Clinical Pharmacology, Early Clinical Development, Worldwide Research & Development, Pfizer, 1 Portland Street, Cambridge, MA, 02139, USA.
Clinical Pharmacology, Global Product Development, Pfizer, Groton, CT, USA.
J Pharmacokinet Pharmacodyn. 2019 Dec;46(6):605-616. doi: 10.1007/s10928-019-09661-4. Epub 2019 Oct 29.
The International Council for Harmonisation (ICH) guidelines have been revised allowing for modeling of concentration-QT (C-QT) data from Phase I dose-escalation studies to be used as primary analysis for QT prolongation risk assessment of new drugs. This work compares three commonly used Phase I dose-escalation study designs regarding their efficiency to accurately identify drug effects on QT interval through C-QT modeling. Parallel group design and 4-period crossover designs with sequential or interleaving cohorts were evaluated. Clinical trial simulations were performed for each design and across different scenarios (e.g. different magnitudes of drug effect, QT variability), assuming a pre-specified linear mixed effect (LME) model for the relationship between drug concentration and change from baseline QT (ΔQT). Analyses suggest no systematic bias in either the predictions of placebo-adjusted ΔQT (ΔΔQT) or the LME model parameter estimates across all evaluated designs. Additionally, false negative rates remained similar and adequately controlled across all evaluated designs. However, compared to the crossover designs, the parallel design had significantly less power to correctly exclude a clinically significant QT effect, especially in the presence of substantial intercept inter-individual variability. In such cases, parallel design is associated with increased uncertainty around ΔΔQT prediction, mainly attributed to the uncertainty around the estimation of the treatment-specific intercept in the model. Throughout all the evaluated scenarios, the crossover design with interleaving cohorts had consistently the best performance characteristics. The results from this investigation will further facilitate informed decision-making during Phase I study design and the interpretation of the associated C-QT modeling output.
国际协调理事会（ICH）指南已经修订，允许将 I 期剂量递增研究中的浓度- QT（C-QT）数据建模作为新药 QT 延长风险评估的主要分析。这项工作比较了三种常用的 I 期剂量递增研究设计，评估它们通过 C-QT 建模准确识别药物对 QT 间期影响的效率。评估了平行组设计和具有顺序或交错队列的 4 期交叉设计。对每个设计和不同场景（例如，药物效应的不同程度、QT 变异性）进行了临床试验模拟，假设药物浓度与 QT 从基线变化（ΔQT）之间的关系为预指定的线性混合效应（LME）模型。分析表明，在所有评估的设计中，安慰剂调整的 ΔQT（ΔΔQT）或 LME 模型参数估计的预测均没有系统偏差。此外，在所有评估的设计中，假阴性率保持相似且得到充分控制。然而，与交叉设计相比，平行设计正确排除临床显著 QT 效应的能力显著降低，尤其是在存在大量个体间截距变异性的情况下。在这种情况下，平行设计与 ΔΔQT 预测的不确定性增加有关，主要归因于模型中治疗特异性截距估计的不确定性。在所有评估的场景中，具有交错队列的交叉设计始终具有最佳的性能特征。这项研究的结果将进一步促进 I 期研究设计过程中的知情决策，并有助于解释相关的 C-QT 建模结果。
