Non-compartmental analysis (NCA) is a model-independent approach to analyzing pharmacokinetic data that does not assume a specific compartmental structure. Unlike compartmental modeling, which requires fitting data to a predefined mathematical model, NCA derives pharmacokinetic parameters directly from the concentration-time curve using numerical integration and statistical moment theory. This approach is widely preferred by regulatory agencies for bioavailability and bioequivalence studies because it makes fewer assumptions and provides robust, reproducible parameter estimates.
曲线下面积计算
The fundamental parameter in NCA is the area under the concentration-time curve (AUC) , which represents total systemic drug exposure over time. AUC is calculated using the linear trapezoidal rule, where the area between consecutive concentration measurements is approximated as a trapezoid.对于从时间 t1 到 t2 的时间间隔，面积等于两个时间点浓度的平均值乘以时间差。
The total AUC from time zero to infinity (AUC0-inf) is calculated as the sum of AUC from time zero to the last measurable concentration (AUC0-last) plus the extrapolated area from the last time point to infinity.外推部分的计算方式为最后测量的浓度除以末端消除速率常数 (lambda z)。理想情况下，外推面积的贡献应小于总 AUC 的 20%，以确保可靠的参数估计。
一阶矩曲线下的面积
一阶矩曲线下面积 (AUMC) 是浓度乘以时间与时间的乘积下的面积。 AUMC 使用相同的梯形规则计算，但应用于 C 乘以 t，而不是单独的 C。 AUMC requires extrapolation from the last time point to infinity: the extrapolated portion is calculated as t_last multiplied by C_last divided by lambda z plus C_last divided by lambda z squared.
AUMC 与 AUC 一起用于计算 平均停留时间 (MRT)：MRT 等于 AUMC 除以 AUC。 MRT 代表药物分子在被不可逆消除之前在体内花费的平均时间。对于静脉推注，MRT 等于平均吸收时间和平均通过体内时间的总和。
清除率和分布量
在 NCA 中，清除率 (CL) 的计算方式为静脉给药的剂量除以 AUC。该计算直接根据间隙的定义得出，不依赖于任何隔室假设。对于血管外给药，清除率等于剂量乘以生物利用度除以 AUC。
稳态分布体积 (Vss) 的计算方式为清除率乘以 MRT。这种关系之所以成立，是因为对于静脉推注给药，Vss 等于 CL 乘以 MRT。 For extravascular routes, Vss is calculated as CL multiplied by MRT multiplied by the fraction of dose absorbed relative to intravenous administration.
末端消除半衰期由末端消除速率常数计算得出，t½ 等于 0.693 除以 lambda z。 The terminal rate constant lambda z is estimated by linear regression of the natural logarithm of concentration versus time during the terminal phase. Careful selection of the points included in the regression is important: too few points reduces precision, while including points from the distribution phase biases the estimate.
相对于隔室模型的优势
与房室建模相比，NCA 具有多种优势。它不需要对模型的隔室数量或结构进行假设，避免了模型指定错误的风险。计算是标准化且可重复的，可以为不同的分析师和软件包提供一致的结果。 NCA 特别适合采样计划相对稀疏的研究，其中分区模型可能无法识别。
NCA 的主要局限性是它不能提供对潜在药代动力学过程的机制洞察。 NCA 描述了发生的情况，但没有解释原因。 It also does not readily predict concentration-time profiles under alternative dosing regimens, which requires a compartmental model or physiologically based modeling approach. NCA 独立处理每次剂量给药，并且不纳入有关药物药代动力学的先验知识。
统计矩理论
NCA is grounded in statistical moment theory, which treats the concentration-time curve as a statistical distribution of drug molecule residence times.零阶矩是AUC，代表总暴露量。一阶矩是MRT，代表平均停留时间。存在更高矩，但在实践中很少使用。
矩方法的主要优点是它将分析与任何特定的模型结构分开。 The moments are calculated directly from the observed data using numerical integration, and the derived parameters (CL, Vss, MRT) are model-independent provided the data adequately characterize the entire concentration-time profile. Regulatory agencies worldwide accept NCA as the standard method for analyzing pharmacokinetic data in clinical trials, and it remains the primary analytical approach for bioequivalence assessment and new drug applications.
