Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
As Drug Development Costs Skyrocket, AI Offers Some Hope
Posted on November 20th, 2017 by Betsy Davis in Pharma R&D
Last year, researchers from the Tufts Center for the Study of Drug Development published a paper in the Journal of Health Economics that provided an assessment of the current cost for pharmaceutical companies to discover and develop new drugs and biologics. This wasn’t the first time that they had done such a study, so the researchers were also able to compare results over time. Unfortunately, what they found confirmed what most pharma firms have been feeling all-too-acutely over recent years—that developing drugs is a very high-cost, high-risk endeavor.
The study estimated that total out-of-pocket and capitalized R&D cost per new drug is $2558 million dollars, which is more than double the cost those same researchers calculated in a study some 10 years earlier. Furthermore, they also found that over that time, the actual success rate that pharmaceutical companies experienced dropped 10 percentage points. While the researchers say it is difficult to be sure exactly why failure rates have increased, hypotheses include the scope and length of clinical trials, the fact that regulators have become more risk averse and that the industry has tended to focus its efforts in areas where the science is more challenging and the risk of failure is higher.
With costs so incredibly high, pharma companies have been trying many different ways to adapt to this expensive reality. Desperately searching for that elusive “blockbuster” drug that would improve fortunes has given way to strategic mergers and acquisitions. Partnerships with innovative biotech companies and collaborations with brilliant minds in academia have provided a path to cutting R&D costs. And, as always, companies are searching for any possible way to save time in the arduous discovery and development process, since time equals money.
When it comes to saving time in the research process, it may seem like few stones have been left unturned at this point. However, new technologies keep providing new ways to speed up the process. Digital tools and research solutions are already helping scientists retrieve literature and data much faster, so they waste less of their precious time hunting for information. But there are even more exciting technologies that are being groomed to help drug development efforts.
One such technology is artificial intelligence (AI), or machine learning. While AI might be better known to most people through science fiction, it is fast becoming scientific reality. As Arvind Dilawar recently reported in a Newsweek article, a Baltimore-based biotech called Insilico Medicine “hopes to revolutionize drug development by slashing the time necessary for research with the help of artificial intelligence.” How are they doing it? By using sophisticated computer networks to vet millions of compounds, identifying those with the best cancer-fighting potential.
Being able to put these intelligent machines to the enormous task of sorting through mountains of data can help researchers leapfrog ahead to concentrate on the most promising compounds, saving a tremendous amount of time. By directing researchers to the very best leads, AI can also save money that might be spent on attempting to develop drugs destined to fail, which is one of the main reasons that drug development costs are so high to begin with.
Pharmaceutical companies are undoubtedly feeling tremendous pressure and strain as they work to develop new drugs and to get them to market quickly, all while attempting to rein in exploding costs. But a willingness to explore and embrace advanced technologies like artificial intelligence could ultimately result in time saved, lower costs and a distinct competitive edge.
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.
Senior Strategic Marketing Manager
- 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
- Machine Learning Could Make Drug Discovery Faster, Cheaper, Better
- How Drug Repurposing Is Revitalizing the Pharma Industry