Folguera-Blasco Núria, Boshier Florencia A T, Uatay Aydar, Pichardo-Almarza Cesar, Lai Massimo, Biasetti Jacopo, Dearden Richard, Gibbs Megan, Kimko Holly
Systems Medicine, Clinical Pharmacology and Safety Sciences, R and D BioPharmaceuticals, AstraZeneca, Cambridge, United Kingdom.
Systems Medicine, Clinical Pharmacology and Safety Sciences, R and D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden.
Front Syst Biol. 2024 Jul 12;4:1380685. doi: 10.3389/fsysb.2024.1380685. eCollection 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 () that the symbiosis can offer, it does also carry potential challenges () such as how to assess and quantify uncertainty, bias and error. Hence, to ensure a successful implementation, arising 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.
定量系统药理学（QSP）已成为药物研发领域的一项强大工具。为了促进其持续应用并进一步提高其适用性，将QSP与人工智能（AI）和机器学习（ML）相结合的共生方法似乎是关键所在。本手稿展示了四个案例，在这些案例中，共生方法的应用能够从包括真实世界数据在内的多维数据中获得新的见解，这有可能在药物研发方面带来突破。除了这种共生关系能够带来的显著益处外，它也确实存在一些潜在挑战，比如如何评估和量化不确定性、偏差和误差。因此，为确保成功实施，必须认识到并谨慎应对出现的问题。共生的QSP与ML/AI方法的成功实施有可能成为一种催化剂，为药物研发的范式转变铺平道路。
