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Status of QSP Modeling in the Pharmaceutical Industry

A primary cause of failures in pharmaceutical research and development (R&D) has been attributed to lack of efficacy,1 suggesting inadequate understanding in therapeutic targets’ biology and their relevance to disease progression or modulation. Quantitative systems pharmacology (QSP) has the promise of increasing the probability of success in R&D by bridging scientific gaps between disciplines to enable target validation.2 In 2014, a group of pharmaceutical industry representatives of the Simcyp consortium along with scientists from Certara initiated discussions on forming a QSP consortium, with the objective of developing, validating and sharing pre-competitive QSP models. The idea of an industry sponsored QSP consortium managed by Certara was well received by the Simcyp member companies. In this blog post, I’ll present the results of a recent survey conducted by AbbVie to assess the QSP landscape in the industry. All 33 consortium members received the survey, and 21 companies returned the questionnaire. A poster of the results was presented at the New York Academy of Sciences Meeting on this topic last May.

Results of the survey

  • QSP modeling is widely used: 62% of companies consider QSP modeling to be an integrated part of their modeling and simulations activities; 52% have dedicated QSP modelers; 60% have an established or ongoing IT infrastructure to support QSP modeling. Matlab/Simbiology was the most used software or platform for QSP applications; the majority of applications are for in silico mechanistic hypothesis testing and selecting the right pathway/target.
  • QSP has broad applications in drug development: 92% and 79% of companies stated their primary interest in developing QSP models for oncology and immunology, respectively; 68% were interested in developing models adapted to pre-clinical pharmacology species; 90% were interested in developing safety/toxicity systems models with a primary focus on cardiovascular, liver and kidney. 70% have external collaborators to develop QSP models, of which 60% utilize both consulting and academic organizations. A significant number of companies (67%) plan to share pre-competitive data, experience and modeling input with the QSP consortium.
  • A number of challenges must still be surmounted: The largest barriers to further innovation include intellectual property limitations, existing software interface and computation limitations, modeling and simulation technical challenges, logistics of sharing models with non-modelers, and the need for consortia management.


QSP and the drug development process

QSP translates PK exposure into pharmacological effects. It sits at the interface between modeling and simulation, and systems biology. A mechanistic approach, QSP facilitates the study of “what if” scenarios to determine the likely efficacy of a drug without doing any experimental analysis, facilitating lead development early in the drug development process.

Key benefits of QSP include:

  • Support discovery of new drugs: QSP will enable Pharma to leverage the huge amount of data now being generated from the ‘omics sciences’ (genomics, proteomics and metabolomics) to support new therapies.
  • Provide insight into underlying mechanisms that determine pharmacological response: For example, QSP determines the exposure at various organs to predict potential side effects, or explore which drug combinations may have the best chance of success in specific cancers.
  • Increase the likelihood of demonstrating drug efficacy: QSP builds on insights gained from physiologically-based pharmacokinetic (PBPK) modeling. Once we know how much drug is at the site of action, how will it modulate cellular signaling to exert a pharmacological effect? What pharmacological action will it have at that particular organ? Answering these questions will provide insight into the mechanisms of drug efficacy.
  • Support precision medicine: In the past, we treated many diseases as monolithic. We now recognize that many diseases are actually a plethora of different diseases, affecting distinct subpopulations of patients. By leveraging QSP, sponsors can rationally select patient subgroups to target before running a Phase 2 trial.


[1] Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014;32(1):40-51.

[2] Sorger PK, Allerheiligen SRB, Abernethy DR, RB Altman, Brouwer KLR, Califano A, D’Argenio DZ, Iyengar R, Jusko WJ, Lalonde R, Lauffenburger DA, Shoichet B, Stevens JL, Subramaniam S, Van der Graaf P, Vicini P, Ward R. Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms. An NIH White Paper by the QSP Workshop Group. 2011. Accessed on June 27, 2016

All information presented derive from public source materials.

Learn how QSP modeling can inform drug programs

My colleague, Dr. Neil Benson, recently gave a webinar where he presented a case study showing how a QSP model was used to evaluate the clinical development path for a FAAH inhibitor as a pain medication. I hope that you’ll watch the webinar and let me know what you think in the comments section.

About the author

By: Steve Toon

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