Development of a Minimal Physiologically-Based Pharmacokinetic Modeling / Machine Learning Framework for Early Target Pharmacology Assessment

Abstract

Development of antibodies often begins with the assessment and optimizing of their physicochemical properties, and their efficient engagement to the target of interest. Decisions at the early optimization stage are critical for the success of the drug candidate but are constrained due to the limited knowledge of the antibody and target pharmacology. n the present work we propose a model-based target pharmacology assessment framework based on which optimal physicochemical properties of antibodies can be inferred from minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML). Towards this goal, we aim to perform a high-throughput virtual exploration of physicochemical properties of antibody drug candidates and relate them to target occupancy (TO). We use a mPBPK model previously developed by our group that incorporates a multivariate quantitative relationship between antibodies’ physicochemical properties such as molecular weight (MW), size, charge, and in silico + in vitro derived descriptors with a known relation to PK properties. In this study, we perform an exploration of virtual antibody drug candidates with varying physicochemical properties, and virtual target candidates with varying characteristics to unravel rules for optimal antibody drug candidates and feasible drug-target interaction. We also identify that varying the antibody dose and dosing scheme, target form (soluble or membrane-bound), antibody charge, and site of action had significant effect on the optimal properties for antibody drug candidate selection. By unravelling new design rules for antibody drug properties that are dependent on model-based TO assessment, we deliver a first-in-class model-based framework towards better understanding of the biology-specific PK and ADME processes of antibody drug candidates proteins and reducing the overall time for drug development.