Exploring Cannabis sativa L for Anti-Alzheimer Potential: An extensive Computational Study including Molecular Docking, Molecular Dynamics, and ADMET Assessments

Exploring Cannabis sativa L for Anti-Alzheimer Potential: An extensive Computational Study including Molecular Docking, Molecular Dynamics, and ADMET Assessments | 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 Research Article Exploring Cannabis sativa L for Anti-Alzheimer Potential: An extensive Computational Study including Molecular Docking, Molecular Dynamics, and ADMET Assessments Hassan Nour, Imane Yamari, Oussama Abchir, Nouh Mounadi, Abdelouahid Samadi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3986384/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 Cholinesterase enzymes play a pivotal role in hydrolyzing acetylcholine, a neurotransmitter crucial for memory and cognition, into its components, acetic acid, and choline. A primary approach in addressing Alzheimer's disease symptoms is by inhibiting the action of these enzymes. With this context, our study embarked on a mission to pinpoint potential Cholinesterase (ChE) inhibitors using a comprehensive computational methodology. A total of 49 phytoconstituents derived from Cannabis sativa L underwent in silico screening via molecular docking, pharmacokinetic and pharmacotoxicological analysis, to evaluate their ability to inhibit cholinesterase enzymes. Out of these, two specific compounds, namely tetrahydrocannabivarin and Δ-9-tetrahydrocannabinol, belonging to cannabinoids, stood out as prospective therapeutic agents against Alzheimer's due to their potential as cholinesterase inhibitors. These candidates showcased commendable binding affinities with the cholinesterase enzymes, highlighting their interaction with essential enzymatic residues. They were predicted to exhibit greater binding affinities than Rivastigmine and Galantamine. Their ADMET assessments further classified them as viable oral pharmaceutical drugs. They are not expected to induce any mutagenic or hepatotoxic effects and cannot produce skin sensitization. In addition, these phytoconstituents are predicted to be BBB permeable and can reach the central nervous system (CNS) and exert their therapeutic effects. To delve deeper, we explored molecular dynamics (MD) simulations to examine the stability of the complex formed between the best candidate (Δ-9-tetrahydrocannabinol) and the target proteins under simulated biological conditions. The MD study affirmed that the ligand-ChE recognition is a spontaneous reaction leading to stable complexes. Our research outcomes provide valuable insights, offering a clear direction for the pharmaceutical sector in the pursuit of effective anti-Alzheimer treatments. Cannabis sativa L Molecular Docking Molecular Dynamics Alzheimer’s disease Cholinesterase inhibitors ADMET Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Cannabis sativa L , commonly known as hemp or marijuana, is a plant species belonging to the Cannabaceae family [ 1 ]. It is widely cultivated for its miscellaneous therapeutic properties, such as anti-anxiety, anti-inflammatory, anti-epileptic, and neuroprotective properties [ 2 – 8 ]. Morocco is among the world's largest producers of cannabis [ 9 ]. In May 2021, the Moroccan Parliament adopted Law 13–21, regulating the legitimate commercial use of cannabis for therapeutic, cosmetic, and industrial purposes. Cannabinoids are the most well-known and studied phytoconstituents in cannabis [ 10 ]. These compounds interact with the body's endocannabinoid system, regulating miscellaneous physiological processes, such as appetite, pain and mood [ 11 – 13 ]. Terpenes are another class of phytoconstituents found in cannabis and are responsible for the plant's characteristic aroma and flavor [ 14 ]. They also have potential therapeutic benefits, such as anti-inflammatory and anti-anxiety effects [ 15 – 17 ]. Flavonoids are a third class of phytoconstituents found in cannabis and have been shown to have antioxidant and anti-inflammatory properties [ 18 , 19 ]. Many investigations have examined the potential benefits of cannabis in addressing neurodegenerative diseases that affect cognitive abilities [ 20 – 26 ]. Additionally, the neuroprotective effect of this plant is widely studied and proven [ 27 ]. In 2021, Xia Jiang has highlighted cannabidiol − carbamate hybrids as selective potent BuChE inhibitors [ 28 ]. In addition, an experimental study by Tess Puopolo showed that certain cannabinoids can have moderate inhibitory effects on the activities of the AChE and BChE enzymes [ 29 ]. Overall, Cannabis sativa L is a complex plant that contains a variety of phytoconstituents with potential therapeutic benefits, yet further investigations are required to explore other therapeutic effects. Alzheimer's disease is a neurologically progressive illness afflicting the human brain, provoking impaired memory, declining cognition, and behavioral and character changes. It is one of the most frequent factors causing dementia, a group of symptoms that affect a person's ability to think, reason, and remember [ 30 , 31 ]. Alzheimer's disease typically affects older adults but can also occur in younger people [ 32 ]. Right now, there is no effective therapy for Alzheimer's disease, but commercial medications are helpful in managing its symptoms and enhancing patients' quality of life [ 33 ]. Acetylcholinesterase (AChE) and Butyrylcholinesterase (BuChE), commonly known as cholinesterase enzymes, are biological catalysts ensuring the hydrolysis of acetylcholine (Ach) into acetic acid and choline [ 34 ]. In the normal brain, AChE is the primary enzyme that is responsible for breaking down Ach [ 35 ]. As Alzheimer's disease progresses, the importance of BuChE in cholinergic neurotransmission is expected to increase. This is due to a potential decrease in AChE activity by up to 45%, while BuChE activity could increase by up to 40%, ultimately reaching 90% [ 36 ]–[ 38 ]. Moreover, Alzheimer's disease is associated with decreased levels of acetylcholine due to intensive loss of cholinergic neurons [ 39 ]. Since acetylcholine is a neurotransmitter implicated in memory and learning, its hydrolysis leads to an altered neurotransmission process in Alzheimer’s disease patients. Consequently, cholinesterase enzymes inhibition is a pertinent approach for the management of Alzheimer's disease symptoms. Cholinesterase inhibitors, such as donepezil, rivastigmine, and galantamine, are commonly used to treat the symptoms of Alzheimer's disease by increasing the levels of acetylcholine in the brain. These drugs work by inhibiting the breakdown of acetylcholine, which can help improve cognitive function and alleviate some of the symptoms of the disease. Donepezil and galantamine acts as selective inhibitors of AChE, while rivastigmine acts as dual inhibitor of both AChE and BuChE [ 35 , 40 ]. Unfortunately, these drugs can induce serious side effects such as hepatotoxicity and several gastrointestinal disorders [ 41 , 42 ]. In this context, the current study attempts to explore new cholinesterase inhibitors more puissant and safer than existing ones. To reach this goal, combined molecular modeling technics, including molecular docking, molecular dynamics, and pharmacokinetics analysis, were implemented. Forty-nine phytoconstituents extracted from Cannabis sativa L were in silico screened to estimate their inhibitory effects towards both AChE and BuChE enzymes, and their drug-likeness properties were predicted. The outcomes of the current investigation could provide scientific support to develop new generation of cannabis-based acetylcholinesterase inhibitors. 2. Materials and methods 2.1. Screened compounds Forty-nine phytoconstituents of Cannabis sativa L , previously extracted and identified, were selected by analyzing several phytochemical studies [ 43 ]–[ 49 ]. These phytoconstituents belong to two classes namely cannabinoids and terpenes. Table 1 illustrates the structures of the investigated phytoconstituents. 2.2. Molecular Docking Molecular docking studies were done to predict the possible interactions that may be involved between the selected phytoconstituents and the cholinesterase enzymes, namely AChE and BuChE, and also to evaluate their binding affinities [ 50 , 51 ]. This investigation makes it possible to explore compounds whose geometry and energy allow them to be easily recognized by the active sites of the biological targets. In the current study, AutoDock vina [ 52 ] software was used to perform the docking study. By default, this software uses a semi-flexible docking algorithm. Indeed, the enzyme structures have been considered rigid, while the ligand structures were conserved flexibles. Other parameters including exhaustiveness (Exh), energy range (ER), and number of binding modes (NBM) have been assigned default values (Exh = 8; ER = 4; NBM = 9). The interactions involved between the investigated phytoconstituents and the target enzymes were visualized using Discovery Studio 2021 software [ 53 ]. Moreover, Rivastigmine, Galantamine, and Donepezil, which are cholinesterase inhibitors approved by the FDA, were also docked to serve as references. 2.3. Ligand preparation Initially, the 3D structures of our chosen phytoconstituents were sourced from the PubChem online database. Subsequently, structural optimization was carried out using the MMFF94 force field, with the steepest Descent methodology [ 54 ]–[ 56 ], available in Avogadro software [ 57 ]. Afterward, using AutoDockTools-1.5.6 [ 39 ], PDBQT files for the investigated structures were produced, serving as docking input data [ 58 ]. 2.4. Protein preparation From the protein Data Bank accessible at http://www.rcsb.org , the three-dimensional structure of human BuChE combined with Rivastigmine (PDB ID: 6eul), was extracted. Similarly, the database yielded the 3D representation of AChE in association with Donepezil (PDB ID: 4ey7). Next, all non-protein molecules were removed from the collected structures. Additionally, polar hydrogens and Kollman charges were added employing AutoDockTools-1.5.6 [ 20 – 21 ] to prepare their corresponding PDBQT files. The central point of the docking box, representing BuChE’s active regions, was derived from Rivastigmine’s coordinates (x = 42.834, y = 19.853, z = 24.398). On the other hand, for defining AChE’s active areas, we relied on Donepezil’s coordinates (X= -14.108, Y = -43.833, Z = 27.670). The axe sizes of the docking boxes were set to: OX = 40 Å, OY = 40 Å and OZ = 40 Å. 2.5. Molecular dynamics simulation To support the docking outcomes and to gain insights into their consistency, the dynamic behavior of the complexes corresponding to the best docked ligands was simulated during 100 ns, under an aqueous environment, using GROMACS [ 60 ]. The enzyme topologies were prepared with pdb2gmx, which is a GROMACS module, by applying the CHARMM27 all-atom force field [ 61 ]. The docked phytoconstituents’ topologies were generated with the SwissParam server [ 62 ]. The topology files contain all the information necessary to define the input system within a simulation. Once the topology files were generated, dodecahedral boxes filled with TIP3P water molecules were defined as unit cell surrounding the systems under study. Following that, the investigated systems were neutralized by adding counter ions like sodium and chlorine. Afterward, the steepest descent technique was employed to reduce the system's energy, progressing through 50,000 iterations. Before starting the MD simulation, the systems under study were undergone NVT and NPT balancing for 2 ns, through a V-rescale thermostat at 300 K and a Parrinello Rahman barostat at 1 atm, respectively [ 63 ]. Finally, the system’s dynamics was simulated for 100 ns and the trajectories of various parameters, including the Root Mean Square Deviation (RMSD), the Root Mean Square Fluctuation (RMSF), Radius of Gyration (RoG), the Solvent Accessible Surface Area (SASA), were generated. 2.6. Pharmacokinetics and drug-likeness properties Inadequate pharmacological characteristics often result in many prospective medications not advancing past preclinical or clinical evaluations, causing setbacks in the drug development timeline and unnecessary resource expenditure. It is therefore highly helpful to consider the pharmacokinetics features in silico assessed to rationalize the discovery process of novel medication candidates. Through the pKCSM web tool, the pharmacokinetic characteristics of the examined phytoconstituents were evaluated. This assessment covered various aspects such as absorption, distribution, metabolism, and potential toxicity, along with drug-likeness indicators, offering insights into the potential bioavailability of a drug [ 64 ]. ADMET analysis stands for Absorption, Distribution, Metabolism, Excretion, and Toxicity analysis. It is a crucial step in drug development that assesses how a potential drug candidate is absorbed, distributed, metabolized, and excreted within the body, as well as its potential toxicity. This analysis helps pharmaceutical researchers understand the potential efficacy and safety of a drug candidate. 3. Results and discussion 3.1. Docking outcomes Forty-nine natural compounds were investigated for their possible use as therapeutic agents against Alzheimer disease. Molecular docking studies were done to evaluate the matching ability of the studied phytoconstituents and the active sites of cholinesterase receptors, namely, AChE and BuChE. The binding free energies corresponding to the docked phytoconstituents are summarized in Table 2 . Ligand BE (Kcal/mol) Ligand BE (Kcal/mol) Ligand BE (Kcal/mol) Table 2 Binding free energies corresponding to the docked ligands (BE). AChE BuChE AChE BuChE AChE BuChE P_1 -9.2 -8.9 P_18 -7.2 -6.4 P_2 -9.7 -8.0 P_19 -7.2 -6.6 P_35 -7.1 -6.4 P_3 -9.1 -8.0 P_20 -7.2 -6.2 P_36 -8.1 -7.9 P_4 -9.0 -8.4 P_21 -6.6 -5.7 P_37 -8.1 -8.0 P_5 -9.9 -8.7 P_22 -7.3 -6.6 P_38 -8.4 -6.6 P_6 -10.7 -9.6 P_23 -7.3 -6.5 P_39 -9.5 -8.1 P_7 -10.3 -9.8 P_24 -6.5 -5.6 P_40 -8.6 -8.5 P_8 -9.6 -9.0 P_25 -6.7 -6.2 P_41 -8.6 -8.3 P_9 -10.2 -9.2 P_26 -7.0 -6.2 P_42 -8.6 -7.0 P_10 -9.2 -8.4 P_27 -9.0 -8.4 P_43 -8.7 -8.6 P_11 -9.4 -9.1 P_28 -8.7 -8.8 P_44 -8.8 -8.4 P_12 -9.8 -8.8 P_29 -8.0 -7.0 P_45 -8.6 -8.3 P_13 -6.9 -6.2 P_30 -9.0 -8.5 P_46 -9.5 -8.5 P_14 -6.6 -6.1 P_31 -8.4 -7.8 P_47 -9.8 -8.7 P_15 -6.9 -6.2 P_32 -6.9 -6.3 P_48 -9.9 -8.6 P_16 -6.6 -5.7 P_33 -7.1 -6.5 P_49 -9.0 -8.5 P_17 -7.2 -6.3 P_34 -6.5 -6.4 BE (Kcal/mol) of reference drugs Galan (a) -9.7 Riva (b) -8.1 -7.3 Donepezil -11.4 (a) Galantamine (b) Rivastigmine As depicted in Table 2 , all docked compounds exhibited negative binding free energies, revealing that the recognition process between the targeted enzymes and the investigated phytoconstituents is thermodynamically favorable. The binding energies of the three FDA approved drugs, namely Rivastigmine, Donepezil and Galantamine, was defined as a selection criterion to choose the best docked ligands. Based on the obtained results, P_5, P_6, P_7 and P_9 are the best docked phytoconstituents. Indeed, they were predicted to exhibit greater binding affinities than Rivastigmine and Galantamine, indicating their strong matching with the active sites of the target enzymes. Therefore, these phytoconstituents are likely to form coherent complexes with the target bioactive sites by involving strong interactions, leading to the inhibition of the biological activity of BuChE and AChE enzymes. A study carried out by Karolina A. Wojtunik-Kulesza showed experimentally that certain terpenes investigated in our studies are AChE inhibitors [ 65 ], corroborating the in silico obtained results. The interactions implicated between the selected phytoconstituents, and the target enzymes are depicted in Table 3 and schematized in Figs. 2– 6 . Furthermore, to get insights into the key residues that could influence the enzymatic activity of AChE and BuChE, the interactions implicated with their co-crystallized ligands, namely Donepezil and Rivastigmine, respectively, are depicted in Fig. 1 . Table 3 The interactions involved between the investigated ligands and their receptors. Ligands AChE residues Interaction types Distance BuChE residues Interaction types Distance P_5 PHE 295 TRP 286 TYR 341 LEU 289 TYR 337 PHE 338 Conventional hydrogen bond Pi-Sigma Pi-Sigma Pi-Pi Stacked Pi-Alkyl Pi-Pi Stacked Pi-Alkyl Alkyl Pi-Alkyl Pi-Alkyl 2.568 3.666 3.543 4.618 4.905 4.452 4.114 4.631 4.636 4.049 ALA 328 MET 437 PRO 285 TRP 82 TRP 430 HIS 438 TYR 440 Alkyl Alkyl Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl 4.022 5.061 4.057 5.058 4.137 4.590 4.757 4.973 5.171 5.304 5.050 P_6 TYR 124 HIS 447 TRP 86 TYR 337 PHE 338 LEU 130 TYR 341 Conventional hydrogen bond Carbon hydrogen bond Pi-Sigma Pi-Pi Stacked Pi-Pi Stacked Pi-Pi Stacked Pi-Pi Stacked Pi-Alkyl Alkyl Pi-Alkyl 2.421 3.391 3.683 4.686 4.492 4.997 5.862 4.385 5.407 3.544 HIS 438 TRP 82 PRO 285 Pi-Cation Pi-Pi Stacked Pi-Pi Stacked Pi-Alkyl Pi-Alkyl Alkyl Pi-Alkyl 4.684 4.025 4.127 5.150 4.199 4.110 5.141 P_7 PHE 295 SER 293 VAL 294 TRP 286 TYR 337 PHE 338 TYR 341 LEU 289 HIS 447 Conventional hydrogen bond Conventional hydrogen bond Carbon hydrogen bond Pi-Sigma Pi-Sigma Pi-Pi Stacked Pi-Alkyl Pi-Sigma Pi-Sigma Pi-Alkyl Pi-Pi Stacked Pi-Alkyl Alkyl Pi-Alkyl 2.652 2.818 2.826 3.704 3.626 4.611 4.892 3.642 3.997 4.702 4.504 4.018 4.577 4.922 TYR 120 ASP 70 TRP 82 PRO 285 HIS 438 Conventional hydrogen bond Pi-Anion Pi-Sigma Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl 2.744 4.373 3.859 3.634 4.704 4.238 4.458 5.443 4.927 P_9 TYR 124 TRP 286 TYR 341 TRP 86 TYR 337 Conventional Hydrogen bond Pi-Sigma Pi-Alkyl Pi-Pi Stacked Pi-Alkyl Pi-Alkyl Pi-Alkyl 2.122 3.673 4.942 3.579 5.002 4.832 3.911 THR 120 GLY 116 TRP 82 PRO 285 Conventional hydrogen bond Carbon hydrogen bond Pi-Sigma Pi-Sigma Pi-Alkyl Alkyl Pi-Alkyl 2.506 3.521 3.849 3.728 4.665 4.421 5.092 In Fig. 1 , it's evident that Donepezil settles within the AChE cavity, forming interactions with residues such as TRP A 286, TYR A 72, TYR A 337, PHE A 338, TRP A 86, TYR A 341, PHE A 295, and SER A 293. These specific residues may be fundamental in influencing AChE's enzymatic functions. Conversely, Rivastigmine's association with BuChE is highlighted by its hydrophobic engagements with residues like ALA A: 328, PHE A: 329, TYR A: 332, TRP A: 82 and PRO A: 285, which could be critical in determining the enzymatic behavior of BuChE. As for P_5, it was found to involve a conventional hydrogen bond with PHE 295 and nine hydrophobic interactions, including Pi-Sigma, Pi-Pi Stacked, Alkyl and Pi-Alkyl interactions, with five AChE residues, namely TRP 286, TYR 341, LEU 289, TYR 337and PHE 338 (Fig. 2, Table 3 ). Except LEU 289, these residues are key sites for the enzymatic activity of AChE, as long as they are among the interaction sites privileged by Donepezil. The same phytoconstituent (P_5) exhibited its ability to be complexed at the BuChE pocket by involving eleven hydrophobic interactions with ALA 328, MET 437, PRO 285, TRP 82, TRP 430, HIS 438 and TYR 440. Furthermore, ALA 328, PRO 285, TRP 82 are among the key sites influencing BuChE enzymatic activity. Regarding P_6, it was found to involve two hydrogen bonds with TYR 124 and HIS 447, in addition to eight hydrophobic interactions with TRP 86, TYR 337, PHE 338, LEU 130 and TYR 341. It can also be noted that P_6 could interact with key sites of AChE enzymatic activity, namely TYR A: 337, PHE A: 338, TRP A: 86, TYR A: 341. While it was predicted to be docked at the BuChE pocket by involving an electrostatic interaction with HIS 438 and six hydrophobic interactions with two key residues namely TRP 82 and PRO 285 (Fig. 3 ). As depicted in Table 3 , P_7 was docked at the AChE pocket by involving three hydrogen bindings with PHE 295, SER 293, and VAL 294, reinforced by eleven hydrophobic interactions with TRP 286, TYR 337, PHE 338, TYR 341, LEU 289 and HIS 447. Furthermore, this phytoconstituent was found to be able to interact with key residues namely PHE 295, SER 293, TRP 286, TYR 337, PHE 338, TYR 341 (Fig. 4 ). The interactions are well distributed over the ligand’s backbone, which can lead to stable complex. While the recognition process between P_7 and BuChE was achieved by the implication of a conventional hydrogen bond, an electrostatic interaction, and seven hydrophobic interactions. As revealed for P_5 and P_6, TRP 82 and PRO 285 are among the interacting sites for P_7. Concerning P_9, it was complexed with AChE by forming a hydrogen bond with TYR 124, in addition to six hydrophobic interactions with four key residues including TRP 286, TYR 341, TRP 86, TYR 337. On the other hand, this phytoconstituent was complexed with BuChE by involving two hydrogen bonds with THR 120 and GLY 116, in addition to five hydrophobic interactions with two key sites namely TRP 82 and PRO 285 (Fig. 5 ). Overall, all investigated phytoconstituents have implicated interactions with two key residues, namely TRP 82 and PRO 285, during their docking process at the BuChE pocket. Similar to Donepezil, P_5 and P_7 were able to form a conventional bond with PHE 295. It’s noted also that hydrogen bonds are stronger than hydrophobic interactions. Indeed, hydrogen bindings are revealed to be able to bring the studied compounds very close to their targets. Additionally, compared to the other phytoconstituents, P_7 established several hydrogen bindings with AChE, and it interacted with the highest number of AChE key residues, highlighting its adequate geometry with the AChE pocket. Furthermore, an experimental study showed that a synthetic compound inhibits AChE by interacting with Phe295 Trp286, Tyr341, His447 [ 66 ], confirming that the residues with which P_7 interacts are key sites of AChE enzymatic activity, and their blocking by forming a stable complex can lead to the inhibition of the enzymatic activity of AChE. Accordingly, P_7 can be proposed as cholinesterase inhibitor candidate, as soon as it presents stable interactions and a good ADMET profile. 3.2. Drug-likeness properties The drug likeness properties corresponding to P_5, P_6, P_7 and P_9, were evaluated based on Lipinski and Veber rules. The obtained results are reported in Table 4 . All the tested phytoconstituents are likely to be bioavailable and can be considered as oral drugs candidates. Indeed, according to Lipinski and Veber rules, bioavailable structures should have less than 500 Daltons in molecular weight (MW), less than ten acceptors of hydrogen bond (HBA), less than five donors of hydrogen bond (HBD), log P less than five, polar surfaces area (PSA) less than 140 Å 2 and less than ten rotatable bonds (RB). Table 4 Drug-likeness characteristics corresponding to the investigated phytoconstituents. Ligand Lipinski and Veber rules Number of violations MW Log P RB HBA HBD PSA P_5 261.215 1.389 2 2 1 127.382 0 P_6 285.237 2.021 4 2 1 138.675 0 P_7 285.237 1.552 4 2 1 140.112 0 P_9 285.237 1.397 4 2 1 139.796 0 Donepezil 350.269 1.635 6 4 0 167.005 1 Galantamine 255.188 0.822 1 4 1 118.844 0 Rivastigmine 228.166 0.722 4 3 0 109.146 0 Threshold MW ≤ 500 LogP ≤ 5 RB ≤ 10 HBA ≤ 10 HBD ≤ 5 PSA ≤ 140 N.Viol ≤ 1 3.3. ADMET properties Predicting ADMET properties is crucial in the early stages of drug research. It aids to exclude substances whose qualities prevent them from reaching a therapeutic target or whose characteristics could produce health issues if they were used as medications. In the current investigation, the ADMET proprieties corresponding to the selected phytoconstituents (P_5, P_6, P_7, P_9), were highlighted trough pKCSM server [ 67 ]. The outcomes are shown in Tables 5 – 7 . Table 5 details that all evaluated compounds demonstrate high absorptive potential within the human intestine (HIA ≥ 90%). The Caco-2 cell line, a prevalent in-vitro representation of human intestinal mucosa, is often employed to estimate the absorption rates of orally taken drugs. For all the compounds under examination, the predicted Caco-2 permeability is notably high, with values exceeding 0.9[ 64 ]. P-glycopotien (Pgp) is a transmembrane protein that expels substances from cells to protect them from toxins and xenobiotics. Except for Donepezil and P_6, the tested compounds cannot be expelled from cells. Furthermore, Donepezil and P_6 are predicted to be Pgp inhibitors. The blood-brain barrier (BBB) is a biological barrier protecting the brain from exogenous compounds. Since the brain is therapeutic target for Alzheimer disease treatment, the investigated phytoconstituents are expected to be BBB permeable. As highlighted in Table 5 , all tested phytoconstituents are predicted to have log BB > -1 and log PS > -3, indicating that they are BBB permeable and can reach the central nervous system (CNS) [ 64 ]. Table 5 The predicted absorption and distribution metrics. Ligand Absorption Distribution Caco2 HIA (%) P-gp substrate P-gp Inhibitor I/II BBB permeability (Log BB) CNS permeability (Log PS) P_5 1.739 99.324 No No/No 0.009 -2.118 P_6 1.663 100 Yes Yes/Yes 0.877 -1.636 P_7 1.737 100 No Yes/No 0.243 -2.135 P_9 1.652 100 No Yes/No 0.128 -1.778 Donepezil 1.262 99.816 Yes Yes/Yes 0.107 -1.422 Galantamine 1.749 92.857 No No/No -0.058 -2.808 Rivastigmine 1.741 98.088 No No/No -0.005 -2.751 Cytochrome P450 (CYP) is a crucial enzyme for drug metabolism in the body. Thus, it is crucial to determine whether a substance has the potential to become a cytochrome P450 substrate or inhibitor. As presented in Table 6 , except for Rivastigmine and Galantamine, all compounds are predicted to be CYP3A4 substrate. Besides, Donepezil is predicted to be CYP inhibitor, which can affect the metabolism of other administrated medications. Renal OCT2 is a transporter implicated in renal clearance of medicines and endogenous compounds. According to the obtained results, except for P_6, Rivastigmine and Galantamine, the investigated phytoconstituents are susceptible to be Renal OCT2 substrate. Table 6 The predicted metabolic and excretion properties. Ligand Metabolism Excretion CYP2D6 substrate CYP3A4 substrate CYP2D6 inhibitor CYP3A4 inhibitor Total Clearance Renal OCT2 substrate P_5 No Yes No No 1.085 Yes P_6 No Yes No No 1.152 No P_7 No Yes No No 1.249 Yes P_9 No Yes No No 1.120 Yes Donepezil Yes Yes Yes Yes 1.430 Yes Galantamine No No No No 1.203 No Rivastigmine No No No No 0.569 No Regarding the toxicity profile, except for P_6, no tested compound is predicted to produce mutagenic effects. Also, they are not hepatotoxic and cannot produce Skin Sensitization effects, except for P_9. In addition to their pharmacological properties, it’s crucial to note that evaluating the safety of the examined compounds is of paramount importance. Their LD50 values reveal that these compounds present risks only when introduced in notably elevated doses. Another cardinal consideration in drug assessment is the potential interaction with specific potassium ion channels, notably the hERG channels. These channels are instrumental in maintaining the heart's rhythmic electrical activity. An obstruction can induce severe ventricular arrhythmias, which could be life-threatening. Data presented in Table 7 confirms that the compounds in our study do not inhibit hERG I, reinforcing their relative safety in this context. Overall, P_5 and P_7 can be considered as promising drug candidates, given their good AMET profiles and high affinity towards AChE and BuChE. Table 7 Metrics related to toxicity of tested compounds. Ligand Toxicity AMES toxicity Max. tolerated dose (human) hERG I inhibitor hERG II inhibitor LD50 LOAEL Hepatotoxicity Skin Sensitisation P_5 No -0.141 No No 1.956 1.825 No No P_6 Yes 0.176 No Yes 2.078 1.948 No No P_7 No -0.224 No Yes 2.050 1.941 No No P_9 No -0.465 No Yes 2.148 1.952 No Yes Donepezil No -0.413 No Yes 2.484 2.054 No No Galantamine No -0.303 No No 2.546 1.011 No No Rivastigmine No 0.238 No No 2.397 0.764 No No 3.4. Molecular dynamics studies The behavior of the P_7 when interacting with enzymes BuChE and AChE were simulated to gain insights into their resilience and stability in a water-based setting. To gauge this stability, we analyzed the evolution of several metrics, over a period of 100 ns, such as RMSD, RMSF, RoG, SASA, and the count of hydrogen bonds. Figure 6 shows the RMSD graph. As illustrated on this graph, the RMSD values calculated for the unbounded proteins (BuChE and AChE) are relatively lower than those calculated for their respective complexes, which reflects the effect of the molecular recognition process occurred between the ligands and their target proteins. This behavior confirms the existence of interactions between the P7 ligand and the target proteins, as described in the molecular docking section. Moreover, the profiles of the RMSD trajectories corresponding to the unbound proteins are relatively like their respective complexes. Indeed, the RMSD fluctuates at the beginning of the simulation, which is a transition state, until to reach a stationary state. This means that, at the beginning, the studied systems adapt to the simulation conditions and afterwards they stabilize. It can also be noted that the RMSD values of AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChEne are not very dispersed, and they are close to their average values, which are 0.145, 0.208,0.229, 0.181, 0.243 and 0.217 nm, respectively, indicating that the studied systems did not undergo radical conformational changes during their simulation. The RMSF is a numerical measure reflecting the flexibility of the residues of a protein during a simulation period. The RMSF plot obtained from the MD simulation is illustrated in Fig. 7 . The first remark that can be revealed by analyzing the RMSF graph, is that the unbound proteins and their respective complexes have almost similar trajectories. On the other hand, the RMSF values corresponding to the unbound proteins are relatively lower than those exhibited by their relative complexes, which is due to the dynamic behavior of the docked ligands, but this increased flexibility is not at all significant to alter the binding coordination of the P7 ligand. Indeed, almost all residues have RMSF values below 0.2 nm, which means that they are moderately flexible. The average RMSF values are 0.077, 0.102 ,0.108, 0.096, 0.124 and 0.110 nm for AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChE, respectively. As depicted in Fig. 8 , the RoG evolution was illustrated to further elucidate the dynamic tendencies of the analyzed systems within a water-based setting. At the beginning of the simulation, the RoG increased and quickly reached a steady state, indicating that the studied systems could be stabilized in the aqueous medium after a brief period of adaptation. The RoG values corresponding to the unbound proteins are relatively higher than those calculated for their respective complexes, which expresses that the molecular recognition between the evaluated chemicals and the target proteins leads to compact complexes. The average RoG values are 2.310, 2.307, 2.315, 2.336, 2.331 and 2.328 nm for AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChE, respectively. The number of hydrogen bindings established during a simulation period between a particular small molecule and its biological receptor is an essential parameter for judging the stability of ligand-protein complexes. Indeed, a high hydrogen bonding number is an indicator of good molecular recognition leading to stable complexes. As shown in Fig. 9 , the P7 ligand successfully established a significant number of hydrogen bindings with BuChE throughout the simulation, which may contribute to its stability at the BuChE binding site. The Solvent Accessible Surface Area (SASA) for BuChE and AChE, both pre and post ligand introduction, was computed and showcased in Fig. 10 . Throughout the entire simulation, the SASA for these systems largely maintains a balanced state, which reflects their stability in the aqueous medium. Moreover, the SASA values corresponding to the unbound proteins are relatively higher than those calculated for their respective complexes, verifying that the interaction between the examined ligands and the target proteins results in compact complexes. The average SASA values are 213.863, 211.643 ,212.938, 225.843, 223.251 and 221.269 nm 2 for AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChE, respectively. Conclusion The present study explored potential Cholinesterase (ChE) inhibitors from phytochemicals belong to Cannabis Sativa L by applying a comprehensive computational approach based on molecular docking, ADMET analysis and molecular dynamics technics. From 49 phytoconstituents in-silico screened, four chemicals have been exhibited high binding affinity towards the cholinesterase enzymes and implicated various interactions to form complexes at the binding pocket of the target enzymes. They were predicted to exhibit greater binding affinities than Rivastigmine and Galantamine, indicating their strong matching with the active sites of the target enzymes. Regarding their ADMET profiles, P_5 and P_7 are suggested as potential hit compounds. They are not expected to induce any mutagenic or hepatotoxic effects and cannot produce skin sensitization. In addition, these phytoconstituents are likely to be bioavailable and predicted to be BBB permeable and can reach the central nervous system (CNS) and exert their therapeutic effects. The molecular dynamics simulation highlighted that the P7-ChE recognition is a spontaneous reaction leading to stable and compact complexes. These complexes can inhibit the enzymatic activity of ChEs. Based on this study's outcomes, P_7 stands out as a potential candidate for cholinesterase inhibition. It is important to note that theoretical studies cannot replace experimental tests but can streamline scientific research by reducing costs and time associated with drug development. Therefore, P_7 deserves to be tested experimentally to evaluate its enzymatic activity in vitro and in vivo. Declarations Author Contribution • Hassan Nour: Conceptualization, Data curation, Writing-Reviewing and Editing Original draft preparation.• Imane Yamari, Oussama Abchir and Nouh Mounadi: Conceptualization, Writing- Original draft preparation. • Abdelouahid Samadi: Visualization, Funding• Salah Belaidi: Visualization,• Samir Chtita: Conceptualization, Methodology, Software, Visualization, Supervision Acknowledgments: The authors gratefully acknowledge the financial support received from the Deanship of Graduate Studies Scientific Research, Middle East University, MEU, Department of Pharmacy. 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Chtita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYJCCAyDExsDA+ADI4eEjVosEGxsDswFICxuxFkkArWGTADEJapFvP514uKLmTh2ffPOzyq85djJA2x4+uoFHi8GZ3A0Hzxx7BnQYm9lt2W3JQIexGRvn4NPCANTS2HAY5Bez25LbmIFaeNik8WmR738L08L+rVhyWz1hLQw34LbwmDF+3HaYsBaDG0BbGo4dlmxjyymWZtx2nIeNmYBf5PtzN39sqDnML998fOPHn9uq7fnZmx8+xuswZMDMAyaJVQ4CjD9IUT0KRsEoGAUjBgAA++lHkDxcJLYAAAAASUVORK5CYII=","orcid":"","institution":"Hassan II University of Casablanca","correspondingAuthor":true,"prefix":"","firstName":"Samir","middleName":"","lastName":"Chtita","suffix":""}],"badges":[],"createdAt":"2024-02-25 00:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3986384/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3986384/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51785415,"identity":"1ca6bf4f-442e-4a6b-9cc6-b324874a9a8b","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174479,"visible":true,"origin":"","legend":"\u003cp\u003eThe Donepezil-AChE interactions (a) and Rivastigmine-BuChE interactions (b) in bidimensional diagram.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/6e2e55f38a86bb2570815db9.png"},{"id":51785419,"identity":"c74541cd-3f45-4e13-821b-dda9e9e9b453","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":411226,"visible":true,"origin":"","legend":"\u003cp\u003eThe (P_5)-AChE interactions (a) and (P_5)-BuChE interactions (b) in bidimensional diagram.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/3575cfa95d2ee504f89c45a4.png"},{"id":51785418,"identity":"57df21e4-87c2-4561-836c-ba5f45475f7c","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":339589,"visible":true,"origin":"","legend":"\u003cp\u003eThe (P_6)-AChE interactions (c) and (P_6)-BuChE interactions (d) in bidimensional diagram.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/41bacfa1203798324b05a4c9.png"},{"id":51785420,"identity":"30a91eb6-7a21-4cb6-84fe-809b90208d74","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":390381,"visible":true,"origin":"","legend":"\u003cp\u003eThe (P_7)-AChE interactions (e) and (P_7)-BuChE interactions (f) in bidimensional diagram.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/7aa8d358564984406e244ee8.png"},{"id":51785417,"identity":"97762c1d-b456-468e-acc6-d70eb777f5f1","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":350063,"visible":true,"origin":"","legend":"\u003cp\u003eThe (P_9)-AChE interactions (g) and (P_9)-BuChE interactions (h) in bidimensional diagram.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/edb86334a07d9870628bacac.png"},{"id":51785426,"identity":"0cc95942-26ff-4637-9b18-80f46a54bb05","added_by":"auto","created_at":"2024-02-29 03:04:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103642,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Rmsd graph corresponding to AChE, P7-AChE complex, and Donepezil-AChE complex. (b) Rmsd graph corresponding to BuChE, P7-BuChE complex, and Rivastigmine-BuChE complex.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/d137203a76d460c3ae482d76.png"},{"id":51785421,"identity":"a1ea00f7-8d5f-4fe7-818f-5e501d7414c9","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":48560,"visible":true,"origin":"","legend":"\u003cp\u003e(a) RMSF of Cα atoms of AChE in the absence and presence of P7 and Donepezil. (b) RMSF of Cα atoms of BuChE in the absence and presence of P7 and Rivastigmine.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/a9b3132efa7cc0030ebceb65.png"},{"id":51785422,"identity":"798ec61a-b07c-4f09-a9de-6a657d41589e","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":39933,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Radius of gyration (RoG) of AChE, P7-AChE complex, and Donepezil-AChE complex as function of time. (b) Radius of gyration (RoG) of BuChE, P7-BuChE complex, and Rivastigmine-BuChE complex.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/9156c08075614d687138d67d.png"},{"id":51785423,"identity":"8ca83ae8-ccc4-42e6-b193-a8af4f3ac98d","added_by":"auto","created_at":"2024-02-29 03:04:13","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":22345,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of hydrogens formed between P7 and its receptors during the MD simulation.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/677cec8e0d2ca86dbab2b627.png"},{"id":51785802,"identity":"2df4bdad-9711-4017-b396-1ac74979ef13","added_by":"auto","created_at":"2024-02-29 03:12:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":108929,"visible":true,"origin":"","legend":"\u003cp\u003e(a) SASA of AChE, P7-AChE complex, and Donepezil-AChE complex as function of time. (b) SASA of BuChE, P7-BuChE complex, and Rivastigmine-BuChE complex.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/d7610d6a534d41755ee86747.png"},{"id":51914744,"identity":"bcc43380-1e4d-476c-a955-0144f3d6600c","added_by":"auto","created_at":"2024-03-03 13:39:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2186373,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/cc9293ff-1bec-45fa-9c91-27705c0ac534.pdf"},{"id":51785801,"identity":"d5a71d9e-5707-448c-b6c5-c26b155ddf9a","added_by":"auto","created_at":"2024-02-29 03:12:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":341205,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3986384/v1/e6f68293d42061f8a791b458.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Cannabis sativa L for Anti-Alzheimer Potential: An extensive Computational Study including Molecular Docking, Molecular Dynamics, and ADMET Assessments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eCannabis sativa L\u003c/em\u003e, commonly known as hemp or marijuana, is a plant species belonging to the Cannabaceae family [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is widely cultivated for its miscellaneous therapeutic properties, such as anti-anxiety, anti-inflammatory, anti-epileptic, and neuroprotective properties [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Morocco is among the world's largest producers of cannabis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In May 2021, the Moroccan Parliament adopted Law 13\u0026ndash;21, regulating the legitimate commercial use of cannabis for therapeutic, cosmetic, and industrial purposes.\u003c/p\u003e \u003cp\u003eCannabinoids are the most well-known and studied phytoconstituents in cannabis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These compounds interact with the body's endocannabinoid system, regulating miscellaneous physiological processes, such as appetite, pain and mood [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Terpenes are another class of phytoconstituents found in cannabis and are responsible for the plant's characteristic aroma and flavor [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. They also have potential therapeutic benefits, such as anti-inflammatory and anti-anxiety effects [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Flavonoids are a third class of phytoconstituents found in cannabis and have been shown to have antioxidant and anti-inflammatory properties [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany investigations have examined the potential benefits of cannabis in addressing neurodegenerative diseases that affect cognitive abilities [\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the neuroprotective effect of this plant is widely studied and proven [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2021, Xia Jiang has highlighted cannabidiol\u0026thinsp;\u0026minus;\u0026thinsp;carbamate hybrids as selective potent BuChE inhibitors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, an experimental study by Tess Puopolo showed that certain cannabinoids can have moderate inhibitory effects on the activities of the AChE and BChE enzymes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, \u003cem\u003eCannabis sativa L\u003c/em\u003e is a complex plant that contains a variety of phytoconstituents with potential therapeutic benefits, yet further investigations are required to explore other therapeutic effects.\u003c/p\u003e \u003cp\u003eAlzheimer's disease is a neurologically progressive illness afflicting the human brain, provoking impaired memory, declining cognition, and behavioral and character changes. It is one of the most frequent factors causing dementia, a group of symptoms that affect a person's ability to think, reason, and remember [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Alzheimer's disease typically affects older adults but can also occur in younger people [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Right now, there is no effective therapy for Alzheimer's disease, but commercial medications are helpful in managing its symptoms and enhancing patients' quality of life [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcetylcholinesterase (AChE) and Butyrylcholinesterase (BuChE), commonly known as cholinesterase enzymes, are biological catalysts ensuring the hydrolysis of acetylcholine (Ach) into acetic acid and choline [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the normal brain, AChE is the primary enzyme that is responsible for breaking down Ach [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As Alzheimer's disease progresses, the importance of BuChE in cholinergic neurotransmission is expected to increase. This is due to a potential decrease in AChE activity by up to 45%, while BuChE activity could increase by up to 40%, ultimately reaching 90% [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, Alzheimer's disease is associated with decreased levels of acetylcholine due to intensive loss of cholinergic neurons [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Since acetylcholine is a neurotransmitter implicated in memory and learning, its hydrolysis leads to an altered neurotransmission process in Alzheimer\u0026rsquo;s disease patients. Consequently, cholinesterase enzymes inhibition is a pertinent approach for the management of Alzheimer's disease symptoms.\u003c/p\u003e \u003cp\u003eCholinesterase inhibitors, such as donepezil, rivastigmine, and galantamine, are commonly used to treat the symptoms of Alzheimer's disease by increasing the levels of acetylcholine in the brain. These drugs work by inhibiting the breakdown of acetylcholine, which can help improve cognitive function and alleviate some of the symptoms of the disease. Donepezil and galantamine acts as selective inhibitors of AChE, while rivastigmine acts as dual inhibitor of both AChE and BuChE [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Unfortunately, these drugs can induce serious side effects such as hepatotoxicity and several gastrointestinal disorders [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, the current study attempts to explore new cholinesterase inhibitors more puissant and safer than existing ones. To reach this goal, combined molecular modeling technics, including molecular docking, molecular dynamics, and pharmacokinetics analysis, were implemented. Forty-nine phytoconstituents extracted from \u003cem\u003eCannabis sativa L\u003c/em\u003e were \u003cem\u003ein silico\u003c/em\u003e screened to estimate their inhibitory effects towards both AChE and BuChE enzymes, and their drug-likeness properties were predicted. The outcomes of the current investigation could provide scientific support to develop new generation of cannabis-based acetylcholinesterase inhibitors.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Screened compounds\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eForty-nine phytoconstituents of \u003cem\u003eCannabis sativa L\u003c/em\u003e, previously extracted and identified, were selected by analyzing several phytochemical studies [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u0026ndash;[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. These phytoconstituents belong to two classes namely cannabinoids and terpenes. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the structures of the investigated phytoconstituents.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Molecular Docking\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eMolecular docking studies were done to predict the possible interactions that may be involved between the selected phytoconstituents and the cholinesterase enzymes, namely AChE and BuChE, and also to evaluate their binding affinities [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. This investigation makes it possible to explore compounds whose geometry and energy allow them to be easily recognized by the active sites of the biological targets. In the current study, AutoDock vina [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e] software was used to perform the docking study. By default, this software uses a semi-flexible docking algorithm. Indeed, the enzyme structures have been considered rigid, while the ligand structures were conserved flexibles. Other parameters including exhaustiveness (Exh), energy range (ER), and number of binding modes (NBM) have been assigned default values (Exh\u0026thinsp;=\u0026thinsp;8; ER\u0026thinsp;=\u0026thinsp;4; NBM\u0026thinsp;=\u0026thinsp;9). The interactions involved between the investigated phytoconstituents and the target enzymes were visualized using Discovery Studio 2021 software [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Moreover, Rivastigmine, Galantamine, and Donepezil, which are cholinesterase inhibitors approved by the FDA, were also docked to serve as references.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Ligand preparation\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eInitially, the 3D structures of our chosen phytoconstituents were sourced from the PubChem online database. Subsequently, structural optimization was carried out using the MMFF94 force field, with the steepest Descent methodology [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]\u0026ndash;[\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e], available in Avogadro software [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. Afterward, using AutoDockTools-1.5.6 [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], PDBQT files for the investigated structures were produced, serving as docking input data [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Protein preparation\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eFrom the protein Data Bank accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org\u003c/span\u003e\u003c/span\u003e, the three-dimensional structure of human BuChE combined with Rivastigmine (PDB ID: 6eul), was extracted. Similarly, the database yielded the 3D representation of AChE in association with Donepezil (PDB ID: 4ey7). Next, all non-protein molecules were removed from the collected structures. Additionally, polar hydrogens and Kollman charges were added employing AutoDockTools-1.5.6 [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] to prepare their corresponding PDBQT files. The central point of the docking box, representing BuChE\u0026rsquo;s active regions, was derived from Rivastigmine\u0026rsquo;s coordinates (x\u0026thinsp;=\u0026thinsp;42.834, y\u0026thinsp;=\u0026thinsp;19.853, z\u0026thinsp;=\u0026thinsp;24.398). On the other hand, for defining AChE\u0026rsquo;s active areas, we relied on Donepezil\u0026rsquo;s coordinates (X= -14.108, Y = -43.833, Z\u0026thinsp;=\u0026thinsp;27.670). The axe sizes of the docking boxes were set to: OX\u0026thinsp;=\u0026thinsp;40 \u0026Aring;, OY\u0026thinsp;=\u0026thinsp;40 \u0026Aring; and OZ\u0026thinsp;=\u0026thinsp;40 \u0026Aring;.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Molecular dynamics simulation\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eTo support the docking outcomes and to gain insights into their consistency, the dynamic behavior of the complexes corresponding to the best docked ligands was simulated during 100 ns, under an aqueous environment, using GROMACS [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. The enzyme topologies were prepared with pdb2gmx, which is a GROMACS module, by applying the CHARMM27 all-atom force field [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. The docked phytoconstituents\u0026rsquo; topologies were generated with the SwissParam server [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. The topology files contain all the information necessary to define the input system within a simulation. Once the topology files were generated, dodecahedral boxes filled with TIP3P water molecules were defined as unit cell surrounding the systems under study. Following that, the investigated systems were neutralized by adding counter ions like sodium and chlorine. Afterward, the steepest descent technique was employed to reduce the system\u0026apos;s energy, progressing through 50,000 iterations. Before starting the MD simulation, the systems under study were undergone NVT and NPT balancing for 2 ns, through a V-rescale thermostat at 300 K and a Parrinello Rahman barostat at 1 atm, respectively [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. Finally, the system\u0026rsquo;s dynamics was simulated for 100 ns and the trajectories of various parameters, including the Root Mean Square Deviation (RMSD), the Root Mean Square Fluctuation (RMSF), Radius of Gyration (RoG), the Solvent Accessible Surface Area (SASA), were generated.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Pharmacokinetics and drug-likeness properties\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eInadequate pharmacological characteristics often result in many prospective medications not advancing past preclinical or clinical evaluations, causing setbacks in the drug development timeline and unnecessary resource expenditure. It is therefore highly helpful to consider the pharmacokinetics features \u003cem\u003ein silico\u003c/em\u003e assessed to rationalize the discovery process of novel medication candidates. Through the pKCSM web tool, the pharmacokinetic characteristics of the examined phytoconstituents were evaluated. This assessment covered various aspects such as absorption, distribution, metabolism, and potential toxicity, along with drug-likeness indicators, offering insights into the potential bioavailability of a drug [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]. ADMET analysis stands for Absorption, Distribution, Metabolism, Excretion, and Toxicity analysis. It is a crucial step in drug development that assesses how a potential drug candidate is absorbed, distributed, metabolized, and excreted within the body, as well as its potential toxicity. This analysis helps pharmaceutical researchers understand the potential efficacy and safety of a drug candidate.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Docking outcomes\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eForty-nine natural compounds were investigated for their possible use as therapeutic agents against Alzheimer disease. Molecular docking studies were done to evaluate the matching ability of the studied phytoconstituents and the active sites of cholinesterase receptors, namely, AChE and BuChE. The binding free energies corresponding to the docked phytoconstituents are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBE (Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBE (Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBE (Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBinding free energies corresponding to the docked ligands (BE).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAChE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuChE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAChE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuChE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAChE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBuChE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" align=\"left\"\u003e\n \u003cp\u003eBE (Kcal/mol) of reference drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGalan \u003csup\u003e(a)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiva \u003csup\u003e(b)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDonepezil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003csup\u003e(a) Galantamine (b) Rivastigmine\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, all docked compounds exhibited negative binding free energies, revealing that the recognition process between the targeted enzymes and the investigated phytoconstituents is thermodynamically favorable. The binding energies of the three FDA approved drugs, namely Rivastigmine, Donepezil and Galantamine, was defined as a selection criterion to choose the best docked ligands. Based on the obtained results, P_5, P_6, P_7 and P_9 are the best docked phytoconstituents. Indeed, they were predicted to exhibit greater binding affinities than Rivastigmine and Galantamine, indicating their strong matching with the active sites of the target enzymes. Therefore, these phytoconstituents are likely to form coherent complexes with the target bioactive sites by involving strong interactions, leading to the inhibition of the biological activity of BuChE and AChE enzymes. A study carried out by Karolina A. Wojtunik-Kulesza showed experimentally that certain terpenes investigated in our studies are AChE inhibitors [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e], corroborating the \u003cem\u003ein silico\u003c/em\u003e obtained results.\u003c/p\u003e\n \u003cp\u003eThe interactions implicated between the selected phytoconstituents, and the target enzymes are depicted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and schematized in Figs.\u0026nbsp;2\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Furthermore, to get insights into the key residues that could influence the enzymatic activity of AChE and BuChE, the interactions implicated with their co-crystallized ligands, namely Donepezil and Rivastigmine, respectively, are depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe interactions involved between the investigated ligands and their receptors.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLigands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAChE\u003c/p\u003e\n \u003cp\u003eresidues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInteraction types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBuChE\u003c/p\u003e\n \u003cp\u003eresidues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInteraction types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance\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\u003eP_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE 295\u003c/p\u003e\n \u003cp\u003eTRP 286\u003c/p\u003e\n \u003cp\u003eTYR 341\u003c/p\u003e\n \u003cp\u003eLEU 289\u003c/p\u003e\n \u003cp\u003eTYR 337\u003c/p\u003e\n \u003cp\u003ePHE 338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConventional hydrogen bond\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.568\u003c/p\u003e\n \u003cp\u003e3.666\u003c/p\u003e\n \u003cp\u003e3.543\u003c/p\u003e\n \u003cp\u003e4.618\u003c/p\u003e\n \u003cp\u003e4.905\u003c/p\u003e\n \u003cp\u003e4.452\u003c/p\u003e\n \u003cp\u003e4.114\u003c/p\u003e\n \u003cp\u003e4.631\u003c/p\u003e\n \u003cp\u003e4.636\u003c/p\u003e\n \u003cp\u003e4.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALA 328\u003c/p\u003e\n \u003cp\u003eMET 437\u003c/p\u003e\n \u003cp\u003ePRO 285\u003c/p\u003e\n \u003cp\u003eTRP 82\u003c/p\u003e\n \u003cp\u003eTRP 430\u003c/p\u003e\n \u003cp\u003eHIS 438\u003c/p\u003e\n \u003cp\u003eTYR 440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.022\u003c/p\u003e\n \u003cp\u003e5.061\u003c/p\u003e\n \u003cp\u003e4.057\u003c/p\u003e\n \u003cp\u003e5.058\u003c/p\u003e\n \u003cp\u003e4.137\u003c/p\u003e\n \u003cp\u003e4.590\u003c/p\u003e\n \u003cp\u003e4.757\u003c/p\u003e\n \u003cp\u003e4.973\u003c/p\u003e\n \u003cp\u003e5.171\u003c/p\u003e\n \u003cp\u003e5.304\u003c/p\u003e\n \u003cp\u003e5.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR 124\u003c/p\u003e\n \u003cp\u003eHIS 447\u003c/p\u003e\n \u003cp\u003eTRP 86\u003c/p\u003e\n \u003cp\u003eTYR 337\u003c/p\u003e\n \u003cp\u003ePHE 338\u003c/p\u003e\n \u003cp\u003eLEU 130\u003c/p\u003e\n \u003cp\u003eTYR 341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConventional hydrogen bond\u003c/p\u003e\n \u003cp\u003eCarbon hydrogen bond\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.421\u003c/p\u003e\n \u003cp\u003e3.391\u003c/p\u003e\n \u003cp\u003e3.683\u003c/p\u003e\n \u003cp\u003e4.686\u003c/p\u003e\n \u003cp\u003e4.492\u003c/p\u003e\n \u003cp\u003e4.997\u003c/p\u003e\n \u003cp\u003e5.862\u003c/p\u003e\n \u003cp\u003e4.385\u003c/p\u003e\n \u003cp\u003e5.407\u003c/p\u003e\n \u003cp\u003e3.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS 438\u003c/p\u003e\n \u003cp\u003eTRP 82\u003c/p\u003e\n \u003cp\u003ePRO 285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePi-Cation\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.684\u003c/p\u003e\n \u003cp\u003e4.025\u003c/p\u003e\n \u003cp\u003e4.127\u003c/p\u003e\n \u003cp\u003e5.150\u003c/p\u003e\n \u003cp\u003e4.199\u003c/p\u003e\n \u003cp\u003e4.110\u003c/p\u003e\n \u003cp\u003e5.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE 295\u003c/p\u003e\n \u003cp\u003eSER 293\u003c/p\u003e\n \u003cp\u003eVAL 294\u003c/p\u003e\n \u003cp\u003eTRP 286\u003c/p\u003e\n \u003cp\u003eTYR 337\u003c/p\u003e\n \u003cp\u003ePHE 338\u003c/p\u003e\n \u003cp\u003eTYR 341\u003c/p\u003e\n \u003cp\u003eLEU 289\u003c/p\u003e\n \u003cp\u003eHIS 447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConventional hydrogen bond\u003c/p\u003e\n \u003cp\u003eConventional hydrogen bond\u003c/p\u003e\n \u003cp\u003eCarbon hydrogen bond\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.652\u003c/p\u003e\n \u003cp\u003e2.818\u003c/p\u003e\n \u003cp\u003e2.826\u003c/p\u003e\n \u003cp\u003e3.704\u003c/p\u003e\n \u003cp\u003e3.626\u003c/p\u003e\n \u003cp\u003e4.611\u003c/p\u003e\n \u003cp\u003e4.892\u003c/p\u003e\n \u003cp\u003e3.642\u003c/p\u003e\n \u003cp\u003e3.997\u003c/p\u003e\n \u003cp\u003e4.702\u003c/p\u003e\n \u003cp\u003e4.504\u003c/p\u003e\n \u003cp\u003e4.018\u003c/p\u003e\n \u003cp\u003e4.577\u003c/p\u003e\n \u003cp\u003e4.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR 120\u003c/p\u003e\n \u003cp\u003eASP 70\u003c/p\u003e\n \u003cp\u003eTRP 82\u003c/p\u003e\n \u003cp\u003ePRO 285\u003c/p\u003e\n \u003cp\u003eHIS 438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConventional hydrogen bond\u003c/p\u003e\n \u003cp\u003ePi-Anion\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.744\u003c/p\u003e\n \u003cp\u003e4.373\u003c/p\u003e\n \u003cp\u003e3.859\u003c/p\u003e\n \u003cp\u003e3.634\u003c/p\u003e\n \u003cp\u003e4.704\u003c/p\u003e\n \u003cp\u003e4.238\u003c/p\u003e\n \u003cp\u003e4.458\u003c/p\u003e\n \u003cp\u003e5.443\u003c/p\u003e\n \u003cp\u003e4.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR 124\u003c/p\u003e\n \u003cp\u003eTRP 286\u003c/p\u003e\n \u003cp\u003eTYR 341\u003c/p\u003e\n \u003cp\u003eTRP 86\u003c/p\u003e\n \u003cp\u003eTYR 337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConventional\u003c/p\u003e\n \u003cp\u003eHydrogen bond\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Pi Stacked\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.122\u003c/p\u003e\n \u003cp\u003e3.673\u003c/p\u003e\n \u003cp\u003e4.942\u003c/p\u003e\n \u003cp\u003e3.579\u003c/p\u003e\n \u003cp\u003e5.002\u003c/p\u003e\n \u003cp\u003e4.832\u003c/p\u003e\n \u003cp\u003e3.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHR 120\u003c/p\u003e\n \u003cp\u003eGLY 116\u003c/p\u003e\n \u003cp\u003eTRP 82\u003c/p\u003e\n \u003cp\u003ePRO 285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConventional hydrogen bond\u003c/p\u003e\n \u003cp\u003eCarbon hydrogen bond\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Sigma\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003cp\u003ePi-Alkyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.506\u003c/p\u003e\n \u003cp\u003e3.521\u003c/p\u003e\n \u003cp\u003e3.849\u003c/p\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003cp\u003e4.665\u003c/p\u003e\n \u003cp\u003e4.421\u003c/p\u003e\n \u003cp\u003e5.092\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=\"BlockQuote\"\u003e\n \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, it\u0026apos;s evident that Donepezil settles within the AChE cavity, forming interactions with residues such as TRP A 286, TYR A 72, TYR A 337, PHE A 338, TRP A 86, TYR A 341, PHE A 295, and SER A 293. These specific residues may be fundamental in influencing AChE\u0026apos;s enzymatic functions. Conversely, Rivastigmine\u0026apos;s association with BuChE is highlighted by its hydrophobic engagements with residues like ALA A: 328, PHE A: 329, TYR A: 332, TRP A: 82 and PRO A: 285, which could be critical in determining the enzymatic behavior of BuChE.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAs for P_5, it was found to involve a conventional hydrogen bond with PHE 295 and nine hydrophobic interactions, including Pi-Sigma, Pi-Pi Stacked, Alkyl and Pi-Alkyl interactions, with five AChE residues, namely TRP 286, TYR 341, LEU 289, TYR 337and PHE 338 (Fig.\u0026nbsp;2, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Except LEU 289, these residues are key sites for the enzymatic activity of AChE, as long as they are among the interaction sites privileged by Donepezil. The same phytoconstituent (P_5) exhibited its ability to be complexed at the BuChE pocket by involving eleven hydrophobic interactions with ALA 328, MET 437, PRO 285, TRP 82, TRP 430, HIS 438 and TYR 440. Furthermore, ALA 328, PRO 285, TRP 82 are among the key sites influencing BuChE enzymatic activity.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003eRegarding P_6, it was found to involve two hydrogen bonds with TYR 124 and HIS 447, in addition to eight hydrophobic interactions with TRP 86, TYR 337, PHE 338, LEU 130 and TYR 341. It can also be noted that P_6 could interact with key sites of AChE enzymatic activity, namely TYR A: 337, PHE A: 338, TRP A: 86, TYR A: 341. While it was predicted to be docked at the BuChE pocket by involving an electrostatic interaction with HIS 438 and six hydrophobic interactions with two key residues namely TRP 82 and PRO 285 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, P_7 was docked at the AChE pocket by involving three hydrogen bindings with PHE 295, SER 293, and VAL 294, reinforced by eleven hydrophobic interactions with TRP 286, TYR 337, PHE 338, TYR 341, LEU 289 and HIS 447. Furthermore, this phytoconstituent was found to be able to interact with key residues namely PHE 295, SER 293, TRP 286, TYR 337, PHE 338, TYR 341 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The interactions are well distributed over the ligand\u0026rsquo;s backbone, which can lead to stable complex. While the recognition process between P_7 and BuChE was achieved by the implication of a conventional hydrogen bond, an electrostatic interaction, and seven hydrophobic interactions. As revealed for P_5 and P_6, TRP 82 and PRO 285 are among the interacting sites for P_7.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eConcerning P_9, it was complexed with AChE by forming a hydrogen bond with TYR 124, in addition to six hydrophobic interactions with four key residues including TRP 286, TYR 341, TRP 86, TYR 337. On the other hand, this phytoconstituent was complexed with BuChE by involving two hydrogen bonds with THR 120 and GLY 116, in addition to five hydrophobic interactions with two key sites namely TRP 82 and PRO 285 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eOverall, all investigated phytoconstituents have implicated interactions with two key residues, namely TRP 82 and PRO 285, during their docking process at the BuChE pocket. Similar to Donepezil, P_5 and P_7 were able to form a conventional bond with PHE 295. It\u0026rsquo;s noted also that hydrogen bonds are stronger than hydrophobic interactions. Indeed, hydrogen bindings are revealed to be able to bring the studied compounds very close to their targets. Additionally, compared to the other phytoconstituents, P_7 established several hydrogen bindings with AChE, and it interacted with the highest number of AChE key residues, highlighting its adequate geometry with the AChE pocket. Furthermore, an experimental study showed that a synthetic compound inhibits AChE by interacting with Phe295 Trp286, Tyr341, His447 [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e], confirming that the residues with which P_7 interacts are key sites of AChE enzymatic activity, and their blocking by forming a stable complex can lead to the inhibition of the enzymatic activity of AChE. Accordingly, P_7 can be proposed as cholinesterase inhibitor candidate, as soon as it presents stable interactions and a good ADMET profile.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Drug-likeness properties\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe drug likeness properties corresponding to P_5, P_6, P_7 and P_9, were evaluated based on Lipinski and Veber rules. The obtained results are reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. All the tested phytoconstituents are likely to be bioavailable and can be considered as oral drugs candidates. Indeed, according to Lipinski and Veber rules, bioavailable structures should have less than 500 Daltons in molecular weight (MW), less than ten acceptors of hydrogen bond (HBA), less than five donors of hydrogen bond (HBD), log P less than five, polar surfaces area (PSA) less than 140 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e and less than ten rotatable bonds (RB).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDrug-likeness characteristics corresponding to the investigated phytoconstituents.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eLipinski and Veber rules\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNumber of violations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMW\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog P\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRB\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBA\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e\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\u003eP_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDonepezil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.635\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=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGalantamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRivastigmine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eThreshold\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMW\u0026thinsp;\u0026le;\u0026thinsp;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRB\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA\u0026thinsp;\u0026le;\u0026thinsp;140\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN.Viol\u0026thinsp;\u0026le;\u0026thinsp;1\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\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. ADMET properties\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePredicting ADMET properties is crucial in the early stages of drug research. It aids to exclude substances whose qualities prevent them from reaching a therapeutic target or whose characteristics could produce health issues if they were used as medications. In the current investigation, the ADMET proprieties corresponding to the selected phytoconstituents (P_5, P_6, P_7, P_9), were highlighted trough pKCSM server [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. The outcomes are shown in Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e details that all evaluated compounds demonstrate high absorptive potential within the human intestine (HIA\u0026thinsp;\u0026ge;\u0026thinsp;90%). The Caco-2 cell line, a prevalent in-vitro representation of human intestinal mucosa, is often employed to estimate the absorption rates of orally taken drugs. For all the compounds under examination, the predicted Caco-2 permeability is notably high, with values exceeding 0.9[\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eP-glycopotien (Pgp) is a transmembrane protein that expels substances from cells to protect them from toxins and xenobiotics. Except for Donepezil and P_6, the tested compounds cannot be expelled from cells. Furthermore, Donepezil and P_6 are predicted to be Pgp inhibitors.\u003c/p\u003e\n \u003cp\u003eThe blood-brain barrier (BBB) is a biological barrier protecting the brain from exogenous compounds. Since the brain is therapeutic target for Alzheimer disease treatment, the investigated phytoconstituents are expected to be BBB permeable. As highlighted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, all tested phytoconstituents are predicted to have log BB \u0026gt; -1 and log PS \u0026gt; -3, indicating that they are BBB permeable and can reach the central nervous system (CNS) [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe predicted absorption and distribution metrics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAbsorption\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaco2\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-gp substrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-gp\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBBB\u003c/strong\u003e permeability\u003c/p\u003e\n \u003cp\u003e(Log BB)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCNS\u003c/strong\u003e permeability\u003c/p\u003e\n \u003cp\u003e(Log PS)\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\u003eP_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo/No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes/No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes/No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDonepezil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGalantamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo/No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRivastigmine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo/No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.751\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=\"BlockQuote\"\u003e\n \u003cp\u003eCytochrome P450 (CYP) is a crucial enzyme for drug metabolism in the body. Thus, it is crucial to determine whether a substance has the potential to become a cytochrome P450 substrate or inhibitor. As presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, except for Rivastigmine and Galantamine, all compounds are predicted to be CYP3A4 substrate. Besides, Donepezil is predicted to be CYP inhibitor, which can affect the metabolism of other administrated medications.\u003c/p\u003e\n \u003cp\u003eRenal OCT2 is a transporter implicated in renal clearance of medicines and endogenous compounds. According to the obtained results, except for P_6, Rivastigmine and Galantamine, the investigated phytoconstituents are susceptible to be Renal OCT2 substrate.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe predicted metabolic and excretion properties.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eExcretion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCYP2D6 substrate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCYP3A4 substrate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCYP2D6 inhibitor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCYP3A4 inhibitor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Clearance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRenal OCT2 substrate\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\u003eP_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDonepezil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGalantamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRivastigmine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\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=\"BlockQuote\"\u003e\n \u003cp\u003eRegarding the toxicity profile, except for P_6, no tested compound is predicted to produce mutagenic effects. Also, they are not hepatotoxic and cannot produce Skin Sensitization effects, except for P_9. In addition to their pharmacological properties, it\u0026rsquo;s crucial to note that evaluating the safety of the examined compounds is of paramount importance. Their LD50 values reveal that these compounds present risks only when introduced in notably elevated doses. Another cardinal consideration in drug assessment is the potential interaction with specific potassium ion channels, notably the hERG channels. These channels are instrumental in maintaining the heart\u0026apos;s rhythmic electrical activity. An obstruction can induce severe ventricular arrhythmias, which could be life-threatening. Data presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e confirms that the compounds in our study do not inhibit hERG I, reinforcing their relative safety in this context.\u003c/p\u003e\n \u003cp\u003eOverall, P_5 and P_7 can be considered as promising drug candidates, given their good AMET profiles and high affinity towards AChE and BuChE.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMetrics related to toxicity of tested compounds.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eToxicity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMES toxicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax. tolerated dose (human)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ehERG I inhibitor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ehERG II inhibitor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLD50\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLOAEL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHepatotoxicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkin Sensitisation\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\u003eP_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDonepezil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGalantamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRivastigmine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Molecular dynamics studies\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe behavior of the P_7 when interacting with enzymes BuChE and AChE were simulated to gain insights into their resilience and stability in a water-based setting. To gauge this stability, we analyzed the evolution of several metrics, over a period of 100 ns, such as RMSD, RMSF, RoG, SASA, and the count of hydrogen bonds.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the RMSD graph. As illustrated on this graph, the RMSD values calculated for the unbounded proteins (BuChE and AChE) are relatively lower than those calculated for their respective complexes, which reflects the effect of the molecular recognition process occurred between the ligands and their target proteins. This behavior confirms the existence of interactions between the P7 ligand and the target proteins, as described in the \u003cspan class=\"InternalRef\"\u003emolecular docking\u003c/span\u003e section. Moreover, the profiles of the RMSD trajectories corresponding to the unbound proteins are relatively like their respective complexes. Indeed, the RMSD fluctuates at the beginning of the simulation, which is a transition state, until to reach a stationary state. This means that, at the beginning, the studied systems adapt to the simulation conditions and afterwards they stabilize. It can also be noted that the RMSD values of AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChEne are not very dispersed, and they are close to their average values, which are 0.145, 0.208,0.229, 0.181, 0.243 and 0.217 nm, respectively, indicating that the studied systems did not undergo radical conformational changes during their simulation.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe RMSF is a numerical measure reflecting the flexibility of the residues of a protein during a simulation period. The RMSF plot obtained from the MD simulation is illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The first remark that can be revealed by analyzing the RMSF graph, is that the unbound proteins and their respective complexes have almost similar trajectories. On the other hand, the RMSF values corresponding to the unbound proteins are relatively lower than those exhibited by their relative complexes, which is due to the dynamic behavior of the docked ligands, but this increased flexibility is not at all significant to alter the binding coordination of the P7 ligand. Indeed, almost all residues have RMSF values below 0.2 nm, which means that they are moderately flexible. The average RMSF values are 0.077, 0.102 ,0.108, 0.096, 0.124 and 0.110 nm for AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChE, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, the RoG evolution was illustrated to further elucidate the dynamic tendencies of the analyzed systems within a water-based setting. At the beginning of the simulation, the RoG increased and quickly reached a steady state, indicating that the studied systems could be stabilized in the aqueous medium after a brief period of adaptation. The RoG values corresponding to the unbound proteins are relatively higher than those calculated for their respective complexes, which expresses that the molecular recognition between the evaluated chemicals and the target proteins leads to compact complexes. The average RoG values are 2.310, 2.307, 2.315, 2.336, 2.331 and 2.328 nm for AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChE, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe number of hydrogen bindings established during a simulation period between a particular small molecule and its biological receptor is an essential parameter for judging the stability of ligand-protein complexes. Indeed, a high hydrogen bonding number is an indicator of good molecular recognition leading to stable complexes. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, the P7 ligand successfully established a significant number of hydrogen bindings with BuChE throughout the simulation, which may contribute to its stability at the BuChE binding site.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe Solvent Accessible Surface Area (SASA) for BuChE and AChE, both pre and post ligand introduction, was computed and showcased in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. Throughout the entire simulation, the SASA for these systems largely maintains a balanced state, which reflects their stability in the aqueous medium. Moreover, the SASA values corresponding to the unbound proteins are relatively higher than those calculated for their respective complexes, verifying that the interaction between the examined ligands and the target proteins results in compact complexes. The average SASA values are 213.863, 211.643 ,212.938, 225.843, 223.251 and 221.269 nm\u003csup\u003e2\u003c/sup\u003e for AChE, P7-AChE, Donepezil-AChE, BuChE, P7-BuChE and Rivastigmine-BuChE, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study explored potential Cholinesterase (ChE) inhibitors from phytochemicals belong to \u003cem\u003eCannabis Sativa L\u003c/em\u003e by applying a comprehensive computational approach based on molecular docking, ADMET analysis and molecular dynamics technics. From 49 phytoconstituents \u003cem\u003ein-silico\u003c/em\u003e screened, four chemicals have been exhibited high binding affinity towards the cholinesterase enzymes and implicated various interactions to form complexes at the binding pocket of the target enzymes. They were predicted to exhibit greater binding affinities than Rivastigmine and Galantamine, indicating their strong matching with the active sites of the target enzymes.\u003c/p\u003e\n\u003cp\u003eRegarding their ADMET profiles, P_5 and P_7 are suggested as potential hit compounds. They are not expected to induce any mutagenic or hepatotoxic effects and cannot produce skin sensitization. In addition, these phytoconstituents are likely to be bioavailable and predicted to be BBB permeable and can reach the central nervous system (CNS) and exert their therapeutic effects. The molecular dynamics simulation highlighted that the P7-ChE recognition is a spontaneous reaction leading to stable and compact complexes. These complexes can inhibit the enzymatic activity of ChEs. Based on this study's outcomes, P_7 stands out as a potential candidate for cholinesterase inhibition. It is important to note that theoretical studies cannot replace experimental tests but can streamline scientific research by reducing costs and time associated with drug development.\u0026nbsp; Therefore, P_7 deserves to be tested experimentally to evaluate its enzymatic activity in vitro and in vivo.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026bull; Hassan Nour: Conceptualization, Data curation, Writing-Reviewing and Editing Original draft preparation.\u0026bull; Imane Yamari, Oussama Abchir and Nouh Mounadi: Conceptualization, Writing- Original draft preparation. \u0026bull; Abdelouahid Samadi: Visualization, Funding\u0026bull; Salah Belaidi: Visualization,\u0026bull; Samir Chtita: Conceptualization, Methodology, Software, Visualization, Supervision\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eThe authors gratefully acknowledge the financial support received from the Deanship of Graduate Studies Scientific Research, Middle East University, MEU, Department of Pharmacy. Dr. Abdelouahid Samadi thanks the UAEU for an internal Start-up grant 2023 (Grant Code G00004400) for support.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeclaration\u003c/b\u003e: There are no conflicts of interest that could potentially bias or influence the information being presented or discussed.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcPartland JM (Oct. 2018) Cannabis Systematics at the Levels of Family, Genus, and Species. 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Open Chem 15(1):332\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/CHEM-2017-0040\u003c/span\u003e\u003cspan address=\"10.1515/CHEM-2017-0040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsghar A, Yousuf M, Fareed G, Nazir R (May 2020) Synthesis, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) activities, and molecular docking studies of a novel compound based on combination of flurbiprofen and isoniazide. RSC Adv 10(33):19346\u0026ndash;19352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1039/d0ra02339f\u003c/span\u003e\u003cspan address=\"10.1039/d0ra02339f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePires DEV, Blundell TL, Ascher DB pkCSM: predicting small-molecule pharmacokinetic properties using graph-based signatures\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cannabis sativa L, Molecular Docking, Molecular Dynamics, Alzheimer’s disease, Cholinesterase inhibitors, ADMET","lastPublishedDoi":"10.21203/rs.3.rs-3986384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3986384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCholinesterase enzymes play a pivotal role in hydrolyzing acetylcholine, a neurotransmitter crucial for memory and cognition, into its components, acetic acid, and choline. A primary approach in addressing Alzheimer's disease symptoms is by inhibiting the action of these enzymes. With this context, our study embarked on a mission to pinpoint potential Cholinesterase (ChE) inhibitors using a comprehensive computational methodology. A total of 49 phytoconstituents derived from \u003cem\u003eCannabis sativa L\u003c/em\u003e underwent \u003cem\u003ein silico\u003c/em\u003e screening via molecular docking, pharmacokinetic and pharmacotoxicological analysis, to evaluate their ability to inhibit cholinesterase enzymes. Out of these, two specific compounds, namely tetrahydrocannabivarin and Δ-9-tetrahydrocannabinol, belonging to cannabinoids, stood out as prospective therapeutic agents against Alzheimer's due to their potential as cholinesterase inhibitors. These candidates showcased commendable binding affinities with the cholinesterase enzymes, highlighting their interaction with essential enzymatic residues. They were predicted to exhibit greater binding affinities than Rivastigmine and Galantamine. Their ADMET assessments further classified them as viable oral pharmaceutical drugs. They are not expected to induce any mutagenic or hepatotoxic effects and cannot produce skin sensitization. In addition, these phytoconstituents are predicted to be BBB permeable and can reach the central nervous system (CNS) and exert their therapeutic effects. To delve deeper, we explored molecular dynamics (MD) simulations to examine the stability of the complex formed between the best candidate (Δ-9-tetrahydrocannabinol) and the target proteins under simulated biological conditions. The MD study affirmed that the ligand-ChE recognition is a spontaneous reaction leading to stable complexes. Our research outcomes provide valuable insights, offering a clear direction for the pharmaceutical sector in the pursuit of effective anti-Alzheimer treatments.\u003c/p\u003e","manuscriptTitle":"Exploring Cannabis sativa L for Anti-Alzheimer Potential: An extensive Computational Study including Molecular Docking, Molecular Dynamics, and ADMET Assessments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 03:04:07","doi":"10.21203/rs.3.rs-3986384/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":"063626a9-7b1e-4f24-a533-59b472bb0c5c","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-03T13:30:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-29 03:04:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3986384","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3986384","identity":"rs-3986384","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}