Kinetic Optimization of the Batch Crystallization of an Active Pharmaceutical Ingredient in the Presence of a Low-Solubility, Precipitating Impurity

The presence of impurities above regulatory thresholds has been responsible for recent recalls of pharmaceutical drugs. Crystallization is one of the most used separation processes to control impurities in the final drug. A particular issue emerges when impurities are poorly soluble in the crystallization solvent and simultaneously precipitate with the product. This publication reports the development of a population balance model to investigate if the impurity crystallization kinetics can be selectively inhibited in a seeded batch crystallization system containing acetaminophen (ACM), a commonly used small-molecule active pharmaceutical ingredient (API), and curcumin (CUR), a simulated low-solubility/co-precipitating impurity. Raman spectroscopy was used in combination with a partial least squares (PLS) model for in situ monitoring of the crystallization process. The Raman data were integrated to calibrate a population balance model in gPROMS FormulatedProducts, to predict the evolution of the product’s purity throughout the process. Process optimization demonstrated that a high purity close to equilibrium is feasible within the first 2 h of crystallization, with ACM seed purity being the primary factor controlling this phenomenon. The optimal approach for kinetically rejecting impurities requires a low nucleation rate for the impurity, high product seed purities, and an adjustable crystallization time so the process can be stopped before equilibrium without allowing the impurity to nucleate. Overall, an improvement in product purity before equilibrium is attainable if there is enough difference in growth kinetics between the product and impurity, and if one can generate relatively pure seed crystals

Potential of near-infrared chemical imaging as process analytical technology tool for continuous freeze-drying

Near-infrared chemical imaging (NIR-CI) is an emerging tool for process monitoring because it combines the chemical selectivity of vibrational spectroscopy with spatial information. 'Whereas traditional near-infrared spectroscopy is an attractive technique for water content determination and solid-state investigation of lyophilized products, chemical imaging opens up possibilities for assessing the homogeneity of these critical quality attributes (CQAs) throughout the entire product. In this contribution, we aim to evaluate NIR-CI as a process analytical technology (PAT) tool for at-line inspection of continuously freeze-dried pharmaceutical unit doses based on spin freezing. The chemical images of freeze-dried mannitol samples were resolved via multivariate curve resolution, allowing us to visualize the distribution of mannitol solid forms throughout the entire cake. Second, a mannitol-sucrose formulation was lyophilized with variable drying times for inducing changes in water content. Analyzing the corresponding chemical images via principal component analysis, vial-to-vial variations as well as within-vial inhomogeneity in water content could be detected. Furthermore, a partial least-squares regression model was constructed for quantifying the water content in each pixel of the chemical images. It was hence concluded that NIR-CI is inherently a most promising PAT tool for continuously monitoring freeze-dried samples. Although some practicalities are still to be solved, this analytical technique could be applied in-line for CQA evaluation and for detecting the drying end point