Writing in the July 12, 2021 online issue of Nature Communications, researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval.
The study findings could measurably change how researchers sift through big data to find meaningful information with significant benefit to patients, the pharmaceutical industry and the nation’s health care systems.
Academic labs and pharmaceutical and biotech companies have access to unlimited amounts of ‘big data’ and better tools than ever to analyze such data. However, despite these incredible advances in technology, the success rates in drug discovery are lower today than in the 1970s.”
Pradipta Ghosh, MD, senior author of the study and professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine
“This is mostly because drugs that work perfectly in preclinical inbred models, such as laboratory mice, that are genetically or otherwise identical to each other, don’t translate to patients in the clinic, where each individual and their disease is unique. It is this variability in the clinic that is believed to be the Achilles heel for any drug discovery program.”
In the new study, Ghosh and colleagues replaced the first and last steps in preclinical drug discovery with two novel approaches developed within the UC San Diego Institute for Network Medicine (iNetMed), which unites several research disciplines to develop new solutions to advance life sciences and technology and enhance human health.
The researchers used the disease model for inflammatory bowel disease (IBD), which is a complex, multifaceted, relapsing autoimmune disorder characterized by inflammation of the gut lining. Because it impacts all ages and reduces the quality of life in patients, IBD is a priority disease area for drug discovery and is a challenging condition to treat because no two patients behave similarly.
The first step, called target identification, used an artificial intelligence (AI) methodology developed by The Center for Precision Computational System Network (PreCSN), the computational arm of iNetMed. The AI approach helps model a disease using a map of successive changes in gene expression at the onset and during the progression of the disease. What sets this mapping apart from other existing models is the use of mathematical precision to recognize and extract all possible fundamental rules of gene expression patterns, many of which are overlooked by current methodologies.
The underlying algorithms ensure that the identified gene expression patterns are ‘invariant’ regardless of different disease cohorts. In other words, PreCSN builds a map that extracts information that applies to all IBD patients.
“In head-to-head comparisons, we demonstrated the superiority of this approach over existing methodologies to accurately predict ‘winners’ and ‘losers’ in clinical trials,” said Ghosh.
The last step, called target validation in preclinical models, was conducted in a first-of-its-kind Phase ‘0’ clinical trial using a living biobank of organoids created from IBD patients at The HUMANOID Center of Research Excellence (CoRE), the translational arm of iNetMed.
