Semi-quantitative prediction of a multiple API solid dosage form with a combination of vibrational spectroscopy methods.

Quality control (QC) in the pharmaceutical industry is a key activity in ensuring medicines have the required quality, safety and efficacy for their intended use. QC departments at pharmaceutical companies are responsible for all release testing of final products but also all incoming raw materials. Near-infrared spectroscopy (NIRS) and Raman spectroscopy are important techniques for fast and accurate identification and qualification of pharmaceutical samples. Tablets containing two different active pharmaceutical ingredients (API) [bisoprolol, hydrochlorothiazide] in different commercially available dosages were analysed using Raman- and NIR Spectroscopy. The goal was to define multivariate models based on each vibrational spectroscopy to discriminate between different dosages (identity) and predict their dosage (semi-quantitative). Furthermore the combination of spectroscopic techniques was investigated. Therefore, two different multiblock techniques based on PLS have been applied: multiblock PLS (MB-PLS) and sequential-orthogonalised PLS (SO-PLS). NIRS showed better results compared to Raman spectroscopy for both identification and quantitation. The multiblock techniques investigated showed that each spectroscopy contains information not present or captured with the other spectroscopic technique, thus demonstrating that there is a potential benefit in their combined use for both identification and quantitation purposes.

Advanced qualification of pharmaceutical excipient suppliers by multiple analytics and multivariate analysis combined.

Pharmaceutical excipients have different functions within a drug formulation, consequently they can influence the manufacturability and/or performance of medicinal products. Therefore, critical to quality attributes should be kept constant. Sometimes it may be necessary to qualify a second supplier, but its product will not be completely equal to the first supplier product. To minimize risks of not detecting small non-similarities between suppliers and to detect lot-to-lot variability for each supplier, multivariate data analysis (MVA) can be used as a more powerful alternative to classical quality control that uses one-parameter-at-a-time monitoring. Such approach is capable of supporting the requirements of a new guideline by the European Parliament and Council (2015/C-95/02) demanding appropriate quality control strategies for excipients based on their criticality and supplier risks in ensuring quality, safety and function. This study compares calcium hydrogen phosphate from two suppliers. It can be assumed that both suppliers use different manufacturing processes. Therefore, possible chemical and physical differences were investigated by using Raman spectroscopy, laser diffraction and X-ray powder diffraction. Afterwards MVA was used to extract relevant information from each analytical technique. Both CaHPO4 could be discriminated by their supplier. The gained knowledge allowed to specify an enhanced strategy for second supplier qualification.

A PAT-based qualification of pharmaceutical excipients produced by batch or continuous processing.

Pharmaceutical excipients have an influence on the main requirements for medicinal products (viz., quality, safety and efficacy) but also on their manufacturability. During product lifecycle it may become necessary to introduce minor changes (e.g., to continuously improve it) or major changes in the validated process (e.g., moving it to a new production site, replacing process version or even disruptively changing processing type). Those changes can influence the critical to quality attributes of the product. Therefore, it is important to enhance process understanding to avoid the risk of any significant quality changes. Process analytical technology can support better decision making and risk-management as required in quality by design - viz., by many pharmaceutical regulatory authorities. This study compares the quality of the pharmaceutical excipient sodium carbonate (anhydrous) produced either in a batch or a continuous process. For continuous processing two different production lines were available that differed on the dryer and crystallizer types used. Therefore their influence on critical to quality attributes of sodium carbonate was investigated for each of the three processing alternatives. The overall goal was to identify which of the continuous processes ensures a similar product quality to batch processing. Namely, changes on chemical and physical attributes of the product were investigated with Raman spectroscopy, laser diffraction and X-ray powder diffraction. Principal component analysis, a very common multivariate analysis technique, was applied to extract relevant information from small differences at multiple spectral regions from samples from each process type and from each analytical technique used. Changing processing from batch to continuous improved consistency of certain attributes (e.g., particle size distribution) but affected others. However, the increased process/product knowledge gained can lead to an enhanced control strategy and ensure a similar product quality is obtained from distinct process versions.

A novel multivariate approach using science-based calibration for direct coating thickness determination in real-time NIR process monitoring.

This study demonstrates an approach, using science-based calibration (SBC), for direct coating thickness determination on heart-shaped tablets in real-time. Near-Infrared (NIR) spectra were collected during four full industrial pan coating operations. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film up to a film thickness of 28 µm. The application of SBC permits the calibration of the NIR spectral data without using costly determined reference values. This is due to the fact that SBC combines classical methods to estimate the coating signal and statistical methods for the noise estimation. The approach enabled the use of NIR for the measurement of the film thickness increase from around 8 to 28 µm of four independent batches in real-time. The developed model provided a spectroscopic limit of detection for the coating thickness of 0.64 ± 0.03 µm root-mean square (RMS). In the commonly used statistical methods for calibration, such as Partial Least Squares (PLS), sufficiently varying reference values are needed for calibration. For thin non-functional coatings this is a challenge because the quality of the model depends on the accuracy of the selected calibration standards. The obvious and simple approach of SBC eliminates many of the problems associated with the conventional statistical methods and offers an alternative for multivariate calibration.