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This study includes the synthesis of monomethyl auristatin E (MMAE) payload conjugated Trastuzumab (TRA) by an enzyme-mediated glycan-remodelling reaction, followed by comprehensive multilevel characterization of conjugation end products. Intact proteoforms of TRA and TRA-MMAE antibody-drug conjugates (ADC) were separated, quantitated, and identified by microfluidic chip-based ultraviolet imaged channel isoelectric focusing mass spectrometry (icIEF-UV/MS). Isoelectric point and deconvoluted intact mass shifts observed with TRA-MMAE allowed for the determination of the drug-to-antibody ratio (DAR) and detection of trace levels of enzymatic and conjugation intermediates. Parallel analysis by reversed-phase high-performance liquid chromatography (RP-HPLC) peptide mapping with electron-activated dissociation (EAD) fragmentation was also performed. Peptide level results corroborated the putative intact identifications, localized posttranslational modifications (PTMs) on the underivatized TRA structure, and validated site-specific conjugation of the MMAE payload to the Asn-300 attached glycan structure of the ADC. Combined icIEF-UV/MS and RP-HPLC peptide map with EAD fragmentation effectively confirmed the high yield of TRA-MMAE DAR 2 produced by the evaluated chemoenzymatic conjugation reaction. Overall, these results establish a streamlined production and analytical workflow capable of providing well-characterized, highly homogeneous ADC structures for downstream preclinical screening and eventual scale-up.

Most current forensic applications concerning DNA mixtures primarily focus on individual identification, specifically determining the presence of a person of interest (POI). However, when the target individual is unavailable due to death or disappearance, traditional methods often prove inadequate. Our previous exploratory research has demonstrated that integrating Bayesian network algorithms with the novel genetic marker-microhaplotype-typing technology can effectively facilitate forensic DNA mixture analysis across various scenarios. However, it is important to note that short tandem repeat (STR) typing based on capillary electrophoresis (CE) remains the most widely employed method in forensic practice, and the profiling data for the majority of DNA mixed samples are still derived from this established technology. Therefore, further investigation into various scenarios devoid of POIs based on traditional STR genotyping data is warranted. In this study, we undertook an investigation into three scenarios involving DNA mixtures in the absence of POIs, leveraging relatedness information through Bayesian network algorithms. The analyses were based on traditional CE-based STR genotyping data derived from artificially synthesized and simulated mixed samples. The Bayesian network framework offers considerable flexibility, enabling the incorporation of kinship information for various interpretive purposes, including assessing the potential contribution of a missing pedigree member to a DNA mixture and evaluating the relatedness among contributors within or between mixed DNA profiles. The aforementioned research offers a referable experimental framework for addressing complex DNA mixtures within the realm of forensic practice.

Recent advances in membrane protein biochemistry have enabled the isolation of complexes in detergent-free, near-native states using synthetic amphipathic copolymers such as styrene-maleic acid (SMA) and diisobutylene maleic acid (DIBMA). However, these polymers often interfere with conventional protein detection methods, particularly in SDS-PAGE and Clear Native PAGE (CN-PAGE), hindering visualization and quantification. Here, we systematically evaluated 15 staining and detection methods-including Coomassie Brilliant Blue, silver, zinc, copper, Ponceau S, and SYPRO fluorescent dyes-on proteins solubilized by 13 different agents, including detergent n-dodecyl-β-d-maltoside (DDM), five SMA variants, and three DIBMA variants, from bovine heart mitochondria and cyanobacterial thylakoids. A photochemical, stain-free detection method using trichloroethanol (TCE) and UV activation proved to be optimal. This method covalently labels solvent-accessible tryptophan and tyrosine residues, generating robust fluorescence signals that are unaffected by polymer interference. TCE-modified proteins display dual emission peaks at ∼460 nm and a shoulder near 490 nm, likely corresponding to tyrosine and tryptophan adducts, respectively. The polymer-insensitive nature of TCE labeling allows sharp band resolution, particularly for low molecular weight proteins, and is compatible with high-throughput microplate analysis. This approach significantly enhances the qualitative and quantitative assessment of membrane proteins solubilized in polymer nanodiscs, enabling improved detection sensitivity, reduced background, and precise visualization of subunits. By facilitating accurate biochemical characterization of membrane proteins in their native-like lipid environments, this method provides a powerful tool for structural and functional proteomics across diverse biological systems.

This study investigated the synthesis and chiral separation of a series of bio-based amide derivatives. Moreover, the greenness of both separation techniques, that is, high-performance liquid chromatography (HPLC) and supercritical fluid chromatography (SFC), was compared. The synthesis employed a two-step, solvent-free, one-pot procedure at 60°C, yielding esters quantitatively and amides with yields of 31%-70%. Chiral separations were performed on polysaccharide stationary phases under the most optimal green conditions. Using a 70/30 heptane/ethanol mobile phase in HPLC, racemate 13 exhibited the highest resolution (R_s = 11.8) and retention time (72.5 min); however, using a 40% ethanol phase reduced both resolution and retention times. SFC, using a 50/50 CO₂/ethanol mobile phase, provided baseline resolution (R_s = 1.51-9.13) for all analytes and significantly reduced analysis times (maximum retention time of 12.6 min for the second eluting enantiomer of racemate 13). Greenness evaluation was carried out using the AGREE and Analytical Method Greenness Score (AMGS) tools. The AGREE score averaged at 0.55 for HPLC and 0.64 for SFC, indicating a slight preference for SFC due to reduced solvent hazards and waste. The AMGS scores confirmed this trend: HPLC showed an average of 40.9 (up to 128 for racemate 13), whereas SFC averaged 14.9 (maximum 31.1). Instrument energy consumption was the dominant contributor to the AMGS score for both techniques, accounting for over 95% of the score for HPLC and over 93% for SFC. For racemate 13, this proportion increased to 99% and 96%, respectively.

Following the valsartan scandal in 2018, the testing of drug substances and drug products for N-nitrosamines has become a critical and mandatory quality control measure. The European Pharmacopoeia chapter 2.5.42 currently describes three analytical methods for this purpose: HPLC-MS/MS, GC-MS, and GC-MS/MS. The US Pharmacopeia monograph 〈1469〉 adds four other methods, LC-high-resolution mass spectrometry (HRMS), headspace GC-MS, LC-MS/MS, and GC-MS/MS. In addition, our group has developed a universal method on the basis of supercritical fluid chromatography (SFC), capable of separating 16 different N-nitrosamines within just 4 min. These eight methods differ significantly in terms of sustainability, with particular emphasis on the reagents used, the separation techniques employed, and their performance characteristics. When assessing the sustainability of such analytical methods, it is essential to consider not only ecological but also economic factors.

Electrodialysis (ED) is a promising seawater desalination technology using electricity. However, the existing research studies on ED mainly focus on design of electrode materials and device structure. The ED is a multiscale and multi-physical process with multiple influencing parameters. Under these circumstances, the complicated ED process needs to be unified for understanding its physical essence and further optimization. In the current work, a similarity principle-based multiscale model is constructed to analyze ion migration mechanism inside ED device. The multiscale model is developed by correlating cation and anion concentration difference in a mesoscopic nanopore with macroscopic space charge density. On the basis of non-dimensionalization of Poisson-Nernst-Planck equations, the mesoscopic model of ED is unified with three dimensionless variables instead of eight-dimensional input parameters, which can be categorized as representative of ion absorption capability, ion transport characteristic, and nanopore characteristic. Then, the macroscopic model of ED is further unified using 6 dimensionless variables instead of 12-dimensional input parameters, and their physical meaning include ion absorption capability, ion transport characteristic, ion migration driving force, and desalination tank characteristic. The similarity principle of multiscale ED process is verified through nine dimensional different cases with identical dimensionless variables. The dimensionless cation-anion difference in nanopores of mesoscopic model varies within 0.25%, and the dimensionless outlet Na⁺ concentration of macroscopic model changes within 0.05%. Besides, a multi-physical sensitivity analysis is also carried out using the Taguchi method to clarify dominant parameters for ED. The Taguchi sensitivity analysis quantifies parameter contribution to seawater desalination rate in ED as seawater temperature 39.74%, initial ion concentration 15.94%, applied electric potential 15.91%, desalination tank length 11.45%, ion exchange membrane porosity 8.76%, and seawater flow velocity 8.19%. The current work lays a theoretical foundation for developing experimental correlations of ED, and it also contributes to rapid sampling generation in artificial intelligence prediction.

Cyclodextrins (CDs) and their derivatives are among the most versatile and effective pseudophases used for chiral capillary electrophoresis (CE). They provide a variety of interactions that could be further expanded by derivatization. Among the three native CDs used in CE, $\beta$ -d-CD and its derivatives have been the most effective chiral selectors and have shown enantioselectivity for a variety of pharmaceutical analytes. The advent of mirror-image CDs has provided great potential in chiral CE applications. In this study, the chiral separation of 38 analytes was investigated with capillary electrophoresis and the enantiomeric selectivity was compared utilizing $\beta$ -d-CD and $\beta$ -l-CD as CE pseudophases. Additionally, the parameters affecting the CE separation, including pH of the run buffer, capillary voltage, and ionic strength of the buffer, were examined. For the first time, the opposite enantioselectivity of the $\beta$ -l-CD was utilized to facilitate challenges in the quantitation of chiral impurities in pharmaceutical compounds.

Hemoglobin A1c (HbA1c) measurement is crucial for diabetes monitoring. Validation of analytical methods ensures the reliability of results. We verified the analytical performance of HbA1c measurement in each capillary of the Capillarys OCTA3 automated capillary analyzer, following the French accreditation committee guide. For each capillary and each HbA1c level, we evaluated repeatability, intermediate precision, accuracy, robustness, and measurement uncertainty. Results were compared to acceptance limits from French Society of Clinical Biology (SFBC), European Federation of Clinical Chemistry and Laboratory Medicine (EFLM), Réseau d'identification et de Communication en Oncologie et Sénologie (RICOS), and Clinical Laboratory Improvement Amendments (CLIA). A comparison with the high-performance liquid chromatography (HPLC) method was performed on 37 samples using linear regression and Bland-Altman analysis. Repeatability coefficient of variations (CVs) were less than 1.1% for both levels, consistent with supplier and SFBC (3.8%) thresholds. Precision CVs were 1.24% (normal) and 1.3% (pathological), below SFBC and supplier limits but above the strict EFLM standard. Accuracy showed consistent biases between 0% and 1.02%. Method comparison showed acceptable agreement. Expanded uncertainties were 3.10% (normal) and 2.89% (pathological), exceeding EFLM recommendations (1.8%). Westgard and CLIA sigma indices confirmed satisfactory robustness; EFLM indices were lower for some capillaries, limiting Six Sigma use. The OCTA3 analyzer demonstrated excellent analytical performance. Individual capillary verification yielded satisfactory results. Despite uncertainty exceeding EFLM recommendations and variable robustness, this method is reliable for routine clinical use.

Green analytical chemistry (GAC) aims to achieve faster, safer, accurate, and sustainable analysis by minimizing the hazardous impact of organic solvents on the environment. Here, the focus will be on impurity profiling of pharmaceuticals. This can be done by employing green chromatographic techniques using eco-friendly solvents, advanced spectroscopic approaches, microextraction, and miniaturization techniques. Pharmaceutical analysis's greenness can be evaluated using various assessment tools and the advanced analytical greenness metric (AGREE). This review comparatively discusses all these tools by highlighting their eco-friendly nature, methodology, adherence to the 12 major principles of green chemistry, and validation of their results. Furthermore, state-of-the-art case studies utilizing green chemistry principles for impurity profiling of pharmaceuticals have also been discussed, along with their employed techniques and observations, thus offering insights into successful implementation. Still, cost, affordability, scalability, and reproducibility vary across techniques, with, for example, supercritical fluid chromatography (SFC) showing more substantial potential than nuclear magnetic resonance (NMR). Greenness metrics also lack global harmonization, limiting comparability. Challenges like stringent regulatory frameworks and a lack of standardized global policies should be overcome by integrating artificial intelligence (AI) and machine learning (ML), as well as more interdisciplinary collaboration among associated professionals, for the widespread adoption of efficient, sustainable, and eco-friendlier pharmaceutical analysis techniques. For AI/ML, trustworthiness will require validation and regulatory alignment to ensure they complement rather than replace existing protocols.