Bennani Fatima Ezzahra, Karrouchi Khalid, Doudach Latifa, Scrima Mario, Rahman Noor, Rastrelli Luca, Tallei Trina Ekawati, Rudd Christopher E, Faouzi My El Abbes, Ansar M'hammed
Laboratory of Pharmacology and Toxicology, Bio Pharmaceutical and Toxicological Analysis Research Team, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat BP6203, Morocco.
Division of Immunology-Oncology, Centre de Recherche Hôpital Maisonneuve-Rosemont (CR-HMR), Montreal, QC H1T 2M4, Canada.
Curr Issues Mol Biol. 2022 Oct 31;44(11):5312-5351. doi: 10.3390/cimb44110361.
Despite continual efforts being made with multiple clinical studies and deploying cutting-edge diagnostic tools and technologies, the discovery of new cancer therapies remains of severe worldwide concern. Multiple drug resistance has also emerged in several cancer cell types, leaving them unresponsive to the many cancer treatments. Such a condition always prompts the development of next-generation cancer therapies that have a better chance of inhibiting selective target macromolecules with less toxicity. Therefore, in the present study, extensive computational approaches were implemented combining molecular docking and dynamic simulation studies for identifying potent pyrazole-based inhibitors or modulators for CRMP2, C-RAF, CYP17, c-KIT, VEGFR, and HDAC proteins. All of these proteins are in some way linked to the development of numerous forms of cancer, including breast, liver, prostate, kidney, and stomach cancers. In order to identify potential compounds, 63 in-house synthesized pyrazole-derivative compounds were docked with each selected protein. In addition, single or multiple standard drug compounds of each protein were also considered for docking analyses and their results used for comparison purposes. Afterward, based on the binding affinity and interaction profile of pyrazole compounds of each protein, potentially strong compounds were filtered out and further subjected to 1000 ns MD simulation analyses. Analyzing parameters such as RMSD, RMSF, RoG and protein-ligand contact maps were derived from trajectories of simulated protein-ligand complexes. All these parameters turned out to be satisfactory and within the acceptable range to support the structural integrity and interaction stability of the protein-ligand complexes in dynamic state. Comprehensive computational analyses suggested that a few identified pyrazole compounds, such as M33, M36, M72, and M76, could be potential inhibitors or modulators for HDAC, C-RAF, CYP72 and VEGFR proteins, respectively. Another pyrazole compound, M74, turned out to be a very promising dual inhibitor/modulator for CRMP2 and c-KIT proteins. However, more extensive study may be required for further optimization of the selected chemical framework of pyrazole derivatives to yield improved inhibitory activity against each studied protein receptor.
尽管通过多项临床研究并采用前沿的诊断工具和技术不断努力,但新型癌症治疗方法的发现仍然是全球严重关切的问题。多种癌细胞类型也出现了多药耐药性,导致它们对许多癌症治疗无反应。这种情况总是促使开发下一代癌症治疗方法,这些方法更有可能以较低的毒性抑制选择性靶标大分子。因此,在本研究中,实施了广泛的计算方法,结合分子对接和动态模拟研究,以鉴定针对CRMP2、C-RAF、CYP17、c-KIT、VEGFR和HDAC蛋白的强效吡唑基抑制剂或调节剂。所有这些蛋白都以某种方式与多种形式癌症的发展相关,包括乳腺癌、肝癌、前列腺癌、肾癌和胃癌。为了鉴定潜在化合物,将63种内部合成的吡唑衍生物化合物与每种选定的蛋白进行对接。此外,还考虑了每种蛋白的单一或多种标准药物化合物进行对接分析,并将其结果用于比较目的。之后,根据每种蛋白的吡唑化合物的结合亲和力和相互作用概况,筛选出潜在的强效化合物,并进一步进行1000 ns的分子动力学模拟分析。从模拟的蛋白-配体复合物轨迹中得出诸如均方根偏差(RMSD)、均方根波动(RMSF)、回转半径(RoG)和蛋白-配体接触图等分析参数。所有这些参数都令人满意且在可接受范围内,以支持蛋白-配体复合物在动态状态下的结构完整性和相互作用稳定性。综合计算分析表明,一些鉴定出的吡唑化合物,如M33、M36、M72和M76,可能分别是HDAC、C-RAF、CYP72和VEGFR蛋白的潜在抑制剂或调节剂。另一种吡唑化合物M74被证明是一种非常有前景的CRMP2和c-KIT蛋白的双重抑制剂/调节剂。然而,可能需要更广泛的研究来进一步优化所选的吡唑衍生物化学框架,以提高对每种研究的蛋白受体的抑制活性。