Two-Dimensional High-Performance Liquid Chromatography as a Powerful Tool for Bioanalysis: The Paradigm of Antibiotics

A technique based on infrared spectroscopy for determining sulfanilamide levels sustainably: Progress and comparisons of greenness and whiteness using ComplexGAPI, AGREE, and RGB

The study aimed to determine the potential of the infrared (IR) spectrophotometric technique for measuring the content of sulphanilamide with the sulfonamide group. The study aimed to obtain the IR spectra of sulfanilamide and use the -SO2 band at 1114.37 for the quantitative assay, determining its area under the curve (AUC). The study gives an alternative approach to existing analytical techniques that require vast amounts of organic solvents, which are costly and can be toxic, thus impacting the environment and increasing the analysis cost. The study evaluated the method's whiteness and greenness by utilizing the Complex green analytical procedure index, analytical GREEness calculator and Red Green Blue algorithm tool. The linierity was found to be 5 to 30 µg/ml. The present study has developed an infrared (IR) spectroscopic method that employs a straightforward sample preparation technique in methanol. The IR spectroscopic method's linearity range was determined to be 5-30 µg/ml. The p-value was 0.001 at 95 % confidence level assuring better recovery. This method is evaluated according to the Q2R1 ICH guideline. It is applicable to routine quality control analysis without pre-extraction using green IR spectroscopy. In conclusion, the study demonstrated that IR spectrophotometric techniques can quantify sulfanilamide while reducing the use of organic solvents, contributing to the green-and-white analytical chemistry approach. The developed methods are reliable, accurate, and cost-effective and have the potential to be implemented in routine analysis of sulfanilamide.

Two-dimensional liquid chromatography measurement of meropenem concentration in synovial fluid of patients with periprosthetic joint infection

Periprosthetic joint infection (PJI) is a catastrophic complication following joint replacement surgery. One potential treatment approach for PJI could be the combination of one-stage revision and intra-articular infusion of antibiotics. Meropenem is one of the commonly used intra-articular antibiotics in our institution. Determining the concentration of meropenem in the joint cavity could be crucial for optimizing its local application, effectively eradicating biofilm infection, and improving PJI treatment outcomes. In this study, we developed a simple, precise, and accurate method of two-dimensional liquid chromatography (2D-LC) for determining the concentration of meropenem in human synovial fluid. The method was then validated based on the guidelines of the Food and Drug Administration and the Chinese Pharmacopoeia. Meropenem showed good linearity in the range of 0.31-25.01 μg/mL (r ≥ .999). Selectivity, intra-day and inter-day precision and accuracy, extraction recovery, and stability validation results were all within the acceptance range. This method has been successfully applied to the determination of synovial fluid samples from PJI patients, providing a useful detection method for meropenem therapeutic drug monitoring (TDM) in PJI patients.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.