Chen Xing, Ren Biao, Chen Ming, Wang Quanxin, Zhang Lixin, Yan Guiying
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China.
Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
PLoS Comput Biol. 2016 Jul 14;12(7):e1004975. doi: 10.1371/journal.pcbi.1004975. eCollection 2016 Jul.
Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.
真菌感染已成为医院获得性感染的主要原因之一，死亡率很高。此外，耐药性在真菌引起的疾病中很常见。联合用药可能是克服耐药性的有效策略。同时，联合用药可以提高治疗效果，降低药物剂量以避免毒性。因此，针对真菌引起的疾病进行联合用药的计算预测变得很有吸引力。在本研究中，我们提出了联合用药的相似性：与相似辅助药物产生协同作用的主要药物通常相似，反之亦然。此外，我们开发了一种名为基于网络的拉普拉斯正则化最小二乘协同药物组合预测（NLLSS）的新算法，通过整合已知协同药物组合、药物-靶点相互作用和药物化学结构等不同类型的信息来预测潜在的协同药物组合。我们应用NLLSS预测抗真菌协同药物组合，并表明它在交叉验证和独立预测方面均表现出色。最后，我们对真菌病原体白色念珠菌进行了生物学实验，以确认13种预测的抗真菌协同药物组合中的7种。NLLSS为识别潜在的协同抗真菌组合提供了一种有效的策略。
