Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules - Mavroudis, Panteleimon D.; Teutonico, Donato; Abos, Alexandra; Pillai, Nikhil (Frontiers in Systems Biology, 2023)
Prediction of a new molecule’s exposure in plasma, is a critical first step towards understanding its efficacy/toxicity profile and conclude whether it’s a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. In this work, we propose a novel framework based on which pre-clinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and next the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-sim Standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using in-vivo, or in-vitro clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underlines the necessity to investigate the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims to enable exposure prediction earlier in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible.