AUTHOR=Folguera-Blasco Núria , Boshier Florencia A. T. , Uatay Aydar , Pichardo-Almarza Cesar , Lai Massimo , Biasetti Jacopo , Dearden Richard , Gibbs Megan , Kimko Holly 

TITLE=Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns

JOURNAL=Frontiers in Systems Biology

VOLUME=4

YEAR=2024

URL=https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2024.1380685

DOI=10.3389/fsysb.2024.1380685

ISSN=2674-0702

ABSTRACT=<p>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 (<italic>gAIns</italic>) that the symbiosis can offer, it does also carry potential challenges (<italic>pAIns</italic>) such as how to assess and quantify uncertainty, bias and error. Hence, to ensure a successful implementation, arising <italic>pAIns</italic> 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.</p>