Saliva is valuable in exposure assessment having been successfully used for drug and environmental pollutant detection, providing a surrogate measure of plasma concentrations. Pyrethroid biomarkers have not previously been assessed in saliva, although are prime candidates for saliva detection. This study's objectives were to 1) develop a liquid-liquid extraction (LLE) method to quantify six pyrethroid metabolites using gas chromatography/ion trap mass spectrometry and 2) assess its application for an occupationally exposed population. Several solvents and mixing protocols were optimized for metabolite recovery. The optimized method was applied to a population of pest control operators (PCOs) and compared against a urine sample before and after a full workday using pesticides. A questionnaire collected demographic information, occupational history, and occupational and non-occupational exposure data. LLE recoveries ranged from 85% - 104% and 72% - 88% for toluene and dichloromethane using slow mixing, and 49% - 103% for methyl tert-butyl ether by fast mixing. Urinary 3-Phenoxybenzoic acid (3PBA) was detected in 100% of pre- and post-work urine samples. Three PCOs had increased urinary pyrethroid metabolite levels post-work. Salivary 3PBA was present below detection limit in two of the three PCO's post-work saliva samples, demonstrating that salivary 3PBA could be measured in PCOs after the workday. This study presents preliminary findings of a potential, low-risk biomonitoring technique that may be utilized in future occupational pyrethroid exposure and risk assessment research.
Keyphrases
- risk assessment
- gas chromatography
- mass spectrometry
- aedes aegypti
- human health
- polycystic ovary syndrome
- loop mediated isothermal amplification
- tandem mass spectrometry
- ms ms
- real time pcr
- label free
- liquid chromatography
- high resolution mass spectrometry
- ionic liquid
- heavy metals
- adipose tissue
- zika virus
- high resolution
- big data
- capillary electrophoresis
- deep learning
- sensitive detection
- data analysis