A computation-based approach streamlines the identification and validation of novel drug compounds
The pharmaceutical industry is infamous for big numbers: it takes 10-15 years and can cost over $2 billion to bring a new drug from the lab to the market. Up to 6 of those years and $1 billion are spent in laborious preclinical trials to identify and validate new potential drug compounds, and much of that expense is passed on to patients in the form of high drug prices. And, even after such investment, many of those drugs fail Phase I clinical trials due to toxicity or poor efficacy that was not observed in preclinical animal models.
The Wyss Institute is launching a new initiative combining a diverse array of technologies that aims to revolutionize the drug discovery process, from molecular design and development to experimental testing. Its multi-stage, iterative, computational approach has the potential to identify promising drug candidates in months rather than the years it currently takes.
A novel algorithm designed by the Predictive BioAnalytics Initiative team sifts through the gene expression pattern data of tens of thousands of known drug compounds and identifies those that have the potential to revert a disease-state expression pattern to a normal one. These candidate compounds are tested in a cell-based assay to evaluate whether they can successfully treat the disease in vitro. In tandem with the experimental work, an iterative medicinal chemistry approach is initiated to generate novel chemical compounds. Lastly, this series of de novo compounds is tested both in cell-based assays and in Human Organ Chips to validate that they can effectively treat the disease without harmful side effects before they move on to clinical trials, thus saving time, money, and lives.
The team is currently using this method to identify new drugs to treat non-small-cell lung cancer, influenza, and Barrett’s esophagus, as well as investigating repurposing drugs for anti-virals, acute radiation syndrome, colon cancer, and esophageal cancer.