Matrix removal in state of the art sample preparation methods for serum by charged aerosol detection and metabolomics-based LC-MS

Removal of Cu (II) by ion exchange resin and its re-utilization of the residual solution from the distilled Lycium barbarum wine

In order to re-utilize the residual from the distillation of the Chinese wolfberry wine and reduce the environmental pollution, the residual is firstly filtered by the ceramic membrane of 50 nm, then the Cu (II) has transferred from the distillation is removed using the ion exchange resin, and the treated solution is recombined with the distilled liquor to make the Chinese wolfberry brandy and the comparison has conducted on the physicochemical properties, antioxidant activity and flavor compounds between the recombined brandy and the finished brandy. The results indicate that the Cu (II) was effectively removed by ceramic membrane combined with the D401 resin. Compared with finished brandy, the recombined brandy contains high contents of polysaccharides, phenols and flavonoids, thus contributing to the improvement of antioxidant capacity. The gas chromatography-ion mobility spectrometry (GC-IMS) reveals that 25 volatile compounds like esters and alcohols have identified in the brandy samples, and the differences are significant between the recombined and the finished brandy. In summary, the distilled residual from the Chinese wolfberry wine might be re-used after the appropriate treatment so as to reduce the discharge and environmental pollution.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

μPESI-MS/MS System for Screening and Quantitating Drugs in Plasma Samples

Recently, we developed a novel microprobe electrospray ionization (μPESI) source and its coupled MS (μPESI-MS/MS) system. Here, we aimed to widely validate the μPESI-MS/MS method for quantitative analysis of drugs in plasma samples. Furthermore, the relationship between the quantitative performance of the μPESI-MS/MS method and the physicochemical properties of target drugs was analyzed. The μPESI-MS/MS methods for quantitative analysis of 5 representative drugs with a relatively wide range of molecular weight, pK_a, and log P values were developed and validated. The results showed that the linearity, accuracy, and precision of these methods met the requirements of the European Medicines Agency (EMA) guidance. Then a total of 75 drugs from plasma samples were primarily detected using the μPESI-MS/MS methods, among which 48 drugs could be quantitatively measured. Logistics regression suggested that drugs with significantly greater log P and physiological charge had better quantitative performance using the μPESI-MS/MS method. Collectively, these results clearly demonstrate the practical application of the μPESI-MS/MS system as a rapid approach to the quantitative analysis of drugs in plasma samples.

Dermal open flow microperfusion for PK-based clinical bioequivalence studies of topical drug products

Topically applied drug products have experienced an extraordinary price increase in the United States, mostly due to a lack of generic products. Generic drug development is hindered by high costs and risks associated with clinical endpoint studies required to show bioequivalence (BE) of prospective generic products relative to their reference products. There is a continued need for cost- and time-efficient alternatives to clinical endpoint studies to determine BE of topically applied dermal drug products. Cutaneous PK-based BE studies present such an alternative and dOFM (dermal open flow microperfusion) has already been successfully used in several verifications studies to show an accurate and sensitive assessment of the rate and extent at which drugs become available in the skin. dOFM technology is discussed as well as the dOFM setup of clinical pilot and main studies to achieve BE assessment with a minimum number of participants and an outlook is given on the use of dOFM technology for other drug products.

Revealing the inner workings of the power function algorithm in Charged Aerosol Detection: A simple and effective approach to optimizing power function value for quantitative analysis

In recent years, charged aerosol detection (CAD) has become a valuable tool for fast and efficient quantitative chromatographic analysis of drug substances with weak UV absorption. In analytical method development using CAD, the power function settings available in the instrument software are key for linearization of the signal response with respect to analyte concentration. However, the relatively poor understanding of the power function algorithm has limited a more widespread use of CAD for quantitative assays, especially in the late stage of method validation and GMP laboratories. Herein, we present an approach to understand the inner workings of the power function value (PFV), the PFV optimization algorithm, as well as a method to determine the optimum PFV based on the signals acquired at PFV = 1 (default CAD settings). The exponent and the constant in the PFV equation used for modeling follow a trend as a function of PFV. The CAD signal at any PFV was modeled based on the signal acquired at PFV = 1, the modelling was successful for two analytes at different concentration levels on two different CAD detectors of the same model. This method reveals the functionality of the PFV which substantially simplifies the workflow needed to optimize the detector signal. The accuracy between the experimental and theoretical results showed high correlation and always resulted in the same optimum PFV determined by both ways. The approach described in this investigation simplifies the selection of the optimum PFV at which the signal is more linear, the signal-to-noise is higher, and the area reproducibility is better. The power function algorithm elucidated herein enables determination of optimum PFV from minimal experimental output and excellent overall accuracy. This paper provides an approach that includes no data transformation outside the vendor software, a very important requirement to easily validate and report results in a GMP environment.

Evaluating the performance of sample preparation methods for ultra-performance liquid chromatography/mass spectrometry based serum metabonomics

RATIONALE

Metabonomics investigating perturbation to endogenous metabolism in response to external stimuli is emerging as a powerful tool for clinical diagnosis as well as in many other areas. The ability to retrieve reliable and reproducible information from complex biological fluids such as serum is crucial for its further applications.

METHODS

In this study, the performance of the commonly used sample preparation methods for ultra-performance liquid chromatography/mass spectrometry (UPLC/MS)-based metabonomics was investigated. Specifically, we compared the extraction efficiencies, the method reproducibility, and the ability to identify potential biomarkers using solvent-based protein precipitation and solid-phase extraction (SPE) for serum metabonomic studies. Differences between extraction methods were explored using principal component analysis (PCA) and orthogonal partial least squares-discrimination analysis (OPLS-DA).

RESULTS

Among the sample preparation methods tested, solvent-based protein precipitation using methanol has demonstrated the best analytical precision and extraction efficiency. Furthermore, this study revealed, for the first time, gender-specific differences in levels of two lysophosphatidylcholines (lysoPC 18:0 and lysoPC 18:1) in rat serum samples.

CONCLUSIONS

The performance of sample preparation methods for UPLC/MS-based serum metabonomics was evaluated systematically. Results showed sample preparation by solvent precipitation using methanol provided the best analytical precision and extraction efficiency for UPLC/MS-based serum metabonomics.

Development of a sensitive and quantitative UHPLC-MS/MS method to study the whole-body uptake of pharmaceuticals in zebrafish

An analytical procedure to measure the whole-body uptake of pharmaceuticals in zebrafish has been developed using state-of-the-art methodologies. A sample preparation procedure for 9 pharmaceuticals displaying a variety in physicochemical properties was developed using 10-day old zebrafish (TG898). For an efficient homogenization of the samples and subsequent recovery of the compounds of interest, different amounts of organic solvents in combination with acidic modifiers were added to zebrafish samples. Samples were subsequently processed using a powerful bath sonicator and centrifuged. Supernatant was then removed and evaporated in a vacuum oven before being reconstituted in a mobile phase-like solvent. Samples were analyzed using ultra-high performance liquid chromatography (UHPLC) on an Acquity BEH C₁₈ column (100 × 2.1mm, d_p=1.7µm) coupled to a Waters Xevo TQ-S mass spectrometer. For this purpose, a generic gradient was run, wherein the percentage of acetonitrile was varied from 3% to 82% in 10.5min at a flow rate of 0.41mL/min. Linearity of the method was demonstrated for all compounds (R² > 0.997) in a practically relevant concentration range. Matrix effects were between 81% and 106%, except for amitriptyline (51%). Using this method, it was demonstrated that a sample pretreatment using 1:2 (v/v) water:methanol in combination with 0.1% formic acid resulted in acceptable recoveries between 74% and 100% for all compounds. Together with the obtained lower limits of quantification of the analytical method (between 0.005 and 1.5ng/mL), this allowed the use of a single zebrafish to study the whole-body uptake of a particular drug, after incubating zebrafish at the maximum tolerated concentration for this drug.