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

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 (f_u) human data set (n = 1812) and a classification-based model with the hepatocyte intrinsic clearance (Cl_int) 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.