Beyond Deterministic Models in Drug Discovery and Development

Itziar Irurzun-Arana; Christopher Rackauckas; Thomas O McDonald; Iñaki F Trocóniz

doi:10.1016/j.tips.2020.09.005

Beyond Deterministic Models in Drug Discovery and Development

Trends Pharmacol Sci. 2020 Nov;41(11):882-895. doi: 10.1016/j.tips.2020.09.005. Epub 2020 Oct 5.

Authors

Itziar Irurzun-Arana¹, Christopher Rackauckas², Thomas O McDonald³, Iñaki F Trocóniz⁴

Affiliations

¹ Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain. Electronic address: itzirurzun@alumni.unav.es.
² Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
³ Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
⁴ Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain; Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, 31080, Spain. Electronic address: itroconiz@unav.es.

Abstract

The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.

Keywords: MID3; deterministic; infectious diseases; nonlinear mixed-effects models; oncology; stochastic.

Publication types

Review

MeSH terms

Drug Development / methods*
Drug Discovery / methods*
Humans
Models, Biological*
Stochastic Processes