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

Objective

This study aims on the one hand to study the physicochemical data of olive oils collected from six regions of Morocco during two consecutive years 2020 and 2021.

Methods

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 in order to reduce the noise, the effect of signal variation and 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) were developed to provide a powerful classification approach.

Results

Based on principal component analysis (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 showed by the high value of CCR varying between 84.09% and 100%.

Conclusion

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.

Highlight

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.

Tracing the Geographical Origin of Moroccan Saffron by Mid-Infrared Spectroscopy and Multivariate Analysis

This work aims to investigate the potential of mid-infrared spectroscopy (MIR) and chemometrics algorithms for the determination of geographical origin and detection of adulteration of Moroccan saffron samples. First, the determination of the geographical origin of five saffron varieties was analyzed by linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). As a result, the developed models correctly classified saffron samples in a subset of external validation with 100% predictive ability. Next, partial least squares regression (PLS-R) was conducted to estimate the amount of adulterants (safflower) in the saffron samples. A good performance was found with Coefficient of Determination (R2) between 0.97 and 0.99. Compared to other techniques, the main advantage of the proposed methods are non-destructive, fast and sensitive which allows to achieve very precise and accurate results.

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.

Exploring Acceptability Drivers of Oral Antibiotics in Children: Findings from an International Observational Study

Antibiotics are among the most commonly prescribed drugs in children. Adherence to the treatment with these drugs is of the utmost importance to prevent the emergence of resistant bacteria, a global health threat. In children, medicine acceptability is likely to have a significant impact on compliance. Herein we used a multivariate approach, considering simultaneously the many aspects of acceptability to explore the drivers of oral antibiotic acceptability in children under twelve, especially in toddlers and in preschoolers. Based on 628 real-life observer reports of the intake of 133 distinct medicines, the acceptability reference framework highlighted the influence of many factors such as age and sex of patients, previous exposure to treatment, place of administration, administration device, flavor agent in excipients and active pharmaceutical ingredient. These findings from an international observational study emphasize the multidimensional nature of acceptability. Therefore, it is crucial to consider all these different aspects for assessing this multi-faceted concept and designing or prescribing a medicine in order to reach adequate acceptability in the target population.

Assessment of a Non-Destructive 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-infrared spectroscopy (MIR) and chemometric models to discriminate eight date palm varieties.

METHODS

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). 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. Partial least-squares 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

Alternative analytical method to discriminate of date palm cultivars by FTIR-ATR spectroscopy coupled with chemometric approaches.

Medicines Acceptability in Hospitalized Children: An Ongoing Need for Age-Appropriate Formulations

Although knowledge on medicine acceptability remains fragmented, this multi-faceted concept has emerged as a key factor for compliance in pediatrics. In order to investigate the acceptability of medicines used in the University Medical Centre Ibn Sina (CHIS) of Rabat, Morocco, an observational study was conducted. Using a multivariate approach integrating the many aspects of acceptability, standardized observer reports were collected for 570 medicine intakes in patients up to the age of 16, then analyzed on a reference framework. Tablets appeared to be well accepted in children greater than 6 years old, but were crushed/dissolved for 90% of the 40 children aged from 3 to 5, and 100% of the 38 patients younger than 3. Moreover, the prescribed dose was fully taken for only 52% and 16% of these younger children, respectively. Despite this, tablets represented 24% of evaluations in children from 3 to 5 and 20% in infants and toddlers. Oral liquid preparations appeared to be better accepted than tablets in preschoolers, but not for those under 3. Overall, these findings highlight the lack of suitable alternatives for the younger children, especially for formulations of antiepileptics, antithrombotic, and psycholeptic agents in the local context.

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.