Clinical Trial Data Used in New Ways To Finance Cures
Continuous investment in research and development is needed to eradicate cancer and tackle human health challenges such as Alzheimer’s, antibiotic resistance and rare and orphan diseases.
But bringing new cures to market can be challenging because of long development timeframes, high costs and a high failure rate for new drug candidates: only 10% of drugs tested in clinical trials succeed, and the cost of developing a drug is over $2 billion and takes ten years.
In 2016, experts from our Business Intelligence Division began work with Professor Andrew Lo, Director of MIT’s Laboratory for Financial Engineering, to find new ways to finance the development of new medicines, with the idea of creating new financial instruments that would attract additional investment capital into drug development.
Data science and machine learning was applied to the drug and clinical trials data we publish, to see if the probability of the success of drugs in various clinical trial stages could be predicted, allowing the creation of risk models to enable funding.
Professor Lo’s team and drug and clinical trial experts succeeded in establishing more detailed benchmarks on drug development success rates than ever before, and the MIT research team has submitted two papers for publication.
Lo commented: “We seem to be at an inflection point in treating many forms of cancer and other disease – now’s the time to be increasing our investments in biomedicine. Better predictive analytics can help us, and we’re grateful to Informa for giving us access to their data and expertise.”
// OUR CHALLENGE WAS TO SEE IF WE COULD PREDICT THE PROBABILITY OF THE SUCCESS OF DRUGS IN VARIOUS CLINICAL TRIAL STAGES BY APPLYING DATA SCIENCE AND MACHINE LEARNING TO OUR DRUG AND CLINICAL TRIALS DATA. //