Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
Eur J Drug Metab Pharmacokinet. 2024 Jan;49(1):1-6. doi: 10.1007/s13318-023-00861-5. Epub 2023 Oct 21.
The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug-drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system.
In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1-7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models.
The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy.
Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.
基于药物的溶解度和代谢情况，生物药剂学药物处置分类系统（BDDCS）将药物分为四类。该框架允许研究生物制药中的转运体和酶代谢的药代动力学，以及体内的药物相互作用。本研究的目的是通过神经网络模型和结构参数及物理化学性质建立计算模型，以预测 BDDCS 系统中药物的类别。
本研究利用深度学习方法探索人工神经网络（ANNs）和卷积神经网络（CNNs）在预测 721 种物质的 BDDCS 类别方面的潜力。利用各种软件计算结构参数和物理化学性质[Abraham 溶剂化参数、辛醇-水分配系数（log P）和 pH 值 1-7.5 范围内的分配系数（log D）、可旋转键的数量、氢键受体数以及氢键供体数]。然后，将这些化合物分为包含 602 个分子的训练集和包含 119 个化合物的测试集，以验证模型。
本研究结果表明，使用药物应用参数（即 log D 和 Abraham 溶剂化参数）的神经网络模型能够很好地预测 BDDCS 系统中的溶解度和代谢类别。
神经网络模型能够很好地处理药物的结构参数和物理化学性质与 BDDCS 类别之间的关系。此外，与 log P 相比，log D 更适合用于预测 BDDCS。
