Abstract
Scope of the study was (1) to develop a lean quantitative calibration for real-time near-infrared (NIR) blend monitoring, which meets the requirements in early development of pharmaceutical products and (2) to compare the prediction performance of this approach with the results obtained from stratified sampling using a sample thief in combination with off-line high pressure liquid chromatography (HPLC) and at-line near-infrared chemical imaging (NIRCI). Tablets were manufactured from powder blends and analyzed with NIRCI and HPLC to verify the real-time results. The model formulation contained 25% w/w naproxen as a cohesive active pharmaceutical ingredient (API), microcrystalline cellulose and croscarmellose sodium as cohesive excipients and free-flowing mannitol. Five in-line NIR calibration approaches, all using the spectra from the end of the blending process as reference for PLS modeling, were compared in terms of selectivity, precision, prediction accuracy and robustness. High selectivity could be achieved with a “reduced” approach i.e. API and time saving approach (35% reduction of API amount) based on six concentration levels of the API with three levels realized by three independent powder blends and the additional levels obtained by simply increasing the API concentration in these blends. Accuracy and robustness were further improved by combining this calibration set with a second independent data set comprising different excipient concentrations and reflecting different environmental conditions. The combined calibration model was used to monitor the blending process of independent batches. For this model formulation the target concentration of the API could be achieved within 3min indicating a short blending time. The in-line NIR approach was verified by stratified sampling HPLC and NIRCI results. All three methods revealed comparable results regarding blend end point determination. Differences in both mean API concentration and RSD values could be attributed to differences in effective sample size and thief sampling errors. This conclusion was supported by HPLC and NIRCI analysis of tablets manufactured from powder blends after different blending times. In summary, the study clearly demonstrates the ability to develop efficient and robust quantitative calibrations for real-time NIR powder blend monitoring with a reduced set of powder blends while avoiding any bias caused by physical sampling.
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