Mozaffarilegha Mozhgan, Gharaghani Sajjad
Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Front Pharmacol. 2025 Jul 17;16:1614758. doi: 10.3389/fphar.2025.1614758. eCollection 2025.
Combination therapies play a crucial role in the treatment of complex diseases, such as cancer. They enhance efficacy, minimize resistance, and reduce toxicity by leveraging synergistic effects. However, identifying effective combinations is challenging due to the vast number of possible pairings and the high-priced costs of experimental validation. Machine learning (ML) and deep learning (DL) models have advanced drug synergy prediction by integrating diverse datasets and modeling the interactions between drugs and cell lines. Despite these advancements, most algorithms primarily rely on drug-specific features, such as chemical structures, with limited incorporation of functional drug information and cellular content features.
We propose a novel approach that integrates Drug Resistance Signatures (DRS) as a biologically informed representation of drug information. This approach provides a more comprehensive framework for identifying effective combination therapies. We evaluated the predictive power of DRS features across various machine learning models (LASSO, Random Forest, AdaBoost, and XGBoost) and the deep learning model SynergyX. We compared their performance with that of conventional drug signatures and chemical structure-based descriptors.
Our results demonstrate that models incorporating DRS features consistently outperform traditional approaches across all evaluated algorithms. Validation on independent datasets, including ALMANAC, O'Neil, OncologyScreen, and DrugCombDB, confirms the robustness and generalizability of the proposed framework.
These findings emphasize the importance of integrating resistance-informed transcriptomic features into computational models. By capturing drug functionality in a biologically relevant context, DRS improves both the accuracy and interpretability of drug synergy prediction, offering a powerful strategy for guiding the discovery of effective combination therapies.
联合疗法在癌症等复杂疾病的治疗中发挥着关键作用。它们通过利用协同效应提高疗效、最小化耐药性并降低毒性。然而，由于可能的配对数量众多以及实验验证成本高昂，确定有效的联合疗法具有挑战性。机器学习（ML）和深度学习（DL）模型通过整合不同的数据集并对药物与细胞系之间的相互作用进行建模，推动了药物协同作用预测的发展。尽管有这些进展，但大多数算法主要依赖于药物特异性特征，如化学结构，而对功能药物信息和细胞内容特征的纳入有限。
我们提出了一种新颖的方法，将耐药特征（DRS）整合为药物信息的生物学信息表示。这种方法为确定有效的联合疗法提供了一个更全面的框架。我们评估了DRS特征在各种机器学习模型（LASSO、随机森林、AdaBoost和XGBoost）以及深度学习模型SynergyX中的预测能力。我们将它们的性能与传统药物特征和基于化学结构的描述符的性能进行了比较。
我们的结果表明，在所有评估算法中，纳入DRS特征的模型始终优于传统方法。在包括ALMANAC、奥尼尔、肿瘤筛查和药物组合数据库在内的独立数据集上的验证证实了所提出框架的稳健性和通用性。
这些发现强调了将耐药性信息转录组特征整合到计算模型中的重要性。通过在生物学相关背景下捕获药物功能，DRS提高了药物协同作用预测的准确性和可解释性，为指导有效联合疗法的发现提供了一种强大的策略。
