Analytical and Bioanalytical Chemistry最新文献

Water, renowned for its sustainability and minimal toxicity, is an ideal candidate for environmentally friendly solvent-based microextraction. However, its potential as an extractant solvent in miniaturized sample preparation remains largely unexplored. This paper pioneers using water as the extraction solvent in headspace single-drop microextraction (HS-SDME) for N-nitrosamines from losartan tablets. Autonomous HS-SDME is executed by an Arduino-controlled, lab-made Cartesian robot, using water for the online preconcentration of enriched extracts through direct injection into a column-switching system. Critical experimental parameters influencing HS-SDME performance are systematically explored through univariate and multivariate experiments. While most previously reported methods for determining N-nitrosamines in pharmaceutical formulations rely on highly selective mass spectrometry detection techniques to handle the strong matrix effects typical of pharmaceutical samples, the water-based HS-SDME method efficiently eliminates the interfering effects of a large amount of the pharmaceutical active ingredient and tablet excipients, allowing straightforward analysis using high-performance liquid chromatography with ultraviolet detection (HPLC-UV-Vis). Under optimized conditions, the developed method exhibits linear responses from 100 to 2400 ng g^-1, demonstrating appropriate detectability, precision, and accuracy for the proposed application. Additionally, the environmental sustainability of the method is assessed using the AGREEprep methodology, positioning it as an outstanding green alternative for determining hazardous contaminants in pharmaceutical products.

Understanding the relationship between the concentration of a drug and its therapeutic efficacy or side effects is crucial in drug development, especially to understand therapeutic efficacy in central nervous system drug, quantifying drug-induced site-specific changes in the levels of endogenous metabolites, such as neurotransmitters. In recent times, evaluation of quantitative distribution of drugs and endogenous metabolites using matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) has attracted much attention in drug discovery research. However, MALDI-MSI quantification (quantitative mass spectrometry imaging, QMSI) is an emerging technique, and needs to be further developed for practicable and convenient use in drug discovery research. In this study, we developed a reliable QMSI method for quantification of clozapine (antipsychotic drug) and dopamine and its metabolites in the rat brain using MALDI-MSI. An improved mimetic tissue model using powdered frozen tissue for QMSI was established as an alternative method, enabling the accurate quantification of clozapine levels in the rat brain. Furthermore, we used the improved method to evaluate drug-induced fluctuations in the concentrations of dopamine and its metabolites. This method can quantitatively evaluate drug localization in the brain and drug-induced changes in the concentration of endogenous metabolites, demonstrating the usefulness of QMSI.

In recent decades, the compositions of preen oil and feathers have been studied to achieve insights into the chemistry of avian odours, which play a significant role in birds' social behaviour. Fewer studies are available regarding volatiles originating from other sources, such as faeces, eggs or a bird's whole body. The aims of this study were (i) to identify odour-active and further volatile compounds in zebra finch whole body odour and (ii) to semi-quantify selected volatiles and use the information to evaluate two different adsorbents for their suitability for whole body odour sampling. Volatiles from the headspace above zebra finches were sampled using an open loop system equipped with either activated charcoal or Tenax® TA. Samples were analysed by olfactory-guided approaches as well as gas chromatography-mass spectrometry. Using activated charcoal as sorbent, 26 odour-active and 73 further volatile compounds were detected, whereas with Tenax® TA 27 odour-active and 81 further volatile compounds were detected. In total, 104 compounds were (tentatively) identified, of which 22 had not been identified previously in zebra finch odour and 12 had not been described in any birds. Hints towards a chemical sex signature became evident for qualitative but not for quantitative differences. With the exception of some compounds, notably carboxylic acids and alkanes, relative peak areas obtained with the two adsorbent types were comparable. The approach described herein is proposed for future studies aiming to determine volatiles emitted by birds when, for example, parent birds are approaching the nest.

This study introduces an innovative approach for the real-time and efficient detection of alkaline phosphatase (ALP) activity, using a calcein fluorescence probe and leveraging the static quenching properties of calcein fluorescence by Ce³⁺ metal ions. In this method, calcein serves as the signal element, with its fluorescence effectively preserved through energy transfer or charge transfer when coordinated with Ce³⁺. Conversely, ALP catalyzes the phosphopeptide substrate to generate a substantial amount of Pi, preventing calcein fluorescence quenching due to the higher affinity between Pi and Ce³⁺ compared with that between calcein and Ce³⁺. The fluorescence intensity ratio (F-F₀/F₀) exhibited excellent linearity, facilitating sensitive ALP detection. The proposed ALP detection method covers a range from 0 to 1.4 mU/mL (R² = 0.9942), with the limit of detection at 0.069 mU/mL (S/N = 3). Additionally, this method was successfully applied for detecting ALP in serum samples and studying its inhibitors. This research introduces a novel clinical diagnosis approach for ALP sensing while broadening the potential applications of calcein.

In recent years, the use of lipid nanoparticles (LNPs) for delivery of messenger RNA (mRNA)-based therapies has gained substantial attention in the field of drug development. In such an application, multiple LNP attributes have to be carefully characterized to ensure product safety and quality, whereas accurate and efficient characterization of these complex mRNA-LNP formulations remains a challenging endeavor. Here, we present the development and application of an online separation and characterization platform designed for the isolation and in-depth analysis of mRNAs and mRNA-loaded LNPs. Our asymmetrical flow field-flow fractionation with a multi-detector (MD-AF4) method has demonstrated exceptional resolution between mRNA-LNPs and mRNAs, delivering excellent recoveries (over 70%) for both analytes and exceptional repeatability. Notably, this platform allows for comprehensive and multi-attribute LNP characterization, including online particle sizing, morphology characterization, and determination of encapsulation efficiency, all within a single injection. Furthermore, real-time online sizing by synchronizing multi-angle light scattering (MALS) and dynamic light scattering (DLS) presented higher resolution over traditional batch-mode DLS, particularly in differentiating heterogeneous samples with a low abundance of large-sized particles. Additionally, our method proves to be a valuable tool for monitoring LNP stability under varying stress conditions. Our work introduces a robust and versatile analytical platform using MD-AF4 that not only efficiently provides multi-attribute characterizations of mRNA-LNPs but also holds promise in advancing studies related to formulation screening, quality control, and stability assessment in the evolving field of nanoparticle delivery systems for mRNAs.

Currently, research in the development of high-performance sensing platforms has increased to fulfill the needs of analysis and detection. In this study, we developed a novel type of surface-enhanced Raman scattering (SERS) chip composed of a covalent organic framework (COF)-silver nanoparticles (AgNPs) nanocomposite, and this nanocomposite was fabricated by a one-step method of ultrasonically mixing the obtained COF and AgNPs. The fabricated chip exhibited high sensitivity and repeatable SERS effects. Practical application results showed that the chip was highly sensitive and reliable and capable of detecting DNA bases (adenine) to fit an equation in the range from 0.01 pM to 1 nM, with an R-square of 0.97253 and a detection limit of ~0.026 pM (signal-to-noise ratio (S/N) = 3). Therefore, the proposed SERS system has potential applications in biological assays.

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X–y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Graphical Abstract

"Purple Drank", a soft drink containing promethazine (PMZ) and codeine (COD), has gained global popularity for its hallucinogenic effects. Consuming large amounts of this combination can lead to potentially fatal events. The binding of these drugs to plasma proteins can exacerbate the issue by increasing the risk of drug interactions, side effects, and/or toxicity. Herein, the binding affinity to human serum albumin (HSA) of PMZ and its primary metabolites [N-desmethyl promethazine (DMPMZ) and promethazine sulphoxide (PMZSO)], along with COD, was investigated by high-performance affinity chromatography (HPAC) though zonal approach. PMZ and its metabolites exhibited a notable binding affinity for HSA (%b values higher than 80%), while COD exhibited a %b value of 65%. To discern the specific sites of HSA to which these compounds were bound, displacement experiments were performed using warfarin and (S)-ibuprofen as probes for sites I and II, respectively, which revealed that all analytes were bound to both sites. Molecular docking studies corroborated the experimental results, reinforcing the insights gained from the empirical data. The in silico data also suggested that competition between PMZ and its metabolites with COD can occur in both sites of HSA, but mainly in site II. As the target compounds are chiral, the enantioselectivity for HSA binding was also explored, showing that the binding for these compounds was not enantioselective.

We present a novel approach for iodide sensing based on the heavy-atom effect to quench the green fluorescent emission of organosilicon nanoparticles (OSiNPs). The fluorescence of OSiNPs was significantly quenched (up to 97.4% quenching efficiency) in the presence of iodide ions (I^-) through oxidation by hydrogen peroxide. Therefore, OSiNPs can serve as a fluorescent probe to detect I^- with high selectivity and sensitivity. The highly selective response is attributed to the hydrophilic surface enabling good dispersion in aqueous solutions and the lipophilic core allowing the generated liposoluble I₂ to approach and quench the fluorescence of OSiNPs. The linear working range for I^- was from 0 to 50 μM, with a detection limit of 0.1 μM. We successfully applied this nanosensor to determine iodine content in edible salt. Furthermore, the fluorescent OSiNPs can be utilized for the determination of total antioxidant capacity (TAC). Antioxidants reduce I₂ to I^-, and the extent of quenching by the remaining I₂ on the OSiNPs indicates the TAC level. The responses to ascorbic acid, pyrogallic acid, and glutathione were investigated, and the detection limit for ascorbic acid was as low as 0.03 μM. It was applied to the determination of TAC in ascorbic acid tablets and fruit juices, indicating the potential application of the OSiNP-based I₂ sensing technique in the field of food analysis.

Currently, there is a significant demand in forensic toxicology for biomarkers of cannabis exposure that, unlike ∆⁹-tetrahydrocannabinol, can reliably indicate time and frequency of use, be sampled with relative ease, and correlate with impairment. Oral fluid (OF) and exhaled breath condensate (EBC) are alternative, non-invasive sample matrices that hold promise for identifying cannabis exposure biomarkers. OF, produced by salivary glands, is increasingly utilized in drug screening due to its non-invasive collection and is being explored as an alternative matrix for cannabinoid analysis. EBC is an aqueous specimen consisting of condensed water vapor containing water-soluble volatile and non-volatile components present in exhaled breath. Despite potential advantages, there are no reports on the use of EBC for cannabinoid detection. This study developed a supported liquid extraction approach and LC-QqQ-MS dMRM analytical method for quantification of 25 major and minor cannabinoids and metabolites in OF and EBC. The method was validated according to the ANSI/ASB 036 standard and other published guidelines. LOQ ranged from 0.5 to 6.0 ng/mL for all cannabinoids in both matrices. Recoveries for most analytes were 60-90%, with generally higher values for EBC compared to OF. Matrix effects were observed with some cannabinoids, with effects mitigated by use of matrix-matched calibration. Bias and precision were within ± 25%. Method applicability was demonstrated by analyzing ten authentic OF and EBC samples, with positive detections of multiple analytes in both matrices. The method will facilitate comprehensive analysis of cannabinoids in non-invasive sample matrices for the development of reliable cannabis exposure biomarkers.