Natural product based anticancer drug combination discovery assisted by deep learning and network analysis

Elton L. Cao
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Abstract

Drug combination therapies have shown effective performance in treating cancer through increased efficacy and circumvention of drug resistance through synergy. Two avenues can be used to discover drug combinations: a novel approach that utilizes natural products compared with the textbook approach of utilizing existing chemotherapy drug combinations. Many natural products achieve efficacy due to synergistic interactions between the active ingredients. Therefore, the pharmacophore relationships in herbal compounds which synergize can potentially be applied to chemotherapy drugs to drive combination discovery. Machine learning approaches have been developed to identify drug combinations, especially deep neural networks (DNN), which have achieved state-of-the-art performance in many drug discovery tasks. Here, a drug protein interaction (DPI) prediction DNN, DeepDPI, was developed to employ DPI drug representations and achieved state-of-the-art performance using the DrugBank database. Two DNNs were also developed to predict novel drug combinations: DeepNPD, which predicts combinations in natural products, and DeepCombo, which predicts synergy in chemotherapy drugs, using the HERB and DrugCombDB databases respectively. An ensemble architecture enhanced with a novel similarity based weight adjustment (SBWA) approach was used and both models accurately predicted drug combinations for both known and unknown drugs. Lastly, a screening was conducted using each model where DeepNPD predicted combinations where drugs had similar targets, while DeepCombo predicted combinations where one agent potentiated the other, with both models’ predicted combinations investigated through a network-based analysis and identifying as a synergistic combinations in literature. DeepNPD notably identified Thioguanine and Hydroxyurea and DeepCombo discovered Vinblastine and Dasatinib as hits for potential new anticancer drug combinations. DeepNPD illustrates how natural products are a novel path where new drug combinations can be discovered.
深度学习和网络分析辅助基于天然产物的抗癌药物组合发现
联合用药疗法通过协同作用提高疗效和规避耐药性,在治疗癌症方面表现出有效的性能。有两种途径可用于发现药物组合:一种是利用天然产品的新方法,另一种是利用现有化疗药物组合的教科书方法。许多天然产品的疗效是通过活性成分之间的协同作用实现的。因此,草药化合物中能产生协同作用的药理关系有可能被应用到化疗药物中,从而推动组合药物的发现。人们已经开发出机器学习方法来识别药物组合,特别是深度神经网络(DNN),它在许多药物发现任务中都取得了最先进的性能。在此,我们开发了一种药物蛋白相互作用(DPI)预测 DNN--DeepDPI,它采用了 DPI 药物表征,并在使用 DrugBank 数据库时取得了最先进的性能。此外,还开发了两个 DNN 来预测新型药物组合:DeepNPD 用于预测天然产品的组合,DeepCombo 用于预测化疗药物的协同作用,分别使用 HERB 和 DrugCombDB 数据库。这两个模型都能准确预测已知和未知药物的药物组合。最后,使用每个模型进行了筛选,DeepNPD 预测了药物具有相似靶点的组合,而 DeepCombo 预测了一种药物对另一种药物具有增效作用的组合,通过基于网络的分析对两个模型预测的组合进行了研究,并将其确定为文献中的协同组合。DeepNPD 发现了硫鸟嘌呤和羟基脲,DeepCombo 发现了长春新碱和达沙替尼,它们都是潜在的抗癌新药组合。DeepNPD 说明了天然产品如何成为发现新的药物组合的新途径。
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