Scroll to:
Prospects of pharmacotranscriptomics in understanding the effects of antiepileptic drugs and searching for new classes of antiepileptic drugs
https://doi.org/10.37489/2588-0527-2025-4-10-17
EDN: FYBPPD
Abstract
Pharmacotranscriptomics is one of the important components of the multiomics approach to evaluating the efficacy and safety of drugs, along with pharmacometabolomics and pharmacogenomics. Pharmacotranscriptomics helps to understand how a patient's gene expression (transcriptome) changes in response to drug exposure (dose, duration of administration, etc.), especially during long-term use. This explains the researchers' interest in the pharmacotranscriptomics of antiepileptic drugs (AEDs), since lifelong AED therapy is required for up to 60-70% of people with epilepsy. This component of pharmacomultiomics can help in understanding the mechanisms of action of antiepileptic drugs, predicting treatment response, and identifying potential drug targets or biomarkers (for example, microRNAs). On the other hand, the prospects of pharmacotranscriptomics in the search for potentially new classes of AEDs are undeniable.
Keywords
For citations:
Shnayder N.A., Bader V.V., Nasyrova R.F. Prospects of pharmacotranscriptomics in understanding the effects of antiepileptic drugs and searching for new classes of antiepileptic drugs. Pharmacogenetics and Pharmacogenomics. 2025;(4):10-17. (In Russ.) https://doi.org/10.37489/2588-0527-2025-4-10-17. EDN: FYBPPD
Introduction
Epilepsy is a genetically and clinically heterogeneous, prevalent, and socially significant disorder affecting all age groups, with approximately 1-2% of the global population diagnosed with the condition [1]. It is characterized by recurrent unprovoked seizures caused by an imbalance between excitation and inhibition in neuronal circuits. This disease requires long-term, and in some cases lifelong, administration of antiepileptic drugs (AEDs) [1]. Chronic AED use, high AED doses, and AED polytherapy are associated with a high risk of adverse drug reactions (ADRs), including teratogenicity [2], neurotoxicity [3], cardiotoxicity [4], metabolic syndrome [5, 6], among others, as well as the development of therapeutic resistance with inadequate seizure control in a significant proportion of patients. Pharmacometabolomics (therapeutic drug monitoring (TDM) of AEDs, gas-liquid chromatography-mass spectrometry (GLC-MS) of active AED metabolites) and pharmacogenomics (pharmacogenetic testing of non-functional polymorphisms in genes encoding key AED metabolism and transport enzymes) are actively developing fields and represent priority areas in personalized neurology [7]. Despite growing researcher interest in the relationship between epigenetic biomarkers and the efficacy or safety of epilepsy pharmacotherapy, which influence the risk of AED-induced ADRs, approaches based on pharmacotranscriptomics [8] remain in their infancy and are not yet applied in routine clinical epileptology practice. The success of the multiomics approach (pharmacometabolomics, pharmacogenomics, and pharmacotranscriptomics) to epilepsy pharmacotherapy will largely depend on the criteria used for selecting methods to analyze AEDs and their active metabolites in biological fluids (plasma, serum, saliva, urine, hair), pharmacogenetic panels for identifying polymorphisms (variants) in candidate genes encoding AED metabolism and transport pathways, and epigenetic biomarkers (primarily microRNAs influencing changes in patient gene expression (transcriptome) in response to AED exposure (dose, duration, etc.), especially during long-term use. Identifying promising epigenetic biomarkers will increase the chances of success in microRNA-based association studies and may ultimately facilitate the development of a new class of drugs for patients with therapeutically resistant epilepsy.
Pharmacotranscriptomics and Epigenetic Biomarkers of Antiepileptic Drugs
Pharmacotranscriptomics is an emerging research field that has only recently begun to develop and holds promise for identifying targets, defining epigenetic biomarkers, and assessing AED efficacy [9] beyond the scope of pharmacogenomics and pharmacometabolomics [10]. Key research directions in the field of AED pharmacotranscriptomics include:
Elucidating AED mechanisms of action:
a) RNA sequencing (RNA-seq) to analyze the entire transcriptome (all RNA molecules) in cells or tissues exposed to AEDs, enabling the determination of which genes are activated or suppressed by AEDs;
b) Investigation of hub genes and their pathways to identify genes that play a central role in AED-induced changes in gene expression and the pathways in which they are involved;Discovering new AEDs:
a) Drug repurposing, where transcriptomic data identify drugs that modify the transcriptomic signature of epilepsy, including achieving seizure freedom; pharmacotranscriptomics-based drug repurposing helps identify new classes of drugs with potential antiepileptic effects;Personalized neurology/epileptology:
a) Predicting response to AEDs (efficacy and safety), where analyzing transcriptome data from epilepsy patients can aid in developing predictive models to determine which AEDs are most likely to be effective for a specific individual and which might lead to clinically significant ADRs (teratogenesis, neurotoxicity, cardiotoxicity, metabolic syndrome);Understanding epilepsy pathophysiology, where transcriptomic studies can reveal molecular pathways involved in the pathogenesis, progression of epilepsy, and development of therapeutic resistance to AEDs, potentially leading to the identification of novel drug targets [11].
The mechanism of action of AEDs is linked to their effects on various molecular targets that selectively reduce neuronal excitability and provide adequate seizure control. First-generation and newer AEDs possess diverse mechanisms of action, which can be broadly categorized into two groups based on their regulatory functions concerning voltage-gated ion channels and synaptic excitability [12]. However, recent studies convincingly demonstrate that AEDs and their active metabolites can exert regulatory effects on gene expression as epigenetic modifiers [13].
Epigenetic modifications and regulators represent potential molecular elements that control relevant physiological and pathological processes, thereby influencing the natural course of epilepsy and the response to AEDs in a given individual. These epigenetic modulators can be used as biomarkers of AED efficacy and safety because they offer several advantages and provide information about gene function, thus explaining differences between endophenotypes of individual patients with epilepsy. Pharmacotranscriptomic technologies employed for analyzing epigenetic biomarkers are being developed and refined, becoming simpler and more accessible for use [9, 11, 13].
In 2017, García-Giménez et al. [8] proposed a modified definition of an epigenetic biomarker as any epigenetic mark or altered epigenetic mechanism that: 1) is stable and reproducible upon sample processing and can be measured in biological fluids or primary tissue specimen types (fresh, frozen, and formalin-fixed paraffin-embedded); 2) predicts the future risk of disease development (risk); 3) identifies disease (diagnosis); 4) reveals information about the natural history of the disease; 5) predicts disease outcome (prognosis); 6) responds to therapy (prediction); 6) monitors response to therapy or drugs (therapy monitoring); 7) allows simultaneous diagnosis and targeted therapy (theragnosis).
The advantages of pharmacotranscriptomics over pharmacometabolomics and pharmacogenomics in epileptology are explained by the fact that epigenetic biomarkers: 1) can provide crucial information about gene function in individual cell types, filling clinical gaps and showing the extent to which specific genetic programs are controlled; 2) can incorporate environmental information and may include data on the lifestyle of the patient with epilepsy, thereby explaining, for example, how nutrition and metabolic factors affect patient health and disease course; 3) can provide information about the natural history of epilepsy, acting as true bioarchives; 4) a wide range of epigenetic biomarkers (particularly microRNAs and post-translational histone modifications) are extremely stable in fluids (e.g., plasma, serum, urine, saliva, etc.) and most are also highly stable in major types of tissue specimens (e.g., fresh and frozen tissues, dried blood spots (Guthrie cards), paraffin-embedded tissue samples, etc.); 5) microRNAs are very stable molecules even in low-quality samples; 6) can provide valuable information for disease diagnosis, prognostication, and treatment monitoring; 7) can enable simultaneous diagnosis and targeted therapy, thus facilitating theranosis [8].
Significant epigenetic biomarkers include DNA methylation, histone protein modifications, and the functions of non-coding RNAs.
Cell-free circulating DNA (cfDNA) has been proposed as an epigenetic biomarker in various pathological conditions [8, 9] and could potentially be used in epileptology. The amount of cfDNA in healthy individuals is typically very low (less than 5 ng/mL in plasma) and can increase 8-10 fold in individuals with some forms of epilepsy. Limitations for the clinical use of cfDNA include challenges in its isolation from biological fluids and quantification due to the small amount and fragmented nature of cfDNA in available biospecimens. Furthermore, the extraction and purification step is critical for developing reproducible, standardized cfDNA isolation methods, including quality control to measure extraction efficiency, fragment size bias, and yield [14].
Histone proteins. The use of histone proteins as disease epigenetic biomarkers is based on analyzing post-translational histone modifications and their variations in the disease context and investigating histones in the extracellular environment (in blood). In the latter case, analyzing post-translational modifications of histone proteins is a valuable tool for diagnosing and/or predicting disease progression [8, 9]. Most kits are designed for rapid isolation of core histone proteins through simple procedures, providing acceptable yield, although they do not preclude the simultaneous isolation of other nuclear proteins. Their primary application is functional analysis performed via western blotting. However, using histone proteins in epigenetic regulation research is far from their application as epigenetic biomarkers of clinical significance, for instance, in assessing AED efficacy and safety. Limitations also include challenges underlying histone protein isolation methods with contamination by other nuclear proteins and components. Most kits and purification methods require high cell densities, which can be obtained through tissue homogenization or isolation of blood cells. There are currently few available methods for purifying histone proteins from biological fluids [11, 13].
It is known that histone modifications influence transcription and other functions of DNA as a template [13]. This process is regulated by specific enzymatic mechanisms in which metabolites act as co-substrates or activators/inhibitors. One of the most common ways to modify histone proteins is acetylation, which neutralizes the positively charged lysine residues abundant in histones, thereby "opening" chromatin and making DNA more accessible to other protein factors [15]. Histone acetylation status is regulated by the balance between histone acetyltransferase and histone deacetylase (HDAC) activity. HDAC inhibition causes an accumulation of acetylated histone forms, thus regulating gene expression, cell proliferation, and cell death. Some AEDs may act as HDAC inhibitors and play a crucial role in multiple mechanisms of gene expression. For example, valproic acid (VPA) was the first AED known to non-selectively inhibit HDAC [16, 17]. Subsequently, carbamazepine (CBZ), topiramate (TPM), and lacosamide (LCM) have also been shown to be HDAC inhibitors [18, 19]. Levetiracetam (LEV) cannot directly affect HDAC activity, but 2-pyrrolidinone-n-butyric acid (the major metabolite of LEV) promotes histone deacetylation in HeLa cells [34].
Circulating microRNAs. MicroRNAs can also be detected in biological fluids or via liquid biopsy, and since some show altered levels in patients with various clinical forms of epilepsy [20], the number of studies demonstrating their promise as epigenetic biomarkers of therapeutic resistance to AEDs and the development of ADRs (e.g., AED-induced metabolic syndrome [6]) has increased in recent years. Depending on the laboratory diagnostic protocol used, one can distinguish: free circulating microRNAs; protein-bound microRNAs; microvesicle-associated microRNAs; all microRNAs present in a blood sample. However, a limitation of using circulating microRNAs as epigenetic biomarkers is the lower efficiency and yield from blood plasma and serum compared to microRNA isolation from cells and tissues [21].
Abnormal microRNA expression can lead to aberrant protein expression, and these unintended responses may be induced by AEDs. For instance, prenatal exposure to VPA leads to overexpression of miR-132 in the mouse embryonic brain, subsequently reducing the levels of its molecular targets — methyl-CpG-binding protein 2 (MECP2) and Rho-GTPase-activating protein (p250GAP) — which may result in autistic-like behavior and pathological changes in the mouse cerebral cortex [22]. Phenobarbital (PB) can cause changes in the expression levels of the gene encoding delta-like homolog 1 and the gene encoding type 3 deiodinase (Dlk1-Dio3), which can express microRNA clusters, resulting in hepatocyte hypertrophy and reprogramming, increasing the risk of PB-induced liver cancer in rodents [23]. CBZ-induced dermatotoxicity (specifically Stevens-Johnson syndrome) is associated with microRNA dysregulation in an experimental analysis of immune cells [24].
Discussion
Recent studies have shown that AEDs can: alter DNA methylation; influence histone protein modification by affecting enzymes such as DNA methyltransferases, histone deacetylases, and methyl-binding proteins; change the expression levels of microRNAs [25]. In this context, cfDNA, modified histone proteins, and circulating microRNAs can be considered promising epigenetic biomarkers of AED efficacy and safety, influencing the expression of target genes of AED action. This explains the predictive, preventive, diagnostic, and therapeutic role of pharmacotranscriptomics based on the aforementioned biomarkers in epilepsy, therapeutic resistance to AEDs, and AED-induced ADRs [26, 27], alongside pharmacogenomics [7, 28, 29] and pharmacometabolomics [10, 17].
Thus, it has been shown that VPA-induced hepatotoxicity with the development of non-alcoholic fatty liver disease is associated with DNA methylation and dysregulation of the PPARγ, PPARα, AHR, and CD36 genes [30], while VPA-induced disruption of folate metabolism is associated with DNA methylation and dysregulation of the MTHFR gene [2, 31]. In animal models (rodents), VPA-induced overexpression of miR-132 and miR-134-5p has been associated with the development of autism spectrum disorders [22, 32]. PB-induced disruption of histone deacetylase H3 acetylation and methylation affects the metabolism of this drug [33], and overexpression of miR-200b and miR-221 is associated with PB-induced carcinogenesis [34]. CBZ-induced disruption of histone acetylation alters the regulation of the CYP3A4 gene, slowing the metabolism of this drug [35], and CBZ-induced overexpression of miR-155, miR-18a, and miR-21 is associated with dermatotoxicity [24]. AED-induced neurotoxicity, particularly concerning the fetal brain in pregnant women with epilepsy, is explained by multiple pathogenetic mechanisms, including disruption of folate metabolism and altered expression of placental transporter proteins [2, 36]. In the future, pharmacotranscriptomics may help develop new strategies for predicting, preventing, and correcting these ADRs [37, 38].
Pharmacotranscriptomics helps reshape our understanding of AED mechanisms of action. For example, VPA increases methylation of the -39C locus in the SCN3A gene promoter and may increase levels of fat mass and obesity-associated protein (FTO), which in turn inhibits the expression of MBD2 and NaV1.3 genes, providing a new explanation for the antiseizure effect of this drug [39]. The antiseizure effect of ethosuximide is associated with overexpression of DNMT gene mRNA in the cerebral cortex in an animal model of epilepsy (rats) [40].
Furthermore, results from pharmacotranscriptomics research are leading to AED repurposing. For example, the molecular effects of VPA include DNA methylation, histone acetylation, and modification of histones H3 and H4 by histone deacetylases [41, 42], and altered expression of microRNAs (hsa-miR-124, hsa-miR-125a, hsa-miR-125b, hsa-miR-133b, hsa-miR-145-5p, hsa-miR-205) [43, 44, 45]. As a result of VPA-induced DNA methylation, the regulation of various genes and their pathways is altered (e.g., BRD1, CD133, NANOG, NGN1, OCT4, SCN3A, SOX2, etc.), enabling the repurposing of VPA from an antiseizure and mood-stabilizing drug to an antineoplastic and immunomodulatory agent [46].
The molecular effects of CBZ and LEV include DNA methylation, histone acetylation, and modification of histone deacetylase H3 [13, 33, 37, 47]. CBZ-induced DNA methylation has been shown to alter the regulation of the BRD1 gene [13]. Administration of VPA and CBZ can induce transcription activation of the BRD1 gene, associated with schizophrenia predisposition, through demethylation of its promoter, making BRD1 a new target for these drugs in treating schizophrenia spectrum disorders [48].
Molecular effects of lacosamide include histone acetylation and altered microRNA expression [49]. Lacosamide and brivaracetam decrease hsa-miR-107 expression and increase hsa-miR-195-5p expression, explaining their antineoplastic effect [50]. Oxcarbazepine-induced DNA methylation leads to altered regulation of the GABRB2 gene, through which its psychotropic effect is achieved [51]. Lamotrigine affects histone acetylation [33], and ethosuximide affects DNA methylation, altering the regulation of DNMT1 and DNMT3 genes [38]. Experiments using animal models have shown that cannabidiol influences DNA methylation, altering the regulation of the CB1 gene and mitochondrial ferritin, mediating its psychotropic and neuroprotective effects [52, 53].
In recent years, the pharmaceutical industry has faced declining productivity in research and new development in epileptology, resulting in fewer AEDs reaching the market despite increased investment. This is because some AED candidates fail during later stages of development due to safety concerns and/or previously undetected ADRs. The QSTAR project demonstrated that pharmacotranscriptomics, through gene expression profiling, can identify compound ADRs and is a valuable tool for decision-making in the early stages of new AED development [54]. Single-cell RNA sequencing (scRNA-Seq) combined with parallel CRISPR-based systems, also known as Perturb-seq, CRISP-Seq, and CROP-seq, could aid in developing new AEDs [55]. This approach allows screening of genes involved in therapeutic resistance to AEDs or specific cellular targets, combining the resolution of massively parallel scRNA-Seq with the scale of genome editing in pooled CRISPR screens, providing functional information about the impact of a specific genetic perturbation on the measurable epilepsy phenotype in an individual patient [56, 57].
A study by Lin WH et al. [58] demonstrated that RNA interference screening could help identify novel targets for next-generation AEDs based on increased expression of the homeostatic regulator pumilio (Pum). Pum activity is known to be regulated by depolarization of brain neurons. Enhanced synaptic excitation increases Pum expression and enhances translational repression of voltage-gated sodium channel (Nav) transcripts, which is sufficient to inhibit Na+ ion current (INa) in neurons and ultimately reduce the frequency of action potential generation and epileptic seizures.
Wang L et al. [59] showed that miR-139-5p increases sensitivity to AEDs in therapeutically resistant epilepsy by inhibiting multidrug resistance-associated protein 1 (MRP1). Furthermore, miR-139-5 expression levels influence cerebral cortex development, with miR-139-5p overexpression potentially attenuating cerebral cortex damage through regulatory effects on cortical migration by targeting Lis1 [60]. miR-139-5p agonists (ago-miR-139-5p) attenuate damage in epilepsy patients by inhibiting human transforming growth factor [59]. Thus, a new class of AEDs that increases miR-139-5p expression could prevent further epilepsy development and reduce the risk of developing therapeutic resistance to AEDs.
Conclusion
Knowledge of transcriptome variants and their influence in the context of molecular changes causing epigenetic modification of epilepsy course and individual response to AEDs in patients with epilepsy, using non-integrated technologies, may help reduce the risk of developing therapeutic resistance to AEDs and serious ADRs. The multifaceted nature of epilepsy as a genetically and clinically heterogeneous disease and its subcellular heterogeneity play a crucial role in AED efficacy and safety, therapeutic resistance, and toxicity. Pharmacotranscriptomics is a powerful tool for understanding the molecular mechanisms of AED action, discovering new microRNA-based classes of AEDs, and advancing personalized medicine to achieve an optimal balance between the efficacy and safety of epilepsy pharmacotherapy.
References
1. Карлов В.А. Эпилепсия у детей и взрослых женщин и мужчин : Руководство для врачей. 2-е издание. БИНОМ. 2019; 896 с. ISBN 978 5-6042641-0-2. [Karlov V.A. Epilepsy in children and adult women and men: A guide for doctors. 2nd edition. BINOM. 2019; 896 p. ISBN 978-5-6042641 0-2 (In Russ.)].
2. Бочанова Е.Н., Дмитренко Д.В., Егорова А.Т. [и др.]. Эпилепсия и беременность. 2-е издание, переработанное и дополненное. ГЭОТАР-Медиа. 2022;296 с. [Bochanova E.N., Dmitrenko D.V., Egorova A.T. et al. Epilepsy and pregnancy. 2nd edition, revised and supplemented. GEOTAR-Media. 2022; 296 p. (In Russ.)].
3. Rubio C, Gatica F, Uribe E, et al. Molecular and Genetic Mechanisms of Neurotoxicity During Anti-seizure Medications Use. Rev de Investig Clínica. 2023;75(1):1–12. doi:10.24875/RIC.22000260
4. Zhuravlev NM, Shnayder NA, Vaiman EE, et al. Interindividual Variability of Anticonvulsant-Induced QT Prolongation Risk. Pers Psychiatry Neurol. 2022;2(1):22-45. doi:10.52667/2712-9179-2022-2-1-23-45
5. Shnayder NA, Pekarets NA, Pekarets NI, et al. MicroRNAs as Epigenetic Biomarkers of Pathogenetic Mechanisms of the Metabolic Syndrome Induced by Antiseizure Medications: Systematic Review. J Clin Med. 2025;14(7):2432. doi:10.3390/jcm14072432
6. Шнайдер Н.А., Пекарец Н.А., Пекарец Н.И., и др. Роль микроРНК как регуляторов системной воспалительной реакции при метаболическом синдроме, вызванном противосудорожными препаратами. Эпилепсия и пароксизмальные состояния. 2025;17(2):208-226. https://doi.org/10.17749/2077-8333/epi.par.con.2025.239 [Shnayder NA, Pekarets NA, Pekarets NI, et al. The role of microRNAs as regulators of systemic inflammatory response in anticonvulsant-induced metabolic syndrome. Epilepsy paroxysmal cond. 2025;17(2):208-26. doi:10.17749/2077-8333/epi.par.con.2025.239 (In Russ.)]
7. Насырова P.Ф., Сивакова Н.А., Липатова Л.В., и др. Биологические маркеры эффективности и безопасности противоэпилептических препаратов: фармакогенетика и фармакокинетика. Сибирское медицинское обозрение. 2017;(1):17-25. doi: 10.20333/2500136-2017-1-17-25 [Nasyrova RF, Sivakova NA, Lipatova LV, et al. Biological Markers of the Antiepileptic Drugs Efficacy and Safety: Pharmacogenetics and Pharmacokinetics. Siberian Med Rev. 2017;(1):17-25. (In Russ.)]
8. García-Giménez JL, Seco-Cervera M, Tollefsbol TO, et al. Epigenetic biomarkers: Current strategies and future challenges for their use in the clinical laboratory. Crit Rev Clin Lab Sci. 2017;54(7-8):529-50. doi:10.1080/10408363.2017.1410520
9. Xicota L, De toma I, Maffioletti E, et al. Recommendations for pharmacotranscriptomic profiling of drug response in CNS disorders. Eur Neuropsychopharmacol. 2022;54:41-53. doi:10.1016/j.euroneuro.2021.10.005
10. Шнайдер Н.А., Гречкина В.В., Архипов В.В., Насырова Р.Ф. Фармакогенетически-информированная фармакометаболомика как инновационный подход к оценке безопасности и риска фармакотерапии препаратами вальпроевой кислоты. Безопасность и риск фармакотерапии. 2023;11(4):450-462. doi: 10.30895/2312-7821-2023-386 [Shnayder NA, Grechkina VV, Arkhipov VV, Nasyrova RF. Pharmacogenetics Informed Pharmacometabolomics as an Innovative Approach to Assessing the Safety and Risk of Pharmacotherapy with Valproic Acid. Saf Risk Pharmacother. 2023;11(4):450-62.]
11. Якимов А.М., Тимечко Е.Е., Парамонова А.И., и др. Гипотезы развития и стратегии преодоления лекарственной устойчивости при эпилепсии. Часть I: Гипотезы развития. Эпилепсия и пароксизмальные состояния. 2024;16(4):375-384. Doi: 10.17749/2077-8333/epi.par.con.2024.210 [Yakimov AM, Timechko EE, Paramonova AI, et al. Hypotheses of development and strategies for overcoming drug resistance in epilepsy. Part I: Hypotheses of development. Epilepsy paroxysmal cond. 2025;16(4):375-84. (In Russ.)].
12. Stafstrom CE. Mechanisms of action of antiepileptic drugs: the search for synergy. Curr Opin Neurol. 2010;23(2):157-63. doi:10.1097/WCO.0b013e32833735b5
13. Kong F, Ma C, Zhong M. Epigenetic Effects Mediated by Antiepileptic Drugs and their Potential Application. Curr Neuropharmacol. 2020;18(2):153-66. doi:10.2174/1570159X17666191010094849
14. Devonshire AS, Whale AS, Gutteridge A, et al. Towards standardisation of cell-free DNA measurement in plasma: controls for extraction efficiency, fragment size bias and quantification. Anal Bioanal Chem. 2014;406(26): 6499-6512. doi:10.1007/s00216-014-7835-3
15. Fan J, Krautkramer KA, Feldman JL, Denu JM. Metabolic Regulation of Histone Post-Translational Modifications. ACS Chem Biol. 2015;10(1):95 108. doi:10.1021/cb500846u
16. Gottlicher M. Valproic acid defines a novel class of HDAC inhibitors inducing differentiation of transformed cells. Embo J. 2001;20(24):6969-78. doi:10.1093/emboj/20.24.6969
17. Shnayder NA, Grechkina VV, Khasanova AK, et al. Therapeutic and Toxic Effects of Valproic Acid Metabolites. Metabolites. 2023;13(1):134. doi:10.3390/metabo13010134
18. Salminen JK, Tammela TL, Auvinen A, Murtola TJ. Antiepileptic drugs with histone deacetylase inhibition activity and prostate cancer risk: a population-based case-control study. Cancer Causes Control. 2016;27(5): 637-45. doi:10.1007/s10552-016-0737-2
19. Salminen JK, Tammela TL, Auvinen A, Murtola TJ. Antiepileptic drugs with histone deacetylase inhibition activity and prostate cancer risk: a population-based case-control study. Cancer Causes Control. 2016;27(5): 637-45. doi:10.1007/s10552-016-0737-219.
20. Stettner M, Krämer G, Strauss A, et al. Long-term antiepileptic treatment with histone deacetylase inhibitors may reduce the risk of prostate cancer. Eur J Cancer Prev. 2012;21(1):55-64. doi:10.1097/cej.0b013e32834a7e6f
21. Walker HK, Hall WD, Hurst JW. Epstein CM. Epilepsy. Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition. Boston: Butterworths. 1990. https://www.ncbi.nlm.nih.gov/books/NBK379/
22. Kroh EM, Parkin RK, Mitchell PS, Tewari M. Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods. 2010;50(4):298-301. doi:10.1016/j.ymeth.2010.01.032
23. Hara Y, Ago Y, Takano E, et al. Prenatal exposure to valproic acid increases miR-132 levels in the mouse embryonic brain. Mol Autism. 2017;8(1):33. doi:10.1186/s13229-017-0149-5
24. Pouché L, Vitobello A, Römer M, et al. Xenobiotic CAR Activators Induce Dlk1-Dio3 Locus Noncoding RNA Expression in Mouse Liver. Toxicol Sci. 2017;158(2):367-78. doi:10.1093/toxsci/kfx104
25. Monroy-Arreola A, Durán-Figueroa NV, Méndez-Flores S, et al. Up-Regulation of T-Cell Activation MicroRNAs in Drug-Specific CD4+ T-Cells from Hypersensitive Patients. Chem Res Toxicol. 2018;31(6):454-61. doi:10.1021/acs.chemrestox.7b00330
26. Timechko EE, Lysova KD, Yakimov AM, et al. Circulating microRNAs as Biomarkers of Various Forms of Epilepsy. Med Sci. 2025;13(1):7. doi:10.3390/medsci13010007
27. Yakovleva KD, Dmitrenko DV, Panina IS, et al. Expression Profile of miRs in Mesial Temporal Lobe Epilepsy: Systematic Review. Int J Mol Sci. 2022;23(2):951. doi:10.3390/ijms23020951
28. Panina YS, Timechko EE, Usoltseva AA, et al. Biomarkers of Drug Resistance in Temporal Lobe Epilepsy in Adults. Metabolites. 2023;13(1):83. doi:10.3390/metabo13010083
29. Сычев Д.А. Генетические особенности пациента могут влиять на профиль эффективности и безопасности лекарственного препарата. Безопасность и риск фармакотерапии. 2024;12(2):127-131. Doi: 10.30895/2312-7821-2024-12-2-127-131 [Sychev DA. Genetic features of a patient may influence the efficacy and safety profile of a medicinal product. Safety and Risk of Pharmacotherapy. 2024;12(2):127-131. (In Russ.)]
30. Bochanova EA, Gusev SD. The Frequency and Structure of Adverse Drug Reactions in the Pharmacotherapy of Epilepsy. Pers Psychiatry Neurol. 2024;4(1):18-25. doi:10.52667/10.52667/2712-9179-2024-4-1-18-25
31. Van breda SG, Claessen SM, Van herwijnen M, et al. Integrative omics data analyses of repeated dose toxicity of valproic acid in vitro reveal new mechanisms of steatosis induction. Toxicology. 2018;393:160-70. doi:10.1016/j.tox.2017.11.013
32. Ni G, Qin J, Chen Z, et al. Associations between genetic variation in one-carbon metabolism and leukocyte DNA methylation in valproate-treated patients with epilepsy. Clin Nutr. 2018;37(1):308-12. doi:10.1016/j.clnu.2017.01.004
33. Hirsch MM, Deckmann I, Fontes-Dutra M, et al. Behavioral alterations in autism model induced by valproic acid and translational analysis of circulating microRNA. Food Chem Toxicol. 2018;115:336-43. doi:10.1016/j.fct.2018.02.061
34. Sakakibara Y, Katoh M, Kondo Y, Nadai M. Effects of Phenobarbital on Expression of UDP-Glucuronosyltransferase 1a6 and 1a7 in Rat Brain. Drug Metab Dispos. 2016;44(3):370-7. doi:10.1124/dmd.115.067439
35. Miousse IR, Murphy LA, Lin H, et al. Dose-response analysis of epigenetic, metabolic, and apical endpoints after short-term exposure to experimental hepatotoxicants. Food Chem Toxicol. 2017;109:690-702. doi:10.1016/j.fct.2017.05.013
36. Ookubo M, Kanai H, Aoki H, Yamada N. Antidepressants and mood stabilizers effects on histone deacetylase expression in C57BL/6 mice: Brain region specific changes. J Psychiatr Res. 2013;47(9):1204-14. doi:10.1016/j.jpsychires.2013.05.028
37. Al-Ansari A, Robertson NP. Anti-epileptics and pregnancy: an update. J Neurol. 2018;265(11):2749-51. doi:10.1007/s00415-018-9058-6
38. Pavlovic S, Kotur N, Stankovic B, et al. Pharmacogenomic and Pharmacotranscriptomic Profiling of Childhood Acute Lymphoblastic Leukemia: Paving the Way to Personalized Treatment. Genes. 2019;10(3):191. doi:10.3390/genes10030191
39. Dini A, Barker H, Piki E, et al. A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery. Nat Chem Biol. 2025;21(3):432-42. doi:10.1038/s41589-024-01761-8
40. Tan N, Tang H, Lin G, et al. Epigenetic Downregulation of Scn3a Expression by Valproate: a Possible Role in Its Anticonvulsant Activity. Mol Neurobiol. 2017;54(4):2831-42. doi:10.1007/s12035-016-9871-9
41. Dezsi G, Ozturk E, Stanic D, Powell KL, Blumenfeld H, O'Brien TJ, Jones NC. Ethosuximide reduces epileptogenesis and behavioral comorbidity in the GAERS model of genetic generalized epilepsy. Epilepsia. 2013;54(4):635-43. doi:10.1111/epi.12118
42. Vukićević V, Qin N, Balyura M, et al. Valproic acid enhances neuronal differentiation of sympathoadrenal progenitor cells. Mol Psychiatry. 2015;20(8):941-50. doi:10.1038/mp.2015.3
43. Zhang C, Zhang E, Yang L, et al. Histone deacetylase inhibitor treated cell sheet from mouse tendon stem/progenitor cells promotes tendon repair. Biomaterials. 2018;172:66-82. doi:10.1016/j.biomaterials.2018.03.043
44. Oikawa H, Goh WW, Lim VK, et al. Valproic acid mediates miR-124 to down-regulate a novel protein target, GNAI1. Neurochem Int. 2015;91: 62-71. doi:10.1016/j.neuint.2015.10.010
45. Lin T, Ren Q, Zuo W, et al. Valproic acid exhibits anti-tumor activity selectively against EGFR/ErbB2/ErbB3-coexpressing pancreatic cancer via induction of ErbB family members-targeting microRNAs. J Exp Clin Cancer Res. 2019;38(1):150. doi:10.1186/s13046-019-1160-9
46. Bellissimo T, Ganci F, Gallo E, et al. Thymic Epithelial Tumors phenotype relies on miR-145-5p epigenetic regulation. Mol Cancer. 2017;16(1):88. doi:10.1186/s12943-017-0655-2
47. Houtepen LC, Van bergen AH, Vinkers CH, Boks MP. DNA Methylation Signatures of Mood Stabilizers and Antipsychotics in Bipolar Disorder. Epigenomics. 2016;8(2):197-208. doi:10.2217/epi.15.98
48. Scicchitano BM, Sorrentino S, Proietti G, Lama G, Dobrowolny G, Catizone A, Binda E, Larocca LM, Sica Get al. Levetiracetam enhances the temozolomide effect on glioblastoma stem cell proliferation and apoptosis. Cancer Cell Int. 2018;18(1):136. doi:10.1186/s12935-018-0626-8
49. Dyrvig M, Qvist P, Lichota J, et al. DNA Methylation Analysis of BRD1 Promoter Regions and the Schizophrenia rs138880 Risk Allele. Plos One. 2017;12(1):e0170121. doi:10.1371/journal.pone.0170121
50. Bang SR, Ambavade SD, Jagdale PG, et al. Lacosamide reduces HDAC levels in the brain and improves memory: Potential for treatment of Alzheimer's disease. Pharmacol Biochem Behav. 2015;134:65-9. doi:10.1016/j.pbb.2015.04.011
51. Rizzo A, Donzelli S, Girgenti V, et al. In vitro antineoplastic effects of brivaracetam and lacosamide on human glioma cells. J Exp Clin Cancer Res. 2017;36(1):76. doi:10.1186/s13046-017-0546-9
52. Zong L, Zhou L, Hou Y, et al. Genetic and epigenetic regulation on the transcription of GABRB2 : Genotype-dependent hydroxymethylation and methylation alterations in schizophrenia. J Psychiatr Res. 2017;88:9-17. doi:10.1016/j.jpsychires.2016.12.019
53. Stark T, Ruda-Kucerova J, Iannotti FA, et al. Peripubertal cannabidiol treatment rescues behavioral and neurochemical abnormalities in the MAM model of schizophrenia. Neuropharmacology. 2019;146:212-21. doi:10.1016/j.neuropharm.2018.11.035
54. Da silva VK, De freitas BS, Dornelles VC, et al. Novel insights into mitochondrial molecular targets of iron-induced neurodegeneration: Reversal by cannabidiol. Brain Res Bull. 2018;139:1-8. doi:10.1016/j.brainresbull.2018.01.014
55. Verbist B, Klambauer G, Vervoort L, et al. Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project. Drug Discov Today. 2015;20(5):505-13. doi:10.1016/j.drudis.2014.12.014
56. Garcia-Rosa S, De freitas brenha B, Da rocha VF, et al. Personalized Medicine Using Cutting Edge Technologies for Genetic Epilepsies. Curr Neuropharmacol. 2021;19(6):813-31. doi:10.2174/1570159X18666200915151909
57. Jaitin DA, Weiner A, Yofe I, et al. Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. Cell. 2016;167(7):1883-1896.e15. doi:10.1016/j.cell.2016.11.039
58. Kurata M, Yamamoto K, Moriarity BS, et al. CRISPR/Cas9 library screening for drug target discovery. J Hum Genet. 2018;63(2):179-86. doi:10.1038/s10038-017-0376-9
59. Lin W, He M, Fan YN, Baines RA. An RNAi-mediated screen identifies novel targets for next-generation antiepileptic drugs based on increased expression of the homeostatic regulator pumilio. J Neurogenetics. 2018;32(2):106-17. doi:10.1080/01677063.2018.1465570
60. Wang L, Song L, Chen X, et al. microRNA-139-5p confers sensitivity to antiepileptic drugs in refractory epilepsy by inhibition of MRP1. Cns Neurosci Ther. 2020;26(4):465-74. doi:10.1111/cns.13268
61. Huang Y, Jiang J, Zheng G, et al. miR-139-5p modulates cortical neuronal migration by targeting Lis1 in a rat model of focal cortical dysplasia. Int J Mol Med. 2014;33(6):1407-14. doi:10.3892/ijmm.2014.1703
About the Authors
N. A. ShnayderRussian Federation
Natalia A. Shnayder — Dr. Sci. (Med.), Professor, Chief Researcher of the Institute of Personalized Psychiatry and Neurology; Leading Researcher of the Centre for Collective Use "Molecular and Cellular Technologies"
V. V. Bader
Russian Federation
Violetta V. Bader — Yunior Researcher of the Institute of Personalized Psychiatry and Neurology neurologist, City Epileptology Centre
R. F. Nasyrova
Russian Federation
Regina F. Nasyrova — Dr. Sci. (Med.), Chief Scientific Officer, Head of the Institute of Personalized Psychiatry and Neurology; Professor, Department of Psychiatry, General and Clinical Psychology
What is already known about this topic?
Standard Approaches: Pharmacogenomics (studying the influence of genetic polymorphisms on drug metabolism) and pharmacometabolomics (therapeutic drug monitoring) are already actively used to personalize epilepsy therapy.
Therapeutic Challenge: Up to 60–70% of epilepsy patients require lifelong antiepileptic drug (AED) therapy, which is associated with a high risk of adverse reactions (teratogenicity, neurotoxicity, metabolic syndrome) and the development of therapeutic resistance.
Role of Epigenetics: It is known that some AEDs (e.g., valproic acid) can influence not only ion channels but also epigenetic mechanisms (histone deacetylase inhibition). However, a systematic approach to studying these effects has been lacking.
Potential of microRNAs: microRNAs were considered promising biomarkers for various diseases, including epilepsy, but their role in response to pharmacotherapy had been studied only fragmentarily.
What is new in the article?
Conceptualizing the Approach: The article comprehensively presents pharmacotranscriptomics as an independent and necessary "piece" of the multiomics puzzle in epileptology. It focuses on how AEDs change a patient's gene expression (transcriptome), not just the structure of their DNA.
New Mechanisms of Action: It shows that AEDs (valproates, carbamazepine, lamotrigine, etc.) can act as epigenetic modifiers, altering DNA methylation, histone modification, and microRNA expression. For example, this explains some of the anti-tumor or psychotropic effects of older AEDs.
Drug Repurposing: Systematizing transcriptomic data opens pathways for discovering new classes of drugs. For instance, understanding how valproic acid alters the expression of genes (BRD1, SCN3A) substantiates its use not only for epilepsy but also in oncology and for schizophrenia spectrum disorders.
Targets for New AEDs: The article proposes specific directions for creating new drug classes based on microRNAs (e.g., miR-139-5p agonists to overcome drug resistance).
Future Technologies: It describes modern methods (CRISPR-based screening, single-cell RNA sequencing) that will allow for the early detection of toxicity in AED candidates and the search for genes responsible for drug resistance.
How can this affect clinical practice in the foreseeable future?
New Diagnostic Panels: The emergence of "liquid biopsies" based on detecting circulating microRNAs in blood or saliva for early prediction of therapy failure or the risk of developing severe adverse reactions (e.g., metabolic syndrome).
Predictive Models: Implementation of algorithms that, based on a patient's transcriptome analysis, help a physician choose the AED with the highest probability of efficacy and minimal risk of toxicity for that specific individual (a shift from the "average dose for the population" to an "individual transcriptomic response").
Emergence of New Drug Classes: The development of fundamentally new molecules targeting not only ion channels but also the regulation of gene expression (e.g., drugs based on antisense oligonucleotides targeting microRNAs). This is particularly important for treating pharmacoresistant forms of epilepsy.
Refining Indications for Existing Drugs: Pharmacotranscriptomics will allow for more precise prescribing of AEDs in patients with comorbid conditions (e.g., choosing valproates when epilepsy co-occurs with bipolar disorder, considering its effect on the BRD1 gene).
Review
For citations:
Shnayder N.A., Bader V.V., Nasyrova R.F. Prospects of pharmacotranscriptomics in understanding the effects of antiepileptic drugs and searching for new classes of antiepileptic drugs. Pharmacogenetics and Pharmacogenomics. 2025;(4):10-17. (In Russ.) https://doi.org/10.37489/2588-0527-2025-4-10-17. EDN: FYBPPD
JATS XML



































