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[NMusers] Webinar with Dr. Bansal, GSK on QSP Models

From: Rebecca Baillie <>
Date: Wed, 5 Jun 2019 15:00:49 +0000

Webinar: Modular development and application of platform QSP models to supp=
ort a broad R&D portfolio: Examples from immuno-oncology and respiratory th=
erapeutic 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

Abstract: QSP modeling provides an integrated systems approach to model the=
 mechanism of action of drugs as well as obtain a deeper understanding of t=
he pathogenesis of diseases. It has thus emerged as an important tool to ad=
vance the discovery and development of therapeutic drugs in the pharmaceuti=
cal industry. However, one of the major challenges facing QSP modelers is r=
apid 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 strat=
egy for widely applying QSP models for several disease areas in GSK has bee=
n developed by leveraging modular development to allow extensive re-use of =
developed models and automation tools for accelerating model development an=
d analysis.

In this talk, developments on application strategy of QSP modeling and its =
impact on programs will be discussed. Model development workflow will be il=
lustrated for a QSP platform for evaluating immune-oncology (IO) therapeuti=
cs which covers description of several immune cells and templates for corec=
eptor-ligand interactions on the surface of cells that can be applied to nu=
mber of different coreceptors to evaluate IO combination therapies. Secondl=
y, a QSP modeling platform has been developed to support the diverse COPD p=
ortfolio in GSK. The model is supporting translation of in-vitro drug effec=
ts to patients to enable efficacious dose prediction, selection of biomarke=
rs that can be used as early indicators of efficacy, as well as clinical tr=
ial design by estimating the length of study required to observe clinical b=

Received on Wed Jun 05 2019 - 11:00:49 EDT

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