The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and |$C_{\max}$| are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the |$90\%$| confidence interval for these ratios falls between |$0.8$| and |$1.25 $|⁠. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of |$\rm AUC$| and |$C_{\max}$| using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of |$0.05$|⁠, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

中文翻译：

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高效的基于模型的生物等效性测试。

在旨在比较两种不同制剂的生物等效性研究中分析药代动力学 (PK) 数据的经典方法是进行非隔室分析 (NCA)，然后进行两次单侧测试 (TOST)。对此，PK参数曲线下面积（AUC）和|$C_{\max}$| 对两个治疗组都获得了，并考虑了它们的几何平均比率。根据美国食品和药物管理局和欧洲药品管理局的现行指南，如果|$90\%$| 这些比率的置信区间介于|$0.8$|之间 和|$1.25 $|⁠. 由于 NCA 在稀疏设计的情况下不是一种可靠的方法，因此已经提出了一种基于模型的替代方案来估计|$\rm AUC$|。和|$C_{\max}$| 使用非线性混合效应模型。在这里，我们提出了另一个比 TOST 更强大的测试，并通过 NCA 和基于模型的方法的模拟研究证明了它的优越性。对于 PK 参数具有高度可变性的产品，该方法似乎具有更接近传统接受的显着性水平|$0.05$|⁠的 I 型错误，这表明它在常规生物等效性分析不适用的情况下具有潜在用途。