Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
Reducing the Risk of Late-Stage Failure with In Silico Modeling
Posted on June 15th, 2016 by Christy J. Wilson in Pharma R&D
In silico modeling is arguably one of the hottest trends in life sciences R&D today. We know that bringing drug discovery to the desktop impacts cost. Research shows companies that invest in in silico tools have a 50% higher probability of technical success in Phase II at a cost reduction of 30% per new medical entity.
But for human health, perhaps the most significant impact is on speed. A recent example really brought this point home. Specifically, Silicon Valley startup, twoXAR, used a machine learning system to identify promising drugs to combat Parkinson’s disease. In an interview in Datanami, twoXAR co-founder Andrew A. Radin said, “We loaded a bunch of data on Parkinson’s disease into the system, pressed the go button, a few minutes later we had a list of drugs that were listed as highly efficacious.”
When Radin searched further, one of the top hits was a drug that Dr. Tim Collier, director of NIH Udall Center of Excellence in Parkinson’s Disease Research at Michigan State University, had identified as being potentially important. The system had managed in minutes to help validate work that had been underway in Collier’s lab for years. The result is an ongoing collaboration between twoXAR and the center that promises to accelerate the discovery and development of candidate anti-Parkinson’s drugs.
Yet many organizations are missing the opportunity to use in silico tools and modeling, not only for drug discovery, but also to better predict outcomes of selected compounds in the preclinical stages, thereby mitigating the risk of late-stage failure. For example, in a webinar last September on preclinical safety strategies that can impact early decision making, Dr. Laszlo Urban of the Novartis Institutes for Biomedical Research, Cambridge, UK, discussed the importance of secondary pharmacology in reducing safety-related drug attrition. Novel computational methods are expanding the ability of secondary pharmacology to identify small-molecule effects early on—important from a proactive pharmacovigilance perspective.
Also in the webinar, Dr. Bernard Fermini of Pfizer, Groton, CT noted that using stem cells with in silico modeling to generate the most robust predictive models of cardiovascular safety is an approach in keeping with the tenets of the CiPA [Comprehensive in Vitro Proarrhythmia Assay] initiative, of which he is co-chair of the Ion Channel Working Group and a steering committee member. These and other in silico strategies also are enabling companies to work toward the European Union’s directive of the ‘three Rs’—replace, reduce and refine the use of animals for scientific purposes, he said.
Underpinning all these efforts is the ability to effectively mine, curate and analyze a vast amount of relevant data from diverse sources, enabling companies to find new connections in their discovery and development data and carry them forward, as appropriate. None of this would be possible without the latest silico tools, which are being continually refined and enhanced to make better predictions, faster, allowing deeply informed decision making at every step of the discovery and development process—and reducing pre- and post-market risks of failure.
All opinions shared in this post are the author’s own.
R&D Solutions for Pharma & Life SciencesWe're happy to discuss your needs and show you how Elsevier's Solution can help.
Christy J. Wilson
Sr. Director, Pharma and Biotech Segment
Connect on LinkedIn
Follow on Twitter
- AI in Drug Discovery Has Great Potential – But Also Significant Barriers
- How Big Data Transforms Reactive Drug Safety to Proactive Pharmacovigilance
- Emulating start-up success with purpose-built data tools
- Finding Answers Where You Least Expect Them
- AI in the life sciences – the good, the bad and the ugly