Robustness considerations and effects of moisture variations on near infrared method performance for solid dosage form assay

摘要

Quantitative analysis by near infrared (NIR) spectroscopy involves the establishment of the relationship between spectra, related to both physical and chemical information of a sample, and the corresponding parameter(s) of interest. To make a model useful and robust, other sources of variability, not directly related to the element(s) to predict should be included in the calibration set. One of the potential sources of variability is moisture. Raw materials may have different moisture levels as a function of the manufacturing lots, the geographic situation of a plant, storage conditions or the season. In a traditional calibration effort, tablets are often made at the same time and no robustness to moisture is built into the model. The present article investigates how moisture variations affect the predictive ability of a NIR calibration model for active ingredient in solid oral dosage forms. Examples of variable selection and orthogo-nalisation techniques are presented as an alternative to including the moisture variability in the calibration data. Tablets composed of acetaminophen, lactose, microcrystalline cellulose, hypromellose and magnesium stearate were manufactured using laboratory scale equipment. A full-factorial design was used to vary acetaminophen (five levels) and excipient ratios (three levels) to generate tablets for calibration and test. Tablets were placed in humidity chambers over saturated salt solutions and equilibrated to 11%, 32%, 52% and 75% relative humidity, respectively. Calibration and test tablets were scanned at each moisture level. Following spectral collection, the acetaminophen content was determined by HPLC. From each sample set representing tablets equilibrated at a single relative humidity, individual calibration models for acetaminophen were constructed. Test samples, stored at the alternate relative humidity conditions, were predicted. When the moisture level was different between calibration and test sets, the prediction error increased, indicating a degradation of the model performance when moisture variance was unaccounted for. Models developed using selected variable, orthogonalisation and global approaches gave significantly lower prediction errors for the test set than the individual models applied to all samples. These findings demonstrated the importance of accounting for expected sources of variance, such as moisture in order to achieve robust calibrations.