Sunildutt Naina, Ahmed Faheem, Salih Abdul Rahim Chethikkattuveli, Kim Hyung Chul, Choi Kyung Hyun
Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
Department of Mechatronics Engineering, Jeju National University, Republic of Korea; Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, US; BioSpero, Inc, Jeju, Republic of Korea.
Comput Biol Med. 2025 Feb;185:109481. doi: 10.1016/j.compbiomed.2024.109481. Epub 2024 Dec 6.
Pancreatic cancer, a malignancy notorious for its late-stage diagnosis and low patient survival rates, remains a formidable global health challenge. The currently available FDA-approved treatments for pancreatic cancer, notably chemotherapeutic agents, exhibit suboptimal efficacy, often accompanied by concerns regarding toxicity. Given the intricate nature of pancreatic cancer pathogenesis and the time-intensive nature of in silico drug discovery approaches, drug repurposing emerges as a compelling strategy to expedite the development of novel therapeutic interventions. In our study, we harnessed transcriptomic data from an exhaustive exploration of four diverse databases, ensuring a rigorous and unbiased analysis of differentially expressed genes, with a particular focus on upregulated genes associated with pancreatic cancer. Leveraging these pancreatic cancer-associated host protein targets, we employed a battery of cutting-edge bioinformatics tools, including Cytoscape STRING, GeneMANIA, Connectivity Map, and NetworkAnalyst, to identify potential small molecule drug candidates and elucidate their interactions. Subsequently, we conducted meticulous docking and redocking simulations for the selected drug-protein target pairs. This rigorous computational approach culminated in the identification of two promising broad-spectrum drug candidates against four pivotal host genes implicated in pancreatic cancer. Our findings strongly advocate for further investigation and preclinical validation of these candidates. Specifically, we propose prioritizing Dasatinib for evaluation against MMP3, MMP9, and EGFR due to their remarkable binding affinities, as well as Pioglitazone against MMP3, MMP2 and MMP9. These discoveries hold great promise in advancing the therapeutic landscape for pancreatic cancer, offering new avenues for improving patient outcomes.
胰腺癌因其晚期诊断和低患者生存率而臭名昭著，仍然是一项严峻的全球健康挑战。目前美国食品药品监督管理局（FDA）批准的胰腺癌治疗方法，尤其是化疗药物，疗效欠佳，且常常伴随着毒性方面的问题。鉴于胰腺癌发病机制的复杂性以及计算机辅助药物发现方法耗时较长的特点，药物重新利用成为加快新型治疗干预措施开发的一种引人注目的策略。在我们的研究中，我们利用了来自四个不同数据库详尽探索的转录组数据，确保对差异表达基因进行严格且无偏倚的分析，特别关注与胰腺癌相关的上调基因。利用这些与胰腺癌相关的宿主蛋白靶点，我们使用了一系列前沿的生物信息学工具，包括Cytoscape STRING、GeneMANIA、Connectivity Map和NetworkAnalyst，以识别潜在的小分子药物候选物并阐明它们之间的相互作用。随后，我们对选定的药物 - 蛋白靶点对进行了细致的对接和重新对接模拟。这种严格的计算方法最终确定了两种有前景的针对与胰腺癌相关的四个关键宿主基因的广谱药物候选物。我们的研究结果强烈主张对这些候选物进行进一步研究和临床前验证。具体而言，由于其显著的结合亲和力，我们建议优先评估达沙替尼针对基质金属蛋白酶3（MMP3）、基质金属蛋白酶9（MMP9）和表皮生长因子受体（EGFR）的效果，以及吡格列酮针对MMP3、基质金属蛋白酶2（MMP2）和MMP9的效果。这些发现对于推进胰腺癌的治疗前景具有很大的潜力，为改善患者预后提供了新途径。
