Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan.

Incorporating inter-individual differences in drug disposition and responses is essential for ensuring the safe and effective use of drugs in real-world patients. Despite ongoing efforts, lower participation of children, older individuals, pregnant and breastfeeding women, postmenopausal women, and people with disease states and disabilities in drug clinical trials is frequent, and it requires multifaceted strategies and tools to evaluate drug exposure and responses in broad populations. The availability of modeling and simulation tools, such as physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology/toxicology (QSP/QST) modeling, enables the application of virtual populations that reflect the differences in drug disposition and responses for disease states and different stages of the lifespan. These models integrate clinical trial and real-world data (RWD) to predict drug exposure, efficacy, and safety. Additionally, machine learning (ML) and artificial intelligence (AI) offer powerful tools for analyzing large datasets and identifying key physiological determinants of drug response across the lifespan. This review discusses the application of in silico and AI models to advance the prediction of drug exposure and responses across the lifespan, including examples of virtual populations in PBPK and QSP/QST models. A case study on QST modeling for drug-induced liver injury (DILI) in postmenopausal women is presented, along with opportunities and challenges in applying AI for modeling physiological determinants of drug dosing in individuals ranging in age from 12 to > 80 years old in drug development.

Duke Scholars

Author Daniel Gonzalez Medicine, Clinical Pharmacology