Drug repurposing for reducing the risk of cataract extraction in patients with diabetes mellitus: integration of artificial intelligence-based drug prediction and clinical corroboration

Abstract

Diabetes mellitus (DM) increases the incidence of age-related cataracts. Currently, no medication is approved or known to delay clinical cataract progression. Using a novel approach based on AI, we searched for drugs with potential cataract surgery-suppressing effects. We developed a drug discovery strategy that combines AI-based potential candidate prediction among 2650 Food and Drug Administration (FDA)-approved drugs with clinical corroboration leveraging multicenter electronic health records (EHRs) of approximately 800,000 cataract patients from the TriNetX platform. Among the top-10 AI-predicted repurposed candidate drugs, we identified three DM diagnostic ICD code groups, such as cataract patients with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), or hyperglycemia, and conducted retrospective cohort analyses to evaluate the efficacy of these candidate drugs in reducing the risk of cataract extraction. Aspirin, melatonin, and ibuprofen were associated with a reduced 5-, 10-, and 20-year cataract extraction risk in all types of diabetes. Acetylcysteine was associated with a reduced 5-, 10-, and 20-year cataract extraction risk in T2DM and hyperglycemia but not in T1DM patient groups. The suppressive effects of aspirin, acetylcysteine, and ibuprofen waned over time, while those of melatonin became stronger in both genders. Thus, the four repositioned drugs have the potential to delay cataract progression in both genders. All four drugs share the ability to directly or indirectly inhibit cyclooxygenase-2 (COX-2), an enzyme that is increased by multiple cataractogenic stimuli.