Immunological Modeling: Bridging Gaps in Clinical Data

The potential impact of modeling in clinical research is more profound than in discovery where approaches to controlled experimental perturbation are available, in a relatively short period of time. In particular, questions during pharmaceutical development often arise in a timeframe that does not allow for data to be generated in a dedicated clinical trial. Clinical data are often generated from healthy volunteers, are observational rather than interventional, and undergo varied quality control. Yet these data are intended to inform predicting responses of interventions in patient populations. Two vignettes of modeling – one, data mining, the other mechanistic – to address pharmaceutical development questions will be discussed, to illustrate considerations for clinical data preparedness. Moderator: James A. Glazier, PhD, Indiana University, Bloomington For more information see: Visser, Sandra AG, Bhargava Kandala, Craig Fancourt, Alexander W. Krug, and Carolyn R. Cho. "A model‐informed drug discovery and development strategy for the novel glucose‐responsive insulin MK‐2640 enabled rapid decision making." Clinical Pharmacology & Therapeutics 107, no. 6 (2020): 1296-1311. https://ascpt.onlinelibrary.wiley.com/doi/full/10.1002/cpt.1729 For the slides in this video, visit: https://drive.google.com/file/d/1BtZwyCYdM6luMG--hVV4tjOHi2Hgp2oE/view If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://tinyurl.com/GLIMPRINTVideos 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/