Mathematical artificial intelligence searches for disease targets and predicts success

July 13, 2021

In a new study, 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 approval from the U.S. Food and Drug Administration (FDA).

“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,” said Pradipta Ghosh, MD, Professor in the Departments of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine.

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.

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.

The Phase ‘0’ approach involves testing the efficacy of the drugs identified using the AI model on human disease organoid models — cultured human cells in a 3D environment that mimic diseases outside of the body. In this case, an IBD-afflicted gut-in-a-dish.

Biopsy tissues for the study were taken during colonoscopy procedures involving IBD patients. Those biopsies were used as the source of stem cells to grow organoids.

The researchers found the computational approach had a surprisingly high level of accuracy across diverse cohorts of IBD patients, and together with the Phase ‘0’ approach, they developed a first-in-class therapy to restore and protect the leaky gut barrier in IBD.

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