Liquid chromatography coupled with photodiode array and a multivariate curve resolution - Alternating least square algorithm for identification and quantification of co-eluting impurities in pharmaceutical analysis

Investigating the quality of extraction and quantification of bioactive compounds in berries through liquid chromatography and multivariate curve resolution

Berries are a rich source of natural antioxidant compounds, which are essential to profile, as they add to their nutritional value. However, the complexity of the matrix and the structural diversity of these compounds pose challenges in extraction and chromatographic separation. By relying on multivariate curve resolution alternating least squares (MCR-ALS) ability to extract components from complex spectral mixtures, our study evaluates the contributions of various extraction techniques to interference, extractability, and quantifying different groups of overlapping compounds using liquid chromatography diode array detection (LC-DAD) data. Additionally, the combination of these methods extends its applicability to evaluate polyphenol degradation in stored berry smoothies, where evolving factor analysis (EFA) is also used to elucidate degradation products. Results indicate that among the extraction techniques, ultrasonication-assisted extraction employing 1% formic acid in methanol demonstrated superior extractability and selectivity for the different phenolic compound groups, compared with both pressurized liquid extraction and centrifugation of the fresh berry smoothie. Employing MCR-ALS on the LC-DAD data enabled reliable estimation of total amounts of compound classes with high spectral overlaps. Degradation studies revealed significant temperature-dependent effects on anthocyanins, with at least 50% degradation after 7 months of storage at room temperature, while refrigeration and freezing maintained fair stability for at least 12 months. The EFA model estimated phenolic derivatives as the main possible degradation products. These findings enhance the reliability of quantifying polyphenolic compounds and understanding their stability during the storage of berry products.

Application of digital-intelligence technology in the processing of Chinese materia medica

Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.

Application of digital-intelligence technology in the processing of Chinese materia medica

Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.

Estimation of confidence intervals for quantitation of coeluted peaks in liquid chromatography-Photodiode array detection through a combination of multivariate curve resolution-alternating least-square and Bayesian inference techniques

There is a dramatic increase in drug candidates that exhibit complex structures and do not comply with Lipinski's rule of five. One of the most critical and complex technical challenges in the quality control of such drug candidates is the control of analogous substances contained in active pharmaceutical ingredients and related formulations. Although the development of ultrahigh-performance liquid chromatography and high-performance columns has improved efficiency per unit time, the difficulty of peak separation to quantify impurities with similar structures and physicochemical properties continues to rise, and so does the probability of failure to achieve the necessary separation. Coeluting peaks observed in the case of high-performance liquid chromatography (HPLC) with photodiode array detection can be separated using the multivariate curve resolution-alternating least-square (MCR-ALS) method exploiting differences in analyte UV spectra. However, relatively large quantitation errors have been observed for coeluting analogous substances, and the reliability of the corresponding quantitative data requires improvement. Herein, Bayesian inference is applied to separation by the MCR-ALS method to develop an algorithm assigning a confidence interval to the quantitative data of each analogous substance. The usefulness and limitations of this approach are tested using two analogs of telmisartan as models. For this test, a simulated two-component HPLC-UV dataset with an intensity ratio (relative to the main peak) of 0.1-1.0 and a resolution of 0.5-1.0 is used. The developed algorithm allows the prediction confidence interval, including the true value, to be assigned to the peak area in almost all cases, even when the intensity ratio, resolution, and signal-to-noise ratio are changed. Finally, the developed algorithm is also evaluated on a real HPLC-UV dataset to confirm that reasonable prediction confidence intervals including true values are assigned to peak areas. In addition to allowing the separation and quantitation of substances such as impurities challenging to separate by HPLC in a scientifically valid manner, which is impossible for conventional HPLC-UV detection, our method can assign confidence intervals to quantitative data. Therefore, the adopted approach is expected to resolve the issues associated with assessing impurities in the quality control of pharmaceuticals.

Beta-Blocker Separation on Phosphodiester Stationary Phases-The Application of Intelligent Peak Deconvolution Analysis

Beta-blockers are a class of medications predominantly used to manage abnormal heart rhythms. They are also widely used to treat high blood pressure. From the liquid chromatography separation point of view, beta-blockers are interesting molecules due to their hydrophobic-hydrophilic properties. Thus, the study aimed to investigate the beta-blocker separation selectivity on four phosphodiester stationary phases in reversed-phase liquid chromatography (RP LC) and hydrophilic interactions liquid chromatography (HILIC). On tested stationary phases, beta-blockers provide retention in both chromatographic systems, RP LC and HILIC. Additionally, it was found that cation-exchange mechanisms have a significant contribution to retention. Separations were enhanced by applying ChromSword software for gradient optimization and Intelligent Peak Deconvolution Analysis to separate unseparated peaks digitally.