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Irurzun-Arana I, Rackauckas C, McDonald TO, Trocóniz IF. Beyond Deterministic Models in Drug Discovery and Development. Trends Pharmacol Sci 2020; 41:882-895. [PMID: 33032836 PMCID: PMC7534664 DOI: 10.1016/j.tips.2020.09.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/28/2020] [Accepted: 09/10/2020] [Indexed: 02/06/2023]
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.
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Affiliation(s)
- Itziar Irurzun-Arana
- 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.
| | - Christopher Rackauckas
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thomas O McDonald
- 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
| | - Iñaki F Trocóniz
- 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.
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Cao Y, Linda P, Seitaridou E. Stochastic Simulation of Biochemical Systems: In Memory of Dan T. Gillespie's contributions. Bull Math Biol 2019; 81:2819-2821. [PMID: 31264134 DOI: 10.1007/s11538-019-00633-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Yang Cao
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA.
| | - Petzold Linda
- Department of Computer Science and Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Effrosyni Seitaridou
- Department of Physics, Oxford College of Emory University, 100 Hamill St., Oxford, GA, 30054, USA
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