The promises of quantitative systems pharmacology modelling for drug development

Knight-Schrijver VR, Chelliah V, Cucurull-Sanchez L, Le Novère N.

Comput Struct Biotechnol J. 2016 Sep;14:363-370

PMID: 27761201

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5064996/

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.