Using artificial intelligence to identify anti‐hypertensives as possible disease modifying agents in Parkinson's disease

Autor: Scott Spangler, Sharon Hensley Alford, Lorraine V. Kalia, Italo Buleje, Piyush Madan, Alix M. B. Lacoste, Connie Marras, Yanyan Han, Naomi P. Visanji
Rok vydání: 2020
Předmět:
Zdroj: Pharmacoepidemiology and Drug Safety. 30:201-209
ISSN: 1099-1557
1053-8569
Popis: PURPOSE Drug repurposing is an effective means of increasing treatment options for diseases, however identifying candidate molecules for the indication of interest from the thousands of approved drugs is challenging. We have performed a computational analysis of published literature to rank existing drugs according to predicted ability to reduce alpha synuclein (aSyn) oligomerization and analyzed real-world data to investigate the association between exposure to highly ranked drugs and PD. METHODS Using IBM Watson for Drug Discoveryâ (WDD) we identified several antihypertensive drugs that may reduce aSyn oligomerization. Using IBM MarketScanâ Research Databases we constructed a cohort of individuals with incident hypertension. We conducted univariate and multivariate Cox proportional hazard analyses (HR) with exposure as a time-dependent covariate. Diuretics were used as the referent group. Age at hypertension diagnosis, sex, and several comorbidities were included in multivariate analyses. RESULTS Multivariate results revealed inverse associations for time to PD diagnosis with exposure to the combination of the combination of angiotensin receptor II blockers (ARBs) and dihydropyridine calcium channel blockers (DHP-CCB) (HR = 0.55, p
Databáze: OpenAIRE