Smart Multivariate Spectrophotometric Assisted Techniques for Simultaneous Determination of Ephedrine Hydrochloride and Naphazoline Nitrate in the Presence of Interfering Parabens

Application of smart chemometric models for spectra resolution and determination of challenging multi-action quaternary mixture: statistical comparison with greenness assessment

A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories.

Smart chemometrics-assisted spectrophotometric methods for efficient resolution and simultaneous determination of paracetamol, caffeine, drotaverine HCl along with three of their corresponding related impurities

Three novel, simple and accurate multivariate spectrophotometric assisted mathematical techniques were developed for determination of paracetamol, caffeine, drotaverine HCl and their related impurities. The used multivariate algorithms are principal component regression (PCR), partial least squares (PLS), and synergy intervals partial least squares (siPLS). Linearity of the suggested methods was found to be (1.00-14.60, 1.40-7.00, 1.40-3.80, 1.00-3.00, 1.50-3.50 and 2.50-4.50 µg/mL) for paracetamol, caffeine, drotaverine HCl, and their related impurities; p-aminophenol, theophylline and homoveratric acid, correspondingly. The presented methods were effectively implemented in the determination of the cited compounds in their laboratory prepared mixtures. Commercially available tablet preparation was also analyzed using the applied methods where no impurities were detected and without interference from tablet additives. Moreover, statistical analysis did not reveal any noticeable differences between the obtained results and those acquired from the reported method in terms of accuracy and precision. The developed multivariate algorithms were validated by means of internal and external validation sets. The obtained results showed the siPLS algorithm's superiority to PCR and PLS according to the values of correlation coefficient values (r) and the lowest root mean square error of prediction (RMSEP). The combination of four subintervals [10, 12, 14, and 17] produced the highest efficiency model. Furthermore, these methods may be an applicable substitute to HPLC ones in quality control laboratories during rush of analyses where several samples have to be analyzed in a short time.

Chemometric quality assessment of Paracetamol and Phenylephrine Hydrochloride with Paracetamol impurities; comparative UV-spectrophotometric implementation of four predictive models

Spectrophotometric data analysis using multivariate approaches has many useful applications. One of these applications is the analysis of active ingredients in presence of impurities. Four chemometric-assisted spectrophotometric methods, namely, principal component regression (PCR), partial least-squares (PLS), artificial neural networks (ANN) and multivariate curve resolution-alternating least squares (MCR-ALS) were proposed and validated. The developed chemometric methods were compared to resolve the severely overlapped spectrum of Paracetamol (PAR) and Phenylephrine HCl (PHE) along with PAR impurities namely, P-Aminophenol (PAP), P-Nitrophenol (PNP), Acetanilide (ACT) and P-Chloroacetanilide (CAC). The four multivariate calibration methods succeeded in simultaneous determination of PAR and PHE with further quantification of PAR impurities. So, the proposed methods could be used with no need of any separation step and successfully applied for pharmaceutical formulation analysis. Furthermore, statistical comparison between the results obtained by the proposed chemometric methods and the official ones showed no significant differences.

Coupling of Trastuzumab chromatographic profiling with machine learning tools: A complementary approach for biosimilarity and stability assessment

Biosimilar products present a growing opportunity to improve the global healthcare systems. The amount of accepted variability during the comparative assessments of biosimilar products introduces a significant challenge for both the biosimilar developers and the regulatory authorities. The aim of this study was to explore unsupervised machine learning tools as a mathematical aid for the interpretation and visualization of such comparability under control and stress conditions using data extracted from high throughput analytical techniques. For this purpose, a head-to-head analysis of the physicochemical characteristics of three Trastuzumab (TTZ) approved biosimilars and the originator product (Herceptin®) was performed. The studied quality attributes included the primary structure and identity by peptide mapping (PM) with reversed-phase chromatography-UV detection, size and charge profiles by stability-indicating size exclusion and cation exchange chromatography. Stress conditions involved pH and thermal stress. Principal component analysis (PCA) and two of the widely used cluster analysis tools, namely, K-means and Density-based Spatial Clustering of Applications with Noise (DBSCAN), were explored for clustering and feature representation of varied analytical datasets. It has been shown that the clustering patterns delineated by the used algorithms changed based on the included chromatographic profiles. The applied data analysis tools were found effective in revealing patterns of similarity and variability between i) intact and stressed as well as ii) originator and biosimilar samples.

Chemometric-enhanced metabolic profiling of five Pinus species using HPLC-MS/MS spectrometry: Correlation to in vitro anti-aging, anti-Alzheimer and antidiabetic activities

Detailed metabolic profiling of needles of five Pinus species was investigated using complementary HPLC-MS/MS techniques together with supervised and unsupervised chemometric tools. This resulted in putative identification of 44 compounds belonging to flavonoids, phenolics, lignans, diterpenes and fatty acids. Unsupervised principal component analysis showed that differences were maintained across the metabolites characteristic of each Pinus species, are mainly related to di-O-p-coumaroyltrifolin, p-coumaroyl quinic acid derivative, arachidonic acid, hydroxypalmitic acid, isopimaric acid and its derivative. A supervised Partial Least Squares regression analysis was performed to correlate HPLC-MS/MS profiles with the variation observed in the in vitro anticholinesterase, antiaging and anti-diabetic potential. All investigated Pinus extracts exerted their antiaging activity via increasing telomerase and TERT levels in normal human melanocytes cells compared to the control (untreated cells). Profound inhibition activities of acetylcholinesterase and dipeptidyl peptidase-4 were also observed with P. pinea and P. canariensis extracts having comparable antidiabetic activities to sitagliptin as a standard antidiabetic drug. Our findings suggested that pine needles are a good source of phenolics and diterpenoids that have possible health promoting activities in management and alleviation of diabetic conditions and Alzheimer disease.