Multivariate analysis of quaternary carbamazepine-saccharin mixtures by X-ray diffraction and infrared spectroscopy

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

In materials and earth science, but also in chemistry, pharmaceutics and engineering, the quantification of elements and crystal phases in solid samples is often essential for a full characterization of materials. The most frequently used techniques for this purpose are X-ray fluorescence (XRF) for elemental analysis and X-ray powder diffraction (XRPD) for phase analysis. In both methods, relations between signal and quantity do exist but they are expressed in terms of complex equations including many parameters related to both sample and instruments, and the dependence on the active element or phase amounts to be determined is convoluted among those parameters. Often real-life samples hold relations not suitable for a direct quantification and, therefore, estimations based only on the values of the relative intensities are affected by large errors. Preferred orientation (PO) and microabsorption (MA) in XRPD cannot usually be avoided, and traditional corrections in Rietveld refinement, such as the Brindley MA correction, are not able, in general, to restore the correct phase quantification. In this work, a multivariate approach, where principal component analysis is exploited alone or combined with regression methods, is used on XRPD profiles collected on ad hoc designed mixtures to face and overcome the typical problems of traditional approaches. Moreover, the partial or no known crystal structure (PONKCS) method was tested on XRPD data, as an example of a hybrid approach between Rietveld and multivariate approaches, to correct for the MA effect. Particular attention is given to the comparison and selection of both method and pre-process, the two key steps for good performance when applying multivariate methods to obtain reliable quantitative estimations from XRPD data, especially when MA and PO are present. A similar approach was tested on XRF data to deal with matrix effects and compared with the more classical fundamental-parameter approach. Finally, useful indications to overcome the difficulties of the general user in managing the parameters for a successful application of multivariate approaches for XRPD and XRF data analysis are given.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Miscibility and Solubility of Caffeine and Theophylline in Hydroxypropyl Methylcellulose

As amorphization may improve the solubility and bioavailability of a drug substance, the aim of this work was to assess to what extent the crystallinity of caffeine (CAF) and theophylline (TF) can be reduced by homogenization with a polymeric excipient. To realize this purpose, the physical mixtures of both methylxanthines with hydroxypropyl methylcellulose (HPMC) were examined using differential scanning calorimetry (DSC), hot-stage microscopy (HSM), Fourier-transform infrared (FTIR) and Raman spectroscopy. Moreover, phase diagrams for the physical mixtures were calculated using theoretical data. Results of DSC experiments suggested that both CAF and TF underwent amorphization, which indicated proportional loss of crystallinity for methylxanthines in the mixtures with HPMC. Additionally, HSM revealed that no other crystalline or amorphous phases were created other than those observed for CAF and TF. FTIR and Raman spectra displayed all the bands characteristic for methylxanthines in mixtures with HPMC, thereby excluding changes in their chemical structures. However, changes to the intensity of the bands created by hydrogen bonds imply the formation of hydrogen bonding in the carbonyl group of methylxanthines and the methyl polymer group. This is consistent with data obtained using principal component analysis. The findings of these studies revealed the quantities of methylxanthines which may be dissolved in the polymer at a given temperature and the composition at which methylxanthines and polymer are sufficiently miscible to form a solid solution.

Multivariate Analysis Applications in X-ray Diffraction

Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail.

Formation of Prenylated Chalcone Xanthohumol Cocrystals: Single Crystal X-Ray Diffraction, Vibrational Spectroscopic Study Coupled with Multivariate Analysis

Four novel xanthohumol (XN) cocrystals with pharmaceutically acceptable coformers, such as nicotinamide (NIC), glutarimide (GA), acetamide (AC), and caffeine (CF) in the 1:1 stoichiometry were obtained by the slow evaporation solution growth technique. The structure of the cocrystals was determined by single crystal X-ray diffraction. The analysis of packing and interactions in the crystal lattice revealed that molecules in the target cocrystals were packed into almost flat layers, formed by the O–H⋅⋅⋅O, O–H⋅⋅⋅N, and N–H⋅⋅⋅O-type contacts between the xanthohumol and coformer molecules. The results provided details about synthons responsible for crystal net stabilization and all hydrogen bonds observed in the crystal lattice. The main synthon was formed via the hydrogen bond between the hydroxyl group in the B ring of XN and coformers. The three-dimensional crystal lattice was stabilized by the hydrogen XN−XN interactions whereas the π–π stacking interactions played an additional role in layer binding, with the exception of low quality cocrystals formed with caffeine. Application of FTIR and Raman spectroscopy confirmed that the crystalline phase of obtained cocrystals was not a simple combination of individual components and completely different crystal phases resulted from the effect of intermolecular interactions. The multivariate analysis showed the changes in the spectra, and this technique can be applied in a combination with vibrational spectroscopy for fast screening of new crystal phases. Additionally, the solubility studies of pure XN and its cocrystals exhibited a 2.6-fold enhancement in XN solubility in aqueous solution for XN–AC and, to a lesser extent, for other cocrystals.

FTIR and TG analyses coupled with factor analysis in a compatibility study of acetazolamide with excipients

A compatibility study of drug substance with excipients is a crucial step in the drug development process in order to generate potent final drug formulations for efficient and safe therapy for various diseases. Thus, the development of new methods for compatibility studies is a great challenge. For this reason, a new approach based on improvement of FTIR spectra and TG curves interpretation using factor analysis (FA) was developed as a screening technique for assessing the compatibility of acetazolamide with selected excipients. This multivariate method demonstrates that in some cases acetazolamide and mixtures with high acetazolamide content formed one cluster, while a second cluster consisted of excipient and mixtures with high excipient content. Such clustering of the analyzed samples (drug substance, excipient and their mixtures) demonstrates the compatibility between ingredients. This in turn means that the FTIR spectra and TG curves of mixtures are the sum of absorption bands or of the thermal profiles of ingredients. In the case of incompatibility, the FTIR spectra and TG curves of mixtures differ from those of ingredients. The FA score scatter plot shows that clusters consist of mixtures which differ with respect to ingredient content. In conclusion, FA proved the incompatibility of acetazolamide mixtures with β-cyclodextrin, chitosan, lactose, mannitol, meglumine and starch. This was also confirmed by complementary techniques such as DSC and PXRD. Hence, the application of FA can be helpful for better comprehension of data obtained from FTIR spectra and TG curves.

Quantifying API polymorphs in formulations using X-ray powder diffraction and multivariate standard addition method combined with net analyte signal analysis

The direct quantification of Active Pharmaceutical Ingredients in solid formulations is a challenging open issue. A consolidated analytical technique based on X-ray Powder Diffraction is available, being the definitive test for the identification of polymorphs and crystal phases. However, its application for quantitative analysis is hindered by matrix effects: refinement methods (e.g. Rietveld method) require a complete knowledge of samples' composition, while univariate calibration methods require the matrix effect to be studied and severely suffer from the co-presence of crystalline and amorphous phases in the sample. Multivariate analysis is the only way to bypass problems affecting refinements procedures and univariate calibration. In particular, the multivariate standard addition method (SAM) is promising; however, it is straightforward only when the analytical blank (matrix devoid of analyte) is available: in that case SAM is applied by simply extrapolating the SAM model to the matrix experimental signal. In this work, the quantitative analysis of polymorphic forms of Active Pharmaceutical Ingredients based on X-ray Powder Diffraction is performed for the first time by a method based on multivariate standard addition method combined with net analyte signal procedure; it allows for reliable quantification of polymorphs of active principles in solid formulations, which are rapidly analyzed without any sample pre-treatment. Two test cases are presented: quantification of two polymorphs of piracetam in binary mixtures (forms II and III), and quantification of paracetamol (form I) in Tachifludec®.

DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

In-line monitoring of cocrystallization process and quantification of carbamazepine-nicotinamide cocrystal using Raman spectroscopy and chemometric tools

A cocrystallization process may involve several molecular species, which are generally solid under ambient conditions. Thus, accurate monitoring of different components that might appear during the reaction is necessary, as well as quantification of the final product. This work reports for the first time the synthesis of carbamazepine-nicotinamide cocrystal in aqueous media with a full conversion. The reactions were monitored by Raman spectroscopy coupled with Multivariate Curve Resolution - Alternating Least Squares, and the quantification of the final product among its coformers was performed using Raman spectroscopy and Partial Least Squares regression. The slurry reaction was made in four different conditions: room temperature, 40°C, 60°C and 80°C. The slurry reaction at 80°C enabled a full conversion of initial substrates into the cocrystal form, using water as solvent for a greener method. The employment of MCR-ALS coupled with Raman spectroscopy enabled to observe the main steps of the reactions, such as drug dissolution, nucleation and crystallization of the cocrystal. The PLS models gave mean errors of cross validation around 2.0 (% wt/wt), and errors of validation between 2.5 and 8.2 (% wt/wt) for all components. These were good results since the spectra of cocrystals and the physical mixture of the coformers present some similar peaks.

The Effect of an Optimized Wet Milling Technology on the Crystallinity, Morphology and Dissolution Properties of Micro- and Nanonized Meloxicam

This article reports on the effects of a new combined wet milling technique on the physicochemical properties of meloxicam (MEL). The influence of milling time on the particle size, the crystallinity, the morphology and the dissolution rate of MEL has been studied in the presence and absence of polyvinyl alcohol (PVA) as a stabilizer agent. Micronized MEL particles were produced in aqueous medium which did not contain additive after milling for 10 min. For nanonization an additive and longer milling time were required. After particle size determination the structural and morphological characterization of the wet milled, dried products containing MEL were studied. X-ray powder diffractometry (XRPD) and differential scanning calorimetry (DSC) examinations revealed the change in the crystallinity of MEL. Scanning electron microscopy (SEM) images showed that aggregates of nanosized MEL particles were formed, regardless of the presence of PVA. The nanonized MEL crystals (D50 = 126 nm) exhibited a regular shape and a smooth surface. The increased specific surface area resulted in a high dissolution rate and concentration of free MEL. According to the results, the produced samples could be applied as a basic material (micronized MEL) and intermediate product (micronized and nanonized MEL with PVA) for the design of dosage forms.

Multivariate Quantification of the Solid State Phase Composition of Co-Amorphous Naproxen-Indomethacin

To benefit from the optimized dissolution properties of active pharmaceutical ingredients in their amorphous forms, co-amorphisation as a viable tool to stabilize these amorphous phases is of both academic and industrial interest. Reports dealing with the physical stability and recrystallization behavior of co-amorphous systems are however limited to qualitative evaluations based on the corresponding X-ray powder diffractograms. Therefore, the objective of the study was to develop a quantification model based on X-ray powder diffractometry (XRPD), followed by a multivariate partial least squares regression approach that enables the simultaneous determination of up to four solid state fractions: crystalline naproxen, γ-indomethacin, α-indomethacin as well as co-amorphous naproxen-indomethacin. For this purpose, a calibration set that covers the whole range of possible combinations of the four components was prepared and analyzed by XRPD. In order to test the model performances, leave-one-out cross validation was performed and revealed root mean square errors of validation between 3.11% and 3.45% for the crystalline molar fractions and 5.57% for the co-amorphous molar fraction. In summary, even four solid state phases, involving one co-amorphous phase, can be quantified with this XRPD data-based approach.

Chemical selectivity in structure determination by the time dependent analysis of in situ XRPD data: a clear view of Xe thermal behavior inside a MFI zeolite

PSD/PCA analysis of MED data allowed to enhance the chemical selectivity in X-ray powder diffraction and to obtain Xe substructure into MFI zeolite.

Simultaneous quantification of three polymorphic forms of carbamazepine in the presence of excipients using Raman spectroscopy

The occurrence of polymorphic transitions is a serious problem for pharmaceutical companies, because it can affect the bioavailability of the final product. With several known polymorphic forms carbamazepine is one of the most problematic drugs in this respect. Raman spectroscopy is a vibrational technique that is becoming very important in the pharmaceutical field, mainly due to its highly specific molecular fingerprint capabilities and easy use as a process analytical tool. However, multivariate methods are necessary both for identification and quantification. In this work an analytical methodology using Raman spectroscopy and interval Partial Least Squares Regression (iPLS), was developed in order to quantify mixtures of carbamazepine polymorphs in the presence of the most common excipients. The three polymorphs CBZ I, CBZ III and CBZ DH (which is a dihydrate) were synthesized and characterized by PXRD and DSC. Subsequently, tablets were manufactured using excipients and 15 different mixtures of carbamazepine polymorphs. The iPLS model presented average prediction validation errors of 1.58%, 1.04% and 0.22% wt/wt, for CBZ I, CBZ III and CBZ DH, respectively, considering the whole mass of the tablet. The model presents a good prediction capacity and the proposed methodology could be used to perform quality control in final products.

Protein phasing at non-atomic resolution by combining Patterson and VLD techniques

Phasing proteins at non-atomic resolution is still a challenge for any ab initio method. A variety of algorithms [Patterson deconvolution, superposition techniques, a cross-correlation function (C map), the VLD (vive la difference) approach, the FF function, a nonlinear iterative peak-clipping algorithm (SNIP) for defining the background of a map and the free lunch extrapolation method] have been combined to overcome the lack of experimental information at non-atomic resolution. The method has been applied to a large number of protein diffraction data sets with resolutions varying from atomic to 2.1 Å, with the condition that S or heavier atoms are present in the protein structure. The applications include the use of ARP/wARP to check the quality of the final electron-density maps in an objective way. The results show that resolution is still the maximum obstacle to protein phasing, but also suggest that the solution of protein structures at 2.1 Å resolution is a feasible, even if still an exceptional, task for the combined set of algorithms implemented in the phasing program. The approach described here is more efficient than the previously described procedures: e.g. the combined use of the algorithms mentioned above is frequently able to provide phases of sufficiently high quality to allow automatic model building. The method is implemented in the current version of SIR2014.

Evaluation of analytical tools and multivariate methods for quantification of co-former crystals in ibuprofen-nicotinamide co-crystals

Co-crystals are multicomponent substances designed by the addition of two or more different molecules in a same crystallographic pattern, in which it differs from the crystallographic motif of its co-formers. The addition of highly soluble molecules, like nicotinamide, in the crystallographic pattern of ibuprofen enhances its solubility more than 7.5 times, improving the properties of this widely used drug. Several analytical solid state techniques are used to characterize the ibuprofen-nicotinamide co-crystal, being the most used: mid-infrared (ATR-FTIR), differential scanning calorimetry (DSC), X-ray diffraction (XRPD) and Raman spectroscopy. These analytical solid state techniques were evaluated to quantify a mixture of ibuprofen-nicotinamide co-crystal and its co-formers in order to develop a calibration model to evaluate the co-crystal purity after its synthesis. Raman spectroscopy showed better result than all other techniques with a combination of multivariate calibration tools, presenting lower values of calibration and prediction errors. The partial least squares regression model gave a mean error lower than 5% for all components presented in the mixture. DSC and mid-infrared spectroscopy proved to be insufficient for quantification of the ternary mixture. XRPD presented good results for quantification of the co-formers, ibuprofen and nicotinamide, but fair results for the co-crystal. This is the first report of quantification of ibuprofen-nicotinamide co-crystal, among its co-formers. The quantification is of great importance to determine the yield of the co-crystallization reactions and the purity of the product obtained.

A combined approach for characterisation of fresh and brined vine leaves by X-ray powder diffraction, NMR spectroscopy and direct infusion high resolution mass spectrometry

X-ray powder diffraction was combined, for the first time, with Nuclear Magnetic Resonance spectroscopy and direct infusion mass spectrometry to characterise fresh and brined grape leaves. Covariance analysis of data generated by the three techniques was performed with the aim to correlate information deriving from the solid part with those obtained for soluble metabolites. The results obtained indicate that crystalline components can be correlated to the metabolites contained in the grape leaves, paving the way to the use of X-ray diffraction analysis for food fingerprinting purposes. Moreover it was ascertained that, differently from most of the metabolites present in the fresh vine leaves, linolenic acid (an omega-3-fatty acid) and quercetin-3-O-glucuronide (a polyphenol metabolite) do not undergo sensible degradation during the brining process, which is used as preservative method for the grape leaves.