QST and the Transformation in Drug Safety Assessment Paul B. Watkins, M.D. FAASLD Director, Institute for Drug Safety Sciences , University of North Carolina in Chapel Hill
Wednesday December 16, 2020, 12:00 to 1:00 pm EDT Register for free at https://www.rosaandco.com/webinars Abstract: Establishing the safety of new drug candidates is a major hurdle to drug development as standard preclinical toxicology does not reliably predict human adverse drug events. Liver toxicity is a potentially fatal adverse event that has been particularly challenging to predict from preclinical studies. Moreover, abnormalities in serum liver chemistries are commonly observed in clinical trials raising suspicion of liver safety liability that can currently only be removed with very large clinical trials. This talk will focus on the progress of a public-private partnership (the DILI-sim Initiative) that for the last decade has been developing a Quantitative Systems Toxicology (QST) model (DILIsym(r)) to improve mechanistic understanding and therefore prediction of liver safety liabilities of new drug candidates. The DILIsym model uses PBPK and other available data to determine the concentration of parent drug and major metabolites inside the hepatocyte during various dosing regimens. Also fed into the model are the exposure dependent effects of parent drug and major metabolites on oxidative stress, bile acid homeostasis, and mitochondrial function as measured in in vitro or cellular systems. Parameters in the model have been varied to reflect genetic and non-genetic variability to create a virtual healthy human population as well as disease-specific populations. With the data inputs, DILIsym will predict the incidence and severity of liver injury that will be observed in a simulated patient population as a function of dosing regimen. Results of DILIsym modeling are increasingly used in decision making within Pharma and have also been helpful in interactions with regulators. DILIsym provides an example of how increased application of QST modeling should transform the safety assessment of new drug candidates as well as risk management in clinical trials and post-approval.
