Mathematical modeling and simulation in animal health. Part I: Moving beyond pharmacokinetics

• Published in: Journal of veterinary pharmacology and therapeutics Vol. 39; no. 3; pp. 213 - 223
• Main Authors: Riviere, J.E., Gabrielsson, J., Fink, M., Mochel, J.
• Format: Journal Article
• Language: English
• Published: England Blackwell Scientific Publications 01.06.2016; Blackwell Publishing Ltd
• Subjects: Animals; Annan klinisk medicin; Computer Simulation; Models, Biological; Other Clinical Medicine; Veterinary Drugs - pharmacokinetics
• ISSN: 0140-7783; 1365-2885; 1365-2885
• DOI: 10.1111/jvp.12278

The application of mathematical modeling to problems in animal health has a rich history in the form of pharmacokinetic modeling applied to problems in veterinary medicine. Advances in modeling and simulation beyond pharmacokinetics have the potential to streamline and speed‐up drug research and development programs. To foster these goals, a series of manuscripts will be published with the following goals: (i) expand the application of modeling and simulation to issues in veterinary pharmacology; (ii) bridge the gap between the level of modeling and simulation practiced in human and veterinary pharmacology; (iii) explore how modeling and simulation concepts can be used to improve our understanding of common issues not readily addressed in human pharmacology (e.g. breed differences, tissue residue depletion, vast weight ranges among adults within a single species, interspecies differences, small animal species research where data collection is limited to sparse sampling, availability of different sampling matrices); and (iv) describe how quantitative pharmacology approaches could help understanding key pharmacokinetic and pharmacodynamic characteristics of a drug candidate, with the goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans, enabling critical decision making, and eventually bringing safe and effective medicines to patients. This study introduces these concepts and introduces new approaches to modeling and simulation as well as clearly articulate basic assumptions and good practices. The driving force behind these activities is to create predictive models that are based on solid physiological and pharmacological principles as well as adhering to the limitations that are fundamental to applying mathematical and statistical models to biological systems.