Two heads are better than one: current landscape of integrating QSP and machine learning
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Published:2022-02
Issue:1
Volume:49
Page:5-18
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ISSN:1567-567X
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Container-title:Journal of Pharmacokinetics and Pharmacodynamics
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language:en
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Short-container-title:J Pharmacokinet Pharmacodyn
Author:
Zhang Tongli, Androulakis Ioannis P., Bonate Peter, Cheng Limei, Helikar Tomáš, Parikh Jaimit, Rackauckas Christopher, Subramanian Kalyanasundaram, Cho Carolyn R.ORCID, Androulakis Ioannis P., Bonate Peter, Borisov Ivan, Broderick Gordon, Cheng Limei, Damian Valeriu, Dariolli Rafael, Demin Oleg, Ellinwood Nicholas, Fey Dirk, Gulati Abhishek, Helikar Tomas, Jordie Eric, Musante Cynthia, Parikh Jaimit, Rackauckas Christopher, Saez-Rodriguez Julio, Sobie Eric, Subramanian Kalyanasundaram, Cho Carolyn R.,
Abstract
AbstractQuantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.
Funder
National Institutes of Health Advanced Research Projects Agency National Science Foundation Army Research Office
Publisher
Springer Science and Business Media LLC
Reference93 articles.
1. Maharao N, Antontsev V, Wright M, Varshney J (2020) Entering the era of computationally driven drug development. Drug Metab Rev 52(2):283–298 2. Azer K, Kaddi CD, Barrett JS, Bai JP, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, and Lombardo R (2021) History and future perspectives on the discipline of Quantitative Systems Pharmacology modeling and its applications. Front. Physiol. 12. 3. Bluestone JA, Auchincloss H, Nepom GT, Rotrosen D, Clair EWS, Turka LA (2010) The immune tolerance network at 10 years: tolerance research at the bedside. Nat Rev Immunol 10(11):797–803 4. Hu H, Correll M, Kvecher L, Osmond M, Clark J, Bekhash A, Schwab G, Gao D, Gao J, Kubatin V (2011) DW4TR: a data warehouse for translational research. J Biomed Inform 44(6):1004–1019 5. Holmes JH, Elliott TE, Brown JS, Raebel MA, Davidson A, Nelson AF, Chung A, La Chance P, Steiner JF (2014) Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature. J Am Med Inform Assoc 21(4):730–736
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