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首页> 外文期刊>Journal of pharmacokinetics and biopharmaceutics >Estimation of confidence intervals for area under the curve from destructively obtained pharmacokinetic data.
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Estimation of confidence intervals for area under the curve from destructively obtained pharmacokinetic data.

机译：从破坏性获得的药代动力学数据估算曲线下面积的置信区间。

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摘要

The area under the curve (AUC) of the concentration-time curve for a drug or metabolite, and the variation associated with the AUC, are primary results of most pharmacokinetic (PK) studies. In nonclinical PK studies, it is often the case that experimental units contribute data for only a single time point. In such cases, it is straightforward to apply noncompartmental methods to determine an estimate of the AUC. In this report, we investigate noncompartmental estimation of the AUC using the long-trapezoidal rule during the elimination phase of the concentration-time profile, and we account for the underlying distribution of data at each sampling time. For data that follow a normal distribution, the log-trapezoidal rule is applied to arithmetic means at each time point of the elimination phase of the concentration-time profile. For data that follow a lognormal distribution, as is common with PK data, the log-trapezoidal rule is applied to geometric means at each time point during elimination. Since the log-trapezoidal rule incorporates nonlinear combinations of mean concentrations at each sampling time, obtaining an estimate of the corresponding variation about the AUC is not straightforward. Estimation of this variance is further complicated by the occurrence of lognormal data. First-order approximations to the variance of AUC estimates are derived under the assumptions of normality, and lognormality, of concentrations at each sampling time. AUC estimates and variance approximations are utilized to form confidence intervals. Accuracies of confidence intervals are tested using simulation studies.
机译：药物或代谢物的浓度-时间曲线的曲线下面积（AUC）以及与AUC相关的变化是大多数药代动力学（PK）研究的主要结果。在非临床PK研究中，通常是实验单位仅在单个时间点上提供数据。在这种情况下，直接应用非分区方法来确定AUC的估算值很简单。在本报告中，我们研究了浓度-时间曲线消除阶段中使用长梯形规则的AUC非隔室估计，并说明了每个采样时间的潜在数据分布。对于遵循正态分布的数据，在浓度-时间曲线的消除阶段的每个时间点，将对数梯形规则应用于算术平均值。对于遵循对数正态分布的数据（与PK数据一样），在消除过程中的每个时间点将对数梯形规则应用于几何均数。由于对数梯形法则结合了每个采样时间的平均浓度的非线性组合，因此获得关于AUC的相应变化的估计并不容易。对数正态数据的出现使这种方差的估计更加复杂。在每个采样时间浓度的正态性和对数正态性的假设下，得出AUC估计方差的一阶近似值。 AUC估计和方差近似可用于形成置信区间。使用模拟算例测试置信区间的准确性。

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