364–72. Trifonova OP, Maslov DL, Balashova EE, Urazgildeeva GR, Abaimov DA, Fedotova EY, et al. Parkinson’s Disease: Available Clinical and Promising Omics Tests for Diagnostics, Disease Risk Assessment, and Pharmacotherapy Personalization. Diagnostics. 2020 May;10(5):339. Hossein-Nezhad A, Fatemi RP, Ahmad R, Peskind ER, Zabetian CP, Hu SC, et al. Transcriptomic Profiling of Extracellular RNAs Present in Cerebrospinal Fluid Identifies Differentially Expressed Transcripts in Parkinson’s Disease. J Parkinsons Dis. 2016;6(1):109–17. Nido GS, Dick F, Toker L, Petersen K, Alves G, Tysnes OB, et al. Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition. acta neuropathol commun. 2020 Apr 21;8(1):55. Fan LY, Yang J, Liu RY, Kong Y, Guo GY, Xu YM. Integrating single-nucleus sequence profiling to reveal the transcriptional dynamics of Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis. J Transl Med. 2023 Sept 21;21(1):649. Zhang FL, Li AY, Niu YL, Zhang K, Zhao MH, Huang JJ, et al. Identification of biomarkers in Parkinson’s disease by comparative transcriptome analysis and WGCNA highlights the role of oligodendrocyte precursor cells. Front Aging Neurosci. 2024;16:1485722. Dumitriu A, Golji J, Labadorf AT, Gao B, Beach TG, Myers RH, et al. Integrative analyses of proteomics and RNA transcriptomics implicate mitochondrial processes, protein folding pathways and GWAS loci in Parkinson disease. BMC Med Genomics. 2016 Jan 21;9(1):5. Hernández-Silva D, Cabello C, Cayuela ML, Pérez-Sánchez H, Alcaraz-Pérez F. Synergizing Virtual Screening and Zebrafish Models to Identify Resveratrol-Derived Antiaging Polyphenols. Pharmaceuticals. 2025 Nov;18(11):1630. Siddiqui S, Siddiqui H, Riguene E, Nomikos M. Zebrafish: A Versatile and Powerful Model for Biomedical Research. Bioessays. 2025 Dec;47(12):e70080. Hermann AC, Millard PJ, Blake SL, Kim CH. Development of a respiratory burst assay using zebrafish kidneys and embryos. Journal of Immunological Methods. 2004 Sept 1;292(1):119–29. Chakraborty C, Hsu CH, Wen ZH, Lin CS, Agoramoorthy G. Zebrafish: a complete animal model for in vivo drug discovery and development. Curr Drug Metab. 2009 Feb;10(2):116–24. Patton EE, Zon LI, Langenau DM. Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials. Nat Rev Drug Discov. 2021 Aug;20(8):611–28. Fernandes DC, Tambourgi DV. Complement System Inhibitory Drugs in a Zebrafish (Danio rerio) Model: Computational Modeling. Int J Mol Sci. 2023 Sept 9;24(18):13895. Mwaffo V, Butail S, Porfiri M. In-silico experiments of zebrafish behaviour: modeling swimming in three dimensions. Sci Rep. 2017 Jan 10;7(1):39877. Wang W, Gao X, Liu L, Guo S, Duan J ao, Xiao P. Zebrafish as a vertebrate model for high-throughput drug toxicity screening: Mechanisms, novel techniques, and future perspectives. J Pharm Anal. 2025 Sept;15(9):101195. Valderrama B, Daly I, Gunnigle E, O’Riordan KJ, Chichlowski M, Banerjee S, et al. From in silico screening to in vivo validation in zebrafish - a framework for reeling in the right psychobiotics. Food Funct. 2025 Mar 3;16(5):2018–30. Tripathi P, Ganeshpurkar A, Singh SK, Krishnamurthy S. Identification of novel glucocerebrosidase chaperone for potential treatment of Parkinson’s disease: An approach using in silico virtual screening, molecular docking and molecular dynamics, and in vitro studies. International Journal of Biological Macromolecules. 2023 Feb 15;228:453–66. Priyadharshini GB, Kondapuram SK, Jaynthy C. Revealing the neuroprotection efficacy of marine sponge extract in Parkinson’s disease using gene expression and molecular docking. Gene Reports. 2025 Sept 1;40:102288. Lukovikov DA, Kolesnikova TO, Ikrin AN, Prokhorenko NO, Shevlyakov AD, Korotaev AA, et al. A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish. J Neurosci Methods. 2024 Nov;411:110256. Taylor KL, Grant NJ, Temperley ND, Patton EE. Small molecule screening in zebrafish: an in vivo approach to identifying new chemical tools and drug leads. Cell Commun Signal. 2010 June 12;8(1):11. Del Rosario Hernández T, Gore SV, Kreiling JA, Creton R. Drug repurposing for neurodegenerative diseases using Zebrafish behavioral profiles. Biomedicine & Pharmacotherapy. 2024 Feb 1;171:116096. Gendelev L, Taylor J, Myers-Turnbull D, Chen S, McCarroll MN, Arkin MR, et al. Deep phenotypic profiling of neuroactive drugs in larval zebrafish. Nat Commun. 2024 Nov 17;15(1):9955. Lescouzères L, Hassen‐Khodja C, Baudot A, Bordignon B, Bomont P. A multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN. EMBO Mol Med. 2023 May 5;15(7):EMMM202216267. Shankar P, McClure RS, Waters KM, Tanguay RL. Gene co-expression network analysis in zebrafish reveals chemical class specific modules. BMC Genomics. 2021 Sept 13;22(1):658. Wang RL, Bencic D, Biales A, Flick R, Lazorchak J, Villeneuve D, et al. Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio). BMC Genomics. 2012 Aug 1;13(1):358. Schunck F, Kodritsch B, Busch W, Krauss M, Focks A. Integrating time-resolved $nrf2$ gene-expression data into a full GUTS model as a proxy for toxicodynamic damage in zebrafish embryo. Environ Sci Technol. 2024 Dec 17;58(50):21942–53. Son A, Park J, Kim W, Yoon Y, Lee S, Ji J, et al. Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy. Toxics. 2024 Nov;12(11):822. Motsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, et al. Gene-environment interactions within a precision environmental health framework. Cell Genom. 2024 June 25;4(7):100591. Cannon JR, Greenamyre JT. Gene-environment interactions in Parkinson’s disease: specific evidence in humans and mammalian models. Neurobiol Dis. 2013 Sept;57:10.1016/j.nbd.2012.06.025. Yin JH, Horzmann KA. Embryonic Zebrafish as a Model for Investigating the Interaction between Environmental Pollutants and Neurodegenerative Disorders. Biomedicines. 2024 July;12(7):1559. Cannon JR, Greenamyre JT. The Role of Environmental Exposures in Neurodegeneration and Neurodegenerative Diseases. Toxicol Sci. 2011 Dec 1;124(2):225–50. Taylor TN, Greene JG, Miller GW. Behavioral phenotyping of mouse models of Parkinson’s Disease. Behav Brain Res. 2010 July 29;211(1):1–10. Dehestani M, Liu H, Gasser T. Polygenic Risk Scores Contribute to Personalized Medicine of Parkinson’s Disease. J Pers Med. 2021 Oct 15;11(10):1030. Kshreeraja S S, Ganesan Rajalekshmi Saraswathy, Giri Ritesh, Kamatchi Sundara Saravanan, Aarti Krishnan, Janhavi Bhargava, et al. Exploring cutting-edge strategies for drug repurposing in female cancers – An insight into the tools of the trade. In: Progress in Molecular Biology and Translational Science [Internet]. Academic Press; 2024 [cited 2025 Dec 9]. p. 355–415. Available from: https://www.sciencedirect.com:5037/science/chapter/bookseries/abs/pii/S1877117324001339 Bangeppagari M, Manjunath A, Srinivasa A, Lee SJ. Tiny Fish, Big Hope: Zebrafish Unlocking Secrets to Fight Parkinson’s Disease. Biology (Basel). 2025 Oct 12;14(10):1397. Le Bras A. Screening Zebrafish for Parkinson’s disease drugs. Lab Anim. 2021 Nov;50(11):310–310. Fusco C, Allen A. Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour [Internet]. arXiv; 2022 [cited 2025 Dec 9]. Available from: http://arxiv.org/abs/2211.03051 Fan YL, Hsu FR, Wang Y, Liao LD. Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization. Med Biol Eng Comput. 2023 Nov 1;61(11):2797–814. Bashirzade AAO, Cheresiz SV, Belova AS, Drobkov AV, Korotaeva AD, Azizi-Arani S, et al. MPTP-Treated Zebrafish Recapitulate ‘Late-Stage’ Parkinson’s-like Cognitive Decline. Toxics. 2022 Feb 4;10(2):69. Kabashi E, Brustein E, Champagne N, Drapeau P. Zebrafish models for the functional genomics of neurogenetic disorders. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 2011 Mar 1;1812(3):335–45. Billingsley KJ, Bandres-Ciga S, Saez-Atienzar S, Singleton AB. Genetic risk factors in Parkinson’s disease. Cell Tissue Res. 2018 July;373(1):9–20. Jilanee SDA, Saeed M, Ahsan MU, Farooq MU, Ahmed S, Shahid H, et al. Neurological consequences of climate change: a review of emerging challenges and potential impacts on brain health. Ann Med Surg (Lond). 2025 May 26;87(7):4209–21. Dorsey ER, Bloem BR. Parkinson’s Disease Is Predominantly an Environmental Disease. Journal of Parkinson’s Disease. 2024 Apr 13;14(3):451–65. Fu P, Guo X, Cheung FMH, Yung KKL. The association between PM2.5 exposure and neurological disorders: A systematic review and meta-analysis. Science of The Total Environment. 2019 Mar 10;655:1240–8. Murata H, Barnhill LM, Bronstein JM. Air Pollution and the Risk of Parkinson’s Disease A Review. Mov Disord. 2022 May;37(5):894–904. Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD, et al. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environmental Toxicology and Chemistry. 2010 Mar 1;29(3):730–41. Ilieva NM, Hoffman EK, Ghalib MA, Greenamyre JT, De Miranda BR. LRRK2 kinase inhibition protects against Parkinson’s disease-associated environmental toxicants. Neurobiol Dis. 2024 June 15;196:106522. Gouri H, Bararia Y, Malhotra V. The Mitochondrial Interactome in Parkinson’s and Huntington’s Diseases. Nat Cell Sci [Internet]. 2025 Sept 30 [cited 2025 Dec 9];3(3). Available from: https://cellnatsci.com/2958-695X/article/10-61474-ncs-2025-00017 Iida M, Takemoto K. A network biology-based approach to evaluating the effect of environmental contaminants on human interactome and diseases. Ecotoxicology and Environmental Safety. 2018 Sept;160:316–27. Vijayanathan Y, Lim FT, Lim SM, Long CM, Tan MP, Majeed ABA, et al. 6-OHDA-Lesioned Adult Zebrafish as a Useful Parkinson’s Disease Model for Dopaminergic Neuroregeneration. Neurotox Res. 2017 Oct;32(3):496–508. Md Hamzah N, Lim SM, Vijayanathan Y, Lim FT, Abdul Majeed AB, Tan MP, et al. Locomotor Assessment of 6-Hydroxydopamine-induced Adult Zebrafish-based Parkinson’s Disease Model. J Vis Exp. 2021 Dec 28;(178). Kogan V, Molodtcov I, Menshikov LI, Reis RJS, Fedichev P. Stability analysis of a model gene network links aging, stress resistance, and negligible senescence. Sci Rep. 2015 Aug 28;5(1):13589. Twala B. AI-driven precision diagnosis and treatment in Parkinson’s disease: a comprehensive review and experimental analysis. Front Aging Neurosci. 2025 July 28;17:1638340. Petersen BD, Bertoncello KT, Bonan CD. Standardizing Zebrafish Behavioral Paradigms Across Life Stages: An Effort Towards Translational Pharmacology. Front Pharmacol. 2022 Jan 20;13:833227. Gerlai R. Reproducibility and replicability in zebrafish behavioral neuroscience research. Pharmacol Biochem Behav. 2019 Mar;178:30–8. Johnson AL, Hurd PL, Mathot KJ, Hamilton TJ. Behavioural variability and repeatability in adult zebrafish (Danio rerio) using the novel tank dive test. PLoS One. 2025 Oct 27;20(10):e0335308. Hsieh JH, Behl M, Parham F, Ryan K. Exploring the Influence of Experimental Design on Toxicity Outcomes in Zebrafish Embryo Tests. Toxicol Sci. 2022 Aug 1;188(2):198–207. Jarema KA, Hunter DL, Hill BN, Olin JK, Britton KN, Waalkes MR, et al. Developmental Neurotoxicity and Behavioral Screening in Larval Zebrafish with a Comparison to Other Published Results. Toxics. 2022 May 17;10(5):256. Hageter J, Efromson J, Alban B, DelGaudio A, Saliu V, Wassef M, et al. High throughput machine learning pipeline to characterize larval zebrafish motor behavior. PLoS One. 2025 Oct 17;20(10):e0335029. Bozhko DV, Myrov VO, Kolchanova SM, Polovian AI, Galumov GK, Demin KA, et al. Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses. Prog Neuropsychopharmacol Biol Psychiatry. 2022 Jan 10;112:110405. Alexandre Vallée. Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins. Journal of Medical Internet Research [Internet]. 2024 Jan 1 [cited 2025 Dec 10];26. Available from: https://www.sciencedirect.com:5037/org/science/article/pii/S1438887124002425 Liu JJ, Borsari B, Li Y, Liu SX, Gao Y, Xin X, et al. Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell. 2025 Jan 23;188(2):515-529.e15. Martínez Lozada PS, Leon-Rojas JE. Neurological and mental health in the era of climate change: mechanisms, clinical impacts, and adaptation. Front Public Health. 2025 Sept 11;13:1630975. Hirth F. Drosophila melanogaster in the Study of Human Neurodegeneration. CNS Neurol Disord Drug Targets. 2010 Aug;9(4):504–23. Burton EA, Burgess HA. A Critical Review of Zebrafish Neurological Disease Models−2. Application: Functional and Neuroanatomical Phenotyping Strategies and Chemical Screens. Oxf Open Neurosci. 2023 Feb 1;2:kvac019. GP2: The Global Parkinson’s Genetics Program. Mov Disord. 2021 Apr;36(4):842–51. Tables References Razali K, Othman N, Mohd Nasir MH, Doolaanea AA, Kumar J, Ibrahim WN, et al. The Promise of the Zebrafish Model for Parkinson’s Disease: Today’s Science and Tomorrow’s Treatment. Front Genet [Internet]. 2021 Apr 15 [cited 2025 Nov 27];12. Available from: https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.655550/full Tagkalidou N, Stevanović M, Romero-Alfano I, Elizalde-Velázquez GA, Herrera-Vázquez SE, Prats E, et al. Motor and Non-Motor Effects of Acute MPTP in Adult Zebrafish: Insights into Parkinson’s Disease. International Journal of Molecular Sciences. 2025 Jan;26(4):1674. Subhan I, Siddique YH. Effect of Rotenone on the Neurodegeneration among Different Models. Curr Drug Targets. 2024;25(8):530–42. Kalyn M, Hua K, Mohd Noor S, Wong CED, Ekker M. Comprehensive Analysis of Neurotoxin-Induced Ablation of Dopaminergic Neurons in Zebrafish Larvae. Biomedicines. 2019 Dec 28;8(1):1. Noor SM, Norazit A. Fishing for synucleinopathy models. Fisheries and Aquatic Sciences. 2022;25(3):117–39. Ren G, Xin S, Li S, Zhong H, Lin S. Disruption of LRRK2 Does Not Cause Specific Loss of Dopaminergic Neurons in Zebrafish. PLOS ONE. 2011 June 16;6(6):e20630. Flinn LJ, Keatinge M, Bretaud S, Mortiboys H, Matsui H, De Felice E, et al. TigarB causes mitochondrial dysfunction and neuronal loss in PINK1 deficiency. Ann Neurol. 2014 Jan 21;74(6):837–47. Flinn L, Bretaud S, Lo C, Ingham PW, Bandmann O. Zebrafish as a new animal model for movement disorders. Journal of Neurochemistry. 2008;106(5):1991–7. Keatinge M, Bui H, Menke A, Chen YC, Sokol AM, Bai Q, et al. Glucocerebrosidase 1 deficient Danio rerio mirror key pathological aspects of human Gaucher disease and provide evidence of early microglial activation preceding alpha-synuclein-independent neuronal cell death. Hum Mol Genet. 2015 Dec 1;24(23):6640–52. Noda S, Sato S, Fukuda T, Tada N, Uchiyama Y, Tanaka K, et al. Loss of Parkin contributes to mitochondrial turnover and dopaminergic neuronal loss in aged mice. Neurobiology of Disease. 2020 Mar 1;136:104717. Han R, Wang Q, Xiong X, Chen X, Tu Z, Li B, et al. Deficiency of parkin causes neurodegeneration and accumulation of pathological α -synuclein in monkey models. J Clin Invest [Internet]. 2024 Oct 15 [cited 2025 Dec 3];134(20). Available from: https://www.jci.org/articles/view/179633 Doyle JM, Croll RP. A Critical Review of Zebrafish Models of Parkinson’s Disease. Front Pharmacol. 2022 Mar 15;13:835827. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8426422","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":572400131,"identity":"2f159330-7998-4967-b283-882a7c63558a","order_by":0,"name":"Mwafaq Kmail","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mwafaq","middleName":"","lastName":"Kmail","suffix":""},{"id":572400132,"identity":"5146d2f4-c9f8-4624-bc57-387fdc8998e0","order_by":1,"name":"Jaya Kumar","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Jaya","middleName":"","lastName":"Kumar","suffix":""},{"id":572400137,"identity":"509a1062-8121-4587-9321-5ff963a918c4","order_by":2,"name":"Teoh Seong Lin","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Teoh","middleName":"Seong","lastName":"Lin","suffix":""},{"id":572400138,"identity":"b8a550c4-01d7-4322-82c3-7952c8cef326","order_by":3,"name":"Wael Mohamed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYJCCA4n/bIAUY+MBYnUwHvjAlgaiG4jWwnxwBtthiHVEqTe4dsbgMA/Pebu17YeBttTYRBPWcjsHqEXidvK2M4lALcfSchsIaTEDazG4nWx2AKiFseEwsVoSziWbnX9IgpaDMw4csDO7Qawt9rfTCg58bEhOMLsBtCWBGL9Izk7e/CGxwc7e7Hz6wwcfamwIa2Fg4DAAkYlglQmElYMA+wOwA4lTPApGwSgYBSMSAACP5k3ccN9G3QAAAABJRU5ErkJggg==","orcid":"","institution":"International Islamic University Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Wael","middleName":"","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2025-12-22 15:23:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8426422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8426422/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100364930,"identity":"4c1501d8-4e0b-46c2-93d4-f4fc7008828d","added_by":"auto","created_at":"2026-01-16 07:54:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7821530,"visible":true,"origin":"","legend":"","description":"","filename":"ListofFiguresandTablesPD.docx","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/45b72267f282606b8cc72329.docx"},{"id":100365073,"identity":"0ee0bccd-a966-4f19-9da0-81bf08ab7edf","added_by":"auto","created_at":"2026-01-16 07:54:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6388816,"visible":true,"origin":"","legend":"","description":"","filename":"1HybridModelingofPDAmended.docx","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/9544d6ed787a75dcac235377.docx"},{"id":100112323,"identity":"ed2d8b70-9a6a-4a75-9172-7c46a5839865","added_by":"auto","created_at":"2026-01-13 06:54:53","extension":"json","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6081,"visible":true,"origin":"","legend":"","description":"","filename":"273cca7323d04d958abd348c66ac50fd.json","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/eb9d2a80893eb44d7c8c5767.json"},{"id":100365316,"identity":"3d2bce39-2aa0-4b4b-9c31-54b93754f319","added_by":"auto","created_at":"2026-01-16 07:55:02","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":344713,"visible":true,"origin":"","legend":"","description":"","filename":"273cca7323d04d958abd348c66ac50fd1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/9cb746d4c7722446b6d20231.xml"},{"id":100112321,"identity":"6cc833bb-519e-4780-a274-8e49ce189a5c","added_by":"auto","created_at":"2026-01-13 06:54:53","extension":"gif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.gif","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/a0d09d567a5943e1b4b65d53.gif"},{"id":100112340,"identity":"51a8c882-a055-4932-b32e-996b7db95a9a","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1637990,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/7880544aa14c8df2b72c5f66.png"},{"id":100366096,"identity":"06ac4819-d6f8-4221-951b-c69b6b760647","added_by":"auto","created_at":"2026-01-16 07:55:57","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1663257,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/8968b371b036d6af7b612943.png"},{"id":100367210,"identity":"19349250-5c2f-4ca5-a5b3-245884349e00","added_by":"auto","created_at":"2026-01-16 07:56:50","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":251189,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/ebe936f3007d2fa946c25695.jpeg"},{"id":100112329,"identity":"294cd1bf-6ad9-4016-af38-d7c57fb1a463","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":291922,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/aecf70d2a9ab7f02cf6b4dbe.jpeg"},{"id":100112333,"identity":"d1e9718c-7004-40e7-ab5b-aad1863d8c47","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1270842,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/75dc35c8e42b837b0a160f03.png"},{"id":100112338,"identity":"f5b2ff7f-39f9-43f8-99a5-f93effa00c4b","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1589385,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/55977319f796e1189cb853cd.png"},{"id":100112344,"identity":"c8f17a0b-ffe5-4ccd-b7bb-45c23270037e","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1637990,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/fcf6c328e32e4c12898d5961.png"},{"id":100366368,"identity":"421e0c3f-0dc7-48bf-abd4-29d8e1e48c4e","added_by":"auto","created_at":"2026-01-16 07:56:17","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1663257,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/539ed024ced0ced602b8d032.png"},{"id":100365583,"identity":"eb358d09-0c36-4ecf-8307-bf3547417779","added_by":"auto","created_at":"2026-01-16 07:55:24","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212483,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/4129be95be556066616356a7.jpeg"},{"id":100112349,"identity":"b6576f8a-4b95-4437-a996-2e80d7003fe0","added_by":"auto","created_at":"2026-01-13 06:54:55","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":292166,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/bb507e23e9cf51c93a6e457d.jpeg"},{"id":100112336,"identity":"3b429fa3-1f90-40c6-9e06-b264138df9aa","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1270842,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/3ff7f09130aa84809f89c350.png"},{"id":100365957,"identity":"0d429e9a-ffd2-4e61-9953-14383190c3cc","added_by":"auto","created_at":"2026-01-16 07:55:47","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1589385,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/2d0899a6cef301968b796a6e.png"},{"id":100112325,"identity":"f6d28170-2aba-45a6-ac82-52f159743301","added_by":"auto","created_at":"2026-01-13 06:54:53","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":308,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/d13b0922d84f66a1d769b58a.png"},{"id":100112331,"identity":"18fe4266-355f-4102-9ab9-d6e2eb23c797","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":263569,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/2de08345f317f02dab21be5e.png"},{"id":100112330,"identity":"6dfd3c09-3718-4802-8f1e-589d6555678d","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":268319,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/06786228e7fe67a5cd15aeab.png"},{"id":100367022,"identity":"03c13730-7d07-4a3c-ad3a-fa89937f5345","added_by":"auto","created_at":"2026-01-16 07:56:44","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63252,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/02957a97cdbea1cdb9149498.png"},{"id":100365257,"identity":"d3306634-91e7-4f30-9f7f-238cc68486df","added_by":"auto","created_at":"2026-01-16 07:54:55","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64787,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/64ac84658298991c1a78ea54.png"},{"id":100367182,"identity":"0375e370-ec35-4062-a4e2-9a0a434a63aa","added_by":"auto","created_at":"2026-01-16 07:56:49","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":227494,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/3b5d3f3d3ff80095453d7327.png"},{"id":100365199,"identity":"de6963d2-413f-4162-a29d-ca7ef9980044","added_by":"auto","created_at":"2026-01-16 07:54:49","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":289951,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/c32c88375a3319cd2aca557f.png"},{"id":100112351,"identity":"d9f4a419-8b04-4705-af6d-932f96135dad","added_by":"auto","created_at":"2026-01-13 06:54:55","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":263569,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/8df39d31224cf93d713ce86a.png"},{"id":100365588,"identity":"a98e28ec-a2d4-4344-8bfb-205a42a90c0c","added_by":"auto","created_at":"2026-01-16 07:55:24","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":268319,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/0bec3e92c60a219a03c9b7fc.png"},{"id":100112342,"identity":"2b195285-5b30-4827-a5ce-da902a429312","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52162,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/0f2dc7483864c72055c5d924.png"},{"id":100367031,"identity":"d2249fcd-4ff6-4fe2-a87c-70d36c882e46","added_by":"auto","created_at":"2026-01-16 07:56:44","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64794,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/ba1c211a8740803627f2e66a.png"},{"id":100112348,"identity":"7ae7a0d9-4d7c-413a-8c69-0ae2a54971cf","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":227494,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/8b611ee655729aa31d34ff9a.png"},{"id":100112353,"identity":"a63989fd-0d7d-4d61-8259-8bda67f17f8c","added_by":"auto","created_at":"2026-01-13 06:54:55","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":289951,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/2e8f85969df38d45e0bc38ab.png"},{"id":100112347,"identity":"a4b86248-f691-4547-a842-d3367de3ad5b","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"xml","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342331,"visible":true,"origin":"","legend":"","description":"","filename":"273cca7323d04d958abd348c66ac50fd1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/11b0eabb795ed16b6ef0dc8f.xml"},{"id":100112341,"identity":"c01f340d-5dee-4107-8b34-716e1e3d0bac","added_by":"auto","created_at":"2026-01-13 06:54:54","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":367128,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/3bb0a84ce9afc1e7612954de.html"},{"id":100366584,"identity":"ef729065-360d-418a-8b34-306749ac1192","added_by":"auto","created_at":"2026-01-16 07:56:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138527,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between healthy and degenerated dopaminergic signaling in Parkinson’s disease. Figure generated with BioRender.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/6362afd8d05bf40edafd8be5.png"},{"id":100112318,"identity":"9bb5b30a-e80b-42e9-8eba-04b1da39889e","added_by":"auto","created_at":"2026-01-13 06:54:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143151,"visible":true,"origin":"","legend":"\u003cp\u003eNeuroinflammatory pathway in PD. DAMPs, aging, ROS, and protein aggregation activate microglia to shift from M2 to M1, leading to mitochondrial dysfunction and excess ROS production. This activates the NLRP3 inflammasome and leads to activate IL-1β, which then attaches to the receptor on the cell surface, leading to activate NF-κB. NF-κB-produce inflammatory cytokines and leads to apoptosis, contributing to dopaminergic neuron loss in PD. The schematic was created with BioRender.com.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/d454f0db03f4d7ea2644eaaa.png"},{"id":100365920,"identity":"e89aa6c2-e64c-44d6-828b-797f97bb36f3","added_by":"auto","created_at":"2026-01-16 07:55:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165789,"visible":true,"origin":"","legend":"\u003cp\u003eAdvantages of using zebrafish as a model for PD research. The schematic highlights key benefits of the zebrafish model. The schematic was created with BioRender.com.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/1fb54d501ea4ef737412125a.png"},{"id":100112322,"identity":"e7046213-1f6e-4f00-b868-f27b6158ec59","added_by":"auto","created_at":"2026-01-13 06:54:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163364,"visible":true,"origin":"","legend":"\u003cp\u003eA) MPTP-induced dopaminergic neurotoxicity leading to PD. MPTP crosses the BBB and is converted into MPP⁺ via MAO-B within astrocytic. MPP⁺ enters dopaminergic neurons through DAT, accumulates in mitochondria, and inhibits Complex I. This leads to ATP loss, OS, calcium imbalance, and eventual degeneration of dopaminergic terminals, contributing to PD. The schematic was created with BioRender.com.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/1bed12bb643e4951f92255ad.png"},{"id":100364942,"identity":"7569ca6b-6d64-4d09-96c5-515556cb21b7","added_by":"auto","created_at":"2026-01-16 07:54:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":177804,"visible":true,"origin":"","legend":"\u003cp\u003eA\u003cstrong\u003e \u003c/strong\u003eSchematic shows\u003cstrong\u003e \u003c/strong\u003eenvironmental pollution and PD mechanisms. (A) Inhaled and ingested pollutants enter the body through the respiratory and gastrointestinal systems and reach the brain via the vagus nerve, olfactory pathways, and blood (B). These exposures increase neuroinflammation, OS, BBB permeability, mitochondrial dysfunction, and α-synuclein accumulation, while reducing dopamine levels, lysosomal function, energy production, antioxidant defenses, and neuronal survival. The schematics were created with BioRender.com.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/3ab6c411e211eedfc8018338.png"},{"id":104202158,"identity":"59664863-3624-4170-a35e-5ac4bb1e3398","added_by":"auto","created_at":"2026-03-09 05:56:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2288255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8426422/v1/c0b0fce2-acd4-4b88-9724-146009972c50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid modeling of Parkinsons disease integrating zebrafish neurobiology with in silico predictive analytics","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a chronic, progressive neurodegenerative disorder characterized by both motor and non-motor symptoms, affecting patients ' muscle control and overall quality of life (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). PD results from the gradual loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) and the accumulation of misfolded α‑synuclein in the nigrostriatal system (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). PD is the most common movement disorder, and after Alzheimer\u0026rsquo;s is the second most prevalent neurodegenerative disease (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the previous few years, the prevalence of PD has increased significantly worldwide, and this increase is expected to continue. According to the Global Burden of Disease Study, PD in 1990there affected 2.5\u0026nbsp;million patients, 6.1\u0026nbsp;million in 2016, and by 2050, this number is projected to reach up to 25.2\u0026nbsp;million. This increase results from population ageing and global growth, underscoring the urgent need for effective therapies and preventive strategies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The etiology of PD is unknown, but it is known as a multifactorial disorder influenced by genetic and environmental factors (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While monogenic forms are rare, genetic risk factors are identified in 5\u0026ndash;10% of cases, often with a hereditary predisposition (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Pesticides, herbicides, and industrial chemicals are all examples of environmental factors that increase the risk of PD (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Age is the strongest risk factor, with a median onset around 60 years and incidence peaking in those aged 70\u0026ndash;79. Prevalence varies across regions, being higher in Europe, North and South America than in Africa, Asia, and the Middle East (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). PD is biologically complex, encompassing both monogenic and sporadic forms and involving multiple interacting pathways, including mitochondrial dysfunction, oxidative stress (OS), impaired proteostasis, neuroinflammation, and synaptic failure. This complexity leads to variable symptoms and disease progression, indicating that single-pathway or single-cell models cannot fully capture the mechanisms of PD or predict patient responses to treatment (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnimal models for preclinical research should be well-characterized, manageable, and translationally relevant. Suitable species share physiological, anatomical, and genetic similarities with humans. Common examples include roundworms, fruit flies, zebrafish, rodents, and non-human primates (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While small models like yeast, worms, and fruit flies can express human PD genes to study protein roles, they cannot fully replicate protein interactions, neuronal loss, or clinical symptoms (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Rodents are widely used in research because of their availability and genetic tractability, whereas larger animals face ethical and financial constraints. \u003cem\u003eIn vitro\u003c/em\u003e systems allow controlled mechanistic studies but lack whole-organism context, including neural circuitry, pharmacokinetics, and immune-vascular interactions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Hybrid modeling is a new approach to PD research, which combines biological studies with computational and AI-driven analysis. The identification of early biomarkers and the creation of more effective therapies are supported by the integration of \u003cem\u003ein vivo\u003c/em\u003e research with \u003cem\u003ein silico\u003c/em\u003e methods, such as AI analysis of omics data, which improves knowledge of the cellular mechanisms behind illness start and progression.\u003c/p\u003e \u003cp\u003eThe complex and multifactorial nature of PD makes it extremely challenging to develop a single model that captures all key neuropathological features. Studying α-SYN aggregation and its clearance \u003cem\u003ein vivo\u003c/em\u003e remains crucial, yet suitable models for this are lacking (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In many recent PD studies, wet-lab experiments are increasingly used with \u003cem\u003ein silico\u003c/em\u003e approaches to enhance mechanistic understanding and accelerate discovery. For example, network-based analyses and docking workflows can screen large chemical libraries against PD-related proteins, with promising compounds subsequently validated in cellular or animal models, creating a feedback loop that continuously refines both experimental and computational predictions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In a recent study using an \u003cem\u003eex vivo\u003c/em\u003e mouse brain model, metabolomic analysis revealed energy-related abnormalities, which were further examined with an \u003cem\u003ein silico\u003c/em\u003e kinetic model that simulated mitochondrial dysfunction, predicted ATP loss, and identified stress-response pathways not detectable through experiments alone (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein silico\u003c/em\u003e approaches are used to model PD and investigate underlying cellular changes. Omics data from these models can be analyzed using genome-scale metabolic models and AI tools, linking molecular alterations to disease outcomes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In this context, the zebrafish model is particularly valuable because of its unique advantages, such as optical transparency, a vertebrate central nervous system with conserved composition and organization, and ease of genetic manipulation, making it an ideal platform for investigating PD mechanisms. Zebrafish are a strong model for behavioral neuroscience research because they are like humans exhibit a range of cognitive processes, including learning, memory, fear, anxiety, perception, social interactions, and sleep patterns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Zebrafish help in combining \u003cem\u003ein silico\u003c/em\u003e and phenotypic drug screening, which leads to accelerated discovery at lower cost. Molecules can first be prioritized computationally against a target, then tested in zebrafish disease models for efficacy and toxicity. This approach allows rapid evaluation of many compounds before costly preclinical phases (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis review presents the current landscape of zebrafish and \u003cem\u003ein silico\u003c/em\u003e modeling in PD research, emphasizing the advantages, challenges, and future directions of hybrid approaches for precision medicine applications.\u003c/p\u003e"},{"header":"2. Pathophysiology of Parkinson’s Disease","content":"\u003cp\u003ePD is characterized by progressive degeneration of dopaminergic neurons in the SNpc (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This leads to striatal dopamine exhaustion and disruption of basal ganglia circuitry that leads to cardinal motor symptoms of bradykinesia, rigidity, tremor, and postural instability. In parallel, widespread extranigral pathology and non‑dopaminergic involvement underlie the prominent non‑motor features that begin years before motor onset (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the difference between the healthy and PD substantia nigra.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe key neurotransmitter dopamine (C₈H₁₁NO₂) is mainly produced in the substantia nigra, with additional synthesis in the ventral tegmental area and hypothalamus. Within the dopaminergic system, there are four main pathways nigrostriatal, mesocortical, mesolimbic, and tuberoinfundibular. Dopamine is important because of its function to control movement, reward, motivation, and several cognitive and hormonal functions. For motor activity, it is produced in the substantia nigra and in the ventral tegmental area for reward signaling and acts as a precursor for norepinephrine and epinephrine. Dopamine levels rise in response to pleasurable stimuli or certain drugs, and balanced signaling is essential for coordinated motor control (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The five subtypes of dopamine receptors, D1, D2, D3, D4, and D5, belong to the G-protein\u0026ndash;coupled receptor superfamily. The two main families of these receptors are the D1-like family includes (D1 and D5), and the D2-like family includes (D2, D3, and D4). While D2, D3, and D4 receptors are structurally connected, D1 and D5 receptors have similar structural characteristics and chemical sensitivities. Dopamine receptors communicate via transduction routes mediated by Gi or Gs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prodromal stage of PD can begin 12\u0026ndash;14 years before diagnosis. Studies suggest that pathology may first appear in the peripheral autonomic system or the olfactory bulb before spreading through the brainstem and reaching the substantia nigra. This early progression leads to non-motor symptoms such as hyposmia, constipation, and rapid eye movement sleep disturbances that precede motor signs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, the precise pathogenesis of PD is still unclear, but evidence indicates that the oxidation of endogenous dopamine can trigger OS in dopaminergic neurons (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Early symptoms include mild tremors, stiffness, slow movements, soft speech, reduced facial expression, fatigue, irritability, or cognitive slowing. These symptoms develop slowly, making detection difficult (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Multiple mechanisms lead to Neuronal degeneration in PD, such as α-synuclein aggregation, OS, mitochondrial dysfunction, and apoptosis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Toxic α-synuclein affects synapses, disrupts calcium balance and mitochondria, and may spread extracellularly, contributing to disease progression. It is also linked to genetic forms of PD (e.g., GBA, LRRK2) through effects on autophagy and lysosomal function, making its regulation a potential therapeutic target (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn PD, alpha-synuclein accumulates to form Lewy bodies, while disruption of neuronal function results from mitochondrial dysfunction, OS, and impaired protein clearance. These changes, along with neuroinflammation, lead to the loss of dopamine-producing neurons (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Recent studies showed that neuron-derived exosomal α-synuclein in plasma correlates with motor dysfunction, highlighting its potential as a non-invasive biomarker for early PD detection (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).When reactive oxygen species (ROS) exceed the cell\u0026rsquo;s antioxidant defenses, this leads to OS, resulting in damage to proteins, lipids, and enzymes, and ultimately causing neuronal death, particularly in dopaminergic neurons (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, ROS play vital physiological roles in cell signaling, immune responses, and apoptosis; excessive levels can result from environmental stressors or xenobiotics, leading to exacerbation of neuronal damage. Antioxidants, such as vitamin E, flavonoids, and polyphenols, may help reduce OS. Additionally, dysfunction of the autophagy\u0026ndash;lysosomal pathway (ALP) impairs α-synuclein degradation and promotes its release through exosomes, potentially accelerating disease progression (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarly interventions such as antioxidant therapy, dopaminergic precursors, or strategies to reduce α-synuclein toxicity have the potential to slow PD progression and provide neuroprotection. Understanding these mechanisms and early pathological changes is important for the development of biomarkers and preventive strategies capable of detecting and mitigating PD before the onset of motor symptoms (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). When two mitochondria merge to form a single elongated organelle, allowing the exchange of proteins and mitochondrial DNA (mtDNA) and promoting the renewal of mitochondrial components, this process is known as mitochondrial fusion. This fusion is important because it is primarily regulated by mitofusins located on the outer mitochondrial membrane and optic atrophy protein 1 on the inner membrane. In contrast, the division of one mitochondrion into two smaller ones, known as mitochondrial fission, involves. This process is primarily controlled by the dynamin-related GTPase Drp1 and the mitochondrial fission protein Fis1. Fission supports mitochondrial quality control by facilitating organelle transport and the removal of damaged mitochondria (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). α-Synuclein controls mitochondrial fusion\u0026ndash;fission, transport, and mitophagy, processes important for neurons' polarized structure (25).\u003c/p\u003e \u003cp\u003eSeveral PD-linked genes, including PINK1, Parkin, DJ-1, LRRK2, and VPS35, disrupt mitochondrial homeostasis, increase OS, and contribute to Lewy body formation, all of this along with impaired autophagy, inflammation, and pharmacologically induced mitochondrial dysfunction (e.g., by MPTP, rotenone, or paraquat) promotes α-synuclein accumulation, highlighting mitochondrial correction as a promising therapeutic strategy for PD (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The process of PD starts when DAMPs released from damaged neurons or toxins like MPTP, rotenone, 6-OHDA, and misfolded α-synuclein activate microglia, which increase ROS and nitric oxide, causing OS and further neuronal damage (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). ROS and DAMPs trigger the NLRP3 inflammasome, generating inflammatory markers such as IL-1β and other cytokines, while NF-κB amplifies pro-inflammatory gene expression (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Chronic stress shifts microglia from anti-inflammatory M2 to pro-inflammatory M1, leading to an increase the inflammation. The adaptive immune system (CD4\u0026thinsp;+\u0026thinsp;T cells, complement) and peripheral inflammation (gut-derived LPS, systemic TNF) further exacerbate CNS damage. This cumulative response leads to dopaminergic neuron death, α-synuclein aggregation, and mitochondrial dysfunction, driving PD motor symptoms (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the inflammatory pathway in the substantia nigra.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Zebrafish as a Model for Parkinson’s Disease","content":"\u003cp\u003eZebrafish have emerged as a strong model for studying PD because of their genetic and neuroanatomical similarity to humans, as well as their display of human-like behaviors including learning, memory, and locomotion. The optical transparency of embryos and larvae enables high-resolution \u003cem\u003ein vivo\u003c/em\u003e imaging, making zebrafish a versatile platform for preclinical and translational PD research (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the advantages of zebrafish.\u003c/p\u003e \u003cp\u003eMoreover, zebrafish offer flexibility in modelling different aspects of PD pathogenesis; both neurotoxin-induced and genetic models have been established to induce PD (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Each replicating a different part of dopaminergic degeneration and selected non-motor symptoms (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Among the toxin-based models, 6-hydroxydopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), and rotenone are the most widely employed (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Transgenic or knock-in models carrying PD-related genes such as synuclein alpha gene (SNCA), leucine-rich repeat kinase 2 (LRRK2), PTEN-induced kinase 1 (PINK1), Parkin RBR E3 ubiquitin protein ligase (PARK2), and glucosylceramidase beta 1 (GBA1) produce a gradual PD-like change (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of Experimental PD Induction Methods in Zebrafish\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgent / Gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMechanism of Action\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhenotypes in Zebrafish\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdvantages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eToxin-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-OHDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget dopamine neuron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDA neuron loss; reduced locomotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRapid and reproducible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRequires injection; lacks progressive pathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConverted to MPP⁺ targeting complex I inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMitochondrial dysfunction \u0026amp; motor deficits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConnect to strong mitochondria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSingle MAO differs from mammals, and MPTP effects are transient, limiting progressive PD modeling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConverted to MPP⁺ targeting complex I inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMotor impairment; DA neuron degeneration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMimic environmental PD toxicants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh variability; systemic toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGenetic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNCA (α-synuclein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein aggregation and Lewy body formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα-syn aggregation; dopaminergic dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModels synucleinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverexpression artifacts, no direct SNCA ortholog, expression differences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLRRK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKinase dysregulation; mitochondrial stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubtle motor deficits; altered mitochondrial dynamics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModels\u0026rsquo; common familial PD mutation; potential for neuron loss and behavioral defects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhenotype inconsistent; normal function unclear; reliability as a PD model uncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePINK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefective mitophagy and mitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMitochondrial defects; DA neuron vulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong mechanistic link to PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMorpholino PINK1 knockdown is transient and sometimes off-target, giving inconsistent results.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParkin (PARK2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpaired ubiquitin-mediated mitochondrial turnover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMitochondrial dysfunction; mild motor changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConserved pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo observable behavioral deficits, limiting the model\u0026rsquo;s ability to fully recapitulate PD symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLysosomal dysfunction and impaired lipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLipid buildup; α-syn accumulation; neurobehavioral changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong link to sporadic PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebiological differences from humans and technical limitations in modeling a chronic, complex neurodegenerative disease (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1.MPTP\u003c/h2\u003e \u003cp\u003eMPTP is a lipophilic protoxin that easily crosses the blood\u0026ndash;brain barrier (BBB) and enters brain cells, MPTP underlies most animal models of PD. \u003cem\u003eIn vivo\u003c/em\u003e, MPTP is metabolized by monoamine oxidase B (MAO-B) into MPP⁺, which selectively induces the loss of dopaminergic neurons in the substantia nigra in mammals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). MPTP, when it enters the brain, is initially stored in acidic organelles, mainly lysosomes, of astrocytes because of its amphiphilic nature. Then MPP⁺ is then released into the extracellular space and via the dopamine transporter (DAT) is taken up by dopaminergic nerve terminals. However, the exact mechanisms of MPP⁺-induced cell death are not fully understood; several key toxic events have been well studied in detail (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The mechanism through which MPTP induces PD is illustrated \u003cb\u003ein\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAccording to the exposure pattern, dopaminergic neurons may die through apoptosis or necrosis. Although MPTP/MPP⁺ is removed from the brain within 12 hours, and ATP returns to its normal level after 24 hours, degeneration of dopaminergic neurons occurs over a much longer period; this means MPTP triggers additional harmful processes beyond the initial energy loss, indicating additional toxic cascades (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn zebrafish, acute MPTP exposure decreases brain dopamine and leads to motor and sensorimotor impairments; however, it still cannot fully mimic human PD pathology because it does not trigger dopaminergic neuron loss or α-synuclein pathology (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. 6-OHDA\u003c/h2\u003e \u003cp\u003e6‑OHDA, a hydroxylated derivative of dopamine and norepinephrine, is selectively taken up by catecholaminergic neurons via dopamine and noradrenaline transporters (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Once it is inside the neuron, it induces rapid neuronal cell death through different mechanisms, including OS, mitochondrial dysfunction, and the generation of ROS (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Unlike the slow neurodegeneration seen in PD, 6-OHDA does not form Lewy bodies, causing rapid neuron degeneration (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). 6‑OHDA does not cross the BBB, so it is delivered by stereotaxic intracerebral injection, especially into the medial forebrain bundle, substantia nigra pars compacta (SNpc), or striatum to lesion the nigrostriatal pathway. Unilateral lesions are typically used to induce asymmetric dopaminergic loss, leading to characteristic amphetamine‑ or apomorphine‑induced rotational behavior that correlates with the extent of dopamine depletion (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients with PD usually experience neuropsychiatric symptoms, including anxiety and depression. Recent \u003cem\u003ein vivo\u003c/em\u003e studies in mice showed that 6-OHDA injected into the striatum causes both motor and non-motor PD symptoms, including anxiety and depression. Unilateral 6-OHDA injection selectively degenerates SNc dopamine neurons while sparing VTA neurons (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Andrea Sl\u0026eacute;zia et al. showed that unilateral striatal injection of 6-OHDA in mice induces progressive degeneration of SNc dopaminergic neurons, and one and two weeks post-lesion, there was significant cell loss (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Rotenone\u003c/h2\u003e \u003cp\u003eRotenone is a highly lipophilic compound derived from Lonchocarpus and Derris species. Rotenone easily crosses the BBB and directly impairs dopaminergic neurons, contributing to progressive neurodegeneration because of its lipophilicity. Its primary inhabitant mitochondrial Complex I, leading to blocking electron transfer to ubiquinone and reducing ATP synthesis. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Rotenone not only damages mitochondria but also, by binding tubulin, disrupts microtubule assembly, causing mitotic arrest and inhibiting cell proliferation (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA recent study showed that rotenone induces mitochondrial dysfunction and mitophagy in BmN cells via the PINK1/Parkin pathway (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Rotenone through Complex I inhibition triggers mitophagy by reducing mitochondrial membrane potential. Loss of membrane potential allows PINK1 accumulation on the outer mitochondrial membrane, where it activates MFN2 and recruits Parkin(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Genetic model\u003c/h2\u003e \u003cp\u003eGene-editing methods, such as transgene insertion, gene disruption, gene inactivation, expression reduction, and mutant gene silencing, enable researchers to modify gene function in controlled ways (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). These methods are usually used to model PD by overexpressing dominant mutant genes such as α-synuclein and LRRK2 or by knocking out/knocking down recessive genes like Parkin, DJ-1, and PINK1 (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. SNCA\u003c/h2\u003e \u003cp\u003eSNCA (synuclein alpha gene) is located on chromosome 4q22.1, encodes the 140\u0026ndash;amino acid presynaptic protein α-synuclein (α-SYN) from its last five exons, and is the first gene linked to familial PD (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). α-SYN is located at presynaptic terminals, its function is to regulate neurotransmitter release, and when it accumulates and aggregates leads to neurodegenerative disorders called synucleinopathies, including PD, dementia with Lewy bodies, and multiple system atrophy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The toxicity of α-SYN also leads to increased amyloid-β and tau aggregation, which highlights its central role in neurodegeneration (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). α-SYN can disrupt autophagy at multiple stages, including autophagosome formation and lysosomal fusion, and alterations in chaperone-mediated autophagy (CMA) and proteasomal pathways have been observed in PD brains, correlating with α-SYN accumulation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Variation in this gene is considered a risk factor for idiopathic PD and may modestly influence age at onset as well as the variability of motor and non-motor symptoms, including cognitive decline, sleep disturbances, psychiatric features, and hyposmia (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Experimental SNCA models, such as transgenic animals have been developed to mimic key features in human PD pathology, including Lewy body-like inclusions, dopaminergic neuron loss, and motor deficits, and show their ability to be valuable tools to study disease mechanisms and evaluate therapies, including gene therapy and neuroprotective strategies (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. GBA1\u003c/h2\u003e \u003cp\u003eGlucocerebrosidase (GBA1), located on chromosome 1q21, encodes the lysosomal enzyme β-glucocerebrosidase (GCase), which hydrolyzes glucosylceramide into glucose and ceramide, and mutations in GBA1 form the most important risk factor for PD (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Around 5% of PD cases result from GBA1 mutation, and both heterozygous and homozygous mutations increase the risk of PD by 20\u0026ndash;30-fold (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).PD patients with GBA1 mutations show different symptoms, but compared with non-GBA1 PD, patients with GBA1 mutations generally develop the disease at a younger age (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). In research models, no single model reproduces all disease features because GBA1 dysfunction affects PD models differently. Therefore \u003cem\u003ein vivo\u003c/em\u003e model is necessary to select the appropriate model for studying specific mechanisms or therapeutic strategies (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). In animal models, GBA1mutation often fails to mimic human PD because the mutations often show weak pathology in animals (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Experimental GBA1 models show mixed results. Such as increased α-synuclein accumulation and mild motor deficits, but largely intact nigrostriatal neurons in mice with reduced GCase activity (D409V) and α-synuclein overexpression display. Furthermore, α-synuclein fibril injection into D409V mice does not significantly worsen pathology. These results suggest that while GBA1 mutation promotes α-synuclein accumulation, additional factors are often required to mimic human PD (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3. Parkin\u003c/h2\u003e \u003cp\u003eThe PARK2 gene, located on chromosome 6q26, contains 12 exons and encodes \u003cem\u003eparkin\u003c/em\u003e, an RBR-type E3 ubiquitin ligase. The main function of parkin is to control mitochondrial quality and also acts as a transcriptional repressor of p53 (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Mutations in the \u003cem\u003eparkin\u003c/em\u003e gene lead to autosomal recessive PD (AR-PD). Parkin mutations represent the primary genetic basis of AR-PD. Parkin contains two RING finger domains, with an in-between RING (IBR) domain positioned between them. Ubiquitin is a small, 76\u0026ndash;amino acid protein generated from several precursor proteins encoded in the human genome. During the ubiquitination process, ubiquitin is covalently attached to lysine residues on target proteins, marking them for specific cellular outcomes (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). More than 100 parkin mutations have been identified, representing about half of familial PD cases and at least 20% of sporadic cases with young-onset PD (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).When it is activated by PINK1, parkin marks damaged mitochondrial proteins with ubiquitin, improving the removal of dysfunctional mitochondria through autophagy. Lysosome pathway mutations in parkin cause autosomal recessive early-onset PD. A recent \u003cem\u003ein vitro\u003c/em\u003e study using patient-derived dopaminergic neurons shows that parkin has a PINK1-independent synaptic role. Mutant parkin led to increased oxidized dopamine because it disrupts synaptic vesicle recycling. When PINK1 and parkin mutations are combined lead to higher dopamine oxidation and earlier disease onset, indicating that partial loss of parkin worsens pathology (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows different animal models used to induce PD by parkin protein knockout or knockdown, highlighting their effectiveness.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnimal Models for Inducing PD via Parkin Loss-of-Function\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffectiveness for PD Induction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Phenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdvantages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisadvantages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMouse Parkin KO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (mild spontaneous; strong with toxins)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo major neuron loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstablished genetics, affordable, good for circuit studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLacks robust degeneration, short lifespan, and limits aging effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePig Parkin KO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-moderate (biochemical more than degenerative change)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMitochondrial defects with small motor issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLarge brain size, closer to human physiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpensive, low throughput, ethical concerns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimate Parkin KO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (age-dependent full pathology)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNigral loss, α-syn pathology, motor deficits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh translational relevance, complex behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVery costly, long timelines, and welfare issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZebrafish Parkin KD/KO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (early dopaminergic vulnerability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAround 20% loss of diencephalic DA neurons, MPP\u0026thinsp;+\u0026thinsp;sensitivity, and motor dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransparent embryos for live imaging and high-throughput screening,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimpler brain, immature immunity, basic behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4. PINK1\u003c/h2\u003e \u003cp\u003ePINK1 (PTEN-induced kinase 1), encoded by PARK6 on chromosome 1p36, is a mitochondrial serine/threonine kinase. It maintains mitochondrial quality in dopaminergic neurons by detecting damaged mitochondria. Loss-of-function PINK1 mutations cause autosomal recessive early-onset PD, with nigrostriatal degeneration, motor deficits, slower progression, and variable Lewy-like pathology (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Misfolded mitochondrial protein (dOTC) expression in dopaminergic neurons causes neurodegeneration and motor deficits, which are worsened by PINK1 knockout and lead to loss of L-DOPA responsiveness, as shown in a recent \u003cem\u003ein vivo\u003c/em\u003e study (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). However, PD models typically induce pathology through PINK1 knockout, knockdown, or patient-mutation knock-in, mimicking recessive human variants to study mitophagy defects, OS, and dopamine dysregulation. These methods reveal early changes but often require aging or additional stressors for robust nigral loss in rodents (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.4.5. LRRK2\u003c/h2\u003e \u003cp\u003eLRRK2 (leucine-rich repeat kinase 2) is a large multidomain protein with kinase and GTPase activities that help to regulate vesicular trafficking, autophagy, and inflammation (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Pathogenic mutations in LRRK2, such as G2019S, R1441C/G/H, Y1699C, I2020T, and N1437H, form the most common genetic cause of familial PD. Around 10%\u0026ndash;15% of cases result from mutations in LRRK2, and are also found in 1%\u0026ndash;2% of sporadic cases in Western populations (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). These mutations lead to late-onset nigrostriatal degeneration, α-synuclein aggregation, and immune dysregulation in dopaminergic neurons due to increased kinase activity and impair normal GTPase and kinase function (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). In recent study by Qing Xu et al. showed that age-dependent motor deficits developed in transgenic mice expressing mutant LRRK2, leading to early nigrostriatal axonopathy, hyperphosphorylated tau, and impaired dopamine transmission, which are the main features of PD (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Although various LRRK2 knock-in and transgenic models exist, knock-in mice generally do not show PD-like behavioural or pathological changes. The motor deficits and pathological phenotypes of PD only reliably reproduce in transgenic mice with LRRK2 overexpression or additional exogenous stress (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Key Readouts in Zebrafish PD Studies\u003c/h2\u003e \u003cp\u003eZebrafish are used to study human diseases, especially neuropsychiatric and neurodegenerative disorders, because of their high anatomical and physiological similarities to the human brain (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). As a vertebrate model, zebrafish have many advantages for PD research, such as high genetic conservation, optical transparency for real-time neural imaging, rapid development, and high fecundity, leading to efficient, high-throughput studies of PD pathogenesis and therapeutic interventions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).PD research using zebrafish, many readouts are routinely used to characterize pathology and assess potential treatments (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). For example, dopaminergic neuron counts, mitochondrial activity, ROS measurements, transcriptomic profiling, and locomotor/behavioural phenotyping. Together, these measures provide a comprehensive, high-resolution overview of PD-related changes in a vertebrate system. Zebrafish lack a clearly defined midbrain area; their tyrosine hydroxylase (TH)-positive neurons are widely accepted as functional equivalents of mammalian DA neurons (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). According to recent study shows region-specific DA neuron distribution in zebrafish, using TH immunofluorescence, DC2 and DC4 in the ventral diencephalon, as well as neurons in the pretectum and telencephalon (Vd/Vv), are predominantly dopaminergic, whereas DC1, DC3, DC5, DC6, the locus coeruleus (LC), and raphe nuclei (Ra) are largely non-dopaminergic or noradrenergic (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. In Silico Approaches in Parkinson’s Disease Research","content":"\u003cp\u003eThere is advanced predictive power, rapid screening capacity, and mechanistic insights that surpass many traditional experimental models when used \u003cem\u003ein silico\u003c/em\u003e model for PD drug discovery (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). \u003cem\u003eIn silico\u003c/em\u003e approaches are now important experimental and clinical studies because they enable large-scale data integration, mechanistic disease modelling, and rapid virtual drug discovery, leading to provide powerful computational support that accelerates and improves PD research (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Computational modelling techniques\u003c/h2\u003e \u003cp\u003eBiophysical computational models work with experimental data and theoretical frameworks, providing powerful analytical tools for investigating neurological diseases. Computational models using medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) usually represent striatal activity. In PD, loss of dopaminergic input from the SNc leads to reduced direct-pathway (D1 MSN) activity and improved indirect-pathway (D2 MSN) signalling, producing excessive thalamic inhibition. Computational program used to mimic neurodynamic models in PD by altering MSN connectivity, inhibitory balance, and dopamine-dependent parameters (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Bayesian and machine learning (ML) predictive computer models in PD research refer to probabilistic and data-driven models that predict disease risk, progression, or prodromal markers by analyzing complex interdependencies among clinical, genetic, and environmental variables (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvolutionary algorithms (EAs) offer a strong ML approach for analysing motor dysfunction in PD. EAs can automatically identify movement features that differ in PD patients from healthy controls by iteratively evolving a population of candidate classifiers. Using simple sensor data collected during clinical tasks like finger tapping or free movement, this enables EA-based models to achieve high diagnostic accuracy. This EA framework can be applied to animal models, where movement data from video tracking can be classified to detect PD-related genetic mutations. This cross-species applicability provides an objective and scalable computational tool for assessing motor impairment and evaluating the effects of existing or novel PD therapies (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e). Traditional model interactions between PD risk and prodromal markers treat the marker independently; Bayesian networks overcome these limitations by combining prior knowledge with longitudinal TREND study data. This approach identifies key marker interdependencies, predicts PD risk probabilistically, and generates realistic synthetic patient profiles, providing a more accurate and comprehensive framework for understanding and predicting prodromal PD (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e).The prediction of protein\u0026ndash;protein interaction sites, including antigen\u0026ndash;antibody interfaces, has been enhanced by recent deep learning approaches, such as graph convolutional networks with attention mechanisms. Combining these predicted binding regions into docking workflows improves accuracy and reduces false positives, highlighting their potential application to PD-related targets such as α-synuclein and LRRK2 in structure-based drug discovery (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrug discovery is traditionally slow, but advances in computing have greatly accelerated this process by streamlining hit identification and hit-to-lead optimization, making drug development more efficient (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). CADD improves the hit rate of new drug candidates by using targeted computational searches that outperform traditional high-throughput screening and combinatorial chemistry. Overall, CADD approaches fall into two major categories: structure-based and ligand-based (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). It helps explain the molecular basis of activity and predicts improved derivatives. CADD in drug discovery is mainly used to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) filter large compound libraries into smaller sets of predicted actives, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) guide lead optimization by improving affinity and ADMET properties, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) design new compounds through fragment-based or stepwise modifications (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). Common \u003cem\u003ein silico\u003c/em\u003e methods in PD drug research include virtual high-throughput screening (vHTS), molecular docking, quantitative structure\u0026ndash;activity relationship (QSAR) models, pharmacophore modelling, molecular dynamics (MD) simulations, and ADME / toxicity prediction (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). \u003cem\u003eIn silico\u003c/em\u003e virtual screening identifies neuroprotective candidates for PD by scanning large chemical libraries against key protein targets. Studies screened the ZINC database for structural analogs of known neuroprotectants, decreasing candidates from 50 to 7 through toxicity, carcinogenicity, and docking filters, yielding molecules like SS2 with strong DJ-1 binding. This approach accelerates the discovery of non-toxic agents capable of modulating neurodegeneration (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Virtual Screening \u0026amp; Molecular Docking Against PD Targets\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eLRRK2\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe kinase domain of LRRK2 is composed of 14 secondary structural elements, made up from nine α-helices, three β-sheets, and two intrinsically disordered regions (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e). LRRK2 is a key target in PD, as its mutations contribute to dopaminergic neuron damage. Three promising candidates, including CRA_1801 identified in a recent study with predicted high potency (pIC₅₀ \u0026gt; 7). When applying machine-learning (ensemble) QSAR modelling on pIC₅₀ data for LRRK2 inhibitors and screening existing drugs from DrugBank. Molecular docking was then performed to predict how these compounds bind to the LRRK2 kinase domain, revealing key interactions and helping prioritize them for further validation with molecular dynamics simulations (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eα-synuclein\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePrevious studies used high-throughput docking to identify α-synuclein fibril-binding compounds (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). A main event in PD is the aggregation of α-synuclein into fibrils. Using 43 diverse ligands, a ligand-based pharmacophore model identified critical features for inhibition: two hydrogen-bond acceptors, one hydrophobic region, and two aromatic rings. Based on this, a 3D-QSAR model (R\u0026sup2; = 0.920, Q\u0026sup2; = 0.752) was developed to predict activity, guiding the design of novel indolinone derivatives. \u003cem\u003eIn vitro\u003c/em\u003e testing with the thioflavin-T assay confirmed inhibitory activity, with the best compound achieving\u0026thinsp;~\u0026thinsp;45% inhibition, demonstrating the models\u0026rsquo; reliability for designing α-synuclein aggregation inhibitors (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e).To identify new candidate compounds, large databases of known α-synuclein inhibitors were explored to explore chemical space via QSAR modelling and virtually screened natural products, such as those from the LOTUS database. In a separate study, 875 phytochemicals were assessed using molecular docking and molecular dynamics simulations, revealing compounds like Crebanine with favourable binding energies and stable interactions over ~\u0026thinsp;40\u0026ndash;60 ns, suggesting their potential for further experimental validation (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e). According to these findings that shows combining \u003cem\u003ein-silico\u003c/em\u003e docking, pharmacophore/QSAR modelling, and MD simulations is a good strategy to identify both synthetic and natural small molecules that may inhibit α-synuclein aggregation (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). Such approaches offer a promising avenue for developing disease-modifying therapies for PD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMAO-B\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eMAO‑B is a main therapeutic target in PD, as its inhibition slows dopamine degradation, thereby enhancing dopaminergic transmission, leading to a reduction in PD symptoms (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). Many studies have used virtual screening, molecular docking, and MD simulations to identify novel MAO‑B inhibitors, exploring both synthetic scaffolds and natural compounds. MAO-B inhibitors have been discovered by docking FDA-approved drugs and new compounds into the enzyme\u0026rsquo;s active site, followed by validation using MD simulations and free energy calculations (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e). For example, ten indanone derivatives evaluated, ligands L3 and L5 exhibited the strongest binding affinities (\u0026minus;\u0026thinsp;8.809 and \u0026minus;\u0026thinsp;9.276 kcal/mol, respectively) with stable interactions, and ADME predictions indicated good oral bioavailability and gastrointestinal absorption, highlighting them as promising drug candidates to inhibit MAO-B (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). In a multi-stage \u003cem\u003ein-silico\u003c/em\u003e workflow combining 3D-pharmacophore modelling, 2D-QSAR, ADMET filtering, docking, MD simulations, and MM/PBSA binding free energy calculations was applied to four chemical databases (ZINC, DrugBank, TCM, and UNPD) for selective MAO-B inhibition. From this screening, 22 top candidates were identified. Among these, four compounds, ZINC21285023, ZINC79651118, ZINC58283019, and UNPD89644 (crotafuran E), displayed stable binding, better interactions with key residues such as Cys172 and Tyr435, and performance comparable to or better than the reference drug safinamide, making them strong leads for further experimental validation (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eQSAR Models, Pharmacophore Modelling \u0026amp; Machine-Learning Integration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eQuantitative structure\u0026ndash;activity relationship (QSAR) modelling is a main ligand-based approach in drug design, complementing methods like molecular docking and virtual screening by enabling the prediction of a compound\u0026rsquo;s biological activity from its chemical features without the need for synthesis (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e). Studies \u003cem\u003ein silico\u003c/em\u003e for neurodegeneration follow a sequential workflow that combines ligand-based approaches (such as QSAR and pharmacophore modelling) with structure-based techniques (like docking and molecular dynamics) (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). For α-synuclein, a ligand-based pharmacophore and 3D-QSAR model built from known inhibitors enabled the design of new indolinone derivatives, several of which showed validated anti-aggregation activity \u003cem\u003ein vitro\u003c/em\u003e (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). For MAO-B, QSAR\u0026thinsp;+\u0026thinsp;docking has been used to propose bioisosteres of known inhibitors (e.g., Rasagiline), improving the pool of candidate molecules (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e). Machine-learning predictive models with QSAR have been used to estimate docking scores or potency, enabling much faster screening; furthermore MAO-inhibitor study showed that ML models could predict docking scores thousands of times faster than standard docking with little loss in accuracy (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eOmics-Driven Computational Discovery\u003c/b\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOmics technologies, including genomics, proteomics, transcriptomics, and metabolomics, enable the study of high-throughput analysis of biological processes by combining multiple omics, which provides deeper insights into disease mechanisms and normal physiology across different molecular levels (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e). Metabolomics, combined with other omics and clinical data, can enable early PD detection and provide system-level insights for therapy (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e). RNA-seq and single-cell RNA-seq and other transcriptomic studies of PD-relevant tissues reveal differentially expressed genes and cell-type\u0026ndash;specific changes. For example, CSF RNA profiling identified protein-coding and non-coding transcripts altered in PD, showing potential minimally invasive biomarkers. Another study utilizing blood transcriptome data derived a two-gene prognostic signature associated with motor progression and linked with peripheral immune cell alterations (increase in neutrophils, decrease in CD4\u0026thinsp;+\u0026thinsp;T-cells), offering a potential blood-based predictor of disease trajectory (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e). However, transcriptomic studies still have some limitations, for example, it is difficult to separate genuine disease-driven transcriptional changes from shifts in cellular populations because bulk post-mortem brain analyses in PD can be confounded by changes in cell-type composition, making (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e). This has spurred the adoption of more refined single-cell and cell-type deconvolution methods, as well as network-based and multi-layer computational analyses. A recent integrative snRNA-seq study across neurodegenerative diseases (including PD) detected both shared and disease-specific transcriptional changes at single-cell resolution, identifying novel regulators (e.g., stress-response genes) and offering deeper insight into cell-type\u0026ndash;specific pathology in PD (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e). By combining differential expression analysis, network modelling (e.g., weighted gene co-expression network analysis, WGCNA), hub-gene detection, and machine-learning-based\u0026ndash;based prioritization, this led to the successful identification of potential biomarkers and therapeutic targets using comprehensive bioinformatics pipelines. When single-cell transcriptomic data from PD patients were used to reveal that oligodendrocyte precursor cells (OPCs) play a more sensitive role than mature oligodendrocytes in PD\u0026ndash;PD-associated transcriptomic changes (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e).Nevertheless, there are challenges to this computational omics paradigm. Bulk RNA-seq analyses can be confounded by cell-type composition changes, reducing the specificity of DEGs unless carefully corrected (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e). Also, overlap between transcriptomic and proteomic data remains limited, emphasizing that RNA expression does not always predict protein abundance or functional change (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. The Hybrid Model: Integrating Zebrafish with In Silico Tools","content":"\u003cp\u003eCombining \u003cem\u003ein silico\u003c/em\u003e methods with \u003cem\u003ein vivo\u003c/em\u003e zebrafish models helps in improving therapeutic discovery by allowing each approach to overcome the other\u0026rsquo;s limitations, resulting in a more precise, efficient, and biologically meaningful hybrid research strategy (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eWhy Hybrid Modelling Works\u003c/b\u003e \u003c/p\u003e \u003cp\u003eZebrafish have many advantages, such as being genetically tractable, an optically transparent vertebrate model with strong human homology. Because of this, it offers rapid, cost-effective disease modelling and high-throughput drug discovery across cardiovascular, neurological, metabolic, and cancer research (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e). Researchers can observe developmental processes, organogenesis, and cellular dynamics in real time with high-resolution phenotypic readouts at the whole-organism level, because zebrafish embryos are externally fertilized and transparent, enabling (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e). Moreover, zebrafish have high fecundity, rapid development, and low maintenance cost compared with mammalian models; because of these advantages, hundreds of embryos can be produced, facilitating large-scale experiments with robust statistical power (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e). Zebrafish is a strong whole-organism model for studying development, disease phenotypes, and drug responses in a system that closely reflects human biology. That is because zebrafish and humans share many disease pathways; drugs often act on similar pathways in both species (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e). \u003cem\u003eIn silico\u003c/em\u003e methods allow researchers to rapidly screen large libraries of compounds or genetic perturbations, prioritize promising candidates, and generate hypotheses by using computational modelling, virtual screening, molecular docking, and mechanistic modelling. For example, molecular docking studies have been used to predict interactions of therapeutic peptides before \u003cem\u003ein vivo\u003c/em\u003e testing (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e). \u003cem\u003eIn silico\u003c/em\u003e reduces the need for extensive early-stage animal experiments because it enables simulation of complex biological or behavioural processes. For example, computational models have been developed to simulate three-dimensional swimming behaviour of zebrafish, replicating observed dynamics and allowing virtual experiments to test hypotheses on behaviour (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e). \u003cem\u003eIn silico\u003c/em\u003e tools save time and resources, enabling more focused, efficient downstream \u003cem\u003ein vivo\u003c/em\u003e studies by reducing the pool of candidates (compounds, targets, pathways). This greatly reduces the workload and increases throughput compared to purely empirical screening (\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e). Both computational and zebrafish model work in a continuous loop; computational predictions can be validated in zebrafish, and the empirical data from zebrafish can, in turn, refine computational models. This loop improves both predictive power and biological relevance. For example, in studies of immune or complement-system inhibitors, \u003cem\u003ein silico\u003c/em\u003e molecular docking on zebrafish ortholog proteins predicted effective binding, which can then be followed up with \u003cem\u003ein vivo\u003c/em\u003e functional assays in zebrafish (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e). Furthermore, by using zebrafish phenotypic readouts (e.g., development, organ toxicity, and behaviour), researchers can validate computationally generated hypotheses about drug effects or genetic perturbations, combining the scalability of \u003cem\u003ein silico\u003c/em\u003e approaches with the realism of a living organism (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e). Many \u003cem\u003ein silico\u003c/em\u003e hypotheses can be triaged rapidly, the most promising ones validated in zebrafish, and only the top candidates move on to more complex mammalian or clinical stages, reducing time, cost, and animal use, so this bidirectional process provides a powerful, ethically and economically advantageous pipeline (\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cb\u003eHybrid Workflows: In silico \u0026harr; Zebrafish Integration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe hybrid workflows typically proceed in two complementary directions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) from in silico \u0026rarr; zebrafish (prediction followed by \u003cem\u003ein vivo\u003c/em\u003e validation) (\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e), and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) from zebrafish \u0026rarr; \u003cem\u003ein silico\u003c/em\u003e (behavioural or molecular readouts from zebrafish plugged into AI/data pipelines for deeper analysis) (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e). These hybrids overcome the limitations of purely computational or purely experimental approaches.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico \u0026rarr; Zebrafish: From Prediction to Validation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the \u003cem\u003ein silico\u003c/em\u003e \u0026rarr; zebrafish workflow, computational methods include molecular docking, QSAR, and AI-based predictive tools that are used to identify the best molecules or targets relevant to PD, including LRRK2, α-synuclein, and MAO-B. Then, by assessing PD-relevant phenotypes, including gene expression, locomotor behaviour, dopaminergic neuron integrity, and stress-related responses, zebrafish are used to validate these predictions. This approach helps to narrow candidates with favourable ADMET properties and brain penetration (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e). This approach has been demonstrated effectively. An example for this approach, a study to predict telomerase-binding compounds that used virtual screening of a polyphenolic library using ligand- and structure-based docking, followed by validation in zebrafish models of premature aging and chronic inflammation, confirming biologically relevant anti-aging and anti-inflammatory effects (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eZebrafish \u0026rarr; In silico: Phenotypes to Data, then to Insights\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExperimental data from zebrafish are used to improve predictions, uncover mechanisms, and guide further drug discovery from a computer model. AI or QSAR models are used to analyse behavioural tracking, neuronal imaging, and gene expression data from zebrafish PD models to identify drug effects, predict affected pathways, and uncover new PD-relevant correlations (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e). An example of this approach, a study on zebrafish for behavioural and gene expression changes that used the bioactive peptide xenin, extracted from a marine sponge. Results from molecular docking and modelling suggested xenin could stabilize the PINK1-ubiquitin complex and improve Parkin production, connecting \u003cem\u003ein vivo\u003c/em\u003e observations to mechanistic predictions (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e). Such zebrafish \u0026rarr; \u003cem\u003ein silico\u003c/em\u003e approaches have many advantages, such as improving throughput, and allowing quantitative comparison across treatments or genotypes, by providing objective, scalable, and reproducible phenotyping, reducing observer bias (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e). Additionally, zebrafish-derived data can also be used to train machine-learning models to predict outcomes in other settings by clustering phenotypic \u0026ldquo;barcodes\u0026rdquo; and inferring possible modes of action, even for previously uncharacterized compounds (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDemonstrated Achievements in Hybrid PD Modeling\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHybrid workflows that integrate zebrafish models with \u003cem\u003ein silico\u003c/em\u003e tools have shown their ability to accelerate drug discovery, mechanistic analysis, and the overall understanding of PD (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e). Many examples show the practical applications and successes of these combined approaches. One successful application for AI is using it for drug repurposing. For example, zebrafish larvae exposed to large libraries of FDA-approved drugs generated behavioural signatures that helped identify compounds with neuroprotective or neuroactive potential (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e). Combining zebrafish screening with AI significantly enhances drug discovery, lowers costs, and prioritizes safe candidates for follow-up. A study accurately predicting neuroactive molecules across diverse structures, with 58% validated in human protein assays, using deep metric learning on zebrafish behavioural data from 650 CNS-active compounds (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e). A valuable basis for computational modeling is offered by zebrafish transcriptomics. Gene-expression profiles from zebrafish exposed to different chemical classes were used to construct co-expression networks, revealing transcriptional modules associated with neurobehavioral and toxic responses (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e). A study using a zebrafish microarray data to develop tissue- and transcription-factor-specific gene, several of which were validated across independent datasets, showing both the reproducibility and mechanistic insight offered by zebrafish-derived molecular signatures (\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e). Hybrid approaches have also been used to study gene\u0026ndash;environment interaction (G\u0026times;E). Zebrafish are increasingly used to examine how environmental pollutants can affect neurodevelopmental and neurodegenerative pathways, providing experimental data that improve computational simulations of G\u0026times;E mechanisms (\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e). Finally, combining zebrafish behavioral, developmental, and transcriptomic readouts with \u003cem\u003ein silico\u003c/em\u003e toxicokinetic and toxicodynamic models, such as stress-responsive gene network models, leads to more accurate prediction of chemical risk, dose\u0026ndash;response behavior, and long-term neurodegenerative trajectories (\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. Opportunities for Advancing Parkinson’s Disease Research Through Emerging Experimental and Computational Strategies","content":"\u003cp\u003eBoth machine learning and \u003cem\u003ein silico\u003c/em\u003e tools play an important role in predictive toxicology, with models like QSAR and deep learning improving toxicity forecasting and biomarker discovery. There is improvement in the translation of preclinical findings this due to the use of micro-physiological systems and PBPK modeling, which further offer human-relevant drug response predictions (\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrecision Toxicology: Integrating Environmental Exposures with Genetic Background\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor clarifying disease origins, it is important to understand how genetic and environmental factors interact across human development (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e). In PD, G\u0026times;E interactions play a major role in disease onset and progression (\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e). Environmental contributors, including pesticides, industrial chemicals, and heavy metals, have long been associated with elevated PD risk. However, no single pollutant has been confirmed as a primary cause (\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e). Occupational exposures to metals and solvents have also been explored for their potential involvement (\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e). Combining controlled genetic variation in model organisms with high-resolution computational analyses, this will enable precision toxicology to study these complex G\u0026times;E interactions (\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e). For example, transcriptomic studies in zebrafish exposed to different toxicants have shown that different chemical classes lead to different gene-expression patterns and co-expression networks, indicating that pollutants leave distinct molecular signatures. Integrating these signatures with genetic variation data can help target biological pathways that confer vulnerability to PD (\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e). For example, animal models that use genetic susceptibility with environmental toxins such as rotenone or MPTP can mimic PD features. These models show that SNCA mutations amplify pesticide-induced α-synuclein pathology, while LRRK2 variants heighten sensitivity to metal exposures, offering mechanistic links between sporadic and familial PD and enabling targeted intervention testing (\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e). Computational methods have advantages as they lead to precision toxicology. Polygenic risk scores and epigenetic modeling predict how environmental exposures influence gene regulation in PD-relevant cell types (\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e). Combining multi-omics datasets with exposure histories supports personalized risk assessment, informs drug repurposing strategies, and accelerates biomarker discovery for early diagnosis and therapeutic monitoring (\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRapid Drug Discovery Enabled by Zebrafish and Computational/AI Tools\u003c/b\u003e \u003c/p\u003e \u003cp\u003eZebrafish have formed a powerful vertebrate platform for high-throughput screening of neuroprotective or neurorestorative compounds relevant to PD (\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e). For example, a recent study targeting the renin-angiotensin-aldosterone system (RAAS), using a zebrafish model of dopaminergic-neuron ablation, screened over 1,400 bioactive compounds and identified several candidate neuroprotective agents, showing the potential for rapid preclinical drug discovery (\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e). Zebrafish assays are highly scalable and compatible with automated imaging and behavioral tracking, allowing for combination with computational drug-matching algorithms, phenotypic clustering, and machine-learning frameworks for hit prioritization. Larval zebrafish offer behavioral data that can be used as input for deep learning or neural-network classification pipelines to discriminate genotypes or treatment effects, streamlining candidate selection (\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAI applications in zebrafish research help in high image recognition and automated analysis, improving behavioral, genetic, and neural assessments. This led to enhanced identification of gene\u0026ndash;function relationships, disease modeling, and therapeutic development. The advance now enables automated tracking, image recognition, and large-scale data processing, offering objective, reproducible, and high-throughput analysis of the large datasets generated in experimental zebrafish studies (\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e). AI in new research is increasingly used to analyze the behavior of many individual zebrafish, ranging from a few to hundreds, while also detecting the effects of chemical exposures and their interactions. Both conventional behavioral analyses and AI-based approaches were used to assess cognitive and locomotor effects in adult zebrafish that were treated with the neurotoxin MPTP to model PD. Using the Y-maze test, zebrafish exposed to MPTP exhibited impaired spatial working memory, indicating cognitive deficits. AI analysis further revealed a distinct swimming-pattern cluster specific to the high-dose group, demonstrating that MPTP produced unique, quantifiable behavioral changes detectable independently of human scoring (\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e). All these findings together highlight how AI enhances the sensitivity, objectivity, and throughput of behavioral phenotyping in experimental neurotoxicology, making it a powerful complement to zebrafish-based drug discovery and disease modeling.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMulti-Ancestry Genetic Insights with Zebrafish Functional Validation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eZebrafish have genetic similarity to humans, easily manipulable gene expression, and relevance to human pathology; because of that, they are a powerful model for human disease research (\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e). Zebrafish allow functional validation of candidate genes and variants identified in large-scale genetic studies, including rare or non-coding variants linked to PD, due to their sequenced genome, high homology, and conserved synteny with humans. The embryos\u0026rsquo; sensitivity to drugs, combined with CRISPR/Cas9 accessibility, enables researchers to assess neurodevelopment, neurodegeneration, and behavior, making zebrafish ideal for testing disease mechanisms and potential therapies. It is important to include diverse ancestries, functional follow-up in systems capable of modeling broad genetic variation. Zebrafish provide a platform, helping ensure that findings are globally relevant and reducing translational bias toward populations. Beyond neurodegeneration, zebrafish have successfully modeled numerous human neurogenetic disorders, further highlighting their utility in neurogenetics and functional genomics (\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e). In PD, mutations in 15 genes have been linked to monogenic forms, yet these account for only\u0026thinsp;~\u0026thinsp;30% of monogenic cases and 3\u0026ndash;5% of genetically complex cases (\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e). Among these, LRRK2 variants are the most common heritable cause, with the p.G2019S mutation contributing to ~\u0026thinsp;1% of sporadic cases and 4% of familial cases (\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e). Zebrafish models provide an important bridge between genetic discovery and functional validation, enabling mechanistic insights that span both common and rare genetic contributors to PD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClimate Change\u0026ndash;Related Neurotoxic Exposures and PD Risk\u003c/b\u003e \u003c/p\u003e \u003cp\u003eClimate change is a major challenge to nervous system health through both gradual environmental changes and acute pollution events (\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e). Among the environmental change factors, neurotoxic pollutants, including certain pesticides, industrial solvents such as trichloroethylene (TCE), and airborne particulate matter, are well-established contributors to PD. Studies show that many of these agents lead to mitochondrial dysfunction, induce OS, and gain access to the body through occupational and environmental exposure pathways (\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e). As illustrated \u003cb\u003ein\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, environmental pollution contributes to PD pathogenesis through interconnected mechanisms. Air pollution, particularly fine particulate matter (PM2.5), has been highlighted as a major concern. PM2.5 can enter the central nervous system via the lungs and bloodstream, where it causes inflammation, OS, and DNA damage, increasing the risk of neurodegenerative diseases and stroke (\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e). Studying these environmental contributors is not easy because of long latency periods and the difficulty in reconstructing lifetime exposure histories. In PD specifically, disease onset often predates clinical diagnosis by many years or even decades (\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e). Making early environmental contributions difficult to trace. Modeling long-term pollution effects is still complex, because risk depends on dose, duration, and timing of exposure. In this context, adverse outcome pathways (AOPs) provide a mechanistic framework linking early molecular biomarkers to later disease outcomes, enabling more systematic study of environmental drivers of PD (\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e). Mechanistically, many PD-associated toxicants, including pesticides and industrial solvents, impair mitochondrial function, increasing OS within dopaminergic neurons. TCE and other mitochondrial toxicants also interact with genetic risk factors, such as inhibition of LRRK2 (a major PD gene) reduces ROS production and mitigates toxicant-induced cellular damage \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e (\u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e). This leads to enhanced gene\u0026ndash;environment synergy, where variation in genes affecting mitochondrial quality control, autophagy, and proteostasis amplifies the neurodegenerative impact of environmental insults. A recent conceptual framework integrates these ideas, proposing that genetic mutations compromise mitochondrial maintenance while environmental toxicants further damage mitochondrial networks, together accelerating dopaminergic neuron loss and PD onset (\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e). To understand the complexity of these interactions, hybrid approaches combining high-throughput animal models with computational toxicology, network biology, and systems-biology tools are increasingly used. An example of this, network analysis used to map how diverse environmental contaminants target the main hub proteins in the human interactome, revealing biological pathways through which exposures may influence neurodegenerative disease risk (\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e). These integrative strategies help clarify how dose, timing, and mixtures of exposures interact with individual genetic backgrounds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModeling PD Progression, Longevity, and Neuroprotection\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePD models focus on acute neuronal loss rather than long-term disease progression, resilience, or recovery, which form a major limitation. Adult zebrafish are an excellent system to study not just degeneration but also spontaneous repair, neuroprotection, and resilience because they can regenerate dopaminergic neurons after neurotoxic injury (\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e). Furthermore, after dopaminergic neuron degeneration (e.g., via 6-hydroxydopamine, 6-OHDA) in adult zebrafish, regeneration occurs within weeks, along with behavioral recovery (\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e). These features make it easy to study factors that affect disease progression, neuronal vulnerability, aging, neuroprotection, and potential longevity-related pathways, especially when integrated with computational models of gene networks, stress response, and aging dynamics(\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e).\u003c/p\u003e"},{"header":"7. Limitations and Challenges","content":"\u003cp\u003eSeveral limitations still exist with full translational impact in PD research, even with rapid advances in zebrafish models, computational tools, and AI-enhanced analytics (\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e). Variability in zebrafish behavioral assays remains a major challenge in neurotoxicology and PD research (\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e). Behavioral outputs such as locomotion, habituation, and learning are highly sensitive to many factors, including age, sex, tank geometry, illumination, water chemistry, and handling, leading to substantial within- and between-laboratory variability (\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e). Even in adult zebrafish, longitudinal behavioral studies reveal significant intra-individual fluctuations over time, reducing statistical robustness and making interpretation of subtle neurobehavioral phenotypes challenging (\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e). Another challenge lies in the lack of standardized computational pipelines for analyzing the increasingly large datasets produced by high-throughput imaging and behavioral tracking (\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e). It is difficult to reproduce findings or compare results across laboratories because of variation between choices in data preprocessing, feature extraction, normalization, and analysis. Reviews emphasize that inconsistent metadata reporting, such as developmental stage, exposure conditions, or behavioral endpoints, remains a major barrier to integration of zebrafish datasets into broader toxicological frameworks (\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e).AI and deep learning help to decrease the bias in behavioral analysis. However, their effectiveness is limited by the need for large, diverse, and well-annotated datasets. Although recent work shows that machine-vision and pose-estimation tools can classify complex zebrafish behaviors and detect treatment-related differences, their performance is often limited by small sample sizes, limited phenotypic variability, and inconsistent labeling common in academic datasets (\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e). Even though zebrafish share major neurotransmitter systems and core molecular pathways with mammals, their brain organization, immune responses, metabolism, and lifespan differ substantially (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). These differences limit mimicking certain behavioral or neurodegenerative phenotypes in human disease. Zebrafish models typically capture acute or sub-acute exposures, whereas human PD often develops after decades of low-dose environmental exposure, an aspect difficult to replicate in short-lived species (\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e). Collectively, these limitations show the need for harmonized experimental protocols, robust computational standards, larger and better-annotated datasets, and cross-model validation strategies to fully realize the translational potential of zebrafish and AI-driven approaches in advancing PD research.\u003c/p\u003e"},{"header":"8. Future Directions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFuture research is expected to focus on technologies that enhance the precision, scalability, and translational relevance of PD models. Automated detection of subtle motor, cognitive, and sensorimotor impairments that are impossible to identify manually, AI-driven zebrafish phenotyping will enable to identification it, especially with high-resolution, video-based behavioral tracking, which will enable earlier and more sensitive identification of neurotoxic effects (\u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e). A recent study demonstrated that an AI-based neural-network system could reliably identify behavioral patterns in adult zebrafish treated with psychoactive drugs, underscoring the feasibility of AI-driven movement-pattern classification in CNS drug and disease research (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording on these advances, we propose a hybrid zebrafish\u0026ndash;AI framework that integrates \u003cem\u003ein vivo\u003c/em\u003e neurobiological modeling with \u003cem\u003ein silico\u003c/em\u003e predictive analytics to support early disease detection, mechanistic insight, and therapeutic evaluation in PD \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eDigital twins a virtual patient replicas built from molecular, behavioral, and environmental data, forming a powerful tool in personalized medicine, enabling simulation of disease progression and prediction of individual treatment responses (\u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e163\u003c/span\u003e). This concept parallels recent advances in other fields where wearable-based digital phenotyping has been integrated with genomic data and AI to predict psychiatric and neurological disorders (\u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e164\u003c/span\u003e). Climate change is increasingly identified as a global health threat with unclear impacts on brain disorders (\u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e165\u003c/span\u003e), it is important to include this change, like temperature shifts, pollutant patterns, and extreme weather, into neurodegeneration models to clarify population-level PD risk, using the zebrafish model, which offers a scalable platform for studying environmental neurotoxicity under changing climate conditions.\u003c/p\u003e \u003cp\u003eDrosophila melanogaster is usually used as a model for neurodegeneration research because of its highly conserved dopaminergic circuitry, rapid generation time, and ease of genetic manipulation (\u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e166\u003c/span\u003e). Multi-model hybrid platforms offer a strong strategy for enhancing mechanistic discovery in PD by integrating the strengths of multiple experimental systems. Combining zebrafish, invertebrate models (e.g., Drosophila), mammalian systems, and advanced \u003cem\u003ein silico\u003c/em\u003e tools will further increase mechanistic discovery by capturing conserved pathways while allowing high-throughput hypothesis testing. Reviews of zebrafish neurological disease models highlight their flexibility in genetic manipulation, neuroanatomical imaging, and compatibility with chemical screens, making them well-suited for integration into hybrid pipelines (\u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e167\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMulti-ancestry genomic datasets, such as the Global Parkinson\u0026rsquo;s Genetics Program (GP2), identify ancestry-specific risk variants and their interactions with exposures, which help to understand how genetic diversity affects susceptibility to environmental neurotoxins (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e). The use of federated, multi-ancestry genomic datasets, such as those generated by the GP2, leads to enhanced genetic analyses, improved identification of ancestry-specific risk variants, and a deeper understanding of how genomic diversity shapes susceptibility to environmental neurotoxins. GP2\u0026rsquo;s global scale and multi-ancestry design make it a powerful resource for linking genetic variation to disease risk across populations (\u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e168\u003c/span\u003e). Together, these directions point toward an integrated, data-driven ecosystem for PD research that connects molecular biology, computational modeling, environmental science, and global population genetics.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProposed hybrid zebrafish\u0026ndash;AI workflow for PD modeling and translational research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecific Aim\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific Question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperimental (In Vivo) Workflow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIn Silico / AI Workflow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKey Outputs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAim 1: Establish graded zebrafish models of Parkinson\u0026rsquo;s disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan zebrafish models recapitulate early and progressive PD phenotypes?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Induce PD using MPTP/rotenone (larvae \u0026amp; adults) \u0026bull; Include low, medium, and high doses to model prodromal \u0026rarr; advanced PD \u0026bull; Optional genetic models (\u003cem\u003epink1\u003c/em\u003e, \u003cem\u003epark2\u003c/em\u003e, \u003cem\u003egba1\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Label datasets by exposure level and disease stage \u0026bull; Create baseline phenotypic clusters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; Validated PD severity spectrum \u0026bull; Reference dataset for AI training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAim 2: Capture high-resolution behavioral phenotypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan subtle, early behavioral changes be detected before overt motor deficits?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Continuous video-based tracking (30\u0026ndash;60 fps) \u0026bull; Longitudinal monitoring (days\u0026ndash;weeks) \u0026bull; Quantify locomotion, turning, freezing, startle response, circadian activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Extract time-series behavioral features \u0026bull; Generate digital behavioral biomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; High-dimensional behavioral dataset \u0026bull; Early PD behavioral signatures\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAim 3: Anchor AI predictions to neurobiological pathology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDo AI-detected phenotypes correlate with dopaminergic neurodegeneration?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; TH\u0026thinsp;+\u0026thinsp;neuron quantification \u0026bull; Dopamine measurement \u0026bull; Oxidative stress \u0026amp; mitochondrial markers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Regression models linking behavior to neuronal loss \u0026bull; Feature importance analysis (SHAP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull;Biologically validated AI outputs \u0026bull; Interpretable biomarkers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAim 4: Develop AI models for early PD detection and staging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan AI detect PD earlier than conventional assays?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Use blinded biological samples \u0026bull; Include early-stage and sub-threshold phenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Train ML/DL models (RF, XGBoost, CNN-LSTM) \u0026bull; Binary \u0026amp; multiclass classification (control/early/late PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; Early-detection AI model \u0026bull; PD staging algorithm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAim 5: Validate the hybrid model using therapeutic perturbation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan AI detect treatment response earlier and more sensitively than manual scoring?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Treat PD zebrafish with L-DOPA, MAO-B inhibitors, or neuroprotective compounds \u0026bull; Assess behavioral and molecular rescue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Predict treatment response trajectories \u0026bull; Compare AI sensitivity vs traditional endpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; AI-based treatment response markers \u0026bull; Translational relevance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAim 6: Establish an iterative hybrid biology\u0026ndash;AI framework\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan biological insight continuously refine AI performance?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Use AI-identified features to refine assays \u0026bull; Design targeted follow-up experiments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Retrain models with refined datasets \u0026bull; Improve prediction accuracy and explainability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; Scalable hybrid PD modeling platform\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"9. Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCombining zebrafish biology with advanced computational and AI-driven technologies is reshaping the landscape of PD research. Researchers can explore disease mechanisms with high resolution and scale by integrating high-throughput \u003cem\u003ein vivo\u003c/em\u003e models with advanced behavioral analytics, digital simulations, and multi-model hybrid platforms. These approaches not only increase the quality of detection of subtle neurotoxic effects and gene\u0026ndash;environment interactions but also enhance the predictive power and translational relevance of experimental results. Currently, climate-related environmental risks, complex genetic architectures, and long preclinical disease phases form a challenge to traditional research frameworks. Here is hybrid models offer a path toward more precise, integrative, and human-relevant insights. Combining zebrafish systems alongside invertebrate, mammalian, and \u003cem\u003ein silico\u003c/em\u003e tools will accelerate mechanistic discoveries and support the development of more effective strategies for early detection, intervention, and therapeutic innovation in PD.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed or generated during this study are included in this published article and its supplementary information files. No new datasets were generated or analyzed beyond those already available in the public domain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies involving human participants or animals performed by any of the authors. Therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual person’s data are included in this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMK and JK conceptualized the review framework and conducted the literature survey on zebrafish models of Parkinson\u0026rsquo;s disease. TS contributed to the in silico and computational neuroscience components, including AI-driven predictive analytics, systems biology approaches, and data integration strategies. WM conceived and supervised the overall study, integrated the experimental and computational perspectives, and led the writing and critical revision of the manuscript. All authors contributed to manuscript drafting, reviewed the final version, and approved it for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRazali K, Othman N, Mohd Nasir MH, Doolaanea AA, Kumar J, Ibrahim WN, et al. The Promise of the Zebrafish Model for Parkinson\u0026rsquo;s Disease: Today\u0026rsquo;s Science and Tomorrow\u0026rsquo;s Treatment. Front Genet [Internet]. 2021 Apr 15 [cited 2025 Nov 27];12. Available from: https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.655550/full\u003c/li\u003e\n\u003cli\u003eDeMaagd G, Philip A. Parkinson\u0026rsquo;s Disease and Its Management. P T. 2015 Aug;40(8):504\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eLopez A, Gorb A, Palha N, Fleming A, Rubinsztein DC. A New Zebrafish Model to Measure Neuronal \u0026alpha;-Synuclein Clearance In Vivo. Genes (Basel). 2022 May 12;13(5):868. \u003c/li\u003e\n\u003cli\u003eLuo Y, Qiao L, Li M, Wen X, Zhang W, Li X. Global, regional, national epidemiology and trends of Parkinson\u0026rsquo;s disease from 1990 to 2021: findings from the Global Burden of Disease Study 2021. Front Aging Neurosci [Internet]. 2025 Jan 10 [cited 2025 Nov 27];16. Available from: https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2024.1498756/full\u003c/li\u003e\n\u003cli\u003eSu D, Cui Y, He C, Yin P, Bai R, Zhu J, et al. Projections for prevalence of Parkinson\u0026rsquo;s disease and its driving factors in 195 countries and territories to 2050: modelling study of Global Burden of Disease Study 2021. BMJ. 2025 Mar 5;388:e080952. \u003c/li\u003e\n\u003cli\u003eKouli A, Torsney KM, Kuan WL. Parkinson\u0026rsquo;s Disease: Etiology, Neuropathology, and Pathogenesis. In: Stoker TB, Greenland JC, editors. Parkinson\u0026rsquo;s Disease: Pathogenesis and Clinical Aspects [Internet]. Brisbane (AU): Codon Publications; 2018 [cited 2025 Nov 27]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK536722/\u003c/li\u003e\n\u003cli\u003eTysnes OB, Storstein A. Epidemiology of Parkinson\u0026rsquo;s disease. J Neural Transm. 2017 Aug 1;124(8):901\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eSubramanian G, Fanai HL, Chand J, Ahmad SF, Attia SM, Emran TB. System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson\u0026rsquo;s disease: a network pharmacology and in-silico evaluation. Sci Rep. 2024 Oct 8;14(1):23414. \u003c/li\u003e\n\u003cli\u003ePotashkin JA, Blume SR, Runkle NK. Limitations of Animal Models of Parkinson\u0026prime;s Disease. Parkinson\u0026rsquo;s Disease. 2011;2011(1):658083. \u003c/li\u003e\n\u003cli\u003eLaub V, Devraj K, Elias L, Schulte D. Bioinformatics for wet-lab scientists: practical application in sequencing analysis. BMC Genomics. 2023 July 7;24:382. \u003c/li\u003e\n\u003cli\u003ePoliquin PO, Chen J, Cloutier M, Trudeau L\u0026Eacute;, Jolicoeur M. Metabolomics and In-Silico Analysis Reveal Critical Energy Deregulations in Animal Models of Parkinson\u0026rsquo;s Disease. PLoS One. 2013 July 23;8(7):e69146. \u003c/li\u003e\n\u003cli\u003eCesur MF, Strazdins R, Sarkar S, \u0026Ccedil;akır T. In vivo and in silico models of Drosophila for Parkinson\u0026rsquo;s disease. FEBS J. 2025 Sept;292(18):4753\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eCornet C, Di Donato V, Terriente J. Combining Zebrafish and CRISPR/Cas9: Toward a More Efficient Drug Discovery Pipeline. Front Pharmacol. 2018 July 3;9:703. \u003c/li\u003e\n\u003cli\u003eMoustafa AA, Chakravarthy S, Phillips JR, Gupta A, Keri S, Polner B, et al. Motor symptoms in Parkinson\u0026rsquo;s disease: A unified framework. Neuroscience \u0026amp; Biobehavioral Reviews. 2016 Sept 1;68:727\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eLatif S, Jahangeer M, Maknoon Razia D, Ashiq M, Ghaffar A, Akram M, et al. Dopamine in Parkinson\u0026rsquo;s disease. Clinica Chimica Acta. 2021 Nov 1;522:114\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eGurevich EV, Gainetdinov RR, Gurevich VV. G protein-coupled receptor kinases as regulators of dopamine receptor functions. Pharmacol Res. 2016 Sept;111:1\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eZhou ZD, Yi LX, Wang DQ, Lim TM, Tan EK. Role of dopamine in the pathophysiology of Parkinson\u0026rsquo;s disease. Transl Neurodegener. 2023 Sept 18;12(1):44. \u003c/li\u003e\n\u003cli\u003eMaiti P, Manna J, Dunbar GL. Current understanding of the molecular mechanisms in Parkinson\u0026rsquo;s disease: Targets for potential treatments. Transl Neurodegener. 2017 Oct 25;6(1):28. \u003c/li\u003e\n\u003cli\u003eDong-Chen X, Yong C, Yang X, Chen-Yu S, Li-Hua P. Signaling pathways in Parkinson\u0026rsquo;s disease: molecular mechanisms and therapeutic interventions. Sig Transduct Target Ther. 2023 Feb 21;8(1):73. \u003c/li\u003e\n\u003cli\u003eVekrellis K, Xilouri M, Emmanouilidou E, Rideout HJ, Stefanis L. Pathological roles of \u0026alpha;-synuclein in neurological disorders. The Lancet Neurology. 2011 Nov 1;10(11):1015\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eZaman V, Shields DC, Shams R, Drasites KP, Matzelle D, Haque A, et al. Cellular and molecular pathophysiology in the progression of Parkinson\u0026rsquo;s disease. Metab Brain Dis. 2021 June;36(5):815\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eSep\u0026uacute;lveda D, Cisternas-Olmedo M, Arcos J, Nassif M, Vidal RL. Contribution of Autophagy-Lysosomal Pathway in the Exosomal Secretion of Alpha-Synuclein and Its Impact in the Progression of Parkinson\u0026rsquo;s Disease. Front Mol Neurosci [Internet]. 2022 Feb 17 [cited 2025 Nov 29];15. Available from: https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2022.805087/full\u003c/li\u003e\n\u003cli\u003ePizzino G, Irrera N, Cucinotta M, Pallio G, Mannino F, Arcoraci V, et al. Oxidative Stress: Harms and Benefits for Human Health. Oxid Med Cell Longev. 2017;2017:8416763. \u003c/li\u003e\n\u003cli\u003ePozo Devoto VM, Falzone TL. Mitochondrial dynamics in Parkinson\u0026rsquo;s disease: a role for \u0026alpha;-synuclein? Dis Model Mech. 2017 Sept 1;10(9):1075\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eAdebayo M, Singh S, Singh AP, Dasgupta S. Mitochondrial Fusion and Fission: The fine-tune balance for cellular homeostasis. FASEB J. 2021 June;35(6):e21620. \u003c/li\u003e\n\u003cli\u003eLi W, Fu Y, Halliday GM, Sue CM. PARK Genes Link Mitochondrial Dysfunction and Alpha-Synuclein Pathology in Sporadic Parkinson\u0026rsquo;s Disease. Front Cell Dev Biol [Internet]. 2021 July 6 [cited 2025 Nov 29];9. Available from: https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.612476/full\u003c/li\u003e\n\u003cli\u003eGrotemeyer A, McFleder RL, Wu J, Wischhusen J, Ip CW. Neuroinflammation in Parkinson\u0026rsquo;s Disease \u0026ndash; Putative Pathomechanisms and Targets for Disease-Modification. Front Immunol [Internet]. 2022 May 18 [cited 2025 Nov 29];13. Available from: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.878771/full\u003c/li\u003e\n\u003cli\u003eLi H, Pan H, Xiao C, Li H, Long L, Wang X, et al. IL-1\u0026beta;-mediated inflammatory responses in intervertebral disc degeneration: Mechanisms, signaling pathways, and therapeutic potential. Heliyon. 2023 Sept 7;9(9):e19951. \u003c/li\u003e\n\u003cli\u003ePansera L, Mhalhel K, Cavallaro M, Aragona M, Laur\u0026agrave; R, Levanti M, et al. Zebrafish as an Integrative Model for Central Nervous System Research: Current Advances and Translational Perspectives. Life. 2025 Nov 14;15(11):1751. \u003c/li\u003e\n\u003cli\u003eKin K, Yasuhara T, Kameda M, Date I. Animal Models for Parkinson\u0026rsquo;s Disease Research: Trends in the 2000s. Int J Mol Sci. 2019 Oct 30;20(21):5402. \u003c/li\u003e\n\u003cli\u003eSchober A. Classic toxin-induced animal models of Parkinson\u0026rsquo;s disease: 6-OHDA and MPTP. Cell Tissue Res. 2004 Oct;318(1):215\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eThe rotenone model of Parkinson\u0026rsquo;s disease: genes, environment and mitochondria. Parkinsonism \u0026amp; Related Disorders. 2003 Aug 1;9:59\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eKonnova EA, Swanberg M. Animal Models of Parkinson\u0026rsquo;s Disease. In: Stoker TB, Greenland JC, editors. Parkinson\u0026rsquo;s Disease: Pathogenesis and Clinical Aspects [Internet]. Brisbane (AU): Codon Publications; 2018 [cited 2025 Nov 30]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK536725/\u003c/li\u003e\n\u003cli\u003eTagkalidou N, Stevanović M, Romero-Alfano I, Elizalde-Vel\u0026aacute;zquez GA, Herrera-V\u0026aacute;zquez SE, Prats E, et al. Motor and Non-Motor Effects of Acute MPTP in Adult Zebrafish: Insights into Parkinson\u0026rsquo;s Disease. International Journal of Molecular Sciences. 2025 Jan;26(4):1674. \u003c/li\u003e\n\u003cli\u003eSubhan I, Siddique YH. Effect of Rotenone on the Neurodegeneration among Different Models. Curr Drug Targets. 2024;25(8):530\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eKalyn M, Hua K, Mohd Noor S, Wong CED, Ekker M. Comprehensive Analysis of Neurotoxin-Induced Ablation of Dopaminergic Neurons in Zebrafish Larvae. Biomedicines. 2019 Dec 28;8(1):1. \u003c/li\u003e\n\u003cli\u003eNoor SM, Norazit A. Fishing for synucleinopathy models. Fisheries and Aquatic Sciences. 2022;25(3):117\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eRen G, Xin S, Li S, Zhong H, Lin S. Disruption of LRRK2 Does Not Cause Specific Loss of Dopaminergic Neurons in Zebrafish. PLOS ONE. 2011 June 16;6(6):e20630. \u003c/li\u003e\n\u003cli\u003eFlinn LJ, Keatinge M, Bretaud S, Mortiboys H, Matsui H, De Felice E, et al. TigarB causes mitochondrial dysfunction and neuronal loss in PINK1 deficiency. Ann Neurol. 2014 Jan 21;74(6):837\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eFlinn L, Bretaud S, Lo C, Ingham PW, Bandmann O. Zebrafish as a new animal model for movement disorders. Journal of Neurochemistry. 2008;106(5):1991\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKeatinge M, Bui H, Menke A, Chen YC, Sokol AM, Bai Q, et al. Glucocerebrosidase 1 deficient Danio rerio mirror key pathological aspects of human Gaucher disease and provide evidence of early microglial activation preceding alpha-synuclein-independent neuronal cell death. Hum Mol Genet. 2015 Dec 1;24(23):6640\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eMeredith GE, Rademacher DJ. MPTP Mouse Models of Parkinson\u0026rsquo;s Disease: An Update. J Parkinsons Dis. 2011 Jan 1;1(1):19\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eSian J, Youdim MBH, Riederer P, Gerlach M. MPTP-Induced Parkinsonian Syndrome. In: Basic Neurochemistry: Molecular, Cellular and Medical Aspects 6th edition [Internet]. Lippincott-Raven; 1999 [cited 2025 Nov 30]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK27974/\u003c/li\u003e\n\u003cli\u003eBradbury AJ, Costall B, Domeney AM, Testa B, Jenner PG, Marsden CD, et al. The toxic actions of MPTP and its metabolite MPP+ are not mimicked by analogues of MPTP lacking an N-methyl moiety. Neurosci Lett. 1985 Oct 24;61(1\u0026ndash;2):121\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eBREESE GR, TRAYLOR TD. EFFECT OF 6-HYDROXYDOPAMINE ON BRAIN NOREPINEPHRINE AND DOPAMINE: EVIDENCE FOR SELECTIVE DEGENERATION OF CATECHOLAMINE NEURONS. J Pharmacol Exp Ther. 1970 Sept;174(3):413\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eVare\u0026scaron;lija D, Tipton KF, Davey GP, McDonald AG. 6-Hydroxydopamine: a far from simple neurotoxin. J Neural Transm (Vienna). 2020 Feb;127(2):213\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eMasini D, Plewnia C, Bertho M, Scalbert N, Caggiano V, Fisone G. A Guide to the Generation of a 6-Hydroxydopamine Mouse Model of Parkinson\u0026rsquo;s Disease for the Study of Non-Motor Symptoms. Biomedicines. 2021 June;9(6):598. \u003c/li\u003e\n\u003cli\u003eLau K, Kotzur R, Richter F. Blood\u0026ndash;brain barrier alterations and their impact on Parkinson\u0026rsquo;s disease pathogenesis and therapy. Transl Neurodegener. 2024 July 29;13(1):37. \u003c/li\u003e\n\u003cli\u003eSl\u0026eacute;zia A, Heged\u0026uuml;s P, Rusina E, Lengyel K, Solari N, Kaszas A, et al. Behavioral, neural and ultrastructural alterations in a graded-dose 6-OHDA mouse model of early-stage Parkinson\u0026rsquo;s disease. Sci Rep. 2023 Nov 9;13(1):19478. \u003c/li\u003e\n\u003cli\u003eZhang H, Yang J, Guo Y, L\u0026uuml; P, Gong X, Chen K, et al. Rotenone-induced PINK1/Parkin-mediated mitophagy: establishing a silkworm model for Parkinson\u0026rsquo;s disease potential. Front Mol Neurosci [Internet]. 2024 Apr 19 [cited 2025 Dec 1];17. Available from: https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2024.1359294/full\u003c/li\u003e\n\u003cli\u003eHeinz S, Freyberger A, Lawrenz B, Schladt L, Schmuck G, Ellinger-Ziegelbauer H. Mechanistic Investigations of the Mitochondrial Complex I Inhibitor Rotenone in the Context of Pharmacological and Safety Evaluation. Sci Rep. 2017 Apr 4;7:45465. \u003c/li\u003e\n\u003cli\u003eBhatia D, Chung KP, Nakahira K, Patino E, Rice MC, Torres LK, et al. Mitophagy-dependent macrophage reprogramming protects against kidney fibrosis. JCI Insight. 2019 Dec 5;4(23):e132826. \u003c/li\u003e\n\u003cli\u003eBarazzuol L, Giamogante F, Brini M, Cal\u0026igrave; T. PINK1/Parkin Mediated Mitophagy, Ca2+ Signalling, and ER\u0026ndash;Mitochondria Contacts in Parkinson\u0026rsquo;s Disease. International Journal of Molecular Sciences. 2020 Jan;21(5):1772. \u003c/li\u003e\n\u003cli\u003ePeng Y, Jiang D yi, Yao S yu, Zhang X, Kazuo S, Liu J, et al. Gene-modified animal models of Parkinson\u0026rsquo;s disease. Experimental Neurology. 2025 Aug 1;390:115287. \u003c/li\u003e\n\u003cli\u003eDawson TM, Ko HS, Dawson VL. Genetic Animal Models of Parkinson\u0026rsquo;s Disease. Neuron. 2010 June 10;66(5):646\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eZhang C, Chen S, Li X, Xu Q, Lin Y, Lin F, et al. Progress in Parkinson\u0026rsquo;s disease animal models of genetic defects: Characteristics and application. Biomedicine \u0026amp; Pharmacotherapy. 2022 Nov;155:113768. \u003c/li\u003e\n\u003cli\u003eWong YC, Krainc D. \u0026alpha;-synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies. Nat Med. 2017 Feb;23(2):1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003ePedersen CC, Lange J, F\u0026oslash;rland MGG, Macleod AD, Alves G, Maple-Gr\u0026oslash;dem J. A systematic review of associations between common SNCA variants and clinical heterogeneity in Parkinson\u0026rsquo;s disease. NPJ Parkinsons Dis. 2021 July 1;7:54. \u003c/li\u003e\n\u003cli\u003ePiper DA, Sastre D, Sch\u0026uuml;le B. Advancing Stem Cell Models of Alpha-Synuclein Gene Regulation in Neurodegenerative Disease. Front Neurosci [Internet]. 2018 Apr 9 [cited 2025 Dec 2];12. Available from: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00199/full\u003c/li\u003e\n\u003cli\u003eSzunyogh S, Carroll E, Wade-Martins R. Recent developments in gene therapy for Parkinson\u0026rsquo;s disease. Molecular Therapy. 2025 May 7;33(5):2052\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eHuh YE, Usnich T, Scherzer CR, Klein C, Chung SJ. GBA1 Variants and Parkinson\u0026rsquo;s Disease: Paving the Way for Targeted Therapy. J Mov Disord. 2023 Sept;16(3):261\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eSmith L, Schapira AHV. GBA Variants and Parkinson Disease: Mechanisms and Treatments. Cells. 2022 Apr 8;11(8):1261. \u003c/li\u003e\n\u003cli\u003ePolinski NK, Martinez TN, Ramboz S, Sasner M, Herberth M, Switzer R, et al. The GBA1 D409V mutation exacerbates synuclein pathology to differing extents in two alpha-synuclein models. Dis Model Mech. 2022 May 25;15(6):dmm049192. \u003c/li\u003e\n\u003cli\u003eFarfel-Becker T, Do J, Tayebi N, Sidransky E. Can GBA1-Associated Parkinson Disease Be Modeled in the Mouse? Trends Neurosci. 2019 Sept;42(9):631\u0026ndash;43. \u003c/li\u003e\n\u003cli\u003eMigdalska-Richards A, Wegrzynowicz M, Rusconi R, Deangeli G, Di Monte DA, Spillantini MG, et al. The L444P Gba1 mutation enhances alpha-synuclein induced loss of nigral dopaminergic neurons in mice. Brain. 2017 Oct 1;140(10):2706\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eAhmad A, Nkosi D, Iqbal MA. PARK2 Microdeletion or Duplications Have Been Implicated in Different Neurological Disorders Including Early Onset Parkinson Disease. Genes (Basel). 2023 Feb 27;14(3):600. \u003c/li\u003e\n\u003cli\u003eDawson TM, Dawson VL. The Role of Parkin in Familial and Sporadic Parkinson\u0026rsquo;s Disease. Mov Disord. 2010;25(0 1):S32\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSong P, Peng W, Sauve V, Fakih R, Xie Z, Ysselstein D, et al. Parkinson\u0026rsquo;s disease-linked parkin mutation disrupts recycling of synaptic vesicles in human dopaminergic neurons. Neuron. 2023 Dec;111(23):3775-3788.e7. \u003c/li\u003e\n\u003cli\u003eNoda S, Sato S, Fukuda T, Tada N, Uchiyama Y, Tanaka K, et al. Loss of Parkin contributes to mitochondrial turnover and dopaminergic neuronal loss in aged mice. Neurobiology of Disease. 2020 Mar 1;136:104717. \u003c/li\u003e\n\u003cli\u003eHan R, Wang Q, Xiong X, Chen X, Tu Z, Li B, et al. Deficiency of parkin causes neurodegeneration and accumulation of pathological \u003cstrong\u003e\u0026alpha;\u003c/strong\u003e-synuclein in monkey models. J Clin Invest [Internet]. 2024 Oct 15 [cited 2025 Dec 3];134(20). Available from: https://www.jci.org/articles/view/179633\u003c/li\u003e\n\u003cli\u003eDoyle JM, Croll RP. A Critical Review of Zebrafish Models of Parkinson\u0026rsquo;s Disease. Front Pharmacol. 2022 Mar 15;13:835827. \u003c/li\u003e\n\u003cli\u003eWang Z, Chan SW, Zhao H, Miu KK, Chan WY. Outlook of PINK1/Parkin signaling in molecular etiology of Parkinson\u0026rsquo;s disease, with insights into Pink1 knockout models. Zool Res. 2023 May 18;44(3):559\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eMoisoi N, Fedele V, Edwards J, Martins LM. Loss of PINK1 enhances neurodegeneration in a mouse model of Parkinson\u0026rsquo;s disease triggered by mitochondrial stress. Neuropharmacology. 2014 Feb;77:350\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eRen X, Butterfield DA. Fidelity of the \u003cem\u003ePINK1\u003c/em\u003e knockout rat to oxidative stress and other characteristics of Parkinson disease. Free Radical Biology and Medicine. 2021 Feb 1;163:88\u0026ndash;101. \u003c/li\u003e\n\u003cli\u003eZhu C, Herbst S, Lewis PA. Leucine-rich repeat kinase 2 at a glance. J Cell Sci. 2023 Sept 12;136(17):jcs259724. \u003c/li\u003e\n\u003cli\u003eGan LH, Sun YM, Zhou XY, Qi ZY, Liu FT, Tang YL, et al. The homozygous LRRK2.p.N1437D point mutation mouse is a novel model of parkinsonism. npj Parkinsons Dis. 2025 Mar 28;11(1):61. \u003c/li\u003e\n\u003cli\u003eDomenicale C, Magnabosco S, Morari M. Modeling Parkinson\u0026rsquo;s disease in LRRK2 rodents. Neuronal Signal. 2023 Aug 16;7(3):NS20220040. \u003c/li\u003e\n\u003cli\u003eXu Q, Shenoy S, Li C. Mouse models for LRRK2 Parkinson\u0026rsquo;s disease. Parkinsonism \u0026amp; Related Disorders. 2012 Jan 1;18:S186\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFontana BD, Mezzomo NJ, Kalueff AV, Rosemberg DB. The developing utility of zebrafish models of neurological and neuropsychiatric disorders: A critical review. Experimental Neurology. 2018 Jan 1;299:157\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eDu Y, Guo Q, Shan M, Wu Y, Huang S, Zhao H, et al. Spatial and Temporal Distribution of Dopaminergic Neurons during Development in Zebrafish. Front Neuroanat. 2016 Nov 28;10:115. \u003c/li\u003e\n\u003cli\u003eHimwaba M, Jarnda KV, Dweh MT, Liu X, Xiang Y, Zou X, et al. The role of in silico and in vitro models in Parkinson\u0026rsquo;s disease: Drug discovery and therapy applications. Ageing Research Reviews. 2026 Jan 1;113:102953. \u003c/li\u003e\n\u003cli\u003eSchmidt BJ, Papin JA, Musante CJ. Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today. 2013 Feb;18(0):116\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eYu Y, Wang X, Wang Q, Wang Q. A review of computational modeling and deep brain stimulation: applications to Parkinson\u0026rsquo;s disease. Appl Math Mech. 2020;41(12):1747\u0026ndash;68. \u003c/li\u003e\n\u003cli\u003eZhang J, Zhou W, Yu H, Wang T, Wang X, Liu L, et al. Prediction of Parkinson\u0026rsquo;s Disease Using Machine Learning Methods. Biomolecules. 2023 Dec 8;13(12):1761. \u003c/li\u003e\n\u003cli\u003eSood M, Suenkel U, von Thaler AK, Zacharias HU, Brockmann K, Eschweiler GW, et al. Bayesian network modeling of risk and prodromal markers of Parkinson\u0026rsquo;s disease. PLoS One. 2023 Feb 24;18(2):e0280609. \u003c/li\u003e\n\u003cli\u003eSmith SL, Lones MA, Bedder M, Alty JE, Cosgrove J, Maguire RJ, et al. Computational approaches for understanding the diagnosis and treatment of Parkinson\u0026rsquo;s disease. IET Systems Biology. 2015 Dec;9(6):226\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eSunny S, Prakash PB, Gopakumar G, Jayaraj PB. DeepBindPPI: Protein\u0026ndash;Protein Binding Site Prediction Using Attention Based Graph Convolutional Network. Protein J. 2023 May 18;1\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eMakhouri FR, Ghasemi JB. In Silico Studies in Drug Research Against Neurodegenerative Diseases. Curr Neuropharmacol. 2018 July;16(6):664\u0026ndash;725. \u003c/li\u003e\n\u003cli\u003eSliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational Methods in Drug Discovery. Pharmacol Rev. 2014 Jan;66(1):334\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eSharan S, Kumar P, Ambasta RK. Discovery of Novel Compounds Targeting DJ-1 as Neuroprotectants for Parkinson\u0026rsquo;s Disease by Virtual Screening and In Silico Method. Curr Comput Aided Drug Des. 2021;17(3):351\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eWei C, Heh CH, Lit LC, Chin SP. Comparative Molecular Dynamics Reveals How LRRK2 Inhibitors Distinguish G2019S from Wild-Type. Neurochem Res. 2025;50(4):259. \u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a MC, Cuesta SA, Mora JR, Paz JL, Marrero-Ponce Y, Alexis F, et al. Using computer modeling to find new LRRK2 inhibitors for parkinson\u0026rsquo;s disease. Sci Rep. 2025 Feb 3;15(1):4085. \u003c/li\u003e\n\u003cli\u003eChia S, Faidon Brotzakis Z, Horne RI, Possenti A, Mannini B, Cataldi R, et al. Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of \u0026alpha;-Synuclein Aggregates. Mol Pharmaceutics. 2023 Jan 2;20(1):183\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eYang J, Hu J, Zhang G, Qin L, Wen H, Tang Y. Pharmacophore modeling and 3D-QSAR study for the design of novel \u0026alpha;-synuclein aggregation inhibitors. J Mol Model. 2021 Aug 25;27(9):260. \u003c/li\u003e\n\u003cli\u003eBoulaamane Y, Jangid K, Britel MR, Maurady A. Probing the molecular mechanisms of \u0026alpha;-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations. Mol Divers. 2024 Aug;28(4):2495\u0026ndash;511. \u003c/li\u003e\n\u003cli\u003eGupta G, Joshi D, Narayan G, Sharma S. Exploration of novel phytochemicals as \u0026alpha;-synuclein aggregation inhibitors in the context of Parkinson\u0026rsquo;s disease therapy: an in-silico approach. In Silico Pharmacol. 2025;13(1):37. \u003c/li\u003e\n\u003cli\u003eMettai M, Daoud I, Melkemi N. In Silico Approaches for the Study of New Anti-Parkinson\u0026rsquo;s Agents. Chemistry Proceedings. 2023 Nov 1;14(1):55. \u003c/li\u003e\n\u003cli\u003eKhan A, Kaushik AC, Shujait Ali S, Ahmad N, Wei DQ. Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson\u0026rsquo;s disease. RSC Advances. 2019;9(18):10326\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eThai KM, Pham DT, Ngo TM, Nguyen HT, Nguyen PV, Pham TQ, et al. Targeting MAO-B selectivity: computational screening, docking, and molecular dynamics insights. SAR QSAR Environ Res. 2025 July;36(7):583\u0026ndash;619. \u003c/li\u003e\n\u003cli\u003eAdeniji SE, Uba S, Uzairu A. QSAR Modeling and Molecular Docking Analysis of Some Active Compounds against Mycobacterium tuberculosis Receptor (Mtb CYP121). Journal of Pathogens. 2018;2018(1):1018694. \u003c/li\u003e\n\u003cli\u003eSpeck-Planche A, Kleandrova VV. QSAR and molecular docking techniques for the discovery of potent monoamine oxidase B inhibitors: computer-aided generation of new rasagiline bioisosteres. Curr Top Med Chem. 2012;12(16):1734\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eCieślak M, Danel T, Krzysztyńska-Kuleta O, Kalinowska-Tłuścik J. Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors. Sci Rep. 2024 Apr 8;14(1):8228. \u003c/li\u003e\n\u003cli\u003eRazali K, Algantri K, Loh SP, Cheng SH, Mohamed W. Integrating nutriepigenomics in Parkinson\u0026rsquo;s disease management: New promising strategy in the omics era. IBRO Neurosci Rep. 2022 Oct 12;13:364\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eTrifonova OP, Maslov DL, Balashova EE, Urazgildeeva GR, Abaimov DA, Fedotova EY, et al. Parkinson\u0026rsquo;s Disease: Available Clinical and Promising Omics Tests for Diagnostics, Disease Risk Assessment, and Pharmacotherapy Personalization. Diagnostics. 2020 May;10(5):339. \u003c/li\u003e\n\u003cli\u003eHossein-Nezhad A, Fatemi RP, Ahmad R, Peskind ER, Zabetian CP, Hu SC, et al. Transcriptomic Profiling of Extracellular RNAs Present in Cerebrospinal Fluid Identifies Differentially Expressed Transcripts in Parkinson\u0026rsquo;s Disease. J Parkinsons Dis. 2016;6(1):109\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eNido GS, Dick F, Toker L, Petersen K, Alves G, Tysnes OB, et al. Common gene expression signatures in Parkinson\u0026rsquo;s disease are driven by changes in cell composition. acta neuropathol commun. 2020 Apr 21;8(1):55. \u003c/li\u003e\n\u003cli\u003eFan LY, Yang J, Liu RY, Kong Y, Guo GY, Xu YM. Integrating single-nucleus sequence profiling to reveal the transcriptional dynamics of Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, and multiple sclerosis. J Transl Med. 2023 Sept 21;21(1):649. \u003c/li\u003e\n\u003cli\u003eZhang FL, Li AY, Niu YL, Zhang K, Zhao MH, Huang JJ, et al. Identification of biomarkers in Parkinson\u0026rsquo;s disease by comparative transcriptome analysis and WGCNA highlights the role of oligodendrocyte precursor cells. Front Aging Neurosci. 2024;16:1485722. \u003c/li\u003e\n\u003cli\u003eDumitriu A, Golji J, Labadorf AT, Gao B, Beach TG, Myers RH, et al. Integrative analyses of proteomics and RNA transcriptomics implicate mitochondrial processes, protein folding pathways and GWAS loci in Parkinson disease. BMC Med Genomics. 2016 Jan 21;9(1):5. \u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez-Silva D, Cabello C, Cayuela ML, P\u0026eacute;rez-S\u0026aacute;nchez H, Alcaraz-P\u0026eacute;rez F. Synergizing Virtual Screening and Zebrafish Models to Identify Resveratrol-Derived Antiaging Polyphenols. Pharmaceuticals. 2025 Nov;18(11):1630. \u003c/li\u003e\n\u003cli\u003eSiddiqui S, Siddiqui H, Riguene E, Nomikos M. Zebrafish: A Versatile and Powerful Model for Biomedical Research. Bioessays. 2025 Dec;47(12):e70080. \u003c/li\u003e\n\u003cli\u003eHermann AC, Millard PJ, Blake SL, Kim CH. Development of a respiratory burst assay using zebrafish kidneys and embryos. Journal of Immunological Methods. 2004 Sept 1;292(1):119\u0026ndash;29. \u003c/li\u003e\n\u003cli\u003eChakraborty C, Hsu CH, Wen ZH, Lin CS, Agoramoorthy G. Zebrafish: a complete animal model for in vivo drug discovery and development. Curr Drug Metab. 2009 Feb;10(2):116\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003ePatton EE, Zon LI, Langenau DM. Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials. Nat Rev Drug Discov. 2021 Aug;20(8):611\u0026ndash;28. \u003c/li\u003e\n\u003cli\u003eFernandes DC, Tambourgi DV. Complement System Inhibitory Drugs in a Zebrafish (Danio rerio) Model: Computational Modeling. Int J Mol Sci. 2023 Sept 9;24(18):13895. \u003c/li\u003e\n\u003cli\u003eMwaffo V, Butail S, Porfiri M. In-silico experiments of zebrafish behaviour: modeling swimming in three dimensions. Sci Rep. 2017 Jan 10;7(1):39877. \u003c/li\u003e\n\u003cli\u003eWang W, Gao X, Liu L, Guo S, Duan J ao, Xiao P. Zebrafish as a vertebrate model for high-throughput drug toxicity screening: Mechanisms, novel techniques, and future perspectives. J Pharm Anal. 2025 Sept;15(9):101195. \u003c/li\u003e\n\u003cli\u003eValderrama B, Daly I, Gunnigle E, O\u0026rsquo;Riordan KJ, Chichlowski M, Banerjee S, et al. From in silico screening to in vivo validation in zebrafish - a framework for reeling in the right psychobiotics. Food Funct. 2025 Mar 3;16(5):2018\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eTripathi P, Ganeshpurkar A, Singh SK, Krishnamurthy S. Identification of novel glucocerebrosidase chaperone for potential treatment of Parkinson\u0026rsquo;s disease: An approach using \u003cem\u003ein silico\u003c/em\u003e virtual screening, molecular docking and molecular dynamics, and \u003cem\u003ein vitro\u003c/em\u003e studies. International Journal of Biological Macromolecules. 2023 Feb 15;228:453\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003ePriyadharshini GB, Kondapuram SK, Jaynthy C. Revealing the neuroprotection efficacy of marine sponge extract in Parkinson\u0026rsquo;s disease using gene expression and molecular docking. Gene Reports. 2025 Sept 1;40:102288. \u003c/li\u003e\n\u003cli\u003eLukovikov DA, Kolesnikova TO, Ikrin AN, Prokhorenko NO, Shevlyakov AD, Korotaev AA, et al. A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish. J Neurosci Methods. 2024 Nov;411:110256. \u003c/li\u003e\n\u003cli\u003eTaylor KL, Grant NJ, Temperley ND, Patton EE. Small molecule screening in zebrafish: an in vivo approach to identifying new chemical tools and drug leads. Cell Commun Signal. 2010 June 12;8(1):11. \u003c/li\u003e\n\u003cli\u003eDel Rosario Hern\u0026aacute;ndez T, Gore SV, Kreiling JA, Creton R. Drug repurposing for neurodegenerative diseases using Zebrafish behavioral profiles. Biomedicine \u0026amp; Pharmacotherapy. 2024 Feb 1;171:116096. \u003c/li\u003e\n\u003cli\u003eGendelev L, Taylor J, Myers-Turnbull D, Chen S, McCarroll MN, Arkin MR, et al. Deep phenotypic profiling of neuroactive drugs in larval zebrafish. Nat Commun. 2024 Nov 17;15(1):9955. \u003c/li\u003e\n\u003cli\u003eLescouz\u0026egrave;res L, Hassen‐Khodja C, Baudot A, Bordignon B, Bomont P. A multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN. EMBO Mol Med. 2023 May 5;15(7):EMMM202216267. \u003c/li\u003e\n\u003cli\u003eShankar P, McClure RS, Waters KM, Tanguay RL. Gene co-expression network analysis in zebrafish reveals chemical class specific modules. BMC Genomics. 2021 Sept 13;22(1):658. \u003c/li\u003e\n\u003cli\u003eWang RL, Bencic D, Biales A, Flick R, Lazorchak J, Villeneuve D, et al. Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio). BMC Genomics. 2012 Aug 1;13(1):358. \u003c/li\u003e\n\u003cli\u003eSchunck F, Kodritsch B, Busch W, Krauss M, Focks A. Integrating time-resolved $nrf2$ gene-expression data into a full GUTS model as a proxy for toxicodynamic damage in zebrafish embryo. Environ Sci Technol. 2024 Dec 17;58(50):21942\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eSon A, Park J, Kim W, Yoon Y, Lee S, Ji J, et al. Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy. Toxics. 2024 Nov;12(11):822. \u003c/li\u003e\n\u003cli\u003eMotsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, et al. Gene-environment interactions within a precision environmental health framework. Cell Genom. 2024 June 25;4(7):100591. \u003c/li\u003e\n\u003cli\u003eCannon JR, Greenamyre JT. Gene-environment interactions in Parkinson\u0026rsquo;s disease: specific evidence in humans and mammalian models. Neurobiol Dis. 2013 Sept;57:10.1016/j.nbd.2012.06.025. \u003c/li\u003e\n\u003cli\u003eYin JH, Horzmann KA. Embryonic Zebrafish as a Model for Investigating the Interaction between Environmental Pollutants and Neurodegenerative Disorders. Biomedicines. 2024 July;12(7):1559. \u003c/li\u003e\n\u003cli\u003eCannon JR, Greenamyre JT. The Role of Environmental Exposures in Neurodegeneration and Neurodegenerative Diseases. Toxicol Sci. 2011 Dec 1;124(2):225\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eTaylor TN, Greene JG, Miller GW. Behavioral phenotyping of mouse models of Parkinson\u0026rsquo;s Disease. Behav Brain Res. 2010 July 29;211(1):1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eDehestani M, Liu H, Gasser T. Polygenic Risk Scores Contribute to Personalized Medicine of Parkinson\u0026rsquo;s Disease. J Pers Med. 2021 Oct 15;11(10):1030. \u003c/li\u003e\n\u003cli\u003eKshreeraja S S, Ganesan Rajalekshmi Saraswathy, Giri Ritesh, Kamatchi Sundara Saravanan, Aarti Krishnan, Janhavi Bhargava, et al. Exploring cutting-edge strategies for drug repurposing in female cancers \u0026ndash; An insight into the tools of the trade. In: Progress in Molecular Biology and Translational Science [Internet]. Academic Press; 2024 [cited 2025 Dec 9]. p. 355\u0026ndash;415. Available from: https://www.sciencedirect.com:5037/science/chapter/bookseries/abs/pii/S1877117324001339\u003c/li\u003e\n\u003cli\u003eBangeppagari M, Manjunath A, Srinivasa A, Lee SJ. Tiny Fish, Big Hope: Zebrafish Unlocking Secrets to Fight Parkinson\u0026rsquo;s Disease. Biology (Basel). 2025 Oct 12;14(10):1397. \u003c/li\u003e\n\u003cli\u003eLe Bras A. Screening Zebrafish for Parkinson\u0026rsquo;s disease drugs. Lab Anim. 2021 Nov;50(11):310\u0026ndash;310. \u003c/li\u003e\n\u003cli\u003eFusco C, Allen A. Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour [Internet]. arXiv; 2022 [cited 2025 Dec 9]. Available from: http://arxiv.org/abs/2211.03051\u003c/li\u003e\n\u003cli\u003eFan YL, Hsu FR, Wang Y, Liao LD. Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization. Med Biol Eng Comput. 2023 Nov 1;61(11):2797\u0026ndash;814. \u003c/li\u003e\n\u003cli\u003eBashirzade AAO, Cheresiz SV, Belova AS, Drobkov AV, Korotaeva AD, Azizi-Arani S, et al. MPTP-Treated Zebrafish Recapitulate \u0026lsquo;Late-Stage\u0026rsquo; Parkinson\u0026rsquo;s-like Cognitive Decline. Toxics. 2022 Feb 4;10(2):69. \u003c/li\u003e\n\u003cli\u003eKabashi E, Brustein E, Champagne N, Drapeau P. Zebrafish models for the functional genomics of neurogenetic disorders. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 2011 Mar 1;1812(3):335\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eBillingsley KJ, Bandres-Ciga S, Saez-Atienzar S, Singleton AB. Genetic risk factors in Parkinson\u0026rsquo;s disease. Cell Tissue Res. 2018 July;373(1):9\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eJilanee SDA, Saeed M, Ahsan MU, Farooq MU, Ahmed S, Shahid H, et al. Neurological consequences of climate change: a review of emerging challenges and potential impacts on brain health. Ann Med Surg (Lond). 2025 May 26;87(7):4209\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eDorsey ER, Bloem BR. Parkinson\u0026rsquo;s Disease Is Predominantly an Environmental Disease. Journal of Parkinson\u0026rsquo;s Disease. 2024 Apr 13;14(3):451\u0026ndash;65. \u003c/li\u003e\n\u003cli\u003eFu P, Guo X, Cheung FMH, Yung KKL. The association between PM2.5 exposure and neurological disorders: A systematic review and meta-analysis. Science of The Total Environment. 2019 Mar 10;655:1240\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMurata H, Barnhill LM, Bronstein JM. Air Pollution and the Risk of Parkinson\u0026rsquo;s Disease A Review. Mov Disord. 2022 May;37(5):894\u0026ndash;904. \u003c/li\u003e\n\u003cli\u003eAnkley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD, et al. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environmental Toxicology and Chemistry. 2010 Mar 1;29(3):730\u0026ndash;41. \u003c/li\u003e\n\u003cli\u003eIlieva NM, Hoffman EK, Ghalib MA, Greenamyre JT, De Miranda BR. LRRK2 kinase inhibition protects against Parkinson\u0026rsquo;s disease-associated environmental toxicants. Neurobiol Dis. 2024 June 15;196:106522. \u003c/li\u003e\n\u003cli\u003eGouri H, Bararia Y, Malhotra V. The Mitochondrial Interactome in Parkinson\u0026rsquo;s and Huntington\u0026rsquo;s Diseases. Nat Cell Sci [Internet]. 2025 Sept 30 [cited 2025 Dec 9];3(3). Available from: https://cellnatsci.com/2958-695X/article/10-61474-ncs-2025-00017\u003c/li\u003e\n\u003cli\u003eIida M, Takemoto K. A network biology-based approach to evaluating the effect of environmental contaminants on human interactome and diseases. Ecotoxicology and Environmental Safety. 2018 Sept;160:316\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eVijayanathan Y, Lim FT, Lim SM, Long CM, Tan MP, Majeed ABA, et al. 6-OHDA-Lesioned Adult Zebrafish as a Useful Parkinson\u0026rsquo;s Disease Model for Dopaminergic Neuroregeneration. Neurotox Res. 2017 Oct;32(3):496\u0026ndash;508. \u003c/li\u003e\n\u003cli\u003eMd Hamzah N, Lim SM, Vijayanathan Y, Lim FT, Abdul Majeed AB, Tan MP, et al. Locomotor Assessment of 6-Hydroxydopamine-induced Adult Zebrafish-based Parkinson\u0026rsquo;s Disease Model. J Vis Exp. 2021 Dec 28;(178). \u003c/li\u003e\n\u003cli\u003eKogan V, Molodtcov I, Menshikov LI, Reis RJS, Fedichev P. Stability analysis of a model gene network links aging, stress resistance, and negligible senescence. Sci Rep. 2015 Aug 28;5(1):13589. \u003c/li\u003e\n\u003cli\u003eTwala B. AI-driven precision diagnosis and treatment in Parkinson\u0026rsquo;s disease: a comprehensive review and experimental analysis. Front Aging Neurosci. 2025 July 28;17:1638340. \u003c/li\u003e\n\u003cli\u003ePetersen BD, Bertoncello KT, Bonan CD. Standardizing Zebrafish Behavioral Paradigms Across Life Stages: An Effort Towards Translational Pharmacology. Front Pharmacol. 2022 Jan 20;13:833227. \u003c/li\u003e\n\u003cli\u003eGerlai R. Reproducibility and replicability in zebrafish behavioral neuroscience research. Pharmacol Biochem Behav. 2019 Mar;178:30\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eJohnson AL, Hurd PL, Mathot KJ, Hamilton TJ. Behavioural variability and repeatability in adult zebrafish (Danio rerio) using the novel tank dive test. PLoS One. 2025 Oct 27;20(10):e0335308. \u003c/li\u003e\n\u003cli\u003eHsieh JH, Behl M, Parham F, Ryan K. Exploring the Influence of Experimental Design on Toxicity Outcomes in Zebrafish Embryo Tests. Toxicol Sci. 2022 Aug 1;188(2):198\u0026ndash;207. \u003c/li\u003e\n\u003cli\u003eJarema KA, Hunter DL, Hill BN, Olin JK, Britton KN, Waalkes MR, et al. Developmental Neurotoxicity and Behavioral Screening in Larval Zebrafish with a Comparison to Other Published Results. Toxics. 2022 May 17;10(5):256. \u003c/li\u003e\n\u003cli\u003eHageter J, Efromson J, Alban B, DelGaudio A, Saliu V, Wassef M, et al. High throughput machine learning pipeline to characterize larval zebrafish motor behavior. PLoS One. 2025 Oct 17;20(10):e0335029. \u003c/li\u003e\n\u003cli\u003eBozhko DV, Myrov VO, Kolchanova SM, Polovian AI, Galumov GK, Demin KA, et al. Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses. Prog Neuropsychopharmacol Biol Psychiatry. 2022 Jan 10;112:110405. \u003c/li\u003e\n\u003cli\u003eAlexandre Vall\u0026eacute;e. Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins. Journal of Medical Internet Research [Internet]. 2024 Jan 1 [cited 2025 Dec 10];26. Available from: https://www.sciencedirect.com:5037/org/science/article/pii/S1438887124002425\u003c/li\u003e\n\u003cli\u003eLiu JJ, Borsari B, Li Y, Liu SX, Gao Y, Xin X, et al. Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell. 2025 Jan 23;188(2):515-529.e15. \u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez Lozada PS, Leon-Rojas JE. Neurological and mental health in the era of climate change: mechanisms, clinical impacts, and adaptation. Front Public Health. 2025 Sept 11;13:1630975. \u003c/li\u003e\n\u003cli\u003eHirth F. Drosophila melanogaster in the Study of Human Neurodegeneration. CNS Neurol Disord Drug Targets. 2010 Aug;9(4):504\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eBurton EA, Burgess HA. A Critical Review of Zebrafish Neurological Disease Models\u0026minus;2. Application: Functional and Neuroanatomical Phenotyping Strategies and Chemical Screens. Oxf Open Neurosci. 2023 Feb 1;2:kvac019. \u003c/li\u003e\n\u003cli\u003eGP2: The Global Parkinson\u0026rsquo;s Genetics Program. Mov Disord. 2021 Apr;36(4):842\u0026ndash;51.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables References","content":"\u003col\u003e\n\u003cli\u003eRazali K, Othman N, Mohd Nasir MH, Doolaanea AA, Kumar J, Ibrahim WN, et al. The Promise of the Zebrafish Model for Parkinson\u0026rsquo;s Disease: Today\u0026rsquo;s Science and Tomorrow\u0026rsquo;s Treatment. Front Genet [Internet]. 2021 Apr 15 [cited 2025 Nov 27];12. Available from: https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.655550/full\u003c/li\u003e\n\u003cli\u003eTagkalidou N, Stevanović M, Romero-Alfano I, Elizalde-Vel\u0026aacute;zquez GA, Herrera-V\u0026aacute;zquez SE, Prats E, et al. Motor and Non-Motor Effects of Acute MPTP in Adult Zebrafish: Insights into Parkinson\u0026rsquo;s Disease. International Journal of Molecular Sciences. 2025 Jan;26(4):1674. \u003c/li\u003e\n\u003cli\u003eSubhan I, Siddique YH. Effect of Rotenone on the Neurodegeneration among Different Models. Curr Drug Targets. 2024;25(8):530\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eKalyn M, Hua K, Mohd Noor S, Wong CED, Ekker M. Comprehensive Analysis of Neurotoxin-Induced Ablation of Dopaminergic Neurons in Zebrafish Larvae. Biomedicines. 2019 Dec 28;8(1):1. \u003c/li\u003e\n\u003cli\u003eNoor SM, Norazit A. Fishing for synucleinopathy models. Fisheries and Aquatic Sciences. 2022;25(3):117\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eRen G, Xin S, Li S, Zhong H, Lin S. Disruption of LRRK2 Does Not Cause Specific Loss of Dopaminergic Neurons in Zebrafish. PLOS ONE. 2011 June 16;6(6):e20630. \u003c/li\u003e\n\u003cli\u003eFlinn LJ, Keatinge M, Bretaud S, Mortiboys H, Matsui H, De Felice E, et al. TigarB causes mitochondrial dysfunction and neuronal loss in PINK1 deficiency. Ann Neurol. 2014 Jan 21;74(6):837\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eFlinn L, Bretaud S, Lo C, Ingham PW, Bandmann O. Zebrafish as a new animal model for movement disorders. Journal of Neurochemistry. 2008;106(5):1991\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKeatinge M, Bui H, Menke A, Chen YC, Sokol AM, Bai Q, et al. Glucocerebrosidase 1 deficient Danio rerio mirror key pathological aspects of human Gaucher disease and provide evidence of early microglial activation preceding alpha-synuclein-independent neuronal cell death. Hum Mol Genet. 2015 Dec 1;24(23):6640\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eNoda S, Sato S, Fukuda T, Tada N, Uchiyama Y, Tanaka K, et al. Loss of Parkin contributes to mitochondrial turnover and dopaminergic neuronal loss in aged mice. Neurobiology of Disease. 2020 Mar 1;136:104717. \u003c/li\u003e\n\u003cli\u003eHan R, Wang Q, Xiong X, Chen X, Tu Z, Li B, et al. Deficiency of parkin causes neurodegeneration and accumulation of pathological \u003cstrong\u003e\u0026alpha;\u003c/strong\u003e-synuclein in monkey models. J Clin Invest [Internet]. 2024 Oct 15 [cited 2025 Dec 3];134(20). Available from: https://www.jci.org/articles/view/179633\u003c/li\u003e\n\u003cli\u003eDoyle JM, Croll RP. A Critical Review of Zebrafish Models of Parkinson\u0026rsquo;s Disease. Front Pharmacol. 2022 Mar 15;13:835827. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, neurodegeneration, in silico, zebrafish, hybrid modelling","lastPublishedDoi":"10.21203/rs.3.rs-8426422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8426422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson\u0026rsquo;s Disease (PD) remains a major neurodegenerative disorder lacking disease-modifying therapies. Traditional single model approaches often fail to capture the complex molecular, environmental, and genetic interactions that drive disease heterogeneity. This review highlights the emerging paradigm of hybrid modeling, combining zebrafish (Danio rerio) experimentation with \u003cem\u003ein silico\u003c/em\u003e computational and AI-driven pipelines to advance PD research. Zebrafish provide a powerful \u003cem\u003ein vivo\u003c/em\u003e system to study dopaminergic neurodegeneration, mitochondrial dysfunction, oxidative stress, and behavioral phenotypes with high translational value. In parallel, computational neuroscience and systems biology tools, including network pharmacology, molecular docking, virtual screening, transcriptomic profiling, and machine-learning\u0026ndash;based predictive models, enable rapid hypothesis generation and therapeutic discovery. By integrating these two modalities, hybrid platforms offer a multiscale understanding of PD pathogenesis and allow efficient identification of biomarkers, drug candidates, and gene\u0026ndash;environment interactions. This review synthesizes current evidence, methodological advances, challenges, and future directions for establishing zebrafish\u0026ndash;\u003cem\u003ein\u0026ndash;silico\u003c/em\u003e hybrid pipelines as next-generation tools for PD precision research.\u003c/p\u003e","manuscriptTitle":"Hybrid modeling of Parkinsons disease integrating zebrafish neurobiology with in silico predictive analytics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 06:54:48","doi":"10.21203/rs.3.rs-8426422/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"507f0db3-7048-4eca-b957-084164f6a90b","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T05:55:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 06:54:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8426422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8426422","identity":"rs-8426422","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}