History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications.
Karim Azer, Chanchala D Kaddi, Jeffrey S Barrett, Jane P F Bai, Sean T McQuade, Nathaniel J Merrill, Benedetto Piccoli, Susana Neves-Zaph, Luca Marchetti, Rosario Lombardo, Silvia Parolo, Selva Rupa Christinal Immanuel, Nitin S Baliga
Author Information
Karim Azer: Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States.
Chanchala D Kaddi: Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States.
Jeffrey S Barrett: Critical Path Institute, Tucson, AZ, United States.
Jane P F Bai: Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States.
Sean T McQuade: Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States.
Nathaniel J Merrill: Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States.
Benedetto Piccoli: Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States.
Susana Neves-Zaph: Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States.
Luca Marchetti: Fondazione the Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Rosario Lombardo: Fondazione the Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Silvia Parolo: Fondazione the Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Selva Rupa Christinal Immanuel: Institute for Systems Biology, Seattle, WA, United States.
Nitin S Baliga: Institute for Systems Biology, Seattle, WA, United States.
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
