Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, Ontario, Canada.
IBM Watson Health, Ann Arbor, Michigan, USA.
Pharmacoepidemiol Drug Saf. 2021 Feb;30(2):201-209. doi: 10.1002/pds.5176. Epub 2020 Nov 30.
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.
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.
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 < 0.01) and angiotensin converting enzyme inhibitors (ACEi) and diuretics (HR = 0.60, p-value <0.01). Increased risk was observed with exposure to alpha-blockers alone (HR = 1.81, p < 0.001) and the combination of alpha-blockers and CCB (HR = 3.17, p < 0.05).
We present evidence that a computational approach can efficiently identify leads for disease-modifying drugs. We have identified the combination of ARBs and DHP-CCBs as of particular interest in PD.
药物重利用是增加疾病治疗选择的有效手段，然而，从数千种已批准的药物中确定候选分子用于感兴趣的适应症是具有挑战性的。我们对已发表的文献进行了计算分析，根据预测降低α-突触核蛋白（aSyn）寡聚化的能力对现有药物进行排名，并分析了真实世界的数据，以调查暴露于高排名药物与 PD 之间的关联。
使用 IBM Watson for Drug Discoveryâ（WDD），我们确定了几种可能降低 aSyn 寡聚化的降压药物。使用 IBM MarketScanâ Research Databases，我们构建了一个患有高血压的个体队列。我们进行了单变量和多变量 Cox 比例风险分析（HR），将暴露作为时间相关的协变量。将利尿剂作为参考组。多变量分析中包括高血压诊断时的年龄、性别和几种合并症。
多变量结果显示，与 PD 诊断时间相关的因素包括：暴露于血管紧张素受体 II 阻滞剂（ARBs）和二氢吡啶钙通道阻滞剂（DHP-CCB）联合治疗（HR = 0.55，p < 0.01）和血管紧张素转换酶抑制剂（ACEi）和利尿剂（HR = 0.60，p 值 < 0.01）。单独暴露于α-受体阻滞剂（HR = 1.81，p < 0.001）和α-受体阻滞剂与钙通道阻滞剂（HR = 3.17，p < 0.05）联合治疗的风险增加。
我们提供了证据表明，计算方法可以有效地识别疾病修饰药物的先导化合物。我们已经确定 ARBs 和 DHP-CCBs 的联合治疗对 PD 具有特别的关注。
