Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona, 18-26, 08034 Barcelona, Spain.
Talanta. 2013 Dec 15;117:492-504. doi: 10.1016/j.talanta.2013.09.037. Epub 2013 Sep 27.
The application of the Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) method to model and control blend processes of pharmaceutical formulations is assessed. Within the MCR-ALS framework, different data analysis approaches have been tested depending on the objective of the study, i.e., knowing the effect of different factors in the evolution of the blending process (modeling) or detecting the blend end-point and monitoring the concentration of the different species during and at the end of the process (control). Data analysis has been carried out studying multiple blending runs simultaneously taking advantage of the multiset mode of the MCR-ALS method. During the ALS optimization, natural constraints, such as non-negativity (spectral and concentration directions) have been applied for blend modeling. When blending control is the main purpose, a variant of the MCR-ALS algorithm with correlation constraint in the concentration direction has been additionally used. This constraint incorporates an internal calibration procedure, which relates resolved concentration values (in arbitrary units) with the real reference concentration values in the calibration samples (known references) providing values in real concentration scale in the final MCR-ALS results. Two systems consisting of pharmaceutical mixtures of an active principle (acetaminophen) with two or four excipients have been investigated. In the first case, MCR results allowed the description of the evolution of the individual compounds and the assessment of some physical effects in the blending process. In the second case, MCR analysis allowed the detection of the end-point of the process and the assessment of the effects linked to variations in the concentration level of the compounds.
交替最小二乘法多变量曲线分辨（MCR-ALS）方法在药物制剂混合过程的建模和控制中的应用进行了评估。在 MCR-ALS 框架内，根据研究的目的测试了不同的数据分析方法，即了解不同因素对混合过程演化的影响（建模）或检测混合终点，并监测过程中和过程结束时不同物质的浓度（控制）。通过利用 MCR-ALS 方法的多数据集模式同时研究多个混合运行，进行了数据分析。在 ALS 优化过程中，对混合建模应用了自然约束，例如非负性（光谱和浓度方向）。当混合控制是主要目的时，还额外使用了在浓度方向上具有相关约束的 MCR-ALS 算法的变体。该约束结合了内部校准程序，该程序将解析的浓度值（任意单位）与校准样品中的实际参考浓度值（已知参考值）相关联，在最终的 MCR-ALS 结果中提供实际浓度尺度上的值。研究了由活性成分（对乙酰氨基酚）与两种或四种赋形剂组成的两种药物混合物系统。在第一种情况下，MCR 结果允许描述各个化合物的演变，并评估混合过程中的一些物理效应。在第二种情况下，MCR 分析允许检测过程的终点，并评估与化合物浓度水平变化相关的效应。
