An Algorithmic Framework for Predicting Side Effects of Drugs
An Algorithmic Framework for Predicting Side Effects of Drugs
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DOI：
10.1089/cmb.2010.0255
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发表时间：
2011-03-01
影响因子：
1.7
通讯作者：
Sharan, Roded
中科院分区：
文献类型：
--
作者：
Atias, Nir;Sharan, Roded
药物开发的关键阶段之一是识别有前途的先导药物的潜在副作用。旨在发现此类副作用的大规模临床实验成本非常高，并且可能会错过微妙或罕见的副作用。以前系统地预测副作用的尝试很少，并且独立地考虑每个副作用。在这项工作中，我们报告了一种新方法来预测给定药物的副作用，同时考虑其他药物及其副作用的信息。从查询药物开始，结合典型相关分析和基于网络的扩散来预测其副作用。我们通过使用 692 种药物的综合数据集及其来自包装说明书的已知副作用来测量交叉验证设置中的性能来评估我们的方法。对于 34% 的药物，得分最高的副作用与药物的已知副作用相匹配。值得注意的是，即使对于看不见的数据，我们的方法也能够推断出与现有知识高度匹配的副作用。此外，我们表明我们的方法优于单独考虑每个副作用的预测方案。因此，我们的方法代表了朝着缩短过程和降低副作用阐明成本迈出的有希望的一步。
One of the critical stages in drug development is the identification of potential side effects for promising drug leads. Large-scale clinical experiments aimed at discovering such side effects are very costly and may miss subtle or rare side effects. Previous attempts to systematically predict side effects are sparse and consider each side effect independently. In this work, we report on a novel approach to predict the side effects of a given drug, taking into consideration information on other drugs and their side effects. Starting from a query drug, a combination of canonical correlation analysis and network-based diffusion is applied to predict its side effects. We evaluate our method by measuring its performance in a cross validation setting using a comprehensive data set of 692 drugs and their known side effects derived from package inserts. For 34% of the drugs, the top scoring side effect matches a known side effect of the drug. Remarkably, even on unseen data, our method is able to infer side effects that highly match existing knowledge. In addition, we show that our method outperforms a prediction scheme that considers each side effect separately. Our method thus represents a promising step toward shortcutting the process and reducing the cost of side effect elucidation.