Voltammetric method for simultaneous determination of ascorbic acid, paracetamol and guaifenesin using a sequential experimentation strategy

Analytical Quality by Design for Chiral Pharmaceuticals: A Robust HPLC Method for Upadacitinib Enantiomeric Quantification

Ensuring the enantiomeric purity of chiral pharmaceuticals is paramount for patient safety and therapeutic efficacy. Upadacitinib (UPA), a vital Janus kinase 1 (JAK-1) inhibitor for rheumatoid arthritis treatment, exemplifies this need. This study represents the development of a robust HPLC method, engineered using analytical quality by design (AQbD), for the simultaneous quantification of UPA and its enantiomeric impurity in pharmaceutical formulations. Our AQbD approach systematically optimized chromatographic separation on a Chiralpak IG column under isocratic elution using n-hexane/ethanol mixture (70:30, v/v) at a flow rate of 1.8 mL/min, UV detection at 230 nm, and a column temperature of 40 °C. Rigorous validation using accuracy profiles confirmed the method suitability. Recognizing the growing imperative for sustainable analytical practices, we further assessed the method environmental impact through comprehensive greenness metrics, while method applicability and sustainability were assessed using Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB12) algorithms, respectively. This innovation empowers pharmaceutical manufacturers with a reliable and sustainable tool to guarantee the quality and regulatory compliance of UPA formulations, ultimately contributing to safer and more effective rheumatoid arthritis therapies.

Voltammetric and flow amperometric determination of drug guaifenesin in pharmaceutical and biological samples using screen-printed sensor with boron doped diamond electrode

New voltammetric and flow amperometric methods for the determination of guaifenesin (GFE) using a perspective screen-printed sensor (SPE) with boron-doped diamond electrode (BDDE) were developed. The electrochemical oxidation of GFE was studied on the surface of the oxygen-terminated BDDE of the sensor. The GFE provided two irreversible anodic signals at a potential of 1.0 and 1.1 V (vs. Ag|AgCl|KCl sat.) in Britton-Robinson buffer (pH 2), which was chosen as the supporting electrolyte for all measurements. First, a voltammetric method based on differential pulse voltammetry was developed and a low detection limit (LOD = 41 nmol L-1), a wide linear dynamic range (LDR = 0.1-155 μmol L-1), and a good recovery in the analysis of model and pharmaceutical samples (RSD <3.0 %) were obtained. In addition, this sensor demonstrated excellent sensitivity and reproducibility in the analysis of biological samples (RSD <3.2 %), where the analysis took place in a drop of serum (50 μL) without pretreatment and additional electrolyte. Subsequently, SP/BDDE was incorporated into a flow-through 3D printed electrochemical cell and a flow injection analysis method with electrochemical detection (FIA-ED) was developed, resulting in excellent analytical parameters (LOD = 86 nmol L-1, LDR = 0.1-50 μmol L-1). Moreover, the mechanism of electrochemical oxidation of GFE was proposed based on calculations of HOMO spatial distribution and spectroelectrochemical measurements focused on IR identification of intermediates and products.

Green highly sensitive and selective spectroscopic detection of guaifenesin in multiple dosage forms and spiked human plasma

Guaifenesin (GUA) is determined in dosage forms and plasma using two methods. The spectrofluorimetric technique relies on the measurement of native fluorescence intensity at 302 nm upon excitation wavelength "223 nm". The method was validated according to ICH and FDA guidelines. A concentration range of 0.1-1.1 μg/mL was used, with limit of detection (LOD) and quantification (LOQ) values 0.03 and 0.08 µg/mL, respectively. This method was used to measure GUA in tablets and plasma, with %recovery of 100.44% ± 0.037 and 101.03% ± 0.751. Furthermore, multivariate chemometric-assisted spectrophotometric methods are used for the determination of GUA, paracetamol (PARA), oxomemazine (OXO), and sodium benzoate (SB) in their lab mixtures. The concentration ranges of 2.0-10.0, 4.0-16.0, 2.0-10.0, and 3.0-10.0 µg/mL for OXO, GUA, PARA, and SB; respectively, were used. LOD and LOQ were 0.33, 0.68, 0.28, and 0.29 µg/mL, and 1.00, 2.06, 0.84, and 0.87 µg/mL for PARA, GUA, OXO, and SB. For the suppository application, the partial least square (PLS) model was used with %recovery 98.49% ± 0.5, 98.51% ± 0.64, 100.21% ± 0.36 & 98.13% ± 0.51, although the multivariate curve resolution alternating least-squares (MCR-ALS) model was used with %recovery 101.39 ± 0.45, 99.19 ± 0.2, 100.24 ± 0.12, and 98.61 ± 0.32 for OXO, GUA, PARA, and SB. Analytical Eco-scale and Analytical Greenness Assessment were used to assess the greenness level of our techniques.

A sustainable model for the simultaneous determination of tadalafil and dapoxetine hydrochloride in their binary mixture: Overcoming the challenges of environmental, operational, and instrumental variability for spectral data

This study employed functional principal component analysis (FPCA) and covariate design of experiments (DoE) to mitigate the susceptibility of chemometric methods to unexpected variations stemming from operational and instrumental factors. A comparative analysis with partial least squares (PLS) revealed that our proposed approach effectively reduced variability across different analysts, days, and instruments. Specifically, FPCA was utilized to compress available spectral wavelength information, while covariate DoE aided in selecting an optimal training set within the experimental space. Subsequently, PLS was applied for the simultaneous determination of tadalafil (TD) and dapoxetine hydrochloride (DP) in their binary mixture. Validation of the proposed method through accuracy profiles demonstrated its reliability, paving the way for its application in pharmaceutical analysis.

A highly sensitive and green electroanalytical method for the determination of favipiravir in pharmaceutical and biological fluids

BACKGROUND

Favipiravir is currently used for the treatment of coronavirus disease-2019 (COVID-19).

OBJECTIVE

A highly sensitive and eco-friendly electroanalytical method was developed to quantify favipiravir.

METHOD

The voltammetric method optimized a sensor composed of reduced graphene oxide / modified carbon paste electrode in the presence of an anionic surfactant, improving the favipiravir detection limit. The investigation reveals that favipiravir-oxidation is a diffusion-controlled irreversible process. The effects of various pH and scan rates on oxidation anodic peak current were investigated.

RESULTS

The developed method offers a wide linear dynamic range of 1.5-420 ng/mL alongside a higher sensitivity with a limit of detection in the nanogram range (0.44 ng/mL) and a limit of quantification in the low nanogram range (1.34 ng/mL).

CONCLUSION

The proposed method was applied for the determination of favipiravir in the dosage form, human plasma and urine samples. The developed method exhibited good selectivity in the presence of two potential electroactive biological interferants, uric acid which increases during favipiravir therapy and the recommended co-administered vitamin C. The organic solvent-free method greenness was evaluated via the Green Analytical Procedure Index, The present work offers a simple, sensitive and environment-friendly method fulfilling green chemistry concepts.

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.