Identifying drugs with disease-modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology

摘要

Purpose: The aim of the study was to assess the feasibility of an approach combining computational methods and pharmacoepidemiology to identify potentially disease-modifying drugs in Parkinson's disease (PD). Methods: We used a two-step approach; (a) computational method using artificial intelligence to rank 620 drugs in the Ontario Drug Benefit formulary based on their predicted ability to inhibit alpha-synucleinaggregation, a pathogenic hallmark of PD; and (b) case-control study using administrative databases in Ontario, Canada. Persons aged 70-110 years with incident PD from April 2002-March 2013. Controls were randomly selected from persons with no previous diagnosis of PD. Results: A total of 15 of the top 50 drugs were deemed feasible for pharmacoepidemiologic analysis, of which seven were significantly associated with incident PD after adjustment, with five of these seven associated with a decreased odds of PD. Methylxanthine drugs pentoxifylline (OR, 0.72; 95 Cl, 0.59-0.89) and theophylline (OR, 0.77; 95 Cl, 0.66-0.91), and the corticosteroid dexamethasone (OR, 0.72; 95 Cl, 0.61-0.85) were associated with decreased odds of PD. Conclusions: Our findings demonstrate the feasibility of this approach to focus the search for disease-modifying drugs. Corticosteroids and methylxanthines should be further investigated as potential disease-modifyingdrugs in PD.