Quantitative solid-state analysis of three solid forms of ranitidine hydrochloride in ternary mixtures using Raman spectroscopy and X-ray powder diffraction

The aim of the study was to develop a reliable quantification procedure for mixtures of three solid forms of ranitidine hydrochloride using X-ray powder diffraction (XRPD) and Raman spectroscopy combined with multivariate analysis. The effect of mixing methods of the calibration samples on the calibration model quality was also investigated. Thirteen ternary samples of form 1, form 2 and the amorphous form of ranitidine hydrochloride were prepared in triplicate to build a calibration model. The ternary samples were prepared by three mixing methods (a) manual mixing (MM) and ball mill mixing (BM) using two (b) 5 mm (BM5) or (c) 12 mm (BM12) balls for 1 min. The samples were analyzed with XRPD and Raman spectroscopy. Principal component analysis (PCA) was used to study the effect of mixing method, while partial least squares (PLS) regression was used to build the quantification models. PCA score plots showed that, in general, BM12 resulted in the narrowest sample clustering indicating better sample homogeneity. In the quantification models, the number of PLS factors was determined using cross-validation and the models were validated using independent test samples with known concentrations. Multiplicative scattering correction (MSC) without scaling gave the best PLS regression model for XPRD, and standard normal variate (SNV) transformation with centering gave the best model for Raman spectroscopy. Using PLS regression, the root mean square error of prediction (RMSEP) values of the best models were 5.0–6.9% for XRPD and 2.5–4.5% for Raman spectroscopy. XRPD and Raman spectroscopy in combination with PLS regression can be used to quantify the amount of single components in ternary mixtures of ranitidine hydrochloride solid forms. Raman spectroscopy gave better PLS regression models than XRPD, allowing a more accurate quantification.     

The use of net analyte signal orthogonalization in the separation of multi-component diffraction patterns obtained from X-ray powder diffraction of intact compacts

X-ray powder diffraction (XRPD) analysis of intact multi-component consolidated mixtures has significant potential owing to the ability to non-destructively quantify and discriminate between solid phases in composite bodies with minimal sample preparation. There are, however, limitations to the quantitative power using traditional univariate methods on diffraction data containing features from all components in the system. The ability to separate multi-component diffraction data into patterns representing single constituents allows both composition as well as physical phenomena associated with the individual components of complex systems to be probed. Intact, four-component compacts, consisting of two crystalline and two amorphous constituents were analyzed using XRPD configured in both traditional Bragg–Brentano reflectance geometry and parallel-beam transmission geometry. Two empirical, model-based methods consisting of a multiple step net analyte signal (NAS) orthogonalization are presented as ways to separate multi-component XRPD patterns into single constituent patterns. Multivariate figures of merit (FOM) were calculated for each of the isolated constituents to compare method-specific parameters such as sensitivity, selectivity, and signal-to-noise, enabling quantitative comparisons between the two modes of XRPD analysis.     

Chemometric Methods for the Quantification of Crystalline Tacrolimus in Solid Dispersion by Powder X‐Ray Diffractrometry

The objective of this study was to develop powder X‐ray diffraction (XRPD) chemometric model for quantifying crystalline tacrolimus from solid dispersion (SD). Three SDs (amorphous tacrolimus component) with varying drug to excipient ratios (24.4%, 6.7%, and 4.3% drug) were prepared. Placebo SDs were mixed with crystalline tacrolimus to make their composition equivalent to three SD (crystalline tacrolimus component). These two components were mixed to cover 0%–100% of crystalline drug. Uniformity of the sample mixtures was confirmed by near‐infrared chemical imaging. XRPD showed three distinct peaks of crystalline drug at 8.5°, 10.3°, and 11.2° (2θ), which were nonoverlapping with the excipients. Principal component regressions (PCR) and partial least square (PLS) regression used in model development showed high R² (>0.99) for all the mixtures. Overall, the model showed low root mean square of standard error, standard error, and bias, which was smaller in PLS than PCR‐based model. Furthermore, the model performance was evaluated on the formulations with known percentage of crystalline drug. Model‐calculated crystalline drug percentage values were close to actual value. Therefore, these studies strongly suggest the application of chemometric‐XRPD models as a quality control tool to quantitatively predict the crystalline drug in the formulation. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:2819–2828, 2014     

Establishing quantitative in-line analysis of multiple solid-state transformations during dehydration

The aim of the study was to conduct quantitative solid phase analysis of piroxicam (PRX) and carbamazepine (CBZ) during isothermal dehydration in situ, and additionally exploit the constructed quantitative models to analyze the solid-state forms in-line during fluidized bed drying. Vibrational spectroscopy (near-infrared (NIR), Raman) was employed for monitoring the dehydration and the quantitative model was based on partial least squares (PLS) regression. PLS quantification was confirmed experimentally using isothermal thermogravimetric analysis (TGA) and X-ray powder diffractometry (XRPD). To appraise the quality of quantitative models several model parameters were evaluated. The hot-stage spectroscopy quantification results were found to be in reasonable agreement with TGA and XRPD results. Quantification of PRX forms showed complementary results with both spectroscopic techniques. The solid-state forms observed during CBZ dihydrate dehydration were quantified with Raman spectroscopy, but NIR spectroscopy failed to differentiate between the anhydrous solid-state forms of CBZ. In addition to in situ dehydration quantification, Raman spectroscopy in combination with PLS regression enabled in-line analysis of the solid-state transformations of CBZ during dehydration in a fluidized bed dryer.     

Influence of sample characteristics on quantification of carbamazepine hydrate formation by X-ray powder diffraction and Raman spectroscopy.

This study aimed to assess the suitability of two widely utilized solid state characterization techniques namely powder X-ray diffraction (XRPD) and Raman spectroscopy, in polymorph detection and quantification for carbamazepine anhydrate and dihydrate mixtures. The influences of particle size, particle morphology, mixing, and in particular, surface bias on quantitation were investigated. Binary mixtures of carbamazepine anhydrate (form III) and dihydrate were prepared and analyzed using both XRPD and Raman spectroscopy in combination with partial least squares analysis. It was found that in principle both XRPD and Raman spectroscopy could be used to build calibration models for quantitative analysis, and a satisfactory correlation between the two techniques could be achieved. However, Raman spectroscopy appeared to be a more reliable quantification method because problems such as different particle size, morphology, and special distribution of the two solid state forms of the drug seemed to have no significant influence on Raman scattering in this study. The robust nature of Raman analysis greatly facilitates the whole quantification process from the preparation of calibration models to the quantification of in situ CBZ-DH conversion.     

Better understanding of dissolution behaviour of amorphous drugs by in situ solid-state analysis using Raman spectroscopy

Amorphous drugs have a higher kinetic solubility and dissolution rate than their crystalline counterparts. However, this advantage is lost if the amorphous form converts to the stable crystalline form during the dissolution as the dissolution rate will gradually change to that of the crystalline form. The purpose of this study was to use in situ Raman spectroscopy in combination with either partial least squares discriminant analysis (PLS-DA) or partial least squares (PLS) regression analysis to monitor as well as quantify the solid-phase transitions that take place during the dissolution of two amorphous drugs, indomethacin (IMC) and carbamazepine (CBZ). The dissolution rate was higher from amorphous IMC compared to the crystalline α- and γ-forms. However, the dissolution rate started to slow down during the experiment. In situ Raman analysis verified that at that time point the sample started to crystallize to the α-form. Amorphous CBZ instantly started to crystallize upon contact with the dissolution medium. The transition from the amorphous form to CBZ dihydrate appears to go through the anhydrate form I. Based on the PLS analysis the amount of form I formed in the sample during the dissolution affected the dissolution rate. Raman spectroscopy combined with PLS-DA was also more sensitive to the solid-state changes than X-ray powder diffraction (XRPD) and was able to detect changes in the solid-state that could not be detected with XRPD.     

Quantitative determination of crystallinity of alpha-lactose monohydrate by Near Infrared Spectroscopy (NIRS)

The purpose of this study was to determine quantitatively the crystallinity in crystalline/amorphous powder mixtures of lactose, to asses the capability of Near Infrared Spectroscopy (NIRS) for quantitative determination of crystallinity and to compare the accuracy of the NIRS method with that of conventional X-ray powder diffraction (XRPD). Amorphous lactose was prepared by spray drying. Samples with different crystallinity were prepared by physical mixing of 100% amorphous and 100% crystalline materials. The samples were characterized by XRPD and NIRS. Analysis was performed on the data sets by multiple linear regression (MLR). There is a close correlation between the predicted and the actual crystallinity of physical mixtures of crystalline and amorphous lactose, determined by NIRS (R(2)=0.9994). NIRS results were compared to the XRPD using the same sample sets. The correlation coefficients was 0.9981. The results showed that NIRS is an useful method for accurately determining low quantities of the crystalline lactose in a physical mixture. Therefore, NIRS can be used for the quantitative determination of crystallinity of materials during pharmaceutical procedures.     

Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms

This paper attempted the feasibility to determine content total polyphenols content in green tea with near infrared (NIR) spectroscopy coupled with an appropriate multivariate calibration method. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were performed comparatively to calibrate regression model. The number of PLS components and the number of intervals were optimized according to root mean square error of cross-validation (RMSECV) in calibration set. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. Experimental results showed that the performance of siPLS model is the best in contrast to PLS and iPLS. The optimal model was achieved with R=0.9583 and RMSEP=0.7327 in prediction set. This study demonstrated that NIR spectroscopy with siPLS algorithm could be used successfully to analysis of total polyphenols content in green tea, and revealed superiority of siPLS algorithm in contrast with other multivariate calibration methods.     

An efficient method-development strategy for quantitative chemical imaging using terahertz pulse spectroscopy

The purpose of our research was to investigate efficient procedures for generating multivariate prediction vectors for quantitative chemical analysis of solid dosage forms using terahertz pulse imaging (TPI) reflection spectroscopy. A set of calibration development and validation tablet samples was created following a ternary mixture of anhydrous theophylline, lactose monohydrate, and microcrystalline cellulose (MCC). Spectral images of one side of each tablet were acquired over the range of 8 cm⁻¹ to 60 cm⁻¹. Calibration models were generated by partial least-squares (PLS) type II regression of the TPI spectra and by generating a pure-component projection (PCP) basis set using net analyte signal (NAS) processing. Following generation of the calibration vectors, the performance of both methods at predicting the concentration of theophylline, lactose, and MCC was compared using the validation spectra and by generating chemical images from samples with known composition patterns. Sensitivity was observed for the PLS calibration over the range of all constituents for both the calibration and the validation datasets; however, some of the calibration statistics indicate that PLS overfits the spectra. Multicomponent prediction images verified the spatial and composition fidelity of the system. The NAS-PCP calibration procedure yielded accurate linear predictions of theophylline and lactose, whereas the results for MCC prediction were poor. The poor sensitivity for MCC is assumed to be related to the relative lack of phonon absorption bands, which concurs with the characterization of MCC as being semi-crystalline. The results of this study demonstrate the use of TPI reflection spectroscopy and efficient NAS-PCP for the quantitative analysis of crystalline pharmaceutical materials.     

Application of diffuse reflectance near-infrared spectroscopy for determination of crystallinity

Studies were conducted to investigate the use of near-infrared spectroscopy (NIRS) for determining degree of crystallinity. Physical mixtures of amorphous/crystalline indomethacin and amorphous/crystalline sucrose were prepared over several composition ranges. Spectra were obtained on powder samples contained in glass vials using diffuse reflectance sampling. Parallel studies were conducted using X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC) for comparison. NIRS standard curves were constructed by plotting crystalline weight percent against the ratio of responses at two wavelengths or by partial least squares regression. NIRS standard curves demonstrated higher coefficients of determination and lower standard errors than either XRPD or DSC. Validation standards confirmed the accuracy of NIRS over XRPD. Method error analysis demonstrated comparable accuracy for NIRS and XRPD, with NIRS showing slightly better precision in repeated crystallinity determinations for a 50% crystalline sucrose sample. Interpretive analysis of the NIRS spectra was performed using neutron scattering and polarized Raman spectroscopy data obtained from the literature. Results indicated that the NIRS differences between crystalline and amorphous sucrose may be attributed to the disruption of regular vibrational modes when crystalline sucrose is rendered amorphous.     

Formation,Physical Stability,and Quantification of Process-Induced Disorder in Cryomilled Samples of a Model Polymorphic Drug

The formation and physical stability of amorphous sulfathiazole obtained from polymorphic forms I and III by cryomilling was investigated by X‐ray powder diffraction (XRPD) and near‐infrared (NIR) spectroscopy. Principal component analysis was applied to the NIR data to monitor the generation of crystalline disorder with milling time and to study subsequent recrystallization under different storage conditions. Complete conversion into the amorphous phase was observed for both forms after 45 (form I) and 150 min (form III) milling time. Upon storage under vacuum over silica gel for 14 days at 4°C, amorphous samples remained amorphous. However, under the same conditions at ambient temperature, recrystallization occurred. Amorphous samples obtained from form I had crystallized back to the original polymorph, whereas those prepared from form III had partially crystallized to mixtures of polymorphs. Amorphous samples stored at ambient temperature and humidity absorbed moisture, which facilitated crystallization to a mixture of polymorphs in both cases. Quantitative analyses of amorphous content in binary mixtures with forms I and III were carried out by XRPD and NIR spectroscopy combined with partial least squares regression. The calibration models had root mean square error of prediction values of <2.0% and were applied to quantify the extent of crystalline disorder during cryomilling.     

IR spectroscopy together with multivariate data analysis as a process analytical tool for in-line monitoring of crystallization process and solid-state analysis of crystalline product

Crystalline product should exist in optimal polymorphic form. Robust and reliable method for polymorph characterization is of great importance. In this work, infra red (IR) spectroscopy is applied for monitoring of crystallization process in situ. The results show that attenuated total reflection Fourier transform infra red (ATR-FTIR) spectroscopy provides valuable information on process, which can be utilized for more controlled crystallization processes. Diffuse reflectance Fourier transform infra red (DRIFT-IR) is applied for polymorphic characterization of crystalline product using X-ray powder diffraction (XRPD) as a reference technique. In order to fully utilize DRIFT, the application of multivariate techniques are needed, e.g., multivariate statistical process control (MSPC), principal component analysis (PCA) and partial least squares (PLS). The results demonstrate that multivariate techniques provide the powerful tool for rapid evaluation of spectral data and also enable more reliable quantification of polymorphic composition of samples being mixtures of two or more polymorphs. This opens new perspectives for understanding crystallization processes and increases the level of safety within the manufacture of pharmaceutics.     

Simultaneous estimation of ramipril, acetylsalicylic acid and atorvastatin calcium by chemometrics assisted UV-spectrophotometric method in capsules

In the present work, three different spectrophotometric methods for simultaneous estimation of ramipril, aspirin and atorvastatin calcium in raw materials and in formulations are described. Overlapped data was quantitatively resolved by using chemometric methods, viz. inverse least squares (ILS), principal component regression (PCR) and partial least squares (PLS). Calibrations were constructed using the absorption data matrix corresponding to the concentration data matrix. The linearity range was found to be 1-5, 10-50 and 2-10 μg mL-1 for ramipril, aspirin and atorvastatin calcium, respectively. The absorbance matrix was obtained by measuring the zero-order absorbance in the wavelength range between 210 and 320 nm. A training set design of the concentration data corresponding to the ramipril, aspirin and atorvastatin calcium mixtures was organized statistically to maximize the information content from the spectra and to minimize the error of multivariate calibrations. By applying the respective algorithms for PLS 1, PCR and ILS to the measured spectra of the calibration set, a suitable model was obtained. This model was selected on the basis of RMSECV and RMSEP values. The same was applied to the prediction set and capsule formulation. Mean recoveries of the commercial formulation set together with the figures of merit (calibration sensitivity, selectivity, limit of detection, limit of quantification and analytical sensitivity) were estimated. Validity of the proposed approaches was successfully assessed for analyses of drugs in the various prepared physical mixtures and formulations.     

Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches

Near-infrared chemical imaging (NIR-CI) is the fusion of near-infrared spectroscopy and image analysis. It can be used to visualize the spatial distribution of the chemical compounds in a sample (providing a chemical image). Each sample measurement generates a hyperspectral data cube containing thousands of spectra. An important part of a NIR-CI analysis is the data processing of the hyperspectral data cube. The aim of this study was to compare the ability of different commonly used calibration methods to generate accurate chemical images. Three common calibration approaches were compared: (1) using single wavenumber, (2) using classical least squares regression (CLS) and (3) using partial least squares regression (PLS1). Each method was evaluated using two different preprocessing methods. A calibration data set of tablets with five constituents was used for analysis. Chemical images of the active pharmaceutical ingredient (API) and the two major excipients cellulose and lactose in the formulation were made. The accuracy of the generated chemical images was evaluated by the concentration prediction ability. The most accurate predictions for all three compounds were generated by PLS1. The drawback of PLS1 is that it requires a calibration data set and CLS, which does not require a calibration data set, therefore proved to be an excellent alternative. CLS also generated accurate predictions and only requires the pure compound spectrum of each constituent in the sample. All three calibration approaches were found applicable for hyperspectral image analysis but their relevance of use depends on the purpose of analysis and type of data set. As expected, the single wavenumber method was primarily found useful for compounds with a distinct spectral band that was not overlapped by bands of other constituents. This paper also provides guidance for hyperspectral image (or NIR-CI) analysis describing each of the typical steps involved.     

Rapid quantification of constituents in St. John's wort extracts by NIR spectroscopy

A quantitative near-infrared reflectance spectroscopy (NIRS) method was established for the determination of two major constituents (hyperforin and I3,II8-biapigenin) in St. John's wort extracts. Hyperforin was chosen due to the fact that it is found in a concentration range from 1 to 5%, a common one for NIRS determinations. I3,II8-Biapigenin on the other hand was selected as a constituent with very low concentrations (0.1-0.7%) but an extensive chromophore that allows very precise measurements in the ultraviolet (UV) and thus exact reference values that are vital for proper NIRS calibrations. Reference measurements were performed by reversed-phase high performance liquid chromatography (HPLC), determining the constituents' content in 35 pharmaceutical dry extracts of different origins. The reference method was validated according to the ICH guideline Q2B. Using partial-least squares (PLS) regression a multivariate calibration was done for the two ingredients each (PLS1). Satisfactory calibration statistics were obtained for hyperforin with a root mean square error of calibration (RMSEC) of 0.17 and a root mean square error of prediction (RMSEP) of 0.22 at a concentration range from 1 to 6% in the dry extracts. Due to the very low concentrations of I3,II8-biapigenin the accuracy of prediction is somewhat lower. However, it is possible to obtain very good results and reliable prediction by dividing the concentration range at 0.35%. The study emphasizes the potential of NIRS as a rapid and highly effective alternative method to conventional quantitative analysis of plant extracts.     

Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations

Near infrared (NIR) spectroscopy combined with multivariate calibration was attempted to analyze free amino acid content of Radix Pseudostellariae. The original spectra of Pseudostellariae samples in wavelength range of 10000–4000 cm⁻¹ were acquired. Partial least squares (PLS), kernel PLS (k-PLS), back propagation neural network (BP-NN), and support vector regression (SVR) algorithms were performed comparatively to develop calibration models. Some parameters of the calibration models were optimized by cross-validation. The performance of BP-NN model was better than PLS, k-PLS, and SVR models. The root mean square error of prediction (RMSEP) and the correlation coefficient (R) of BP-NN model were 0.687 and 0.889 in prediction set respectively. Results showed that NIR spectroscopy combined with multivariate calibration has significant potential in quantitative analysis of free amino acid content in Radix Pseudostellariae.     

A comparison of reflectance and transmittance near-infrared spectroscopic techniques in determining drug content in intact tablets

Drug contents of intact tablets were determined using non-destructive near infrared (NIR) reflectance and transmittance spectroscopic techniques. Tablets were compressed from blends of Avicel PH-101 and 0.5% w/w magnesium stearate with varying concentrations of anhydrous theophylline (0, 1, 2, 5, 10, 20 and 40% w/w). Ten tablets from each drug content batch were randomly selected for spectral analysis. Both reflectance and transmittance NIR spectra were obtained from these intact tablets. Actual drug contents of the tablets were then ascertained using a UV-spectrophotometer at 268 nm. Multiple linear regression (MLR) models at 1116 nm and partial least squares (PLS) calibration models were generated from the second derivative spectral data of the tablets in order to predict drug contents of intact tablets. Both the reflectance and the transmittance techniques were able to predict the drug contents in intact tablets over a wide range. However, a comparison of the results of the study indicated that the lowest percent errors of prediction were provided by the PLS calibration models generated from spectral data obtained using the transmittance technique.     

Determination of average crystallite shape by X-ray diffraction and computational methods

The objective of this work was to develop a method to estimate the average shape and habit of organic crystalline material using X-ray powder diffraction (XRPD), the single-crystal structure, and computational methods. It is proposed that the relative intensities of the peaks in an XRPD pattern from a sample exhibiting a "standard" preferred orientation correlates with the shape of the crystallites present. Models were developed to yield a quantitative "enhancement" factor for each face. The combined simple-forms morphology (CSM) of the material was then produced by indexing the observed faces and modifying the simulated Bravais-Friedel-Donnay-Harker (BFDH) morphology. The average shape of crystallites can be estimated from the CSM by multiplying each face by its enhancement factor. Acetaminophen crystals in two different habits and ibuprofen crystallized from toluene were used. The predicted shapes closely resembled the average shapes observed with microscopy. Results suggested the average shapes of the organic crystalline materials can be estimated by XRPD and the computational simulation. The current limitations are the need to "index" the faces, the size of the crystallites, and the unknown impact of a polydisperse size distribution on the calculation. The method must be used within the limits described; however, it is the only method found that may be adapted to large, more representative sample sizes. The determination of the average morphology is often a "bottle neck" in elucidating other important behaviors of large quantities of crystalline powders used in pharmaceutical development and processing.     

Quantification of glycine crystallinity by near-infrared (NIR) spectroscopy

The object of this investigation was to use near-infrared (NIR) spectroscopy for quantification of glycine crystallinity. Glycine samples, with different degrees of crystallinity, were obtained by physically mixing different proportions of crystalline beta-glycine with amorphous glycine. NIR spectra were obtained, directly from samples in glass vials, over the wavelength range of 1100-2500 nm. A partial least squares (PLS) model was developed to correlate the NIR spectral changes with the degree of crystallinity. Using this model, a standard error of calibration (SEC) of 2.1% was obtained with an r(2) value of 0.996. Cross validation was used to test the precision of the quantitative model, resulting in a standard error of prediction (SEP) of 3.2%. These results indicate that NIR spectroscopy is well suited to the measurement of glycine crystallinity in lyophilized products. Employing the PLS model, the crystallinity of glycine in freeze-dried sucrose-glycine mixtures was evaluated. At a sucrose to glycine ratio >4, glycine crystallization during lyophilization was inhibited. Conversely, at ratios < or =0.67, glycine remained substantially crystalline. At intermediate compositions, the glycine was partially crystalline.     

Differentiation and quantitative determination of surface and hydrate water in lyophilized mannitol using NIR spectroscopy

Mannitol hydrate is a metastable form produced during lyophilization. It is unstable, and therefore can undergo dehydration to release water to the surrounding environment at room temperature. The analysis of this form is challenging due to its thermodynamic instability. This study describes the development of a fast and non-invasive method to determine the mannitol hydrate and surface water content in a lyophilized product using near-infrared (NIR) spectroscopy. The mannitol hydrate was produced through lyophilization and characterized using XRPD, TGA, and NIR spectroscopy. Quantitative methods for hydrate and surface water were developed for NIR spectra with curve fitting and partial least square (PLS) regression models. The curve fitting method deconvoluted the NIR spectra into hydrate and surface water peaks and generated a calibration model by correlating pure spectra peak area to concentration. The standard error of prediction (SEP) for hydrate and surface water content were 0.65 and 0.40%, respectively. The PLS model developed for the same sample set was better than the curve fitting model; SEP = 0.50% for hydrate water and 0.22% for surface water, respectively. The methods can be used to monitor the formation and stability of mannitol hydrate in mannitol-containing formulations during the lyophilization process.