Atias Nir, Sharan Roded
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
J Comput Biol. 2011 Mar;18(3):207-18. doi: 10.1089/cmb.2010.0255.
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.
药物研发中的关键阶段之一是确定有前景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试验成本高昂，且可能会遗漏细微或罕见的副作用。此前系统预测副作用的尝试较少，且是独立考虑每种副作用。在这项工作中，我们报告了一种预测给定药物副作用的新方法，该方法考虑了其他药物及其副作用的信息。从一种查询药物开始，应用典型相关分析和基于网络的扩散相结合的方法来预测其副作用。我们使用一个包含692种药物及其从药品说明书中获取的已知副作用的综合数据集，通过在交叉验证设置中测量其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该药物的已知副作用相匹配。值得注意的是，即使在未见数据上，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种副作用的预测方案。因此，我们的方法代表了朝着简化副作用阐明过程和降低成本迈出的有前景的一步。
