Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Clinical Pharmacy, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
Eur J Drug Metab Pharmacokinet. 2024 Jul;49(4):449-465. doi: 10.1007/s13318-024-00892-6. Epub 2024 May 11.
The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug.
A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression.
Initial analysis showed that compounds with logD > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize.
A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.
口服首过代谢是药物药代动力学特征的关键因素。基于化学结构参数预测口服首过代谢在药物设计过程中可能很有用。开发具有可接受药代动力学特征的口服药物对于降低与评估候选化合物在临床前研究中的首过代谢程度相关的成本和时间至关重要。本研究旨在预测口服药物的首过代谢。
收集了一组具有报告的首过代谢数据的化合物。此外,从文献中提取了人类肠道吸收百分比和口服生物利用度数据,提出了一种分类系统,根据药物的首过代谢程度对药物进行分类。为每个化合物计算了各种结构参数。使用逻辑回归获得了每个化合物的结构和物理化学值与化合物所属类别之间的关系。
初步分析表明,logD>1 或糙度因子>1.5 的化合物更有可能具有高首过代谢。引入了四个不同的模型,可以用可接受的误差预测口服首过代谢。模型的整体准确率在 72%(具有简单描述符的模型)到 78%(具有复杂描述符的模型)之间。尽管具有简单描述符的模型的准确性低于复杂模型,但它们更具可解释性,研究人员更容易使用。
引入了一种基于口服首过代谢程度的新型药物分类,并开发了机制模型将候选化合物分配到适当的拟议类别中。