On-line near infrared spectroscopy as a Process Analytical Technology (PAT) tool to control an industrial seeded API crystallization.

The final step of an active pharmaceutical ingredient (API) manufacturing synthesis process consists of a crystallization during which the API and residual solvent contents have to be quantified precisely in order to reach a predefined seeding point. A feasibility study was conducted to demonstrate the suitability of on-line NIR spectroscopy to control this step in line with new version of the European Medicines Agency (EMA) guideline [1]. A quantitative method was developed at laboratory scale using statistical design of experiments (DOE) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. NIR models were built to quantify the API in the range of 9-12% (w/w) and to quantify the residual methanol in the range of 0-3% (w/w). To improve the predictive ability of the models, the development procedure encompassed: outliers elimination, optimum model rank definition, spectral range and spectral pre-treatment selection. Conventional criteria such as, number of PLS factors, R(2), root mean square errors of calibration, cross-validation and prediction (RMSEC, RMSECV, RMSEP) enabled the selection of three model candidates. These models were tested in the industrial pilot plant during three technical campaigns. Results of the most suitable models were evaluated against to the chromatographic reference methods. Maximum relative bias of 2.88% was obtained about API target content. Absolute bias of 0.01 and 0.02% (w/w) respectively were achieved at methanol content levels of 0.10 and 0.13% (w/w). The repeatability was assessed as sufficient for the on-line monitoring of the 2 analytes. The present feasibility study confirmed the possibility to use on-line NIR spectroscopy as a PAT tool to monitor in real-time both the API and the residual methanol contents, in order to control the seeding of an API crystallization at industrial scale. Furthermore, the successful scale-up of the method proved its capability to be implemented in the manufacturing plant with the launch of the new API process.