Unveiling Impurities: A Comprehensive RP-HPLC Method for Accurate Atorvastatin Impurity Analysis in Pharmaceuticals Using Accuracy Profile Approach.

BACKGROUND: Accurate determination of active pharmaceutical ingredients and impurities is essential for ensuring the safety and effectiveness of medications. This study focuses on the validation of a high-performance liquid chromatography (HPLC) method for quantifying Atorvastatin and its impurities, addressing a critical aspect of pharmaceutical analysis. OBJECTIVE: The primary objective is to conduct a comprehensive validation study for the HPLC method, covering specificity assessment, response function establishment, and a detailed analysis of precision, trueness, and tolerance intervals. The emphasis is on demonstrating the method's precision, accuracy, and stability-indicating capabilities across various concentrations and compounds. METHODS: The HPLC method is validated through rigorous assessments, including specificity, response function establishment, and analyses of precision, trueness, and tolerance intervals. Induced degradation experiments are conducted to explore Atorvastatin's behavior under extreme conditions. Insights into the compound's synthesis and degradation pathways are provided through a proposed mechanism for intramolecular esterification. RESULTS: The results affirm the precision, accuracy, and stability-indicating capabilities of the validated HPLC method. The method effectively differentiates between Atorvastatin and its impurities, showcasing its suitability for pharmaceutical quality control. CONCLUSION: The validated HPLC method emerges as a robust and reliable tool for Atorvastatin analysis, contributing significantly to pharmaceutical research and quality control. Its application ensures the safety and efficacy of medications, reinforcing its role in pharmaceutical analysis. HIGHLIGHTS: This study not only validates a crucial HPLC method for Atorvastatin analysis but also provides insights into the compound's behavior under extreme conditions and its synthesis and degradation pathways. The validated method serves as a cornerstone in pharmaceutical research and quality control, ensuring medication safety and efficacy.

Validation of an HPLC Method for the Determination of Diclofenac Diethylamine and Three of Its Impurities in a Gel Pharmaceutical Form.

BACKGROUND: Monitoring impurities in drug products is a principal requirement of pharmaceutical regulatory authorities all over the world to ensure drug safety. For this reason, there is a great need for analytical QC of dugs products. OBJECTIVE: In this study, a simple, efficient, and direct HPLC method was developed for the determination of three impurities of diclofenac. METHODS: The HPLC method was developed using a mobile phase which consisted of an HPLC grade mixture, acetonitrile-0.01M phosphoric acid adjusted to pH 2.3 (1 + 3, by volume). RESULTS: The separation was performed in 15 min. The calibration curves of the three impurities were linear; the correlation coefficients were 0.999 at concentrations of 0.00015-0.003 µg/mL. CONCLUSION: The validation of this method shows that it meets all validation criteria. This shows the reliability of this method for the routine control of diclofenac impurities. HIGHLIGHTS: The validation of a robust HPLC method for the determination of diclofenac impurities is of great importance for the pharmaceutical industry to control its products.

A Novel Approach for Therapeutic Drug Monitoring of Valproic Acid Using FT-IR Spectroscopy and Nonlinear Support Vector Regression.

BACKGROUND: Recent technological progress has bolstered efforts to bring personalized medicine from theory into clinical practice. However, progress in areas such as therapeutic drug monitoring (TDM) has remained somewhat stagnant. In drugs with well-known dose-response relationships, TDM can enhance patient outcomes and reduce health care costs. Traditional monitoring methods such as chromatography-based or immunoassay techniques are limited by their higher costs and slow turnaround times, making them unsuitable for real-time or onsite analysis. OBJECTIVE: In this work, we propose the use of a fast, direct, and simple approach using Fourier transform infrared spectroscopy (FT-IR) combined with chemometric techniques for the therapeutic monitoring of valproic acid (VPA). METHOD: In this context, a database of FT-IR spectra was constructed from human plasma samples containing various concentrations of VPA; these samples were characterized by the reference method (immunoassay technique) to determine the VPA contents. The FT-IR spectra were processed by two chemometric regression methods: partial least-squares regression (PLS) and support vector regression (SVR). RESULTS: The results provide good evidence for the effectiveness of the combination of FT-IR spectroscopy and SVR modeling for estimating VPA in human plasma. SVR models showed better predictive abilities than PLS models in terms of root-mean-square error of calibration and prediction RMSEC, RMSEP, R2Cal, R2Pred, and residual predictive deviation (RPD). CONCLUSIONS: This analytical tool offers potential for real-time TDM in the clinical setting. HIGHLIGHTS: FTIR spectroscopy was evaluated for the first time to predict VPA in human plasma for TDM. Two regressions were evaluated to predict VPA in human plasma, and the best-performing model was obtained using nonlinear SVR.

A Preliminary Study on the Potential of FT-IR Spectroscopy and Chemometrics for Tracing the Geographical Origin of Moroccan Virgin Olive Oils.

BACKGROUND: The authentication of the geographical origin of virgin olive oils (VOO) generally requires the use of sophisticated and time-consuming analytical techniques. There is a need for quick and simple analytical techniques to predict the origin of olive oils. OBJECTIVE: This study aims to examine the physico-chemical data of olive oils collected in six regions of Morocco during two consecutive years 2020 and 2021, and also to evaluate the ability of FT-IR in combination with discrimination tools to study the geographical origin of Moroccan olive oils. METHOD: Fourier transform infrared spectroscopy (FTIR) was used in this study as an emerging analytical technique to express a unique "fingerprint." A preliminary processing of the ATR-FTIR spectral data was performed by preprocessing algorithm to reduce the noise and the effect of signal variation as well as to minimize the effects of light scattering to extract the maximum analytical information from the spectra. A multivariate statistical procedure based on principal component analysis (PCA) coupled with linear discriminant analysis (LDA) as well as partial least-squares discriminant analysis (PLS-DA) was developed to provide a powerful classification approach. RESULTS: Based on the PCA, six clusters were identified. The application of PCA-LDA and PLS-DA procedures demonstrate a powerful capacity in predicting the geographic origin of olive oils; this capacity is shown by the high value of correct classification rate (CCR), varying between 84.09 and 100%. CONCLUSIONS: The suggested procedure has given reliable results for the classification of olive oils according to their geographical origin, with advantages such as being fast, inexpensive, and not requiring any prior separation process. HIGHLIGHTS: The performance of this approach is significantly faster and possesses a higher degree of selectivity and sensitivity. The implementation of this technique for routine analysis of olive oil would save significant time, resources, and solvents.

Comparative study of three fingerprint analytical approaches based on spectroscopic sensors and chemometrics for the detection and quantification of argan oil adulteration.

BACKGROUND: Argan oil is one of the purest and rarest oils in the world, so that the addition of any further product is strictly prohibited by international regulations. Consequently, it is necessary to establish reliable analytical methods to ensure its authenticity. In this study, three multivariate approaches have been developed and validated using fluorescence, UV-visible, and attenuated total reflectance Fourier transform mid-infrared (FT-MIR) spectroscopies. RESULTS: The application of a partial least squares discriminant analysis model showed an accuracy of 100%. The quantification of adulteration have been evaluated using partial least squares (PLS) regression. The PLS model developed from fluorescence spectroscopy provided the best results for the calibration and cross-validation sets, as it showed the highest R2 (0.99) and the lowest root mean square error of calibration and cross-validation (0.55, 0.79). The external validation of the three multivariate approaches by the accuracy profile shows that these approaches guarantee reliable and valid results of 0.5-32%, 7-32%, and 10-32% using fluorescence, FT-MIR and UV-visible spectroscopies respectively. CONCLUSION: This study confirmed the feasibility of using spectroscopic sensors (routine technique) for rapid determination of argan oil falsification. © 2021 Society of Chemical Industry.

Chemometric Analysis of UV-Visible Spectral Fingerprints for the Discrimination and Quantification of Clinical Anthracycline Drug Preparation Used in Oncology.

In clinical treatment, the analytical quality assessment of the delivery of chemotherapeutic preparations is required to guarantee the patient's safety regarding the dose and most importantly the appropriate anticancer drug. On its own, the development of rapid analytical methods allowing both qualitative and quantitative control of the formulation of prepared solutions could significantly enhance the hospital's workflow, reducing costs, and potentially providing optimal patient care. UV-visible spectroscopy is a nondestructive, fast, and economical technique for molecular characterization of samples. A discrimination and quantification study of three chemotherapeutic drugs doxorubicin, daunorubicin, and epirubicin was conducted, using clinically relevant concentration ranges prepared in 0.9% NaCl solutions. The application of the partial least square discriminant analysis PLS-DA method on the UV-visible spectral data shows a perfect discrimination of the three drugs with a sensitivity and specificity of 100%. The use of partial least square regression PLS shows high quantification performance of these molecules in solution represented by the low value of root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSCECV) on the one hand and the high value of R-square on the other hand. This study demonstrated the viability of UV-visible fingerprinting (routine approach) coupled with chemometric tools for the classification and quantification of chemotherapeutic drugs during clinical preparation.

Assessment of a Nondestructive Method for Rapid Discrimination of Moroccan Date Palm Varieties via Mid-Infrared Spectroscopy Combined with Chemometric Models.

BACKGROUND: Morocco is an important world producer and consumer of several varieties of date palm. In fact, the discrimination between varieties remains difficult and requires the use of complex and high-cost techniques. OBJECTIVE: We evaluated in this work the potential of mid-IR (MIR) spectroscopy and chemometric models to discriminate eight date palm varieties. METHOD: Four chemometric models were applied for the analysis of the spectral data, including principal-component analysis (PCA), support-vector machine discriminant analysis (SVM-DA), linear discriminant analysis (LDA), and partial-least-squares (PLS) analysis. MIR spectroscopic data were recorded from the wavenumber range 4000-600 cm-1, with a spectral resolution of 4 cm-1. RESULTS: The discriminant analysis was performed by LDA and SVM-DA with a 100% correct classification rate for the date mesocarp. PLS analysis was applied as a complementary chemometric tool aimed at quantifying moisture content; the validation of this model shows a good predictive capacity with a regression coefficient of 84% and a root-mean-square error of cross-validation of 0.50. CONCLUSIONS: The present study clearly demonstrates that MIR spectroscopy combined with chemometric approaches constitutes a promising analytical method to classify date palms according to their varietal origin and to establish a regression model for predicting moisture content. HIGHLIGHTS: An alternative analytical method to discriminate date palm cultivars by FTIR-attenuated total reflection spectroscopy coupled with chemometric approaches is described.

Rapid Analytical Method to Characterize the Freshness of Olive Oils Using Fluorescence Spectroscopy and Chemometric Algorithms.

One of the most important issues in the field of quality assurance of olive oils is the detection of the freshness of olive oil. In this study, 400 nm laser-induced fluorescence spectroscopy was used with supervised and unsupervised multivariate analysis methods to develop a rapid method able to discriminate between freshly produced olive oils and oil that has been stored for a period of time ranging from 12 to 24 months. The fluorescence spectral data were firstly processed by the PCA. This method shows strong discrimination of the three oil classes using the first three components which present 96% of the total variability of the initial data, and then supervised classification models were constructed using the discriminant partial least square regression PLS-DA, support vector machine SVM, and linear discriminant analysis LDA. These methods show a high capacity in the classification of the three classes of olive oil. The validation of these classification models by external samples shows a high capacity of classification of the samples in their class with an accuracy of 100%. This study demonstrated the feasibility of the fluorescence spectroscopy fingerprint (routine technique) for the classification of olive oils according to their freshness and storage time.

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools.

In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000-600 cm-1, with a spectral resolution of 4 cm-1. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R 2 greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.