Can Bias Evaluation Provide Protection Against False-Negative Results in QT Studies Without a Positive Control Using Exposure-Response Analysis?
详细信息 查看全文
- 作者：Georg Ferber ; Meijian Zhou ; Corina Dota ; Christine Garnett ; James Keirns ; Marek Malik ; Norman Stockbridge and Borje Darpo
- 刊名：The Journal of Clinical Pharmacology
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：57
- 期：1
- 页码：85-95
- 全文大小：618K
- ISSN：1552-4604
文摘
The revised ICH E14 document allows the use of exposure-response analysis to exclude a small QT effect of a drug. If plasma concentrations exceeding clinically relevant levels is achieved, a positive control is not required. In cases when this cannot be achieved, there may be a need for metrics to protect against false-negative results. The objectives of this study were to create bias in electrocardiogram laboratory QT-interval measurements and define a metric that can be used to detect bias severe enough to cause false-negative results using exposure-response analysis. Data from the IQ-CSRC study, which evaluated the QT effect of 5 QT-prolonging drugs, were used. Negative bias using 3 deterministic and 2 random methods was introduced into the reported QTc values and compared with fully automated data from the underlying electrocardiogram algorithm (COMPAS). The slope estimate of the Bland-Altman plot was used as a bias metric. With the deterministic bias methods, negative bias, measured between electrocardiogram laboratory values and COMPAS, had to be larger than approximately −20 milliseconds over a QTcF range of 100 milliseconds to cause failures to predict the QT effect of ondansetron, quinine, dolasetron, moxifloxacin, and dofetilide. With the random methods, the rate of false-negatives was ≤5% with bias severity < –10 milliseconds for all 5 drugs when plasma levels exceeded those of interest. Severe and therefore detectable bias has to be introduced into reported QTc values to cause false-negative predictions with exposure-response analysis.