Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854.
Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, Pennsylvania 15282.
J Pharm Sci. 2019 Jan;108(1):494-505. doi: 10.1016/j.xphs.2018.06.031. Epub 2018 Aug 10.
Accurate assessment of tablet content uniformity is critical for narrow therapeutic index drugs such as phenytoin sodium. This work presents a near-infrared (NIR)-based analytical method for rapid prediction of content uniformity based on a large number of phenytoin sodium formulation tablets. Calibration tablets were generated through an integrated experimental design by varying formulation and process parameters, and scale of manufacturing. A partial least squares model for individual tablet content was developed based on tablet NIR spectra. The tablet content was obtained from a modified United States Pharmacopeia phenytoin sodium high-performance liquid chromatography assay method. The partial least squares model with 4 latent variables explained 92% of the composition variability and yielded a root mean square error of prediction of 0.48% w/w. The resultant NIR model successfully assayed the composition of tablets manufactured at the pilot scale. For one such batch, bootstrapping was applied to calculate the confidence intervals on the mean, acceptance value, and relative SD for different sample sizes, n = 10, 30, and 100. As the bootstrap sample size increased, the confidence interval on the mean, acceptance value, and relative SD became narrower and symmetric. Such a 'large N' NIR-based process analytical technology method can increase reliability of quality assessments in solid dosage manufacturing.
对于苯妥英钠等治疗指数较窄的药物，准确评估片剂的含量均匀度至关重要。本工作提出了一种基于近红外（NIR）的分析方法，可快速预测苯妥英钠制剂大量片剂的含量均匀度。通过改变制剂和工艺参数以及生产规模，采用综合实验设计生成了校准片剂。基于片剂 NIR 光谱，建立了个体片剂含量的偏最小二乘模型。片剂含量通过美国药典苯妥英钠高效液相色谱法测定。具有 4 个潜在变量的偏最小二乘模型解释了 92%的组成变化，预测的均方根误差为 0.48%w/w。所得的 NIR 模型成功地检测了中试规模生产的片剂的组成。对于这样的一批，采用自举法计算了不同样本大小（n=10、30 和 100）时均值、接受值和相对标准偏差的置信区间。随着自举样本量的增加，均值、接受值和相对标准偏差的置信区间变得更窄且对称。这种“大 N”基于 NIR 的过程分析技术方法可以提高固体制剂质量评估的可靠性。
