School of Chemistry and Molecular Engineering at East China Normal University, Shanghai, 200062, China.
Center of Biotechnology, PPGBCM, Federal University of Rio Grande Do Sul (UFRGS), Porto Alegre, Rio Grande Do Sul, 91501970, Brazil.
BMC Cancer. 2024 Mar 12;24(1):335. doi: 10.1186/s12885-024-12057-4.
The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations.
To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses.
By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails.
In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.
耐药性的发展是癌症治疗失败的主要原因。为了抑制耐药性，通常将多种药物一起作为联合疗法进行治疗。特别是协同药物组合，以较低的浓度杀死癌细胞，可保证癌症患者更好的预后和更少的副作用。许多研究通过小规模基于功能的靶向生长测定或大规模非靶向生长测定来寻找协同组合，但由于技术问题（例如可能的测试组合数量多），它们的发现总是具有挑战性。
为了解决这个问题，我们在非小细胞肺癌细胞系中进行了中等规模的光学药物协同筛选，并通过高内涵图像分析进一步研究了组合药物反应中的单个药物相互作用。光学高内涵分析细胞反应最近在药物发现、功能基因组学和毒理学领域引起了广泛关注。在这里，我们采用类似的方法来研究组合药物反应。
通过检查 6 种不同药物类别（例如 mTOR 抑制剂、HDAC 抑制剂、HSP90 抑制剂、MT 抑制剂、DNA 抑制剂和蛋白酶体抑制剂）的 12 种药物化合物的所有可能组合，我们成功地鉴定了 INK128（一种 mTOR 抑制剂）与 HDAC 抑制剂之间的协同作用，这也在其他地方有报道。我们的高内涵分析进一步表明，当 HDAC 抑制剂、HSP90 抑制剂和蛋白酶体抑制剂与 MT 抑制剂、DNA 抑制剂或 mTOR 抑制剂混合时，在组合药物反应中起主导作用，这表明隐性药物作为多药鸡尾酒的成分可能优先级较低。
总之，我们的光学药物筛选平台在非小细胞肺癌细胞系中有效地鉴定了协同药物组合，我们的高内涵分析进一步揭示了药物混合物中的单个药物如何相互作用以产生组合药物反应。
