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.

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