Insights into pharmacokinetic properties for exposure chemicals: predictive modelling of human plasma fraction unbound (fu) and hepatocyte intrinsic clearance (Clint) data using machine learning†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-08-15 DOI:10.1039/D4DD00082J
Souvik Pore and Kunal Roy
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Abstract

An external chemical substance (which may be a medicinal drug or an exposome), after ingestion, undergoes a series of dynamic movements and metabolic alterations known as pharmacokinetic events while exerting different physiological actions on the body (pharmacodynamics events). Plasma protein binding and hepatocyte intrinsic clearance are crucial pharmacokinetic events that influence the efficacy and safety of a chemical substance. Plasma protein binding determines the fraction of a chemical compound bound to plasma proteins, affecting the distribution and duration of action of the compound. The compounds with high protein binding may have a smaller free fraction available for pharmacological activity, potentially altering their therapeutic effects. On the other hand, hepatocyte intrinsic clearance represents the liver's capacity to eliminate a chemical compound through metabolism. It is a critical determinant of the elimination half-life of the chemical substance. Understanding hepatic clearance is essential for predicting chemical toxicity and designing safety guidelines. Recently, the huge expansion of computational resources has led to the development of various in silico models to generate predictive models as an alternative to animal experimentation. In this research work, we developed different types of machine learning (ML) based quantitative structure–activity relationship (QSAR) models for the prediction of the compound's plasma protein fraction unbound values and hepatocyte intrinsic clearance. Here, we have developed regression-based models with the protein fraction unbound (fu) human data set (n = 1812) and a classification-based model with the hepatocyte intrinsic clearance (Clint) human data set (n = 1241) collected from the recently published ICE (Integrated Chemical Environment) database. We have further analyzed the influence of the plasma protein binding on the hepatocyte intrinsic clearance, by considering the compounds having both types of target variable values. For the fraction unbound data set, the support vector machine (SVM) model shows superior results compared to other models, but for the hepatocyte intrinsic clearance data set, random forest (RF) shows the best results. We have further made predictions of these important pharmacokinetic parameters through the similarity-based read-across (RA) method. A Python-based tool for predicting the endpoints has been developed and made available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/pkpy-tool.

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揭示暴露化学品的药代动力学特性:利用机器学习† 建立人体血浆非结合分数(fu)和肝细胞固有清除率(Clint)数据的预测模型
外来化学物质(可能是药物或暴露体)摄入人体后,在对人体产生不同生理作用(药效学事件)的同时,会发生一系列被称为药代动力学事件的动态变化和代谢改变。血浆蛋白结合和肝细胞固有清除率是影响化学物质疗效和安全性的关键药代动力学事件。血浆蛋白结合率决定了化合物与血浆蛋白结合的比例,从而影响化合物的分布和作用时间。蛋白结合率高的化合物可用于药理活性的游离部分可能较小,从而可能改变其治疗效果。另一方面,肝细胞固有清除率代表肝脏通过新陈代谢消除化合物的能力。它是决定化学物质消除半衰期的关键因素。了解肝脏清除率对于预测化学毒性和设计安全指南至关重要。最近,随着计算资源的大幅扩展,人们开发出了各种硅学模型来生成预测模型,以替代动物实验。在这项研究工作中,我们开发了不同类型的基于机器学习(ML)的定量结构-活性关系(QSAR)模型,用于预测化合物的血浆蛋白部分未结合值和肝细胞固有清除率。在此,我们利用从最近发布的 ICE(集成化学环境)数据库中收集的未结合蛋白分数(fu)人类数据集(n = 1812)开发了基于回归的模型,并利用肝细胞固有清除率(Clint)人类数据集(n = 1241)开发了基于分类的模型。通过考虑两种类型目标变量值的化合物,我们进一步分析了血浆蛋白结合对肝细胞固有清除率的影响。对于非结合分数数据集,支持向量机(SVM)模型显示出优于其他模型的结果,但对于肝细胞固有清除率数据集,随机森林(RF)显示出最佳结果。我们还通过基于相似性的read-across(RA)方法进一步预测了这些重要的药代动力学参数。我们开发了一个基于 Python 的终点预测工具,可从 https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/pkpy-tool 获取。
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Back cover Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing Artificial intelligence-enabled optimization of battery-grade lithium carbonate production†
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