Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model

Abstract

The pharmaceutical industry has increasingly adopted model-informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and disease complexities to predict clinical endpoints and biomarkers is central to MID3. QSP modeling has proven successful in metabolic and cardiovascular diseases and has expanded into oncology, immunotherapy, and infectious diseases. Despite its benefits, QSP model validation through clinical trial simulations using virtual patients (VPs) is challenging because of parameter variability and high computational costs. To address these challenges, this study proposes a hybrid method that combines Bayesian optimization with machine learning for efficient parameter screening. Our approach achieved an acceptance rate of 27.5% in QSP simulations, which is in sharp contrast with the 2.5% rate of conventional random search methods, indicating more than 10-fold improvement in efficiency. By facilitating faster and more diverse VPs generation, this method promises to advance clinical trial simulations and accelerate drug development in pharmaceutical research.