Computational drug repositioning approach to predict first-line therapeutics for epilepsy

Computational drug repositioning approach to predict first-line therapeutics for epilepsy | 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 Article Computational drug repositioning approach to predict first-line therapeutics for epilepsy Pawan Kumar, Vivek Kumar, Raveena Chauhan, Vandana Saini, Ajit Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6851614/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 18 You are reading this latest preprint version Abstract Epilepsy affects millions of people globally, with approximately one-third of patients experiencing drug-resistant seizures. Developing new anti-epileptic drugs is time-intensive and costly, prompting interest in computational drug repositioning strategies. Here we report about a comprehensive drug repositioning approach to identify the first-line therapeutic option(s) for epileptic seizures. All approved drugs from the DrugBank database were screened for their anti-epileptic properties that involved their blood brain permeability prediction and clustering them for structural similarity with the marketed anti-epilepsy drugs. The screened drugs were subjected to molecular docking against previously identified therapeutic target proteins (Voltage-Gated Sodium Channel α2; GABA receptor α1-β1; and Voltage-Gated Calcium Channel α1G), A total of 46 drugs showed better binding affinity than the respective standard drugs - Carbamazepine, Clonazepam and Pregabalin for the selected target proteins - Voltage-Gated Sodium Channel α2; GABA receptor α1-β1; and Voltage-Gated Calcium Channel α1G, respectively. The binding pocket and literature data mining revealed three drugs, Oxaprozin, Pizotifen, and Cyproheptadine, that bind within the precise binding pocket and have no reported severe side effects related to seizure onset. The molecular dynamic simulation studies showed all three compounds with better and more stable binding interactions against the corresponding drug targets. Oxaprozin, among identified 3 drugs, showed a very stable binding and can be a considered a potential repurposed drug against epilepsy, inviting further pre-clinical trials. Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Computational neuroscience Biological sciences/Computational biology and bioinformatics/Virtual drug screening AED Drug repurposing/repositioning Epilepsy Molecular docking Molecular dynamic simulations Seizures Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Epilepsy is one of the most prevalent neurological disorders globally, affecting millions of individuals of all ages 1 , 2 . It encompasses a spectrum of neurological disorders characterised by abnormal electrical activity in the brain, leading to seizures. These seizures can manifest in various forms, ranging from momentary lapses of consciousness to convulsions. The underlying causes of epilepsy are diverse, including genetic predispositions, brain injuries, infections, and developmental abnormalities 3 , 4 . Epilepsy poses significant challenges to patients' quality of life, societal integration, and healthcare systems worldwide. Numerous antiepileptic drugs (AEDs) are available, but a substantial proportion of epilepsy patients experience inadequate seizure control or intolerable side effects with standard medications 5 . Moreover, developing novel AEDs entails substantial time, resources, and regulatory hurdles, making it a challenging endeavour. Therefore, alternative strategies, such as drug repositioning, which can expedite the discovery and approval of effective therapies, are imperative. Drug repositioning, also known as drug repurposing or reprofiling, involves identifying new therapeutic applications for existing drugs beyond their initially intended indications 6 . Unlike traditional drug discovery approaches, which often start from scratch, drug repositioning leverages existing pharmacological data, clinical experience, and safety profiles of approved drugs. This strategy offers several advantages, including reduced development costs, shorter timelines, and increased likelihood of success in clinical trials 7 , 8 . Recent years have witnessed a growing interest in exploring drug repositioning strategies for epilepsy treatment, resulting in systematic screening of approved drug libraries and investigational compounds to identify candidates with antiepileptic properties. These efforts have yielded promising findings, as the repurposed drug Lorcaserin 9 demonstrates its efficacy in preclinical models and is under early-stage clinical trials for epilepsy. The present study employed a virtual high-throughput screening (VHTS)-based, multi-target drug repositioning strategy to identify drug compound(s) with antiepileptic activity. The DrugBank database was selected to screen approved drug compounds based on blood-brain barrier (BBB) permeability, structural similarity, molecular docking and binding site analysis. Drug(s) showing better binding affinity were evaluated for binding stability and free binding energy using molecular dynamics simulation (MDS) for 100 ns. Materials and Methodology Retrieval of structural and physicochemical data of drug molecules The DrugBank database is one of the most significant structural databases containing chemical, physicochemical, pharmacokinetic, pharmacodynamic, target, and metabolomic information of marketed drugs, including approved, small-molecule drugs, biotech drugs, investigational and withdrawn drugs 10 . Due to safety concerns, experimental and withdrawn drug compounds were not considered, while approved drugs, including illicit and nutraceutical drugs, were selected for further in-silico screening. The 3D molecular files for all drug compounds were retrieved from the DrugBank database's File Transfer Protocol (FTP) server as a single Structure Data File (SDF). Screening of drugs for BBB permeability The Central Nervous System (CNS) acting drug compounds must cross the blood-brain barrier (BBB) to reach the neural system for pharmacological activity. In-vivo or in-vitro BBB permeability prediction is a complex, time- and cost-intensive process. So, as an alternative, Machine Learning (ML) based in-silico BBB permeability prediction is a time-efficient and economical process with superior accuracy. Different BBB permeability prediction tools are available with great accuracy, but to gain maximum positive results, we used three different tools: (a) an in-house developed tool, BBBper (Blood Brain Barrier permeability prediction tool) 11 , AdmetSAR 12 and LightBBB 13 to screen the selected approved drug compounds for BBB permeability. Clustering of structurally similar drugs Most studied phenomena of drug repositioning focus on similarity among drugs' structural fingerprints, stating, "similar structures will have similar activity" 8 , 14 , 15 . So, all selected BBB permeable drugs were further filtered for their structural similarity with available marketed AEDs (Table 1 ) 16 . The ChemMine tool 17 , an online molecular data analysis program supported by the R library ChemMineR 18 , was used to cluster selected approved and BBB-permeable drugs using binning clustering applications. The binning clustering method is used to partition a dataset into clusters by quantising the data points into bins and further assigning each bin to a cluster. Different drug clusters were generated using different similarity cutoff values ranging from 0.4 to 0.9. A lower cut-off value means lower similarity between compounds and results in the clustering of less similar compounds. In contrast, a higher similarity cut-off value results in the clustering of more similar compounds, leading to smaller cluster sizes. Finally, the similarity cut-off, which grouped most marketed AEDs within a single cluster, was selected for grouping drug compounds with similar structures to marketed AEDs. All compounds within the selected AEDs cluster were chosen for subsequent molecular docking analysis, based on the hypothesis that their structural conformations were analogous to those of established AEDs and, therefore, may elicit comparable therapeutic effects. Table 1 List of marketed anti-epileptic drugs. Drug Name DrugBank ID Mechanism of action (from DrugBank) Acetazolamide DB00819 • Carbonic anhydrase inhibitor. Brivaracetam DB05541 • Synaptic vesicle glycoprotein 2A (SV2A) agonist. • VGSC alpha 1B inhibitor. Cannabidiol DB09061 • Weak partial agonist activity at Cannabinoid receptors CB1R and CB2R. • Inhibits noradrenaline, dopamine, serotonin and GABA uptake 19 . • Block T-type (low voltage-activated) Ca channel 19 . • Antagonise mu-opioid receptor. Carbamazepine DB00564 • Inhibits VGSC alpha subunit 20 . • Decrease dopamine turnover (dopamine antagonist) by reducing dopamine (D2) receptor density and phosphorylation. • Enhance GABA synthesis. • Inhibits Serotonin uptake. • Decrease Norepinephrine release. Clobazam DB00349 • GABA-A receptor partial agonist (alpha and gamma 2 subunits). Clonazepam DB01068 • GABA - A receptor agonist 21 . Diazepam DB00829 • GABA - A receptor agonist. Dronabinol DB00470 • Cannabinoid receptors 1 and 2 agonist. Eslicarbazepine acetate DB09119 • Inhibits VGSC 22 . • Inhibits T-type calcium channel. Ethosuximide DB00593 • Inhibits T-type VGCC alpha 1G 23 . Ethotoin DB00754 • Inhibits VGSC alpha 5. Ezogabine DB04953 • VGPC (Kv7.2-7.5) KCNQ2,3,4,5) agonist. Felbamate DB00949 • Antagonize Glutamate receptor (NMDA 2A,3A, 2B). FosPhenytoin DB01320 • Inhibits VGSC alpha 5. Gabapentin DB00996 • Structural analogue of GABA 24 . • Inhibits VGCC subunit alpha 2, delta 1,2. • Activates VGPC subfamily KQT member 3,5. Lacosamide DB06218 • Inhibits VGSC alpha 3,9,10 22,25 . Lamotrigine (phenyl triazine) DB00555 • Inhibits VGSC 23 . • Inhibits adenosine A1/A2 receptor. • Inhibits K-opioid receptor. Levetiracetam DB01202 • Agonist for Synaptic vesicle glycoprotein 2A (SV2A) 26 . • Inhibits VGSC alpha 1B 27 , 28 . Lorazepam DB00186 • GABA-alpha receptor agonist. Methsuximide DB05246 • Inhibits VGCC T-type subunit alpha 1G. Methylphenobarbital DB00849 • GABA-alpha receptor agonist. • Glutamate receptor antagonist. Midazolam DB00683 • GABA-alpha receptor agonist. Nitrazepam DB01595 • GABA R agonist. • Inhibits Voltage-dependent sodium channels. Oxcarbazepine DB00776 • Inhibits VGSC 20 , 22 . Perampanel DB08883 • Glutamate receptor 1 antagonist 29 . Phenacemide DB01121 • Inhibits Sodium channel protein type 1 subunit alpha Phenobarbital DB01174 • GABA R agonist. • Glutamate receptor antagonist. • Inhibits Calcium channels. • NMDA channel antagonist. Phenytoin DB00252 • VGSC blocker alpha 1, 3 and 5 subunits. Pregabalin DB00230 • Inhibits VGCC subunit alpha 2/ delta 1 30,31 . Primidone DB00794 • GABA alpha receptor agonist. • Glutamate receptor antagonist. Rufinamide DB06201 • Inhibits VGSC 32 . Stiripentol DB09118 • GABA alpha receptor agonist. Tiagabine DB00906 • Inhibits GABA transferase. Topiramate DB00273 • Inhibits VGSC type 1 alpha subunit 29 . • GABA alpha 1 receptor agonist. Trimethadione DB00347 • Inhibits Voltage-dependent T-type calcium channel subunit alpha-1G Valproic acid DB00313 • Inhibits succinic semialdehyde dehydrogenase (SSADH) 33 . • Inhibits VGSC 34 , 35 . • Inhibits GABA transferase 33 . • Inhibits Histone deacetylase 2 and 9. Vigabatrin DB01080 • GABA analogue 36 . • Irreversible inhibitor of 4-aminobutyrate transaminase. • GABA beta receptor agonist 36 . Zonisamide DB00909 • Inhibits VGSC alpha 1,2,3,4,5,9,11 subunits 37 . • Inhibits VGSC beta 1,2,3,4 subunits 37 . • Inhibits VGCC T-type subunit alpha 1G, 1H, 1I. • Inhibits Carbonic anhydrase 1,2,3,4,5A,5B,6,7,10,11,1213,14. Selection and preparation of the tertiary structure of epilepsy target receptors Previously, we have identified three 1st line epilepsy therapeutic targets: Voltage-Gated Sodium Channel (VGSC) α2 (Nav1.2), Gamma-Aminobutyric Acid (GABA) receptor α1-β1, and Voltage-Gated Calcium Channel (VGCC) α1G (Cav3.1) 16 . The tertiary protein structures of receptors Nav1.2, GABA receptor α1, and Cav3.1 were available in the RCSB-PDB database with PDB-IDs 6J8E-A, 6HUJ-A, and 6KZP, respectively. However, the tertiary structure of GABA receptor β1 was unavailable in the RCSB-PDB database, and hence, its 3D structure was generated by homology modelling using the SWISS-MODEL web server 38 . The GABA receptor β1 fasta sequence was retrieved from UniProt (UniProt ID: P18505) and subjected to homology modelling using the Swiss Model web server, with 6HUJ-B as the template. The modelled structure was further validated using the QMEAN score 39 , Ramachandran plot 40 , Verify3D 41 , and ProSA Z-score 42 . To form the functional GABA receptor α1-β1 complex, the available GABA receptor α1 chain and modelled GABA receptor β1 chain were subjected to protein-protein docking using Hex tool v8.0.0 43 . A total of 25 searches were performed, taking “shape + electro + DARS” as correlation types and 3D FFT mode. A side-by-side (parallel) conformation showing head-to-head and tail-to-tail orientation for both chains was selected, and the result was saved as a combined PDB file. The conformation for the generated GABA receptor α1-β1 file was also validated by aligning the generated structure against GABA receptor α1-β3 (PDB ID: 6HUJ) using open-source PyMOL v2.5.0 44 . All the selected epilepsy target receptor proteins (Nav1.2, GABA receptor α1-β1, and Cav3.1) were subjected to energy minimisation using UCSF Chimera v1.6 45 for 100 steepest descent and 10 conjugate gradient steps under AMBER ff99bsc force field with a step size of 0.02Å. The energy-minimised structures were saved as PDB files for further molecular docking studies. Molecular docking study The selected epilepsy target receptor proteins (Nav1.2, GABA receptor α1-β1, and Cav3.1) were prepared for molecular docking by adding polar hydrogen and assigning Kollman and Gasteiger charges using Autodock tools. Autodock v4.2.6 46 was used for molecular docking of selected drug compounds against selected therapeutic target proteins. The target proteins Nav1.2 and Cav3.1 are channel proteins, and to block the channel, the Grid file parameters (GPF) were assigned around the pore region, while the grid parameters for GABA receptor α1-β1 were assigned between the α1 and β1 chains within the GABA binding area to find a GABA agonist 47 (Table 2 ). Molecular docking was performed for 100 independent runs using the Lamarckian genetic algorithm with a population size of 150, taking a gene mutation rate of 0.02 and a crossover rate of 0.8. The dock conformation with the lowest binding energy and maximum cluster size was selected for each drug. The highest-selling AEDs, Carbamazepine, Clonazepam and Pregabalin, were chosen as standard drugs for target proteins - Nav1.2, GABA receptor α1-β1, and Cav3.1, respectively 16 . The drugs showing better binding affinities than the corresponding standard AEDs against all three receptors were selected for further study. Table 2 Grid box parameters for molecular docking study of voltage-gated sodium channel 2A (Nav1.2), GABA receptor α1-β, and voltage-gated calcium channel α1G (Cav3.1) Nav1.2 GABA Receptor α1-β1 Cav3.1 Size-X 54 56 64 Size-Y 66 76 76 Size-Z 94 76 90 Center-X 129.988 119.412 176.584 Center-Y 132.695 134.518 168.642 Center-Z 135.591 159.123 192.98 Grid Box Binding pocket analysis of selected drugs The residues within the binding pockets are significant factors in a drug's pharmacodynamic effect. Hence, the binding pockets of selected drugs were compared with the binding pockets of standard drugs, hypothesising that drugs with binding pockets similar to the standard AEDs would produce similar therapeutic effects. Therefore, the dock complexes of standard AEDs and selected drugs against the epilepsy target proteins were generated using the Autodock tool, and the binding pockets (nearby amino acids and hydrogen bond-forming amino acids) for each dock complexes were analysed using the Java-based tool LigPlot + 48 . Drugs showing similar binding pockets to the standards and better binding affinities than corresponding standard drugs would have similar but better therapeutic effects than the existing standard/marketed drugs, and were hence selected for further repositioning study. Literature data mining Selected drugs with better binding affinities and similar binding pockets to their corresponding standard drugs were analysed for previous reports regarding epilepsy/seizure or any other severe side effects related to suicidal thinking, abnormal heartbeats, etc. Drugs showing any seizure-persuading or severe side effects cannot serve as a potential repositioned drug candidate. Therefore, drugs with no report or any prior study on seizure reduction with acceptable side effects were selected for further repositioning study. Molecular dynamics simulation study Selected repositioned drugs with better binding energies than corresponding standard drugs against selected epilepsy target receptors were subjected to Molecular Dynamics Simulation (MDS) using GROMACS v2022.1 49 . The charmm36m force field 50 was used to generate topology files for the docked complex of the selected drugs and the epilepsy receptor(s). The entire system was solvated with TIP3P water molecules in a rectangular box, followed by the addition of sodium (Na+) and chloride (Cl-) ions to neutralise the system at a concentration of 0.15 M, to mimic physiological conditions. To relieve the system's geometric strain, a maximum of 5,000 energy minimisation steps were performed using the steepest descent algorithm to lower the potential energy up to 1,000 KJ/mol. Then, the whole system was equilibrated under constant temperature (310 K) and pressure (1 bar) conditions for 1000 ps (1 ns) using the Nose-Hoover thermostat and Parrinello-Rahman barostat, respectively. After equilibration, MDS was performed for 100 ns (500,000,000 steps) in triplicate with a time step of 2 fs at constant temperature and pressure using Periodic Boundary Conditions (PBC). Trajectories were recorded every 100 ps. Protein-drug interactions throughout the whole simulation were monitored for stability examination. The obtained trajectory of the simulated protein-ligand complexes was analysed based on Root Mean Square Deviation (RMSD), Root Mean Square Fluctuations (RMSF), Radius of gyration (Rg), Solvent Accessible Surface Area (SASA), Principal Component Analysis (PCA), interaction energy including Lennard-Jones potential and Coulombic interactions, and Hydrogen bond analysis. Further MMPBSA was performed using gmx_MMPBSA 51 to check the free binding energy between the protein and drug for the whole MDS. Results and Discussion Retrieval of approved drugs from the DrugBank database The DrugBank database v5.1.12 (accessed January 1, 2025) contained structural and physicochemical data for 12,699 drugs, including approved, illicit, nutraceuticals, investigational, experimental, veterinary, and withdrawn drugs (Supplementary File: S1). For safety purposes, only approved drugs (2,769) were considered for our drug repositioning study (Fig. 1 ; Supplementary File: S2; Table 3 ). Among these, 188 drugs were found to be withdrawn from the market after their initial approval. Hence, the remaining 2,581 approved drugs were selected for further drug repositioning screening as first-line anti-epileptic therapeutics. The structural (SDF) and physicochemical data for the selected 2581 drugs were retrieved using the FTP service of the DrugBank database. The selected approved drugs also included 38 marketed AEDs (Table 1 ), which were used for structural similarity analyses. Table 3 Classification of approved drugs from the DrugBank database Drug Groups Number Approved 1099 Approved; Experimental 152 Approved; Experimental; Investigational 22 Approved; Experimental; Investigational; Withdrawn 1 Approved; Experimental; Vet Approved 2 Approved; Experimental; Withdrawn 1 Approved; Illicit 39 Approved; Illicit; Investigational 12 Approved; Illicit; Investigational; Vet Approved 4 Approved; Illicit; Investigational; Withdrawn 5 Approved; Illicit; Vet Approved 1 Approved; Illicit; Withdrawn 5 Approved; Investigational 963 Approved; Investigational; Nutraceutical 24 Approved; Investigational; Nutraceutical; Vet Approved 3 Approved; Investigational; Nutraceutical; Withdrawn 1 Approved; Investigational; Vet Approved 77 Approved; Investigational; Vet Approved; Withdrawn 8 Approved; Investigational; Withdrawn 57 Approved; Nutraceutical 36 Approved; Nutraceutical; Vet Approved 10 Approved; Nutraceutical; Withdrawn 1 Approved; Vet Approved 137 Approved; Vet Approved; Withdrawn 15 Approved; Withdrawn 94 Total 2769 Screening of drugs for BBB permeability AEDs must cross the BBB to perform their action within the CNS. Three different ML algorithm-based BBB permeability prediction programs/tools were used for better accuracy. Our in-house developed tool - BBBper 11 predicted 1553 BBB-permeable drugs, admetSAR 12 predicted the maximum number of 1575 drugs as BBB-permeable, while LightBBB 13 predicted 1393 drugs to cross the BBB (Supplementary File: S3). All three selected programs/tools predicted 895 drugs as BBB-permeable (Fig. 2 ). BBBper predicted all 38 AEDs as BBB permeable, while admetSAR and LightBBB showed 37 AEDs as BBB permeable, and FosPhenytoin and Eslicarbazepine as BBB impermeable, respectively. Hence, to remove the adverse selection, the drug compounds predicted to be BBB-permeable by any two programs/tools were selected for further study. The selection criteria resulted in 1606 drug compounds (including 38 marketed AEDs) as BBB permeable. Clustering of structurally similar drugs To find structurally similar drugs, all the selected BBB-permeable drugs were clustered based on structure similarity using the ChemMine tool 17 . Different similarity cutoff values of 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 resulted in the generation of 645, 927, 1128, 1296, 1449, and 1492 bins, respectively (Supplementary File: S4). The lower similarity cutoff value of 0.4 resulted in the lowest bin count but the largest bin size, with 360 and 185 compounds in bins 2 and 12, respectively. Within bin 2, 18 AEDs were clustered based on their structural similarity along with 342 other drug compounds. At a similarity cutoff value of 0.5, the largest bin (bin-12) of 133 compounds was observed, but it did not have any AEDs. A bin size of 92 compounds (bin-38) was observed at the similarity cutoff of 0.6. At higher similarity cut-off values, no significant bin size was observed (Fig. 3 ). Bin-2, at the cutoff value of 0.4, was observed to have a maximum (18) number of AEDs (Carbamazepine, Clobazam, Clonazepam, Diazepam, Eslicarbazepine, Ethotoin, FosPhenytoin, Lorazepam, Methylphenobarbital, Midazolam, Methsuximide, Nitrazepam, Oxcarbazepine, Perampanel, Phenacemide, Phenobarbital, Phenytoin and Primidone). Hence, Bin-2 at a 0.4 cutoff value with at least 40% structural similarity and with most of the marketed AEDs was selected for further study. Along with Bin-2, other bins at a similarity cutoff of 0.4 were checked for the remaining 20 AEDs. Most of the bins were singular with AED only (Bin-65: Pregabalin; Bin-96: Topiramate; Bin-120: Valproate; Bin-286: Ethosuximide; Bin-475: Tiagabine; Bin-477: Zonisamide; Bin-501: Felbamate; Bin-531: Gabapentin; Bin-578: Vigabatrin; Bin-824: Ezogabine; Bin-855: Rufinamide; Bin-863: Lacosamide and Bin-1029: Stiripentol), representing unique structure of these 13 marketed AEDs. In contrast, the Bin-213 and Bin-649 were observed to have three drug compounds, including two AEDs (Bin-213: Cannabidiol, Dronabinol and Bin-649: Brivaracetam, Levetiracetam) and one other structurally similar drug to these AEDs. Bin-140 and Bin-348 were observed to have two drug compounds, including one AED (Bin-140: Trimethadione and Bin-348: Acetazolamide) and one structurally similar drug compound. In Bin-54, Lamotrigine was observed with three other structurally similar drug compounds. From all these bins, 349 drug compounds were selected, having > 40% structural similarities with most of the marketed AEDs and were considered for further repositioning studies. Selection and preparation of the tertiary structure of epilepsy target receptors Three epilepsy drug targets, VGSC α2 (Nav1.2), GABA receptor α1-β1, and VGCC α1G (Cav3.1), were reportedly identified as 1st line therapy targets against epilepsy 16 . The tertiary protein structures of receptors Nav1.2, GABA receptor α1, and Cav3.1 were available in the RCSB-PDB database with PDB-IDs 6J8E-A (Fig. 4 A), 6HUJ-A (Fig. 4 B), and 6KZP (Fig. 4 C), respectively. The tertiary structure of GABA receptor β1 (UniProt ID: P18505) was unavailable in the RCSB-PDB database and was generated by homology modelling using the Swiss model web server and PDB-ID: 6HUJ-B as a template (Fig. 4 D). The modelled 3D structure was evaluated with a QMEAN score of -2.88, a Ramachandran Z-score of -2.818, and a pass verify-3D status. Ramachandran plot (Fig. 4 E) analysis revealed that 93.4% of the amino acids reside under the favoured region, and the remaining 6.6% reside in the allowed region. The overall validation criterion represented an exemplary structure of the predicted GABA receptor β1 model and was suitable for further studies. The functional GABA receptor 3D structures are reportedly observed in parallel chains, i.e ., tail-to-tail and head-to-head conformation 52 – 54 . Hence, to form a functional GABA receptor α1-β1 complex, the retrieved GABA receptor α1 subunit was docked against the modelled GABA receptor β1 using Hex tool 8.0.0 43 . The functional conformation of GABA receptor α1-β1 with parallel binding (head-to-head and tail-to-tail) was selected for further study, with a binding energy of -1206.05 KJ/mol. The structural alignment of the selected GABA receptor α1-β1 docked complex (Fig. 4 F) against the reported tertiary structure of GABA receptor α1-β3 (PDB ID: 6HUJ) showed the RMSD of 0.099, revealing a very high accuracy of the modelled structure. The selected structure of GABA receptor α1-β1 was subjected to energy minimisation using UCSF Chimera for further docking studies. Molecular docking study The selected 349 drug molecules were energy-minimised and docked against the selected epilepsy target proteins, Nav1.2, GABA receptor α1-β1, and Cav3.1, using AutoDock v4.2.6. Top-selling AEDs, Carbamazepine, Clonazepam and Pregabalin, were chosen as standard drugs for the studied target receptors - Nav1.2, GABA receptor α1-β1, and Cav3.1, respectively, for comparing the binding affinities of the docked drug complexes 16 , 47 . The minimum binding energies of standard drugs, Carbamazepine, Clonazepam and Pregabalin, were observed to be -7.13 Kcal/mol, -6.14 Kcal/mol and − 5.76 Kcal/mol when docked against protein receptors Nav1.2, GABA receptor α1-β1, and Cav3.1, respectively. A total of 136 drugs showed better binding affinities for the epilepsy receptor Nav1.2 compared to the standard Carbamazepine, while 72 drugs showed lower binding energies for GABA receptor α1-β1 when compared to GABA agonist Clonazepam. A total of 275 drugs, screened against Cav3.1, showed better binding affinities than the corresponding standard drug, Pregabalin (Supplementary File: S5). These 275 Cav3.1 binding drugs included all 136 Nav1.2 binding drugs and 56 out of 72 GABA receptor α1-β1 binding drugs. Overall, 46 drugs, in common, showed better binding affinities against their corresponding therapeutic target receptors (Fig. 5 ; Table 4 ). These 46 drug compounds were also predicted to be BBB permeable and showed structural similarity with already marketed AEDs and thus can be predicted as potential candidates for AED repositioning. Table 4 Drugs having better binding energies (Kcal/mol) than standard drugs against selected epileptic receptors: Voltage-gated sodium channel α2 (Nav1.2), GABA receptor α1-β1, and Voltage-gated calcium channel α1G (Cav3.1) DrugBank ID Name Nav1.2 GABA receptor α1-β1 Cav3.1 DB00564 Carbamazepine -7.13 -- -- DB01068 Clonazepam -- -6.14 -- DB00230 Pregabalin -- -- -5.76 DB00192 Indecainide -7.84 -6.45 -9.73 DB00321 Amitriptyline -7.94 -6.53 -9.56 DB00340 Metixene -8.53 -6.65 -9.61 DB00344 Protriptyline -7.68 -6.38 -8.99 DB00427 Triprolidine -7.89 -6.16 -9.70 DB00434 Cyproheptadine -8.23 -6.63 -8.46 DB00486 Nabilone -8.60 -6.50 -8.28 DB00496 Darifenacin -9.83 -7.22 -9.41 DB00540 Nortriptyline -8.01 -6.84 -10.16 DB00561 Doxapram -8.53 -6.63 -8.73 DB00568 Cinnarizine -9.12 -6.48 -9.44 DB00693 Fluorescein -7.70 -6.41 -8.01 DB00719 Azatadine -7.81 -6.47 -8.58 DB00850 Perphenazine -8.12 -6.25 -10.45 DB00920 Ketotifen -7.76 -6.67 -8.70 DB00924 Cyclobenzaprine -8.16 -6.84 -9.29 DB00933 Mesoridazine -9.12 -6.35 -9.38 DB00934 Maprotiline -8.15 -7.06 -9.97 DB00972 Azelastine -9.12 -6.62 -10.26 DB00991 Oxaprozin -7.61 -6.60 -8.11 DB01009 Ketoprofen -7.38 -7.26 -7.26 DB01100 Pimozide -9.11 -6.80 -10.22 DB01142 Doxepin -7.69 -6.37 -9.46 DB01146 Diphenylpyraline -8.07 -6.32 -8.96 DB01148 Flavoxate -8.01 -6.49 -9.08 DB01501 Difenoxin -8.70 -7.33 -9.46 DB01544 Flunitrazepam -7.23 -6.27 -7.64 DB01608 Periciazine -8.30 -6.52 -9.13 DB01623 Thiothixene -8.83 -6.25 -11.47 DB01624 Zuclopenthixol -8.17 -6.33 -11.30 DB01628 Etoricoxib -7.92 -6.63 -7.68 DB04841 Flunarizine -9.18 -6.21 -9.28 DB05015 Belinostat -7.41 -7.02 -8.07 DB06077 Lumateperone -8.84 -7.08 -8.73 DB06153 Pizotifen -7.69 -6.57 -8.39 DB06413 Armodafinil -7.19 -6.23 -6.96 DB06626 Axitinib -8.51 -6.40 -9.38 DB06684 Vilazodone -9.59 -6.16 -10.93 DB09167 Dosulepin -7.96 -6.37 -9.42 DB09488 Acrivastine -7.72 -7.14 -10.23 DB11952 Duvelisib -9.38 -6.34 -8.41 DB12492 Piritramide -9.50 -7.08 -11.46 DB12877 Oxatomide -9.43 -6.43 -9.10 DB13292 Pimethixene -8.36 -6.96 -8.24 DB14033 Acetyl sulfisoxazole -7.58 -6.52 -6.50 DB12792 Boscalid -7.37 -6.14 -7.72 Binding pocket analysis of selected drugs The screened 46 drugs, selected as above, were further evaluated for their binding interactions within the binding pockets of selected target proteins. Most marketed AEDs working as VGSC inhibitors bind within the inner pore-loop (P-loop) region. Standard drug Carbamazepine forms a hydrogen bond (H-bond) with the Leu939 residue presented within the P-loop region of Domain-II. Amino acids Ser1461, Phe1462, and Ile1457 within the P-loop region of VGSC-Domain-III were reported to be essential for the binding of VGSC-inhibiting AEDs to generate an anti-epileptic activity 55 – 57 . 20 out of 46 selected drugs showed their binding within the binding pocket, including the P-loop region. Drugs Belinostat, Dosulepin, Flavoxate, Mesoridazine, and Oxatomide were observed to form H-bonds within the P-loop region of Domain-II. Drugs Amitriptyline, Azatadine, Azelastine, Cyproheptadine, Difenoxin, Maprotiline, Nortriptyline, Oxaprozin, and Piritramide were observed to form H-bonds within the P-loop region of Domain-III (Supplementary File: S6). Drugs Cinnarizine, Darifenacin, Flunarizine, Lumateperone, Pimozide, and Pizotifen did not show any bonded interaction but bound within the P-loop region through non-bonded interactions, while the remaining 26 drugs were not observed to bind within the binding pocket as standard drugs. GABA and its agonist bind between the α1 and β1 chains of the GABA receptor 53 . Amino acids Asp149, Pro174, Asp191, and Lys279 within the extracellular region of GABA receptor subunit α1 were reported to be involved in the binding of GABA and its agonist 58 , 59 . Standard drug Clonazepam showed its binding within the GABA binding region by the formation of a single H-bond with Asp184 of GABA receptor subunit α1 and several non-bonding interactions with the amino acids Leu124, Asn125, Met162, Glu178, and Arg187 of GABA receptor subunit β1. Among selected drugs, 16 drugs (Acetyl sulfisoxazole, Acrivastine, Armodafinil, Axitinib, Azatadine, Cyclobenzaprine, Cyproheptadine, Etoricoxib, Flunitrazepam, Ketotifen, Maprotiline, Nortriptyline, Oxaprozin, Pizotifen, Protriptyline, and Vilazodone) showed their binding within the binding region of GABA receptor subunit α1 with the formation of an H-bond. In contrast, Pimethixene and Triprolidine did not show any H-bonding but were observed to bind within the binding pocket of GABA receptor α1-β1 (Supplementary File: S6). The pore-forming intracellular region of S5 and S6 in VGCC has been reportedly necessary for the trafficking of Ca + 2 ions 60 ; hence, drug binding within this region would generate a therapeutic response for epileptic seizures. The standard drug, Pregabalin, showed three H-bonded interactions with amino acids Glu354 (2.72Å), Gln922 (2.77Å), and Glu923 (2.7Å) within the S6 helical region of repeats I and II. All the selected drugs were observed to bind within the binding pocket of Cav3.1, similar to the standard drug. The binding pocket comparison of selected drugs revealed six drugs (Azatadine, Cyproheptadine, Maprotiline, Nortriptyline, Oxaprozin, and Pizotifen) with similar binding pockets as standard drugs for their respective target receptors. These six drug hits were further evaluated for their role in seizure management, using exhaustive text mining. Literature data mining The selected marketed drugs (Azatadine, Cyproheptadine, Maprotiline, Nortriptyline, Oxaprozin, and Pizotifen) belong to different classes of drugs and were analysed for their previous reports in the context of epileptic seizures and other toxicity assays. Azatadine Azatadine is a first-generation antihistamine that primarily functions as an H1 receptor antagonist, making it effective in treating allergic reactions such as rhinitis, conjunctivitis, and urticaria 61 . Its chemical structure is closely related to tricyclic compounds, contributing to its ability to cross the blood-brain barrier. As a result, azatadine can cause significant CNS effects, such as drowsiness and sedation 62 . The sedative effects of azatadine may lower the seizure threshold, potentially increasing the risk of seizures. Additionally, its anticholinergic properties could interfere with the balance of neurotransmitters in the brain, further complicating seizure control 63 . Due to these concerns, Azatadine is generally avoided in epilepsy patients and was omitted from our study outcome. Cyproheptadine Cyproheptadine is also a first-generation antihistamine with anticholinergic, antiserotonergic, and sedative properties. It was initially developed to treat allergies by blocking the H1 histamine receptors; it is also used widely to stimulate appetite, especially in patients experiencing cachexia or malnutrition 64 , 65 . It is also prescribed for the treatment of serotonin syndrome, a potentially life-threatening condition caused by excessive serotonergic activity in the CNS. The serotonin receptors, particularly the 5-HT2A receptor, are also reported to be implicated in seizure activity. In some cases of intractable epilepsy, where patients do not respond well to conventional anticonvulsants, Cyproheptadine has been used off-label to help control seizures by modulation of serotonergic pathways, which can influence neuronal excitability and seizure thresholds. Some animal studies also showed reduced seizure frequency and the number of dying cells in the brain after Cyproheptadine treatment 66 . Maprotiline Maprotiline is a tetracyclic antidepressant (TeCA) primarily used to treat depression, particularly major depressive disorder and dysthymia. It functions by inhibiting the reuptake of norepinephrine, a neurotransmitter, thereby increasing its levels in the brain and alleviating depressive symptoms 67 , 68 . Unlike tricyclic antidepressants (TCAs), which affect multiple neurotransmitters, Maprotiline is more selective for norepinephrine, which reduces some of the side effects typically associated with TCAs. Additionally, it has strong sedative properties, which makes it worthwhile for patients with anxiety or insomnia, but potentially problematic for those who are sensitive to sedatives 67 . Maprotiline has also been explored for its potential effects on epilepsy, but it is not a first-line treatment. The clinical studies suggest that Maprotiline can lower the seizure threshold, but at higher doses or in patients with a predisposition to seizures, it behaves as a pro-convulsant 69 , 70 . Therefore, Maprotiline use in epileptic patients is generally approached with caution, often requiring close monitoring and consideration of alternative therapies. Nortriptyline Nortriptyline is another TCA, primarily used to treat major depressive disorder, but it is also prescribed for conditions such as chronic pain, neuropathic pain, and certain anxiety disorders 71 – 73 . It works by inhibiting the reuptake of neurotransmitters, particularly norepinephrine and, to a lesser extent, serotonin, in the brain, which enhances the mood-stabilising effects of these chemicals. Nortriptyline is reported to lower the seizure threshold, which can increase the risk of seizures in susceptible individuals 74 , 75 . However, it may be considered in cases where an epileptic patient suffers from comorbid conditions like depression or chronic pain, where the benefits of its mood-stabilising effects might outweigh the risks. Hence, the use of nortriptyline in patients with epilepsy involves a careful risk-benefit analysis, considering the individual's seizure history, the severity of comorbid conditions, and the potential for drug interactions. Oxaprozin Oxaprozin is a non-steroidal anti-inflammatory drug (NSAID) used to treat inflammation or pain caused by osteoarthritis or rheumatoid arthritis. It inhibits the cyclooxygenase (COX) enzyme non-selectively, synthesising prostaglandins and lipid compounds; consequently, inflammation, pain and fever are reduced 76 . However, Oxaprozin has not been widely studied or recommended for use in individuals with epilepsy and no seizure-related side effects were reported in the public database. In the recent in vivo study of induced rat seizure models, Oxaprozin has been shown to produce an anticonvulsive effect during the behavioural test 77 , which opens a new therapeutic approach for Oxaprozin. Pizotifen Pizotifen is a tricyclic drug primarily used as a preventive treatment for migraine headaches and cluster headaches. It functions as a serotonin receptor antagonist, explicitly targeting the 5-HT2A and 5-HT2C receptors, and exhibits antihistamine and anticholinergic properties 78 , 79 . Inhibiting serotonin, a neurotransmitter believed to play a role in the dilation of blood vessels in the brain, results in reduced frequency and severity of migraines. Pizotifen is often prescribed when other treatments, such as beta-blockers or antiepileptic drugs, are either ineffective or unsuitable for the patient. Its role in epilepsy is less prominent and more complex, as it has been observed to exhibit some anticonvulsant properties in adult zebrafish 80 due to its ability to modulate serotonin levels in the brain. However, its efficacy in epilepsy is not well-established, and it is generally not considered a standard antiepileptic drug. The use of Pizotifen in epilepsy might be explored in patients with comorbid conditions, such as migraines, where the dual action of preventing headaches and potentially reducing seizures could be beneficial. Nonetheless, more research is needed to better understand its exact mechanism and effectiveness in managing epilepsy. Among the selected six drugs, Azatadine and Cyproheptadine are antihistamines. Azatadine was reported to increase the seizure threshold, but another antihistamine, Cyproheptadine, was reported to control seizures. Hence, Azatadine was not selected for further repositioning study, while based on previous reports of Cyproheptadine, it was considered a strong repurposed drug candidate for further research. Antidepressant drugs Maprotiline and Nortriptyline were reported to behave as pro-convulsants and required close monitoring in epilepsy patients. Hence, both antidepressants were dropped for further study. In animal studies, Oxaprozin and Pizotifen were reported to act as anti-epileptic compounds. However, no clinical trials have been reported for any of these compounds. Hence, we propose Oxaprozin, Pizotifen, along with the antihistamine drug Cyproheptadine, as potential first-line AEDs for different kinds of epileptic seizures, irrespective of their nature (genetic, congenital, physical injury, or environment). Molecular dynamics simulation study The docked complex of selected potential first-line AED candidates - Cyproheptadine, Oxaprozin, and Pizotifen, with the epilepsy target receptors Nav1.2, GABA receptor α1-β1, and Cav3.1, were subjected to molecular dynamics studies to evaluate their stability in physiological conditions. All the receptor-drug complexes showed a minimised energy configuration within a 3000 ps run, followed by equilibration at an average temperature and pressure of 310 K and one psi, respectively. The docked complexes of the selected drugs were finally simulated for a 100-ns runtime in triplicate. Root Mean Square Deviation (RMSD) RMSD measures the average distance between the atoms (usually the backbone atoms) of superimposed molecules. For all simulated complexes, the RMSD graph appeared to be stabilised within the initial run of 10 ns and remained stable until the end of the simulation (Fig. 6 ). For all three epilepsy receptors, the Oxaprozin drug showed the lowest RMSD values, followed by Pizotifen and the standard drugs, while Cyproheptadine showed maximum RMSD values. In Nav1.2, Oxaprozin was observed to have the lowest RMSD of 0.48nm, followed by standard drug Carbamazepine (0.54nm), Pizotifen (0.56nm), and Cyproheptadine, which showed the maximum RMSD of 0.78nm (Fig. 6 A). In the case of GABA receptor α1-β1, Pizotifen showed higher RMSD values, but in the latter half of the simulation, all four drug complexes were observed to have similar RMSD values. Cyproheptadine showed the lowest RMSD, followed by Oxaprozin, standard drug Clonazepam and Pizotifen (Fig. 6 B). The Cav3.1 was observed to be less stable than the other two receptors. From the beginning of the simulation, RMSD was observed to be stable after a run of 7 ns, and was observed to be stable till 60 ns, after which the RMSD of Oxaprozin and Pizotifen started to increase up to the average RMSD of Cyproheptadine and standard drug Pregabalin. Overall, Pizotifen showed the lowest mean RMSD of 0.515nm, followed by Oxaprozin (0.53nm), standard drug Pregabalin (0.775nm), and Cyproheptadine, which showed the maximum RMSD value of 0.81nm (Fig. 6 C). Based on the RMSD analysis, the binding of all the selected drugs, along with the standard drugs, showed a stable protein-ligand complex structure. Root Mean Square Fluctuation (RMSF) RMSF measures the average deviation of a particle (e.g. protein residue) over time from a reference position (typically the time-averaged position of the particle). It analyses the portions of the structure that fluctuate from their mean structure the most or least. The Cyproheptadine drug showed the lowest RMSF, followed by Pizotifen and the standard drugs, while Oxaprozin showed maximum RMS fluctuation upon binding with all three selected epilepsy target receptors (Fig. 7 ). Oxaprozin binding with the Nav1.2 showed a significant increase in RMSF value up to 3.16nm, including H-bond formation with amino acids Ser413, Ile417, Asn418, Leu421, and Ala425, resulting in higher fluctuation in the P-loop region of Domain I of Nav1.2. Later, such H-bonds were observed in the P-loop region of Domains II, III, and IV, resulting in higher RMSF values at the end of each domain. The other three drugs did not show any significant fluctuation in the structure of Nav1.2 upon binding (Fig. 7 A). All four drugs bound to the GABA receptor α1-β1 showed a similar pattern of RMS fluctuations. In case of Cav3.1, Oxaprozin showed maximum RMS fluctuation in Domain I and IV, while standard drug Pregabalin showed higher RMSF in Domain II and III (Fig. 7 C). The rest of the drugs showed a similar pattern of RMSF for all three protein receptors. In comparison, Oxaprozin showed maximum protein fluctuation due to its binding with the selected protein receptors. Radius of Gyration (Rg) Rg is defined as the distribution of protein atoms around its axis. The length representing the distance between the point where it is rotating and the point where the transfer of energy has the maximum effect gives Rg. The distribution of atoms of all three studied target receptors was stable throughout the simulation within the 3.2 to 3.6 nm (Fig. 8 ). The Nav1.2 receptor showed maximum Rg when bound with Oxaprozin, while the remaining three drugs, including the standard drug Carbamazepine, showed similar Rg values around 3.5nm (Fig. 8 A). The observations were opposite for the GABA receptor α1-β1, where Oxaprozin showed the lowest Rg (3.26) while the rest of the three drugs, including the standard drug Clonazepam, showed similar Rg values of 3.38nm (Fig. 8 B). The Rg pattern was observed to be different for target receptor Cav3.1, where Cyproheptadine bound Cav3.1 showed the lowest Rg value of 3.23nm, followed by Pizotifen (3.3nm), Oxaprozin (3.34nm), and the standard drug Pregabalin bound Cav3.1 showed the highest Rg value of 3.35nm (Fig. 8 C). The Rg study revealed that all three selected drugs, along with standard drugs, have a similar role in stabilising all the studied therapeutic target proteins. Solvent Accessible Surface Area (SASA) SASA is a critical parameter describing the area around a macromolecule accessible to solvent molecules. It aids in understanding protein folding, ligand binding, and conformational changes, and plays a crucial role in elucidating molecular interactions at the atomic level. For all drug-receptor complexes, the SASA graphs were observed to be stable, referring to the overall size of the protein (Fig. 9 ). The high SASA values for Nav1.2 (580 nm 2 – 620 nm 2 ) and Cav3.1 (510 nm 2 – 550 nm 2 ) complexes than the GABA receptor α1-β1 (290 nm 2 – 340 nm 2 ) correlate with the massive structure of the protein, where Nav1.2 and Cav3.1 has > 1000 amino acids, while GABA receptor α1-β1 composed of 602 amino acids. The Oxaprozin complex with Nav1.2 showed the highest SAS area (608 nm 2 ), while the remaining three drugs, along with the standard drug Carbamazepine, showed similar SAS areas around 590 nm 2 (Fig. 9 A). In case of the GABA receptor α1-β1, the Oxaprozin-bound complex showed the lowest SASA (302 nm 2 ), while the remaining three drugs, along with the standard drug Clonazepam, showed SAS areas around 321 nm 2 (Fig. 9 B). The Cav3.1 SAS area was found to be lowest for the Pizotifen-bound complex (522 nm 2 ), followed by Oxaprozin (528 nm 2 ), Cyproheptadine (539 nm 2 ), and the standard drug Pregabalin showed maximum SAS area (Fig. 9 C). The SASA graph was similar to the Rg graph, representing a similar protein folding pattern in both studies. All the drugs exhibited similar protein-soluble areas, indicating protein stability and the binding of the drug molecules to the selected proteins. Principal Component Analysis (PCA) PCA was employed to project the backbone fluctuations of selected target proteins and drug complexes onto a reduced two-dimensional (2D) space defined by the first two principal components (PC1 and PC2). These projections help to visualise how different drugs affect the structural flexibility and stability of the epilepsy receptor proteins throughout simulations. Tighter clusters indicate restricted atomic motion and hence more stable binding, while more dispersed clusters suggest greater conformational freedom and less stable interactions. The PCA projection for the Nav1.2 - Cyproheptadine complex showed the most compact and centralised cluster in the PC1-PC2 space, indicating minimal conformational changes during the simulation and, hence, strong structural stabilisation. The Nav1.2 complex with Oxaprozin and Pizotifen showed a moderately dispersed trajectory cluster, suggesting intermediate flexibility. In contrast, the standard drug Carbamazepine showed a wider spread compared to selected drugs, pointing to greater backbone fluctuation and reduced stabilising effect (Fig. 10 A). For the GABA receptor α1-β1, the Oxaprozin drug complex showed the most stable structural behaviour, demonstrated by its tight cluster with minimal spread of ± 3 in the PC2 space. It was followed by the drugs Pizotifen and Cyproheptadine. In contrast, standard drug Clonazepam showed a wider spread in its PCA projection, indicating that its binding led to higher receptor flexibility (Fig. 10 B). In the Cav3.1 receptor system, Cyproheptadine produced the tightest cluster among the other drugs, indicating a more substantial stabilising effect. Oxaprozin and Pizotifen showed slightly more dispersed clustering than Cyproheptadine but retained relatively constrained dynamics. The standard drug Pregabalin showed a scattered cluster, confirming its limited ability to stabilise receptor structure (Fig. 10 C). The PCA results for the three epilepsy receptors demonstrated that Cyproheptadine is the most effective drug, closely followed by Oxaprozin and Pizotifen, inducing minimal structural fluctuations during MD simulations. In contrast, standard drugs resulted in broader conformational spread, indicating reduced structural constraint and weaker binding-induced stabilisation across all three receptors. Interaction energy The interaction energy represents the non-bonding interactions between the receptor and the ligand. Two different non-bonding interaction energies (Lennard-Jones potential and Coulombic interactions) were calculated in our investigation. The analysis of interaction energy revealed the non-bonding behaviours of the drugs with the target protein receptor at certain phases of the entire simulation. Pizotifen showed better and more stable non-bonding interaction energy for all bound complexes than the other drugs, followed by Oxaprozin, Cyproheptadine, and the standard drugs. Compared to the standard drugs, all the selected drugs showed better interaction energy (Fig. 11 ; Table 5 ). For Voltage-gated sodium channel α2 (Nav1.2), the standard drug Carbamazepine showed a total interaction energy of -111.5977 KJ/mol (LJ: -99.8047 KJ/mol; Coul: -11.793 KJ/mol). In comparison, Cyproheptadine showed the lowest interaction energy of -171.111 KJ/mol (LJ: -117.832 KJ/mol; Coul: -53.279 KJ/mol), followed by Pizotifen (Total: -144.0706 KJ/mol; LJ: -122.406 KJ/mol; Coul: -21.6646 KJ/mol) and Oxaprozin (Total: -135.7998 KJ/mol; LJ: -127.217 KJ/mol; Coul: -8.5828 KJ/mol). All the selected drugs showed better interaction energies than the standard drug, Carbamazepine, against Nav1.2 (Fig. 11 A; Table 5 ). Clonazepam binding with GABA receptor α1–β1 showed a total interaction energy of -56.5546 KJ/mol (LJ: -35.405 KJ/mol; Coul: -21.1496 KJ/mol), while Pizotifen showed the lowest interaction energy (Total: -115.0727; LJ: -58.7435 KJ/mol; Coul: -29.3292 KJ/mol), followed by Oxaprozin (Total: -64.5817 KJ/mol; LJ: -47.8904 KJ/mol; Coul: -16.6913 KJ/mol). In contrast, Cyproheptadine showed higher interaction energy (Total: -55.8563 KJ/mol; LJ: -41.7388 KJ/mol; Coul: -14.1175 KJ/mol) than the standard drug (Fig. 11 B; Table 5 ). Standard drug Pregabalin interacted with its receptor Voltage-gated calcium channel α1G with the interaction energy of -65.51398 KJ/mol (LJ: -62.8288 KJ/mol; Coul: -2.68518 KJ/mol). Cyproheptadine showed the lowest interaction energy of -189.5322 KJ/mol (LJ: -107.54 KJ/mol; Coul: -81.9922 KJ/mol), followed by Pizotifen (Total: -158.4378 KJ/mol; LJ: -117.041 KJ/mol; Coul: -47.3968 KJ/mol) and Oxaprozin (Total: -125.84350 KJ/mol; LJ: -118.768 KJ/mol; Coul: -7.07558 KJ/mol) when bound to Cav3.1 (Fig. 11 C; Table 5 ). Based on the binding interaction analyses, Pizotifen was observed to have strong binding affinity with all three target proteins, followed by Oxaprozin. Cyproheptadine showed a low binding interaction compared to Clonazepam against GABA receptor α1-β1; however, it showed better binding affinity with Nav1.2 and Cav3.1. We predicted that all three potential repurposed drugs would bind strongly to the selected first-line target proteins for epilepsy. Table 5 Interaction energy (KJ/mol) between selected drugs and epilepsy receptor Nav1.2, GABA receptor alpha1-beta1 and Cav3.1. Lennard-Jones Potential (KJ/mol) Coulombic Interaction (KJ/mol) Total interactions (KJ/mol) Voltage-Gated Sodium Channel – α2 (Nav1.2) Standard (Carbamazepine) -99.8047 -11.793 -111.5977 Cyproheptadine -117.832 -53.279 -171.111 Oxaprozin -127.217 -8.5828 -135.7998 Pizotifen -122.406 -21.6646 -144.0706 GABA receptor – α1 β1 Standard (Clonazepam) -35.405 -21.1496 -56.5546 Cyproheptadine -41.7388 -14.1175 -55.8563 Oxaprozin -47.8904 -16.6913 -64.5817 Pizotifen -58.7435 -29.3292 -115.0727 Voltage-Gated Calcium Channel – α1G (Cav3.1) Standard (Pregabalin) -62.8288 -2.68518 -65.51398 Cyproheptadine -107.54 -81.9922 -189.5322 Oxaprozin -118.768 -7.07558 -125.84350 Pizotifen -117.041 -47.3968 -158.4378 Hydrogen bond (H-bond) H-bond analysis revealed the number of hydrogen bonds formed between the protein and ligand throughout the simulation. Throughout the simulation studies of Nav1.2, the Oxaprozin drug showed an average of 1.342 Hydrogen Bonds, followed by the standard drugs Carbamazepine (0.199), Pizotifen (0.027), and Cyproheptadine (0.007). The MDS study revealed two H-bonds at the beginning of the simulation, which fluctuated to form a maximum of three H-bonds at 22 ns, but a single H-bond was observed to be the predominant interaction throughout the simulation. The standard drug Carbamazepine showed a single H-bond in the beginning, but in the middle, a maximum of two H-bonds were observed, which again reduced to a single H-bond at the end. The non-bonding interaction energy between Nav1.2 and drugs - Pizotifen and Cyproheptadine were observed to be lowest compared to the other drugs, while the number of H-bonds formed between them was substantially low and a maximum of a single H-bond was observed in between the simulations (Fig. 12 A). GABA receptor α1-β1showed the highest H-bond formation with Oxaprozin, averaging 1.532 H-bonds throughout the simulation period, followed by standard drug Clonazepam (0.296), Pizotifen (0.155) and Cyproheptadine (0.024). The interaction between GABA receptor α1-β1 and Oxaprozin showed a maximum of three H-bonds, in the beginning and middle of the simulation, which drops to single and double H-bonds at the end. The standard drug Clonazepam showed up to four H-bonds at the start of the simulation, but later on, double and single H-bonds were observed towards the end of the simulation. Pizotifen showed the lowest non-bonding interaction energy towards the GABA receptor α1-β1 but did not show any H-bonds at the beginning of the simulation; however, after 60 ns of simulation, a single H-bond was observed. Cyproheptadine was observed to have weak binding with GABA receptor α1-β1, occasionally showing a single H-bond in between the simulations (Fig. 12 B). The number of H-bonds between all the drugs and Cav3.1 was lower than that of the other two receptors. Here again, Oxaprozin was observed to show a maximum number of H-bonds, averaging 0.821 H-bonds throughout the simulation, followed by standard drug Pregabalin (0.086), Cyproheptadine (0.086), and Pizotifen (0.021). Oxaprozin showed an average of a single H-bond throughout the simulation, while standard drug Pregabalin did not have any H-bond at the beginning of the simulation, but in the middle of the simulation, a single H-bond was observed. A similar H-bond pattern was also observed in Pizotifen, but Cyproheptadine showed a single H-bond in the beginning and at the end of the simulation, while no H-bond was observed in the middle of the simulation (Fig. 12 C). The H-bond analyses revealed that Oxaprozin forms a higher number of H-bonds with all the selected target receptor proteins than the other studied drugs and standard drugs. Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) The free binding energies between selected target receptor proteins and drugs were calculated using the MMPBSA method to examine the molecular interactions and stability. The van der Waals energy (VDW), electrostatic energy (EEL) and ΔG GAS (ΔBonds + ΔAngle + ΔDihedral + ΔVDW + ΔEEL) play a significant role in the binding of ligands to the protein, while ΔG SOLV (ΔEGB + ΔESURF) appeared to have an adverse effect on the total binding energy. The VGSC–α2 (Nav1.2) interaction with studied drugs showed lower free binding energy (ΔG Total) with Oxaprozin (-23.98 kcal/mol), followed by standard drug Carbamazepine (-18.10 kcal/mol), Pizotifen (-17.78 kcal/mol), and Cyproheptadine (-16.35 kcal/mol). The ΔEEL and ΔG-GAS were observed to be lowest in the case of Cyproheptadine, followed by Oxaprozin, but ΔVDW and ΔG-SOLV were observed to be lowest in the case of Oxaprozin (Fig. 13 A; Table 6 ). In case of GABA receptor α1 β1, Oxaprozin showed the lowest ΔG Total (-13.40 kcal/mol), followed by standard drug Clonazepam (-12.65 kcal/mol), Pizotifen (-9.32 kcal/mol) and Cyproheptadine (-3.41 kcal/mol). Here, ΔEEL and ΔG-GAS were observed to be lowest in the case of Clonazepam, while ΔVDW and ΔG-SOLV were observed to be lowest for Oxaprozin interaction with GABA receptor α1 β1 (Fig. 13 B; Table 6 ). The interaction between VGCC – α1G (Cav3.1) and drugs showed Oxaprozin having lowest free binding energy of -22.94 kcal/mol, followed by Pizotifen (-17.93 kcal/mol), Cyproheptadine (-13.13 kcal/mol) while the standard drug Pregabalin showed highest free binding energy of -9.64 kcal/mol, representing weak binding affinity of standard drug than other selected drugs for Cav3.1 (Fig. 13 C; Table 6 ). For all three selected first-line target proteins, Oxaprozin showed the best binding affinity, followed by Pizotifen and Cyproheptadine. Table 6 The free binding energy (kcal/mol) between selected epilepsy receptor proteins and drugs using MMPBSA analysis POISSON BOLTZMANN ΔVDW ΔEEL ΔG GAS ΔG SOLV ΔG Total Voltage-Gated Sodium Channel – α2 (Nav1.2) Standard (Carbamazepine) -27.38 -3.59 -30.97 12.87 -18.10 Cyproheptadine -32.62 -170.84 -203.46 187.11 -16.35 Oxaprozin -33.79 -153.70 -187.50 163.52 -23.98 Pizotifen -26.53 -7.40 -33.93 16.15 -17.78 GABA receptor – α1 β1 Standard (Clonazepam) -19.40 -49.29 -68.68 56.04 -12.65 Cyproheptadine -11.37 -12.95 -24.32 20.92 -3.41 Oxaprozin -23.39 -23.09 -46.47 33.07 -13.40 Pizotifen -18.98 -11.98 -30.97 21.65 -9.32 Voltage-Gated Calcium Channel – α1G (Cav3.1) Standard (Pregabalin) -16.79 -2.15 -18.94 9.30 -9.64 Cyproheptadine -30.40 -204.51 -234.91 221.78 -13.13 Oxaprozin -32.44 -150.56 -183.01 160.07 -22.94 Pizotifen -30.95 -9.99 -40.94 23.00 -17.93 The molecular dynamics study revealed that all three selected drugs exhibited stable and good binding affinity against the target receptor proteins. All three drugs outperformed the standard drugs for all the receptors. Oxaprozin showed a more stable and stronger binding affinity among the three selected drugs, followed by Pizotifen and Cyproheptadine. Conclusion A comprehensive in-silico drug repositioning approach was applied to screen the whole of the DrugBank database molecules for identifying new first-line therapeutic option for epilepsy. A total of 2769 FDA-approved drugs were primarily screened for blood-brain permeability and structural similarity with currently marketed AEDs. The Voltage Gated Sodium channel – α2, GABA receptor α1 β1 and Voltage gated calcium channel – α1G, were selected as therapeutic target proteins for the molecular docking studies using Carbamazepine, Clonazepam and Pregabalin, as standard reference drugs, respectively. Only 46 marketed drugs, in common, showed higher binding affinities than the selected standard drugs and the binding pocket analyses and text mining studies narrowed our findings to three drug compounds, namely - Oxaprozin, Pizotifen and Cyproheptadine, as potential candidates for drug repurposing for first-line treatment of epilepsy. The molecular dynamic simulation study revealed that all three selected drug compounds have a stable and strong binding with all three studied therapeutic target proteins. Oxaprozin was observed to show the strongest binding affinity and stability, followed by Pizotifen and Cyproheptadine, and invites further pre-clinical and clinical investigations for their application as a first-line therapeutic option for epilepsy. Abbreviations AEDs Antiepileptic Drugs BBB Blood Brain Barrier GABA-R Gamma-aminobutyric acid receptor Rg Radius of Gyration RMSD Root Mean Square Deviation RMSF Root Mean Square Fluctuations SASA Solvent Accessible Surface Area VGCC Voltage-gated calcium channel VGSC Voltage-gated sodium channel. Declarations Conflict of interest The authors declare no conflict of interest. Funding This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution PK conceptualised, investigated, validated, supervised and wrote the manuscript. VK and RC performed the methodology and validation. VS Conceptualised, validated, and supervised, and AK conceptualised, supervised, and validated the results and manuscript. Acknowledgement The authors thank the CCS Haryana Agricultural University, Hisar and DBT-BUILDER, M. D. University, Rohtak, for providing infrastructure facilities for the study. Data Availability Data is provided within the manuscript or supplementary information files References Klein, P. et al. Repurposed molecules for antiepileptogenesis: Missing an opportunity to prevent epilepsy? Epilepsia 61 , 359–386 (2020). Thijs, R. D., Surges, R., O’Brien, T. J. & Sander, J. W. Epilepsy in adults. Lancet 393 , 689–701 (2019). Lossin, C., Wang, D. W., Rhodes, T. H., Vanoye, C. G. & George, A. L. Molecular basis of an inherited epilepsy. Neuron 34 , 877–884 (2002). Annegers, J. F. & Rocca, W. A. Elsevier,. Causes of epilepsy: Contributions of the rochester epidemiology project. in Mayo Clinic Proceedings vol. 71 570–575 (1996). Galindo-Mendez, B., Mayor, L. C., Velandia-Hurtado, F. & Calderon-Ospina, C. Failure of antiepileptic drugs in controlling seizures in epilepsy: What do we do next? Epilepsy Behav. Case Rep. 4 , 6–8 (2015). Liu, Z. et al. In silico drug repositioning-what we need to know. Drug Discov Today . 18 , 110–115 (2013). Ashburn, T. T. & Thor, K. B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov . 3 , 673–683 (2004). Jourdan, J. P., Bureau, R., Rochais, C. & Dallemagne, P. Drug repositioning: a brief overview. J. Pharm. Pharmacol. 72 , 1145–1151 (2020). Tolete, P. et al. Lorcaserin therapy for severe epilepsy of childhood onset. Neurology 91 , 837–839 (2018). Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 46 , D1074–D1082 (2018). Kumar, P. et al. A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction. CNS Neurol. Disord Drug Targets . 24 , 1–9 (2024). Cheng, F. et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 52 , 3099–3105 (2012). Shaker, B. et al. LightBBB: Computational prediction model of blood-brain-barrier penetration based on LightGBM. Bioinformatics 37 , 1135–1139 (2021). Hurle, M. R. et al. Computational drug repositioning: From data to therapeutics. Clin. Pharmacol. Ther. 93 , 335–341 (2013). Ekins, S., Williams, A. J., Krasowski, M. D. & Freundlich, J. S. In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov Today . 16 , 298–310 (2011). Kumar, P., Sheokand, D., Saini, V. & Kumar, A. In-silico Identification and Analysis of Hub Proteins for Designing Novel First-line Anti-seizure Medications. Lett. Drug Des. Discov . 20 , 662–673 (2022). Backman, T. W. H., Cao, Y. & Girke, T. ChemMine tools: An online service for analyzing and clustering small molecules. Nucleic Acids Res. 39 , W486–W491 (2011). Cao, Y., Charisi, A., Cheng, L. C., Jiang, T. & Girke, T. ChemmineR: A compound mining framework for R. Bioinformatics 24 , 1733–1734 (2008). Silvestro, S., Mammana, S., Cavalli, E., Bramanti, P. & Mazzon, E. Use of cannabidiol in the treatment of epilepsy: Efficacy and security in clinical trials. Molecules 24 , 1459 (2019). Beydoun, A. et al. Current role of carbamazepine and oxcarbazepine in the management of epilepsy. Seizure 83 , 251–263 (2020). Dokkedal-Silva, V. et al. Indications, Side Effects, and Potential for Nonmedical Use. Harv. Rev. Psychiatry . 27 , 279–289 (2019). Hebeisen, S. et al. Eslicarbazepine and the enhancement of slow inactivation of voltage-gated sodium channels: A comparison with carbamazepine, oxcarbazepine and lacosamide. Neuropharmacology 89 , 122–135 (2015). Brigo, F. & Igwe, S. C. Ethosuximide, sodium valproate or lamotrigine for absence seizures in children and adolescents. Cochrane Database of Systematic Reviews (2017). (2017). Markota, M. & Morgan, R. J. Treatment of Generalized Anxiety Disorder with Gabapentin. Case Rep. Psychiatry . 2017 , 1–4 (2017). Rogawski, M. A., Tofighy, A., White, H. S., Matagne, A. & Wolff, C. Current understanding of the mechanism of action of the antiepileptic drug lacosamide. Epilepsy Res. 110 , 189–205 (2015). Stockburger, C. et al. A Mitochondrial Role of SV2a Protein in Aging and Alzheimer’s Disease: Studies with Levetiracetam. J. Alzheimer’s Disease . 50 , 201–215 (2016). Krauss, G. L. et al. Levetiracetam treatment of idiopathic generalised epilepsy. Seizure 12 , 617–620 (2003). Mruk, A. L., Garlitz, K. L. & Leung, N. R. Levetiracetam in neonatal seizures: A review. J. Pediatr. Pharmacol. Ther. 20 , 76–89 (2015). Hansen, C. C., Ljung, H., Brodtkorb, E. & Reimers, A. Mechanisms underlying aggressive behavior induced by antiepileptic drugs: Focus on topiramate, levetiracetam, and perampanel. Behavioural Neurology (2018). (2018). Alles, S. R. A., Cain, S. M. & Snutch, T. P. Pregabalin as a Pain Therapeutic: Beyond Calcium Channels. Front. Cell. Neurosci. 14 , 83 (2020). Manjushree, N., Chakraborty, A., Shashidhar, K. & Narayanaswamy, S. A review of the drug pregabalin. Int J Basic Clin Pharmacol 601–605 (2015). 10.18203/2319-2003.ijbcp20150359 Perucca, E., Cloyd, J., Critchley, D., Fuseau, E. & Rufinamide Clinical pharmacokinetics and concentration-response relationships in patients with epilepsy. Epilepsia 49 , 1123–1141 (2008). Piplani, S., Verma, P. K. & Kumar, A. Neuroinformatics analyses reveal GABAt and SSADH as major proteins involved in anticonvulsant activity of valproic acid. Biomed. Pharmacotherapy . 81 , 402–410 (2016). Romoli, M. et al. Valproic Acid and Epilepsy: From Molecular Mechanisms to Clinical Evidences. Curr. Neuropharmacol. 17 , 926–946 (2018). Zanatta, G. et al. Valproic acid interactions with the NavMs voltage-gated sodium channel. Proc. Natl. Acad. Sci. U S A . 116 , 26549–26554 (2019). Luszczki, J. J., Ratnaraj, N., Patsalos, P. N. & Czuczwar, S. J. Isobolographic and behavioral characterizations of interactions between vigabatrin and gabapentin in two experimental models of epilepsy. Eur. J. Pharmacol. 595 , 13–21 (2008). Biton, V. Clinical pharmacology and mechanism of action of zonisamide. Clin. Neuropharmacol. 30 , 230–240 (2007). Arnold, K., Bordoli, L., Kopp, J. & Schwede, T. The SWISS-MODEL workspace: A web-based environment for protein structure homology modelling. Bioinformatics 22 , 195–201 (2006). Benkert, P., Tosatto, S. C. E. & Schomburg, D. QMEAN: A comprehensive scoring function for model quality assessment. Proteins: Struct. Function Genet. 71 , 261–277 (2008). Agnihotry, S., Pathak, R. K., Singh, D. B., Tiwari, A. & Hussain, I. Protein structure prediction. Bioinformatics: Methods Appl. 177–188. 10.1016/B978-0-323-89775-4.00023-7 (2021). Eisenberg, D., Lüthy, R. & Bowie, J. U. VERIFY3D: Assessment of protein models with three-dimensional profiles. Methods Enzymol. 277 , 396–404 (1997). Wiederstein, M. & Sippl, M. J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35 , W407–W410 (2007). Macindoe, G., Mavridis, L., Venkatraman, V., Devignes, M. D. & Ritchie, D. W. HexServer: An FFT-based protein docking server powered by graphics processors. Nucleic Acids Res 38 , (2010). Delano, L. & Warren Pymol: An open-source molecular graphics tool. CCP4 Newsl. protein Crystallogr. 40 , 82–92 (2002). Pettersen, E. F. et al. UCSF Chimera - A visualization system for exploratory research and analysis. J. Comput. Chem. 25 , 1605–1612 (2004). Morris, G. M. et al. Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30 , 2785–2791 (2009). Kumar, P., Sheokand, D., Grewal, A., Saini, V. & Kumar, A. Clinical side-effects based drug repositioning for anti-epileptic activity. J. Biomol. Struct. Dyn. 42 , 1443–1454 (2024). Laskowski, R. A., Swindells, M. B. & LigPlot+ Multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 51 , 2778–2786 (2011). Abraham, M. J. et al. High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2 , 19–25 (2015). Gromacs. Huang, J. et al. CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nat. Methods . 14 , 71–73 (2016). Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A. & Moreno, E. Gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J. Chem. Theory Comput. 17 , 6281–6291 (2021). Ghit, A., Assal, D., Al-Shami, A. S. & Hussein, D. E. E. GABAA receptors: structure, function, pharmacology, and related disorders. Journal of Genetic Engineering and Biotechnology 19, 1–15 (2021). Masiulis, S. et al. GABAA receptor signalling mechanisms revealed by structural pharmacology. Nature 565 , 454–459 (2019). Sigel, E. & Steinmann, M. E. Structure, function, and modulation of GABAA receptors. J. Biol. Chem. 287 , 40224–40231 (2012). Abdelsayed, M. & Sokolov, S. Voltage-gated sodium channels: Pharmaceutical targets via anticonvulsants to treat epileptic syndromes. Channels 7 , 146–152 (2013). Docherty, R. J. & Farmer, C. E. The pharmacology of voltage-gated sodium channels in sensory neurones. Handb. Exp. Pharmacol. 194 , 519–561 (2009). Lipkind, G. M. & Fozzard, H. A. Molecular model of anticonvulsant drug binding to the voltage-gated sodium channel inner pore. Mol. Pharmacol. 78 , 631–638 (2010). Kash, T. L., Jenkins, A., Kelley, J. C., Trudell, J. R. & Harrison, N. L. Coupling of agonist binding to channel gating in the GABAA receptor. Nature 421 , 272–275 (2003). Newell, J. G. & Czajkowski, C. The GABAA receptor α1 subunit Pro174-Asp191 segment is involved in GABA binding and channel gating. J. Biol. Chem. 278 , 13166–13172 (2003). Cunningham, K. L. & Littleton, J. T. Mechanisms controlling the trafficking, localization, and abundance of presynaptic Ca2 + channels. Front. Mol. Neurosci. 15 , 767 (2023). Katelaris, C. Comparative effects of loratadine and azatadine in the treatment of seasonal allergic rhinitis. Asian Pac. J. Allergy Immunol. 8 , 103–107 (1990). Welch, M. J., Meltzer, E. O., Estelle, F. & Simons, R. H1-antihistamines and the central nervous system. Histamine and H1-Antihistamines in Allergic Disease . Second Ed. Revis. Expanded . 337–388. 10.3109/9780203910375-16 (2002). Hill, T. et al. Antidepressant use and risk of epilepsy and seizures in people aged 20 to 64 years: Cohort study using a primary care database. BMC Psychiatry 15 , (2015). Bertrand, V. et al. Safety of Cyproheptadine, an Orexigenic Drug. Analysis of the French National Pharmacovigilance Data-Base and Systematic Review. Front. Pediatr. 9 , 712413 (2021). Kalpaklioglu, F. & Baccioglu, A. Efficacy and Safety of H1-Antihistamines: An Update. Antiinflamm Antiallergy Agents Med. Chem. 11 , 230–237 (2013). Winkler, J. L., Skovira, J. W. & Kan, R. K. Anticonvulsant efficacy of antihistamine cyproheptadine in rats exposed to the chemical warfare nerve agent soman. Neurotoxicology 58 , 153–160 (2017). Baumann, P. A. & Maitre, L. Neurobiochemical Aspects of Maprotiline (Ludiomil ®) Action. J. Int. Med. Res. 7 , 391–400 (1979). Barbaccia, M. L., Ravizza, L., Costa, E. & Maprotiline An antidepressant with an unusual pharmacological profile. J. Pharmacol. Exp. Ther. 236 , 307–312 (1986). https://pubmed.ncbi.nlm.nih.gov/3003338/ Johannessen Landmark, C., Henning, O. & Johannessen, S. I. Proconvulsant effects of antidepressants — What is the current evidence? Epilepsy Behav. 61 , 287–291 (2016). Hoffman, B. F. & Wachsmuth, R. Maprotiline and seizures. J. Clin. Psychiatry . 43 , 117–118 (1982). https://pubmed.ncbi.nlm.nih.gov/6801027/ Derry, S., Wiffen, P. J., Aldington, D. & Moore, R. A. Nortriptyline for neuropathic pain in adults. Cochrane Database of Systematic Reviews (2015). (2017). Reinert, J. P., Veronin, M. A. & Medina, C. Tricyclic Antidepressants in Nociceptive and Neuropathic Pain: A Review of Their Analgesic Properties in Combination With Opioids. J. Pharm. Technol. 39 , 35–40 (2023). Wiffen, P. J., Moore, R. A., Aldington, D. & Derry, S. Nortriptyline for neuropathic pain in adults. Cochrane Database of Systematic Reviews (2014). (2014). Luchins, D. J., Oliver, A. P. & Wyatt, R. J. Seizures with Antidepressants: An In Vitro Technique to Assess Relative Risk. Epilepsia 25 , 25–32 (1984). Lipper, B., Bell, A. & Gaynor, B. Recurrent hypotension immediately after seizures in nortriptyline overdose. Am. J. Emerg. Med. 12 , 452–453 (1994). Peesa, J. P. et al. Oxaprozin prodrug as safer nonsteroidal anti-inflammatory drug: Synthesis and pharmacological evaluation. Arch. Pharm. (Weinheim) . 351 , 1700256 (2018). Khatami, P., Mirazi, N., Khosravi, M. & Bananej, M. Nonsteroidal Anti-inflammatory Drug Oxaprozin is Beneficial Against Seizure-induced Memory Impairment in an Experimental Model of Seizures in Rats: Impact On Oxidative Stress and Nrf2/HO-1 Signaling Pathway. J. Mol. Neurosci. 72 , 880–887 (2022). Robbins, M. S. & Burch, R. Preventive Migraine Treatment. CONTINUUM Lifelong Learn. Neurol. 27 , 613–632 (2021). Tronvik, E., Giri, S. & Young, W. Preventive treatment of migraine: Non-specific oral agents. Handb. Clin. Neurol. 199 , 67–86 (2024). Batista, F. L. A. et al. Anticonvulsant and anxiolytic-like potential of the essential oil from the Ocimum basilicum Linn leaves and its major constituent estragole on adult zebrafish (Danio rerio). Neurochem Int. 178 , 105796 (2024). Additional Declarations No competing interests reported. Supplementary Files S1DBdatabase.csv S2Approved.csv S3BBB.csv S4ChemMine.csv S5Dock.csv S6Bindinginteraction.pdf Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 09 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 24 Jun, 2025 Editor invited by journal 17 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6851614","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475901899,"identity":"a5eaf4ec-dad1-47ad-9ea2-28b4cdbcbf2d","order_by":0,"name":"Pawan Kumar","email":"","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Pawan","middleName":"","lastName":"Kumar","suffix":""},{"id":475901900,"identity":"2f7f90ab-31e3-466b-b6c4-c41c1bd90c3d","order_by":1,"name":"Vivek Kumar","email":"","orcid":"","institution":"Maharshi Dayanand University","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Kumar","suffix":""},{"id":475901901,"identity":"594d494f-73f9-4302-ad3f-68cb5c2f5f8e","order_by":2,"name":"Raveena Chauhan","email":"","orcid":"","institution":"Maharshi Dayanand University","correspondingAuthor":false,"prefix":"","firstName":"Raveena","middleName":"","lastName":"Chauhan","suffix":""},{"id":475901902,"identity":"e6671c9d-feda-41ea-8a4d-8a07071d19a8","order_by":3,"name":"Vandana Saini","email":"","orcid":"","institution":"Maharshi Dayanand University","correspondingAuthor":false,"prefix":"","firstName":"Vandana","middleName":"","lastName":"Saini","suffix":""},{"id":475901903,"identity":"1abab77f-98f7-4eef-acce-a8503c54e9f8","order_by":4,"name":"Ajit Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACHihtAOHYAEnGxgPEaWEDc9JAWhpI0nIYzMGrhb/njOmGn3sY5M3lG1g3vG07b7e2/TDQlhqbaFxaJM72mN3secZguLONge3m3LbbydvOJAK1HEvLbcCl5zyP2Q2eAwwJBscY2G7zArWYHQBqYWw4jFOLPFDLzT8ILeeSzc4/xK/FAOiw20i2HLAzu0HAFsMzx8puyxyQAPolse3mnHPJCWY3gLYk4PGL3JnkbTffHLCRN2c+fOzGmzI7e7Pz6Q8ffKixwe19CJBgAMUgiJUIJhPwK0cF9qQoHgWjYBSMgpEBAP1rZM+w+lc/AAAAAElFTkSuQmCC","orcid":"","institution":"Maharshi Dayanand University","correspondingAuthor":true,"prefix":"","firstName":"Ajit","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-06-09 07:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6851614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6851614/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27625-2","type":"published","date":"2025-12-16T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85617958,"identity":"178eb971-1ed6-48f3-9ef7-9f2aaab9388e","added_by":"auto","created_at":"2025-06-29 14:48:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":327480,"visible":true,"origin":"","legend":"\u003cp\u003eApproved drug classification at the DrugBank database\u003c/p\u003e","description":"","filename":"Fig1Approveddrugs.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/c735d2e39a0220cc1f91df93.jpg"},{"id":85618001,"identity":"e401b2f9-6ce0-48e9-95b5-713677c990c6","added_by":"auto","created_at":"2025-06-29 14:48:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142456,"visible":true,"origin":"","legend":"\u003cp\u003eDrugs predicted to permeate the Blood-Brain-Barrier using BBBper, LightBBB, and admetSAR\u003c/p\u003e","description":"","filename":"Fig2BBB.png","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/c42e0e6e5b8347e4d0831396.png"},{"id":85619272,"identity":"fc6b2c59-9d84-41a9-84e4-1d0ed46098b3","added_by":"auto","created_at":"2025-06-29 14:56:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":278488,"visible":true,"origin":"","legend":"\u003cp\u003eBin size observed for drug compounds at different cutoff values using the ChemMine tool\u003c/p\u003e","description":"","filename":"Fig3Chemmine.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/14c37dddbcd483bd77ea679f.jpg"},{"id":85617986,"identity":"9673b7e0-953a-4857-b8a3-e70aaaa443d6","added_by":"auto","created_at":"2025-06-29 14:48:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4835389,"visible":true,"origin":"","legend":"\u003cp\u003eTertiary structure of selected epilepsy receptor proteins A). Voltage-gated sodium channel 2A, B). GABA receptor α1 chain, C). Voltage-gated Calcium channel α1G, D). Homology modelled GABA receptor β1 chain, E). Ramachandran plot for homology modelled GABA receptor β1 chain, and F). GABA receptor α1-β1\u003c/p\u003e","description":"","filename":"Fig4Modeling.png","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/4905381c09b00b09ac932229.png"},{"id":85619267,"identity":"5d082ba2-aad2-44b9-a0f7-c2c8cd4466fb","added_by":"auto","created_at":"2025-06-29 14:56:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":137108,"visible":true,"origin":"","legend":"\u003cp\u003eDrugs having better binding affinity as compared to standard drugs for epilepsy receptors, Voltage-gated sodium channel α2 (Nav1.2), GABA receptor α1-β1, and Voltage-gated calcium channel α1G (Cav3.1)\u003c/p\u003e","description":"","filename":"Fig5Drugsdocked.png","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/bead21e527c95de27a53d495.png"},{"id":85619944,"identity":"a64086df-a378-4f1b-8065-7ca614ffdbb9","added_by":"auto","created_at":"2025-06-29 15:04:07","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5313438,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plot depicting the Root Mean Square Deviation (RMSD) between the selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation\u003c/p\u003e","description":"","filename":"Fig6rmsd.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/bd5038c26bc7b50f82069663.jpg"},{"id":85618033,"identity":"32efb747-deb0-4e94-9398-f5c6fcf54773","added_by":"auto","created_at":"2025-06-29 14:48:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4519992,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plot depicting the Root Mean Square Fluctuation (RMSF) between the selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation\u003c/p\u003e","description":"","filename":"Fig7rmsf.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/0883c9bb1cfab02787752d5f.jpg"},{"id":85618022,"identity":"a60e20d5-de99-44a1-b5d9-075e91a6bd5a","added_by":"auto","created_at":"2025-06-29 14:48:08","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5645569,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plot depicting the Radius of Gyration (Rg) between the selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation\u003c/p\u003e","description":"","filename":"Fig8rg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/429fd36b4ce77584109e1bd8.jpg"},{"id":85617995,"identity":"96b5575d-5b97-44b5-bb1c-ec936c6a9a31","added_by":"auto","created_at":"2025-06-29 14:48:06","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":7019428,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plot depicting the Solvent Accessible Surface Area (SASA) between the selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation\u003c/p\u003e","description":"","filename":"Fig9sasa.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/51ca4ef813362e348abb4a5e.jpg"},{"id":85617981,"identity":"700f401f-d172-441c-bf56-0b477e8d8460","added_by":"auto","created_at":"2025-06-29 14:48:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":6561850,"visible":true,"origin":"","legend":"\u003cp\u003ePCA plots illustrating the conformational variability of protein-ligand complexes for selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation.\u003c/p\u003e","description":"","filename":"Fig10PCA.png","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/c272550e37948033df751d93.png"},{"id":85617961,"identity":"131423c5-254d-45aa-aad9-18d10f27568c","added_by":"auto","created_at":"2025-06-29 14:48:04","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":7374597,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plot depicting the interaction energy (Lennard-Jones Potential, Coulombic Interactions and Total interactions) between the selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation\u003c/p\u003e","description":"","filename":"Fig11ie.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/ee9ebca3f92a4cd334384cae.jpg"},{"id":85619285,"identity":"36089949-807d-450d-85a2-a7368ae6ea43","added_by":"auto","created_at":"2025-06-29 14:56:07","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":3484441,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plot depicting the number of hydrogen bonds (H-bonds) formed between the selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation.\u003c/p\u003e","description":"","filename":"Fig12Hbonds.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/ea938b17b99966a338bf1021.jpg"},{"id":85619281,"identity":"b0827a9b-7484-43f4-b957-960825906e35","added_by":"auto","created_at":"2025-06-29 14:56:07","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":2000208,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plot depicting the Molecular Mechanics Poisson- Boltzmann Surface Area (MMPBSA) between the selected epilepsy proteins: A. Voltage-gated sodium channel α2 (Nav1.2); B. GABA receptor α1-β1; and C. Voltage-gated calcium channel α1G (Cav3.1) and selected drugs (Standard, Cyproheptadine, Oxaprozin, Pizotifen) over a 100 ns simulation.\u003c/p\u003e","description":"","filename":"Fig13MMPBSA.png","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/dc63b3ae2ca7605e901266cc.png"},{"id":98815251,"identity":"ec012d6e-8673-4e21-acc0-a179488c86fc","added_by":"auto","created_at":"2025-12-22 16:14:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":49573061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/f4ea8f14-1ab9-452a-b8dd-a3009daa775a.pdf"},{"id":85617987,"identity":"91c38e0e-e0f8-42dc-beda-f1050987202a","added_by":"auto","created_at":"2025-06-29 14:48:06","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4294469,"visible":true,"origin":"","legend":"","description":"","filename":"S1DBdatabase.csv","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/f6404fb1a5e725d708dc6a5c.csv"},{"id":85617966,"identity":"831aca35-a8a5-420e-a399-d7b2b9034b09","added_by":"auto","created_at":"2025-06-29 14:48:05","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":944354,"visible":true,"origin":"","legend":"","description":"","filename":"S2Approved.csv","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/f05ffb05cae5b3e083d8b11d.csv"},{"id":85619273,"identity":"82e61874-9a42-46ce-b07e-082ec82937b1","added_by":"auto","created_at":"2025-06-29 14:56:05","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":230978,"visible":true,"origin":"","legend":"","description":"","filename":"S3BBB.csv","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/99f803712e1a5a8d6d53354a.csv"},{"id":85619943,"identity":"3ee23d76-29a5-4caf-9aa4-4a10f00bf611","added_by":"auto","created_at":"2025-06-29 15:04:05","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":149608,"visible":true,"origin":"","legend":"","description":"","filename":"S4ChemMine.csv","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/2187944dafa3dbf1334c2bff.csv"},{"id":85617983,"identity":"8bdaa55f-3ad4-45a7-b4e0-4e160c3228b2","added_by":"auto","created_at":"2025-06-29 14:48:06","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":36191,"visible":true,"origin":"","legend":"","description":"","filename":"S5Dock.csv","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/b2d8d859e546a7f6fedfc22e.csv"},{"id":85617973,"identity":"0759aa61-94fa-4aa9-a8e6-cb74d24d1581","added_by":"auto","created_at":"2025-06-29 14:48:05","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3748550,"visible":true,"origin":"","legend":"","description":"","filename":"S6Bindinginteraction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6851614/v1/ad706cffb376fc9b91a2311b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational drug repositioning approach to predict first-line therapeutics for epilepsy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEpilepsy is one of the most prevalent neurological disorders globally, affecting millions of individuals of all ages\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It encompasses a spectrum of neurological disorders characterised by abnormal electrical activity in the brain, leading to seizures. These seizures can manifest in various forms, ranging from momentary lapses of consciousness to convulsions. The underlying causes of epilepsy are diverse, including genetic predispositions, brain injuries, infections, and developmental abnormalities\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Epilepsy poses significant challenges to patients' quality of life, societal integration, and healthcare systems worldwide. Numerous antiepileptic drugs (AEDs) are available, but a substantial proportion of epilepsy patients experience inadequate seizure control or intolerable side effects with standard medications\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Moreover, developing novel AEDs entails substantial time, resources, and regulatory hurdles, making it a challenging endeavour. Therefore, alternative strategies, such as drug repositioning, which can expedite the discovery and approval of effective therapies, are imperative.\u003c/p\u003e \u003cp\u003eDrug repositioning, also known as drug repurposing or reprofiling, involves identifying new therapeutic applications for existing drugs beyond their initially intended indications\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Unlike traditional drug discovery approaches, which often start from scratch, drug repositioning leverages existing pharmacological data, clinical experience, and safety profiles of approved drugs. This strategy offers several advantages, including reduced development costs, shorter timelines, and increased likelihood of success in clinical trials\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Recent years have witnessed a growing interest in exploring drug repositioning strategies for epilepsy treatment, resulting in systematic screening of approved drug libraries and investigational compounds to identify candidates with antiepileptic properties. These efforts have yielded promising findings, as the repurposed drug Lorcaserin\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e demonstrates its efficacy in preclinical models and is under early-stage clinical trials for epilepsy.\u003c/p\u003e \u003cp\u003eThe present study employed a virtual high-throughput screening (VHTS)-based, multi-target drug repositioning strategy to identify drug compound(s) with antiepileptic activity. The DrugBank database was selected to screen approved drug compounds based on blood-brain barrier (BBB) permeability, structural similarity, molecular docking and binding site analysis. Drug(s) showing better binding affinity were evaluated for binding stability and free binding energy using molecular dynamics simulation (MDS) for 100 ns.\u003c/p\u003e"},{"header":"Materials and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eRetrieval of structural and physicochemical data of drug molecules\u003c/h2\u003e\n \u003cp\u003eThe DrugBank database is one of the most significant structural databases containing chemical, physicochemical, pharmacokinetic, pharmacodynamic, target, and metabolomic information of marketed drugs, including approved, small-molecule drugs, biotech drugs, investigational and withdrawn drugs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Due to safety concerns, experimental and withdrawn drug compounds were not considered, while approved drugs, including illicit and nutraceutical drugs, were selected for further \u003cem\u003ein-silico\u003c/em\u003e screening. The 3D molecular files for all drug compounds were retrieved from the DrugBank database\u0026apos;s File Transfer Protocol (FTP) server as a single Structure Data File (SDF).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eScreening of drugs for BBB permeability\u003c/h3\u003e\n\u003cp\u003eThe Central Nervous System (CNS) acting drug compounds must cross the blood-brain barrier (BBB) to reach the neural system for pharmacological activity. \u003cem\u003eIn-vivo\u003c/em\u003e or \u003cem\u003ein-vitro\u003c/em\u003e BBB permeability prediction is a complex, time- and cost-intensive process. So, as an alternative, Machine Learning (ML) based \u003cem\u003ein-silico\u003c/em\u003e BBB permeability prediction is a time-efficient and economical process with superior accuracy. Different BBB permeability prediction tools are available with great accuracy, but to gain maximum positive results, we used three different tools: (a) an \u003cem\u003ein-house\u003c/em\u003e developed tool, BBBper (Blood Brain Barrier permeability prediction tool)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, AdmetSAR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and LightBBB\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e to screen the selected approved drug compounds for BBB permeability.\u003c/p\u003e\n\u003ch3\u003eClustering of structurally similar drugs\u003c/h3\u003e\n\u003cp\u003eMost studied phenomena of drug repositioning focus on similarity among drugs\u0026apos; structural fingerprints, stating, \u0026quot;similar structures will have similar activity\u0026quot;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. So, all selected BBB permeable drugs were further filtered for their structural similarity with available marketed AEDs (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The ChemMine tool\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, an online molecular data analysis program supported by the R library ChemMineR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, was used to cluster selected approved and BBB-permeable drugs using binning clustering applications. The binning clustering method is used to partition a dataset into clusters by quantising the data points into bins and further assigning each bin to a cluster. Different drug clusters were generated using different similarity cutoff values ranging from 0.4 to 0.9. A lower cut-off value means lower similarity between compounds and results in the clustering of less similar compounds. In contrast, a higher similarity cut-off value results in the clustering of more similar compounds, leading to smaller cluster sizes. Finally, the similarity cut-off, which grouped most marketed AEDs within a single cluster, was selected for grouping drug compounds with similar structures to marketed AEDs. All compounds within the selected AEDs cluster were chosen for subsequent molecular docking analysis, based on the hypothesis that their structural conformations were analogous to those of established AEDs and, therefore, may elicit comparable therapeutic effects.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of marketed anti-epileptic drugs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrugBank ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMechanism of action (from DrugBank)\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\u003eAcetazolamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Carbonic anhydrase inhibitor.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrivaracetam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB05541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Synaptic vesicle glycoprotein 2A (SV2A) agonist.\u003c/p\u003e\n \u003cp\u003e\u0026bull; VGSC alpha 1B inhibitor.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabidiol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB09061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Weak partial agonist activity at Cannabinoid receptors CB1R and CB2R.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits noradrenaline, dopamine, serotonin and GABA uptake \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Block T-type (low voltage-activated) Ca channel \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Antagonise mu-opioid receptor.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbamazepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC alpha subunit \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Decrease dopamine turnover (dopamine antagonist) by reducing dopamine (D2) receptor density and phosphorylation.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Enhance GABA synthesis.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits Serotonin uptake.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Decrease Norepinephrine release.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClobazam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA-A receptor partial agonist (alpha and gamma 2 subunits).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonazepam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB01068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA - A receptor agonist \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiazepam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA - A receptor agonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDronabinol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Cannabinoid receptors 1 and 2 agonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEslicarbazepine acetate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB09119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits T-type calcium channel.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthosuximide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits T-type VGCC alpha 1G \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthotoin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC alpha 5.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEzogabine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB04953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; VGPC (Kv7.2-7.5) KCNQ2,3,4,5) agonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFelbamate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Antagonize Glutamate receptor (NMDA 2A,3A, 2B).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFosPhenytoin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB01320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC alpha 5.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGabapentin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Structural analogue of GABA \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits VGCC subunit alpha 2, delta 1,2.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Activates VGPC subfamily KQT member 3,5.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLacosamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB06218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC alpha 3,9,10 \u003csup\u003e22,25\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLamotrigine (phenyl triazine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits adenosine A1/A2 receptor.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits K-opioid receptor.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevetiracetam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB01202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Agonist for Synaptic vesicle glycoprotein 2A (SV2A) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC alpha 1B \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLorazepam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA-alpha receptor agonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethsuximide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB05246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGCC T-type subunit alpha 1G.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethylphenobarbital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA-alpha receptor agonist.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Glutamate receptor antagonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMidazolam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA-alpha receptor agonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrazepam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB01595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA R agonist.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits Voltage-dependent sodium channels.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxcarbazepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerampanel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB08883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Glutamate receptor 1 antagonist \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenacemide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB01121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits Sodium channel protein type 1 subunit alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenobarbital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB01174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA R agonist.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Glutamate receptor antagonist.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits Calcium channels.\u003c/p\u003e\n \u003cp\u003e\u0026bull; NMDA channel antagonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenytoin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; VGSC blocker alpha 1, 3 and 5 subunits.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregabalin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGCC subunit alpha 2/ delta 1 \u003csup\u003e30,31\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimidone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA alpha receptor agonist.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Glutamate receptor antagonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRufinamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB06201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStiripentol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB09118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA alpha receptor agonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTiagabine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits GABA transferase.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTopiramate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC type 1 alpha subunit \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; GABA alpha 1 receptor agonist.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrimethadione\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits Voltage-dependent T-type calcium channel subunit alpha-1G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValproic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits succinic semialdehyde dehydrogenase (SSADH) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits GABA transferase \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits Histone deacetylase 2 and 9.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigabatrin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB01080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; GABA analogue \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Irreversible inhibitor of 4-aminobutyrate transaminase.\u003c/p\u003e\n \u003cp\u003e\u0026bull; GABA beta receptor agonist \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZonisamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB00909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC alpha 1,2,3,4,5,9,11 subunits \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits VGSC beta 1,2,3,4 subunits \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits VGCC T-type subunit alpha 1G, 1H, 1I.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Inhibits Carbonic anhydrase 1,2,3,4,5A,5B,6,7,10,11,1213,14.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eSelection and preparation of the tertiary structure of epilepsy target receptors\u003c/h3\u003e\n\u003cp\u003ePreviously, we have identified three 1st line epilepsy therapeutic targets: Voltage-Gated Sodium Channel (VGSC) \u0026alpha;2 (Nav1.2), Gamma-Aminobutyric Acid (GABA) receptor \u0026alpha;1-\u0026beta;1, and Voltage-Gated Calcium Channel (VGCC) \u0026alpha;1G (Cav3.1)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The tertiary protein structures of receptors Nav1.2, GABA receptor \u0026alpha;1, and Cav3.1 were available in the RCSB-PDB database with PDB-IDs 6J8E-A, 6HUJ-A, and 6KZP, respectively. However, the tertiary structure of GABA receptor \u0026beta;1 was unavailable in the RCSB-PDB database, and hence, its 3D structure was generated by homology modelling using the SWISS-MODEL web server\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The GABA receptor \u0026beta;1 fasta sequence was retrieved from UniProt (UniProt ID: P18505) and subjected to homology modelling using the Swiss Model web server, with 6HUJ-B as the template. The modelled structure was further validated using the QMEAN score\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, Ramachandran plot\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, Verify3D\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and ProSA Z-score\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. To form the functional GABA receptor \u0026alpha;1-\u0026beta;1 complex, the available GABA receptor \u0026alpha;1 chain and modelled GABA receptor \u0026beta;1 chain were subjected to protein-protein docking using Hex tool v8.0.0\u003csup\u003e43\u003c/sup\u003e. A total of 25 searches were performed, taking \u0026ldquo;shape\u0026thinsp;+\u0026thinsp;electro\u0026thinsp;+\u0026thinsp;DARS\u0026rdquo; as correlation types and 3D FFT mode. A side-by-side (parallel) conformation showing head-to-head and tail-to-tail orientation for both chains was selected, and the result was saved as a combined PDB file. The conformation for the generated GABA receptor \u0026alpha;1-\u0026beta;1 file was also validated by aligning the generated structure against GABA receptor \u0026alpha;1-\u0026beta;3 (PDB ID: 6HUJ) using open-source PyMOL v2.5.0 \u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAll the selected epilepsy target receptor proteins (Nav1.2, GABA receptor \u0026alpha;1-\u0026beta;1, and Cav3.1) were subjected to energy minimisation using UCSF Chimera v1.6\u003csup\u003e45\u003c/sup\u003e for 100 steepest descent and 10 conjugate gradient steps under AMBER ff99bsc force field with a step size of 0.02\u0026Aring;. The energy-minimised structures were saved as PDB files for further molecular docking studies.\u003c/p\u003e\n\u003ch3\u003eMolecular docking study\u003c/h3\u003e\n\u003cp\u003eThe selected epilepsy target receptor proteins (Nav1.2, GABA receptor \u0026alpha;1-\u0026beta;1, and Cav3.1) were prepared for molecular docking by adding polar hydrogen and assigning Kollman and Gasteiger charges using Autodock tools. Autodock v4.2.6\u003csup\u003e46\u003c/sup\u003e was used for molecular docking of selected drug compounds against selected therapeutic target proteins. The target proteins Nav1.2 and Cav3.1 are channel proteins, and to block the channel, the Grid file parameters (GPF) were assigned around the pore region, while the grid parameters for GABA receptor \u0026alpha;1-\u0026beta;1 were assigned between the \u0026alpha;1 and \u0026beta;1 chains within the GABA binding area to find a GABA agonist\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Molecular docking was performed for 100 independent runs using the Lamarckian genetic algorithm with a population size of 150, taking a gene mutation rate of 0.02 and a crossover rate of 0.8. The dock conformation with the lowest binding energy and maximum cluster size was selected for each drug. The highest-selling AEDs, Carbamazepine, Clonazepam and Pregabalin, were chosen as standard drugs for target proteins - Nav1.2, GABA receptor \u0026alpha;1-\u0026beta;1, and Cav3.1, respectively\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The drugs showing better binding affinities than the corresponding standard AEDs against all three receptors were selected for further study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGrid box parameters for molecular docking study of voltage-gated sodium channel 2A (Nav1.2), GABA receptor \u0026alpha;1-\u0026beta;, and voltage-gated calcium channel \u0026alpha;1G (Cav3.1)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNav1.2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGABA Receptor \u0026alpha;1-\u0026beta;1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCav3.1\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\u003eSize-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize-Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCenter-X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCenter-Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCenter-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrid Box\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1750963276.png\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1750963284.png\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1750963291.png\"\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\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBinding pocket analysis of selected drugs\u003c/h2\u003e\n \u003cp\u003eThe residues within the binding pockets are significant factors in a drug\u0026apos;s pharmacodynamic effect. Hence, the binding pockets of selected drugs were compared with the binding pockets of standard drugs, hypothesising that drugs with binding pockets similar to the standard AEDs would produce similar therapeutic effects. Therefore, the dock complexes of standard AEDs and selected drugs against the epilepsy target proteins were generated using the Autodock tool, and the binding pockets (nearby amino acids and hydrogen bond-forming amino acids) for each dock complexes were analysed using the Java-based tool LigPlot\u0026thinsp;+\u0026thinsp;\u003csup\u003e48\u003c/sup\u003e. Drugs showing similar binding pockets to the standards and better binding affinities than corresponding standard drugs would have similar but better therapeutic effects than the existing standard/marketed drugs, and were hence selected for further repositioning study.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLiterature data mining\u003c/h3\u003e\n\u003cp\u003eSelected drugs with better binding affinities and similar binding pockets to their corresponding standard drugs were analysed for previous reports regarding epilepsy/seizure or any other severe side effects related to suicidal thinking, abnormal heartbeats, etc. Drugs showing any seizure-persuading or severe side effects cannot serve as a potential repositioned drug candidate. Therefore, drugs with no report or any prior study on seizure reduction with acceptable side effects were selected for further repositioning study.\u003c/p\u003e\n\u003ch3\u003eMolecular dynamics simulation study\u003c/h3\u003e\n\u003cp\u003eSelected repositioned drugs with better binding energies than corresponding standard drugs against selected epilepsy target receptors were subjected to Molecular Dynamics Simulation (MDS) using GROMACS v2022.1\u003csup\u003e49\u003c/sup\u003e. The charmm36m force field\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e was used to generate topology files for the docked complex of the selected drugs and the epilepsy receptor(s). The entire system was solvated with TIP3P water molecules in a rectangular box, followed by the addition of sodium (Na+) and chloride (Cl-) ions to neutralise the system at a concentration of 0.15 M, to mimic physiological conditions. To relieve the system\u0026apos;s geometric strain, a maximum of 5,000 energy minimisation steps were performed using the steepest descent algorithm to lower the potential energy up to 1,000 KJ/mol. Then, the whole system was equilibrated under constant temperature (310 K) and pressure (1 bar) conditions for 1000 ps (1 ns) using the Nose-Hoover thermostat and Parrinello-Rahman barostat, respectively. After equilibration, MDS was performed for 100 ns (500,000,000 steps) in triplicate with a time step of 2 fs at constant temperature and pressure using Periodic Boundary Conditions (PBC). Trajectories were recorded every 100 ps. Protein-drug interactions throughout the whole simulation were monitored for stability examination.\u003c/p\u003e\n\u003cp\u003eThe obtained trajectory of the simulated protein-ligand complexes was analysed based on Root Mean Square Deviation (RMSD), Root Mean Square Fluctuations (RMSF), Radius of gyration (Rg), Solvent Accessible Surface Area (SASA), Principal Component Analysis (PCA), interaction energy including Lennard-Jones potential and Coulombic interactions, and Hydrogen bond analysis. Further MMPBSA was performed using gmx_MMPBSA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e to check the free binding energy between the protein and drug for the whole MDS.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRetrieval of approved drugs from the DrugBank database\u003c/h2\u003e \u003cp\u003eThe DrugBank database v5.1.12 (accessed January 1, 2025) contained structural and physicochemical data for 12,699 drugs, including approved, illicit, nutraceuticals, investigational, experimental, veterinary, and withdrawn drugs (Supplementary File: S1). For safety purposes, only approved drugs (2,769) were considered for our drug repositioning study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary File: S2; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these, 188 drugs were found to be withdrawn from the market after their initial approval. Hence, the remaining 2,581 approved drugs were selected for further drug repositioning screening as first-line anti-epileptic therapeutics. The structural (SDF) and physicochemical data for the selected 2581 drugs were retrieved using the FTP service of the DrugBank database. The selected approved drugs also included 38 marketed AEDs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which were used for structural similarity analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of approved drugs from the DrugBank database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Experimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Experimental; Investigational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Experimental; Investigational; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Experimental; Vet Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Experimental; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Illicit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Illicit; Investigational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Illicit; Investigational; Vet Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Illicit; Investigational; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Illicit; Vet Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Illicit; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Investigational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Investigational; Nutraceutical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Investigational; Nutraceutical; Vet Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Investigational; Nutraceutical; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Investigational; Vet Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Investigational; Vet Approved; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Investigational; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Nutraceutical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Nutraceutical; Vet Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Nutraceutical; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Vet Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Vet Approved; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproved; Withdrawn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScreening of drugs for BBB permeability\u003c/h2\u003e \u003cp\u003eAEDs must cross the BBB to perform their action within the CNS. Three different ML algorithm-based BBB permeability prediction programs/tools were used for better accuracy. Our \u003cem\u003ein-house\u003c/em\u003e developed tool - BBBper\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e predicted 1553 BBB-permeable drugs, admetSAR\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e predicted the maximum number of 1575 drugs as BBB-permeable, while LightBBB\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e predicted 1393 drugs to cross the BBB (Supplementary File: S3). All three selected programs/tools predicted 895 drugs as BBB-permeable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). BBBper predicted all 38 AEDs as BBB permeable, while admetSAR and LightBBB showed 37 AEDs as BBB permeable, and FosPhenytoin and Eslicarbazepine as BBB impermeable, respectively. Hence, to remove the adverse selection, the drug compounds predicted to be BBB-permeable by any two programs/tools were selected for further study. The selection criteria resulted in 1606 drug compounds (including 38 marketed AEDs) as BBB permeable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClustering of structurally similar drugs\u003c/h2\u003e \u003cp\u003eTo find structurally similar drugs, all the selected BBB-permeable drugs were clustered based on structure similarity using the ChemMine tool\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Different similarity cutoff values of 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 resulted in the generation of 645, 927, 1128, 1296, 1449, and 1492 bins, respectively (Supplementary File: S4). The lower similarity cutoff value of 0.4 resulted in the lowest bin count but the largest bin size, with 360 and 185 compounds in bins 2 and 12, respectively. Within bin 2, 18 AEDs were clustered based on their structural similarity along with 342 other drug compounds. At a similarity cutoff value of 0.5, the largest bin (bin-12) of 133 compounds was observed, but it did not have any AEDs. A bin size of 92 compounds (bin-38) was observed at the similarity cutoff of 0.6. At higher similarity cut-off values, no significant bin size was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBin-2, at the cutoff value of 0.4, was observed to have a maximum (18) number of AEDs (Carbamazepine, Clobazam, Clonazepam, Diazepam, Eslicarbazepine, Ethotoin, FosPhenytoin, Lorazepam, Methylphenobarbital, Midazolam, Methsuximide, Nitrazepam, Oxcarbazepine, Perampanel, Phenacemide, Phenobarbital, Phenytoin and Primidone). Hence, Bin-2 at a 0.4 cutoff value with at least 40% structural similarity and with most of the marketed AEDs was selected for further study. Along with Bin-2, other bins at a similarity cutoff of 0.4 were checked for the remaining 20 AEDs. Most of the bins were singular with AED only (Bin-65: Pregabalin; Bin-96: Topiramate; Bin-120: Valproate; Bin-286: Ethosuximide; Bin-475: Tiagabine; Bin-477: Zonisamide; Bin-501: Felbamate; Bin-531: Gabapentin; Bin-578: Vigabatrin; Bin-824: Ezogabine; Bin-855: Rufinamide; Bin-863: Lacosamide and Bin-1029: Stiripentol), representing unique structure of these 13 marketed AEDs. In contrast, the Bin-213 and Bin-649 were observed to have three drug compounds, including two AEDs (Bin-213: Cannabidiol, Dronabinol and Bin-649: Brivaracetam, Levetiracetam) and one other structurally similar drug to these AEDs. Bin-140 and Bin-348 were observed to have two drug compounds, including one AED (Bin-140: Trimethadione and Bin-348: Acetazolamide) and one structurally similar drug compound. In Bin-54, Lamotrigine was observed with three other structurally similar drug compounds. From all these bins, 349 drug compounds were selected, having\u0026thinsp;\u0026gt;\u0026thinsp;40% structural similarities with most of the marketed AEDs and were considered for further repositioning studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSelection and preparation of the tertiary structure of epilepsy target receptors\u003c/h2\u003e \u003cp\u003eThree epilepsy drug targets, VGSC α2 (Nav1.2), GABA receptor α1-β1, and VGCC α1G (Cav3.1), were reportedly identified as 1st line therapy targets against epilepsy\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The tertiary protein structures of receptors Nav1.2, GABA receptor α1, and Cav3.1 were available in the RCSB-PDB database with PDB-IDs 6J8E-A (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), 6HUJ-A (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and 6KZP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), respectively. The tertiary structure of GABA receptor β1 (UniProt ID: P18505) was unavailable in the RCSB-PDB database and was generated by homology modelling using the Swiss model web server and PDB-ID: 6HUJ-B as a template (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The modelled 3D structure was evaluated with a QMEAN score of -2.88, a Ramachandran Z-score of -2.818, and a pass verify-3D status. Ramachandran plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) analysis revealed that 93.4% of the amino acids reside under the favoured region, and the remaining 6.6% reside in the allowed region. The overall validation criterion represented an exemplary structure of the predicted GABA receptor β1 model and was suitable for further studies.\u003c/p\u003e \u003cp\u003eThe functional GABA receptor 3D structures are reportedly observed in parallel chains, \u003cem\u003ei.e\u003c/em\u003e., tail-to-tail and head-to-head conformation\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Hence, to form a functional GABA receptor α1-β1 complex, the retrieved GABA receptor α1 subunit was docked against the modelled GABA receptor β1 using Hex tool 8.0.0\u003csup\u003e43\u003c/sup\u003e. The functional conformation of GABA receptor α1-β1 with parallel binding (head-to-head and tail-to-tail) was selected for further study, with a binding energy of -1206.05 KJ/mol. The structural alignment of the selected GABA receptor α1-β1 docked complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) against the reported tertiary structure of GABA receptor α1-β3 (PDB ID: 6HUJ) showed the RMSD of 0.099, revealing a very high accuracy of the modelled structure. The selected structure of GABA receptor α1-β1 was subjected to energy minimisation using UCSF Chimera for further docking studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking study\u003c/h2\u003e \u003cp\u003eThe selected 349 drug molecules were energy-minimised and docked against the selected epilepsy target proteins, Nav1.2, GABA receptor α1-β1, and Cav3.1, using AutoDock v4.2.6. Top-selling AEDs, Carbamazepine, Clonazepam and Pregabalin, were chosen as standard drugs for the studied target receptors - Nav1.2, GABA receptor α1-β1, and Cav3.1, respectively, for comparing the binding affinities of the docked drug complexes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The minimum binding energies of standard drugs, Carbamazepine, Clonazepam and Pregabalin, were observed to be -7.13 Kcal/mol, -6.14 Kcal/mol and \u0026minus;\u0026thinsp;5.76 Kcal/mol when docked against protein receptors Nav1.2, GABA receptor α1-β1, and Cav3.1, respectively.\u003c/p\u003e \u003cp\u003eA total of 136 drugs showed better binding affinities for the epilepsy receptor Nav1.2 compared to the standard Carbamazepine, while 72 drugs showed lower binding energies for GABA receptor α1-β1 when compared to GABA agonist Clonazepam. A total of 275 drugs, screened against Cav3.1, showed better binding affinities than the corresponding standard drug, Pregabalin (Supplementary File: S5). These 275 Cav3.1 binding drugs included all 136 Nav1.2 binding drugs and 56 out of 72 GABA receptor α1-β1 binding drugs. Overall, 46 drugs, in common, showed better binding affinities against their corresponding therapeutic target receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These 46 drug compounds were also predicted to be BBB permeable and showed structural similarity with already marketed AEDs and thus can be predicted as potential candidates for AED repositioning.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrugs having better binding energies (Kcal/mol) than standard drugs against selected epileptic receptors: Voltage-gated sodium channel α2 (Nav1.2), GABA receptor α1-β1, and Voltage-gated calcium channel α1G (Cav3.1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugBank ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNav1.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGABA receptor α1-β1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCav3.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbamazepine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClonazepam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePregabalin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndecainide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmitriptyline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetixene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtriptyline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriprolidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNabilone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDarifenacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNortriptyline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoxapram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCinnarizine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFluorescein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAzatadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerphenazine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKetotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyclobenzaprine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMesoridazine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaprotiline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAzelastine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB00991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKetoprofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePimozide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoxepin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiphenylpyraline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlavoxate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifenoxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlunitrazepam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePericiazine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThiothixene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZuclopenthixol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB01628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtoricoxib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB04841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlunarizine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB05015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB06077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumateperone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB06153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB06413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArmodafinil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB06626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAxitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB06684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVilazodone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB09167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDosulepin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB09488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcrivastine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB11952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuvelisib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB12492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePiritramide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB12877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxatomide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB13292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePimethixene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB14033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcetyl sulfisoxazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB12792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoscalid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBinding pocket analysis of selected drugs\u003c/h2\u003e \u003cp\u003eThe screened 46 drugs, selected as above, were further evaluated for their binding interactions within the binding pockets of selected target proteins. Most marketed AEDs working as VGSC inhibitors bind within the inner pore-loop (P-loop) region. Standard drug Carbamazepine forms a hydrogen bond (H-bond) with the Leu939 residue presented within the P-loop region of Domain-II. Amino acids Ser1461, Phe1462, and Ile1457 within the P-loop region of VGSC-Domain-III were reported to be essential for the binding of VGSC-inhibiting AEDs to generate an anti-epileptic activity\u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. 20 out of 46 selected drugs showed their binding within the binding pocket, including the P-loop region. Drugs Belinostat, Dosulepin, Flavoxate, Mesoridazine, and Oxatomide were observed to form H-bonds within the P-loop region of Domain-II. Drugs Amitriptyline, Azatadine, Azelastine, Cyproheptadine, Difenoxin, Maprotiline, Nortriptyline, Oxaprozin, and Piritramide were observed to form H-bonds within the P-loop region of Domain-III (Supplementary File: S6). Drugs Cinnarizine, Darifenacin, Flunarizine, Lumateperone, Pimozide, and Pizotifen did not show any bonded interaction but bound within the P-loop region through non-bonded interactions, while the remaining 26 drugs were not observed to bind within the binding pocket as standard drugs.\u003c/p\u003e \u003cp\u003eGABA and its agonist bind between the α1 and β1 chains of the GABA receptor\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Amino acids Asp149, Pro174, Asp191, and Lys279 within the extracellular region of GABA receptor subunit α1 were reported to be involved in the binding of GABA and its agonist\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Standard drug Clonazepam showed its binding within the GABA binding region by the formation of a single H-bond with Asp184 of GABA receptor subunit α1 and several non-bonding interactions with the amino acids Leu124, Asn125, Met162, Glu178, and Arg187 of GABA receptor subunit β1. Among selected drugs, 16 drugs (Acetyl sulfisoxazole, Acrivastine, Armodafinil, Axitinib, Azatadine, Cyclobenzaprine, Cyproheptadine, Etoricoxib, Flunitrazepam, Ketotifen, Maprotiline, Nortriptyline, Oxaprozin, Pizotifen, Protriptyline, and Vilazodone) showed their binding within the binding region of GABA receptor subunit α1 with the formation of an H-bond. In contrast, Pimethixene and Triprolidine did not show any H-bonding but were observed to bind within the binding pocket of GABA receptor α1-β1 (Supplementary File: S6).\u003c/p\u003e \u003cp\u003eThe pore-forming intracellular region of S5 and S6 in VGCC has been reportedly necessary for the trafficking of Ca\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e ions\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e; hence, drug binding within this region would generate a therapeutic response for epileptic seizures. The standard drug, Pregabalin, showed three H-bonded interactions with amino acids Glu354 (2.72\u0026Aring;), Gln922 (2.77\u0026Aring;), and Glu923 (2.7\u0026Aring;) within the S6 helical region of repeats I and II. All the selected drugs were observed to bind within the binding pocket of Cav3.1, similar to the standard drug.\u003c/p\u003e \u003cp\u003eThe binding pocket comparison of selected drugs revealed six drugs (Azatadine, Cyproheptadine, Maprotiline, Nortriptyline, Oxaprozin, and Pizotifen) with similar binding pockets as standard drugs for their respective target receptors. These six drug hits were further evaluated for their role in seizure management, using exhaustive text mining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLiterature data mining\u003c/h2\u003e \u003cp\u003eThe selected marketed drugs (Azatadine, Cyproheptadine, Maprotiline, Nortriptyline, Oxaprozin, and Pizotifen) belong to different classes of drugs and were analysed for their previous reports in the context of epileptic seizures and other toxicity assays.\u003c/p\u003e \u003cp\u003eAzatadine\u003c/p\u003e \u003cp\u003eAzatadine is a first-generation antihistamine that primarily functions as an H1 receptor antagonist, making it effective in treating allergic reactions such as rhinitis, conjunctivitis, and urticaria\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Its chemical structure is closely related to tricyclic compounds, contributing to its ability to cross the blood-brain barrier. As a result, azatadine can cause significant CNS effects, such as drowsiness and sedation\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The sedative effects of azatadine may lower the seizure threshold, potentially increasing the risk of seizures. Additionally, its anticholinergic properties could interfere with the balance of neurotransmitters in the brain, further complicating seizure control\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Due to these concerns, Azatadine is generally avoided in epilepsy patients and was omitted from our study outcome.\u003c/p\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003cp\u003eCyproheptadine is also a first-generation antihistamine with anticholinergic, antiserotonergic, and sedative properties. It was initially developed to treat allergies by blocking the H1 histamine receptors; it is also used widely to stimulate appetite, especially in patients experiencing cachexia or malnutrition\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. It is also prescribed for the treatment of serotonin syndrome, a potentially life-threatening condition caused by excessive serotonergic activity in the CNS. The serotonin receptors, particularly the 5-HT2A receptor, are also reported to be implicated in seizure activity. In some cases of intractable epilepsy, where patients do not respond well to conventional anticonvulsants, Cyproheptadine has been used off-label to help control seizures by modulation of serotonergic pathways, which can influence neuronal excitability and seizure thresholds. Some animal studies also showed reduced seizure frequency and the number of dying cells in the brain after Cyproheptadine treatment\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMaprotiline\u003c/p\u003e \u003cp\u003eMaprotiline is a tetracyclic antidepressant (TeCA) primarily used to treat depression, particularly major depressive disorder and dysthymia. It functions by inhibiting the reuptake of norepinephrine, a neurotransmitter, thereby increasing its levels in the brain and alleviating depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Unlike tricyclic antidepressants (TCAs), which affect multiple neurotransmitters, Maprotiline is more selective for norepinephrine, which reduces some of the side effects typically associated with TCAs. Additionally, it has strong sedative properties, which makes it worthwhile for patients with anxiety or insomnia, but potentially problematic for those who are sensitive to sedatives\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Maprotiline has also been explored for its potential effects on epilepsy, but it is not a first-line treatment. The clinical studies suggest that Maprotiline can lower the seizure threshold, but at higher doses or in patients with a predisposition to seizures, it behaves as a pro-convulsant\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Therefore, Maprotiline use in epileptic patients is generally approached with caution, often requiring close monitoring and consideration of alternative therapies.\u003c/p\u003e \u003cp\u003eNortriptyline\u003c/p\u003e \u003cp\u003eNortriptyline is another TCA, primarily used to treat major depressive disorder, but it is also prescribed for conditions such as chronic pain, neuropathic pain, and certain anxiety disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. It works by inhibiting the reuptake of neurotransmitters, particularly norepinephrine and, to a lesser extent, serotonin, in the brain, which enhances the mood-stabilising effects of these chemicals. Nortriptyline is reported to lower the seizure threshold, which can increase the risk of seizures in susceptible individuals\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. However, it may be considered in cases where an epileptic patient suffers from comorbid conditions like depression or chronic pain, where the benefits of its mood-stabilising effects might outweigh the risks. Hence, the use of nortriptyline in patients with epilepsy involves a careful risk-benefit analysis, considering the individual's seizure history, the severity of comorbid conditions, and the potential for drug interactions.\u003c/p\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003cp\u003eOxaprozin is a non-steroidal anti-inflammatory drug (NSAID) used to treat inflammation or pain caused by osteoarthritis or rheumatoid arthritis. It inhibits the cyclooxygenase (COX) enzyme non-selectively, synthesising prostaglandins and lipid compounds; consequently, inflammation, pain and fever are reduced\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. However, Oxaprozin has not been widely studied or recommended for use in individuals with epilepsy and no seizure-related side effects were reported in the public database. In the recent \u003cem\u003ein vivo\u003c/em\u003e study of induced rat seizure models, Oxaprozin has been shown to produce an anticonvulsive effect during the behavioural test\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, which opens a new therapeutic approach for Oxaprozin.\u003c/p\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003cp\u003ePizotifen is a tricyclic drug primarily used as a preventive treatment for migraine headaches and cluster headaches. It functions as a serotonin receptor antagonist, explicitly targeting the 5-HT2A and 5-HT2C receptors, and exhibits antihistamine and anticholinergic properties\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Inhibiting serotonin, a neurotransmitter believed to play a role in the dilation of blood vessels in the brain, results in reduced frequency and severity of migraines. Pizotifen is often prescribed when other treatments, such as beta-blockers or antiepileptic drugs, are either ineffective or unsuitable for the patient. Its role in epilepsy is less prominent and more complex, as it has been observed to exhibit some anticonvulsant properties in adult zebrafish\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e due to its ability to modulate serotonin levels in the brain. However, its efficacy in epilepsy is not well-established, and it is generally not considered a standard antiepileptic drug. The use of Pizotifen in epilepsy might be explored in patients with comorbid conditions, such as migraines, where the dual action of preventing headaches and potentially reducing seizures could be beneficial. Nonetheless, more research is needed to better understand its exact mechanism and effectiveness in managing epilepsy.\u003c/p\u003e \u003cp\u003eAmong the selected six drugs, Azatadine and Cyproheptadine are antihistamines. Azatadine was reported to increase the seizure threshold, but another antihistamine, Cyproheptadine, was reported to control seizures. Hence, Azatadine was not selected for further repositioning study, while based on previous reports of Cyproheptadine, it was considered a strong repurposed drug candidate for further research. Antidepressant drugs Maprotiline and Nortriptyline were reported to behave as pro-convulsants and required close monitoring in epilepsy patients. Hence, both antidepressants were dropped for further study. In animal studies, Oxaprozin and Pizotifen were reported to act as anti-epileptic compounds. However, no clinical trials have been reported for any of these compounds. Hence, we propose Oxaprozin, Pizotifen, along with the antihistamine drug Cyproheptadine, as potential first-line AEDs for different kinds of epileptic seizures, irrespective of their nature (genetic, congenital, physical injury, or environment).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMolecular dynamics simulation study\u003c/h2\u003e \u003cp\u003eThe docked complex of selected potential first-line AED candidates - Cyproheptadine, Oxaprozin, and Pizotifen, with the epilepsy target receptors Nav1.2, GABA receptor α1-β1, and Cav3.1, were subjected to molecular dynamics studies to evaluate their stability in physiological conditions. All the receptor-drug complexes showed a minimised energy configuration within a 3000 ps run, followed by equilibration at an average temperature and pressure of 310 K and one psi, respectively. The docked complexes of the selected drugs were finally simulated for a 100-ns runtime in triplicate.\u003c/p\u003e \u003cp\u003eRoot Mean Square Deviation (RMSD)\u003c/p\u003e \u003cp\u003eRMSD measures the average distance between the atoms (usually the backbone atoms) of superimposed molecules. For all simulated complexes, the RMSD graph appeared to be stabilised within the initial run of 10 ns and remained stable until the end of the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For all three epilepsy receptors, the Oxaprozin drug showed the lowest RMSD values, followed by Pizotifen and the standard drugs, while Cyproheptadine showed maximum RMSD values. In Nav1.2, Oxaprozin was observed to have the lowest RMSD of 0.48nm, followed by standard drug Carbamazepine (0.54nm), Pizotifen (0.56nm), and Cyproheptadine, which showed the maximum RMSD of 0.78nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In the case of GABA receptor α1-β1, Pizotifen showed higher RMSD values, but in the latter half of the simulation, all four drug complexes were observed to have similar RMSD values. Cyproheptadine showed the lowest RMSD, followed by Oxaprozin, standard drug Clonazepam and Pizotifen (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The Cav3.1 was observed to be less stable than the other two receptors. From the beginning of the simulation, RMSD was observed to be stable after a run of 7 ns, and was observed to be stable till 60 ns, after which the RMSD of Oxaprozin and Pizotifen started to increase up to the average RMSD of Cyproheptadine and standard drug Pregabalin. Overall, Pizotifen showed the lowest mean RMSD of 0.515nm, followed by Oxaprozin (0.53nm), standard drug Pregabalin (0.775nm), and Cyproheptadine, which showed the maximum RMSD value of 0.81nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Based on the RMSD analysis, the binding of all the selected drugs, along with the standard drugs, showed a stable protein-ligand complex structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRoot Mean Square Fluctuation (RMSF)\u003c/p\u003e \u003cp\u003eRMSF measures the average deviation of a particle (e.g. protein residue) over time from a reference position (typically the time-averaged position of the particle). It analyses the portions of the structure that fluctuate from their mean structure the most or least. The Cyproheptadine drug showed the lowest RMSF, followed by Pizotifen and the standard drugs, while Oxaprozin showed maximum RMS fluctuation upon binding with all three selected epilepsy target receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Oxaprozin binding with the Nav1.2 showed a significant increase in RMSF value up to 3.16nm, including H-bond formation with amino acids Ser413, Ile417, Asn418, Leu421, and Ala425, resulting in higher fluctuation in the P-loop region of Domain I of Nav1.2. Later, such H-bonds were observed in the P-loop region of Domains II, III, and IV, resulting in higher RMSF values at the end of each domain. The other three drugs did not show any significant fluctuation in the structure of Nav1.2 upon binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). All four drugs bound to the GABA receptor α1-β1 showed a similar pattern of RMS fluctuations. In case of Cav3.1, Oxaprozin showed maximum RMS fluctuation in Domain I and IV, while standard drug Pregabalin showed higher RMSF in Domain II and III (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The rest of the drugs showed a similar pattern of RMSF for all three protein receptors. In comparison, Oxaprozin showed maximum protein fluctuation due to its binding with the selected protein receptors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRadius of Gyration (Rg)\u003c/p\u003e \u003cp\u003eRg is defined as the distribution of protein atoms around its axis. The length representing the distance between the point where it is rotating and the point where the transfer of energy has the maximum effect gives Rg. The distribution of atoms of all three studied target receptors was stable throughout the simulation within the 3.2 to 3.6 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The Nav1.2 receptor showed maximum Rg when bound with Oxaprozin, while the remaining three drugs, including the standard drug Carbamazepine, showed similar Rg values around 3.5nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The observations were opposite for the GABA receptor α1-β1, where Oxaprozin showed the lowest Rg (3.26) while the rest of the three drugs, including the standard drug Clonazepam, showed similar Rg values of 3.38nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The Rg pattern was observed to be different for target receptor Cav3.1, where Cyproheptadine bound Cav3.1 showed the lowest Rg value of 3.23nm, followed by Pizotifen (3.3nm), Oxaprozin (3.34nm), and the standard drug Pregabalin bound Cav3.1 showed the highest Rg value of 3.35nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The Rg study revealed that all three selected drugs, along with standard drugs, have a similar role in stabilising all the studied therapeutic target proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSolvent Accessible Surface Area (SASA)\u003c/p\u003e \u003cp\u003eSASA is a critical parameter describing the area around a macromolecule accessible to solvent molecules. It aids in understanding protein folding, ligand binding, and conformational changes, and plays a crucial role in elucidating molecular interactions at the atomic level. For all drug-receptor complexes, the SASA graphs were observed to be stable, referring to the overall size of the protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The high SASA values for Nav1.2 (580 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e \u0026ndash; 620 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and Cav3.1 (510 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e \u0026ndash; 550 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) complexes than the GABA receptor α1-β1 (290 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e \u0026ndash; 340 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) correlate with the massive structure of the protein, where Nav1.2 and Cav3.1 has \u0026gt;\u0026thinsp;1000 amino acids, while GABA receptor α1-β1 composed of 602 amino acids. The Oxaprozin complex with Nav1.2 showed the highest SAS area (608 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), while the remaining three drugs, along with the standard drug Carbamazepine, showed similar SAS areas around 590 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). In case of the GABA receptor α1-β1, the Oxaprozin-bound complex showed the lowest SASA (302 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), while the remaining three drugs, along with the standard drug Clonazepam, showed SAS areas around 321 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). The Cav3.1 SAS area was found to be lowest for the Pizotifen-bound complex (522 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), followed by Oxaprozin (528 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), Cyproheptadine (539 nm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), and the standard drug Pregabalin showed maximum SAS area (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). The SASA graph was similar to the Rg graph, representing a similar protein folding pattern in both studies. All the drugs exhibited similar protein-soluble areas, indicating protein stability and the binding of the drug molecules to the selected proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e \u003cp\u003ePCA was employed to project the backbone fluctuations of selected target proteins and drug complexes onto a reduced two-dimensional (2D) space defined by the first two principal components (PC1 and PC2). These projections help to visualise how different drugs affect the structural flexibility and stability of the epilepsy receptor proteins throughout simulations. Tighter clusters indicate restricted atomic motion and hence more stable binding, while more dispersed clusters suggest greater conformational freedom and less stable interactions. The PCA projection for the Nav1.2 - Cyproheptadine complex showed the most compact and centralised cluster in the PC1-PC2 space, indicating minimal conformational changes during the simulation and, hence, strong structural stabilisation. The Nav1.2 complex with Oxaprozin and Pizotifen showed a moderately dispersed trajectory cluster, suggesting intermediate flexibility. In contrast, the standard drug Carbamazepine showed a wider spread compared to selected drugs, pointing to greater backbone fluctuation and reduced stabilising effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). For the GABA receptor α1-β1, the Oxaprozin drug complex showed the most stable structural behaviour, demonstrated by its tight cluster with minimal spread of \u0026plusmn;\u0026thinsp;3 in the PC2 space. It was followed by the drugs Pizotifen and Cyproheptadine. In contrast, standard drug Clonazepam showed a wider spread in its PCA projection, indicating that its binding led to higher receptor flexibility (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). In the Cav3.1 receptor system, Cyproheptadine produced the tightest cluster among the other drugs, indicating a more substantial stabilising effect. Oxaprozin and Pizotifen showed slightly more dispersed clustering than Cyproheptadine but retained relatively constrained dynamics. The standard drug Pregabalin showed a scattered cluster, confirming its limited ability to stabilise receptor structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). The PCA results for the three epilepsy receptors demonstrated that Cyproheptadine is the most effective drug, closely followed by Oxaprozin and Pizotifen, inducing minimal structural fluctuations during MD simulations. In contrast, standard drugs resulted in broader conformational spread, indicating reduced structural constraint and weaker binding-induced stabilisation across all three receptors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInteraction energy\u003c/p\u003e \u003cp\u003eThe interaction energy represents the non-bonding interactions between the receptor and the ligand. Two different non-bonding interaction energies (Lennard-Jones potential and Coulombic interactions) were calculated in our investigation. The analysis of interaction energy revealed the non-bonding behaviours of the drugs with the target protein receptor at certain phases of the entire simulation. Pizotifen showed better and more stable non-bonding interaction energy for all bound complexes than the other drugs, followed by Oxaprozin, Cyproheptadine, and the standard drugs. Compared to the standard drugs, all the selected drugs showed better interaction energy (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For Voltage-gated sodium channel α2 (Nav1.2), the standard drug Carbamazepine showed a total interaction energy of -111.5977 KJ/mol (LJ: -99.8047 KJ/mol; Coul: -11.793 KJ/mol). In comparison, Cyproheptadine showed the lowest interaction energy of -171.111 KJ/mol (LJ: -117.832 KJ/mol; Coul: -53.279 KJ/mol), followed by Pizotifen (Total: -144.0706 KJ/mol; LJ: -122.406 KJ/mol; Coul: -21.6646 KJ/mol) and Oxaprozin (Total: -135.7998 KJ/mol; LJ: -127.217 KJ/mol; Coul: -8.5828 KJ/mol). All the selected drugs showed better interaction energies than the standard drug, Carbamazepine, against Nav1.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Clonazepam binding with GABA receptor α1\u0026ndash;β1 showed a total interaction energy of -56.5546 KJ/mol (LJ: -35.405 KJ/mol; Coul: -21.1496 KJ/mol), while Pizotifen showed the lowest interaction energy (Total: -115.0727; LJ: -58.7435 KJ/mol; Coul: -29.3292 KJ/mol), followed by Oxaprozin (Total: -64.5817 KJ/mol; LJ: -47.8904 KJ/mol; Coul: -16.6913 KJ/mol). In contrast, Cyproheptadine showed higher interaction energy (Total: -55.8563 KJ/mol; LJ: -41.7388 KJ/mol; Coul: -14.1175 KJ/mol) than the standard drug (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Standard drug Pregabalin interacted with its receptor Voltage-gated calcium channel α1G with the interaction energy of -65.51398 KJ/mol (LJ: -62.8288 KJ/mol; Coul: -2.68518 KJ/mol). Cyproheptadine showed the lowest interaction energy of -189.5322 KJ/mol (LJ: -107.54 KJ/mol; Coul: -81.9922 KJ/mol), followed by Pizotifen (Total: -158.4378 KJ/mol; LJ: -117.041 KJ/mol; Coul: -47.3968 KJ/mol) and Oxaprozin (Total: -125.84350 KJ/mol; LJ: -118.768 KJ/mol; Coul: -7.07558 KJ/mol) when bound to Cav3.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Based on the binding interaction analyses, Pizotifen was observed to have strong binding affinity with all three target proteins, followed by Oxaprozin. Cyproheptadine showed a low binding interaction compared to Clonazepam against GABA receptor α1-β1; however, it showed better binding affinity with Nav1.2 and Cav3.1. We predicted that all three potential repurposed drugs would bind strongly to the selected first-line target proteins for epilepsy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction energy (KJ/mol) between selected drugs and epilepsy receptor Nav1.2, GABA receptor alpha1-beta1 and Cav3.1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLennard-Jones Potential (KJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoulombic Interaction (KJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal interactions (KJ/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVoltage-Gated Sodium Channel \u0026ndash; α2 (Nav1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard (Carbamazepine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-99.8047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-111.5977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-117.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-53.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-171.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-127.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.5828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-135.7998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-122.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21.6646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-144.0706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eGABA receptor \u0026ndash; α1 β1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard (Clonazepam)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-35.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21.1496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-56.5546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-41.7388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14.1175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-55.8563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-47.8904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-16.6913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-64.5817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-58.7435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29.3292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-115.0727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVoltage-Gated Calcium Channel \u0026ndash; α1G (Cav3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard (Pregabalin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-62.8288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.68518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-65.51398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-107.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-81.9922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-189.5322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-118.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.07558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-125.84350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-117.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-47.3968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-158.4378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHydrogen bond (H-bond)\u003c/p\u003e \u003cp\u003eH-bond analysis revealed the number of hydrogen bonds formed between the protein and ligand throughout the simulation. Throughout the simulation studies of Nav1.2, the Oxaprozin drug showed an average of 1.342 Hydrogen Bonds, followed by the standard drugs Carbamazepine (0.199), Pizotifen (0.027), and Cyproheptadine (0.007). The MDS study revealed two H-bonds at the beginning of the simulation, which fluctuated to form a maximum of three H-bonds at 22 ns, but a single H-bond was observed to be the predominant interaction throughout the simulation. The standard drug Carbamazepine showed a single H-bond in the beginning, but in the middle, a maximum of two H-bonds were observed, which again reduced to a single H-bond at the end. The non-bonding interaction energy between Nav1.2 and drugs - Pizotifen and Cyproheptadine were observed to be lowest compared to the other drugs, while the number of H-bonds formed between them was substantially low and a maximum of a single H-bond was observed in between the simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eGABA receptor α1-β1showed the highest H-bond formation with Oxaprozin, averaging 1.532 H-bonds throughout the simulation period, followed by standard drug Clonazepam (0.296), Pizotifen (0.155) and Cyproheptadine (0.024). The interaction between GABA receptor α1-β1 and Oxaprozin showed a maximum of three H-bonds, in the beginning and middle of the simulation, which drops to single and double H-bonds at the end. The standard drug Clonazepam showed up to four H-bonds at the start of the simulation, but later on, double and single H-bonds were observed towards the end of the simulation. Pizotifen showed the lowest non-bonding interaction energy towards the GABA receptor α1-β1 but did not show any H-bonds at the beginning of the simulation; however, after 60 ns of simulation, a single H-bond was observed. Cyproheptadine was observed to have weak binding with GABA receptor α1-β1, occasionally showing a single H-bond in between the simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe number of H-bonds between all the drugs and Cav3.1 was lower than that of the other two receptors. Here again, Oxaprozin was observed to show a maximum number of H-bonds, averaging 0.821 H-bonds throughout the simulation, followed by standard drug Pregabalin (0.086), Cyproheptadine (0.086), and Pizotifen (0.021). Oxaprozin showed an average of a single H-bond throughout the simulation, while standard drug Pregabalin did not have any H-bond at the beginning of the simulation, but in the middle of the simulation, a single H-bond was observed. A similar H-bond pattern was also observed in Pizotifen, but Cyproheptadine showed a single H-bond in the beginning and at the end of the simulation, while no H-bond was observed in the middle of the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThe H-bond analyses revealed that Oxaprozin forms a higher number of H-bonds with all the selected target receptor proteins than the other studied drugs and standard drugs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMolecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA)\u003c/p\u003e \u003cp\u003eThe free binding energies between selected target receptor proteins and drugs were calculated using the MMPBSA method to examine the molecular interactions and stability. The van der Waals energy (VDW), electrostatic energy (EEL) and ΔG GAS (ΔBonds\u0026thinsp;+\u0026thinsp;ΔAngle\u0026thinsp;+\u0026thinsp;ΔDihedral\u0026thinsp;+\u0026thinsp;ΔVDW\u0026thinsp;+\u0026thinsp;ΔEEL) play a significant role in the binding of ligands to the protein, while ΔG SOLV (ΔEGB\u0026thinsp;+\u0026thinsp;ΔESURF) appeared to have an adverse effect on the total binding energy. The VGSC\u0026ndash;α2 (Nav1.2) interaction with studied drugs showed lower free binding energy (ΔG Total) with Oxaprozin (-23.98 kcal/mol), followed by standard drug Carbamazepine (-18.10 kcal/mol), Pizotifen (-17.78 kcal/mol), and Cyproheptadine (-16.35 kcal/mol). The ΔEEL and ΔG-GAS were observed to be lowest in the case of Cyproheptadine, followed by Oxaprozin, but ΔVDW and ΔG-SOLV were observed to be lowest in the case of Oxaprozin (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In case of GABA receptor α1 β1, Oxaprozin showed the lowest ΔG Total (-13.40 kcal/mol), followed by standard drug Clonazepam (-12.65 kcal/mol), Pizotifen (-9.32 kcal/mol) and Cyproheptadine (-3.41 kcal/mol). Here, ΔEEL and ΔG-GAS were observed to be lowest in the case of Clonazepam, while ΔVDW and ΔG-SOLV were observed to be lowest for Oxaprozin interaction with GABA receptor α1 β1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The interaction between VGCC \u0026ndash; α1G (Cav3.1) and drugs showed Oxaprozin having lowest free binding energy of -22.94 kcal/mol, followed by Pizotifen (-17.93 kcal/mol), Cyproheptadine (-13.13 kcal/mol) while the standard drug Pregabalin showed highest free binding energy of -9.64 kcal/mol, representing weak binding affinity of standard drug than other selected drugs for Cav3.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For all three selected first-line target proteins, Oxaprozin showed the best binding affinity, followed by Pizotifen and Cyproheptadine.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe free binding energy (kcal/mol) between selected epilepsy receptor proteins and drugs using MMPBSA analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOISSON BOLTZMANN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔVDW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔEEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔG GAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔG SOLV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔG Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eVoltage-Gated Sodium Channel \u0026ndash; α2 (Nav1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard (Carbamazepine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-30.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-18.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-32.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-170.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-203.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-16.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-33.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-153.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-187.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-23.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-26.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-33.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eGABA receptor \u0026ndash; α1 β1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard (Clonazepam)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-19.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-49.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-68.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-24.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-23.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-23.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-46.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-30.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eVoltage-Gated Calcium Channel \u0026ndash; α1G (Cav3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard (Pregabalin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproheptadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-30.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-204.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-234.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxaprozin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-32.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-150.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-183.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePizotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-30.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-40.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe molecular dynamics study revealed that all three selected drugs exhibited stable and good binding affinity against the target receptor proteins. All three drugs outperformed the standard drugs for all the receptors. Oxaprozin showed a more stable and stronger binding affinity among the three selected drugs, followed by Pizotifen and Cyproheptadine.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA comprehensive \u003cem\u003ein-silico\u003c/em\u003e drug repositioning approach was applied to screen the whole of the DrugBank database molecules for identifying new first-line therapeutic option for epilepsy. A total of 2769 FDA-approved drugs were primarily screened for blood-brain permeability and structural similarity with currently marketed AEDs. The Voltage Gated Sodium channel \u0026ndash; α2, GABA receptor α1 β1 and Voltage gated calcium channel \u0026ndash; α1G, were selected as therapeutic target proteins for the molecular docking studies using Carbamazepine, Clonazepam and Pregabalin, as standard reference drugs, respectively. Only 46 marketed drugs, in common, showed higher binding affinities than the selected standard drugs and the binding pocket analyses and text mining studies narrowed our findings to three drug compounds, namely - Oxaprozin, Pizotifen and Cyproheptadine, as potential candidates for drug repurposing for first-line treatment of epilepsy. The molecular dynamic simulation study revealed that all three selected drug compounds have a stable and strong binding with all three studied therapeutic target proteins. Oxaprozin was observed to show the strongest binding affinity and stability, followed by Pizotifen and Cyproheptadine, and invites further pre-clinical and clinical investigations for their application as a first-line therapeutic option for epilepsy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAEDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntiepileptic Drugs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBBB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Brain Barrier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGABA-R\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGamma-aminobutyric acid receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadius of Gyration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Fluctuations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSASA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSolvent Accessible Surface Area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVGCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVoltage-gated calcium channel\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVoltage-gated sodium channel.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePK conceptualised, investigated, validated, supervised and wrote the manuscript. VK and RC performed the methodology and validation. VS Conceptualised, validated, and supervised, and AK conceptualised, supervised, and validated the results and manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the CCS Haryana Agricultural University, Hisar and DBT-BUILDER, M. D. University, Rohtak, for providing infrastructure facilities for the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKlein, P. et al. Repurposed molecules for antiepileptogenesis: Missing an opportunity to prevent epilepsy? \u003cem\u003eEpilepsia\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 359\u0026ndash;386 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThijs, R. D., Surges, R., O\u0026rsquo;Brien, T. J. \u0026amp; Sander, J. W. Epilepsy in adults. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e393\u003c/b\u003e, 689\u0026ndash;701 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLossin, C., Wang, D. W., Rhodes, T. H., Vanoye, C. G. \u0026amp; George, A. L. Molecular basis of an inherited epilepsy. \u003cem\u003eNeuron\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 877\u0026ndash;884 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnegers, J. F. \u0026amp; Rocca, W. A. Elsevier,. Causes of epilepsy: Contributions of the rochester epidemiology project. in \u003cem\u003eMayo Clinic Proceedings\u003c/em\u003e vol. 71 570\u0026ndash;575 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalindo-Mendez, B., Mayor, L. C., Velandia-Hurtado, F. \u0026amp; Calderon-Ospina, C. Failure of antiepileptic drugs in controlling seizures in epilepsy: What do we do next? \u003cem\u003eEpilepsy Behav. Case Rep.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 6\u0026ndash;8 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Z. et al. In silico drug repositioning-what we need to know. \u003cem\u003eDrug Discov Today\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 110\u0026ndash;115 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburn, T. T. \u0026amp; Thor, K. B. Drug repositioning: Identifying and developing new uses for existing drugs. \u003cem\u003eNat. Rev. Drug Discov\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 673\u0026ndash;683 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJourdan, J. P., Bureau, R., Rochais, C. \u0026amp; Dallemagne, P. Drug repositioning: a brief overview. \u003cem\u003eJ. Pharm. Pharmacol.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 1145\u0026ndash;1151 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolete, P. et al. Lorcaserin therapy for severe epilepsy of childhood onset. \u003cem\u003eNeurology\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 837\u0026ndash;839 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWishart, D. S. et al. DrugBank 5.0: A major update to the DrugBank database for 2018. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e, D1074\u0026ndash;D1082 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, P. et al. A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction. \u003cem\u003eCNS Neurol. Disord Drug Targets\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 1\u0026ndash;9 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, F. et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. \u003cem\u003eJ. Chem. Inf. Model.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 3099\u0026ndash;3105 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaker, B. et al. LightBBB: Computational prediction model of blood-brain-barrier penetration based on LightGBM. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 1135\u0026ndash;1139 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHurle, M. R. et al. Computational drug repositioning: From data to therapeutics. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e, 335\u0026ndash;341 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkins, S., Williams, A. J., Krasowski, M. D. \u0026amp; Freundlich, J. S. In silico repositioning of approved drugs for rare and neglected diseases. \u003cem\u003eDrug Discov Today\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 298\u0026ndash;310 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, P., Sheokand, D., Saini, V. \u0026amp; Kumar, A. In-silico Identification and Analysis of Hub Proteins for Designing Novel First-line Anti-seizure Medications. \u003cem\u003eLett. Drug Des. Discov\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 662\u0026ndash;673 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBackman, T. W. H., Cao, Y. \u0026amp; Girke, T. ChemMine tools: An online service for analyzing and clustering small molecules. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, W486\u0026ndash;W491 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, Y., Charisi, A., Cheng, L. C., Jiang, T. \u0026amp; Girke, T. ChemmineR: A compound mining framework for R. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 1733\u0026ndash;1734 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilvestro, S., Mammana, S., Cavalli, E., Bramanti, P. \u0026amp; Mazzon, E. Use of cannabidiol in the treatment of epilepsy: Efficacy and security in clinical trials. \u003cem\u003eMolecules\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 1459 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeydoun, A. et al. Current role of carbamazepine and oxcarbazepine in the management of epilepsy. \u003cem\u003eSeizure\u003c/em\u003e \u003cb\u003e83\u003c/b\u003e, 251\u0026ndash;263 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDokkedal-Silva, V. et al. Indications, Side Effects, and Potential for Nonmedical Use. \u003cem\u003eHarv. Rev. Psychiatry\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e, 279\u0026ndash;289 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHebeisen, S. et al. Eslicarbazepine and the enhancement of slow inactivation of voltage-gated sodium channels: A comparison with carbamazepine, oxcarbazepine and lacosamide. \u003cem\u003eNeuropharmacology\u003c/em\u003e \u003cb\u003e89\u003c/b\u003e, 122\u0026ndash;135 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrigo, F. \u0026amp; Igwe, S. C. Ethosuximide, sodium valproate or lamotrigine for absence seizures in children and adolescents. \u003cem\u003eCochrane Database of Systematic Reviews\u003c/em\u003e (2017). (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkota, M. \u0026amp; Morgan, R. J. Treatment of Generalized Anxiety Disorder with Gabapentin. \u003cem\u003eCase Rep. Psychiatry\u003c/em\u003e. \u003cb\u003e2017\u003c/b\u003e, 1\u0026ndash;4 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogawski, M. A., Tofighy, A., White, H. S., Matagne, A. \u0026amp; Wolff, C. Current understanding of the mechanism of action of the antiepileptic drug lacosamide. \u003cem\u003eEpilepsy Res.\u003c/em\u003e \u003cb\u003e110\u003c/b\u003e, 189\u0026ndash;205 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStockburger, C. et al. A Mitochondrial Role of SV2a Protein in Aging and Alzheimer\u0026rsquo;s Disease: Studies with Levetiracetam. \u003cem\u003eJ. Alzheimer\u0026rsquo;s Disease\u003c/em\u003e. \u003cb\u003e50\u003c/b\u003e, 201\u0026ndash;215 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrauss, G. L. et al. Levetiracetam treatment of idiopathic generalised epilepsy. \u003cem\u003eSeizure\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 617\u0026ndash;620 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMruk, A. L., Garlitz, K. L. \u0026amp; Leung, N. R. Levetiracetam in neonatal seizures: A review. \u003cem\u003eJ. Pediatr. Pharmacol. Ther.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 76\u0026ndash;89 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen, C. C., Ljung, H., Brodtkorb, E. \u0026amp; Reimers, A. Mechanisms underlying aggressive behavior induced by antiepileptic drugs: Focus on topiramate, levetiracetam, and perampanel. \u003cem\u003eBehavioural Neurology\u003c/em\u003e (2018). (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlles, S. R. A., Cain, S. M. \u0026amp; Snutch, T. P. Pregabalin as a Pain Therapeutic: Beyond Calcium Channels. \u003cem\u003eFront. Cell. Neurosci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 83 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManjushree, N., Chakraborty, A., Shashidhar, K. \u0026amp; Narayanaswamy, S. A review of the drug pregabalin. \u003cem\u003eInt J Basic Clin Pharmacol\u003c/em\u003e 601\u0026ndash;605 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18203/2319-2003.ijbcp20150359\u003c/span\u003e\u003cspan address=\"10.18203/2319-2003.ijbcp20150359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerucca, E., Cloyd, J., Critchley, D., Fuseau, E. \u0026amp; Rufinamide Clinical pharmacokinetics and concentration-response relationships in patients with epilepsy. \u003cem\u003eEpilepsia\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 1123\u0026ndash;1141 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiplani, S., Verma, P. K. \u0026amp; Kumar, A. Neuroinformatics analyses reveal GABAt and SSADH as major proteins involved in anticonvulsant activity of valproic acid. \u003cem\u003eBiomed. Pharmacotherapy\u003c/em\u003e. \u003cb\u003e81\u003c/b\u003e, 402\u0026ndash;410 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomoli, M. et al. Valproic Acid and Epilepsy: From Molecular Mechanisms to Clinical Evidences. \u003cem\u003eCurr. Neuropharmacol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 926\u0026ndash;946 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanatta, G. et al. Valproic acid interactions with the NavMs voltage-gated sodium channel. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e116\u003c/b\u003e, 26549\u0026ndash;26554 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuszczki, J. J., Ratnaraj, N., Patsalos, P. N. \u0026amp; Czuczwar, S. J. Isobolographic and behavioral characterizations of interactions between vigabatrin and gabapentin in two experimental models of epilepsy. \u003cem\u003eEur. J. Pharmacol.\u003c/em\u003e \u003cb\u003e595\u003c/b\u003e, 13\u0026ndash;21 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiton, V. Clinical pharmacology and mechanism of action of zonisamide. \u003cem\u003eClin. Neuropharmacol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 230\u0026ndash;240 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnold, K., Bordoli, L., Kopp, J. \u0026amp; Schwede, T. The SWISS-MODEL workspace: A web-based environment for protein structure homology modelling. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 195\u0026ndash;201 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenkert, P., Tosatto, S. C. E. \u0026amp; Schomburg, D. QMEAN: A comprehensive scoring function for model quality assessment. \u003cem\u003eProteins: Struct. Function Genet.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 261\u0026ndash;277 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgnihotry, S., Pathak, R. K., Singh, D. B., Tiwari, A. \u0026amp; Hussain, I. Protein structure prediction. \u003cem\u003eBioinformatics: Methods Appl.\u003c/em\u003e 177\u0026ndash;188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-323-89775-4.00023-7\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-323-89775-4.00023-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenberg, D., L\u0026uuml;thy, R. \u0026amp; Bowie, J. U. VERIFY3D: Assessment of protein models with three-dimensional profiles. \u003cem\u003eMethods Enzymol.\u003c/em\u003e \u003cb\u003e277\u003c/b\u003e, 396\u0026ndash;404 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiederstein, M. \u0026amp; Sippl, M. J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, W407\u0026ndash;W410 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacindoe, G., Mavridis, L., Venkatraman, V., Devignes, M. D. \u0026amp; Ritchie, D. W. HexServer: An FFT-based protein docking server powered by graphics processors. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelano, L. \u0026amp; Warren Pymol: An open-source molecular graphics tool. \u003cem\u003eCCP4 Newsl. protein Crystallogr.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 82\u0026ndash;92 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePettersen, E. F. et al. UCSF Chimera - A visualization system for exploratory research and analysis. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 1605\u0026ndash;1612 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris, G. M. et al. Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 2785\u0026ndash;2791 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, P., Sheokand, D., Grewal, A., Saini, V. \u0026amp; Kumar, A. Clinical side-effects based drug repositioning for anti-epileptic activity. \u003cem\u003eJ. Biomol. Struct. Dyn.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 1443\u0026ndash;1454 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaskowski, R. A., Swindells, M. B. \u0026amp; LigPlot+ Multiple ligand-protein interaction diagrams for drug discovery. \u003cem\u003eJ. Chem. Inf. Model.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 2778\u0026ndash;2786 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraham, M. J. et al. High performance molecular simulations through multi-level parallelism from laptops to supercomputers. \u003cem\u003eSoftwareX\u003c/em\u003e \u003cb\u003e1\u0026ndash;2\u003c/b\u003e, 19\u0026ndash;25 (2015). Gromacs.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, J. et al. CHARMM36m: An improved force field for folded and intrinsically disordered proteins. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 71\u0026ndash;73 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVald\u0026eacute;s-Tresanco, M. S., Vald\u0026eacute;s-Tresanco, M. E., Valiente, P. A. \u0026amp; Moreno, E. Gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 6281\u0026ndash;6291 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhit, A., Assal, D., Al-Shami, A. S. \u0026amp; Hussein, D. E. E. GABAA receptors: structure, function, pharmacology, and related disorders. \u003cem\u003eJournal of Genetic Engineering and Biotechnology\u003c/em\u003e 19, 1\u0026ndash;15 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasiulis, S. et al. GABAA receptor signalling mechanisms revealed by structural pharmacology. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e565\u003c/b\u003e, 454\u0026ndash;459 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSigel, E. \u0026amp; Steinmann, M. E. Structure, function, and modulation of GABAA receptors. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e287\u003c/b\u003e, 40224\u0026ndash;40231 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdelsayed, M. \u0026amp; Sokolov, S. Voltage-gated sodium channels: Pharmaceutical targets via anticonvulsants to treat epileptic syndromes. \u003cem\u003eChannels\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 146\u0026ndash;152 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDocherty, R. J. \u0026amp; Farmer, C. E. The pharmacology of voltage-gated sodium channels in sensory neurones. \u003cem\u003eHandb. Exp. Pharmacol.\u003c/em\u003e \u003cb\u003e194\u003c/b\u003e, 519\u0026ndash;561 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipkind, G. M. \u0026amp; Fozzard, H. A. Molecular model of anticonvulsant drug binding to the voltage-gated sodium channel inner pore. \u003cem\u003eMol. Pharmacol.\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e, 631\u0026ndash;638 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKash, T. L., Jenkins, A., Kelley, J. C., Trudell, J. R. \u0026amp; Harrison, N. L. Coupling of agonist binding to channel gating in the GABAA receptor. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e421\u003c/b\u003e, 272\u0026ndash;275 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewell, J. G. \u0026amp; Czajkowski, C. The GABAA receptor α1 subunit Pro174-Asp191 segment is involved in GABA binding and channel gating. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e278\u003c/b\u003e, 13166\u0026ndash;13172 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunningham, K. L. \u0026amp; Littleton, J. T. Mechanisms controlling the trafficking, localization, and abundance of presynaptic Ca2\u0026thinsp;+\u0026thinsp;channels. \u003cem\u003eFront. Mol. Neurosci.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 767 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatelaris, C. Comparative effects of loratadine and azatadine in the treatment of seasonal allergic rhinitis. \u003cem\u003eAsian Pac. J. Allergy Immunol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 103\u0026ndash;107 (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelch, M. J., Meltzer, E. O., Estelle, F. \u0026amp; Simons, R. H1-antihistamines and the central nervous system. \u003cem\u003eHistamine and H1-Antihistamines in Allergic Disease\u003c/em\u003e. \u003cem\u003eSecond Ed. Revis. Expanded\u003c/em\u003e. 337\u0026ndash;388. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/9780203910375-16\u003c/span\u003e\u003cspan address=\"10.3109/9780203910375-16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill, T. et al. Antidepressant use and risk of epilepsy and seizures in people aged 20 to 64 years: Cohort study using a primary care database. \u003cem\u003eBMC Psychiatry\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertrand, V. et al. Safety of Cyproheptadine, an Orexigenic Drug. Analysis of the French National Pharmacovigilance Data-Base and Systematic Review. \u003cem\u003eFront. Pediatr.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 712413 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalpaklioglu, F. \u0026amp; Baccioglu, A. Efficacy and Safety of H1-Antihistamines: An Update. \u003cem\u003eAntiinflamm Antiallergy Agents Med. Chem.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 230\u0026ndash;237 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkler, J. L., Skovira, J. W. \u0026amp; Kan, R. K. Anticonvulsant efficacy of antihistamine cyproheptadine in rats exposed to the chemical warfare nerve agent soman. \u003cem\u003eNeurotoxicology\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 153\u0026ndash;160 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaumann, P. A. \u0026amp; Maitre, L. Neurobiochemical Aspects of Maprotiline (Ludiomil \u0026reg;) Action. \u003cem\u003eJ. Int. Med. Res.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 391\u0026ndash;400 (1979).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbaccia, M. L., Ravizza, L., Costa, E. \u0026amp; Maprotiline An antidepressant with an unusual pharmacological profile. \u003cem\u003eJ. Pharmacol. Exp. Ther.\u003c/em\u003e \u003cb\u003e236\u003c/b\u003e, 307\u0026ndash;312 (1986). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/3003338/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/3003338/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohannessen Landmark, C., Henning, O. \u0026amp; Johannessen, S. I. Proconvulsant effects of antidepressants \u0026mdash; What is the current evidence? \u003cem\u003eEpilepsy Behav.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 287\u0026ndash;291 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman, B. F. \u0026amp; Wachsmuth, R. Maprotiline and seizures. \u003cem\u003eJ. Clin. Psychiatry\u003c/em\u003e. \u003cb\u003e43\u003c/b\u003e, 117\u0026ndash;118 (1982). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/6801027/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/6801027/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerry, S., Wiffen, P. J., Aldington, D. \u0026amp; Moore, R. A. Nortriptyline for neuropathic pain in adults. \u003cem\u003eCochrane Database of Systematic Reviews\u003c/em\u003e (2015). (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReinert, J. P., Veronin, M. A. \u0026amp; Medina, C. Tricyclic Antidepressants in Nociceptive and Neuropathic Pain: A Review of Their Analgesic Properties in Combination With Opioids. \u003cem\u003eJ. Pharm. Technol.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 35\u0026ndash;40 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiffen, P. J., Moore, R. A., Aldington, D. \u0026amp; Derry, S. Nortriptyline for neuropathic pain in adults. \u003cem\u003eCochrane Database of Systematic Reviews\u003c/em\u003e (2014). (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuchins, D. J., Oliver, A. P. \u0026amp; Wyatt, R. J. Seizures with Antidepressants: An In Vitro Technique to Assess Relative Risk. \u003cem\u003eEpilepsia\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 25\u0026ndash;32 (1984).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipper, B., Bell, A. \u0026amp; Gaynor, B. Recurrent hypotension immediately after seizures in nortriptyline overdose. \u003cem\u003eAm. J. Emerg. Med.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 452\u0026ndash;453 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeesa, J. P. et al. Oxaprozin prodrug as safer nonsteroidal anti-inflammatory drug: Synthesis and pharmacological evaluation. \u003cem\u003eArch. Pharm. (Weinheim)\u003c/em\u003e. \u003cb\u003e351\u003c/b\u003e, 1700256 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhatami, P., Mirazi, N., Khosravi, M. \u0026amp; Bananej, M. Nonsteroidal Anti-inflammatory Drug Oxaprozin is Beneficial Against Seizure-induced Memory Impairment in an Experimental Model of Seizures in Rats: Impact On Oxidative Stress and Nrf2/HO-1 Signaling Pathway. \u003cem\u003eJ. Mol. Neurosci.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 880\u0026ndash;887 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobbins, M. S. \u0026amp; Burch, R. Preventive Migraine Treatment. \u003cem\u003eCONTINUUM Lifelong Learn. Neurol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 613\u0026ndash;632 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTronvik, E., Giri, S. \u0026amp; Young, W. Preventive treatment of migraine: Non-specific oral agents. \u003cem\u003eHandb. Clin. Neurol.\u003c/em\u003e \u003cb\u003e199\u003c/b\u003e, 67\u0026ndash;86 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatista, F. L. A. et al. Anticonvulsant and anxiolytic-like potential of the essential oil from the Ocimum basilicum Linn leaves and its major constituent estragole on adult zebrafish (Danio rerio). \u003cem\u003eNeurochem Int.\u003c/em\u003e \u003cb\u003e178\u003c/b\u003e, 105796 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AED, Drug repurposing/repositioning, Epilepsy, Molecular docking, Molecular dynamic simulations, Seizures","lastPublishedDoi":"10.21203/rs.3.rs-6851614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6851614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpilepsy affects millions of people globally, with approximately one-third of patients experiencing drug-resistant seizures. Developing new anti-epileptic drugs is time-intensive and costly, prompting interest in computational drug repositioning strategies. Here we report about a comprehensive drug repositioning approach to identify the first-line therapeutic option(s) for epileptic seizures. All approved drugs from the DrugBank database were screened for their anti-epileptic properties that involved their blood brain permeability prediction and clustering them for structural similarity with the marketed anti-epilepsy drugs. The screened drugs were subjected to molecular docking against previously identified therapeutic target proteins (Voltage-Gated Sodium Channel α2; GABA receptor α1-β1; and Voltage-Gated Calcium Channel α1G), A total of 46 drugs showed better binding affinity than the respective standard drugs - Carbamazepine, Clonazepam and Pregabalin for the selected target proteins - Voltage-Gated Sodium Channel α2; GABA receptor α1-β1; and Voltage-Gated Calcium Channel α1G, respectively. The binding pocket and literature data mining revealed three drugs, Oxaprozin, Pizotifen, and Cyproheptadine, that bind within the precise binding pocket and have no reported severe side effects related to seizure onset. The molecular dynamic simulation studies showed all three compounds with better and more stable binding interactions against the corresponding drug targets. Oxaprozin, among identified 3 drugs, showed a very stable binding and can be a considered a potential repurposed drug against epilepsy, inviting further pre-clinical trials.\u003c/p\u003e","manuscriptTitle":"Computational drug repositioning approach to predict first-line therapeutics for epilepsy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:47:58","doi":"10.21203/rs.3.rs-6851614/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-14T06:36:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T03:04:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-09T06:57:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T06:21:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T15:31:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T12:46:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T08:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329610072423921609146809041367678954191","date":"2025-06-24T05:25:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154519480569871829226781056223343858069","date":"2025-06-24T05:21:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43120113303477288041473173542131715311","date":"2025-06-24T05:14:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318082268734874961955328595273815866609","date":"2025-06-24T05:05:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2400211239122677817137907178103385974","date":"2025-06-24T04:57:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23691743487112302325159468347573644586","date":"2025-06-24T04:54:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T04:50:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T04:32:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-17T08:36:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-16T06:45:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-09T07:00:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2434f21a-3ea0-4180-990e-3771cc0bb24f","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50535498,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":50535499,"name":"Biological sciences/Computational biology and bioinformatics/Computational neuroscience"},{"id":50535500,"name":"Biological sciences/Computational biology and bioinformatics/Virtual drug screening"}],"tags":[],"updatedAt":"2025-12-22T16:11:51+00:00","versionOfRecord":{"articleIdentity":"rs-6851614","link":"https://doi.org/10.1038/s41598-025-27625-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-16 15:57:53","publishedOnDateReadable":"December 16th, 2025"},"versionCreatedAt":"2025-06-29 14:47:58","video":"","vorDoi":"10.1038/s41598-025-27625-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27625-2","workflowStages":[]},"version":"v1","identity":"rs-6851614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6851614","identity":"rs-6851614","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}