The multi-institution project aims to leverage computational models to predict drug success, streamlining the current lengthy, expensive process.

Sanford Burnham Prebys Medical Discovery Institute in La Jolla is now part of a new, multi-institution, $31.7 million contract announced Dec 4 by the Advanced Research Projects Agency for Health (ARPA-H), part of the US Department of Health and Human Services.

Computational or in silico modeling at the molecular level is a quicker, more effective method for assessing how experimental therapeutic compounds behave and interact, speeding drug development./Sanford Burnham Prebys

The contract seeks to streamline the drug discovery process by developing computational models to predict human toxicity, through a project called Pharmacological Research and Evaluation through Digital Integration and Clinical Trial Simulation, or PREDICTS.

The project would shorten the current discovery process by years, reduce the need for laboratory animal models and catch safety problems with new drugs before they are tested in humans.

David A Brenner, MD/Sanford Burnham Prebys

“It is a transformational goal,” David A Brenner, MD, President and chief executive of Sanford Burnham Prebys, said in a press release.

The challenges of getting a drug from discovery through pre-clinical and clinical trials to the clinic are well-known, characterized by the “valley of death,” which takes 10 to 15 years of continuous effort and $2 billion to get a new drug to market.

More than 90 percent of drug candidates fail.

“Drug development has two fundamental parts: a preclinical component that typically uses animal models to demonstrate a new drug is efficacious and safe and a clinical component in which first-in-human studies focus on safety, with subsequent trials in patients to demonstrate efficacy,” said Michael Jackson, PhD, director of the Center for Therapeutics Discovery and senior vice president for drug discovery and development at Sanford Burnham Prebys.

Animal testing isn’t flawless, however, leading to failings in the clinical trial phase.

PREDICTS’ potential to optimize this process could reduce risk to humans as well as offer cost savings, as human trials are expensive.

Current computer models can improve drug quality, but scientists are hoping the PREDICTS platform will vastly improve these efforts through a cloud repository of rich datasets; algorithms trained for safety liabilities; and predictions combined with other models’ information.