Laura C Maclagan1, Naomi P Visanji2, Yi Cheng1, Mina Tadrous1,3, Alix M B Lacoste4, Lorraine V Kalia2, Susan E Bronskill1,3,5, Connie Marras1,2. 1. ICES, Life Stage Research Program, Toronto, Ontario, Canada. 2. Edmond J Safra Program in Parkinson Disease, Toronto Western Hospital, Toronto, Ontario, Canada. 3. Women's College Research Institute, Toronto, Ontario, Canada. 4. Data Science, BenevolentAI, Brooklyn, New York, USA. 5. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
Abstract
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% CI, 0.59-0.89) and theophylline (OR, 0.77; 95% CI, 0.66-0.91), and the corticosteroid dexamethasone (OR, 0.72; 95% CI, 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.
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% CI, 0.59-0.89) and theophylline (OR, 0.77; 95% CI, 0.66-0.91), and the corticosteroid dexamethasone (OR, 0.72; 95% CI, 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.
Authors: Susan E Bronskill; Laura C Maclagan; Colleen J Maxwell; Andrea Iaboni; R Liisa Jaakkimainen; Connie Marras; Xuesong Wang; Jun Guan; Daniel A Harris; Abby Emdin; Aaron Jones; Nadia Sourial; Claire Godard-Sebillotte; Isabelle Vedel; Peter C Austin; Richard H Swartz Journal: JAMA Health Forum Date: 2022-01-21
Authors: Daniel Janitschke; Anna A Lauer; Cornel M Bachmann; Heike S Grimm; Tobias Hartmann; Marcus O W Grimm Journal: Nutrients Date: 2021-02-28 Impact factor: 5.717
Authors: Kevin S Chen; Krystal Menezes; Suneil K Kalia; Lorraine V Kalia; Jarlath B Rodgers; Darren M O'Hara; Nhat Tran; Kazuko Fujisawa; Seiya Ishikura; Shahin Khodaei; Hien Chau; Anna Cranston; Minesh Kapadia; Grishma Pawar; Susan Ping; Aldis Krizus; Alix Lacoste; Scott Spangler; Naomi P Visanji; Connie Marras; Nour K Majbour; Omar M A El-Agnaf; Andres M Lozano; Joseph Culotti; Satoshi Suo; William S Ryu Journal: Mol Neurodegener Date: 2021-11-12 Impact factor: 14.195