Predicting the potency of anti-Alzheimer drug combinations using machine learning
Predicting the potency of anti-Alzheimer drug combinations using machine learning
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使用机器学习预测抗阿尔茨海默病药物组合的效力
DOI：
10.1101/2020.04.28.066340
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
T. Anastasio
中科院分区：
文献类型：
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作者：
T. Anastasio
众所周知，用于治疗阿尔茨海默病（AD）的单一药物的临床试验是不成功的。重新利用药物的组合可以为AD提供有效的治疗。挑战在于确定潜在的有效组合。目的利用机器学习（ML）从两个主要的AD数据库中提取知识，然后利用机器预测两个数据库中共同的药物组合作为AD的治疗方法是最有效的。使用ML训练在其内层具有复合门控单元的三层神经网络（NN），以预测任一数据库中参与者的认知评分，给定其他数据字段，包括年龄，人口统计学变量，合并症和药物。结果来自单独训练的NN的预测是强相关的。最好的药物组合，联合确定从两组预测，是高的非甾体抗炎药，抗凝剂，降脂，抗高血压药物，和女性激素。结论AD是一种多因素的疾病，联合应用再利用药物可有效治疗AD。
BACKGROUND Clinical trials of single drugs for the treatment of Alzheimer Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially potent combinations. OBJECTIVE To use machine learning (ML) to extract the knowledge from two leading AD databases, and then use the machine to predict which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. METHODS Three-layered neural networks (NNs) having compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants in either database, given the other data fields including age, demographic variables, comorbidities, and drugs taken. RESULTS The predictions from the separately trained NNs were strongly correlated. The best drug combinations, jointed determined from both sets of predictions, were high in NSAID, anticoagulant, lipid-lowering, and antihypertensive drugs, and female hormones. CONCLUSION The results suggest that AD, as a multifactorial disorder, could be effectively treated using a combination of repurposed drugs.