Quantitative analysis of solid dosage forms of cefixime using Raman spectroscopy

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.

Identification and quantification techniques of polymorphic forms - A review

In the pharmaceutical industry, the unexpected appearance of crystalline forms could impact the therapeutic efficacy of an Active Pharmaceutical Ingredient (API). For quality control, a thorough qualitative and quantitative monitoring of pharmaceutical solid forms is essential to ensure the detection and the quantification of crystalline forms, wither different or with the same chemical composition (polymorphs) at a low detection level. The purpose of this paper was to review and highlight the importance of choosing adequate solid-state techniques for detection and quantification APIs that present polymorphism - based on limits of detection (LOD) and quantification (LOQ), pharmacopeias specifications, international guidelines and studies reported in the literature. To this study, the powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), Infrared and Raman spectroscopies and solid-state nuclear magnetic resonance (NMR) were the solid-state techniques analyzed. Additionally, the Argentine, Brazilian, British, European, International, Japanese, Mexican and the United States of America pharmacopeias were reviewed. Based on the analysis performed, the advantages and disadvantages of these techniques, as well as the LOD and LOQ values of APIs were reported. In comparison to these solid-state techniques, reference material used for identification analyses should be previously identified with the corresponding polymorph. Without this previous procedure, the patterns, the spectra, and DSC curves of the reference material can only be used to confirm the mixture of solid forms, not being able to specify which polymorphs are contained in the sample. A major advantage of PXRD is the use of the calculated diffraction patterns obtained from the Crystallographic Information Frameworks (CIFs) files which could be used as a reference pattern without any other information, assistance technique, or physical standards. Regarding the quantification aspect, different pharmacopeias suggest various methods such as the PXRD combining with Rietveld method, which can be used to obtain lower LOD values for minority phases in the mixture of different substances without the need for a calibration curve. Raman spectroscopy can detect polymorphs in small particles and solid-state NMR spectroscopy is a powerful technique for quantification not only crystalline but also crystalline-amorphous mixtures. Finally, this review intends to be a useful tool to control, with efficiency and accuracy, the polymorphism of APIs in pharmaceutical compounds.

Quantitative analysis of cephalexin in solid dosage form by Raman spectroscopy and chemometric tools

OBJECTIVE

To use Raman Spectroscopy for qualitative and quantitative evaluation of pharmaceutical formulations of active pharmaceutical ingredient (API) of Cephalexin.

SIGNIFICANCE

Raman Spectroscopy is a noninvasive, nondestructive, reliable and rapid detection technique used for various pharmaceutical drugs quantification. The present study explores the potential of Raman Spectroscopy for quantitative analysis of pharmaceutical drugs.

METHOD

For qualitative and quantitative analysis of Cephalexin API, various standard samples containing less and more concentration of API than commercial tablet was prepared. To study spectral differences, the mean plot of all the samples was prepared. For qualitative analysis, Principal Component Analysis (PCA) and for quantitative analysis Partial Least Square Regression analysis (PLSR) was used. Both of these are Multivariate data analysis techniques and give reliable results as published in previous literature.

RESULTS

PCA model distinguished all the Raman Spectral data related to the various Cephalexin solid dosage formulations whereas the PLSR model was used to calculate the concentration of different unknown formulations. For the PLSR model, RMSEC and RMSEP were determined to be 3.3953 and 3.8972, respectively. The prediction efficiency of this built PLSR model was found to be very good with a goodness of the model value (R2) of 0.98. The PLSR model also predicted the concentrations of Cephalexin formulations in the blind or unknown sample.

CONCLUSION

These findings demonstrate that the Raman spectroscopy coupled to PLSR analysis could be regarded as a fast and effectively reliable tool for quantitative analysis of pharmaceutical drugs.

Raman Spectroscopy for the Quantitative Analysis of Solid Dosage Forms of the Active Pharmaceutical Ingredient of Febuxostat

Raman spectroscopy has been used to characterize and quantify the solid dosage forms of the commercially available drug febuxostat. For this purpose, different formulations consisting of the febuxostat (API) and excipients with different concentrations of the API are prepared and analyzed by Raman spectroscopy to identify different spectral features related to the febuxostat API and excipients. Multivariate data analysis tools such as principal component analysis (PCA) and partial least-squares regression (PLSR) analysis are used for qualitative and quantitative analyses. PCA has been found to be useful for the qualitative monitoring of various solid dosage forms. PLSR analysis has led to the successful prediction of API concentration in the unknown samples with a sensitivity and a selectivity of 98 and 99%, respectively. Moreover, the root-mean-square error (RMSE) of calibration and validation of the PLSR model has been found to be 2.9033 and 1.35, respectively. Notably, it is found to be very helpful for the comparison between the self-made formulations of febuxostat and commercially available febuxostat tablets (40 and 80 mg) of two different brands (Gouric and Zurig). These results showed that Raman spectroscopy can be a useful and reliable technique for identifying and quantifying the active pharmaceutical ingredient (API) in commercially available solid dosage forms.

Qualitative and Quantitative Analysis of Azithromycin as Solid Dosage by Raman Spectroscopy

Active pharmaceutical ingredients (APIs) and excipients are main drug constituents that ought to be identified qualitatively and quantitatively. Raman spectroscopy is aimed to be an efficient technique for pharmaceutical analysis in solid dosage forms. This technique can successfully be used in terms of qualitative and quantitative analysis of pharmaceutical drugs, their APIs, and excipients. In the proposed research, Raman spectroscopy has been employed to quantify Azithromycin based on its distinctive Raman spectral features by using commercially prepared formulations with altered API concentrations and excipients as well. Along with Raman spectroscopy, principal component analysis and partial least squares regression (PLSR), two multivariate data analysis techniques have been used for the identification and quantification of the API. For PLSR, goodness of fit of the model (R2) was found to be 0.99, whereas root mean square error of calibration was 0.46 and root mean square error of prediction was 2.42, which represent the performance of the model. This study highlights the efficiency of Raman spectroscopy in the field of pharmaceutics by preparing pharmaceutical formulations of any drug to quantify their API and excipients to compensate for the commercially prepared concentrations.

Surface-enhanced Raman spectral characterization of antifungal activity of selenium and zinc based organometallic compounds

Surface-enhanced Raman spectroscopy (SERS) is used to identify the biochemical changes associated with the antifungal activities of selenium and zinc organometallic complexes against Aspergillus niger fungus. These biochemical changes identified in the form of SERS peaks can help to understand the mechanism of action of these antifungal agents which is important for development of new antifungal drugs. The SERS spectral changes indicate the denaturation and conformational changes of proteins and fungal cell wall decomposition in complex exposed fungal samples. The SERS spectra of these organometallic complexes exposed fungi are analyzed by using statistical tools like principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). PCA is employed to differentiate the SERS spectra of fungal samples exposed to ligands and complexes. The PLS-DA discriminated different groups of spectra with 99.8% sensitivity, 100% specificity, 98% accuracy and 86 % area under receiver operating characteristic (AUROC) curve.

Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains

The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.

Quantitative analysis of solid dosage forms of Losartan potassium by Raman spectroscopy

Raman spectroscopy is an outstanding analytical tool increasingly utilized in the pharmaceutical field for the solid-state pharmaceutical drug analysis. In current study, the potential of Raman spectroscopy has been investigated for qualitative and quantitative analysis of solid dosage form of Losartan potassium. For this purpose, different solid dosage forms/concentrations of losartan potassium were prepared to compensate the commercially available pharmaceutical drug formulations and their Raman spectral data showed a gradual change in the specific Raman spectral features associated with the active pharmaceutical ingredient (API) of Losartan potassium as a function of change in the concentration. The Raman spectral data was analyzed by using Principal Component Analysis (PCA) for the classification of different spectral data sets of different concentrations of drug. Moreover, partial least square regression (PLSR) analysis was performed for monitoring the quantitative relation among different concentrations of Losartan potassium API and spectral data by constructing a predictive model. From the model, the value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were observed to be 0.38 and 2.98 respectively and the value of goodness of fit was found to be 0.99. Furthermore, the quantity of unknown/blind sample of Losartan potassium formulation was also estimated by using PLSR model. From these results, it is demonstrated that Raman spectroscopy can be considered to be used for quick and reliable quantitative analysis of pharmaceutical solids.

Raman spectrum classification based on transfer learning by a convolutional neural network: Application to pesticide detection

Pesticide detection is of tremendous importance in agriculture, and Raman spectroscopy/Surface-Enhanced Raman Scattering (SERS) has proven extremely effective as a stand-alone method to detect pesticide residues. Machine learning may be able to automate such detection, but conventional algorithms require a complete database of Raman spectra, which is not feasible. To bypass this problem, the present study describes a transfer learning method that improves the algorithm's accuracy and speed to extract features and classify Raman spectra. The transfer learning model described here was developed through the following steps: (1) the classification model was pre-trained using an open-source Raman spectroscopy database; (2) the feature extraction layer was saved after training; and (3) the training model for the Raman spectroscopy database was re-established while using self-tested pesticides and keeping the feature extraction layer unchanged. Three models were evaluated with or without transfer learning: CNN-1D, Resnet-1D, and Inception-1D, and they have improved the accuracy of spectrum classification by 6%, 2%, and 3%, with reduced training time and increased curve smoothness. These results suggest that transfer learning can improve the feature extraction capability and therefore accuracy of Raman spectroscopy models, expanding the range of Raman-based applications where transfer learning model can be used to identify the spectra of different substances.

Confocal Raman Spectroscopic Imaging for Evaluation of Distribution of Nano-Formulated Hydrophobic Active Cosmetic Ingredients in Hydrophilic Films

Film-forming systems are highly relevant to the topical administration of active ingredients (AI) to the body. Enhanced contact with the skin can increase the efficacy of delivery and penetration during prolonged exposure. However, after the evaporation of volatile solvents to form a thin film, the distribution of the ingredient should remain homogenous in order to ensure the effectiveness of the formula. This is especially critical for the use of hydrophobic molecules that have poor solubility in hydrophilic films. In order to address this concern, hydroxyphenethyl esters (PHE) of Punica granatum seed oil were prepared as a nanosuspension stabilised by poloxamers (NanoPHE). NanoPHE was then added to a formulation containing polyvinyl alcohol (PVA) as a film forming agent, Glycerol as a plasticiser and an antimicrobial agent, SepicideTM HB. Despite their reliability, reference methods such as high-performance liquid chromatography are increasingly challenged due to the need for consumables and solvents, which is contrary to current concerns about green industry in the cosmetics field. Moreover, such methods fail to provide spatially resolved chemical information. In order to investigate the distribution of ingredients in the dried film, Confocal Raman imaging (CRI) coupled to Non-negatively Constrained Least Squares (NCLS) analysis was used. The reconstructed heat maps from a range of films containing systematically varying PHE concentrations highlighted the changes in spectral contribution from each of the ingredients. First, using NCLS scores it was demonstrated that the distributions of PVA, Glycerol, SepicideTM HB and PHE were homogenous, with respective relative standard deviations (RSD) of 3.33%, 2.48%, 2.72% and 6.27%. Second, the respective relationships between ingredient concentrations in the films and their Raman responses, and the spectral abundance were established. Finally, a model for absolute quantification for PHE was be constructed using the percentage of spectral abundance. The prepared %w/w concentrations regressed against predicted %w/w concentrations, displaying high correlation (R2 = 0.995), while the Root Mean Squared Error (0.0869% w/w PHE) confirmed the precision of the analysis. The mean percent relative error of 3.75% indicates the accuracy to which the concentration in dried films could be determined, further supporting the suitability of CRI for analysis of composite solid film matrix. Ultimately, it was demonstrated that nanoformulation of hydrophobic PHE provides homogenous distribution in PVA based film-forming systems independent of the concentration of NanoPHE used in the formula.

Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains

Tigecycline (TGC) is recognised as last resort of drugs against several antibiotic-resistant bacteria. Bacterial resistance to tigecycline due to presence of plasmid-mediated mobile TGC resistance genes (tet X3/X4) has broken another defense line. Therefore, rapid and reproducible detection of tigecycline-resistant E. coli (TREC) is required. The current study is designed for the identification and differentiation of TREC from tigecycline-sensitive E. coli (TSEC) by employing SERS by using Ag NPs as a SERS substrate. The SERS spectral fingerprints of E. coli strains associated directly or indirectly with the development of resistance against tigecycline have been distinguished by comparing SERS spectral data of TSEC strains with each TREC strain. Moreover, the statistical analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed to check the diagnostic potential of SERS for the differentiation among TREC and TSEC strains. The qualitative identification and differentiation between resistant and sensitive strains and among individual strains have been efficiently done by performing both PCA and HCA. The successful discrimination among TREC and TSEC at the strain level is performed by PLS-DA with 98% area under ROC curve, 100% sensitivity, 98.7% specificity and 100% accuracy.

Strategies and formulations of freeze-dried tablets for controlled drug delivery

The freeze-drying process has been particularly attractive for preparing tablets for controlled drug release. Although traditional methods, such as granulation or direct compression methods, have been used in various studies to produce tablets with controlled release, freeze-drying processes have been utilized in certain circumstances due to their distinct advantages. However, overall, further development of these strategies, which started with early studies on orally disintegrating tablets, is still necessary. In this review, the incorporation of different formulations into freeze-dried tablets will be discussed. Moreover, the use of excipients, freeze-drying conditions, formulation reconstitution and tablet structure for optimizing the performance of freeze-dried tablets will be reported, including strategies with nanoformulations and natural materials. Generally, this discussion with potential approaches will benefit further development of freeze-dried tablets containing drugs in the pharmaceutical industry.