Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?

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Abstract

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition.

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APA

Tindall, M. J., Cucurull-Sanchez, L., Mistry, H., & Yates, J. W. T. (2023, October 1). Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell? Journal of Pharmacology and Experimental Therapeutics. American Society for Pharmacology and Experimental Therapy (ASPET). https://doi.org/10.1124/jpet.122.001551

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