Application of the near-infrared spectroscopy in the pharmaceutical technology

An evaluation of resolution, accuracy, and precision in FT-IR spectroscopy

Infrared spectroscopy is a foundational technique for the elucidation of chemical structures. The advancements in interferometric spectroscopy, and specifically the development of Fourier transform infrared (FT-IR) spectroscopy, are responsible for the widespread usage of IR spectrometers ranging from teaching labs to pharmaceutical quality control. FT-IR affords an excellent signal-to-noise ratio that permits sensitive sampling with quantitative accuracy and high wavenumber precision based on well documented advantages (Jacquinot, Fellgett, Connes). However, the effect of resolution and instrument-to-instrument variation on wavenumber accuracy is not well understood, with previous work grossly overestimating error. Here, a recommendation of wavenumber accuracy as a function of spectral resolution, accounting for instrument variation among leading manufacturers, is given based on an experimental study of polystyrene and acetaminophen. For peaks that are well resolved and not saturated, the position can be known within 1.1 cm-1 at a spectral resolution of 4 cm-1 or higher, and within 2.2 cm-1 at 8 cm-1 resolution. Other sources of variation are also discussed (e.g., poorly resolved peaks, peak saturation, water interference, spectral noise) to give general recommendations on when IR peak positions can be considered significantly different. Such guidelines are critical for interpreting subtle positional variations, as are often present in different crystal forms of pharmaceuticals.

Rational formulation and industrial manufacturing of lipid-based complex injectables: Landmarks and trends

Lipid-based complex injectables are renowned for their effectiveness in delivering drugs, with many approved products. While significant strides have been made in formulating nanosystems for small molecular weight drugs, a pivotal breakthrough emerged with the recognition of lipid nanoparticles as a promising platform for delivering nucleic acids. This finding has paved the way for tackling long-standing challenges in molecular and delivery aspects (e.g., mRNA stability, intracellular delivery) that have impeded the clinical translation of gene therapy, especially in the realm of immunotherapy. Nonetheless, developing and implementing new lipid-based delivery systems pose significant challenges, as industrial manufacturing of these formulations often involves complex, multi-batch processes, giving rise to issues related to scalability, stability, sterility, and regulatory compliance. To overcome these obstacles, embracing the principles of quality-by-design (QbD) is imperative. Furthermore, adopting cutting-edge manufacturing and process analytical tools (PAT) that facilitate the transition from batch to continuous production is essential. Herein, the key milestones and insights derived from the development of currently approved lipid- nanosystems will be explored. Additionally, a comprehensive and critical overview of the latest technologies and regulatory guidelines that underpin the creation of more efficient, scalable, and flexible manufacturing processes for complex lipid-based nanoformulations will be provided.

THz-TDS as a PAT tool for monitoring blend homogeneity in pharmaceutical manufacturing of solid oral dosage forms: A proof-of-concept study

The process analytical technology (PAT) framework is well established and integral to facilitate process understanding, enable a transition from batch to continuous manufacturing, and improve product quality. Near-infrared (NIR) spectroscopy has been established as a standard PAT tool for many process analytical challenges, including monitoring powder blend homogeneity. However, alternative technologies for monitoring powder blending are of interest due to the importance of the blending step in manufacturing solid oral dosage forms. Terahertz time-domain spectroscopy (THz-TDS) is therefore explored in this study as an alternative tool for monitoring blend homogeneity with the potential for endpoint control in a batch blending process. Powder blends of microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate and blends of MCC and granulated α-lactose monohydrate were investigated non-invasively at various compositions using THz-TDS in transmission mode for acquiring spectra from samples enclosed in the blending container. It was found that attenuation- and phase-related parameters acquired with THz-TDS could reliably resolve physical changes related to the homogeneity of the blend. Further evaluations revealed that changes in the bulk density of the blend, in addition to the intrinsic optical properties of the materials, played a critical role in the observed trends for both systems. In contrast, the scattering contribution of the powder was mainly crucial for the attenuation-related parameter in blends with materials of high refractive indices. Finally, THz-TDS measurements were acquired throughout a blending process mimicking a continuous acquisition. The method could follow blending dynamics and resulted in reasonable predictive errors of the content of 0.5 - 2.5 %. Relative standard deviations for high content blends (20 %) were acceptable (3 - 7 %) whereas at low contents (5 %) significantly higher values (9 - 35 %) were found. Based on these findings, THz-TDS is a feasible PAT tool for monitoring blend homogeneity and controlling high content blend processes, although precision and accuracy is considered to improve with a more suitable interface.

Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy

OBJECTIVE

To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol.

METHODS

Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively.

RESULTS

As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model.

CONCLUSIONS

Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

Optical-parametric-amplification-enhanced background-free spectroscopy

Traditional absorption spectroscopy has a fundamental difficulty in resolving small absorbance from a strong background due to the instability of laser sources. Existing background-free methods in broadband vibrational spectroscopy help to alleviate this problem but face challenges in realizing either low extinction ratios or time-resolved field measurements. Here, we introduce optical-parametric-amplification-enhanced background-free spectroscopy, in which the excitation background is first suppressed by an interferometer, and then the free-induction decay that carries molecular signatures is selectively amplified. We show that this method can improve the limit of detection in linear interferometry by order(s) of magnitude without requiring lower extinction ratios or a time-resolved measurement, which can benefit sensing applications in detecting trace species.

Application of electron paramagnetic resonance spectroscopy to examine free radicals formed in indapamide and torasemide storage under UV irradiation and at the higher temperatures which appear under light exposition

Free radical formation in two diuretics: indapamide and torasemide was examined during UV irradiation and storage at higher temperatures using X-band (9.3 GHz) electron paramagnetic resonance spectroscopy (EPR). The aim of this study was to investigate the possibility of storing indapamide and torasemide under UV irradiation and at higher temperatures, which may occur during exposure to light. The diuretic samples were exposed to UVA irradiation for 15, 30 and 45 minutes, and stored at temperatures of 40 °C and 50 °C by 30 minutes. The EPR spectra were analyzed to determine the amplitudes (A), linewidths (ΔBpp), and integral intensities (I) and g factors. The concentrations of free radical (N) in the diuretic samples were also determined. The influence of microwave power on amplitudes, linewidths and the asymmetry parameter were evaluated. The result showed that the tested indapamide and torasemide samples exhibited high free radical concentrations in the range of 1018-1019 spin/g after UV irradiation and heat treatment. Therefore, due to the significant free radical formation indapamide and torasemide should not be stored under UV light and at temperatures of 40 °C and 50 °C. The complex character of free radical systems in the diuretic samples was proved as evidenced by the changes of the asymmetry parameters of the EPR lines with increasing microwave power. Fast spin-lattice relaxation processes were observed in all tested diuretic samples, regardless of the storage conditions. Electron paramagnetic resonance spectroscopy is proposed as a useful method in pharmacy to determine the appropriate storage conditions for diuretics.

Near-infrared spectroscopy bioprobe estimation of metabolites' responses to Pb2+ in Cladophora rupestris

Near-infrared (NIR) spectroscopy was used as a bioprobe to estimation chlorophyll, total sugar, proline, and metallothionein (MT) response to Pb2+ (0, 1, 2.5, 5, 7.5, 10, and 20 mg/l) in Cladophora rupestris. Spectra between 3775.94 and 12315.19 cm-1 were analysed using a partial least-squares regression (PLS). Principal component analysis (PCA) was performed by evaluating the distance between the C. rupestris samples treated with different concentrations of Pb2+. Results showed that 4000-7500 cm-1 of raw NIR spectra from C. rupestris was linked to carbohydrates and stretching of N-H, and the O-H overtone was associated with proline and MT. NIR spectroscopy technique coupled with PLS quantitative analysis model has high potential. Multiplicative scatter correction and standard normal variate combining first-order derivative with S-G smoothing filter are superior to the other models. The root mean square error of cross of chlorophyll, total sugar, proline, and MT was 0-1, indicating the accuracy of the model is high. The Rc2 values of chlorophyll, total soluble sugar, proline, and MT between measured and predicted values were higher than 0.99. PCA analysis indicate clear boundaries in the spatial distributions of seven Pb2+ concentrations, indicating that NIR spectroscopy has high potential as a tool assisting bioprobes for estimating metabolites' responses to Pb2+ in Cladophora rupestris.

Quantification of ciprofloxacin in pharmaceutical products from various brands using FT-NIR: A comparative investigation of PLS and MCR-ALS

This study aims to quantify ciprofloxacin in commercial tablets with varying excipient compositions using Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and chemometric models: Partial Least Squares (PLS) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Matrix variation, arising from differences in excipient compositions among the tablets, can impact quantification accuracy. We discuss this phenomenon, emphasizing potential issues introduced by varying certain excipients and its importance in reliable ciprofloxacin quantification. We evaluated the performance of PLS and MCR-ALS models independently on two sets of tablets, each containing the same drug substance but different excipients. The statistical results revealed promising results with PLS prediction error of 0.38% w/w of the first set and 0.47% w/w of the second set, while MCR-ALS achieved prediction errors of 0.67% w/w of the first set and 1.76% w/w of the second set. To address the challenge of matrix variation, we developed single models for PLS and MCR-ALS using a dataset combining both first and second sets. The PLS single model demonstrated a prediction error of 4.3% w/w and a relative error of 6.41% w/w, while the MCR-ALS single model showed a prediction error of 1.88% w/w and a relative error of 1.29% w/w. We then assessed the performance of the single PLS and MCR-ALS models developed based on the combination of the first and the second set in quantifying ciprofloxacin in various commercial tablet brands containing new excipients. The PLS model achieved a prediction error ranging between 6.2% w/w and 8.39% w/w, with relative errors varied between 8.53% w/w and 12.82% w/w. On the other hand, the MCR-ALS model had a prediction error between 1.11% w/w and 2.66% w/w, and the relative errors ranging from 0.8% to 1.74% w/w.

Optimization of radial extrusion and pellet coating processes using PAT approaches

FDA's initiative Pharmaceutical CGMPs for the 21st century opened the door for introduction of several risk based approaches in pharmaceutical industry. One significant advancement that has emerged is the implementation of process analytical technology (PAT), which has opened doors for understanding and controlling complex technological processes. Two such processes, radial extrusion and pellet coating, offer a solid foundation for the application of PAT tools due to their numerous critical process parameters. The aim of the first part of the study was to optimize the neutral pellet production to produce the pellets with properties desired for successful film coating using design of experiments (DoE). In the second part the optimized pellets underwent film coating and the coating quantity was predicted in real or near real-time using in-line and at-line NIR probes and the performance of both probes was compared. The desired properties of the pellets, narrow particle size distribution, high sphericity and high process yield, were successfully achieved. Models for film coating quantity prediction using in-line and at-line NIR probe were successfully calibrated and tested by coating two additional batches. Despite the limited sample size for model calibration, at-line NIR exhibited excellent prediction performance and enabled accurate determination of process end-point. The coating quantity determined by UV/VIS spectroscopy in both test batches deviated by less than 2.0 % from the target value. However, the in-line NIR probe, primarily due to its inferior spectral resolution, displayed a slightly lower quality of the calibrated model and notable overprediction for the tested batches.

Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy

Gout is a metabolic disease that can result in the formation of gout stones. It is essential to promptly identify and confirm the type of gout stone to alleviate pain and inflammation in patients and prevent complications associated with gout stones. Traditional detection methods, such as X-ray, ultrasound, CT scanning, and blood uric acid measurement, have limitations in early diagnosis. Therefore, this article aims to explore the use of micro Raman spectroscopy, Fourier transform infrared spectroscopy, and Terahertz time-domain spectroscopy systems to detect gout stone samples. Through comparative analysis, Terahertz technology and Raman spectroscopy have been found to provide chemical composition and molecular structure information of different wavebands of samples. By combining these two technologies, faster and more comprehensive analysis and characterization of samples can be achieved. In the future, handheld portable integrated testing instruments will be developed to improve the efficiency and accuracy of testing. Furthermore, this article proposes establishing a spectral database of gout stones and urinary stones by combining Raman spectroscopy and Terahertz spectroscopy. This database would provide accurate and comprehensive technical support for the rapid diagnosis of gout in clinical practice.

Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning

Spectroscopy data are useful for modelling biological systems such as predicting quality parameters of horticultural products. However, using the wide spectrum of wavelengths is not practical in a production setting. Such data are of high dimensional nature and they tend to result in complex models that are not easily understood. Furthermore, collinearity between different wavelengths dictates that some of the data variables are redundant and may even contribute noise. The use of variable selection methods is one efficient way to obtain an optimal model, andthis was the aim of this work. Taking advantage of a non-contact spectrometer, near infrared spectral data in the range of 800-2500 nm were used to classify bruise damage in three apple cultivars, namely 'Golden Delicious', 'Granny Smith' and 'Royal Gala'. Six prominent machine learning classification algorithms were employed, and two variable selection methods were used to determine the most relevant wavelengths for the problem of distinguishing between bruised and non-bruised fruit. The selected wavelengths clustered around 900 nm, 1300 nm, 1500 nm and 1900 nm. The best results were achieved using linear regression and support vector machine based on up to 40 wavelengths: these methods reached precision values in the range of 0.79-0.86, which were all comparable (within error bars) to a classifier based on the entire range of frequencies. The results also provided an open-source based framework that is useful towards the development of multi-spectral applications such as rapid grading of apples based on mechanical damage, and it can also be emulated and applied for other types of defects on fresh produce.

Shortwave infrared single-pixel spectral imaging based on a GSST phase-change metasurface

Shortwave infrared (SWIR) spectral imaging obtains spectral fingerprints corresponding to overtones of molecular vibrations invisible to conventional silicon-based imagers. However, SWIR imaging is challenged by the excessive cost of detectors. Single-pixel imaging based on compressive sensing can alleviate the problem but meanwhile presents new difficulties in spectral modulations, which are prerequisite in compressive sampling. In this work, we theoretically propose a SWIR single-pixel spectral imaging system with spectral modulations based on a Ge₂Sb₂Se₄Te₁ (GSST) phase-change metasurface. The transmittance spectra of the phase-change metasurface are tuned through wavelength shifts of multipole resonances by varying crystallinities of GSST, validated by the multipole decompositions and electromagnetic field distributions. The spectral modulations constituted by the transmittance spectra corresponding to the 11 phases of GSST are sufficient for the compressive sampling on the spectral domain of SWIR hyperspectral images, indicated by the reconstruction in false color and point spectra. Moreover, the feasibility of optimization on phase-change metasurface via coherence minimization is demonstrated through the designing of the GSST pillar height. The concept of spectral modulation with phase-change metasurface overcomes the static limitation in conventional modulators, whose integratable and reconfigurable features may pave the way for high-efficient, low-cost, and miniaturized computational imaging based on nanophotonics.

Post-Mortem Interval of Human Skeletal Remains Estimated with Handheld NIR Spectrometry

Estimating the post-mortem interval (PMI) of human skeletal remains is a critical issue of forensic analysis, with important limitations such as sample preparation and practicability. In this work, NIR spectroscopy (NIRONE® Sensor X; Spectral Engines, 61449, Germany) was applied to estimate the PMI of 104 human bone samples between 1 day and 2000 years. Reflectance data were repeatedly collected from eight independent spectrometers between 1950 and 1550 nm with a spectral resolution of 14 nm and a step size of 2 nm, each from the external and internal bone. An Artificial Neural Network was used to analyze the 66,560 distinct diagnostic spectra, and clearly distinguished between forensic and archaeological bone material: the classification accuracies for PMIs of 0−2 weeks, 2 weeks−6 months, 6 months−1 year, 1 year−10 years, and >100 years were 0.90, 0.94, 0.94, 0.93, and 1.00, respectively. PMI of archaeological bones could be determined with an accuracy of 100%, demonstrating the adequate predictive performance of the model. Applying a handheld NIR spectrometer to estimate the PMI of human skeletal remains is rapid and extends the repertoire of forensic analyses as a distinct, novel approach.

Real-Time Monitoring of Critical Quality Attributes during High-Shear Wet Granulation Process by Near-Infrared Spectroscopy Effect of Water Addition and Stirring Speed on Pharmaceutical Properties of the Granules

To produce high-quality pharmaceuticals, a real-time monitoring method for the high-shear wet granulation process (HSWG) was developed based on near-infrared spectroscopy (NIRS). Samples consisting of lactose, potato starch, and hydroxypropyl cellulose were prepared using HSWG with varying amounts of purified water (80, 90, and 100 mL) and impeller speed (200, 400, and 600 rpm), which produces granules of different characteristics. Twelve batches of samples were used for the calibration and nine batches were used for validation. After drying, the median particle size (D50), tapped density (TD), and Hauser ratio (HR) were measured. The best calibration models to predict moisture content (MC), D50, TD, and HR were determined based on pretreated NIR spectra using partial least squares regression analysis (PLSR). The temporal changes in the pharmaceutical properties under different amounts of water added and stirring speed were monitored in real time using NIRS/PLSR. Because the most important critical quality attribute (CQA) in the process was MC, granule characteristics such as D50, TD, and HR were analyzed with respect to MC. They might be used as robust and simple monitoring methods based on MC to evaluate the pharmaceutical properties of HSWG granules.

Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

Over the past four decades, near-infrared reflectance spectroscopy (NIRS) has become one of the most attractive and used technique for analysis as it allows for fast and simultaneous qualitative and quantitative characterization of a wide variety of food samples [...].

A weighted twin support vector machine as a potential discriminant analysis tool and evaluation of its performance for near-infrared spectroscopic discrimination of the geographical origins of diverse agricultural products

A weighted twin support vector machine (wTWSVM) was proposed as a potential discriminant analysis tool and its utility was evaluated for near-infrared (NIR) spectroscopic identification of the geographical origins of 12 different agricultural products including black soybean and garlic. In the wTWSVM, weights were applied on each variable in the sample spectra to highlight detailed NIR spectral features and the optimal weights to minimize the discrimination error were iteratively searched. Then, the weighted spectra were employed to determine the samples' geographical origins using a TWSVM adopting two non-parallel hyperplanes for the discrimination. For the performance evaluation, SVM, TWSVM, and wTWSVM were separately used for the two-group discriminations and their accuracies were comparatively analyzed. When the SVM and TWSVM accuracies were compared, the improvements by using the TWSVM were significant (95% confidence level) for 10 out of the 12 products. Moreover, the accuracy improvements with the wTWSVM against SVM were significant for all the 12 products. In the case of the TWSVM-wTWSVM accuracy comparison, the improvements by the wTWSVM were also significant for 10 products, thereby demonstrating superior discrimination performance of wTWSVM. Based on the overall results, the wTWSVM could be a potential chemometric tool for discriminant analysis and expandable to other areas such as spectroscopy-based biomedical disease diagnosis and forensic analysis.

Spectroscopic evaluation on pseudopolymorphs of sodium naproxen

This paper demonstrates the assessment of vibrational spectroscopy methods such as middle infrared, near infrared and Raman spectroscopy (FTIR, FT-NIR, Raman) for the identification of pseudopolymorphic forms of a model active pharmaceutical ingredient (API) - sodium naproxen (NpxNa). NpxNa, in the form of three different pseudopolymorphs, was investigated by methods dedicated for solid state characterization: DSC (differential scanning calorimetry), XPRD (powder X-ray diffraction), SEM (scanning electron microscopy) and Karl Fischer titration. Novelty in the results sourced in the usage of the method not applied so far to identify pseudopolymorphic forms of NpxNa, that is, FTIR and FT-NIR. Based on the obtained reproduceable results, various pseudopolymorphic forms were successfully evaluated. Spectroscopic data were correlated with DSC and XPRD results. It was concluded that the combination of band's variations visible on the spectra of pseudopolymorphic forms will allow to interpretate the results unequivocally in case of crucial stability tests of medicinal substance or during on-line pharmaceutical process development by FTIR, FT-NIR and Raman spectroscopy.

The concentrations of immunoglobulins in bovine colostrum determined by the gold standard method are genetically correlated with their near-infrared prediction

Background

The quality of colostrum administered to calves is based on its concentration in immunoglobulins G (IgG, g/L). Immunoglobulins A (IgA) and M (IgM) are also present but at a lower level. The gold standard reference analysis for these traits, radial immunodiffusion, is time-consuming and expensive. In order to define breeding strategies that are aimed at improving colostrum quality in dairy cattle, a large amount of data is needed, and the use of indicator traits would be beneficial. In the study presented here, we explored the heritabilities of reference (radial immunodiffusion) and near infrared-predicted IgG, IgA, and IgM concentrations and estimated their genetic correlations. First, the colostrum of 765 Holstein cows from nine herds was sampled to perform a reference analysis and the near-infrared spectra (400–2500 nm) were stored. We used a calibration set (28% of the initial samples) that was representative of the herds and cow parity orders to develop prediction equations for IgG, IgA, and IgM concentrations. Finally, these traits were predicted in the validation set (72% of the initial samples) to estimate genetic parameters for the predictions. Genetic correlations between reference and predicted values of each trait were estimated through bivariate linear animal models.

Results

The three near-infrared-predicted immunoglobulin fractions were genetically correlated with their reference value. In particular, the reference and predicted IgG concentrations were strongly correlated at both the genetic (0.854 ± 0.314) and phenotypic level (0.767 ± 0.019). Weaker associations were observed for IgA and IgM concentrations, which were predicted with lower accuracy compared to IgG. Simulation analyses suggested that improving colostrum quality by selective breeding in Holstein cattle based on near-infrared predicted colostrum immunoglobulins concentrations is feasible. In addition, less than 10 mL of colostrum are needed for spectra acquisition and thus implementation of such analyses is possible in the near future.

Conclusions

The concentrations of colostrum immunoglobulins can be predicted from near-infrared spectra and the genetic correlation between the reference and the predicted traits is positive and favourable, in spite of the large standard errors of the estimates. Near-infrared spectroscopy can be exploited in selective breeding of dairy cattle to improve colostral immunoglobulins concentration.

A novel spatially resolved interactance spectroscopy system to estimate degree of red coloration in red-fleshed apple

Reliable information about degree of red coloration in fruit flesh is essential for grading and sorting of red-fleshed apples. We propose a spatially resolved interactance spectroscopy approach as a new rapid and non-destructive technique to estimate degree of red coloration in the flesh of a red-fleshed apple cultivar 'Kurenainoyume'. A novel measurement system was developed to obtain spatially resolved interactance spectra (190-1070 nm) for apple fruits at eight different light source-detector separation (SDS) distances on fruit surface. Anthocyanins in apple were extracted using a solvent extraction technique, and their contents were quantified with a spectrophotometer. Partial least squares (PLS) regression analyses were performed to develop estimation models for anthocyanin content from spatially resolved interactance spectra. Results showed that the PLS models based on interactance spectra obtained at different SDS distances achieved different predictive accuracy. Further, the system demonstrated the possibility to detect the degree of red coloration in the flesh at specific depths by identifying an optimal SDS distance. This might contribute to provide a detailed profile of the red coloration (anthocyanins) that is unevenly distributed among different depths of the flesh. This new approach may be potentially applied to grading and sorting systems for red-fleshed apples in fruit industry.

Application of UV dissolution imaging to pharmaceutical systems

UV-vis spectrometry is widely used in the pharmaceutical sciences for compound quantification, alone or in conjunction with separation techniques, due to most drug entities possessing a chromophore absorbing light in the range 190-800 nm. UV dissolution imaging, the scope of this review, generates spatially and temporally resolved absorbance maps by exploiting the UV absorbance of the analyte. This review aims to give an introduction to UV dissolution imaging and its use in the determination of intrinsic dissolution rates and drug release from whole dosage forms. Applications of UV imaging to non-oral formulations have started to emerge and are reviewed together with the possibility of utilizing UV imaging for physical chemical characterisation of drug substances. The benefits of imaging drug diffusion and transport processes are also discussed.

Interpretability of selected variables and performance comparison of variable selection methods in a polyethylene and polypropylene NIR classification task

Near infrared (NIR) spectra are collected as a high amount of absorption values which usually greatly exceeds the sample size. Variable selection methods are employed in NIR spectroscopy to avoid "curse of dimensionality" related issues. In this paper, we examined the interpretability of selected variables, that is, how much selected wavelengths are related to the chemical structure of the materials studied, and if the relation is important for classification performance. Additionally, we examined classification performance in dependence on the number of selected variables. For this purpose, relative standard deviation (RSD), successive projection algorithm (SPA), stepwise decorrelation of variables (SELECT), genetic algorithm (GA), principal component analysis (PCA), and random (RANDOM) variable selection were applied in two-class classification modelling using linear discriminant analysis (LDA) or a support vector machine (SVM). Different pre-treatments and sample sizes were considered. Variable selection improved classification performance and variables selected by a majority of the methods considered were conveniently related to chemical structure. Interpretability and performance increase/decrease depend greatly on the number of selected variables, however. Since selected variables reveal great chemical interpretability, some variable selection methods could be employed to determine material characteristic absorption bands. SELECT and SPA displayed the best properties among the methods considered. To avoid faulty results, optimization of the number of selected variables should become the crucial stage in the variable selection process.

Near-Infrared Hyperspectral Imaging as a Monitoring Tool for On-Demand Manufacturing of Inkjet-Printed Formulations

This study evaluates the potential use of near-infrared hyperspectral imaging (NIR-HSI) for quantitative determination of the drug amount in inkjet-printed dosage forms. We chose metformin hydrochloride as a model active pharmaceutical ingredient (API) and printed it onto gelatin films using a piezoelectric inkjet printing system. An industry-ready NIR-HSI sensor combined with a motorized movable linear stage was applied for spectral acquisition. Initial API-substrate screening revealed best printing results for gelatin films with TiO₂ filling. For calibration of the NIR-HSI system, escalating drug doses were printed on the substrate. After spectral pre-treatments, including standard normal variate (SNV) and Savitzky-Golay filtering for noise reduction and enhancement of spectral features, principal component analysis (PCA) and partial least squares (PLS) regression were applied to create predictive models for the quantification of independent printed metformin hydrochloride samples. It could be shown that the concentration distribution maps provided by the developed HSI models were capable of clustering and predicting the drug dose in the formulations. HSI model prediction showed significant better correlation to the reference (HPLC) compared to on-board monitoring of dispensed volume of the printer. Overall, the results emphasize the capability of NIR-HSI as a fast and non-destructive method for the quantification and quality control of the deposited API in drug-printing applications.

Advances in differentiation and identification of foodborne bacteria using near infrared spectroscopy

Rapid and sensitive detection of foodborne bacteria is a growing concern for ensuring safe food supply and preventing human foodborne infections. It is difficult for conventional methods to meet these detection requirements because they are often tedious and time-consuming. In the recent years, near infrared (NIR) spectroscopy has been found to be a promising method for all sorts of analyses in microbiology due to its highly specific absorption signature and non-destructive measurements. In this review, we first briefly introduce the fundamental and basic operational procedure of NIR spectroscopy for foodborne bacteria detection. Then we summarize the main advances and contributions of this technique in the study of foodborne bacteria. Finally, we conclude that much work still remains to be done before NIR spectroscopy really becomes a viable alternative in the field of microbiological characterization.

Unveiling meloxicam monohydrate process of dehydration by an at-line vibrational multi-spectroscopy approach

Meloxicam (MLX) is a non-steroidal anti-inflammatory drug, extensively used for inflammatory diseases and pain treatments, which exhibits five known solids forms. Form IV of MLX, a zwitterionic monohydrate (MH), is an emblematic hydrate case with promissory dissolution properties in a poorly soluble drug. However, the lack of information about MH stability regarding the dehydration process and phase transition impedes the development of further stability studies. A multi-spectroscopic/chemometric approach was implemented coupling middle- (MIR), near-infrared (NIR) and Raman spectroscopies to monitor the heat-mediated dehydration process of MH. The application of multivariate curve resolution-alternating least squares (MCR-ALS) to multi-source spectra by data fusion allow a complete view of the phenomena, improving the selectivity and precision to establish the transition temperatures and to identify involved species. It was revealed a two-step mechanism, where MH changes to Form V at 90 °C obtaining its complete dehydration at 130 °C, Form V remains unchanged during the temperature range 130-190 °C and then the polymorphic conversion to Form I starts, which reaches 100 % at 230 °C before melting MLX (248 °C). The findings of this work allow set targets in the process control of products using MH. Additionally, MCR-ALS detected an event not evidenced by conventional thermal analysis, the transformation of Form V to Form I.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.

An agile and robust in-line NIR potency deviation detection method for monitoring and control of a continuous direct compression process

Near Infrared (NIR) method for blend potency estimation has been commonly used as an essential tool for process monitoring and control in continuous manufacturing of solid oral dosage forms. Robustness has been the main challenge for successful application of an NIR method, which often results in a long development time with frequent method update. Robustness deficiency often presents as an offset (bias) on the mean potency estimation. In this paper, the purpose of the NIR method has been redefined from estimating potency to potency deviation. This quantitative approach uses the mean centered potency to estimate potency deviations from the process mean, therefore, detects the non-conforming materials for continuous process monitoring and control. An NIR method was developed at the lab benchtop scale and directly deployed to a direct compression continuous manufacturing platform at Pfizer for mean centered potency estimation. The benchtop calibration provided a speedy and efficient NIR method development and the method showed enhanced robustness for estimating potency deviation in presence of wide process and raw material variations. Integrating with the mean centered approach, the NIR model from the lab could be implemented to different sites using different instruments without requiring model update for the established range of process conditions and raw material properties.

Integrating sensors for monitoring blend content in a pharmaceutical continuous manufacturing plant

In a pharmaceutical manufacturing process, Critical Quality Attributes (CQAs) need to be monitored not only for the final product but also for intermediates. Blend uniformity of powders is one such attribute that needs to be measured to ensure the quality of the final product. Multiple in-line sensors were implemented within a Direct Compaction (DC) continuous tablet manufacturing line to monitor the blend content of the powders. In most cases, since the primary ingredient of interest is the active pharmaceutical ingredient (API), the concentration (potency) of the API was monitored/predicted over the course of manufacturing. For the calibration model building process, a unique setup involving dynamic powder spectral acquisition method was used. This setup was aimed at mimicking the powder flow characteristics within the manufacturing line, while at the same time utilizing a relatively small amount of powder. A Raman probe and a portable NIR were used concurrently at the exit of the blending process before the tableting stage. The performance of the two sensors and their respective models were evaluated in terms of accuracy, precision, operating range, measurement frequency, placement, reliability, robustness, and compared to predictions using gravimetric feed rates. Additionally, their performances were validated by off-line traditional analytical measurements.

Characterization of connective tissues using near-infrared spectroscopy and imaging

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.

Performance indicators for a holistic evaluation of catalyst-based degradation-A case study of selected pharmaceuticals and personal care products (PPCPs)

Considerable efforts have been made to develop effective and sustainable catalysts, e.g., carbon-/biochar-based catalyst, for the decontamination of organic pollutants in water/wastewater. Most of the published studies evaluated the catalytic performance mainly upon degradation efficiency of parent compounds; however, comprehensive and field-relevant performance assessment is still in need. This review critically analysed the performance indicators for carbon-/biochar-based catalytic degradation from the perspectives of: (1) degradation of parent compounds, i.e., concentrations, kinetics, reactive oxidative species (ROS) analysis, and residual oxidant concentration; (2) formation of intermediates and by-products, i.e., intermediates analysis, evolution of inorganic ions, and total organic carbon (TOC); and (3) impact assessment of treated samples, i.e., toxicity evolution, disinfection effect, and biodegradability test. Five most frequently detected pharmaceuticals and personal care products (PPCPs) (sulfamethoxazole, carbamazepine, ibuprofen, diclofenac, and acetaminophen) were selected as a case study to articulate the performance indicators for a holistic evaluation of carbon-/biochar-based catalytic degradation. This review also encourages the development of alternative performance indicators to facilitate the rational design of catalysts in future studies.

Underpinning mechanistic understanding of the segregation phenomena of pharmaceutical blends using a near-infrared (NIR) spectrometer embedded segregation tester

The segregation of an active pharmaceutical ingredient (API) within a powder blend is one of the major manufacturing obstacles in achieving content uniformity. Segregation can be due to differences in physicochemical properties of formulation components and/or perturbations experienced during secondary processing steps, such as granulation, fluidization, die-filling and compression. A near-infrared (NIR) spectrometer embedded segregation tester, which could mimic the external stimulations (vibration and fluidization) experienced by a blend in a manufacturing facility, was used to evaluate and predict blend segregation. Two different GlaxoSmithKline (GSK) product blends with variations in the API particle size and concentration were tested. Drug content was further measured at different locations along the powder bed by NIR to sketch the segregation profile and calculate the overall segregation intensity of each blend. The study indicated that the segregation potential was dependent on the particle sizes of API and excipients, as well as the type of stimulus applied (vibration vs fluidization). Drug concentration profiles obtained from this mode of analysis decoded the underlying segregation mechanisms (sieving, trajectory and air elutriation) easily. The employed NIR-based segregation tester proved to be a useful small-scale predictive tool to evaluate and rank the segregation risk of the studied pharmaceutical blends.

Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool

Piperine is a bioactive alkaloid compound which provides a unique spicy flavor derived from plants of the Piper nigrum L. Black pepper (n = 160) collected from Vietnam was studied using non-destructive near infrared spectroscopy (NIRS). The spectral acquisition ranged from 1100 to 2500 nm, and a chemometrics analysis program was performed to quantify the piperine contents. High performance liquid chromatography (HPLC) analysis was carried out to develop a chemometric model based on reference values. The black pepper samples were divided into two groups used for calibration (n = 120) and prediction (n = 40) sets. The optimum calibration model was developed by pretreatment of the spectra. The analyses results based on the prediction samples included a coefficient of determination (R2) of 0.914, a root mean square error of prediction (RMSEP) and a standard error of prediction (SEP) of about 0.220 g/100 g, and a ratio performance to deviation (RPD) value of 3.378 regarding the partial least square (PLS) regression model, and an R2 of 0.921, an RMSEP and SEP of 0.210 g/100 g, and an RPD of 3.571, with respect to the principal components (PC) regression model. These results indicate that NIRS can be applicable as a control, or as an alternative rapid and effective method to quantify piperine in P. nigrum L.

Feasibility of rapid piperine quantification in whole and black pepper using near infrared spectroscopy and chemometrics

Piperine is a bioactive alkaloid that possesses various health benefits and is responsible for the pungent aroma of pepper. Piperine content in whole and ground black pepper (n = 132) was analyzed by near-infrared spectroscopy (NIRS) in the 950 to 1650 nm wavelength window. Chemometric modeling using partial least square regression was performed, and outliers were checked and removed during the preparation of the calibration curve by considering sample residual variance and sample leverage. Model accuracy was evaluated with a low root-mean-square error of cross-validation (RMSECV) and a high ratio performance to deviation (RPD). The optimal model had a coefficient of determination (R² ) of 0.726, RMSECV of 0.289 g/100 g, and RPD of 1.744 for the whole black pepper. The results of R² , RMSECV, and RPD for the ground black pepper were 0.850, 0.231 g/100 g, and 2.424, respectively. Therefore, based on the perspective of onsite process, the proposed NIRS method can be employed for selecting abnormal samples during the inspection of black pepper raw material and for quantifying piperine contents of final black pepper product. PRACTICAL APPLICATION: Generally, the quality indicators of black pepper are graded solely based on their external appearance, quality, and size. This study discloses the development of a near-infrared spectroscopy-based fast and accurate nondestructive analytical method for the detection of piperine, a bioactive constituent of pepper, as a tool for the quality control of whole and ground black pepper.

Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by near-infrared spectroscopy combined with chemometric methods

Pseudostellaria heterophylla is a very popular traditional Chinese medicine herb, also called "Taizishen." Discrimination of P. heterophylla from different regions is critical for ensuring the effectiveness of drug use, because the drug effects of P. heterophylla from different regions are diversity of each other. To discriminate P. heterophylla from different regions rapidly and effectively, a model extracted by competitive adaptive reweighted sampling (CARS) was established. Original spectra of P. heterophylla in wave number range of 10,000 to 4,000 cm^-1 were acquired. Orthogonal partial least squares discriminant analysis (OPLS-DA) was also used to establish a suitable model. CARS was performed for extracting key wave number variables. We found that the near-infrared spectrum of a series of samples analyzed by Row-center-SG, CARS, and OPLS-DA can effectively distinguish the P. heterophylla from different regions, and the accuracy of OPLS-DA model is also satisfactory in terms of good discrimination rate. These results show that the Row-center-SG, CARS, and OPLS-DA model can be used to identify the P. heterophylla from different regions. PRACTICAL APPLICATION: According to our research results, we can draw a conclusion that our research results may be used to distinguish the traditional Chinese medicine from those from different places of origin and the powder with similar appearance.

The detection and quantification of heptafluoroisobutyronitrile(C4F7N) and its decomposition products by infrared spectroscopy and chemometrics

As an excellent alternative gas for sulfur hexafluoride(SF₆), heptafluoroisobutyronitrile(C₄F₇N) has received widespread attention. C₄F₇N gas mixture has the potential to be applied to gas-insulated electrical equipment due to its good insulation properties. Quick and easy quantification of the gas mixture and its decomposition products has great significance. In this paper, the infrared spectroscopy is used to detect the three decomposition products of hexafluoropropene(C₃F₆), carbon monoxide(CO), and carbonyl fluoride(COF₂). Combining chemometrics, a partial least squares(PLS) analysis model of C₄F₇N and its decomposition products is established. Quantitative analysis of CO, C₃F₆ and C₄F₇N is achieved by infrared spectroscopy and chemometrics. The research provides new ideas for the decomposition products detection and future online monitoring of alternative gas-insulated equipment.

A Comparative Approach to Screen the Capability of Raman and Infrared (Mid- and Near-) Spectroscopy for Quantification of Low-Active Pharmaceutical Ingredient Content Solid Dosage Forms: The Case of Alprazolam

Content uniformity is a critical attribute for potent and low-dosage formulations of active pharmaceutical ingredient (API) that, in addition to the formulation parameters, plays pivotal role during pharmaceutical development and production. However, when API content is low, implementing a vibrational spectroscopic analytical tool to monitor the content and blend uniformity remains a challenging task. The aim of this study was to showcase the potentials of mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy for quantitative analysis of alprazolam (ALZ) in a low-content powder blends with lactose, which is used as a common diluent for tablets produced by direct compression. The offered approach might be further scaled up and exploited for potential application in the process analytical technology (PAT). Partial least square and orthogonal PLS (OPLS) methodologies were employed to build the calibration models from raw and processed spectral data (standard normal variate, first and second derivatives). The models were further compared regarding their main statistical indicators: correlation coefficients, predictivity, root mean square error of estimation (RMSEE), and root mean square error of cross-validation (RMSEEcv). All statistical models presented high regression and predictivity coefficients. The RMSEEcv for the optimal models was 1.118, 0.08, and 0.059% for MIR, NIR, and Raman spectroscopy, respectively. The scarce information content extracted from the ALZ NIR spectra and the major band overlapping with those from lactose monohydrate was the main culprit of poor accuracy in the NIR model, whereas the subsampling instrumental setup (resulting in a non-representative spectral acquisition of the sample) was regarded as a main limitation for the MIR-based calibration model. The OPLS models of the Raman spectra of the powder blends manifested favorable statistical indicators for the accuracy of the calibration model, probably due to the distinctive ALZ Raman pattern resulting in the largest number of predictive spectral points that were used for the mathematical modeling. Furthermore, the Raman scattering calibration model was optimized in narrower scanning range (1700-700 cm^-1) and its prediction power was evaluated (root mean square error of prediction, RMSEP = 0.03%). Thus, the Raman spectroscopy presented the most favorable statistical indicators in this comparative study and therefore should be further considered as a PAT for the quantitative determination of ALZ in low-content powder blends.

Quantification of Monomer Units in Insoluble Polymeric Active Pharmaceutical Ingredients Using Solid-State NMR Spectroscopy I: Patiromer

Although extensive precautions are taken to limit batch-to-batch variation in pharmaceutical manufacturing, differences between lots may still exist, particularly in complex formulations. When polymerization is used in the production process, the potential for varying chain lengths and incorporation of different monomers increases the likelihood of batch-to-batch variation. This poses a significant challenge for demonstrating active pharmaceutical ingredient (API) sameness between the innovator and generic drug under development. Therefore, the ability to accurately analyze and quantify the relative amounts of active ingredients present in a formulated product is critically important. Solid-state nuclear magnetic resonance (SSNMR) spectroscopy was used to identify, quantify, and compare the relative amounts of the three polymer groups in the amorphous block copolymer drug, patiromer (Veltassa^®). Techniques such as cross polarization (CP) and magic angle spinning were used to quantify each polymer group while the importance of understanding CP dynamics to obtain quantitative data was also addressed. It was found that the magnetization transfer rate and chemical shift anisotropy for different functional groups present in patiromer play a large role when optimizing parameters for spectral acquisition. Once accounted for, the average patiromer lot contained 90.9%, 7.6%, and 1.5% carboxylate, aromatic, and aliphatic blocks, respectively, with little lot-to-lot variation between different dosage strengths and expiration dates. SSNMR proved to be a sensitive analytical technique for evaluating and quantifying different monomer groups present in patiromer. This procedure may serve as a guide for similar quantitation studies on complex drug products and for demonstrating API sameness during generic drug development.

Geographic Authentication of Eucommia ulmoides Leaves Using Multivariate Analysis and Preliminary Study on the Compositional Response to Environment

To explore the influences of different cultivated areas on the chemical profiles of Eucommia ulmoides leaves (EUL) and rapidly authenticate its geographical origins, 187 samples from 13 provinces in China were systematically investigated using three data fusion strategies (low, mid, and high level) combined with two discrimination model algorithms (partial least squares discrimination analysis; random forest, RF). RF models constructed by high-level data fusion with different modes of different spectral data (Fourier transform near-infrared spectrum and attenuated total reflection Fourier transform mid-infrared spectrum) were most suitable for identifying EULs from different geographical origins. The accuracy rates of calibration and validation set were 92.86% and 93.44%, respectively. In addition, climate parameters were systematically investigated the cluster difference in our study. Some interesting and novel information could be found from the clustering tree diagram of hierarchical cluster analysis. The Xinjiang Autonomous Region (Region 5) located in the high latitude area was the only region in the middle temperate zone of all sample collection areas in which the samples belonged to an individual class no matter their distance in the tree diagram. The samples were from a relatively high elevation in the Shennongjia Forest District in Hubei Province (>1200 m), which is the main difference from the samples from Xiangyang City (78 m). Thus, the sample clusters from region 9 are different from the sample clusters from other regions. The results would provide a reference for further research to those samples from the special cluster.

Near-infrared spectroscopic study of molecular interaction in ethanol-water mixtures

Given the importance of ethanol-water mixtures in many chemical and biological processes, the molecular interaction in ethanol-water binary system was studied using near-infrared (NIR) spectroscopy. Excess spectra (in form of excess absorption coefficient) and Gaussian fitting were applied to analyze low concentration ethanol-water mixtures, ranging from 0 to 10% (v/v). With the knowledge of aquaphotomics, six kinds of water species were identified for 0-10% ethanol-water system, and it was indicated that water can be a sensitive probe for analyzing the structural changes and the interactions in the solutions. The excess spectra and two-dimensional (2D) correlation spectroscopy were introduced for high concentration mixtures (10-100%) analysis and found that the intermolecular hydrogen bonding strength between ethanol and water reaches to the maximum at 40% ethanol concentration which may be related to some abnormal properties of alcoholic solutions reported previously. In 40-100% mixtures, ethanol molecules tend to initiate the self-association which leads to the weakening of the interaction between ethanol and water. This paper not only deepens the understanding of the structure and dynamics of alcoholic solution, but also opens a new perspective in molecular interaction analysis in aqueous system by understanding the roles of water.

Differential extinction of vibrational molecular overtone transitions with gold nanorods and its role in surface enhanced near-IR absorption (SENIRA)

Resonant coupling between plasmonic nanoantennas and molecular vibrational excitations is employed to amplify the weak overtone transitions that reside in the near-infrared. We explore for the first time the differential extinction of forbidden molecular overtone transitions coupled to the localized surface plasmons. We show a non-trivial interplay between the molecular absorption enhancement and suppression of plasmonic absorption in a coupled system. When the resonance conditions are met at 1.5 μm, two orders of magnitude enhancement of differential extinction as compared to the extinction of the same amount of free probe molecules is achieved. Our results pave a road toward a new class of surface enhanced near-infrared absorption-based sensors.

Studies on the influence of β-cyclodextrin derivatives on the physical stability of famotidine

The usage of Fourier transform infrared spectroscopy, near infrared spectroscopy, differential scanning calorimetry and microscopy is presented in this work focused on the exploration of the effect of CD on the physical stability of famotidine (FAM). The most significant information was achieved by analysis of the second derivatives of near infrared (NIR) spectra recorded. Changes in the shape of spectra derivatives allow to observe the improvement of physical stability of FAM with the usage of various supramolecular systems based on β-cyclodextrin (CD) derivatives. The comparison was performed with the usage of maltodextrin in FAM mixtures. We found out that β-cyclodextrin derivatives in the form of physical mixture and inclusion complexes successfully increased the physical stability of famotidine in solid state while maltodextrin does not show positive effect in imparting physical stability to famotidine.