Evaluation Framework for Systems Models

A wide variety of mathematical methods are used to aid the drug development process. One example is the use of quantitative systems pharmacology (QSP) models. A QSP model is a mathematical, mechanistic representation of a patient’s disease/therapy dynamics. QSP models are typically systems of ordinary differential equations with many nonlinear equations, and many more parameters. Although QSP models have been used to save substantial time and money in drug development, their use is not as widespread as might be expected from these benefits. Lack of buy-in from stakeholders is a major hurdle to adoption and can be attributed to lack of confidence in QSP models and their predictions. In this talk, I make the case that standardization of systems model evaluation methods, either within the biopharma community or more broadly, would support more extensive use of such models, saving resources and producing better outcomes [1]. Proposed model evaluation methods include sensitivity and identifiability analysis, uncertainty quantification, comparison to data, and external review. I will focus on the concepts of sensitivity and identifiability analysis, and will show examples of these methods applied to QSP models. My presentation includes work from the publications below. For more information see: Braakman S, Pathmanathan P, Moore H (2022) Evaluation framework for systems models. CPT Pharmacometrics Syst Pharmacol, 11: 264- 289. https://doi.org/10.1002/psp4.12755 González-Garcia I, Pierre V, Dubois VSF, Morsli N, Spencer S, Baverel PG, Moore H (2021) Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous-cell carcinoma (HNSCC) treated with immunotherapy. CPT Pharmacometrics Syst Pharmacol, 10: 230-240. https://doi.org/10.1002/psp4.12594 Moore H (2018) How to mathematically optimize drug regimens using optimal control. J Pharmacokinet Pharmacodyn, 45: 127-137. https://doi.org/10.1007/s10928-018-9568-y Gallaher J, Larripa K, Ledzewicz U, Renardy M, Shtylla B, Tania N, White D, Wood K, Zhu L, Passey C, Robbins M, Bezman N, Shelat S, Cho HJ, Moore H (2018). A Mathematical Model for Tumor–Immune Dynamics in Multiple Myeloma. In: Radunskaya, A., Segal, R., Shtylla, B. (eds) Understanding Complex Biological Systems with Mathematics. Association for Women in Mathematics Series, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-98083-6_5 Gallaher J, Larripa K, Renardy M, Shtylla B, Tania N, White D, Wood K, Zhu L, Passey C, Robbins M, Bezman N, Shelat S, Cho HJ, Moore H (2018) Methods for determining key components in a mathematical model for tumor–immune dynamics in multiple myeloma. J Theor Biol, 458: 31-46. https://doi.org/10.1016/j.jtbi.2018.08.037 If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/