Detection of cystic fibrosis transmembrane conductance regulator activity in early-phase clinical trials.
Detection of cystic fibrosis transmembrane conductance regulator activity in early-phase clinical trials.
复制标题
DOI：
10.1513/pats.200703-043br
复制
发表时间：
2007-08-01
期刊：
影响因子：
--
通讯作者：
Clancy, John P
中科院分区：
文献类型：
--
作者：
Rowe, Steven M;Accurso, Frank;Clancy, John P
我们对囊性纤维化发病机制的理解的进展已经导致了针对疾病的根本原因而不是疾病相关症状的治疗的策略。为了加快对这些新兴疗法的评估，早期临床试验需要将体内囊性纤维化跨膜传导调节因子（CFTR）检测试验扩展到多中心试验形式，包括鼻电位差和汗液氯化物测量。这两种技术都可以用于满足疾病的诊断标准，并可以区分不同水平的CFTR功能。在多中心临床试验中充分实现这些检测需要识别非生物学研究中心内和研究中心间变异性的来源，并仔细关注研究设计和研究生成数据的统计分析。在这篇综述中，我们讨论了几个重要的问题，这些检测的性能，包括努力识别和解决方面，可能会导致不一致和/或潜在的错误结果。辅助手段检测CFTR，包括mRNA表达，免疫细胞化学定位，和其他方法也进行了讨论。提出建议，以促进我们对这些生物标志物的理解，并提高其预测囊性纤维化结局的能力。
Advances in our understanding of cystic fibrosis pathogenesis have led to strategies directed toward treatment of underlying causes of the disease rather than treatments of disease-related symptoms. To expedite evaluation of these emerging therapies, early-phase clinical trials require extension of in vivo cystic fibrosis transmembrane conductance regulator (CFTR)-detecting assays to multicenter trial formats, including nasal potential difference and sweat chloride measurements. Both of these techniques can be used to fulfill diagnostic criteria for the disease, and can discriminate various levels of CFTR function. Full realization of these assays in multicenter clinical trials requires identification of sources of nonbiological intra- and intersite variability, and careful attention to study design and statistical analysis of study-generated data. In this review, we discuss several issues important to the performance of these assays, including efforts to identify and address aspects that can contribute to inconsistent and/or potentially erroneous results. Adjunctive means of detecting CFTR including mRNA expression, immunocytochemical localization, and other methods are also discussed. Recommendations are presented to advance our understanding of these biomarkers and to improve their capacity to predict cystic fibrosis outcomes.