Webinar with Dr. Bansal, GSK on QSP Models

Webinar: Modular development and application of platform QSP models to support a broad R&D portfolio: Examples from immuno-oncology and respiratory therapeutic areas Loveleena Bansal, PhD Scientific Leader, GSK Associate Fellow, at GSK, Collegeville PA Jun 20, 2019 12:00-1:00 PM EDT Registration (Free) at https://register.gotowebinar.com/register/6137023505483805197?source=website Abstract: QSP modeling provides an integrated systems approach to model the mechanism of action of drugs as well as obtain a deeper understanding of the pathogenesis of diseases. It has thus emerged as an important tool to advance the discovery and development of therapeutic drugs in the pharmaceutical industry. However, one of the major challenges facing QSP modelers is rapid development of these models under strict timelines to allow impactful contributions to programs and scaling up to other targets/drugs within the same disease area as well as other disease areas of interest. Thus, a strategy for widely applying QSP models for several disease areas in GSK has been developed by leveraging modular development to allow extensive re-use of developed models and automation tools for accelerating model development and analysis. In this talk, developments on application strategy of QSP modeling and its impact on programs will be discussed. Model development workflow will be illustrated for a QSP platform for evaluating immune-oncology (IO) therapeutics which covers description of several immune cells and templates for coreceptor-ligand interactions on the surface of cells that can be applied to number of different coreceptors to evaluate IO combination therapies. Secondly, a QSP modeling platform has been developed to support the diverse COPD portfolio in GSK. The model is supporting translation of in-vitro drug effects to patients to enable efficacious dose prediction, selection of biomarkers that can be used as early indicators of efficacy, as well as clinical trial design by estimating the length of study required to observe clinical benefits.