Small molecule exposure prediction (intravenous (IV) and oral (PO)) using Machine learning (ML) in combination with mechanistic modeling
Abstract
Objective: The goal of this proof-of concept work is to evaluate whether using machine learning (ML)-derived parameters in tandem with physiologically based pharmacokinetic (PBPK) modeling can reasonably predict plasma exposure after IV and PO administration of small molecules. This framework aims to provide early PK prediction solely based on molecular structure enabling early go, no-go decisions. Methods: In this study, ML is used to predict pharmacokinetic (PK) and physicochemical (PC) properties that are then used as input to mechanistic models to ultimately predict rat plasma exposure for both IV [1] and PO administration. As a first step, ML models were built to predict parameters required as input to PBPK model. Different ML algorithms such as random forest [2], support vector regression (SVR) [3], XGboost [4] and message-passing neural networks (MPNN) [5] were tested for their performance. For PBPK modeling five distribution models were tested: Berezhkovskiy [6], PK-Sim standard [7], Poulin and Theil [8], Rodgers and Rowland [9] and Schmidt [10]. PBPK model was implemented in PK-Sim, part of the Open Systems Pharmacology Suite version 11.0 [11]. R version 4.2.0 was used to perform the simulations. To ensure data consistency and reduce variability caused by assay choice, we utilized historical data exclusively generated at the Sanofi Boston site. Administered doses were 1 mg/kg for IV administration and 3 mg/kg for PO administration. The test set comprised of 61…
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