Fabric phase sorptive extraction combined with high performance liquid chromatography for the determination of favipiravir in human plasma and breast milk

A fast procedure obtained by the combination of fabric phase extraction (FPSE) with high performance liquid chromatography (HPLC) has been developed and validated for the quantification of favipiravir (FVP) in human plasma and breast milk. A sol-gel polycaprolactone-block-polydimethylsiloxane-block-polycaprolactone (sol-gel PCAP-PDMS-PCAP) coated on 100% cellose cotton fabric was selected as the most efficient membrane for FPSE in human plasma and breast milk samples. HPLC-UV analysis were performed using a RP C18 column under isocratic conditions. Under these optimezed settings, the overall chromatographic analysis time was limited to only 5 min without encountering any observable matrix interferences. Following the method validation procedure, the herein assay shows a linear calibration curve over the range of 0.2–50 µg/mL and 0.5–25 µg/mL for plasma and breast milk, respectively. The method sensitivities in terms of limit of detection (LOD) and limit of quantification (LOQ), validated in both the matrices, have been found to be 0.06 and 0.2 µg/mL for plasma and 0.15 and 0.5 µg/mL for milk, respectively. Intraday and interday precision and trueness, accordingly to the International Guidelines, were validated and were below 3.61% for both the matrices. The herein method was further tested on real samples in order to highlight the applicability and the advantage for therapeutic drug monitoring (TDM) applications. To the best of our knowledge, this is the first validated FPSE-HPLC-UV method in human plasma and breast milk for TDM purposes applied on real samples. The validated method provides fast, simple, cost reduced, and sensitive assay for the direct quantification of favipiravir in real biological matrices, also appliyng a well-known rugged and cheap instrument configuration. © 2022 Elsevier B.V.

Analytical approaches for determination of COVID-19 candidate drugs in human biological matrices.

Since the outbreak of the COVID-19 pandemic, the use of antiviral and other available drugs has been considered to combat or reduce the clinical symptoms of patients. In this regard, it would be necessary to choose sensitive and selective analytical techniques for pharmacokinetic and pharmacodynamic studies, monitoring of drug concentration in biological fluids, and determination of the most appropriate dose to achieve the desired effect on the disease. In the present study, the analytical techniques based on spectroscopy and chromatography with different detectors for diagnosis and determination of candidate drugs in the treatment of COVID-19 in human biological fluids are reviewed during the period 2015-2022. Moreover, various sample preparation and extraction techniques, are being used for this purpose, such as protein precipitation (PP), solid-phase extraction (SPE), liquid-liquid extraction (LLE), and QuEChERS (quick, easy, cheap, effective, rugged, and safe) are investigated.

Use of surgical masks for sampling in the determination of volatile organic compounds

In the SARS-COV-2 pandemic the use of masks has been one of the most efficient and extended practice to reduce the infection rate by virus propagation. Due to the ease of use and reduced cost of surgical masks, we have evaluated them for sampling exhaled breath by retention of volatile organic compounds (VOCs). A method based on headspace–solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS–SPME–GC–MS) was developed for the determination of retained compounds in surgical masks used by volunteers during the period recommended by manufacturers. Analysis of results revealed that masks can retain VOCs from exhaled air, but also from the environment by exposition of users. We tentatively identified 63 compounds associated to 10 different chemical families and characterized the intra-individual (from 18 to 160%) and inter-individuals (from 32 to 260%) variability by analysis of masks collected for six days. Finally, we identified markers associated with the intake of products such as coffee and beer, chewing gum, smoking or use of toohpaste. All this suggests that surgical masks could be used as a simple and inexpensive sampling system for the analysis of volatile organic compounds.

Exhaled VOCs can discriminate subjects with COVID-19 from healthy controls.

COVID-19 detection currently relies on testing by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing. However, SARS-CoV-2 is expected to cause significant metabolic changes in infected subjects due to both metabolic requirements for rapid viral replication and host immune responses. Analysis of volatile organic compounds (VOCs) from human breath can detect these metabolic changes and is therefore an alternative to RT-PCR or antigen assays. To identify VOC biomarkers of COVID-19, exhaled breath samples were collected from two sample groups into Tedlar bags: negative COVID-19 (n= 12) and positive COVID-19 symptomatic (n= 14). Next, VOCs were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Subjects with COVID-19 displayed a larger number of VOCs as well as overall higher total concentration of VOCs (p< 0.05). Univariate analyses of qualified endogenous VOCs showed approximately 18% of the VOCs were significantly differentially expressed between the two classes (p< 0.05), with most VOCs upregulated. Machine learning multivariate classification algorithms distinguished COVID-19 subjects with over 95% accuracy. The COVID-19 positive subjects could be differentiated into two distinct subgroups by machine learning classification, but these did not correspond with significant differences in number of symptoms. Next, samples were collected from subjects who had previously donated breath bags while experiencing COVID-19, and subsequently recovered (COVID Recovered subjects (n= 11)). Univariate and multivariate results showed >90% accuracy at identifying these new samples as Control (COVID-19 negative), thereby validating the classification model and demonstrating VOCs dysregulated by COVID are restored to baseline levels upon recovery.

VOC Fingerprints: Metabolomic Signatures of Biothreat Agents with and without Antibiotic Resistance

Category A and B biothreat agents are deemed to be of great concern by the US Centers for Disease Control and Prevention (CDC) and include the bacteria Francisella tularensis, Yersinia pestis, Burkholderia mallei, and Brucella species. Underscored by the impact of the 2020 SARSCoV-2 outbreak, 2016 Zika pandemic, 2014 Ebola outbreak, 2001 anthrax letter attacks, and 1984 Rajneeshee Salmonella attacks, the threat of future epidemics/pandemics and/or terrorist/criminal use of pathogenic organisms warrants continued exploration and development of both classic and alternative methods of detecting biothreat agents. Volatile organic compounds (VOCs) comprise a large and highly diverse group of carbon-based molecules, generally related by their volatility at ambient temperature. Recently, the diagnostic potential of VOCs has been realized, as correlations between the microbial VOC metabolome and specific bacterial pathogens have been identified. Herein, we describe the use of microbial VOC profiles as fingerprints for the identification of biothreat-relevant microbes, and for differentiating between a kanamycin susceptible and resistant strain. Additionally, we demonstrate microbial VOC profiling using a rapid-throughput VOC metabolomics method we refer to as simultaneous multifiber headspace solid-phase microextraction (simultihSPME). Finally, through VOC analysis, we illustrate a rapid non-invasive approach to the diagnosis of BALB/c mice infected with either F. tularensis SCHU S4 or Y. pestis CO92.

Development of a solid phase microextraction gas chromatography tandem mass spectrometry methodology for the analysis of sixty personal care products in hydroalcoholic gels - hand sanitizers - in the context of COVID-19 pandemic.

Because of the coronavirus pandemic, hydroalcoholic gels have become essential products to prevent the spread of COVID-19. This research aims to develop a simple, fast and sustainable microextraction methodology followed by gas chromatography tandem mass spectrometry (GC-MS/MS) to analyze simultaneously 60 personal care products (PCPs) including fragrances allergens, synthetic musks, preservatives and plasticizers in hand sanitizers. Micro-matrix-solid-phase dispersion (µMSPD) and solid-phase microextraction (SPME) were compared with the aim of obtaining high sensitivity and sample throughput. SPME demonstrated higher efficiency being selected as sample treatment. Different dilutions of the sample in ultrapure water were assessed to achieve high sensitivity but, at the same time, to avoid or minimize matrix effect. The most critical parameters affecting SPME (fibre coating, extraction mode and temperature) were optimized by design of experiments (DOE). The method was successfully validated in terms of linearity, precision and accuracy, obtaining recovery values between 80 and 112% for most compounds with relative standard deviation (RSD) values lower than 10%. External calibration using standards prepared in ultrapure water demonstrated suitability due to the absence of matrix effect. Finally, the simple, fast and high throughput method was applied to the analysis of real hydroalcoholic gel samples. Among the 60 target compounds, 39 of them were found, highlighting the high number of fragrance allergens, at concentrations ranging between 0.01 and 217 µg g-1. Most of the samples were not correctly labelled attending cosmetic Regulation (EU) No 1223/2009, and none of them followed the World Health Organization (WHO) recommendation for hand sanitizers formulation.

Response Surface Methodology to Optimize the Isolation of Dominant Volatile Compounds from Monofloral Greek Thyme Honey Using SPME-GC-MS.

This study aimed at an experimental design of response surface methodology (RSM) in the optimization of the dominant volatile fraction of Greek thyme honey using solid-phase microextraction (SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). For this purpose, a multiple response optimization was employed using desirability functions, which demand a search for optimal conditions for a set of responses simultaneously. A test set of eighty thyme honey samples were analyzed under the optimum conditions for validation of the proposed model. The optimized combination of isolation conditions was the temperature (60 °C), equilibration time (15 min), extraction time (30 min), magnetic stirrer speed (700 rpm), sample volume (6 mL), water: honey ratio (1:3 v/w) with total desirability over 0.50. It was found that the magnetic stirrer speed, which has not been evaluated before, had a positive effect, especially in combination with other factors. The above-developed methodology proved to be effective in the optimization of isolation of specific volatile compounds from a difficult matrix, like honey. This study could be a good basis for the development of novel RSM for other monofloral honey samples.