Can QSP Save Lives? Lessons from the Bial Trial Debacle

On-Demand Webinar

One of the biggest challenges for the pharmaceutical industry is the high rate of drug attrition in Phase 2 clinical trials, which wastes significant amounts of money and time. The major reasons for this attrition are that either candidate drugs do not show efficacy or have unexpected toxicity, in turn implying that we did not fully understand the complexity of the biology the candidate drugs were designed to modulate.

Quantitative Systems Pharmacology (QSP) models are mathematical representations that describe our current understanding of a given biology, with a focus on drug development questions. This approach can help understand system complexity and thus improve assessment and prediction of the benefits of a proposed clinical strategy and increase the predictability and reliability of critical drug development decisions.

In this webinar, Certara’s Dr. Neil Benson presented a case study that illustrates how a QSP model was used to evaluate the clinical development path for a FAAH inhibitor as a pain medication (Benson et al, CPT Pharmacometrics Syst Pharmacol. 2014 Jan 15;3:e91. doi: 10.1038/psp.2013.72). The need for quantitative description of the biology in the QSP model raised numerous open questions about the likely efficacy of FAAH inhibitors in pain and the consequences of perturbing the complex endocannabinoid biology. The lessons were discussed in the context of the recent adverse clinical findings for Bial FAAH inhibitor BIA 10-2474.

By watching this webinar, you will learn how QSP models can help:

  • Describe the cellular and molecular pharmacological system that a drug modulates
  • Identify key gaps in the biological understanding of this system
  • Quantify potential benefits/risks and inform optimal strategies for moving a compound further in clinical development

About Our Speaker

Webinar-1speaker-BensonNeil Benson BSc (Hons), PhD. Head QSP Operations Certara QSP, Simcyp. Neil has 20 years’ experience of working in modeling and simulation in the pharmaceutical industry at SmithKline Beecham and Pfizer. During this time, he held a number of senior leadership positions, most recently as Head of Systems Pharmacology at Pfizer, Sandwich. He was awarded the Pfizer Upjohn award for innovation in developing dose prediction methodology.

He has extensive experience of using modeling and simulation to address questions of critical importance in drug discovery including; clinical dose prediction, optimal target identification and biomarker selection and has authored ~ 30 papers and patents. From 2011-2015 he was Founder and Director at Xenologiq Ltd, a consultancy company focused on the productive application of PKPD and Systems Pharmacology in drug discovery. In 2015, Xenologiq Ltd became part of the Certara group of companies, where he is now Head of QSP Operations.

Educated at the University of East Anglia (UK) and the University of Lund (Sweden), Neil has a First Class Honors degree and a PhD in enzymology. Neil can be contacted at neil.benson@certara.com.

One of the biggest challenges for the pharmaceutical industry is the high rate of drug attrition in Phase 2 clinical trials, which wastes significant amounts of money and time. The major reasons for this attrition are that either candidate drugs do not show efficacy or have unexpected toxicity, in turn implying that we did not fully understand the complexity of the biology the candidate drugs were designed to modulate.

Quantitative Systems Pharmacology (QSP) models are mathematical representations that describe our current understanding of a given biology, with a focus on drug development questions. This approach can help understand system complexity and thus improve assessment and prediction of the benefits of a proposed clinical strategy and increase the predictability and reliability of critical drug development decisions.

In this webinar, Certara’s Dr. Neil Benson presented a case study that illustrates how a QSP model was used to evaluate the clinical development path for a FAAH inhibitor as a pain medication