Chan K K, Langenbucher F, Gibaldi M
J Pharm Sci. 1987 Jun;76(6):446-50. doi: 10.1002/jps.2600760607.
Determination of in vivo drug release using compartmental model analysis is hampered by problems such as flip-flop phenomena and vanishing exponential terms. The usefulness of numerical deconvolution to estimate in vivo drug release was evaluated in this study by means of simulated data comparing solid dosage forms with a solution as a reference standard. Concentration-time data were generated using the standard linear two-compartment body model with various first-order release and absorption rate constants. Random errors of 5 and 10% were added to data sets for further analysis. The results of the study using error-free data afforded excellent agreement with the theoretical values except in one case where the release rate constant was overestimated by 6%. When random error was added to the data, the resulting in vivo release profile showed considerable fluctuation and no single rate constant could be assigned. However, further analysis showed that the method does not create additional error during the calculating process, as previously suggested, but merely reflects the inherent error added to the raw data. If the raw data are poor, no useful information can be obtained without using an arbitrary technique such as smoothing or fitting. In this regard, the time course of drug release obtained after numerical deconvolution merits investigation.
使用房室模型分析来确定体内药物释放受到诸如翻转现象和消失指数项等问题的阻碍。本研究通过将固体剂型与溶液作为参比标准进行模拟数据比较,评估了数值反卷积法估算体内药物释放的有效性。使用具有不同一级释放和吸收速率常数的标准线性二房室模型生成浓度 - 时间数据。向数据集添加5%和10%的随机误差以进行进一步分析。使用无误差数据的研究结果与理论值高度吻合,仅有一例释放速率常数被高估了6%。当向数据中添加随机误差时,所得的体内释放曲线显示出相当大的波动,且无法确定单一的速率常数。然而,进一步分析表明,该方法在计算过程中不会像之前所认为的那样产生额外误差,而仅仅反映了添加到原始数据中的固有误差。如果原始数据质量较差,不使用诸如平滑或拟合等任意技术就无法获得有用信息。在这方面,数值反卷积后获得的药物释放时间进程值得研究。