Why should the pharmaceutical industry claim for the implementation of second-order chemometric models-A critical review

Chemometric-assisted kinetic determination of oxytetracycline using AgInS2 quantum dots as PL sensing platforms

In this work a kinetic fluorometric methodology relying on the time-based monitoring of the photoluminescence quenching of AgInS₂ ternary quantum dots induced by oxytetracycline, was developed. The kinetic approach allowed not only to reduce the LOD and improve sensitivity and selectivity but also to collect second-order data that was explored for the quantification of the target analyte in the presence of uncalibrated interfering species. Upon processing the acquired second-order kinetic PL data by unfolded partial least-squares (U-PLS), oxytetracycline was quantified in commercially available pharmaceutical formulations. The obtained results, namely an R²_P higher than 0.99 and RE lower than 8%, proved the suitability and accuracy of the developed approach.

Liquid Chromatography Analysis of Reactive Oxoammonium Cations

This study presents the first liquid chromatography method for the quantitative and qualitative analysis of highly reactive oxoammonium cations based on a simple derivatization reaction. Rapid 1,2-electrophilic addition reactions with olefins were used to transform these reactive species into analyzable derivates. Three model substances were chosen to represent each of the main application fields of oxoammonium cations and to demonstrate the versatility of the method. The measuring protocol was validated according to the ICH and USP guidelines. The method revealed an excellent linearity (R2 = 0.9980–0.9990) with a low limit of detection (0.16–0.14 mmol L−1) and a low limit of quantification (0.55–0.43 mmol L−1). The protocol was finally used to determine the oxoammonium cations in the presence of their corresponding radical, showing a robustness against impurity concentration of up to approx. 30%.

Three-way calibration using PARAFAC and MCR-ALS with previous synchronization of second-order chromatographic data through a new functional alignment of pure vectors for the quantification in the presence of retention time shifts in peak position and shape

In the present contribution is shown the application of the recently developed functional alignment of pure vectors (FAPV) as a proper algorithm to align second-order chromatographic data with severe retention time shifts in peak position and shape. FAPV decomposed a three-way chromatographic data array in their three modes (sample, spectral and elution time vectors), using a basis function to pre-process the non-linear mode (elution time) and then it aligns the functionalized pure vectors and reshapes the transformed vectors into matrices, restoring the trilinearity of second-order chromatographic data. The well-aligned three-way chromatographic data array is then successfully decomposed by advanced chemometric models such as parallel factor analysis (PARAFAC) and multivariate curve resolution - alternating least-squares (MCR-ALS) with the trilinearity constraint. The performance of this innovative analytical strategy based on PARAFAC and MCR-ALS with previous synchronization of data through FAPV algorithm is properly evaluated using real second-order chromatographic data with multiple artifacts, i.e., shifts in peak position and shape for the simultaneous quantification of amoxicillin and potassium clavulanate in commercial medicinal drugs. The present contribution compares some analytical results achieved by: (1) the usual MCR-ALS as a bilinear model applied in augmented data matrix without previous synchronization and with interval correlation optimized shifting (ICOSHIFT) and FAPV and (2) trilinear models using PARAFAC with ICOSHIFT and FAPV and trilinearity constraint in MCR-ALS with FAPV. Available results suggest that these strongly shifted and warped elution time profiles cause for the loss of trilinearity, which can be adequately restored by FAPV algorithm. PARAFAC performed a successful trilinear decomposition of three-way chromatographic data array with law values of relative prediction error (REP) in the order of 1.34-1.42% in both analytes.

Current challenges in the detection and analysis of falsified medicines

Falsified medicines affect public health all around the globe. Complex distribution routes, illegal online webshops and reuse of packaging materials make them hard to detect. In order to tackle this problem, detection methods for the recognition of suspicious medicines and subsequent confirmation of falsification by analytical techniques is required. In this review, we focus on the developments and challenges that existed in the last five years (2015-2020) in the detection and analysis of falsified medicines. These challenges might have not been solved yet or arisen with new types of falsifications, new analytical techniques or detection strategies. Detection of suspicious medicines starts with visual inspection of packaging materials. However, re-use of packaging materials and high-quality imitations complicate visual inspection. Recent developments in the analysis of packaging by microscopic and spectroscopic techniques such as optical microscopy, X-ray fluorescence, infrared spectroscopy and Raman spectroscopy or microscopy, in combination with multivariate analysis show promising results in the detection of falsified medicines. An ongoing big challenge in the analysis of falsified medicines is the affordability of analytical devices. Yet, recent reports showed that lower cost devices, such as Counterfeit Drug Indicator or Counterfeit Detection device version 3 show promising use in the detection of falsified medicines. Furthermore, combining the outcomes of different low-cost analytical techniques, such as Minilab, colorimetry and Counterfeit Drug Indicator significantly increased selectivity and sensitivity in the detection of falsified medicines. Also, recent developments make it possible to link a low-cost technique, such as TLC, to mobile phones. Proper training of personnel has shown room for improvement and remains a challenge, even for relatively simple techniques. With an increased use of analytical fingerprints, an upcoming challenge is the accessibility of the growing pool of data. There is also the need of validated reference libraries on both national and international levels. Developments of the last few years bring us a step closer in the fight against falsified medicines, however challenges remain in the worldwide accessibility of affordable, easily operable and sensitive techniques.

Eco-friendly multivariate curve resolution-alternating least squares and chromatographic quantifications of some veterinary drug residues in pharmaceutical industrial wastewater

Three eco-friendly and cost-effective analytical methods were developed and optimized for quantitative analysis of some veterinary drug residues in production wastewater samples. The studied drugs were ivermectin, rafoxanide and sulfadimidine. A solid-phase extraction procedure was employed using Bond Elut C18 cartridges, prior to analysis. The first method was a chemometric approach called multivariate curve resolution - alternating least squares (MCR-ALS). A calibration model was developed and several figures of merit (RMSEP, SEP, bias, RE%) were calculated. The second method was a thin layer chromatography followed by densitometric measurements at 245 nm. The separation was performed using silica gel 60 F254 plates and ethyl acetate : acetonitrile : toluene : ammonia (20 : 3 : 2 : 1, by volume) as a developing system. The third method was a high performance liquid chromatographic separation on HiQsil C18 HS column with UV detection at 245 nm. The mobile phase consisted of acetonitrile : methanol : water (60 : 25 : 15, by volume), with a flow rate of 1.5 mL min-1. The proposed methods were validated according to ICH guidelines. The described procedures were applied to quantify the studied drug residues in synthetic and real industrial wastewater samples. The proposed methods were statistically compared with the official and the reported methods, showing no significant difference with respect to accuracy and precision at P = 0.05.

An Advanced Statistical Approach Using Weighted Linear Regression in Electroanalytical Method Development for Epinephrine, Uric Acid and Ascorbic Acid Determination

In this study, the use of weighted linear regression in the development of electrochemical methods for the determination of epinephrine (EP), ascorbic acid (AA), and uric acid (UA) is presented. The measurements were performed using a glassy carbon electrode and square-wave voltammetry (SWV). All electroanalytical methods were validated by determination of the limit of detection, limit of quantification, linear concentration range, accuracy, and precision. The normal distribution of all data sets was checked using the quantile-quantile plot and Kolmogorov-Smirnov statistical tests. The heteroscedasticity of the data was tested using Hartley's test, Bartlett's test, Cochran's C test, and the analysis of residuals. The heteroscedastic behavior was observed with all analytes, justifying the use of weighted linear regression. Six different weighting factors were tested, and the best weighted model was determined using relative percentage error. Such statistical approach improved the regression models by giving greater weight on the values with the smallest error and vice versa. Consequently, accuracy of the analytical results (especially in the lower concentration range) was improved. All methods were successfully used for the determination of these analytes in real samples: EP in an epinephrine auto-injector, AA in a dietary supplement, and UA in human urine. The accuracy and precision of real sample analysis using best weighted model gave satisfactory results with recoveries between 95.21-113.23% and relative standard deviations between 0.85-7.98%. The SWV measurement takes about 40 s, which makes the presented methods for the determination of EP, AA, and UA a promising alternative to chromatographic techniques in terms of speed, analysis, and equipment costs, as the analysis is performed without organic solvents.

Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II)

This work focused on the combination of CdTe and AgInS₂ quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS₂ combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R²_P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7-9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.