Perspectives on training quantitative systems pharmacologists

• Published in: CPT: pharmacometrics and systems pharmacology Vol. 11; no. 6; pp. 669 - 672
• Main Authors: Zhang, Tongli, Cho, Carolyn R., Bonate, Peter L.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.06.2022; John Wiley and Sons Inc; Wiley
• Subjects: Applied mathematics; Communication; Computer science; Dynamical systems; Education; Humans; Interdisciplinary aspects; Parameter estimation; Pharmaceutical industry; Pharmacodynamics; Pharmacokinetics; Pharmacology; Pharmacology, Clinical - education; Physiology; Scientists; Soft skills; System theory
• ISSN: 2163-8306; 2163-8306
• DOI: 10.1002/psp4.12783

Quantitative systems pharmacology has been rapidly adopted by industry and academia alike. 1–3 The potential benefit of QSP in de-risking drug development has led to an encouraging increase in demand from employers, from entry-level interns to mid- and senior-level group leaders. [...]it is not surprising that a QSP scientist would need some data-handling tools in the toolbox. Furthermore, when the mechanism is not clear, or the data are too sparse to support the development of a mechanistic model, data-driven approaches such as machine learning 4,5 and time series analysis would be useful to extract the patterns from the data. [...]a good understanding of differential equations and numerical integration would help avoid numerical errors when integrating stiff dynamical systems, experience with nonlinear dynamical system theory would help with grasping the qualitative boundaries of complex biological models, and experience with various optimization algorithms allow the QSP scientist to increase the computational efficiency without impairing the solidness of the results.