QSAR study of benzylidene hydrazine benzamides derivatives with in vitro anticancer activity against human lung cancer cell line A459

IF 1.2 Q4 PHARMACOLOGY & PHARMACY Journal of Pharmacy & Pharmacognosy Research Pub Date : 2023-11-01 DOI:10.56499/jppres23.1718_11.6.1123
Galih Satrio Putra, Melanny Ika Sulistyowaty, Tegar Achsendo Yuniarta, Yahmin Yahmin, Sumari Sumari, Charinrat Saechan, Takayasu Yamauchi
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Abstract

Context: In the last decade, resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in lung cancer cases has been widespread. The discovery and development of new drugs need to be done to overcome the case. Aims: To develop lung anticancer candidates with benzylidene hydrazine benzamides derivatives that can inhibit the growth of human lung cancer cell line A459. Methods: The in silico approach method, along with the QSAR technique, plays an important role in the process of discovery and development of new drugs. In this study, we focused on developing benzylidene hydrazine benzamides derivatives that are much more potent by making the best QSAR equation of 11 benzylidene hydrazine benzamides that have been tested in vitro for its anticancer activity against human lung cancer cell line A459. Results: The best QSAR equation was obtained from benzylidene hydrazine benzamides derivatives as anticancer activity against human lung cancer cell line A459, with PIC50 = 0.738 (± 0.217) Log S - 0.031 (± 0.007) rerank + 0.017 (± 0.016) MR -1.359 ± (1.381) (n = 11; Sig = 0.003; r = 0.921; R2 = 0.849; F= 13.096; Q2 = 0.61). Conclusions: The best QSAR equation can be a tool to obtain a new chemical structure model with more potential and reduce trials and errors.
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对人肺癌A459细胞体外抗癌活性的苄基肼类苯酰胺衍生物的QSAR研究
背景:在过去的十年中,肺癌患者对表皮生长因子受体-酪氨酸激酶抑制剂(EGFR-TKIs)的耐药性已经很普遍。为了克服这种情况,需要发现和开发新药。目的:寻找能抑制人肺癌细胞系A459生长的苄基肼类苯并胺衍生物。方法:in silico approach方法与QSAR技术在新药的发现和开发过程中发挥着重要作用。在本研究中,我们重点开发了更有效的苯并苄基肼苯酰胺衍生物,通过对11种苯并苄基肼苯酰胺进行体外抗人肺癌细胞系A459的活性测试,建立了最佳的QSAR方程。结果:苄基肼类苯并酰胺类衍生物对人肺癌细胞株A459的抗肿瘤活性得到最佳的QSAR方程,PIC50 = 0.738(±0.217),Log S - 0.031(±0.007),重秩+ 0.017(±0.016),MR -1.359±(1.381)(n = 11;Sig = 0.003;R = 0.921;R2 = 0.849;F = 13.096;Q2 = 0.61)。结论:最佳QSAR方程可作为获得新的化学结构模型的工具,具有更大的潜力,减少试验和误差。
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来源期刊
CiteScore
3.00
自引率
20.00%
发文量
0
审稿时长
8 weeks
期刊介绍: The Journal of Pharmacy & Pharmacognosy Research (JPPRes) is an international, specialized and peer-reviewed open access journal, under the auspices of AVAGAX – Diseño, Publicidad y Servicios Informáticos, which publishes studies in the pharmaceutical and herbal fields concerned with the physical, botanical, chemical, biological, toxicological properties and clinical applications of molecular entities, active pharmaceutical ingredients, devices and delivery systems for drugs, vaccines and biologicals, including their design, manufacture, evaluation and marketing. This journal publishes research papers, reviews, commentaries and letters to the editor as well as special issues and review of pre-and post-graduate thesis from pharmacists or professionals involved in Pharmaceutical Sciences or Pharmacognosy.
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