safetyriskБезопасность и риск фармакотерапииSafety and Risk of Pharmacotherapy2312-78212619-1164Federal State Budgetary Institution ‘Scientific Centre for Expert Evaluation of Medicinal Products’ of the Ministry of Health of the Russian Federation (FSBI ‘SCEEMP’)10.30895/2312-7821-2023-11-4-390-408safetyrisk-380Research ArticleГЛАВНАЯ ТЕМА: ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ЗДРАВООХРАНЕНИИ: ПОЛЬЗА И РИСКИMAIN TOPIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE: RISKS AND BENEFITSПрогноз in silico токсикологических и фармакокинетических характеристик лекарственных соединенийIn Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compoundshttps://orcid.org/0000-0002-8188-5052ВасильевП. М.VassilievP. M.

Васильев Павел Михайлович, д-р биол. наук, с. н. с., доцент

Площадь Павших борцов, д. 1, Волгоград, 400131

Pavel M. Vassiliev, Dr. Sci. (Biol.), Senior Researcher, Associate Professor

1 Pavshikh Bortsov Sq., Volgograd 400131

pvassiliev@mail.ruhttps://orcid.org/0000-0001-8268-8811ГолубеваА. В.GolubevaA. V.

Голубева Арина Владимировна

Площадь Павших борцов, д. 1, Волгоград, 400131

Arina V. Golubeva

1 Pavshikh Bortsov Sq., Volgograd 400131

arina_arina_golubeva@mail.ruhttps://orcid.org/0009-0002-1413-5224КоролеваА. Р.KorolevaA. R.

Королева Анастасия Руслановна

Площадь Павших борцов, д. 1, Волгоград, 400131

Anastasia R. Koroleva

1 Pavshikh Bortsov Sq., Volgograd 400131

korolyova1698@mail.ruhttps://orcid.org/0000-0002-5326-3299ПерфильевМ. А.PerfilevM. A.

Перфильев Максим Алексеевич

Площадь Павших борцов, д. 1, Волгоград, 400131

Maksim A. Perfilev

1 Pavshikh Bortsov Sq., Volgograd 400131

maxim.firu@yandex.ruhttps://orcid.org/0000-0003-3077-1837КочетковА. Н.KochetkovA. N.

Кочетков Андрей Николаевич

Площадь Павших борцов, д. 1, Волгоград, 400131

Andrey N. Kochetkov

1 Pavshikh Bortsov Sq., Volgograd 400131

akocha@mail.ruФедеральное государственное бюджетное образовательное учреждение высшего образования «Волгоградский государственный медицинский институт» Министерства здравоохранения Российской ФедерацииРоссияVolgograd State Medical UniversityRussian Federation202313112023114390408Copyright © Васильев П.М., Голубева А.В., Королева А.Р., Перфильев М.А., Кочетков А.Н., 20232023Васильев П.М., Голубева А.В., Королева А.Р., Перфильев М.А., Кочетков А.Н.Vassiliev P.M., Golubeva A.V., Koroleva A.R., Perfilev M.A., Kochetkov A.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.risksafety.ru/jour/article/view/380Актуальность

Актуальность. Изучение токсикологических и фармакокинетических свойств лекарственных соединений является важным этапом доклинических исследований и в случае неудовлетворительных результатов может привести к невозможности дальнейшей разработки лекарственного препарата. Поэтому создание компьютерных методов для предварительного скрининга свойств химических соединений является актуальной задачей.

Цель

Цель. Обзор современных подходов к компьютерному прогнозу ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) свойств фармакологически активных веществ, в особенности наиболее важных токсикологических и фармакокинетических показателей, и изложение результатов собственных исследований в этой области.

Обсуждение

Обсуждение. В результате проведенного обзора моделей для прогноза токсикологических характеристик химических соединений (острой токсичности, канцерогенности, мутагенности, генотоксичности, эндокринотоксичности, цитотоксичности, кардиотоксичности, гепатотоксичности, иммунотоксичности) установлено, что точность прогноза указанных свойств колеблется от 74,0 до 98,0%. Результаты обзора моделей для прогноза фармакокинетических характеристик химических соединений (всасываемости в желудочно-кишечном тракте (ЖКТ), биодоступности при пероральном приеме, объема распределения, клиренса общего, почечного и печеночного, времени полувыведения) свидетельствуют о том, что коэффициент детерминации при прогнозе указанных свойств колеблется от 0,265 до 0,920. Проведенный обзор литературы показал, что в настоящий момент наиболее распространенными методами оценки in silico параметров ADMET фармакологически активных соединений являются метод случайного леса и метод опорных векторов. Собственные результаты по компьютерному моделированию консенсусным методом в системе IT Microcosm и методом искусственных нейронных сетей двенадцати токсикологических и фармакокинетических характеристик химических соединений показали, что в сравнении с данными литературы IT Microcosm лучше прогнозирует два токсикологических свойства: канцерогенность и проникновение через гематоэнцефалический барьер (точность прогноза до 93,4%); а нейросетевые модели – четыре токсикологических свойства: острую токсичность, канцерогенность, генотоксичность, проникновение через гематоэнцефалический барьер (точность прогноза до 93,8%) и три фармакокинетических свойства: всасываемость в ЖКТ, объем распределения и печеночный клиренс (коэффициент детерминации 0,825).

Выводы

Выводы. Наиболее перспективным и практически значимым направлением в разработке систем in silico прогноза ADMET характеристик новых лекарственных препаратов является использование технологии искусственных нейронных сетей.

Scientific relevance

Scientific relevance. Studies of the toxicological and pharmacokinetic properties of medicinal compounds are a crucial stage of preclinical research; unsatisfactory results may invalidate further drug development. Therefore, the development of in silico methods for a preliminary pre-experimental assessment of toxicological and pharmacokinetic properties is a relevant and crucial task.

Aim

Aim. The study aimed to review current approaches to in silico prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters of pharmacologically active compounds, in particular, the most important toxicological and pharmacokinetic parameters, and to present the results of the authors’ own research in this area.

Discussion

Discussion. According to the review of models for predicting the toxicological properties of chemical compounds (acute toxicity, carcinogenicity, mutagenicity, genotoxicity, endocrine toxicity, cytotoxicity, cardiotoxicity, hepatotoxicity, and immunotoxicity), the accuracy of predictions ranged from 74.0% to 98.0%. According to the review of models for predicting the pharmacokinetic properties of chemical compounds (gastrointestinal absorption; oral bioavailability; volume of distribution; total, renal, and hepatic clearance; and half-life), the coefficient of determination for the predictions ranged from 0.265 to 0.920. The literature review showed that the most widely used methods for in silico assessment of the ADMET parameters of pharmacologically active compounds included the random forest method and the support vector machines method. The authors compared the literature data with the results they obtained by modelling 12 toxicological and pharmacokinetic properties of chemical compounds using the consensus method in the IT Microcosm system and artificial neural networks. IT Microcosm outperformed the models described in the literature in terms of predicting 2 toxicological properties, including carcinogenicity and blood–brain barrier penetration (the prediction accuracy reached 93.4%). Neural network models were superior in predicting 4 toxicological properties, including acute toxicity, carcinogenicity, genotoxicity, and blood–brain barrier penetration (the prediction accuracy reached 93.8%). In addition, neural network models were better in predicting 3 pharmacokinetic properties, including gastrointestinal absorption, volume of distribution, and hepatic clearance (the coefficient of determination reached 0.825).

Conclusions

Conclusions. The data obtained suggest that artificial neural networks are the most promising and practically significant direction for the development of in silico systems for predicting the ADMET characteristics of new medicinal products.

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The authors declare that there are no conflicts of interest present.