Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties

Abstract

Accurate prediction of oral pharmacokinetics remains challenging. This study investigated quantitative approaches for the prediction of the area under the plasma concentration−time curve after oral administration (AUC p,oral ) to rats using the in vitro−in vivo extrapolation (IVIVE), in silico model using machine learning approaches and the combination of the in silico model and in vitro data. A set of 595 structurally diverse compounds with determined AUC p,oral at 1 mg/kg, in vitro intrinsic clearance (CL int ), an unbound fraction in plasma (f u,p ) in rats, and kinetic solubility at pH 6.8 was used for this assessment. Prediction models developed by two different types of machine learning techniques (i.e., random forest regression and Gaussian processes) were evaluated using three validation methods implementing the time and cluster-split training and test set and fivefold cross-validation. The developed machine learning models have a square of correlation coefficient (R 2 ) in the range of 0.381−0.685 with 33−45% of the compounds being predicted within 2fold of the observed AUC p,oral value. The predictivity was improved by incorporating CL int , f u,p , and solubility as explanatory variables with R 2 = 0.554−0.743. In cases where extraction by the liver is the main elimination pathway and intestinal extraction is negligible, AUC p,oral can be expressed by dose, CL int , and f u,p based on a well-stirred model. By using this conventional IVIVE approach, only 1.7−5.0% of compounds were predicted within the 2-fold error with R 2 = 0.354−0.487. Two empirical scaling factors (ESFs) determined by linear regression analysis and machine learning approaches improved the predictivity of AUC p,oral with 33−44% predicted within twofold variability. The IVIVE using ESF predicted by random forest regression showed better predictivity of AUC p,oral with R 2 = 0.471−0.618, while it still showed lower predictivity than machine learning approaches applied directly to AUC p,oral prediction. This study demonstrated that the combination of in silico and in vitro parameters is useful to improve the predictivity of the machine learning model for rat AUC p,oral and supports consideration for predicting AUC p,oral for human and other non-clinical species in a similar manner. KEYWORDS: machine learning, quantitative structure−activity relationship (QSAR), oral clearance prediction, in silico, plasma protein binding, in vitro−in vivo extrapolation (IVIVE), bottom-up approach, well-stirred model

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