Shelper Todd B, Lovitt Carrie J, Avery Vicky M
Discovery Biology, Eskitis Institute for Drug Discovery, Griffith University , Nathan, Australia .
Assay Drug Dev Technol. 2016 Sep;14(7):367-80. doi: 10.1089/adt.2016.737.
Pancreatic cancer continues to have one of the poorest prognoses among all cancers. The drug discovery efforts for this disease have largely failed, with no significant improvement in survival outcomes for advanced pancreatic cancer patients over the past 20 years. Traditional in vitro cell culture techniques have been used extensively in both basic and early drug discovery; however, these systems offer poor models to assess emerging therapeutics. More predictive cell-based models, which better capture the cellular heterogeneity and complexities of solid pancreatic tumors, are urgently needed not only to improve drug discovery success but also to provide insight into the tumor biology. Pancreatic tumors are characterized by a unique micro-environment that is surrounded by a dense stroma. A complex network of interactions between extracellular matrix (ECM) components and the effects of cell-to-cell contacts may enhance survival pathways within in vivo tumors. This biological and physical complexity is lost in traditional cell monolayer models. To explore the predictive potential of a more complex cellular system, a three-dimensional (3D) micro-tumor assay was evaluated. Efficacy of six current chemotherapeutics was determined against a panel of primary and metastatic pancreatic tumor cell lines in a miniaturized ECM-based 3D cell culture system. Suitability for potential use in high-throughput screening applications was assessed, including ascertaining the effects that miniaturization and automation had on assay robustness. Cellular health was determined by utilizing an indirect population-based metabolic activity assay and a direct imaging-based cell viability assay.
胰腺癌仍然是所有癌症中预后最差的癌症之一。针对这种疾病的药物研发工作大多以失败告终，在过去20年里，晚期胰腺癌患者的生存结果没有显著改善。传统的体外细胞培养技术在基础研究和早期药物研发中都得到了广泛应用；然而，这些系统提供的模型对于评估新兴疗法而言效果不佳。迫切需要更具预测性的基于细胞的模型，这些模型能够更好地捕捉胰腺实体瘤的细胞异质性和复杂性，不仅可以提高药物研发的成功率，还能深入了解肿瘤生物学。胰腺肿瘤的特征是其独特的微环境，周围环绕着致密的基质。细胞外基质（ECM）成分之间复杂的相互作用网络以及细胞间接触的影响，可能会增强体内肿瘤的生存途径。这种生物学和物理上的复杂性在传统的细胞单层模型中会丧失。为了探索更复杂细胞系统的预测潜力，对一种三维（3D）微肿瘤检测方法进行了评估。在基于小型化ECM的3D细胞培养系统中，针对一组原发性和转移性胰腺肿瘤细胞系，测定了六种现有化疗药物的疗效。评估了其在高通量筛选应用中的潜在适用性，包括确定小型化和自动化对检测稳健性的影响。通过使用基于群体的间接代谢活性检测和基于成像的直接细胞活力检测来确定细胞健康状况。
