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The Promises Of Quantitative Systems Pharmacology Modelling For Drug Development

Authors: V.R.Knight-Schrijver, V.Chelliah, L.Cucurull-Sanchez, N. Le Novere

Abstract:

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative Systems Pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Keywords

Quantitative systems pharmacology; QSP; Drug discovery; Modelling; Systems biology; New therapeutic entitity.

Citation: V.R. Knight-Schrijver, V. Chelliah, L.Cucurull-Sanchez, N. Le Novere The Promises Of Quantitative Systems Pharmacology Modelling For Drug Development http://dx.doi.org/10.1016/j.csbj.2016.09.002.

Received: 22 June 2016, Revised: 8 September 2016, Accepted: 19 September 2016, Available online: 23 September 2016

Copyright: © 2016 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Funding

Research was funded by a Biotechnology and Biosciences Research Council (BBSRC) industrial CASE studentship in collaboration between the Babraham Institute and GlaxoSmithKline research & development Ltd.

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