Predicting and Quantifying Antagonistic Effects of Natural Compounds Given with Chemotherapeutic Agents: Applications for High-Throughput Screening.
Predicting and Quantifying Antagonistic Effects of Natural Compounds Given with Chemotherapeutic Agents: Applications for High-Throughput Screening.
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预测和量化天然化合物与化疗药物的拮抗作用：高通量筛选的应用。
DOI：
10.3390/cancers12123714
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发表时间：
2020-12-10
期刊：
影响因子：
5.2
通讯作者：
Tiziani S
中科院分区：
文献类型：
--
作者：
Hackman GL;Collins M;Lu X;Lodi A;DiGiovanni J;Tiziani S
越来越多的癌症患者正在转向补充和替代药物（CAM），以促进或取代他们的癌症治疗，或者他们正在从饮食中获得天然产品。这是令人担忧的，因为有证据表明这些生物活性化合物和癌症药物之间存在分子相互作用，可以阻碍或逆转其疗效并防止癌症消退。高通量药物筛选和深度学习技术过去已成功应用于评估协同抗癌药物和天然产物组合。然而，这些技术应该更普遍地应用于药物拮抗作用的背景下，以发现潜在的有害相互作用，并为癌症患者提供更安全的建议。在这篇综述中，我们评估了天然产物和化疗药物之间的拮抗作用，并强调了高通量筛选和深度学习技术的应用如何加强这一研究领域。 几个世纪以来，天然产品一直被用于治疗各种人类疾病。近几十年来，已经确定了利用天然产物协同增强癌症药物治疗效果的多药物组合，并在改善治疗结果方面取得了成功。虽然药物协同作用研究是一个新兴的领域，但在定义和数学参数上存在分歧，这阻碍了术语协同作用，拮抗作用和加和性的标准化和正确使用。这导致关于天然产物对癌症药物的拮抗作用的数据相对较少，这些药物可以降低其治疗效果并防止癌症消退。天然产物潜在降解或逆转癌症治疗剂的分子活性的能力代表了一个重要但高度强调的研究领域，该领域在临床前和临床研究中经常被忽视。本综述旨在评估围绕天然产物与癌症治疗剂之间的拮抗相互作用的工作，并强调高通量筛选（HTS）和深度学习技术在鉴定拮抗癌症药物疗效的天然产物中的应用。
Increasing numbers of cancer patients are turning to complementary and alternative medicines (CAM) to facilitate or replace their cancer treatments, or they are obtaining natural products in their diet. This is concerning, as there is evidence of molecular interactions between these bioactive compounds and cancer drugs that can impede or reverse their efficacy and prevent cancer regression. High-throughput drug screening and deep learning techniques have successfully been applied in the past to evaluate synergistic cancer drug and natural product combinations. However, these techniques should be applied more commonly in the context of drug antagonism to uncover potentially harmful interactions and drive safer recommendations for cancer patients. In this review, we evaluate the antagonistic interactions between natural products and chemotherapeutics and highlight how the application of high-throughput screening and deep learning techniques can strengthen this area of research. Natural products have been used for centuries to treat various human ailments. In recent decades, multi-drug combinations that utilize natural products to synergistically enhance the therapeutic effects of cancer drugs have been identified and have shown success in improving treatment outcomes. While drug synergy research is a burgeoning field, there are disagreements on the definitions and mathematical parameters that prevent the standardization and proper usage of the terms synergy, antagonism, and additivity. This contributes to the relatively small amount of data on the antagonistic effects of natural products on cancer drugs that can diminish their therapeutic efficacy and prevent cancer regression. The ability of natural products to potentially degrade or reverse the molecular activity of cancer therapeutics represents an important but highly under-emphasized area of research that is often overlooked in both pre-clinical and clinical studies. This review aims to evaluate the body of work surrounding the antagonistic interactions between natural products and cancer therapeutics and highlight applications for high-throughput screening (HTS) and deep learning techniques for the identification of natural products that antagonize cancer drug efficacy.
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影响因子：
3
作者：
Bannerman, Bret;Xu, Ling;Jones, Matthew;Tsu, Christopher;Yu, Jie;Hales, Paul;Monbaliu, Johan;Fleming, Paul;Dick, Lawrence;Manfredi, Mark;Claiborne, Christopher;Bolen, Joseph;Kupperman, Erik;Berger, Allison
通讯作者：
Berger, Allison
影响因子：
158.5
作者：
EISENBERG, DM;KESSLER, RC;DELBANCO, TL
通讯作者：
DELBANCO, TL
影响因子：
2.6
作者：
Foucquier, Julie;Guedj, Mickael
通讯作者：
Guedj, Mickael
影响因子：
2.6
作者：
Buckner, C. A.;Lafrenie, R. M.;Want, D. A.
通讯作者：
Want, D. A.
影响因子：
5.8
作者：
Chen, D;Daniel, KG;Dou, QP
通讯作者：
Dou, QP