Targeting the cyclin-dependent kinase family in anticancer drug discovery: From computational to experimental studies

被引:1
作者
Solanki, Priyanka [1 ]
Sarwadia, Shubhangi [1 ]
Athar, Mohd [2 ]
Jha, Prakash C. [3 ]
Manhas, Anu [1 ]
机构
[1] Pandit Deendayal Energy Univ, Dept Chem, Gandhinagar 382426, India
[2] Univ Cagliari, Dept Phys, Monserrato, Cagliari, Italy
[3] Cent Univ Gujarat, Sch Appl Mat Sci, Gandhinagar 382030, India
来源
CHEMICAL PHYSICS IMPACT | 2024年 / 9卷
关键词
CDK enzymes; Cancer; Anticancer compounds; Pharmacophore modeling; Anticancer activity; MOLECULAR DOCKING; PHARMACOPHORE; INHIBITORS; DYNAMICS; DATABASE; IDENTIFICATION; EXPLORATION; DERIVATIVES; ZINC; QSAR;
D O I
10.1016/j.chphi.2024.100768
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Uncontrolled cell proliferation, primarily regulated by cyclin-dependent kinases (CDKs), is a critical driver of cancer progression, with dysregulation of CDKs contributing to various cancer types. CDKs have emerged as wellestablished targets for cancer therapy; however, traditional drug development methods have often proven to be time-consuming, challenging, and expensive. Recent advancements in CDK inhibitors (CDKIs) have shown immense clinical potential but many first-generation CDKIs face issues of non-selectivity and significant toxicity, limiting their clinical approval. To address these challenges, innovative computational approaches, particularly pharmacophore modeling, have the potential to streamline drug discovery. These methods can guide the selection of small molecules through target-specific structure-activity relationship (SAR) models and chemotypes screening across databases, thereby accelerating the identification of effective CDKIs. This review paper summarizes the latest developments on CDK inhibitors, highlights their structural features, and the methodologies (key databases & software tools) that can provide further suggestions for future drug development.
引用
收藏
页数:24
相关论文
共 101 条
[81]   In Silico Drug-Designing Studies on Flavanoids as Anticolon Cancer Agents: Pharmacophore Mapping, Molecular Docking, and Monte Carlo Method-Based QSAR Modeling [J].
Simon, Lalitha ;
Imane, Abdelli ;
Srinivasan, K. K. ;
Pathak, Lokesh ;
Daoud, I. .
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES, 2017, 9 (03) :445-458
[82]   A comprehensive analysis of the role of molecular docking in the development of anticancer agents against the cell cycle CDK enzyme [J].
Solanki, Priyanka ;
Rana, Nisarg ;
Jha, Prakash C. ;
Manhas, Anu .
BIOCELL, 2023, 47 (04) :707-729
[83]   COCONUT online: Collection of Open Natural Products database [J].
Sorokina, Maria ;
Merseburger, Peter ;
Rajan, Kohulan ;
Yirik, Mehmet Aziz ;
Steinbeck, Christoph .
JOURNAL OF CHEMINFORMATICS, 2021, 13 (01)
[84]   ZINC 15-Ligand Discovery for Everyone [J].
Sterling, Teague ;
Irwin, John J. .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2015, 55 (11) :2324-2337
[85]  
Studio D, 2008, DISCOVERY STUDIO
[86]   Molecular Docking Simulation Studies of Curcumin and Its Derivatives as Cyclin-Dependent Kinase 2 Inhibitors [J].
Sumirtanurdin, Riyadi ;
Sungkar, Shafira ;
Hisprastin, Yasarah ;
Sidharta, Kenny Dwi ;
Nurhikmah, Dea Dian .
TURKISH JOURNAL OF PHARMACEUTICAL SCIENCES, 2020, 17 (04) :417-423
[87]   Design and screening of FAK, CDK 4/6 dual inhibitors by pharmacophore model, molecular docking, and molecular dynamics simulation [J].
Sun, Chuance ;
Feng, Lijun ;
Sun, Xiaohua ;
Yu, Rilei ;
Kang, Congmin .
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS, 2021, 39 (15) :5358-5367
[88]   Pharmit: interactive exploration of chemical space [J].
Sunseri, Jocelyn ;
Koes, David Ryan .
NUCLEIC ACIDS RESEARCH, 2016, 44 (W1) :W442-W448
[89]   A Search for Cyclin-Dependent Kinase 4/6 Inhibitors by Pharmacophore-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulations [J].
Susanti, Ni Made Pitri ;
Damayanti, Sophi ;
Kartasasmita, Rahmana Emran ;
Tjahjono, Daryono Hadi .
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2021, 22 (24)
[90]  
SYBYL, 2002, Molecular Modelling System