A genetic algorithm-based approach for quantitative prediction of drug-drug interactions caused by cytochrome P450 3A inhibitors and inducers in dogs and cats

IF 4.7 2区医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Chemico-Biological Interactions Pub Date : 2025-04-29 DOI:10.1016/j.cbi.2025.111537

Veronica Di Paolo , Francesco Maria Ferrari , Italo Poggesi , Mauro Dacasto , Francesca Capolongo , Luigi Quintieri

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引用次数: 0

Abstract

A genetic algorithm (GA)-based framework was developed to predict drug-drug interactions (DDIs) caused by cytochrome P450 3A (CYP3A) inhibition or induction in dogs and cats. Area under the plasma concentration-time curve (AUC) ratios, obtained from published in vivo DDI studies, were used to calculate the following parameters: (a) the contribution ratio (CR), which represents the fraction of the dose of the victim drug metabolized via CYP3A, and (b) the inhibitory potency (inhibition ratio; IR) or inducing potency (IC) of the perpetrator drug. AUC ratios of 3 substrates, 4 inhibitors and 1 inducer of CYP3A in cats, and the AUC ratios of 10 substrates, 12 inhibitors and 3 inducers of CYP3A in dogs were successfully predicted and validated by the developed methodology within 50–200 % of observed values. This approach could represent a useful resource to predict the extent of DDIs in clinical scenarios requiring the simultaneous administration of a CYP3A substrate drug with a CYP3A perpetrator.

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基于遗传算法的定量预测狗和猫中细胞色素P450 3A抑制剂和诱导剂引起的药物-药物相互作用的方法

开发了一个基于遗传算法（GA）的框架，用于预测狗和猫中细胞色素P450 3A （CYP3A）抑制或诱导引起的药物-药物相互作用（ddi）。从已发表的体内DDI研究中获得的血浆浓度-时间曲线（AUC）比下面积用于计算以下参数：(a)贡献比（CR），表示通过CYP3A代谢的受害者药物剂量的比例；(b)抑制效力(抑制比；（IR）或诱导效力（IC）犯罪者的药物。在猫体内，CYP3A的3种底物、4种抑制剂和1种诱导剂的AUC比率，以及在狗体内，CYP3A的10种底物、12种抑制剂和3种诱导剂的AUC比率在观察值的50 - 200%范围内成功预测和验证。在需要同时使用CYP3A底物药物和CYP3A作主药物的临床情况下，这种方法可以作为预测ddi程度的有用资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Chemico-Biological Interactions 生物-毒理学

CiteScore

7.70

自引率

3.90%

发文量

410

审稿时长

36 days

期刊介绍： Chemico-Biological Interactions publishes research reports and review articles that examine the molecular, cellular, and/or biochemical basis of toxicologically relevant outcomes. Special emphasis is placed on toxicological mechanisms associated with interactions between chemicals and biological systems. Outcomes may include all traditional endpoints caused by synthetic or naturally occurring chemicals, both in vivo and in vitro. Endpoints of interest include, but are not limited to carcinogenesis, mutagenesis, respiratory toxicology, neurotoxicology, reproductive and developmental toxicology, and immunotoxicology.