Прогноз in silico токсикологических и фармакокинетических характеристик лекарственных соединений

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

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