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寻找治疗药物过程中针对各种KRAS突变体的计算技术进展:一篇综述文章

Evolution of computational techniques against various KRAS mutants in search for therapeutic drugs: a review article.

作者信息

Mehmood Ayesha, Hakami Mohammed Ageeli, Ogaly Hanan A, Subramaniyan Vetriselvan, Khalid Asaad, Wadood Abdul

机构信息

Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan.

Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Al- Quwayiyah, Riyadh, Saudi Arabia.

出版信息

Cancer Chemother Pharmacol. 2025 Apr 7;95(1):52. doi: 10.1007/s00280-025-04767-8.

Abstract

KRAS was (Kirsten rat sarcoma viral oncogene homolog) revealed as an important target in current therapeutic cancer research because alteration of RAS (rat sarcoma viral oncogene homolog) protein has a critical role in malignant modification, tumor angiogenesis, and metastasis. For cancer treatment, designing competitive inhibitors for this attractive target was difficult. Nevertheless, computational investigations of the protein's dynamic behavior displayed the existence of temporary pockets that could be used to design allosteric inhibitors. The last decade witnessed intensive efforts to discover KRAS inhibitors. In 2021, the first KRAS G12C covalent inhibitor, AMG 510, received FDA (Food and drug administration) approval as an anticancer medication that paved the path for future treatment strategies against this target. Computer-aided drug designing discovery has long been used in drug development research targeting different KRAS mutants. In this review, the major breakthroughs in computational methods adapted to discover novel compounds for different mutations have been discussed. Undoubtedly, virtual screening and molecular dynamic (MD) simulation and molecular docking are the most considered approach, producing hits that can be employed in subsequent refinements. After comprehensive analysis, Afatinib and Quercetin were computationally identified as hits in different publications. Several authors conducted covalent docking studies with acryl amide warheads groups containing inhibitors. Future studies are needed to demonstrate their true potential. In-depth studies focusing on various allosteric pockets demonstrate that the switch I/II pocket is a suitable site for drug designing. In addition, machine learning and deep learning based approaches provide new insights for developing anti-KRAS drugs. We believe that this review provides extensive information to researchers globally and encourages further development in this particular area of research.

摘要

KRAS( Kirsten大鼠肉瘤病毒癌基因同源物)被揭示为当前癌症治疗研究中的一个重要靶点,因为RAS(大鼠肉瘤病毒癌基因同源物)蛋白的改变在恶性转化、肿瘤血管生成和转移中起关键作用。对于癌症治疗而言,设计针对这一有吸引力靶点的竞争性抑制剂颇具难度。尽管如此,对该蛋白动态行为的计算研究显示存在可用于设计变构抑制剂的临时口袋。在过去十年中,人们为发现KRAS抑制剂付出了巨大努力。2021年,首个KRAS G12C共价抑制剂AMG 510获得美国食品药品监督管理局(FDA)批准,成为一种抗癌药物,为针对该靶点的未来治疗策略铺平了道路。计算机辅助药物设计发现长期以来一直用于针对不同KRAS突变体的药物开发研究。在这篇综述中,讨论了适用于发现针对不同突变的新型化合物的计算方法的重大突破。毫无疑问,虚拟筛选、分子动力学(MD)模拟和分子对接是最常被考虑的方法,能产生可用于后续优化的命中化合物。经过全面分析,阿法替尼和槲皮素在不同出版物中通过计算被确定为命中化合物。几位作者对含丙烯酰胺弹头基团的抑制剂进行了共价对接研究。未来需要开展研究以证明它们的真正潜力。针对各种变构口袋的深入研究表明,开关I/II口袋是药物设计的合适位点。此外,基于机器学习和深度学习的方法为开发抗KRAS药物提供了新的见解。我们相信这篇综述为全球的研究人员提供了广泛信息,并鼓励在这一特定研究领域进一步开展研究。

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