Ayesha Mehmood, Mohammed Ageeli Hakami, Hanan A Ogaly, Vetriselvan Subramaniyan, Asaad Khalid, Abdul Wadood
{"title":"Evolution of computational techniques against various KRAS mutants in search for therapeutic drugs: a review article.","authors":"Ayesha Mehmood, Mohammed Ageeli Hakami, Hanan A Ogaly, Vetriselvan Subramaniyan, Asaad Khalid, Abdul Wadood","doi":"10.1007/s00280-025-04767-8","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":9556,"journal":{"name":"Cancer Chemotherapy and Pharmacology","volume":"95 1","pages":"52"},"PeriodicalIF":2.7000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Chemotherapy and Pharmacology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00280-025-04767-8","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Addressing a wide range of pharmacologic and oncologic concerns on both experimental and clinical levels, Cancer Chemotherapy and Pharmacology is an eminent journal in the field. The primary focus in this rapid publication medium is on new anticancer agents, their experimental screening, preclinical toxicology and pharmacology, single and combined drug administration modalities, and clinical phase I, II and III trials. It is essential reading for pharmacologists and oncologists giving results recorded in the following areas: clinical toxicology, pharmacokinetics, pharmacodynamics, drug interactions, and indications for chemotherapy in cancer treatment strategy.