Gamma-aminobutyric acid (GABA) is the brain's primary inhibitory neurotransmitter, and some anti-epileptic drugs work by enhancing its effects. Medications such as Valproic acid and Benzodiazepines increase GABA activity, which inhibits neural firing and suppresses seizure activity. In the present study twelve Valproic acid derivatives were designed and evaluated for their GABA-A mediated inhibition of neural firing by in-silico methods. All novel designed molecules were evaluated by IvLCB: In-vitro like computational bioassay and SwissDock. These molecules were also evaluated for their in-silico biological activity spectrum & predictive toxicological study by MolpredictX and predictive bioactivity score by Molinspiration. As per the analysis performed it was found that designed molecule VAL1 shows strong binding ability to PDB ID: 4COF with 3 hydrogen bonds and binding affinity of -4.4 kcal/mol.
Anti-epileptic drugs (AEDs), also known as anticonvulsants, are a class of medications used to manage seizures in individuals with epilepsy. Epilepsy is a chronic neurological disorder that leads to recurrent, spontaneous seizures caused by abnormal electrical activity in the brain. AEDs are fundamental in controlling these seizures, reducing their frequency and intensity, and allowing individuals with epilepsy to lead more normal lives.
Seizures, which are the hallmark of epilepsy, can vary in nature. These include generalized tonic-clonic seizures, which affect the entire brain, focal (partial) seizures that begin in one area of the brain, and absence seizures, which are brief episodes of impaired awareness. The purpose of AED therapy is to control or prevent these seizures while minimizing adverse effects and improving the overall quality of life for patients.
1.1. History of Anti-Epileptic DrugsThe history of epilepsy treatments stretches back thousands of years, with early civilizations like the Egyptians and Greeks attributing seizures to divine or supernatural forces. Treatments in ancient times were largely symbolic or based on herbal remedies, with little understanding of the neurological mechanisms behind epilepsy.
The first major breakthrough in modern epilepsy treatment occurred in the 19th century with the use of bromides, a class of compounds that helped control seizures, although their use was limited due to toxicity and side effects like sedation.
The true development of modern AEDs began in the early 20th century. One of the first and most significant advances was the introduction of phenytoin (Dilantin) in the 1930s, which provided more effective seizure control without the extreme side effects seen with earlier treatments. This marked the beginning of a new era in epilepsy treatment, with subsequent generations of AEDs offering greater effectiveness and fewer side effects.
Since then, numerous AEDs have been developed, including carbamazepine, valproic acid, lamotrigine, and levetiracetam, expanding treatment options and providing personalized approaches to seizure management 1.
1.2. Mechanisms of Action of Anti-Epileptic DrugsAEDs work primarily by influencing the electrical activity of neurons, preventing or reducing the excessive firing that leads to seizures. Seizures are caused by an imbalance between excitatory and inhibitory signals in the brain, and most AEDs target these mechanisms to restore normal brain function 2.
Many AEDs function by altering the flow of ions (sodium, potassium, calcium) across neuronal membranes. For example, phenytoin, carbamazepine, and lamotrigine block sodium channels, which stabilizes the neuronal membranes and reduces the likelihood of seizure activity. This helps prevent neurons from firing too rapidly and uncontrollably 3.
Gamma-aminobutyric acid (GABA) is the brain's primary inhibitory neurotransmitter, and some AEDs work by enhancing its effects. Medications such as valproic acid and benzodiazepines (e.g., diazepam) increase GABA activity, which inhibits neural firing and suppresses seizure activity 4.
Glutamate is the main excitatory neurotransmitter in the brain. Some AEDs, such as topiramate and felbamate, reduce the action of glutamate, helping to reduce the overstimulation of neurons that leads to seizures 4.
Certain AEDs, like ethosuximide, target calcium channels involved in the generation of seizures, especially in conditions like absence seizures. By blocking specific calcium channels, these drugs help prevent the abnormal electrical discharges that characterize these types of seizures 5.
1.3. Treatment of Epilepsy with Anti-Epileptic DrugsThe treatment of epilepsy with AEDs is tailored to the specific type of seizures and the individual patient. The goal is to provide effective seizure control while minimizing side effects. The selection of an AED depends on factors such as the type of epilepsy, the patient's age, medical history, and any other coexisting conditions 7.
The first-choice AEDs vary based on the type of seizures a person experiences. For generalized tonic-clonic seizures, drugs like valproic acid, amotrigine, and vetiracetam are commonly prescribed. For focal seizures, carbamazepine, phenytoin, and oxcarbazepine are often preferred 8.
If first-line medications do not adequately control seizures, second-line drugs may be considered. These include topiramate, gabapentin, and pregabalin. These drugs can be effective for patients who do not respond to initial therapies or for those who experience intolerable side effects from first-line drugs 9.
In cases where seizures are not fully controlled with a single AED, combination therapy may be necessary. This involves the use of two or more AEDs, which requires careful monitoring to manage potential drug interactions and avoid excessive side effects 10.
The choice of AED must also consider factors like pregnancy, age, and other health conditions. For example, lamotrigine is often chosen as a safer option during pregnancy compared to other drugs, but it still requires careful monitoring 11.
1.4. Side Effects and Complications of Anti-Epileptic DrugsWhile AEDs are essential for controlling seizures, they can cause a variety of side effects. These vary depending on the specific drug and the individual’s response. Some side effects can be mild, while others may require adjustments to the treatment plan 12.
Many AEDs, particularly older ones, can cause drowsiness, dizziness, and cognitive issues like memory problems or difficulty concentrating. Drugs such as phenytoin and phenobarbital are more likely to induce sedation and impair daily functioning.
Nausea, vomiting, and appetite changes are common side effects of AEDs. For example, valproic acid may lead to weight gain, while topiramate can cause weight loss.
Certain AEDs, like carbamazepine and phenytoin, can cause blood-related issues, such as a reduction in white blood cell count (leukopenia) or platelets (thrombocytopenia). Patients on these drugs require regular blood tests to monitor for these complications.
Some AEDs, especially lamotrigine and carbamazepine, can cause rashes, which, in rare cases, may progress to serious allergic reactions such as Stevens-Johnson syndrome. Any unusual skin reactions should be promptly addressed 13.
Several AEDs, particularly valproic acid, are known to increase the risk of birth defects when taken during pregnancy. Therefore, women of childbearing age must carefully plan their treatment, and alternatives may be considered during pregnancy.
AEDs can interact with other medications, altering their effects. For instance, phenytoin and carbamazepine can induce liver enzymes that accelerate the breakdown of other drugs, which might reduce their effectiveness. Monitoring and adjustments are necessary to avoid these interactions 14.
1.5. Common Anti-Epileptic DrugOne of the oldest and most widely used AEDs, phenytoin is effective for both generalized tonic-clonic and focal seizures. It stabilizes sodium channels, preventing abnormal neural firing. However, it has a narrow therapeutic range and requires careful dosing and regular blood monitoring.
This AED is commonly used for focal seizures and generalized tonic-clonic seizures. It also works by stabilizing sodium channels, but like phenytoin, it can interact with other drugs and may require regular blood tests.
Valproic acid is a broad-spectrum AED that can treat a wide range of seizure types, including generalized tonic-clonic and absence seizures. While it is highly effective, it has significant side effects, including potential weight gain and teratogenicity.
Lamotrigine is effective for both generalized and focal seizures and is associated with a lower risk of side effects compared to older AEDs. It is often used as both a first-line and adjunctive therapy.
This AED is favored for its favorable side-effect profile and low likelihood of drug interactions. It can treat a variety of seizure types and is commonly used in combination with other AEDs.
Topiramate is another broad-spectrum AED that is effective for both focal and generalized seizures. It is associated with weight loss as a side effect, which may be beneficial in some patients but cause concerns in others 15, 16.
1.6. In-silico StudyAn in-silico study refers to research that utilizes computational models and simulations to investigate scientific phenomena.
These studies typically involve the use of software tools to analyze data, predict biological or chemical interactions, or simulate complex systems, providing valuable insights without the need for physical experimentation 20, 21.
1.7. DockingDocking in drug design is a computational approach that helps predict how a small molecule, such as a potential drug, interacts with a target protein or receptor. This method simulates the binding process to determine the most favorable configurations for the molecule's attachment. It takes into account factors like the shape compatibility, electrostatic interactions, and hydrophobic effects to assess how strongly and specifically a molecule may bind to its target 17.
In drug discovery, docking plays an essential role during the initial phases, helping researchers identify promising lead compounds while minimizing the need for costly experimental procedures. It enables the virtual screening of large compound libraries, examining millions of molecules for their potential to interact with the target. This approach not only conserves time and resources but also provides valuable insights into the molecular dynamics between the drug and its target, aiding the further refinement of drugs to improve their effectiveness and safety 18, 19.
Identification of pharmacophore group was done from the structure of well-known anti-epileptics and then all the molecules were designed by keeping this pharmacophoric concept in mind. All the novel designed structures are given below in Figure 1.
In-silico studies are refers to research or experiments conducted by using computer simulations, computational software and models, or data analysis techniques to investigate biological, chemical, or physical phenomenon. The term "in-Silico" comes from the Latin word "silicon," referencing the silicon-based computers used to perform these studies. In-silico studies often involve the use of software tools, algorithms, and databases to model complex systems, predict outcomes, or analyze large datasets that would be difficult or costly to examine in a laboratory setting 20.
A descriptor calculation has been performed in order to observe designed compounds with its drug ability property.
Lipinski rule of five is used to differentiate drug like and non-drug like molecules. It predicts the probability of the two like molecules.
• Molecular mass less than 500Dalton.
• High lipophilicity (AlogP less than 5)
• Less than 5 hydrogen bond donors (nHBD)
• Less than 10 hydrogen bond acceptors (nHBA)
• Molar refractivity (MR) should be between 40-130
• Topological polar surface area (TPSA) should be less than 140
Lipinski rule of five has been performed by the online website named as ADME Calculator which required a smile structure of the compound 20.
Computational Bioassay was performed by the Computational Bioassays for Biochemical Experiments (CBBE) accessed via https:// assay. smallmoles.com/cbbeapps/, In-vitro like computational bioassay (IvLCB); a research product of CBBE (Computational Biology for Biochemical Experiments; www.smallmoles.com) which is a web based software that is designed as a tool for evaluating the newly designed derivatives. 20.
“Predictive Toxicology” was mainly focusing on in-silico approaches and applied almost synonymously to computational toxicology it has later been extended to describe models and assays complementary or as replacement to the classical descriptive in vivo toxicology.
Toxicology forms the backbone of the chemical next-generation risk assessment (NGRA), which integrates NAMs to assure human safety without animal testing.
Bioactivity has been performed by the online tool molpredictx (https://www.molpredictx.ufpb.br/home/) which predict the biological activity of the compounds 21.
Bioactivity score of the compound has been calculated by the Web Tool of Cheminformatics Community which is https://www.molinspiration.com/cgi/properties 21.
Docking was performed with the online tool Swissdock. It is virtual online tool for computational drug discovery that can be used to screen libraries of compounds against potential drug targets. It enables medicinal chemists to run virtual screening form any platform. In this tool we directly paste the smile structure of the drug and the PDB Id of the proteins. This tool directly fetches the data from the protein data bank. It helps users in every steps of this process from data preparation to job submission and analysis of the results 18, 19.
All the designed compounds are given in the figure 1.
3.1. Descriptor of Lipinski Rule of FiveA descriptor calculation has been performed in order to observe designed compounds with its drug ability property.
Lipinski rule of five is used to differentiate drug like and non-drug like molecules. It predicts the probability of the two like molecules.
None of the designed molecules are found to be Lipinski failure. (Table 1) Hence, it implies that all the 12 designed compounds have the drug ability properties/drug likeness properties 20.
3.2. Computational Bioassay by IvLCB (In-vitro like Computational Bioassay)A computational evaluation has been performed by licensed subscription of IvLCB (In-vitro like computational bioassay) which estimates the comparison of possible % inhibition of novel designed molecule at predefined concentration gradient as we do in in-vitro experiments (Table 2). It also shows relation between % inhibition variations on the ground of normalized concentration gradient.
It also predicts activity representative of designed molecules in terms of Q-Score & C-Score independently for Query and Control compound along with Activity Pattern. Among all designed molecules, molecule VAL1 is found with best Q-score of 4.71 which is closest to control drug. As far as its activity pattern is concern IvLCB shows High activity pattern which supports the molecule for its activity.
It gives an idea about how our current query compound is close to predefined standard drugs in the form of regression plot (Figure 2). Our designed molecules keep floating in graph very close to Valproic acid which again supports the designed molecules for its better and positive activity as anti-convulsant molecule.
It can compare our designed molecule’s activity among themselves, i.e. which one is better among all designed molecules. On comparing all the parameters of IvLCB, designed molecule VAL1 is found to be most active molecule among all 20.
3.3. Predictive Toxicology“Predictive Toxicology” was mainly focusing on in-silico approaches and applied almost synonymously to computational toxicology it has later been extended to describe models and assays complementary or as replacement to the classical descriptive in-vivo toxicology.
Toxicology forms the backbone of the chemical next-generation risk assessment (NGRA), which integrates NAMs to assure human safety without animal testing 21.
All the designed molecules are checked for their predictive probability against Dengue larvicida, C. albicans, Leishmania amazonensis, Alpjis gossupii, Alzheimer-NADPH, Promastigote Ldonovani etc. The most active designed compound VAL1 is found to be active with all mentioned above. A predictive probability of all designed compounds are given from Table no. 3 to 14.
3.4. Predictive Bioactivity ScoreA prediction bioactivity score estimates a compound's potential effectiveness in the human body. It uses computational models to analyze properties like structure, chemical interactions, and bioavailability, predicting how the compound may interact with biological targets. A higher score suggests a greater chance of beneficial effects, helping researchers prioritize promising drug candidates 21. All the designed molecules were passed from bioactivity score predictor for the most important drug targets like GPCR ligands, kinase inhibitors, ion channel modulators, nuclear receptors etc. All predicted scores are given in Table 15.
After analysis it was observed that 7 designed molecules (VAL2, VAL4M, VAL4O, VAL4P, VAL5M, VAL5O & VAL5P) were found to be with highest score for Enzyme inhibition. 4 molecules (VAL1, VAL6M, VAL6O & VAL6P) were found to be with higher score as protease inhibitor. 1 molecule (VAL3) was found to be with higher score as ion channel modulator.
3.5. Descriptor CalculationSome drug design descriptors were also calculated for general idea of designed molecules which are given in Table 16. All the novel designed compounds were found within the prescribed range.
In drug discovery, docking plays an essential role during the initial phases, helping researchers identify promising lead compounds while minimizing the need for costly experimental procedures. It enables the virtual screening of large compound libraries, examining millions of molecules for their potential to interact with the target. This approach not only conserves time and resources but also provides valuable insights into the molecular dynamics between the drug and its target, aiding the further refinement of drugs to improve their effectiveness and safety 18, 19.
For performing docking all the receptor have been downloaded from RCSB website with PDB Id 4COFall the designed ligands have been docked with protein (receptor) with online SwissDock tool. (Table 17 & Figure 3)
On docking analysis, designed compound VAL1 has been found to be strongly docked with GABA (A) with 3 hydrogen bonds and binding affinity of: -4.48Kcal/mol. On residue study Lle47, Glu182 and Glu182 were found to be significant. On the account of ligand oxygen atom is significant in binding with donor bonds, whereas significant element in receptor is nitrogen.
On docking analysis, designed compound VAL2 has been found to be docked with GABA (A) with 1 hydrogen bonds and binding affinity of: -4.976Kcal/mol. On residue study Gln185 was found to be significant. On the account of ligand oxygen atom is significant in binding with donor bonds, whereas significant element in receptor is nitrogen.
On docking analysis, designed compound VAL3 has been found to be strongly docked with GABA (A) with 1 hydrogen bonds and binding affinity of: -5.528Kcal/mol. On residue study Val50 was found to be significant. On the account of ligand nitrogen atom is significant in binding with donor bonds, whereas significant element in receptor is oxygen.
On docking analysis, designed compound VAL4M has been found to be docked with GABA (A) with 0 hydrogen bonds and binding affinity of: -6.19Kcal/mol.
On docking analysis, designed compound VAL4O has been found to be docked with GABA (A) with 0 hydrogen bonds and binding affinity of: -5.48Kcal/mol.
On docking analysis, designed compound VAL4P has been found to be strongly docked with GABA (A) with 1hydrogen bonds and binding affinity of: -5.93Kcal/mol. On residue study Lys274 was found to be significant. On the account of ligand oxygen atom is significant in binding with donor bonds, whereas significant element in receptor is nitrogen
On docking analysis, designed compound VAL5M has been found to be docked with GABA (A) with 0 hydrogen bonds and binding affinity of: -5.88Kcal/mol.
On docking analysis, designed compound VAL5O has been found to be docked with GABA (A) with 0 hydrogen bonds and binding affinity of: -5.74Kcal/mol.
On docking analysis, designed compound VAL5P has been found to be docked with GABA (A) with 0 hydrogen bonds and binding affinity of: -5.72Kcal/mol.
On docking analysis, designed compound VAL6M has been found to be strongly docked with GABA (A) with 2 hydrogen bonds and binding affinity of: -6.10Kcal/mol. On residue study Glu52 and Thr271 were found to be significant. On the account of ligand nitrogen atom is significant in binding with acceptor bonds, whereas significant element in receptor is oxygen.
On docking analysis, designed compound VAL6O has been found to be strongly docked with GABA (A) with 2 hydrogen bonds and binding affinity of: -5.91Kcal/mol. On residue study Gln185 and Val50 were found to be significant. On the account of ligand oxygen atom is significant in binding with donor bonds, whereas significant element in receptor is nitrogen.
On docking analysis, designed compound VAL6P has been found to be strongly docked with GABA (A) with 2 hydrogen bonds and binding affinity of: -5.78Kcal/mol. On residue study Thr133 and Thr58 were found to be significant. On the account of ligand nitrogen atom is significant in binding with both type of bonds, whereas significant element in receptor is oxygen 25.
Anti-epileptic drugs have dramatically improved the management of epilepsy, allowing many individuals to achieve better control over their seizures. The development of newer AEDs has expanded treatment options and minimized side effects, allowing for more personalized treatment approaches. Gamma-aminobutyric acid (GABA) is the brain's primary inhibitory neurotransmitter, and some anti-epileptic drugs work by enhancing its effects. All novel designed molecules were evaluated by IvLCB: In-vitro like computational bioassay and SwissDock. These molecules were also evaluated for their in-silico biological activity spectrum & predictive toxicological study by MolpredictX and predictive bioactivity score by Molinspiration. It was found that designed molecule VAL1 shows strong binding ability to PDB ID: 4COF with 3 hydrogen bonds and binding affinity of -4.4 kcal/mol and IvLCB: In-vitro like computational bioassay also predicts this compound as active molecule as anti-convulsant molecule. All the above studies supported the molecules for good and potent anti-convulsant activity but then also a biochemical experimental study is required to confirm the findings.
| [1] | Alison Hitchcock, Frank Vajda, John Craig, Terence J. O’Brien, Arjune Sen, Development of EpiRisk: An online clinical tool for estimating the risk of major congenital malformations in pregnant women treated for epilepsy, Epilepsia Open, 2018, 281-285. | ||
| In article | View Article PubMed | ||
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| In article | View Article PubMed | ||
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| In article | View Article PubMed | ||
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| In article | View Article PubMed | ||
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| In article | View Article PubMed | ||
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| In article | |||
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| In article | View Article PubMed | ||
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| In article | View Article PubMed | ||
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| In article | View Article PubMed | ||
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Published with license by Science and Education Publishing, Copyright © 2025 Muskan Alvi, Vikash Pal, Sahil Kumar, Yash Kumar, Ajeet, Babita Kumar, Shabnam Ain and Qurratul Ain
This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by/4.0/
| [1] | Alison Hitchcock, Frank Vajda, John Craig, Terence J. O’Brien, Arjune Sen, Development of EpiRisk: An online clinical tool for estimating the risk of major congenital malformations in pregnant women treated for epilepsy, Epilepsia Open, 2018, 281-285. | ||
| In article | View Article PubMed | ||
| [2] | Juseop Kang, Yoo-Sin Park, Shin-Hee Kim, Sang-Hyun Kim, and Min-Young Jun, Modern Methods for Analysis of Antiepileptic Drugs in the Biological Fluids for Pharmacokinetics, Bioequivalence and Therapeutic Drug Monitoring, Korean J Physiol Pharmacol, Vol 15 67－81, April, 2011. | ||
| In article | View Article PubMed | ||
| [3] | Robert L. Macdonald and Kevin M. Kelly, Antiepileptic Drug Mechanisms of Action, Epilepsia 1995, S2-S12. | ||
| In article | View Article PubMed | ||
| [4] | Kinga K. Borowicz-Reutt, Effects of Antiarrhythmic Drugs on Antiepileptic Drug Action—A Critical Review of Experimental Findings, MDPI, Poland, 2022. | ||
| In article | View Article PubMed | ||
| [5] | Barbara Miziak, Agnieszka Konarzewska, Marzena Ułamek-Kozioł, Monika Dudra-Jastrz˛ ebska, Ryszard Pluta, Stanisław J. Czuczwar, Anti-Epileptogenic Effects of Antiepileptic Drugs, MDPI, Poland, 2020. | ||
| In article | View Article PubMed | ||
| [6] | Jarogniew J. Luszczki, Third-generation antiepileptic drugs: mechanisms of action, pharmacokinetics and interactions,Pharmacological Reports, Poland, 197-216, 2009. | ||
| In article | View Article PubMed | ||
| [7] | H. Zhou, N. Wang, L. Xu, H.-L. Huang, C.-Y. Yu, Clinical study on anti-epileptic drug with B vitamins for the treatment of epilepsy after stroke, European Review for Medical and Pharmacological Sciences, China, 2017. | ||
| In article | |||
| [8] | Rowena M. A. Packer, Luisa De Risio and Holger A. Volk, Investigating the potential of the anti_epileptic drug imepitoin as a treatment for co-morbid anxiety in dogs with idiopathic epilepsy, BMC Veterinary Research, Hertfordshire, 2017. | ||
| In article | View Article PubMed | ||
| [9] | Emilio Perucca, H. Steve White, Meir Bialer, New GABATargeting Therapies for the Treatment of Seizures and Epilepsy: II. Treatments in Clinical Development, CNS Drugs, august 2023. | ||
| In article | View Article PubMed | ||
| [10] | Sanjay M. Sisodiya, Precision medicine and therapies of the future, Epilepsia, Bucks UK, 2020. | ||
| In article | View Article PubMed | ||
| [11] | Carlos A. M. Guerreiro, Epilepsy: Is there hope?, Indian J Med Res, Brazil, 2016. | ||
| In article | View Article PubMed | ||
| [12] | Enes Akyüz, Betul koklu, Cansu Ozenen, Alina Arulsamy and Mohd. Farooq Shaikh, Elucidating the Potential Side Effects of Current Anti-Seizure Drugs for Epilepsy, Bentham Science Publishers, Turkey, 2021. | ||
| In article | View Article PubMed | ||
| [13] | Ngonidzashe Mutanana, Maria Tsvere, Manase K. Chiweshe, General side effects and challenges associated with anti-epilepsy medication: A review of related literature, AOSIS Publishers, Zimbabwe, 2020. | ||
| In article | View Article PubMed | ||
| [14] | Michele Simonato, Amy R Brooks-Kayal, Jerome Engel Jr, Aristea S Galanopoulou, Frances E Jensen, Solomon L Moshe, et al. The challenge and promise of anti-epileptic therapy development in animal models, USA, 2016. | ||
| In article | |||
| [15] | Asher Ornoy, Boniface Echefu, Maria Becker, Valproic Acid in Pregnancy Revisited: Neurobehavioral, Biochemical and Molecular Changes Affecting the Embryo and Fetus in Humans and in Animals: A Narrative Review, MDPI, Israel, 2023. | ||
| In article | View Article PubMed | ||
| [16] | Sarah J Nevitt, Maria Sudell, Sofia Cividini, Anthony G Marson, Catrin Tudur Smith, Antiepileptic drug monotherapy for epilepsy: a network meta_analysis of individual participant data, Cochrane Library, Liverpool, UK, 2022. | ||
| In article | View Article PubMed | ||
| [17] | Leonardo G. Ferreira, Ricardo N. dos Santos, Glaucius Oliva and Adriano D. Andricopulo, Molecular Docking and Structure-Based Drug Design Strategies, MPDI, Brazil, 2015. | ||
| In article | View Article PubMed | ||
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| In article | View Article PubMed | ||
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