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How Model-informed Drug Development Will Increase R&D Productivity

Will 2017 finally be the year that we overcome the blight of late-stage attrition of promising drug candidates? In a recent commentary, “Improving the Tools of Clinical Pharmacology: Goals for 2017 and Beyond,” in Clinical Pharmacology & Therapeutics, Issam Zineh and colleagues describe several areas where clinical pharmacology approaches can help reduce late-stage attrition and increase pharma R&D productivity.

  • Erase the preclinical/clinical dividing line: Drug development is often depicted as being linear. A drug is identified in discovery, advances into pre-clinical studies, progresses into the clinic, and then finally enters the realm of patient care. We must abandon this thinking. In reality, the information flow is circular. Collaborating across historically siloed disciplines and sharing information allows teams to make better decisions about drug candidates. Zineh and coworkers suggest that investing in model-informed drug development (MIDD) is essential to leveraging pre-clinical and early-stage clinical data to inform late-stage decision making.
  • Advance the use of quantitative systems pharmacology approaches: Quantitative systems pharmacology (QSP) is a relatively new discipline with enormous potential to improve pharma R&D productivity. It combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. This emerging discipline integrates quantitative drug data with knowledge of its mechanism of action. QSP models predict how drugs modify cellular networks in space and time and how they impact and are impacted by human pathophysiology. One of the barriers that must be overcome for QSP to be effective is a lack of common tools. As Certara VP of QSP, Dr. Piet van der Graaf says, “To take QSP to the next level, we also need a QSP infrastructure, i.e. software and IT tools, that both companies and regulators can use and understand. Without an infrastructure, converting QSP from a purely academic discipline into one that industry actually uses will be very difficult.”
  • Increase the number of vetted biomarkers: Both internal and regulatory decisions require using trusted biomarkers. Many biomarkers are developed as “one-offs” that cannot be repurposed by other drug programs. Pre-competitive partnerships have been critical in increasing the number of vetted biomarkers to support clinical development. We are proud to have worked with the Critical Path Institute (C-Path) Polycystic Kidney Disease Outcomes Consortium to gain FDA support for a prognostic biomarker for a rare disease, autosomal dominant polycystic kidney disease (ADPKD). Having a validated biomarker will help spur new research and clinical trials to find a treatment for ADPKD.
  • Enhance the “Totality of Evidence” approach: Meeting the regulatory requirements for efficacy entails using an array of data from clinical pharmacology studies. We recommend building parallel, but connected, in vivo and in silico development paths. The virtual drug development program is always ahead in each “time zone” to the in vivo drug development program. This paradigm confers a self-learning process that guides in vivo drug development. Thus, the clinical program gains the ability to move faster, more predictably, and more reliably.
  • Improve pharmacology education: For innovation to continue in drug development, we must cultivate our future pharmacology leaders. At Certara, we believe that education is critical to our mission. That’s why we support training for pharmacokinetic/pharmacodynamic (PK/PD) modeling via Certara University and for physiologically-based pharmacokinetic (PBPK) modeling via our Simcyp workshop program. Our Certara Centers of Excellence program at designated universities is another way that we nurture the next generation of experts in the field of model-informed drug development.

By addressing the above scientific, educational, and cultural issues, will we realize the promise of MIDD to address the problem of late-stage attrition? Only time will tell. But with the growing recognition by industry, regulatory agencies, and academia of the critical role that modeling and simulation plays in drug development, I’m optimistic about the future.

All information presented derive from public source materials.

To learn more the growing use of MIDD, please read this article in “Clinical Researcher.”

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By: Suzanne Minton

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