Computational Identification of Stearic Acid as a Potential PDK1 Inhibitor and In Vitro Validation of Stearic Acid as Colon Cancer Therapeutic in Combination with 5-Fluorouracil.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2021-12-13 eCollection Date: 2021-01-01 DOI:10.1177/11769351211065979
Jonathan Mitchel, Pratima Bajaj, Ketki Patil, Austin Gunnarson, Emilie Pourchet, Yoo Na Kim, Jeffrey Skolnick, S Balakrishna Pai
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引用次数: 5

Abstract

Background: Colorectal cancer is the third largest cause of cancer-related mortality worldwide. Although current treatments with chemotherapeutics have allowed for management of colorectal cancer, additional novel treatments are essential. Intervening with the metabolic reprogramming observed in cancers called "Warburg Effect," is one of the novel strategies considered to combat cancers. In the metabolic reprogramming pathway, pyruvate dehydrogenase kinase (PDK1) plays a pivotal role. Identification and characterization of a PDK1 inhibitor is of paramount importance. Further, for efficacious treatment of colorectal cancers, combinatorial regimens are essential. To this end, we opted to identify a PDK1 inhibitor using computational structure-based drug design FINDSITEcomb and perform combinatorial studies with 5-FU for efficacious treatment of colorectal cancers.

Methods: Using computational structure-based drug design FINDSITEcomb, stearic acid (SA) was identified as a possible PDK1 inhibitor. Elucidation of the mechanism of action of SA was performed using flow cytometry, clonogenic assays.

Results: When the growth inhibitory potential of SA was tested on colorectal adenocarcinoma (DLD-1) cells, a 50% inhibitory concentration (IC50) of 60 µM was recorded. Moreover, SA inhibited the proliferation potential of DLD-1 cells as shown by the clonogenic assay and there was a sustained response even after withdrawal of the compound. Elucidation of the mechanism of action revealed, that the inhibitory effect of SA was through the programmed cell death pathway. There was increase in the number of apoptotic and multicaspase positive cells. SA also impacted the levels of the cell survival protein Bcl-2. With the aim of achieving improved treatment for colorectal cancer, we opted to combine 5-fluorouracil (5-FU), the currently used drug in the clinic, with SA. Combining SA with 5-FU, revealed a synergistic effect in which the IC50 of 5-FU decreased from 25 to 6 µM upon combination with 60 µM SA. Further, SA did not inhibit non-tumorigenic NIH-3T3 proliferation.

Conclusions: We envision that this significant decrease in the IC50 of 5-FU could translate into less side effects of 5-FU and increase the efficacy of the treatment due to the multifaceted action of SA. The data generated from the current studies on the inhibition of colorectal adenocarcinoma by SA discovered by the use of the computational program as well as synergistic action with 5-FU should open up novel therapeutic options for the management of colorectal adenocarcinomas.

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硬脂酸作为潜在PDK1抑制剂的计算鉴定以及硬脂酸与5-氟尿嘧啶联合治疗结肠癌的体外验证。
背景:结直肠癌是全球癌症相关死亡的第三大原因。虽然目前的化疗治疗已经可以治疗结直肠癌,但额外的新治疗方法是必不可少的。干预在癌症中观察到的代谢重编程,被称为“Warburg效应”,被认为是对抗癌症的新策略之一。在代谢重编程途径中,丙酮酸脱氢酶激酶(PDK1)起着关键作用。鉴定和表征PDK1抑制剂是至关重要的。此外,为了有效治疗结直肠癌,联合治疗方案是必不可少的。为此,我们选择使用基于计算结构的药物设计FINDSITEcomb来鉴定PDK1抑制剂,并与5-FU进行联合研究,以有效治疗结直肠癌。方法:使用基于计算结构的药物设计FINDSITEcomb,硬脂酸(SA)被确定为可能的PDK1抑制剂。利用流式细胞术、克隆实验对SA的作用机制进行了分析。结果:测定SA对结直肠癌(DLD-1)细胞的生长抑制电位时,记录到50%的抑制浓度(IC50)为60µM。此外,克隆实验显示,SA抑制了DLD-1细胞的增殖潜力,即使在停用该化合物后仍有持续的反应。作用机制的阐明表明,SA的抑制作用是通过程序性细胞死亡途径实现的。凋亡细胞增多,多aspase阳性细胞增多。SA还影响了细胞存活蛋白Bcl-2的水平。为了改善结直肠癌的治疗,我们选择了目前临床使用的5-氟尿嘧啶(5-FU)与SA联合使用。SA与5-FU联用显示协同效应,5-FU与60µM SA联用时IC50由25µM降至6µM。此外,SA不抑制非致瘤性NIH-3T3的增殖。结论:我们设想,由于SA的多方面作用,5-FU IC50的显著降低可以转化为5-FU副作用的减少和治疗效果的提高。目前使用计算程序发现的SA对结直肠腺癌的抑制作用以及与5-FU的协同作用所产生的数据,应该为结直肠腺癌的治疗开辟新的治疗选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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