Determination of co-crystal phase purity by mid infrared spectroscopy and multiple curve resolution.

Multivariate Curve Resolution (MCR) was used to determine the phase purity of pharmaceutical co-crystals from mid infrared spectra. An in-silico coformer screening was used to choose one of ten potential coformers. This analysis used quantum chemistry simulation to predict which coformers are thermodynamically inclined to form cocrystals with the model drug, hydrochlorothiazide. The coformer chosen was nicotinamide. An experimental solvent screening by ultrasound assisted slurry co-crystallization was performed to evaluate the capacity of the method to determine phase purity. Afterwards, slurry and slow evaporation co-crystallizations were performed at 10, 25, and 40 °C using 7 solvent systems, and two levels of agitation for the evaporation co-crystallization (on and off). Mid infrared spectroscopy (MIRS) analysis of the products of these co-crystallizations was used to develop an MCR model to determine co-crystal phase purity. The MCR results were compared with a reference co-crystal. Experimental design (DoE) was used to investigate the effect of solvents, temperature, and agitation on the purity of co-crystals produced by slurry and evaporation co-crystallization. DoE revealed that evaporation co-crystallization with agitating at 65 rpm formed co-crystals with greater phase purity. The optimal temperature varied with the solvent used.

Pharmaceutical cocrystallization techniques. Advances and challenges.

Cocrystals are homogenous (single-phase) crystalline structures composed by two or more components in a definite stoichiometric ratio bonded together by noncovalent bonds. Pharmaceutical industry has been showing interest in cocrystals due to their ability to improve active pharmaceutical ingredients (API's) properties, such as solubility, dissolution, bioavailability, stability and processability. The necessity for high-throughput screening methods and methods capable of producing cocrystals in an industrial scale still hinders the use of cocrystals by the pharmaceutical industry. The aim of this review is to present an extensive overview of the cocrystallization methods, focusing in the specificities of each technique, its advantages and disadvantages. The review is divided into solvent-based and solvent-free methods. The most appropriate methods to the different stages of cocrystals manufacture, from the screening phase to industrial production are identified. The use of continuous and scalable methods in cocrystal production as well as the implementation of quality-by-design and process analytical technology concepts are also addressed.

Merging vibrational spectroscopic data for wine classification according to the geographic origin.

The wine making procedure is no longer a secret and it is nowadays well described and repeated around the world. Nevertheless, wines present unique features, strongly associated with their geographic origin. Classification systems were developed to catalogue wines according to the provenance, and are currently established by official authorities in order to ensure wine authenticity. The use of near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy for tracing the origin of wine samples, has been reported with different levels of success. This work evaluated and compared the performance of these techniques, as well as their joint use, in terms of geographic origin classification. NIR, MIR and Raman spectra of wine samples belonging to four Portuguese wine regions (Vinhos Verdes, Lisboa, Açores and Távora-Varosa) were analyzed by partial least squares discriminant analysis (PLS-DA). Results revealed the better suitability of MIR spectroscopy (87.7% of correct predictions) over NIR (60.4%) and Raman (60.8%). The joint use of spectral sets did not improve the predictive ability of the models. The best models were achieved by combining MIR and NIR spectra resulting in 86.7% of correct predictions. Multiblock partial least squares (MB-PLS) models were developed to further explore the combination of spectral data. Although these models did not improve the percentage of correct predictions, they demonstrated the higher contribution of MIR spectroscopic data, in the development of the models.

Statistical process control of cocrystallization processes: A comparison between OPLS and PLS.

Orthogonal partial least squares regression (OPLS) is being increasingly adopted as an alternative to partial least squares (PLS) regression due to the better generalization that can be achieved. Particularly in multivariate batch statistical process control (BSPC), the use of OPLS for estimating nominal trajectories is advantageous. In OPLS, the nominal process trajectories are expected to be captured in a single predictive principal component while uncorrelated variations are filtered out to orthogonal principal components. In theory, OPLS will yield a better estimation of the Hotelling's T2 statistic and corresponding control limits thus lowering the number of false positives and false negatives when assessing the process disturbances. Although OPLS advantages have been demonstrated in the context of regression, its use on BSPC was seldom reported. This study proposes an OPLS-based approach for BSPC of a cocrystallization process between hydrochlorothiazide and p-aminobenzoic acid monitored on-line with near infrared spectroscopy and compares the fault detection performance with the same approach based on PLS. A series of cocrystallization batches with imposed disturbances were used to test the ability to detect abnormal situations by OPLS and PLS-based BSPC methods. Results demonstrated that OPLS was generally superior in terms of sensibility and specificity in most situations. In some abnormal batches, it was found that the imposed disturbances were only detected with OPLS.

Near infrared spectroscopy to monitor drug release in-situ during dissolution tests.

Dissolution tests can be used to demonstrate suitable in vivo drug release through in vivo/in vitro correlations. This work explores the possibility of using near infrared spectroscopy (NIRS) to monitor in-situ dissolution tests. It aims at expanding surrogate methods in quality control of drug products. Laboratory designed tablets of an immediate-release formulation containing folic acid and four excipients were used as case study. The dissolution tests were performed on a 1L vessel filled with 500ml of Milli-Q water with a rotating paddle apparatus (apparatus 2, Ph. Eur.) at 50rpm and 37±0.5°C. Near infrared (NIR) spectra were acquired in-situ with a transflectance probe connected to a Fourier-transform near infrared spectrometer. NIR spectra were regressed against folic acid concentration by partial least squares (PLS) regression. Folic acid concentrations during dissolution tests were obtained by periodically sampling the dissolution vessel and resourcing to an UV method. The proposed real-time NIR method was tested on a validation run yielding a root mean squared error of 0.25µgml-1 (0.16µgml-1 for the calibration runs) and a R2 of 0.93 (0.95 for the calibration runs). The results suggest that NIRS is a suitable analytical technique for monitoring in-situ dissolution tests.

Online monitoring of P(3HB) produced from used cooking oil with near-infrared spectroscopy.

Online monitoring process for the production of polyhydroxyalkanoates (PHA), using cooking oil (UCO) as the sole carbon source and Cupriavidus necator, was developed. A batch reactor was operated and hydroxybutyrate homopolymer was obtained. The biomass reached a maximum concentration of 11.6±1.7gL(-1) with a polymer content of 63±10.7% (w/w). The yield of product on substrate was 0.77±0.04gg(-1). Near-infrared (NIR) spectroscopy was used for online monitoring of the fermentation, using a transflectance probe. Partial least squares regression was applied to relate NIR spectra with biomass, UCO and PHA concentrations in the broth. The NIR predictions were compared with values obtained by offline reference methods. Prediction errors to these parameters were 1.18, 2.37 and 1.58gL(-1) for biomass, UCO and PHA, respectively, which indicate the suitability of the NIR spectroscopy method for online monitoring and as a method to assist bioreactor control.

A batch modelling approach to monitor a freeze-drying process using in-line Raman spectroscopy.

Freeze-drying or lyophilisation is a batch wise industrial process used to remove water from solutions, hence stabilizing the solutes for distribution and storage. The objective of the present work was to outline a batch modelling approach to monitor a freeze-drying process in-line and in real-time using Raman spectroscopy. A 5% (w/v) D-mannitol solution was freeze-dried in this study as model. The monitoring of a freeze-drying process using Raman spectroscopy allows following the product behaviour and some process evolution aspects by detecting the changes of the solutes and solvent occurring during the process. Herewith, real-time solid-state characterization of the final product is also possible. The timely spectroscopic measurements allowed the differentiation between batches operated in normal process conditions and batches having deviations from the normal trajectory. Two strategies were employed to develop batch models: partial least squares (PLS) using the unfolded data and parallel factor analysis (PARAFAC). It was shown that both strategies were able to developed batch models using in-line Raman spectroscopy, allowing to monitor the evolution in real-time of new batches. However, the computational effort required to develop the PLS model and to evaluate new batches using this model is significant lower compared to the PARAFAC model. Moreover, PLS scores in the time mode can be computed for new batches, while using PARAFAC only the batch mode scores can be determined for new batches.

The use of net analyte signal (NAS) in near infrared spectroscopy pharmaceutical applications: interpretability and figures of merit.

Near infrared spectroscopy (NIRS) has been extensively used as an analytical method for quality control of solid dosage forms for the pharmaceutical industry. Pharmaceutical formulations can be extremely complex, containing typically one or more active product ingredients (API) and various excipients, yielding very complex near infrared (NIR) spectra. The NIR spectra interpretability can be improved using the concept of net analyte signal (NAS). NAS is defined as the part of the spectrum unique to the analyte of interest. The objective of this work was to compare two different methods to estimate the API's NAS vector of different pharmaceutical formulations. The main difference between the methods is the knowledge of API free formulations NIR spectra. The comparison between the two methods was assessed in a qualitative and quantitative way. Results showed that both methods produced good results in terms of the similarity between the NAS vector and the pure API spectrum, as well as in the ability to predict the API concentration of unknown samples. Moreover, figures of merit such as sensitivity, selectivity, and limit of detection were estimated in a straightforward manner.