Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence

Corrales-Hernández, María; Villarroel-Hagemann, Sebastián; Mendoza-Rodelo, Isabella; Palacios-Sánchez, Leonardo; Gaviria-Carrillo, Mariana; Buitrago-Ricaurte, Natalia; Espinosa-Lugo, Santiago; Calderon-Ospina, Carlos-Alberto; Rodríguez-Quintana, Jesús

doi:10.3390/biomedicines11061632

Cited by 6 publications

(3 citation statements)

References 126 publications

(190 reference statements)

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“…Drug Response Prediction: Utilizing ML to predict patient response to anti-epileptic drugs (AEDs) represents another area of success. By analyzing patient data, including genetic information, seizure types, and treatment history, ML models have been able to predict with significant accuracy which patients are likely to respond well to specific AEDs ( 57 ), paving the way for personalized treatment strategies. Automated Video Analysis for Seizure Detection: An innovative application of ML in epilepsy involves the use of video analysis to detect physical signs of seizures, particularly in monitoring situations such as long-term EEG video telemetry ( 58 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Huo,

Luo,

et al. 2024

Front. Neurol.

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BackgroundEpilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization.MethodsRelevant articles and reviews were searched for 2004–2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio).ResultsA total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper “Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals” was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments.ConclusionsThis study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.

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Section: Discussionmentioning

confidence: 99%

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Huo,

Luo,

et al. 2024

Front. Neurol.

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“…However, we must keep in mind that there is historical evidence that many major drugs, in opposition to what AI can rationally generate, have been discovered fortuitously through random investigations of organisms; this is the so-called phenomenon of serendipity. As a historical illustration of serendipity and drug discovery, in 1962, there was the judicious observation that the presence of valproic acid, used as a solvent in cough syrups, provided a significant decrease in the number of seizures in epileptic patients, thus opening up an unanticipated window in the domain of anti-epileptic drugs [ 10 ]. Serendipity, thus, proves the role of chance in the identification of drugs of potentially great value in oncology.…”

Section: Ai and Preclinical Anticancer Drug Developmentmentioning

confidence: 99%

Artificial Intelligence and Anticancer Drug Development—Keep a Cool Head

Bailleux,

Gal,

Chamorey

et al. 2024

Pharmaceutics

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Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.

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“…The comparison analogs were the anticonvulsant drug Phenytoin (5, 5-diphenylhydantoin -compound №. 1), which is a structural analog of the studied compounds, and the antimanic drug, lithium chloride [12,22]. The acute daily toxicity of the drugs was tested in mice.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and neurotropic activity of new derivatives of some amino acid hydantoins and their lithium salts

Paronikyan,

Grigoryan,

Barkhudaryants

et al. 2024

BCHD

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Background: Amino acid hydantoins are widely used in various fields, particularly in pharmacy. For example, phenytoin is used to treat generalized epileptic seizures. Objective: This study aims to investigate the neurotropic properties of new amino acid hydantoin derivatives in order to identify new anticonvulsants with psychotropic properties.Methods: The compounds mentioned exhibited anticonvulsant properties that were evaluated through a series of tests: maximal electric shock, pentylenetetrazole, thiosemicarbazide, picrotoxin, strychnine, nicotine, and camphor convulsions on outbred mice. The psychotropic effects of the compounds were assessed through various tests,including the elevated plus maze (EPM), forced swimming, and open field tests. Additionally, their effect on the activity of monoamine oxidase (MAO) was investigated under in vitro conditions. The neurotoxic effect of these compounds was further examined by conducting the ''rotating rod'' test on mice. Results: The five studied compounds, which are byproducts of amino acid hydantoins, along with Phenytoin and their lithium salts, display neurotropic properties, demonstrating anticonvulsant and psychotropic effects. Compounds that inhibit clonic pentylenetetrazole, maximal electroshock generalized tonic convulsions, nicotine, and kamphoraconvulsions, as well as exhibiting antithiosemicarbazide action in animals, display pronounced anxiolytic and behavior-activating effects across various internationally recognized models. Simultaneously, the compounds show antidepressant (evidenced by the "forced swimming" model) and anti-MAO effects. The compounds did notdemonstrate muscle relaxant effects in the doses examined. In certain aspects of their neurotropic properties, the compounds outperformed drugs currently used in the clinic, including Phenytoin, Ethosuximide, and lithium chloride(antimanic drug), among others. Conclusion: Hydantoins derived from DL-tryptophan, DL-β-phenyl-α-alanine, and Phenytoin, along with their corresponding lithium salts, were synthesized. Both the anticonvulsant and psychotropic effects of these substances have been thoroughly studied. In several models, compounds that inhibit maximal electroshock convulsions in animals and clonic pentylenetetrazole also display anxiolytic and behavior-activating effects. These compounds also exhibit antidepressant and anti-MAO effects. The investigated compounds can be used in medicine, as drugs, in the treatment of epilepsy with psychotropic disorders. Keywords: antiepileptic drugs, derivatives of amino acid, lithium salts, neurotropic activity, pentylenetetrazole convulsions.

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Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence

Cited by 6 publications

References 126 publications

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Artificial Intelligence and Anticancer Drug Development—Keep a Cool Head

Synthesis and neurotropic activity of new derivatives of some amino acid hydantoins and their lithium salts

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