State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.
Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
J Transl Med. 2024 Sep 28;22(1):873. doi: 10.1186/s12967-024-05660-3.
In the management of complex diseases, the strategic adoption of combination therapy has gained considerable prominence. Combination therapy not only holds the potential to enhance treatment efficacy but also to alleviate the side effects caused by excessive use of a single drug. Presently, the exploration of combination therapy encounters significant challenges due to the vast spectrum of potential drug combinations, necessitating the development of efficient screening strategies.
In this study, we propose a prediction scoring method that integrates heterogeneous data using a weighted Bayesian method for drug combination prediction. Heterogeneous data refers to different types of data related to drugs, such as chemical, pharmacological, and target profiles. By constructing a multiplex drug similarity network, we formulate new features for drug pairs and propose a novel Bayesian-based integration scheme with the introduction of weights to integrate information from various sources. This method yields support strength scores for drug combinations to assess their potential effectiveness.
Upon comprehensive comparison with other methods, our method shows superior performance across multiple metrics, including the Area Under the Receiver Operating Characteristic Curve, accuracy, precision, and recall. Furthermore, literature validation shows that many top-ranked drug combinations based on the support strength score, such as goserelin and letrozole, have been experimentally or clinically validated for their effectiveness.
Our findings have significant clinical and practical implications. This new method enhances the performance of drug combination predictions, enabling effective pre-screening for trials and, thereby, benefiting clinical treatments. Future research should focus on developing new methods for application in various scenarios and for integrating diverse data sources.
在复杂疾病的治疗中，联合治疗策略得到了广泛关注。联合治疗不仅有潜力提高治疗效果，还可以减轻单一药物过度使用带来的副作用。目前，由于潜在药物组合的范围广泛，联合治疗的探索面临着重大挑战，需要开发有效的筛选策略。
在这项研究中，我们提出了一种基于加权贝叶斯方法的药物组合预测评分方法，该方法可以整合异质数据。异质数据是指与药物相关的不同类型的数据，如化学、药理学和靶标特征。通过构建多药物相似性网络，我们为药物对构建了新的特征，并提出了一种新的基于贝叶斯的集成方案，引入权重来整合来自不同来源的信息。该方法为药物组合生成支持强度评分，以评估其潜在的有效性。
与其他方法的全面比较表明，我们的方法在多个指标上都表现出优越的性能，包括接收器操作特征曲线下的面积、准确性、精度和召回率。此外，文献验证表明，许多基于支持强度得分的排名靠前的药物组合，如戈舍瑞林和来曲唑，已经在实验或临床中验证了其有效性。
我们的研究结果具有重要的临床和实际意义。这种新方法提高了药物组合预测的性能，为试验的有效预筛选提供了可能，从而有益于临床治疗。未来的研究应侧重于开发适用于各种场景的新方法，并整合各种数据源。
