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.     

Quality Control of Multi-Component,Intact Pharmaceutical Tablets with Three Different Near-Infrared Apparatuses

The purpose of this study was to develop a robust and versatile near infrared (NIR) analysis protocol for the quality control of intact tablets containing two active pharmaceutical ingredients, acetylsalicylic acid (ASA) and caffeine, as well as three excipients. Reference samples were prepared and a calibration model built for each apparatus. All components of the formulation were characterized by transmission measurements with NIR spectroscopy (NIRS). The study was performed with three different Fourier transform NIR apparatuses and chemometric models. Calibration was carried out by the partial least squares regression method and a pre-processing technique to optimize the efficiency of the models. High performance liquid chromatography was the reference method for obtaining active pharmaceutical ingredient concentration values used in model building. It also served as a reference for chemometric model validation. Eighteen samples were analyzed by chemometric modeling to predict each component's concentration. Four out of five ingredients were quantified precisely with the three chemometric models developed. ASA quantification uncertainty ranges were between 1.0 and 1.1%, and the average error was less than 5% for caffeine. More than 99.9% of tablet content were analyzed and quantified. The results show that a versatile in-line or at-line NIRS method, with three different chemometric models built from three different acquisition apparatuses, can be developed without sample preparation for pharmaceutical tablet quality control of existing products.     

Qualitative in situ analysis of multiple solid-state forms using spectroscopy and partial least squares discriminant modeling

This study used in situ spectroscopy to reveal the multiple solid-state forms that appear during isothermal dehydration. Hydrate forms of piroxicam and carbamazepine (CBZ) were investigated on hot-stage at different temperatures using near-infrared (NIR) and Raman spectroscopy combined with multivariate modeling. Variable temperature X-ray powder diffraction, differential scanning calorimetry, thermogravimetric analysis, and Karl Fisher titrimetry were used as reference methods. Partial least squares discriminant analysis (PLS-DA) was performed to qualitatively evaluate the phase transition. It was shown that the constructed PLS-DA models, where spectral differences were directly correlated to solid-state modifications, enabled differentiation between the multiple forms. Qualitative analysis revealed that during dehydration, hydrates, such as CBZ dihydrate, may go through several solid-state forms, which must be considered in quantitative model construction. This study demonstrates that in situ analysis can be used to monitor the dehydration and reveal associated solid-state forms prior to quantification. The utility of the complementary spectroscopic techniques, NIR and Raman, have been shown.     

Applications of NIR in early stage formulation development. Part II. Content uniformity evaluation of low dose tablets by principal component analysis

A near infrared method based on principal component analysis (PCA) was developed for predicting content uniformity of low dose tablets manufactured by a direct compression process. The work was conducted in early stage formulation development. NIR spectra of one hundred and eighty tablets from three feasibility batches were used as the pseudo-calibration set. A correlation was established between PCA scores and a set of reference values obtained by HPLC analysis. The reference values were also used to define a concentration range for the active pharmaceutical ingredient to facilitate content uniformity prediction by PCA. Analyses of unknown samples were conducted by forming a prediction set that included the calibration and unknown samples, followed by PCA. Samples from two development batches were predicted using the PCA model and the results were consistent with the reference HPLC values. Remarkably, the model was able to predict CU for tablets that were prepared using different grades of lactose (anhydrous versus monohydrate). Additionally, during this study, the impact of spectrum pretreatments on PCA is demonstrated. A brief discussion is given to highlight the advantages of PCA over partial least squares (PLS) regression for analysis of samples generated in early stage formulation development.     

Near-Infrared Imaging for Studying Homogeneity of Protein-Sugar Mixtures

Purpose To investigate the applicability of near-infrared (NIR) imaging for assessing the homogeneity of dried protein-sugar formulations.Methods Physical mixtures of lysozyme and trehalose in different ratios were prepared and analyzed by near-infrared (NIR) imaging with a spatial resolution of 10 or 40 μm. To define and select the best imaging strategy, besides visual inspection of the images, several approaches for data processing were tested: single wavelength intensity, peak/height ratio of two specific wavelengths, correlation coefficient with a reference spectrum and principal component analysis (PCA). In order to relate the contrast directly to concentration differences of lysozyme and trehalose, quantitative models were created based on correlation coefficient and partial least squares (PLS) regression. The selected imaging method was applied to compare the homogeneity of a supercritical fluid (SCF) dried and a freeze-dried lysozyme-trehalose mixture.Results All tested methods confirmed each other and showed spatial heterogeneity in the lysozyme and trehalose contents of the physical mixtures. However, multivariate data processing methods (correlation coefficient and PCA/PLS) resulted in more distinct contrasts than univariate approaches (single wavelength analysis) and allowed a quantitative estimation of the homogeneity. As shown by NIR imaging in combination with the correlation coefficient or the PLS method, the SCF dried lysozyme-trehalose formulation was at least as homogeneous as its lyophilized counterpart, at 10 μm pixel size resolution.Conclusions NIR imaging is a useful tool for studying the homogeneity of dried protein-sugar formulations.     

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.     

Near-Infrared and Fourier Transform Infrared Chemometric Methods for the Quantification of Crystalline Tacrolimus from Sustained-Release Amorphous Solid Dispersion

The objective of the present research was to study the feasibility of using near-infrared (NIR) and Fourier transform infrared (FTIR)-based chemometric models in quantifying crystalline and amorphous tacrolimus from its sustained-release amorphous solid dispersion (ASD). ASD contained ethyl cellulose, hydroxypropyl methyl cellulose, and lactose monohydrate as carriers, and amorphous form of tacrolimus in it was confirmed by X-ray powder diffraction. Crystalline physical mixture was mixed with ASD in various proportions to prepare sample matrices containing 0%–100% amorphous/crystalline tacrolimus. NIR and FTIR of the samples were recorded, and data were mathematically pretreated using multiple scattering correction, standard normal variate, or Savitzky–Golay before multivariate analysis, partial-least-square regression (PLSR), and principle component regression (PCR). The PLSR models were more accurate than PCR for NIR and FTIR data as indicated by low value of root-mean-squared error of prediction, standard error of prediction and bias, and high value of R². Additionally, NIR data-based models were more accurate and precise than FTIR data models. In conclusion, NIR chemometric models provide simple and fast method to quantitate crystalline tacrolimus in the ASD formulation. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:2376–2385, 2014     

Formulation and Evaluation of a Protein-loaded Solid Dispersions by Non-destructive Methods

The purpose of this investigation was to develop solid dispersion (SD) formulation of cyclosporine (CyA) using polyethylene glycol (PEG-6000) to enhance its dissolution rate followed by nondestructive method for the prediction of both drug and carrier. SD formulations were prepared by varying the ratio of CyA and PEG-6000 by solvent evaporation technique and characterized by dissolution, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), Fourier transform infrared (FTIR), powder X-ray diffraction (PXRD), near infrared (NIR) and near infrared chemical imaging (NIR-CI). Dissolution data revealed enhanced dissolution of CyA when compared with pure CyA. DSC results showed that the crystallinity of PEG-6000 has decreased as indicated by decrease in the enthalpy of fusion and melting peak in the formulations. FTIR data demonstrated no chemical interaction between drug and carrier. The surface morphology of SD formulations was similar to PEG-6000 particle. NIR-CI disclosed homogeneity of SD matrix as indicated by symmetrical histograms with smaller values of skewness. Similar to NIR, a multivariate peak evaluation with principal component analysis and partial least square (PLS) were carried out with PXRD spectral data. PLS models with both techniques showed good correlation coefficient and smaller value of root mean square of errors. The accuracy of model for predicting CyA and PEG-6000 in NIR and PXRD data were 5.22%, 5.35%, 5.27%, and 2.10%, respectively. In summary, chemometric applications of non-destructive method sensors provided a valuable means of characterization and estimation of drug and carrier in the novel formulations.     

Near-Infrared Investigations of Novel Anti-HIV Tenofovir Liposomes

Near-infrared (NIR) approaches is considered one of the most well-studied process analyzers evolving from the process analytical technology initiatives. The objective of this study was to evaluate NIR spectroscopy and imaging to assess individual components within a novel tenofovir liposomal formulation. By varying stearylamine, as a positive charge imparting agent, five batches were prepared by the thin film method. Each formulation was characterized in terms of drug entrapment efficiency, release characteristics, particle sizing, and zeta potential. Drug excipients compatibility was tested using Fourier transform infrared spectroscopy, differential scanning calorimetry, and X-ray diffraction. The obtained results showed an increase in drug entrapment and a slower drug release by increasing the incorporated percentage of stearylamine. The compatibility testing revealed a significant interaction between the drug and some of the investigated excipients. The developed NIR calibration model was able to assess drug, phospholipid, and stearylamine levels along the batches. The calibration and prediction plots were linear with correlation coefficients of more than 0.9. The root square standard errors of calibration and prediction did not attain 5% of the measured values confirming the accuracy of the model. In contrast, NIR spectral imaging was capable of clearly distinguishing the different batches, both qualitatively and quantitatively. A linear relationship was obtained correlating the actual drug entrapped and the predicted values obtained from the partial least squares images.     

Near-infrared reflection spectroscopy (NIRS) as a successful tool for simultaneous identification and particle size determination of amoxicillin trihydrate

A successful application of NIR spectroscopy (NIRS) in combination with multivariate data analysis (MVA) for the simultaneous identification and particle size determination of amoxicillin trihydrate particles was developed. Particle size analysis was ascertained by NIRS in diffuse reflection mode on different particle size fractions of amoxicillin trihydrate with D90 particle diameters ranging from 6.9 to 21.7 μm. The present problem of fractionating the powder into good enough size fractions to achieve a stable calibration model was solved. By probing dried suspensions measurement parameters were optimized and further combined with the best suitable chemometric operations. Thereby the quality of established regression models could be improved considerably. A linear coherence between particle size and absorbance signal was found at specific wavenumbers. Satisfactory clustering by particle size was achieved by principal component analysis (PCA) whereas partial least squares regression (PLSR) and principal component regression (PCR) was compared for quantitatively calibrating the NIRS data. PLSR turned out to predict unknown test samples slightly better than PCR.     

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.     

Determination of the scale of segregation of low dose tablets using hyperspectral imaging

In this work, near infrared (NIR) hyperspectral imaging was used to quantify the spatial distribution of drug in tablets containing tolmetin sodium dihydrate. Hyperspectral data cubes were generated by imaging the same spatial region of a sample while illuminated by a laser at a different wavelength for each image. Images were generated for wavelengths ranging from 1100 to 2200 nm. Ten tablets with concentrations ranging from 0.0 to 10.0% w/w tolmetin were imaged, and the scales of segregation were calculated for the tablets. Lactose anhydrous was used as the diluent, and all mixtures contained 0.5% magnesium stearate as a lubricant. This research has shown hyperspectral imaging to be viable tool for quantifying segregation of low dose drugs in tablets.     

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.     

Chemometric evaluation of brompheniramine-tannate complexes

The objective of the current study was to evaluate the performance of Raman and near-infrared (NIR) techniques combined with chemometrics in characterizing the critical quality attributes of brompheniramine (BP)-tannate complexes. Seven complexes were prepared and evaluated for chemical interactions, solubilities, dissolutions, and spatial distributions by NIR chemical imaging (CI). Principal component analysis (PCA) was applied before either partial least squares regression (PLSR) or principal component regression (PCR) models were developed. Complexation was confirmed by Fourier transform IR analysis to yield complexes of lower drug solubilities and sustained-release characteristics in alkaline media. PCA results showed better discrimination ability by NIR than by Raman spectroscopy. Compared with PCR, the PLSR predictions errors, calculated from the Raman and NIR data with second-derivative pretreatment, showed lesser values of 2.68, 0.37, 1.79, and 5.60 and 0.58, 0.25, 0.93, and 0.58 for complex solubilities in acidic and alkaline media and percentages dissolved after 1 and 20 h, respectively. In addition, good correlation (>0.95) was obtained for predicting the drug concentration using PLSR score images explaining the validity of the NIR-CI model for spatial quantitation of BP within its tannate complexes. In conclusion, the chemometric analysis of NIR and/or Raman spectra represented an innovative approach to determine the tannate complexation variability.     

Quantitative analysis of bucillamine and its pharmaceutical formulation using FT-IR spectroscopy

A Fourier transform infrared (FT-IR) spectrometric method was developed for the rapid, direct measurement of bucillamine. Conventional KBr-spectra and DRIFTS spectra were compared for best determination of active substance in its tablet formulation. Two chemometric approaches, partial least squares (PLS) and principal component regression (PCR+) methods were used in data processing. Similar results were obtained with both chemometric methods.     

On-line monitoring of a granulation process by NIR spectroscopy

Near infrared (NIR) spectroscopy has been used in a noninvasively mode to develop qualitative and quantitative methods for the monitoring of a wet granulation process. The formulation contained API (10%w/w) and microcrystalline cellulose and maize starch as main excipients. NIR spectra have been acquired through the glass window of the fluidizer in reflectance mode without causing interference to neither the process nor the formulation. The spectral data has been used to develop a qualitative multivariate model based on principal component analysis (PCA). This qualitative model allows the monitoring of different steps during the granulation process only using the spectral data. Also, a quantitative calibration model based on partial least squares (PLS) methodology has been obtained to predict relevant parameters of the process, such as the moisture content, particle size distribution, and bulk density. The methodology for data acquisition, calibration modeling and method application is relatively low-cost and can be easily performed on most of the pharmaceutical sites. Based on the results, the proposed strategy provides excellent results for the monitoring of granulation processes in the pharmaceutical industry. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:336–345, 2010     

Hyphenated spectroscopy as a polymorph screening tool

Polymorph screening of a model compound (nitrofurantoin) was performed. Nitrofurantoin was crystallized from acetone-water mixtures with varying process parameters. Two anhydrate forms (alpha and beta) and one monohydrate form (II) were crystallized in the polymorph screen. The solid forms were analyzed with three complementary spectroscopic techniques: near-infrared (NIR) spectroscopy, Raman spectroscopy and terahertz pulsed spectroscopy (TPS), and the results of the solid phase analysis were verified with X-ray powder diffraction (XRPD). NIR and Raman spectroscopy were coupled to achieve a rapid and comprehensive method of solid phase analysis. The hyphenated NIR/Raman spectroscopic data were analyzed with a multivariate method, principal component analysis (PCA). The combination was found effective in screening solid forms due to the complementary characteristics of the methods. NIR spectroscopy is powerful in differentiating between anhydrate and hydrate forms and intermolecular features, whereas Raman spectroscopy is sensitive to intramolecular alterations in the molecular backbone.     

Quality control of multi-component, intact pharmaceutical tablets with three different near-infrared apparatuses

The purpose of this study was to develop a robust and versatile near infrared (NIR) analysis protocol for the quality control of intact tablets containing two active pharmaceutical ingredients, acetylsalicylic acid (ASA) and caffeine, as well as three excipients. Reference samples were prepared and a calibration model built for each apparatus. All components of the formulation were characterized by transmission measurements with NIR spectroscopy (NIRS). The study was performed with three different Fourier transform NIR apparatuses and chemometric models. Calibration was carried out by the partial least squares regression method and a pre-processing technique to optimize the efficiency of the models. High performance liquid chromatography was the reference method for obtaining active pharmaceutical ingredient concentration values used in model building. It also served as a reference for chemometric model validation. Eighteen samples were analyzed by chemometric modeling to predict each component's concentration. Four out of five ingredients were quantified precisely with the three chemometric models developed. ASA quantification uncertainty ranges were between 1.0 and 1.1%, and the average error was less than 5% for caffeine. More than 99.9% of tablet content were analyzed and quantified. The results show that a versatile in-line or at-line NIRS method, with three different chemometric models built from three different acquisition apparatuses, can be developed without sample preparation for pharmaceutical tablet quality control of existing products.     

Real‐Time Monitoring of Changes of Adsorbed and Crystalline Water Contents in Tablet Formulation Powder Containing Theophylline Anhydrate at Various Temperatures During Agitated Granulation by Near‐Infrared Spectroscopy

Real‐time monitoring of adsorbed water content (FW) and hydrate formation of theophylline anhydrate (THA) in tablet formulation during agitated granulation was investigated by near‐infrared (NIR) spectroscopy. As the wet‐granulation process of THA tablet formulation involves change in pseudo‐polymorphs between THA and theophylline monohydrate (THM), the pharmaceutical properties of THA tablet depend on the degree of hydration during granulation. After mixing of the powder materials (4 g) containing THA, and excipients and the addition of 600 μL of binding water, the powder was kneaded at 27°C, 40°C, and 50°C and then dried. The mixing, granulating, and drying processes were monitored using NIR. The calibration models to predict THM and total water contents during granulation in THA tablet formulation were obtained by partial least‐squares regression. The FW in the formulation was determined by subtracting THM from the water content. The results of the THA formulation powder bed during granulation by NIR monitoring indicated that the transformation pathway of the THA powder was THA ⇒ THM ⇒ THA at 27°C and 40°C, but that at 50°C was THA ⇒ THA ⇒ THA. The pharmaceutical properties, such as tablet porosity, hardness, tablet disintegration time, and dissolution rate of the final THA tablet products, were affected by the degree of crystalline transformation during granulation. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:2924–2936, 2014     

A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending. Part III: Quantitative near-infrared calibration for prediction of blend homogeneity and characterization of powder mixing kinetics

The Process Analytical Technology (PAT) initiative, undertaken by the Food and Drug Administration (FDA), paves the way for improvement of drug manufacturing through real-time measurements that allow better process understanding. This study is the third and final Part in a series of studies that represent an integrated approach for real-time blend uniformity assessment using near-infrared (NIR) technology. In this study, the development of a quantitative NIR model for prediction of blending end point is presented. Process signature was built into NIR calibration models by using blend samples that were collected from actual blend experiments under different processing conditions. Evaluation of various calibration algorithms including principal component regression (PCR), partial least squares (PLS), and multi-term linear regression (MLR) was performed. It was found that linear regression, using a single wavelength, yielded optimum calibration and prediction results. The blending profiles predicted by the NIR quantitative model correlated well to those determined by the UV reference analytical method. Characterization of intra-shell versus inter-shell powder mixing kinetics and its implication in sensor positioning was also performed and will be discussed.