Savva Kyriaki, Zachariou Margarita, Bourdakou Marilena M, Dietis Nikolas, Spyrou George M
Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
Experimental Pharmacology Laboratory, Medical School, University of Cyprus, Cyprus.
Comput Struct Biotechnol J. 2022 Mar 16;20:1427-1438. doi: 10.1016/j.csbj.2022.03.013. eCollection 2022.
Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common type of dementia. With no disease-curing drugs available and an ever-growing AD-related healthcare burden, novel approaches for identifying therapies are needed. In this work, we propose stage-specific candidate repurposed drugs against AD by using a novel network-based method for drug repurposing against different stages of AD severity. For each AD stage, this approach a) ranks the candidate repurposed drugs based on a novel network-based score emerging from the weighted sum of connections in a network resembling the structural similarity with failed, approved or currently ongoing drugs b) re-ranks the candidate drugs based on functional, structural and a priori information according to a recently developed method by our group and c) checks and re-ranks for permeability through the Blood Brain Barrier (BBB). Overall, we propose for further experimental validation 10 candidate repurposed drugs for each AD stage comprising a set of 26 elite candidate repurposed drugs due to overlaps between the three AD stages. We applied our methodology in a retrospective way on the known clinical trial drugs till 2016 and we show that we were able to highly rank a drug that did enter clinical trials in the following year. We expect that our proposed network-based drug-repurposing methodology will serve as a paradigm for application for ranking candidate repurposed drugs in other brain diseases beyond AD.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，也是最常见的痴呆类型。由于目前尚无治愈该疾病的药物，且与AD相关的医疗负担不断加重，因此需要新的方法来确定治疗方案。在这项研究中，我们提出了针对AD不同严重程度阶段的阶段特异性候选药物，并采用一种基于网络的新方法来重新利用药物治疗AD。对于每个AD阶段，该方法：a）基于一种新的基于网络的评分对候选药物进行排名，该评分来自于一个类似于与已失败、已批准或正在进行的药物结构相似性的网络中连接的加权和；b）根据我们小组最近开发的方法，基于功能、结构和先验信息对候选药物进行重新排名；c）检查并重新排名药物通过血脑屏障（BBB）的通透性。总体而言，由于三个AD阶段之间存在重叠，我们为每个AD阶段提出了10种候选药物进行进一步的实验验证，共包括一组26种优秀的候选药物。我们以回顾性的方式将我们的方法应用于截至2016年的已知临床试验药物，并表明我们能够对次年进入临床试验的一种药物进行高排名。我们期望我们提出的基于网络的药物重新利用方法将成为在AD以外的其他脑部疾病中对候选药物进行排名的应用范例。
