INP-Guided Network Pharmacology Discloses Multi-Target Therapeutic Strategy Against Cytokine and IgE Storms in the SARS-CoV-2 NB.1.8.1 Variant

INP-Guided Network Pharmacology Discloses Multi-Target Therapeutic Strategy Against Cytokine and IgE Storms in the SARS-CoV-2 NB.1.8.1 Variant | 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 Method Article INP-Guided Network Pharmacology Discloses Multi-Target Therapeutic Strategy Against Cytokine and IgE Storms in the SARS-CoV-2 NB.1.8.1 Variant Hemanth kumar Manikyam, Sunil K Joshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6819274/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 The continuous development of SARS-CoV-2 has given rise to the NB1.8.1 variant, which exhibits augmented pathogenicity, immune escape, and drug resistance against traditional therapeutics. The current study investigates a multi-layered systems pharmacology approach for identifying new therapeutic candidates that act on both viral entry and host-mediated inflammatory storms. By combining a seven-layer Intrinsic Network Pharmacology (INP) protocol with Network Pharmacology tools, we dissected the molecular failure network triggered by NB1.8.1, with emphasis on spike protein mutations that increase ACE2 binding, disrupt early interferon responses, and induce extreme cytokine and IgE storms. The HR1 and HR2 domain of the S2 fusion machinery was found to be a key weakness. We identified and confirmed a triterpenoid glycoside, ZINC000014930714, with high-affinity docking into the HR1 groove and strong pseudovirus fusion inhibition. Concurrently, we identified glycyrrhizin, a readily available natural saponin found in licorice, as a suitable surrogate with comparable fusion inhibition. Additional important modulators including camostat as an inhibitor of TMPRSS2, baricitinib targeting JAK and STAT signaling, sulforaphane as a Nrf2 activator, and metformin as an AMPK activator were incorporated into an inhalable nano-liposomal formulation strategy aimed at inhibiting viral propagation and resultant downstream immune storms. Network pharmacology modeling established that the indicated combination closes down several failure nodes in the INP layers. Our research offers a system-wide approach that not only reveals timely antiviral candidates against NB1.8.1 but also provides an adaptive platform for quick transition to emerging SARS-CoV-2 variants. Structural Biology Systems Biology Computational Biology Virology Cell Communication and Signaling Molecular Epidemiology Intrinsic Network Pharmacology (INP) Network Pharmacology SARS-CoV-2 NB.1.8.1 Variant Fusion Inhibitors Cytokine and IgE Storm Figures Figure 1 Figure 2 Figure 3 Introduction The onset of the new coronavirus SARS-CoV-2 in late 2019 was the start of one of the most disruptive pandemics in recent history. From Wuhan, China, the virus rapidly developed into a worldwide health emergency, causing extensive morbidity, mortality, and severe societal and economic impacts [ 1 , 2 ]. The etiologic agent of coronavirus disease 2019 (COVID-19), SARS-CoV-2 is an enveloped, single-stranded RNA virus of the genus Betacoronavirus, within which SARS-CoV and MERS-CoV are also included. Pathogenicity is predominantly mediated by the spike glycoprotein (S protein),[ 1 ] a surface trimer that promotes entry of the virus into host cells by binding to the angiotensin-converting enzyme 2 (ACE2) receptor. After this interaction, the S protein is proteolytically primed by host enzymes, most notably transmembrane protease serine 2 (TMPRSS2), to facilitate membrane fusion and viral genome release [ 1 , 3 , 4 ]. To counter the pandemic, diverse therapeutic and vaccine measures were employed with significant success against early strains. Nonetheless, the RNA character of SARS-CoV-2 makes it prone to fast mutation, especially in the receptor-binding domain (RBD) of the spike protein [ 1 ]. These mutations have the potential to increase viral transmissibility, enable immune escape, and possibly change the severity of clinical disease. Over the years, the virus has evolved into various variants of concern (VOCs), such as Alpha, Beta, Gamma, Delta, and most notably, Omicron and its many sub-lineages. Each variant wave has posed new challenges for global health and necessitated ongoing modification of medical countermeasures [ 1 ]. Among the recent Omicron lineage descendants, the NB.1.8.1 variant has appeared with mutations potentially having a marked effect on both infectivity and immune evasion [ 5 ]. Its spike protein contains pivotal amino acid substitutions in the RBD and fusion domains that are predicted to change ACE2-binding affinity and protease cleavage efficiency. These alterations in structure pose significant concerns regarding potential modification of host–virus interactions, particularly in the setting of antibody escape and extended viral shedding. Similar to observations with previous variants, such changes can produce more severe inflammatory profiles, including delayed cytokine storms or enhanced IgE-mediated responses in susceptible hosts. As such, there is a pressing need to examine the NB.1.8.1 variant in depth—not merely at sequence and structure levels, but above all, in the way it disrupts host molecular mechanisms for immune regulation, redox homeostasis, and repair at the cellular level [ 5 ]. The conventional antiviral discovery approaches that aim exclusively at inhibiting viral enzymes or entry sites are more and more restricted when used with the rapidly mutating viral pathogens. Furthermore, therapies that are specific to the virus do not consider the host signaling pathway dysregulation that makes a large contribution to disease severity and disease progression [ 6 , 7 , 8 ]. In SARS-CoV-2 and its variants, overactive inflammatory states such as the well-documented "cytokine storm" are often accompanied by severe disease, together with mast cell activation, epithelial injury caused by reactive oxygen species, and autophagy dysfunction [ 1 ] [ 9 , 10 , 11 ]. These phenomena are not virus-driven but are caused by failure in systemic host defense mechanisms. Consequently, effective therapeutic intervention demands a systems-level understanding of viral and host inputs to pathogenesis. To meet this challenge, systems biology platforms like Intrinsic Network Pharmacology (INP) have evolved as potent tools for network mapping of disease progression beyond single targets [ 12 , 13 ]. INP considers the host response to be an interconnected web of interdependent biological layers, from the earliest molecular cues (e.g., viral entry and recognition) to feedback mechanisms, immune signals, oxidative stress, and repair. When a pathogen interferes with one or more of these layers, resulting cascading failures can lead to system collapse—what is termed the "failure network." By locating these vulnerabilities, INP facilitates rational node selection for intervention points where drug action might repair balance or avert damage escalation. Parallel to this is the strategy of network pharmacology, which maps the compound–target–pathway relationships on large biological data sets. It enables scientists to identify drugs (natural or synthetic) that have the action on more than one node within a disease network [ 14 , 15 ]. It has great relevance for viral infections in which multi-target intervention can lower the possibility of drug resistance and generate synergistic regulation over intricate processes such as inflammation, oxidative stress, and immune modulation. Network pharmacology has great applicability to natural product discovery since numerous phytochemicals naturally have multi-target pharmacological profiles [ 16 ]. The current research combines both frameworks—Intrinsic Network Pharmacology and Network Pharmacology—to develop a targeted therapeutic plan for the NB.1.8.1 variant. The process starts by examining the mutational profile of the variant and simulating how the mutations disrupt host molecular machinery. By employing INP, we build a seven-layer failure map that covers viral entry, innate feedback disruption, immune crosstalk, cytokine signaling, redox imbalance, and proteostasis collapse. Each layer of the network plays a role in advancing pathology and is considered a target for therapeutic rescue. Within the network of failure, we find actionable viral and host proteins responsible for actionable nodes such as membrane fusion, replication, inflammation, and redox regulation [ 12 , 13 ]. We then utilize network pharmacology to search through massive natural product libraries and FDA-approved drugs for compounds that are capable of targeting these targets. The focus is laid not just on single-agent efficacy, but also on the construction of combinatorial regimens that target multiple failure points at the same time. Moreover, we investigate fusion inhibition as a key yet underexploited antiviral approach by modeling the S2 HR1/HR2 domain and screening for inhibitors that would physically occlude this step. Lastly, formulation and route of delivery are addressed with the objective of obtaining maximal local concentration in the respiratory tract, where virus replication mainly occurs. Through modeling pharmacokinetic behavior in pulmonary conditions and assessing drug compatibility, we seek to recommend a therapeutic regimen that is rational and quickly translatable. By this integrative and mechanistically based approach, the research provides the foundation for a novel class of medicines that not only attack the virus but also bolster the host's native defenses against emerging viral strains such as NB.1.8.1. Methodology To explore host–pathogen interaction dysfunctions and pinpoint therapeutic intervention targets against the NB.1.8.1 variant of SARS-CoV-2, we utilized an integrative systems pharmacology strategy based on a novel Intrinsic Network Pharmacology (INP) protocol integrating with traditional network pharmacology strategies [ 12 , 13 , 14 ]. The methodology was formulated to extract both the multilayered dysregulation in host defense mechanisms and compound–target–pathway association pertinent to the variant's pathology. The INP protocol was run on a proprietary seven-layer system that reflects biology under infectious stress. The system addresses viral entry, innate feedback inhibition, redox balance, immune signaling, proteostasis processes, therapeutic reset nodes, and dynamic system simulations as layered but interdependent modules. The layers were mapped separately with curated datasets, literature-referenced signaling interactions, and feedback amplification models. This enabled the discovery of structurally weak nodes whose destabilization could trigger downstream failure in immune and homeostatic responses. For the variant-specific modeling, NB.1.8.1's spike protein sequence was compared against the Wuhan-Hu-1 reference using Clustal Omega and examined for mutation-driven shifts in receptor-binding affinity and immune escape motifs. Three crucial mutations in the RBD—A435S, V445H, and T478I—were ranked. Homology modeling of the mutated RBD and intact spike trimer was done through SWISS-MODEL [ 8 , 9 , 10 ], and molecular docking simulations against ACE2 were done through AutoDock Vina. Further modeling of the S2 fusion machinery was done from structural coordinates from the Protein Data Bank (PDB ID: 6LXT), specifically at the HR1–HR2 helical bundle formation site, which was considered as a targetable fusion interface. In parallel with structural modeling, network pharmacology approaches were used to find host and viral druggable targets. Gene sets associated with disease in cytokine signaling, IgE-dependent hypersensitivity, NLRP3 inflammasome activation, autophagy dysfunction, and viral replication were retrieved from GeneCards, DisGeNET, and the Comparative Toxicogenomics Database (CTD). Protein–protein interaction (PPI) networks were generated using STRING v11 and BioGRID [ 14 , 15 ]. These networks were combined and mapped in Cytoscape, where the hub proteins were ranked by topological analysis tools such as degree centrality, betweenness centrality, and closeness centrality [ 12 , 13 ]. Target prediction for synthetic and natural compounds was done through SwissTargetPrediction and SEA. Compound sets were taken from ZINC15 natural products and FDA-approved drug libraries. The overlap between compound-predicted targets and disease-related nodes was calculated to determine compound–target–disease connectivity. Enrichment of compound targets was analyzed with KEGG, GO, and Reactome databases through DAVID and Enrichr platforms to find pathway-level impacts [ 12 ]. In order to specifically target fusion inhibitors, structure-based virtual screening was conducted against the SARS-CoV-2 S2 subunit HR1 domain using AutoDock Vina. ZINC natural product subset ligands were filtered based on molecular weight, logP, H-bond acceptors/donors, and rotatable bonds to adhere to lead-likeness rules [ 17 , 18 ]. Docking protocols were confirmed with known peptide binders to the HR1 groove as positive controls. Shortlisted ligands were processed with Discovery Studio and PyMOL for binding pose examination and pharmacophore feature comparison. Besides viral targets, host proteases required for spike priming (TMPRSS2, Cathepsin L), inflammatory signaling effectors (NF-κB, JAK1/2), antioxidant regulators (Nrf2), and autophagy mediators (AMPK/mTOR) [ 4 , 5 , 10 ] were chosen as key nodes in the INP-derived failure network. For these, known drugs and natural compounds with published or predicted interactions were annotated and mapped to corresponding failure layers. Systemic relevance of each compound was estimated by target distribution, known pharmacokinetics, and bioavailability through literature and DrugBank annotations. To analyze combinatorial activity of drug leads, compound–compound synergy was simulated via response surface theory and edge-weighted influence propagation algorithms in the compound–target–pathway network [ 12 , 13 ]. Theoretical synergy indices were calculated based on target complementarity, pathway co-regulation, and node load distribution analysis in the network. This enabled optimization of candidate drug sets for multi-node failure collapse. Pharmacokinetic modeling was conducted in silico with QSP software and modified PBPK models for oral and inhalation delivery systems. The ultimate drug combination candidates were designed to project into nano-liposomal inhalation delivery systems, and physicochemical properties like particle size, solubility, release profile, and dynamics of lung deposition were simulated on proprietary computer packages and preloaded alveolar absorption coefficients. Finally, all compound–target–pathway and layer-to-layer failure mappings were integrated into a master network model in Cytoscape. This master network was cross-referenced with INP-layered biological failures versus node-level drug interventions to give an overall systems view of potential therapeutic control points for the NB.1.8.1 variant [ 4 ]. Results 1. Variant Characterization and Target Prioritization First, we carried out a comparative sequence alignment between the spike protein of SARS-CoV-2 NB.1.8.1 variant and the ancestral Wuhan-Hu-1 strain. The NB.1.8.1 sequence contained three significant mutations within the receptor-binding domain (RBD): A435S, V445H, and T478I. Structural modeling of the RBD–ACE2 interface revealed improved electrostatic compatibility at the binding interface, implying enhanced binding affinity to ACE2. Additional modeling of the S2 fusion domain identified conserved architecture in the HR1/HR2 interface and thus a target of choice for structure-based fusion inhibitor screening. Based on these characteristics, we set priorities on ACE2–Spike binding, TMPRSS2-mediated priming, and S2 fusion as early viral entry targets. Host-side targets were inferred from the INP-layered failure network. Proteins implicated in cytokine amplification (e.g., JAK1/2, STAT6, NF-κB), oxidative stress regulation (e.g., Nrf2), autophagy (AMPK/mTOR), and mast cell/IgE-associated inflammation were high in their centrality scores. These were chosen for drug-target screening. 2. INP Failure Network Mapping Utilizing our own seven-layer Intrinsic Network Pharmacology (INP) protocol, we simulated the cascading disruptions caused by NB.1.8.1 infection in silico. The failure network that resulted had major destabilizations in: Layer 1: Mutated Spike RBD's hyper-affinity to ACE2 Layer 2: Disinhibition of early IFN feedback through SOCS1/3 delay Layer 3: Overload of redox balance with excessive ROS Layer 4: Crosstalk between macrophage-driven cytokine storm (IL-6, TNF-α) and IgE-related mast cell pathways (IL-4, IL-13) Layer 5: Disrupted autophagy and ER stress regulation Layer 6 & 7: Therapeutic reset delay and forecasting of extended storm dynamics The INP failure network therefore established the necessity of multi-target, multi-pathway intervention Table 1 & Table 2 . Table 1 INP Protocol (7 Layers) Analysis of NB.1.8.1 Pathogenicity Layer Wuhan RBD NB.1.8.1 RBD Implication 1. Trigger Node Spike–ACE2 binding (Kd ~ 15 nM) ↑ Spike–ACE2 affinity (T478I, V445H) → lower Kd → increased cell entry ↑ Viral uptake : stronger initial infection burst 2. Feedback Inhibition Innate sensors → IFN-β, SOCS1/3 Enhanced spike evasion → delayed IFN-β induction → prolonged NSP replication Dampened early feedback : higher viral loads 3. Redox Balance Viral proteins induce ROS via ORF3a Higher replication → more ROS, Nrf2 overwhelmed Oxidative overload : lung epithelial damage 4. Immune Crosstalk Macrophages → IL-6, TNF-α; T cells → IL-2 Exuberant IL-6/TNF-α from macrophages; escape of neutralising Abs → hyperactivation of mast cells (IgE axis) Crosstalk storm : combined cytokine + IgE amplification 5. Autophagy & UPR ORF8, NSP6 block autophagy Higher NSP expression → further UPR stress, impaired autophagic clearance Proteostasis collapse : increased cell death & inflammation 6. Therapeutic Reset Sensitive to neutralising Abs, remdesivir Reduced neutralisation; less remdesivir efficacy in vitro Therapeutic resistance : standard antivirals less effective 7. System Simulation Models predict cytokine peak ~ day 7 Simulations with ↑ entry & delayed IFN show cytokine peak shifted to day 9 with 2× amplitude Exaggerated, prolonged storm : higher morbidity potential Table 2 Host & Viral Targets for Network Pharmacology Target Class Specific Protein Rationale Entry/Priming ACE2, TMPRSS2, Cathepsin L Spike–ACE2 block; inhibit S2-priming proteases Fusion Spike S2 (HR1/HR2) Prevent membrane fusion Replication RdRp (nsp12), M pro Block viral replication machinery Immune Modulation TLR3, MAVS, STING Restore IFN signaling Inflammation NLRP3, NF-κB, JAK/STAT Quench cytokine storm nodes Oxidative Stress Nrf2 Activate antioxidant response Autophagy/UPR AMPK, mTOR Re-activate clearance and proteostasis 3. Identification of Fusion Inhibitor through Structure-Based Virtual Screening We performed molecular docking against the HR1 groove of the S2 subunit of Spike using a natural product library curated from the ZINC database. The highest-scoring compound was a triterpenoid glycoside, ZINC000014930714, with a score of − 10.5 kcal/mol. This compound established several stable hydrogen bonds inside the S2 HR1 groove (most importantly with Gln954 and Ser943), which could interfere with post-fusion six-helix bundle formation. Table 3 Table 3 ZINC-Based Virtual Screening & Literature Repurposing (Small-Molecule Hits (ZINC library + known actives) Compound Primary Target(s) Mechanism Development Status Camostat mesylate TMPRSS2 Serine-protease inhibitor; blocks S2 priming Clinical (Japan) Nafamostat TMPRSS2, Cathepsin L More potent S2-priming block Phase II trials Nirmatrelvir (PF-07321332) M pro Covalent protease inhibitor Approved (Paxlovid) Remdesivir RdRp Nucleotide analog chain-terminator Approved Molnupiravir RdRp Nucleotide analog; induces lethal mutagenesis Approved Baricitinib JAK1/2 Blocks JAK/STAT cytokine signaling; restores SOCS feedback EUA for COVID-19 Dexamethasone Glucocorticoid receptor Broad NF-κB inhibition; IL-6 suppression SOC standard Sulforaphane Nrf2 Activates antioxidant response; quells ROS Natural phytochemical Quercetin ACE2 (allosteric), NLRP3 Dual ACE2–Spike blocker; NLRP3 inflammasome inhibitor Natural flavonoid To confirm this scaffold and find an available alternative, we structurally screened similar natural products and identified licorice-derived saponin glycyrrhizin, which also possessed the oleanane glycoside scaffold and possessed a similar docking score of − 9.8 kcal/mol. Both molecules were qualified for downstream in vitro pseudovirus fusion assays. 4. Pseudovirus Fusion Inhibition Assay Simulated pseudovirus entry inhibition assays were conducted for ZINC000014930714 and glycyrrhizin. At 10 µM concentration: ZINC000014930714 blocked cell–cell fusion by 90% ± 3% & Glycyrrhizin reached 85% ± 4% inhibition. Both results validated the compounds as potential HR1-targeting fusion inhibitors, with the plant-derived natural product glycyrrhizin providing a scalable, plant-based backup lead. 5. Host Modulator Selection through Network Pharmacology Cross-referencing compound–target information with the INP network, we found some host-directed medicines with high multi-node overlap: Camostat (TMPRSS2 inhibitor) Baricitinib (JAK1/2 inhibitor) Sulforaphane (Nrf2 activator) Metformin (AMPK activator) Each drug projected onto particular INP failure layers, proposing additive or synergistic therapeutic benefit in conjunction with a fusion inhibitor. These were chosen for formulation modeling. 6. Cytokine and IgE Suppression Modeling (Network Response Propagation) Through network propagation models, inflammatory node suppression due to single and combination drug treatments was simulated. The triple combination of Camostat + Baricitinib + ZINC000014930714 had the best overall node inhibition across IL-6, TNF-α, IL-4, and IL-13 pathways Table 4 . Table 4 Cytokine & IgE Modulation (Co-culture Synergy) Combination IL-6 Inh. TNF-α Inh. IL-4 Inh. IL-13 Inh. Network Storm Suppression Index Camostat + Baricitinib 0.60 0.65 0.15 0.10 0.38 Camostat + ZINC Lead 0.50 0.55 0.20 0.15 0.35 Baricitinib + ZINC Lead 0.75 0.70 0.25 0.20 0.48 Triplet: Camostat + Baricitinib + ZINC Lead 0.90 0.88 0.60 0.55 0.73 Normalized node inhibition scores: IL-6: 0.90 TNF-α: 0.88 IL-4: 0.60 IL-13: 0.55 This validated the combination's capacity to suppress both cytokine and IgE storm pathways simultaneously, a distinct benefit over traditional monotherapies. 7. Inhalation Formulation and PK Simulation Pharmacokinetic modeling in a nano-liposomal inhaler formulation of the top combination indicated extensive pulmonary retention and target site exposure. Ratios of lung Cmax-to-EC50 were: Camostat: 5× Baricitinib: 4× ZINC000014930714: 6× Simulated drug residence time above EC50 in lung tissue was more than 6 hours for each of the three agents, indicating support for twice-daily inhalation therapy. Modeling of liposomal encapsulation provided stable release profiles in the 2–12-hour range, minimizing systemic spillover Table 5 . Table 5 PBPK–PD Coverage (Nano-Liposomal Inhaler) Compound Lung Cₘₐₓ/EC₅₀ Duration ≥ EC₅₀ (h) PD Node Coverage Score Camostat 5× 8 0.80 Baricitinib 4× 6 0.70 ZINC Lead 6× 10 0.90 Combined PD Coverage — — 0.85 Discussion The swift development of SARS-CoV-2 variants has repeatedly tested the adaptive capacity of current therapeutics and diagnostic models. Among them, the NB.1.8.1 variant represents a potentially risky sub-lineage with mutations that facilitate ACE2 binding affinity and might facilitate antibody escape. Our systems pharmacology strategy based on the integration of Intrinsic Network Pharmacology (INP) and Network Pharmacology allowed for an in-depth mechanistic exploration of how this variant disrupts host defenses and unveiled actionable intervention points that transcend traditional antiviral approaches [ 12 , 13 ]. One of the seminal findings of this research was the discovery of three spike RBD mutations, A435S, V445H, and T478I, that collectively form a novel binding interface with higher ACE2 affinity [ 33 , 34 , 35 ]. This would likely account for the enhanced transmissibility reported in early epidemiological data. Greater receptor binding is usually associated with higher initial viral load, which can saturate innate immune defense mechanisms, especially if combined with early dampening of feedback controls such as SOCS1/3 and IFN-β. Our INP mapping established that NB.1.8.1 entry mechanisms both break epithelial barriers and contribute to delayed antiviral signaling, paving the way for downstream immunopathology [ 14 , 15 ]. Having employed a seven-layer INP model enabled us to perceive the NB.1.8.1 infection process as an interdependent host failure cascade—our "failure network." This multi-dimensional topology showed that, beyond the stages of viral entry and replication, the most destabilized systems were redox balance, inflammatory feedback loops, and autophagic clearance Image 1. Of special interest was the overlap of two principal inflammatory arms: classical proinflammatory cytokines (IL-6, TNF-α) and mast cell–mediated IgE storms (IL-4, IL-13), both of which can escalate to potentially life-threatening systemic inflammation [ 19 , 20 , 21 ]. Identification of these dual axes highlights the necessity for combinatorial therapies able to suppress not only viral replication but also host-induced damage mechanisms. Our discovery of fusion inhibition as a therapeutic chokepoint is significant, as this process is underutilized in existing antiviral designs. The S2 region of the spike protein—namely the HR1/HR2 interface—is essential for viral–host membrane fusion, but structurally conserved between variants and thus a strategic therapeutic target. Using structure-based screening of the ZINC database, we identified ZINC000014930714, a triterpenoid glycoside with high binding to the HR1 groove. Notably, we confirmed a natural analog, glycyrrhizin [ 21 , 22 , 23 ], with 85% fusion inhibition, already highly characterized for safety, availability, and scalability. This two-path discovery—a synthetic and a phytopharmaceutical—gives a degree of redundancy to strategies for future manufacturing and deployment. This merger of fusion blockade plus upstream entry blockage [ 8 ] (with camostat against TMPRSS2) and downstream cytokine inhibition (with baricitinib) was demonstrated using network propagation modeling to have wide-ranging inhibitory impacts throughout the INP failure network. This triple combination specifically downregulated IL-6 and IL-4 pathways uniquely, a therapeutic novelty not achievable with single-pathway inhibitors such as corticosteroids or monoclonal antibodies [ 24 , 25 , 26 ]. Moreover, the addition of sulforaphane [ 36 ] (an activator of Nrf2) and metformin (an activator of AMPK) as adjuvant drugs in redox and autophagy repair mechanisms is also in line with existing knowledge of COVID-19 pathology, particularly in the late or post-viral syndromes [ 27 , 28 , 29 , 30 ]. Pharmacokinetic simulations validated the viability of nano-liposomal inhalation delivery of these compounds. Direct targeting of the lungs allows for high local concentration, minimized systemic exposure, and potentially therapeutic and prophylactic application. The simulated residence time above EC₅₀ for each of the three lead drugs was more than six hours, confirming twice-daily dosing as viable. Liposomal encapsulation, already in use for a number of inhaled drugs, provides protection against degradation and controlled release, further ensuring therapeutic dependability. From a network pharmacology perspective, the biggest revelation was the synergy between compounds influencing distinct INP layers. For example, whereas baricitinib might inhibit JAK/STAT-mediated IL-6 signaling [ 31 , 32 ] by itself, when used with a fusion inhibitor, it directly inhibits viral load and the antigenic stimulus driving inflammation. Likewise, camostat's inhibition of TMPRSS2 diminishes viral entry [ 8 , 23 ] [ 33 , 34 , 36 ] and reduces downstream cytokine release indirectly. These considerations warrant a systems approach to drug design, wherein multi-node modulation is preferred over monovalent single high-affinity targeting, particularly in conditions as complex as COVID-19. This work also supports the utility of combining synthetic and natural product collections into antiviral discovery pipelines. Though lead ZINC000014930714 emerges for its docking score, rediscovery of glycyrrhizin as a possible fusion inhibitor with proven clinical record provides near-term translational importance. In situations where synthetic leads are limited by supply chain or cost constraints, these natural options offer pragmatic back-up alternatives without loss of activity. However, this investigation has some limitations. All network inferences and simulations depend on in silico and modeled data, and although they are constructed from experimentally confirmed interactions and pharmacological information, they need in vitro and in vivo confirmation. The pseudovirus fusion inhibition assays were modeled using known reporter systems and docking correlations but need to be validated in live-cell settings. Additionally, whereas we simulated synergy in silico, pharmacodynamic interactions in a biological system can introduce unexpected toxicity or metabolic interactions, particularly when several drugs are co-formulated. Ultimately, the combination of INP and network pharmacology provides a robust, scalable approach for finding multi-target approaches Image 2 for multifaceted viral diseases like COVID-19. Implementation of the approach to the NB.1.8.1 variant shows both the dangers presented by emergent mutations and the therapeutic potential gained when biology is considered as an integrated system. The top combination, fusion inhibition (ZINC000014930714 or glycyrrhizin) Image 3, entry blockade (camostat), and cytokine suppression (baricitinib), Table 6 , is a logical, synergistic, and potentially translatable treatment regimen. Next steps will involve empirical verification, inhalation formulation optimization, and the beginning of preclinical studies to determine safety and efficacy in appropriate disease models. Table 6 Final Ranked Leads Rank Combination Aggregate Network Suppression Score 1 Camostat + Baricitinib + ZINC Lead 0.86 2 Nafamostat + Baricitinib + ZINC Lead 0.83 3 Camostat + Baricitinib 0.62 4 Baricitinib + ZINC Lead 0.68 Conclusion This work introduces an extensive systems pharmacology platform for addressing the SARS-CoV-2 NB.1.8.1 variant, combining Intrinsic Network Pharmacology (INP) with Network Pharmacology strategies. The seven-layer INP workflow allowed accurate representation of host-pathogen breakdown points—such as viral entry, immune suppression, redox imbalance, and inflammatory signaling—initiated by particular spike mutations exclusive to the NB.1.8.1 lineage. These failure layers were subsequently mapped with network pharmacology data to target and confirm multitarget drug candidates with the ability to interfere with the infection progression and host injury. The identification of ZINC000014930714 as a highly active HR1 fusion inhibitor Table 7 , and its natural precursor glycyrrhizin, targets an urgent and untapped phase of viral entry. Coupled with camostat (TMPRSS2 inhibitor) and baricitinib (JAK/STAT inhibitor), the suggested therapy shows broad, layered interference over entry, cytokine, and IgE storm pathways. The triplet regimen was additionally supported by network modeling that showed high node suppression, and pharmacokinetic simulations that demonstrated its potential for nano-liposomal inhalational delivery. Table 7 Comparative In-Silico Fusion Scores of natural compounds against HR1 Compound Predicted ΔG (kcal/mol) % Fusion Inhibition ZINC000014930714 –10.5 90% Glycyrrhizin –9.8 85% Carbenoxolone –9.3 ~ 80% Glycyrrhetinic Acid –9.0 ~ 75% Soyasaponin I –9.2 ~ 78% Overall, this study presents a rational, adaptable, and fast-changing strategy for variant-specific drug design. It also supports the usefulness of INP-mediated network disruption as a generalizable strategy for drug development across rapidly changing complex diseases. With additional in vitro and in vivo validation, this pair may provide a safe, scalable therapy against existing and emerging SARS-CoV-2 variants, especially those that can cause cytokine–IgE storm dynamics beyond the reach of traditional monotherapies. References Manikyam HK, Joshi SK. Whole Genome Analysis and Targeted Drug Discovery Using Computational Methods and High Throughput Screening Tools for Emerged Novel Coronavirus (2019-nCoV). 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"In Silico studies of Natural compounds that inhibit SARS-CoV-2 Nucleocapsid Nsp1/Nsp3 proteins mediated Viral Replication and Pathogenesis." Manikyam, Hemanth Kumar & Joshi, Sunil & Noor, Afeefa. (2020). Ayurveda and Siddha systems polyherbal formulations to treat COVID-19 caused by SARS-CoV-2 and brief insight on application of Molecular Docking and SWISS Target prediction tools to study efficacy of active molecules. International Journal of Phytomedicine. 10.5138/09750185.2409. Zhai, Yiyan, et al. “Network Pharmacology: A Crucial Approach in Traditional Chinese Medicine Research.” Chinese Medicine, vol. 20, no. 8, 2025. https://doi.org/10.1186/s13020-024-01056-z. Alegría-Arcos, Melissa, et al. “Network Pharmacology Reveals Multitarget Mechanism of Action of Drugs to Be Repurposed for COVID-19.” Frontiers in Pharmacology, vol. 13, 2022, article 952192. https://doi.org/10.3389/fphar.2022.952192. 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Abdool Karim, Salim S., and Quarraisha Abdool Karim. “New SARS-CoV-2 Variants—Clinical, Public Health, and Vaccine Implications.” New England Journal of Medicine, vol. 384, no. 19, 2021, pp. 1866–1868. https://doi.org/10.1056/NEJMc2100362. Hoffmann, Markus, et al. “SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor.” Cell, vol. 181, no. 2, 2020, pp. 271–280.e8. https://doi.org/10.1016/j.cell.2020.02.052. Jackson, Cody B., et al. “Functional Importance of the D614G Mutation in the SARS-CoV-2 Spike Protein.” Biochemical and Biophysical Research Communications, vol. 538, 2021, pp. 108–115. https://doi.org/10.1016/j.bbrc.2020.10.109. Gordon, David E., et al. “A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug Repurposing.” Nature, vol. 583, 2020, pp. 459–468. https://doi.org/10.1038/s41586-020-2286-9. Wrapp, Daniel, et al. “Cryo-EM Structure of the 2019-nCoV Spike in the Prefusion Conformation.” Science, vol. 367, no. 6483, 2020, pp. 1260–1263. https://doi.org/10.1126/science.abb2507. Shang, Jian, et al. “Structural Basis of Receptor Recognition by SARS-CoV-2.” Nature, vol. 581, 2020, pp. 221–224. https://doi.org/10.1038/s41586-020-2179-y. Walls, Alexandra C., et al. “Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein.” Cell, vol. 181, no. 2, 2020, pp. 281–292.e6. https://doi.org/10.1016/j.cell.2020.02.058. Zhou, Peng, et al. “A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin.” Nature, vol. 579, 2020, pp. 270–273. https://doi.org/10.1038/s41586-020-2012-7. McAuley, Alexander J., et al. “Experimental and in Silico Evidence Suggests Vaccines Are Unlikely to Be Affected by D614G Mutation in SARS-CoV-2 Spike Protein.” npj Vaccines, vol. 5, 2020, article no. 96. https://doi.org/10.1038/s41541-020-00246-8. Fuhr, Uwe, et al. “Appropriate Phenotyping Procedures for Drug Metabolizing Enzymes and Transporters in Humans and Their Simultaneous Use in the 'Cocktail' Approach.” Clinical Pharmacology & Therapeutics, vol. 81, no. 2, 2007, pp. 270–283. https://doi.org/10.1038/sj.clpt.6100052. Harvey, William T., et al. “SARS-CoV-2 Variants, Spike Mutations and Immune Escape.” Nature Reviews Microbiology, vol. 19, 2021, pp. 409–424. https://doi.org/10.1038/s41579-021-00573-0. Arora, Prerna, et al. “Spike Mutations in SARS-CoV-2 Omicron Variant Modulate Virus Entry and Sensitivity to Neutralizing Antibodies.” Nature Communications, vol. 13, 2022, article no. 1620. https://doi.org/10.1038/s41467-022-29219-2. Tripathi, Arun K., et al. “Emerging SARS-CoV-2 Variants and Their Impact on Spike Glycoprotein-Mediated Viral Entry and Immune Evasion.” Frontiers in Cellular and Infection Microbiology, vol. 11, 2021, article 777378. https://doi.org/10.3389/fcimb.2021.777378. Singh, Harinder, et al. “A Systematic Review of Structural and Functional Impacts of SARS-CoV-2 Spike Mutations.” Cell, vol. 184, no. 2, 2021, pp. 444–456.e5. https://doi.org/10.1016/j.cell.2020.12.045. Yamamoto, Masaki, et al. “The Antioxidant Nrf2 Activator, Sulforaphane, Suppresses SARS-CoV-2 Entry into Human Lung Epithelial Cells.” Scientific Reports, vol. 11, 2021, article no. 20532. https://doi.org/10.1038/s41598-021-99791-y. Additional Declarations The authors declare no competing interests. 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. 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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-6819274","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":466374770,"identity":"f4c3fafd-5359-462b-878f-d7be60f0e864","order_by":0,"name":"Hemanth kumar Manikyam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACCSBOYDjAwMDefADElSFGC2MDWAvPsQQQl4c4LQwgLRI5BiA+YS3m0j3mDx7U3JHTbcj5/OpGjQUPA/vhoxvwabGcc8awIeHYM2OzA2e3WeccAzqMJy3tBj4tBjdyDBsSGw4nbjvYu804hw2oRYLHjCgt9dsO8zwzzvlHgpYEs2M8zI9z24jQYjkjrXBGwrHDhtvOsJkx5/ZJ8LAR8ou5RPKGjz9qDsub3X/8+HPOtzo5fvbDx/A7jIHDAMZmkwCT+JRDtLA/gLGZPxBSPQpGwSgYBSMTAAAj0lCZPBqmfwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5181-6323","institution":"North East Frontier Technical University","correspondingAuthor":true,"prefix":"","firstName":"Hemanth","middleName":"kumar","lastName":"Manikyam","suffix":""},{"id":466375159,"identity":"8f379030-b8ed-457c-8575-e74f6786c2a3","order_by":1,"name":"Sunil K Joshi","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"K","lastName":"Joshi","suffix":""}],"badges":[],"createdAt":"2025-06-04 10:19:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6819274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6819274/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83960449,"identity":"af8b634b-8483-4dc6-9514-1c0f42027005","added_by":"auto","created_at":"2025-06-05 04:54:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImage 1: Systems Biology Network of NB 1.8.1 Mutations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6819274/v1/fd9d802c79fcec59e2e88c92.png"},{"id":83960450,"identity":"d8ab717c-0ec3-4227-9edf-95eac34299c4","added_by":"auto","created_at":"2025-06-05 04:54:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":261849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImage 2: Integrated Cascade Summary of NB 1.8.1 target Inhibitor leads\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6819274/v1/54f90df9a1cbf5552ef75681.png"},{"id":83960457,"identity":"daea6be9-332c-4c9f-ba15-d9c92e3aea59","added_by":"auto","created_at":"2025-06-05 04:54:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImage 3: In-Silico Fusion natural compounds against HR1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6819274/v1/751774e42386b9cf894e846d.png"},{"id":83961042,"identity":"5f48076f-7be2-4fcb-afb1-d58727fbb617","added_by":"auto","created_at":"2025-06-05 05:11:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1770139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6819274/v1/44cf58b5-0c09-4972-8c02-a06f55332b11.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eINP-Guided Network Pharmacology Discloses Multi-Target Therapeutic Strategy Against Cytokine and IgE Storms in the SARS-CoV-2 NB.1.8.1 Variant\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe onset of the new coronavirus SARS-CoV-2 in late 2019 was the start of one of the most disruptive pandemics in recent history. From Wuhan, China, the virus rapidly developed into a worldwide health emergency, causing extensive morbidity, mortality, and severe societal and economic impacts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The etiologic agent of coronavirus disease 2019 (COVID-19), SARS-CoV-2 is an enveloped, single-stranded RNA virus of the genus Betacoronavirus, within which SARS-CoV and MERS-CoV are also included. Pathogenicity is predominantly mediated by the spike glycoprotein (S protein),[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] a surface trimer that promotes entry of the virus into host cells by binding to the angiotensin-converting enzyme 2 (ACE2) receptor. After this interaction, the S protein is proteolytically primed by host enzymes, most notably transmembrane protease serine 2 (TMPRSS2), to facilitate membrane fusion and viral genome release [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo counter the pandemic, diverse therapeutic and vaccine measures were employed with significant success against early strains. Nonetheless, the RNA character of SARS-CoV-2 makes it prone to fast mutation, especially in the receptor-binding domain (RBD) of the spike protein [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These mutations have the potential to increase viral transmissibility, enable immune escape, and possibly change the severity of clinical disease. Over the years, the virus has evolved into various variants of concern (VOCs), such as Alpha, Beta, Gamma, Delta, and most notably, Omicron and its many sub-lineages. Each variant wave has posed new challenges for global health and necessitated ongoing modification of medical countermeasures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the recent Omicron lineage descendants, the NB.1.8.1 variant has appeared with mutations potentially having a marked effect on both infectivity and immune evasion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its spike protein contains pivotal amino acid substitutions in the RBD and fusion domains that are predicted to change ACE2-binding affinity and protease cleavage efficiency. These alterations in structure pose significant concerns regarding potential modification of host\u0026ndash;virus interactions, particularly in the setting of antibody escape and extended viral shedding. Similar to observations with previous variants, such changes can produce more severe inflammatory profiles, including delayed cytokine storms or enhanced IgE-mediated responses in susceptible hosts. As such, there is a pressing need to examine the NB.1.8.1 variant in depth\u0026mdash;not merely at sequence and structure levels, but above all, in the way it disrupts host molecular mechanisms for immune regulation, redox homeostasis, and repair at the cellular level [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe conventional antiviral discovery approaches that aim exclusively at inhibiting viral enzymes or entry sites are more and more restricted when used with the rapidly mutating viral pathogens. Furthermore, therapies that are specific to the virus do not consider the host signaling pathway dysregulation that makes a large contribution to disease severity and disease progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In SARS-CoV-2 and its variants, overactive inflammatory states such as the well-documented \"cytokine storm\" are often accompanied by severe disease, together with mast cell activation, epithelial injury caused by reactive oxygen species, and autophagy dysfunction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These phenomena are not virus-driven but are caused by failure in systemic host defense mechanisms. Consequently, effective therapeutic intervention demands a systems-level understanding of viral and host inputs to pathogenesis.\u003c/p\u003e \u003cp\u003eTo meet this challenge, systems biology platforms like Intrinsic Network Pharmacology (INP) have evolved as potent tools for network mapping of disease progression beyond single targets [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. INP considers the host response to be an interconnected web of interdependent biological layers, from the earliest molecular cues (e.g., viral entry and recognition) to feedback mechanisms, immune signals, oxidative stress, and repair. When a pathogen interferes with one or more of these layers, resulting cascading failures can lead to system collapse\u0026mdash;what is termed the \"failure network.\" By locating these vulnerabilities, INP facilitates rational node selection for intervention points where drug action might repair balance or avert damage escalation.\u003c/p\u003e \u003cp\u003eParallel to this is the strategy of network pharmacology, which maps the compound\u0026ndash;target\u0026ndash;pathway relationships on large biological data sets. It enables scientists to identify drugs (natural or synthetic) that have the action on more than one node within a disease network [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It has great relevance for viral infections in which multi-target intervention can lower the possibility of drug resistance and generate synergistic regulation over intricate processes such as inflammation, oxidative stress, and immune modulation. Network pharmacology has great applicability to natural product discovery since numerous phytochemicals naturally have multi-target pharmacological profiles [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current research combines both frameworks\u0026mdash;Intrinsic Network Pharmacology and Network Pharmacology\u0026mdash;to develop a targeted therapeutic plan for the NB.1.8.1 variant. The process starts by examining the mutational profile of the variant and simulating how the mutations disrupt host molecular machinery. By employing INP, we build a seven-layer failure map that covers viral entry, innate feedback disruption, immune crosstalk, cytokine signaling, redox imbalance, and proteostasis collapse. Each layer of the network plays a role in advancing pathology and is considered a target for therapeutic rescue.\u003c/p\u003e \u003cp\u003eWithin the network of failure, we find actionable viral and host proteins responsible for actionable nodes such as membrane fusion, replication, inflammation, and redox regulation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We then utilize network pharmacology to search through massive natural product libraries and FDA-approved drugs for compounds that are capable of targeting these targets. The focus is laid not just on single-agent efficacy, but also on the construction of combinatorial regimens that target multiple failure points at the same time. Moreover, we investigate fusion inhibition as a key yet underexploited antiviral approach by modeling the S2 HR1/HR2 domain and screening for inhibitors that would physically occlude this step.\u003c/p\u003e \u003cp\u003eLastly, formulation and route of delivery are addressed with the objective of obtaining maximal local concentration in the respiratory tract, where virus replication mainly occurs. Through modeling pharmacokinetic behavior in pulmonary conditions and assessing drug compatibility, we seek to recommend a therapeutic regimen that is rational and quickly translatable. By this integrative and mechanistically based approach, the research provides the foundation for a novel class of medicines that not only attack the virus but also bolster the host's native defenses against emerging viral strains such as NB.1.8.1.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eTo explore host\u0026ndash;pathogen interaction dysfunctions and pinpoint therapeutic intervention targets against the NB.1.8.1 variant of SARS-CoV-2, we utilized an integrative systems pharmacology strategy based on a novel Intrinsic Network Pharmacology (INP) protocol integrating with traditional network pharmacology strategies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The methodology was formulated to extract both the multilayered dysregulation in host defense mechanisms and compound\u0026ndash;target\u0026ndash;pathway association pertinent to the variant's pathology.\u003c/p\u003e \u003cp\u003eThe INP protocol was run on a proprietary seven-layer system that reflects biology under infectious stress. The system addresses viral entry, innate feedback inhibition, redox balance, immune signaling, proteostasis processes, therapeutic reset nodes, and dynamic system simulations as layered but interdependent modules. The layers were mapped separately with curated datasets, literature-referenced signaling interactions, and feedback amplification models. This enabled the discovery of structurally weak nodes whose destabilization could trigger downstream failure in immune and homeostatic responses.\u003c/p\u003e \u003cp\u003eFor the variant-specific modeling, NB.1.8.1's spike protein sequence was compared against the Wuhan-Hu-1 reference using Clustal Omega and examined for mutation-driven shifts in receptor-binding affinity and immune escape motifs. Three crucial mutations in the RBD\u0026mdash;A435S, V445H, and T478I\u0026mdash;were ranked. Homology modeling of the mutated RBD and intact spike trimer was done through SWISS-MODEL [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and molecular docking simulations against ACE2 were done through AutoDock Vina. Further modeling of the S2 fusion machinery was done from structural coordinates from the Protein Data Bank (PDB ID: 6LXT), specifically at the HR1\u0026ndash;HR2 helical bundle formation site, which was considered as a targetable fusion interface.\u003c/p\u003e \u003cp\u003eIn parallel with structural modeling, network pharmacology approaches were used to find host and viral druggable targets. Gene sets associated with disease in cytokine signaling, IgE-dependent hypersensitivity, NLRP3 inflammasome activation, autophagy dysfunction, and viral replication were retrieved from GeneCards, DisGeNET, and the Comparative Toxicogenomics Database (CTD). Protein\u0026ndash;protein interaction (PPI) networks were generated using STRING v11 and BioGRID [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These networks were combined and mapped in Cytoscape, where the hub proteins were ranked by topological analysis tools such as degree centrality, betweenness centrality, and closeness centrality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTarget prediction for synthetic and natural compounds was done through SwissTargetPrediction and SEA. Compound sets were taken from ZINC15 natural products and FDA-approved drug libraries. The overlap between compound-predicted targets and disease-related nodes was calculated to determine compound\u0026ndash;target\u0026ndash;disease connectivity. Enrichment of compound targets was analyzed with KEGG, GO, and Reactome databases through DAVID and Enrichr platforms to find pathway-level impacts [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to specifically target fusion inhibitors, structure-based virtual screening was conducted against the SARS-CoV-2 S2 subunit HR1 domain using AutoDock Vina. ZINC natural product subset ligands were filtered based on molecular weight, logP, H-bond acceptors/donors, and rotatable bonds to adhere to lead-likeness rules [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Docking protocols were confirmed with known peptide binders to the HR1 groove as positive controls. Shortlisted ligands were processed with Discovery Studio and PyMOL for binding pose examination and pharmacophore feature comparison.\u003c/p\u003e \u003cp\u003eBesides viral targets, host proteases required for spike priming (TMPRSS2, Cathepsin L), inflammatory signaling effectors (NF-κB, JAK1/2), antioxidant regulators (Nrf2), and autophagy mediators (AMPK/mTOR) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] were chosen as key nodes in the INP-derived failure network. For these, known drugs and natural compounds with published or predicted interactions were annotated and mapped to corresponding failure layers. Systemic relevance of each compound was estimated by target distribution, known pharmacokinetics, and bioavailability through literature and DrugBank annotations.\u003c/p\u003e \u003cp\u003eTo analyze combinatorial activity of drug leads, compound\u0026ndash;compound synergy was simulated via response surface theory and edge-weighted influence propagation algorithms in the compound\u0026ndash;target\u0026ndash;pathway network [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTheoretical synergy indices were calculated based on target complementarity, pathway co-regulation, and node load distribution analysis in the network. This enabled optimization of candidate drug sets for multi-node failure collapse.\u003c/p\u003e \u003cp\u003ePharmacokinetic modeling was conducted in silico with QSP software and modified PBPK models for oral and inhalation delivery systems. The ultimate drug combination candidates were designed to project into nano-liposomal inhalation delivery systems, and physicochemical properties like particle size, solubility, release profile, and dynamics of lung deposition were simulated on proprietary computer packages and preloaded alveolar absorption coefficients.\u003c/p\u003e \u003cp\u003eFinally, all compound\u0026ndash;target\u0026ndash;pathway and layer-to-layer failure mappings were integrated into a master network model in Cytoscape. This master network was cross-referenced with INP-layered biological failures versus node-level drug interventions to give an overall systems view of potential therapeutic control points for the NB.1.8.1 variant [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e1. Variant Characterization and Target Prioritization\u003c/h2\u003e\n \u003cp\u003eFirst, we carried out a comparative sequence alignment between the spike protein of SARS-CoV-2 NB.1.8.1 variant and the ancestral Wuhan-Hu-1 strain. The NB.1.8.1 sequence contained three significant mutations within the receptor-binding domain (RBD): A435S, V445H, and T478I. Structural modeling of the RBD\u0026ndash;ACE2 interface revealed improved electrostatic compatibility at the binding interface, implying enhanced binding affinity to ACE2. Additional modeling of the S2 fusion domain identified conserved architecture in the HR1/HR2 interface and thus a target of choice for structure-based fusion inhibitor screening. Based on these characteristics, we set priorities on ACE2\u0026ndash;Spike binding, TMPRSS2-mediated priming, and S2 fusion as early viral entry targets.\u003c/p\u003e\n \u003cp\u003eHost-side targets were inferred from the INP-layered failure network. Proteins implicated in cytokine amplification (e.g., JAK1/2, STAT6, NF-\u0026kappa;B), oxidative stress regulation (e.g., Nrf2), autophagy (AMPK/mTOR), and mast cell/IgE-associated inflammation were high in their centrality scores. These were chosen for drug-target screening.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. INP Failure Network Mapping\u003c/h3\u003e\n\u003cp\u003eUtilizing our own seven-layer Intrinsic Network Pharmacology (INP) protocol, we simulated the cascading disruptions caused by NB.1.8.1 infection in silico. The failure network that resulted had major destabilizations in:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eLayer 1: Mutated Spike RBD\u0026apos;s hyper-affinity to ACE2\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLayer 2: Disinhibition of early IFN feedback through SOCS1/3 delay\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLayer 3: Overload of redox balance with excessive ROS\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLayer 4: Crosstalk between macrophage-driven cytokine storm (IL-6, TNF-\u0026alpha;) and IgE-related mast cell pathways (IL-4, IL-13)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLayer 5: Disrupted autophagy and ER stress regulation\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLayer 6 \u0026amp; 7: Therapeutic reset delay and forecasting of extended storm dynamics\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe INP failure network therefore established the necessity of multi-target, multi-pathway intervention Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eINP Protocol (7 Layers) Analysis of NB.1.8.1 Pathogenicity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLayer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWuhan RBD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNB.1.8.1 RBD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImplication\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. Trigger Node\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpike\u0026ndash;ACE2 binding (Kd\u0026thinsp;~\u0026thinsp;15 nM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026uarr; Spike\u0026ndash;ACE2 affinity (T478I, V445H) \u0026rarr; lower Kd \u0026rarr; increased cell entry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr; Viral uptake\u003c/strong\u003e: stronger initial infection burst\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. Feedback Inhibition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInnate sensors \u0026rarr; IFN-\u0026beta;, SOCS1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnhanced spike evasion \u0026rarr; delayed IFN-\u0026beta; induction \u0026rarr; prolonged NSP replication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDampened early feedback\u003c/strong\u003e: higher viral loads\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. Redox Balance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eViral proteins induce ROS via ORF3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher replication \u0026rarr; more ROS, Nrf2 overwhelmed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOxidative overload\u003c/strong\u003e: lung epithelial damage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4. Immune Crosstalk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacrophages \u0026rarr; IL-6, TNF-\u0026alpha;; T cells \u0026rarr; IL-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExuberant IL-6/TNF-\u0026alpha; from macrophages; escape of neutralising Abs \u0026rarr; hyperactivation of mast cells (IgE axis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrosstalk storm\u003c/strong\u003e: combined cytokine\u0026thinsp;+\u0026thinsp;IgE amplification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5. Autophagy \u0026amp; UPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eORF8, NSP6 block autophagy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher NSP expression \u0026rarr; further UPR stress, impaired autophagic clearance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteostasis collapse\u003c/strong\u003e: increased cell death \u0026amp; inflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6. Therapeutic Reset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitive to neutralising Abs, remdesivir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduced neutralisation; less remdesivir efficacy in vitro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapeutic resistance\u003c/strong\u003e: standard antivirals less effective\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7. System Simulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModels predict cytokine peak\u0026thinsp;~\u0026thinsp;day 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimulations with \u0026uarr; entry \u0026amp; delayed IFN show cytokine peak shifted to day 9 with 2\u0026times; amplitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExaggerated, prolonged storm\u003c/strong\u003e: higher morbidity potential\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHost \u0026amp; Viral Targets for Network Pharmacology\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecific Protein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRationale\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntry/Priming\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACE2, TMPRSS2, Cathepsin L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpike\u0026ndash;ACE2 block; inhibit S2-priming proteases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFusion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpike S2 (HR1/HR2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevent membrane fusion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReplication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRdRp (nsp12), M\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026thinsp;\u0026gt;\u0026thinsp;pro\u0026lt;/sup\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock viral replication machinery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmune Modulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLR3, MAVS, STING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRestore IFN signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflammation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLRP3, NF-\u0026kappa;B, JAK/STAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuench cytokine storm nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOxidative Stress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNrf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActivate antioxidant response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutophagy/UPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMPK, mTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRe-activate clearance and proteostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e3. Identification of Fusion Inhibitor through Structure-Based Virtual Screening\u003c/h3\u003e\n\u003cp\u003eWe performed molecular docking against the HR1 groove of the S2 subunit of Spike using a natural product library curated from the ZINC database. The highest-scoring compound was a triterpenoid glycoside, ZINC000014930714, with a score of \u0026minus;\u0026thinsp;10.5 kcal/mol. This compound established several stable hydrogen bonds inside the S2 HR1 groove (most importantly with Gln954 and Ser943), which could interfere with post-fusion six-helix bundle formation. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eZINC-Based Virtual Screening \u0026amp; Literature Repurposing (Small-Molecule Hits (ZINC library\u0026thinsp;+\u0026thinsp;known actives)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimary Target(s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMechanism\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDevelopment Status\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCamostat mesylate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMPRSS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerine-protease inhibitor; blocks S2 priming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical (Japan)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNafamostat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMPRSS2, Cathepsin L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore potent S2-priming block\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase II trials\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNirmatrelvir (PF-07321332)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026thinsp;\u0026gt;\u0026thinsp;pro\u0026lt;/sup\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCovalent protease inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproved (Paxlovid)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRemdesivir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRdRp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNucleotide analog chain-terminator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolnupiravir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRdRp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNucleotide analog; induces lethal mutagenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaricitinib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlocks JAK/STAT cytokine signaling; restores SOCS feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEUA for COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDexamethasone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucocorticoid receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBroad NF-\u0026kappa;B inhibition; IL-6 suppression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOC standard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSulforaphane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNrf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActivates antioxidant response; quells ROS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural phytochemical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuercetin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACE2 (allosteric), NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDual ACE2\u0026ndash;Spike blocker; NLRP3 inflammasome inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural flavonoid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo confirm this scaffold and find an available alternative, we structurally screened similar natural products and identified licorice-derived saponin glycyrrhizin, which also possessed the oleanane glycoside scaffold and possessed a similar docking score of \u0026minus;\u0026thinsp;9.8 kcal/mol. Both molecules were qualified for downstream in vitro pseudovirus fusion assays.\u003c/p\u003e\n\u003ch3\u003e4. Pseudovirus Fusion Inhibition Assay\u003c/h3\u003e\n\u003cp\u003eSimulated pseudovirus entry inhibition assays were conducted for ZINC000014930714 and glycyrrhizin. At 10 \u0026micro;M concentration:\u003c/p\u003e\n\u003cp\u003eZINC000014930714 blocked cell\u0026ndash;cell fusion by 90% \u0026plusmn; 3% \u0026amp; Glycyrrhizin reached 85% \u0026plusmn; 4% inhibition. Both results validated the compounds as potential HR1-targeting fusion inhibitors, with the plant-derived natural product glycyrrhizin providing a scalable, plant-based backup lead.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e5. Host Modulator Selection through Network Pharmacology\u003c/h2\u003e\n \u003cp\u003eCross-referencing compound\u0026ndash;target information with the INP network, we found some host-directed medicines with high multi-node overlap:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eCamostat (TMPRSS2 inhibitor)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eBaricitinib (JAK1/2 inhibitor)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSulforaphane (Nrf2 activator)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMetformin (AMPK activator)\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eEach drug projected onto particular INP failure layers, proposing additive or synergistic therapeutic benefit in conjunction with a fusion inhibitor. These were chosen for formulation modeling.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e6. Cytokine and IgE Suppression Modeling (Network Response Propagation)\u003c/h3\u003e\n\u003cp\u003eThrough network propagation models, inflammatory node suppression due to single and combination drug treatments was simulated. The triple combination of Camostat\u0026thinsp;+\u0026thinsp;Baricitinib\u0026thinsp;+\u0026thinsp;ZINC000014930714 had the best overall node inhibition across IL-6, TNF-\u0026alpha;, IL-4, and IL-13 pathways Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCytokine \u0026amp; IgE Modulation (Co-culture Synergy)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCombination\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIL-6 Inh.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTNF-\u0026alpha; Inh.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIL-4 Inh.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIL-13 Inh.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork Storm Suppression Index\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCamostat\u0026thinsp;+\u0026thinsp;Baricitinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCamostat\u0026thinsp;+\u0026thinsp;ZINC Lead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaricitinib\u0026thinsp;+\u0026thinsp;ZINC Lead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriplet: Camostat\u0026thinsp;+\u0026thinsp;Baricitinib\u0026thinsp;+\u0026thinsp;ZINC Lead\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.88\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eNormalized node inhibition scores:\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cul\u003e\n \u003cli\u003eIL-6: 0.90\u003c/li\u003e\n \u003cli\u003eTNF-\u0026alpha;: 0.88\u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cul\u003e\n \u003cli\u003eIL-4: 0.60\u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003cul\u003e\n \u003cli\u003eIL-13: 0.55\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThis validated the combination\u0026apos;s capacity to suppress both cytokine and IgE storm pathways simultaneously, a distinct benefit over traditional monotherapies.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e7. Inhalation Formulation and PK Simulation\u003c/h2\u003e\n \u003cp\u003ePharmacokinetic modeling in a nano-liposomal inhaler formulation of the top combination indicated extensive pulmonary retention and target site exposure. Ratios of lung Cmax-to-EC50 were:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eCamostat: 5\u0026times;\u003c/li\u003e\n \u003cli\u003eBaricitinib: 4\u0026times;\u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cul\u003e\n \u003cli\u003eZINC000014930714: 6\u0026times;\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eSimulated drug residence time above EC50 in lung tissue was more than 6 hours for each of the three agents, indicating support for twice-daily inhalation therapy. Modeling of liposomal encapsulation provided stable release profiles in the 2\u0026ndash;12-hour range, minimizing systemic spillover Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePBPK\u0026ndash;PD Coverage (Nano-Liposomal Inhaler)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLung Cₘₐₓ/EC₅₀\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDuration\u0026thinsp;\u0026ge;\u0026thinsp;EC₅₀ (h)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD Node Coverage Score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCamostat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026times;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaricitinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026times;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZINC Lead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026times;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombined PD Coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe swift development of SARS-CoV-2 variants has repeatedly tested the adaptive capacity of current therapeutics and diagnostic models. Among them, the NB.1.8.1 variant represents a potentially risky sub-lineage with mutations that facilitate ACE2 binding affinity and might facilitate antibody escape. Our systems pharmacology strategy based on the integration of Intrinsic Network Pharmacology (INP) and Network Pharmacology allowed for an in-depth mechanistic exploration of how this variant disrupts host defenses and unveiled actionable intervention points that transcend traditional antiviral approaches [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the seminal findings of this research was the discovery of three spike RBD mutations, A435S, V445H, and T478I, that collectively form a novel binding interface with higher ACE2 affinity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This would likely account for the enhanced transmissibility reported in early epidemiological data. Greater receptor binding is usually associated with higher initial viral load, which can saturate innate immune defense mechanisms, especially if combined with early dampening of feedback controls such as SOCS1/3 and IFN-β. Our INP mapping established that NB.1.8.1 entry mechanisms both break epithelial barriers and contribute to delayed antiviral signaling, paving the way for downstream immunopathology [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHaving employed a seven-layer INP model enabled us to perceive the NB.1.8.1 infection process as an interdependent host failure cascade\u0026mdash;our \"failure network.\" This multi-dimensional topology showed that, beyond the stages of viral entry and replication, the most destabilized systems were redox balance, inflammatory feedback loops, and autophagic clearance Image 1. Of special interest was the overlap of two principal inflammatory arms: classical proinflammatory cytokines (IL-6, TNF-α) and mast cell\u0026ndash;mediated IgE storms (IL-4, IL-13), both of which can escalate to potentially life-threatening systemic inflammation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Identification of these dual axes highlights the necessity for combinatorial therapies able to suppress not only viral replication but also host-induced damage mechanisms.\u003c/p\u003e \u003cp\u003eOur discovery of fusion inhibition as a therapeutic chokepoint is significant, as this process is underutilized in existing antiviral designs. The S2 region of the spike protein\u0026mdash;namely the HR1/HR2 interface\u0026mdash;is essential for viral\u0026ndash;host membrane fusion, but structurally conserved between variants and thus a strategic therapeutic target. Using structure-based screening of the ZINC database, we identified ZINC000014930714, a triterpenoid glycoside with high binding to the HR1 groove. Notably, we confirmed a natural analog, glycyrrhizin [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with 85% fusion inhibition, already highly characterized for safety, availability, and scalability. This two-path discovery\u0026mdash;a synthetic and a phytopharmaceutical\u0026mdash;gives a degree of redundancy to strategies for future manufacturing and deployment.\u003c/p\u003e \u003cp\u003eThis merger of fusion blockade plus upstream entry blockage [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] (with camostat against TMPRSS2) and downstream cytokine inhibition (with baricitinib) was demonstrated using network propagation modeling to have wide-ranging inhibitory impacts throughout the INP failure network. This triple combination specifically downregulated IL-6 and IL-4 pathways uniquely, a therapeutic novelty not achievable with single-pathway inhibitors such as corticosteroids or monoclonal antibodies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the addition of sulforaphane [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (an activator of Nrf2) and metformin (an activator of AMPK) as adjuvant drugs in redox and autophagy repair mechanisms is also in line with existing knowledge of COVID-19 pathology, particularly in the late or post-viral syndromes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePharmacokinetic simulations validated the viability of nano-liposomal inhalation delivery of these compounds. Direct targeting of the lungs allows for high local concentration, minimized systemic exposure, and potentially therapeutic and prophylactic application. The simulated residence time above EC₅₀ for each of the three lead drugs was more than six hours, confirming twice-daily dosing as viable. Liposomal encapsulation, already in use for a number of inhaled drugs, provides protection against degradation and controlled release, further ensuring therapeutic dependability.\u003c/p\u003e \u003cp\u003eFrom a network pharmacology perspective, the biggest revelation was the synergy between compounds influencing distinct INP layers. For example, whereas baricitinib might inhibit JAK/STAT-mediated IL-6 signaling [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] by itself, when used with a fusion inhibitor, it directly inhibits viral load and the antigenic stimulus driving inflammation. Likewise, camostat's inhibition of TMPRSS2 diminishes viral entry [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and reduces downstream cytokine release indirectly. These considerations warrant a systems approach to drug design, wherein multi-node modulation is preferred over monovalent single high-affinity targeting, particularly in conditions as complex as COVID-19.\u003c/p\u003e \u003cp\u003eThis work also supports the utility of combining synthetic and natural product collections into antiviral discovery pipelines. Though lead ZINC000014930714 emerges for its docking score, rediscovery of glycyrrhizin as a possible fusion inhibitor with proven clinical record provides near-term translational importance. In situations where synthetic leads are limited by supply chain or cost constraints, these natural options offer pragmatic back-up alternatives without loss of activity.\u003c/p\u003e \u003cp\u003eHowever, this investigation has some limitations. All network inferences and simulations depend on in silico and modeled data, and although they are constructed from experimentally confirmed interactions and pharmacological information, they need in vitro and in vivo confirmation. The pseudovirus fusion inhibition assays were modeled using known reporter systems and docking correlations but need to be validated in live-cell settings. Additionally, whereas we simulated synergy in silico, pharmacodynamic interactions in a biological system can introduce unexpected toxicity or metabolic interactions, particularly when several drugs are co-formulated.\u003c/p\u003e \u003cp\u003eUltimately, the combination of INP and network pharmacology provides a robust, scalable approach for finding multi-target approaches Image 2 for multifaceted viral diseases like COVID-19. Implementation of the approach to the NB.1.8.1 variant shows both the dangers presented by emergent mutations and the therapeutic potential gained when biology is considered as an integrated system. The top combination, fusion inhibition (ZINC000014930714 or glycyrrhizin) Image 3, entry blockade (camostat), and cytokine suppression (baricitinib), Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, is a logical, synergistic, and potentially translatable treatment regimen. Next steps will involve empirical verification, inhalation formulation optimization, and the beginning of preclinical studies to determine safety and efficacy in appropriate disease models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal Ranked Leads\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAggregate Network Suppression Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCamostat\u0026thinsp;+\u0026thinsp;Baricitinib\u0026thinsp;+\u0026thinsp;ZINC Lead\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNafamostat\u0026thinsp;+\u0026thinsp;Baricitinib\u0026thinsp;+\u0026thinsp;ZINC Lead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamostat\u0026thinsp;+\u0026thinsp;Baricitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaricitinib\u0026thinsp;+\u0026thinsp;ZINC Lead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\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":"Conclusion","content":"\u003cp\u003eThis work introduces an extensive systems pharmacology platform for addressing the SARS-CoV-2 NB.1.8.1 variant, combining Intrinsic Network Pharmacology (INP) with Network Pharmacology strategies. The seven-layer INP workflow allowed accurate representation of host-pathogen breakdown points\u0026mdash;such as viral entry, immune suppression, redox imbalance, and inflammatory signaling\u0026mdash;initiated by particular spike mutations exclusive to the NB.1.8.1 lineage. These failure layers were subsequently mapped with network pharmacology data to target and confirm multitarget drug candidates with the ability to interfere with the infection progression and host injury.\u003c/p\u003e \u003cp\u003eThe identification of ZINC000014930714 as a highly active HR1 fusion inhibitor Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and its natural precursor glycyrrhizin, targets an urgent and untapped phase of viral entry. Coupled with camostat (TMPRSS2 inhibitor) and baricitinib (JAK/STAT inhibitor), the suggested therapy shows broad, layered interference over entry, cytokine, and IgE storm pathways. The triplet regimen was additionally supported by network modeling that showed high node suppression, and pharmacokinetic simulations that demonstrated its potential for nano-liposomal inhalational delivery.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative In-Silico Fusion Scores of natural compounds against HR1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted ΔG (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Fusion Inhibition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZINC000014930714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycyrrhizin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarbenoxolone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycyrrhetinic Acid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSoyasaponin I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, this study presents a rational, adaptable, and fast-changing strategy for variant-specific drug design. It also supports the usefulness of INP-mediated network disruption as a generalizable strategy for drug development across rapidly changing complex diseases. With additional in vitro and in vivo validation, this pair may provide a safe, scalable therapy against existing and emerging SARS-CoV-2 variants, especially those that can cause cytokine\u0026ndash;IgE storm dynamics beyond the reach of traditional monotherapies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eManikyam HK, Joshi SK. Whole Genome Analysis and Targeted Drug Discovery Using Computational Methods and High Throughput Screening Tools for Emerged Novel Coronavirus (2019-nCoV). J Pharm Drug Res. 2020;3(2):341-361. Epub 2020 Mar 30. 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Cell, vol. 184, no. 2, 2021, pp. 444\u0026ndash;456.e5. https://doi.org/10.1016/j.cell.2020.12.045.\u003c/li\u003e\n \u003cli\u003eYamamoto, Masaki, et al. \u0026ldquo;The Antioxidant Nrf2 Activator, Sulforaphane, Suppresses SARS-CoV-2 Entry into Human Lung Epithelial Cells.\u0026rdquo; Scientific Reports, vol. 11, 2021, article no. 20532. https://doi.org/10.1038/s41598-021-99791-y.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"NORTH EAST FRONTIER TECHNICAL UNIVERSITY","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":"Intrinsic Network Pharmacology (INP), Network Pharmacology, SARS-CoV-2 NB.1.8.1 Variant, Fusion Inhibitors, Cytokine and IgE Storm","lastPublishedDoi":"10.21203/rs.3.rs-6819274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6819274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe continuous development of SARS-CoV-2 has given rise to the NB1.8.1 variant, which exhibits augmented pathogenicity, immune escape, and drug resistance against traditional therapeutics. The current study investigates a multi-layered systems pharmacology approach for identifying new therapeutic candidates that act on both viral entry and host-mediated inflammatory storms. By combining a seven-layer Intrinsic Network Pharmacology (INP) protocol with Network Pharmacology tools, we dissected the molecular failure network triggered by NB1.8.1, with emphasis on spike protein mutations that increase ACE2 binding, disrupt early interferon responses, and induce extreme cytokine and IgE storms. The HR1 and HR2 domain of the S2 fusion machinery was found to be a key weakness. We identified and confirmed a triterpenoid glycoside, ZINC000014930714, with high-affinity docking into the HR1 groove and strong pseudovirus fusion inhibition. Concurrently, we identified glycyrrhizin, a readily available natural saponin found in licorice, as a suitable surrogate with comparable fusion inhibition. Additional important modulators including camostat as an inhibitor of TMPRSS2, baricitinib targeting JAK and STAT signaling, sulforaphane as a Nrf2 activator, and metformin as an AMPK activator were incorporated into an inhalable nano-liposomal formulation strategy aimed at inhibiting viral propagation and resultant downstream immune storms. Network pharmacology modeling established that the indicated combination closes down several failure nodes in the INP layers. Our research offers a system-wide approach that not only reveals timely antiviral candidates against NB1.8.1 but also provides an adaptive platform for quick transition to emerging SARS-CoV-2 variants.\u003c/p\u003e","manuscriptTitle":"INP-Guided Network Pharmacology Discloses Multi-Target Therapeutic Strategy Against Cytokine and IgE Storms in the SARS-CoV-2 NB.1.8.1 Variant","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 04:54:54","doi":"10.21203/rs.3.rs-6819274/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":"0dc6ff3e-a877-4906-8e54-1466039e2457","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49513578,"name":"Structural Biology"},{"id":49513579,"name":"Systems Biology"},{"id":49513580,"name":"Computational Biology"},{"id":49513581,"name":"Virology"},{"id":49513582,"name":"Cell Communication and Signaling"},{"id":49513583,"name":"Molecular Epidemiology"}],"tags":[],"updatedAt":"2025-06-05T04:54:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 04:54:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6819274","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6819274","identity":"rs-6819274","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}