College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
FAST National University of Computer and Emerging Sciences, Karachi, Pakistan.
Sci Rep. 2022 Nov 7;12(1):18935. doi: 10.1038/s41598-022-23649-0.
Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient's mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient's unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at: https://github.com/rizwanqureshi123/PDRP/ .
具有突变表皮生长因子受体 (EGFR) 的肺癌是全球癌症死亡的主要原因。针对 EGFR 开发了靶向酪氨酸激酶抑制剂 (TKI)，这些抑制剂在生存率和生活质量方面显示出可喜的结果。然而，耐药性可能会影响治疗计划，并且大约一年后治疗效果可能会丧失。预测 EGFR 突变型肺癌患者对 EGFR-TKI 的反应是一个关键的研究领域。在这项研究中，我们提出了一种基于分子动力学模拟和机器学习的个性化药物反应预测模型 (PDRP)，用于预测第一代 FDA 批准的小分子 EGFR-TKI，吉非替尼/厄洛替尼，在肺癌患者中的反应。在分子动力学 (MD) 模拟中考虑了患者的突变状态。考虑到 MD 模拟，为每个患者的独特突变状态建模，以提取分子水平的几何特征。此外，还将其他临床特征纳入药物反应预测的机器学习模型中。完整的特征集包括人口统计学和临床信息 (DCI)、药物-靶标结合位点的几何性质以及 MD 模拟中药物-靶标复合物的结合自由能。PDRP 采用 XGBoost 分类器，在 4 类药物反应预测任务中实现了 97.5%的准确率、93%的召回率、96.5%的精度和 94%的 F1 分数，达到了最先进的性能。我们发现，结合结合自由能对结合口袋的几何形状进行建模是药物反应的良好预测指标。然而，我们观察到临床信息对模型性能的影响很小。该模型可以在其他类型的癌症上进行测试。我们相信 PDRP 将支持基于临床基因组信息制定有效的治疗方案。源代码和相关文件可在 GitHub 上获得：https://github.com/rizwanqureshi123/PDRP/ 。
