Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns - Folguera-Blasco, Núria; Boshier, Florencia A. T.; Uatay, Aydar; Pichardo-Almarza, Cesar; Lai, Massimo; Biasetti, Jacopo; Dearden, Richard; Gibbs, Megan; Kimko, Holly (Frontiers in Systems Biology, 2024)
Quantitative Systems Pharmacology (QSP) has become a powerful tool in the drug development landscape. To facilitate its continued implementation and to further enhance its applicability, a symbiotic approach in which QSP is combined with artificial intelligence (AI) and machine learning (ML) seems key. This manuscript presents four case examples where the application of a symbiotic approach could unlock new insights from multidimensional data, including real-world data, potentially leading to breakthroughs in drug development. Besides the remarkable benefits (gAIns) that the symbiosis can offer, it does also carry potential challenges (pAIns) such as how to assess and quantify uncertainty, bias and error. Hence, to ensure a successful implementation, arising pAIns need to be acknowledged and carefully addressed. Successful implementation of the symbiotic QSP and ML/AI approach has the potential to serve as a catalyst, paving the way for a paradigm shift in drug development.