Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
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DOI：
10.1136/amiajnl-2013-002512
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发表时间：
2014-10-01
影响因子：
6.4
通讯作者：
Zhao, Zhongming
中科院分区：
文献类型：
--
作者：
Cheng, Feixiong;Zhao, Zhongming
目的药物-药物相互作用(DDIS)在药物开发和临床应用中都是一个重要的考虑因素，尤其是联合用药。虽然有必要在临床试验期间确定所有可能的DDIS，但DDIS经常在药物获准临床使用后报告，它们是导致药物不良反应(ADR)和增加医疗成本的常见原因。计算预测可能有助于在临床试验中识别潜在的DDIS。方法在这里，我们提出了一个异质网络辅助推理(HNAI)框架来辅助预测DDIS。首先，我们根据DrugBank的数据构建了一个全面的DDI网络，其中包含6946个独特的DDI对，连接了721种批准的药物。接下来，我们使用四个特征来计算药物-药物对的相似性：基于全面的药物-ADR网络的表型相似性、基于药物解剖治疗化学分类系统的治疗相似性、来自SMIRS数据的化学结构相似性、以及基于使用DrugBank和治疗靶标数据库建立的大型药物-靶相互作用网络的基因组相似性。最后将朴素贝叶斯模型、决策树模型、k近邻模型、Logistic回归模型和支持向量机模型应用于HNAI模型中。以抗精神病药物为例，HNAI预测的几种涉及体重增加和细胞色素P450抑制的DDIS得到了文献资源的支持。结论通过基于机器学习的药物表型、治疗、结构和基因组相似性的集成，HNAI有望在药物开发和上市后监测中发现DDIS。
Objective Drug-drug interactions (DDIs) are an important consideration in both drug development and clinical application, especially for co-administered medications. While it is necessary to identify all possible DDIs during clinical trials, DDIs are frequently reported after the drugs are approved for clinical use, and they are a common cause of adverse drug reactions (ADR) and increasing healthcare costs. Computational prediction may assist in identifying potential DDIs during clinical trials.Methods Here we propose a heterogeneous network-assisted inference (HNAI) framework to assist with the prediction of DDIs. First, we constructed a comprehensive DDI network that contained 6946 unique DDI pairs connecting 721 approved drugs based on DrugBank data. Next, we calculated drug-drug pair similarities using four features: phenotypic similarity based on a comprehensive drug-ADR network, therapeutic similarity based on the drug Anatomical Therapeutic Chemical classification system, chemical structural similarity from SMILES data, and genomic similarity based on a large drug-target interaction network built using the DrugBank and Therapeutic Target Database. Finally, we applied five predictive models in the HNAI framework: naive Bayes, decision tree, k-nearest neighbor, logistic regression, and support vector machine, respectively.Results The area under the receiver operating characteristic curve of the HNAI models is 0.67 as evaluated using fivefold cross-validation. Using antipsychotic drugs as an example, several HNAI-predicted DDIs that involve weight gain and cytochrome P450 inhibition were supported by literature resources.Conclusions Through machine learning-based integration of drug phenotypic, therapeutic, structural, and genomic similarities, we demonstrated that HNAI is promising for uncovering DDIs in drug development and postmarketing surveillance.