Quantitative systems toxicology modeling in pharmaceutical research and development: An industry‐wide survey and selected case study examples - Kylie A. Beattie; Meghna Verma; Richard J. Brennan; Diana Clausznitzer; Valeriu Damian; Derek Leishman; Mary E. Spilker; Britton Boras; Zhenhong Li; Elias Oziolor; Theodore R. Rieger; Anna Sher (CPT: Pharmacometrics & Systems Pharmacology, 2024)
Abstract Quantitative systems toxicology (QST) models are increasingly being applied for predicting and understanding toxicity liabilities in pharmaceutical research and development. A European Federation of Pharmaceutical Industries and Associations (EFPIA)‐wide survey was completed by 15 companies. The results provide insights into the current use of QST models across the industry. 73% of responding companies with more than 10,000 employees utilize QST models. The most applied QST models are for liver, cardiac electrophysiology, and bone marrow/hematology. Responders indicated particular interest in QST models for the central nervous system (CNS), kidney, lung, and skin. QST models are used to support decisions in both preclinical and clinical stages of pharmaceutical development. The survey suggests high demand for QST models and resource limitations were indicated as a common obstacle to broader use and impact. Increased investment in QST resources and training may accelerate application and impact. Case studies of QST model use in decision‐making within EFPIA companies are also discussed. This article aims to (i) share industry experience and learnings from applying QST models to inform decision‐making in drug discovery and development programs, and (ii) share approaches taken during QST model development and validation and compare these with recommendations for modeling best practices and frameworks proposed in the literature. Discussion of QST‐specific applications in relation to these modeling frameworks is relevant in the context of the recently proposed International Council for Harmonization (ICH) M15 guideline on general principles for Model‐Informed Drug Development (MIDD).