Hong Yongkai, Chen Dantian, Jin Yaqing, Zu Mian, Zhang Yin
Institute of Health Service and Transfusion Medicine, Academy of Military Medical Sciences, Beijing, China.
Front Mol Biosci. 2022 Oct 18;9:971768. doi: 10.3389/fmolb.2022.971768. eCollection 2022.
Drug combinations can increase the therapeutic effect by reducing the level of toxicity and the occurrence of drug resistance. Therefore, several drug combinations are often used in the management of complex diseases. However, due to the exponential growth in drug development, it would be impractical to evaluate all combinations through experiments. In view of this, we developed Pathway Interaction Network (PINet) biological model to estimate the optimal drug combinations for various diseases. The random walk with restart (RWR) algorithm was used to capture the "disease state" and "drug state," while PINet was used to evaluate the optimal drug combinations and the high-order drug combination. The model achieved a mean area under the curve of a receiver operating characteristic curve of 0.885. In addition, for some diseases, PINet predicted the optimal drug combination. For example, in the case of acute myeloid leukemia, PINet correctly predicted midostaurin and gemtuzumab as effective drug combinations, as demonstrated by the results of a Phase-I clinical trial. Moreover, PINet also correctly predicted the potential drug combinations for diseases that lacked a training dataset that could not be predicted using standard machine learning models.
药物组合可以通过降低毒性水平和耐药性的发生来提高治疗效果。因此，几种药物组合经常用于复杂疾病的治疗。然而，由于药物研发呈指数级增长，通过实验评估所有组合是不切实际的。鉴于此，我们开发了通路相互作用网络（PINet）生物学模型来估计针对各种疾病的最佳药物组合。使用带重启的随机游走（RWR）算法来捕捉“疾病状态”和“药物状态”，而PINet则用于评估最佳药物组合和高阶药物组合。该模型在受试者工作特征曲线下的平均面积为0.885。此外，对于某些疾病，PINet预测了最佳药物组合。例如，在急性髓系白血病的病例中，PINet正确地预测了米哚妥林和吉妥单抗为有效的药物组合，一项I期临床试验的结果证明了这一点。此外，PINet还正确地预测了那些缺乏训练数据集、无法使用标准机器学习模型进行预测的疾病的潜在药物组合。
