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

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

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.
将定量系统药理学建模与机器学习和人工智能相结合,促进药物开发:其 pAIns 和 gAIns
定量系统药理学(QSP)已成为药物开发领域的有力工具。为促进其持续应用并进一步提高其适用性,将定量系统药理学与人工智能(AI)和机器学习(ML)相结合的共生方法似乎至关重要。本手稿介绍了四个案例,在这些案例中,应用共生方法可以从包括真实世界数据在内的多维数据中获得新的见解,从而有可能在药物开发方面取得突破。共生方法除了能带来显著的优势(gAIns)外,也存在潜在的挑战(pAIns),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,必须认识到并认真解决由此产生的 pAIns。QSP 和 ML/AI 共生方法的成功实施有可能成为一种催化剂,为药物开发模式的转变铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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