AI is not the first technology the state has used in the fight against opioid use disorder. This is also not the first AI use case for West Virginia, which has leveraged the technology in areas including communication and fraud detection. Rather, the partnership will establish a program integrating AI into the pharmaceutical research process.
Friday’s announcement recognizes a $5 million investment, approved by the West Virginia Economic Development Authority and the West Virginia Jobs Investment Trust board of directors, in GATC. This investment will support the company’s research and development lab at the West Virginia University Innovation Center, in an effort to position the state to leverage GATC’s AI technology.
West Virginia’s opioid overdose rate is above the national average. The funding will enable GATC to hire dozens of employees to help complete FDA application efforts for a drug candidate for treating opioid use disorder.
“The establishment of a proven AI-enabled R&D program to discover more effective and safer drugs within our state, specifically tailored to treat the health challenges faced by Appalachians, serves as a new model for drug development,” state Sen. Tom Takubo said in a statement.
Medical research is an area in which AI can enable “tremendous advances,” U.S. Rep. Ted Lieu said at a virtual Brookings Institution event in September. Lieu was recently appointed to co-chair the Task Force on AI in the House of Representatives.
The drug candidate was recently the focus of pre-clinical animal studies at the University of California, Irvine (UCI)’s Neurobiology and Behavior School of Biological Sciences, which resulted in a successful outcome. It is non-opioid and non-habit forming and repairs brain areas affected by addiction, aiming to support both short- and long-term recovery. (A drug candidate is a compound suitable for clinical testing, based on an evaluation of its ability to meet specific criteria.)
The experiments were conducted using GATC’s Multiomics Advanced Technology (MAT) platform. The platform uses proprietary AI and machine learning to simulate human physiology and predict outcomes of experiments for efficacy and safety. It can analyze 400 trillion genetic data points in about seven minutes.
The UCI study showed the platform can reduce both the time of preclinical trials, from four to six years to 18 months, and the cost, from more than $300 million to $2 million.
A "outside university study" on the MAT platform found it could successfully predict the positive human outcome of a drug compound with an accuracy rate approximately 11 times better than industry lead optimization success rates, according to the announcement.
