Toxicokinetics of a urinary metabolite of tebuconazole following controlled oral and dermal administration in human volunteers

Oerlemans, Arné; Verscheijden, Laurens F. M.; Mol, J.G.J.; Vermeulen, Roel; Westerhout, Joost; Roeleveld, Nel; Rüssel, Frans G. M.; Scheepers, Paul T.J.

doi:10.1007/s00204-019-02523-5

Cited by 22 publications

(6 citation statements)

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“…An exposure assessment strategy was developed to include personal sampling, environmental sampling, and the collection of contextual information. Additional experimental studies were conducted to generate complementary information on methods of urine collection from non–toilet trained infants [ 27 ], the toxicokinetics of human metabolites of pesticides [ 28 ], as well as experimental applications to better understand pesticide spray drift and volatilization. The study design is shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Following full conformation, dedicated methods for analysis of each biomarker were developed and validated, and all individual samples from each of the volunteers were analyzed. In this way, data on toxicokinetics were generated and conversion factors were derived [ 28 ]. The conversion factors were used to estimate pesticide uptake (Module 7) based on measured urinary biomarker concentrations (Module 2).…”

Section: Methodsmentioning

confidence: 99%

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Pesticide Exposure of Residents Living Close to Agricultural Fields in the Netherlands: Protocol for an Observational Study

Figueiredo¹,

Krop²,

Duyzer³

et al. 2021

JMIR Res Protoc

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Background Application of pesticides in the vicinity of homes has caused concern regarding possible health effects in residents living nearby. However, the high spatiotemporal variation of pesticide levels and lack of knowledge regarding the contribution of exposure routes greatly complicates exposure assessment approaches. Objective The objective of this paper was to describe the study protocol of a large exposure survey in the Netherlands assessing pesticide exposure of residents living close (<250 m) to agricultural fields; to better understand possible routes of exposure; to develop an integrative exposure model for residential exposure; and to describe lessons learned. Methods We performed an observational study involving residents living in the vicinity of agricultural fields and residents living more than 500 m away from any agricultural fields (control subjects). Residential exposures were measured both during a pesticide use period after a specific application and during the nonuse period for 7 and 2 days, respectively. We collected environmental samples (outdoor and indoor air, dust, and garden and field soils) and personal samples (urine and hand wipes). We also collected data on spraying applications as well as on home characteristics, participants' demographics, and food habits via questionnaires and diaries. Environmental samples were analyzed for 46 prioritized pesticides. Urine samples were analyzed for biomarkers of a subset of 5 pesticides. Alongside the field study, and by taking spray events and environmental data into account, we developed a modeling framework to estimate environmental exposure of residents to pesticides. Results Our study was conducted between 2016 and 2019. We assessed 96 homes and 192 participants, including 7 growers and 28 control subjects. We followed 14 pesticide applications, applying 20 active ingredients. We collected 4416 samples: 1018 air, 445 dust (224 vacuumed floor, 221 doormat), 265 soil (238 garden, 27 fields), 2485 urine, 112 hand wipes, and 91 tank mixtures. Conclusions To our knowledge, this is the first study on residents’ exposure to pesticides addressing all major nondietary exposure sources and routes (air, soil, dust). Our protocol provides insights on used sampling techniques, the wealth of data collected, developed methods, modeling framework, and lessons learned. Resources and data are open for future collaborations on this important topic. International Registered Report Identifier (IRRID) RR1-10.2196/27883

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Pesticide Exposure of Residents Living Close to Agricultural Fields in the Netherlands: Protocol for an Observational Study

Figueiredo¹,

Krop²,

Duyzer³

et al. 2021

JMIR Res Protoc

Self Cite

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“…The percentage of excretion of tebuconazole is 1% in urine and faces with CF of 115.4. For tebuconazole metabolite, the percentage of excretion is 27% and CF of 3.6 (Fustinoni et al, 2014;Oerlemans et al, 2019). Unfortunately, no data was available regarding the excretion of the unchanged drug for clotrimazole and epoxiconazole.…”

Section: Estimation Of Community Wide Daily Intake Of Afsmentioning

confidence: 99%

“…Moreover, there was no information found in literature on prochloraz, propiconazole and terbinafine excretion in urine or faces. (Fustinoni et al, 2014;Oerlemans et al, 2019) Terbinafine ---urine (human) No unchanged terbinafine was excreted in the urine (Jensen, 2009) N-desmethylcarboxyterbinafine 53.8 1.98 urine (human) (Humbert et al, 1998) Voriconazole Voriconazole 2.0 50.0 urine (human) (Levêque et al, 2006) WBE AF intake (PNDI) estimates as well as AF per-capita NHS per-postcode prescription (PNDP) are presented in Figures 7 and 8. Average WBE-estimated fluconazole PNDIs were 330 ± 125, 627 ± 1231, 309 ± 12, 163 ± 40 and 8 ± 2 mg day -1 1000inh -1 , and prescription PNDPs were 33, 6, 18, 19, and 12 mg day -1 1000inh -1 for city A, B, C and D. Similarly, average WBE-estimated ketoconazole PNDIs were 3001 ± 891 and 2145 ± 682 mg day -1 1000inh -1 , and prescription PNDPs were 74 and 69 mg day -1 1000inh -1 for city B and D. It is apparent that PNDIs are much higher than PNDPs.…”

Section: Estimation Of Community Wide Daily Intake Of Afsmentioning

confidence: 99%

An integrated One Health framework for holistic evaluation of risks from antifungal agents in a large-scale multi-city study using post-acquisition mass spectral data mining and wastewater-based epidemiology pipelines

Kasprzyk-Hordern

Proctor

Jagadeesan

et al. 2023

Preprint

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A new analytical framework for retrospective mass spectral data mining for antifungal (AFs) agents was developed as part of One Health framework for holistic evaluation of risks from AFs. Post-acquisition data mining of mass spectral repository obtained with a maXis HD QToF mass spectrometer, retrospective quantification and confirmation using bbCID method was undertaken. A large scale, multi-city spatiotemporal profiling programme of antifungal agents was undertaken in the Avon River catchment, South-West England encompassing five contrasting towns/cities served by five major wastewater treatment plants (WWTPs) covering a large geographical area of 2000 km2 and a population of ~1.5 million accounting for > 75% of the overall population in the studied catchment. Key drivers of AFs in the catchment were identified with communal WWTP discharges being the main driver for human AFs (e.g., fluconazole and ketoconazole) and agricultural runoff being the main driver for pesticide AFs (e.g., prochloraz, prothioconazole and tebuconazole). Daily mass loads (DLs) of AF were study/city dependent with several instances of large quantities of AFs (direct disposal) observed. Wastewater-Based Epidemiology (WBE) pipelines were developed to estimate community-wide exposure to antifungal agents and to triangulate the WBE data with per-postcode prescription. Average WBE-estimated human used fluconazole and ketoconazole PNDIs (population normalised daily intake per person) exceeded on average 300 mg day-1 1000inh-1 and 2000 mg day-1 1000inh-1. This is much higher than PNDPs (population normalised daily prescriptions) <40 mg day-1 1000inh-1 and <80 mg day-1 1000inh-1 for fluconazole and ketoconazole respectively. This was expected due to both prescription and OTC (over-the-counter) usage as well as both oral and topical (non-metabolic) application. WBE applied to pesticide-based AFs: prothioconazole and tebuconazole provided variable PNDI estimates across the catchment. Prothioconazole PNDIs were <40,000 mg day-1 1000inh -1, which gave intake per kg of body weight at: 0.43, 0.26, 0.07 mg kg-1 in City A, B and C and is likely due to accounting for external/non-human sources. This is higher than the acceptable daily intake (ADI) set at 0.01 mg kg-1bw day-1, which warrants further study. Intake per kg of body weight estimated using tebuconazole as a xenobiotic chemical residue (XCR) was: 0.86, 1.39, 0.12, 0.13 and 2.7 mg kg-1 in City A-E respectively and is likely due to accounting for external/non-human sources. Intake calculated using the metabolite is in line with ADI: 0.02 and 0.01 mg kg-1 in City B and C respectively, which aligns well with ADI set at 0.03 mg kg-1bw day-1. The environmental risk assessment of the target AAs indicates low/medium risk from fluconazole, prochloraz and tebuconazole, medium risk from epoxiconazole, prothioconazoles metabolite, and tebuconazole, and high risk for prothioconazole in river water. High risk was estimated from fluconazole, epoxiconazole, prothioconazole and its metabolite, tebuconazole, ketoconazole in influent (and/or effluent) samples, which is important due to possible raw sewage discharge via sewer overflows. Due to lack of information on PNEC (predicted no-effect concentration) for other AFs, risk assessment could not be undertaken, which warrants further study.

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“…Urine collection methods for non-toilet-trained children in biological monitoring studies: Validation of a disposable diaper for characterization of tebuconazole exposure. toxicology letters, 298, 201-206. https://doi.org/10.1016/j.toxlet.2018.09.018 oerlemans, a., Verscheijden, l. F. M., Mol, J. G. J.,Vermeulen, R. C. H., Westerhout, J., Roeleveld, n., … Scheepers, P. t. J. (2019).…”

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confidence: 99%

Measured and modelled environmental exposure of residents to pesticides

Figueiredo¹

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Most people, if not all, will be exposed at least once in their life-time to a certain level of pesticide(s). For non-occupationally exposed groups, a great portion of this exposure can be attributed to dietary intake and the remaining to environmental exposure. In the Netherlands, the application of pesticides on agricultural fields has raised concerns from residents living close to these. In order to understand if a certain disease is related to an exposure to any given chemical, we first need to identify the exposed vs non-exposed population and also understand the levels, duration and frequency of exposure. The aim of my thesis is on investigating residents’ environmental exposure to pesticides by studying drivers of this exposure as well as temporal and spatial variability in different pesticides concentration. Chapter 2 details the methodology used to study residential exposure to pesticides and a list of (practical) lessons learned. Chapter 3 described the spatial and temporal variation of 46 different pesticide concentrations in air outside and inside homes. Here, we saw that most pesticide concentrations were 5–10 times higher in outdoor air during the spraying period when comparing with the non-spraying period. Concentrations were overall higher closer to the fields (<250 m) then further away. Environmental exposure to pesticides via air may occur year-round. Chapter 4 focused on the indoor dust matrix. House dust that was collected from vacuuming floors (VFD) and from newly placed clean doormats (DDM). Pesticides were found in indoor dust of all homes included in our study and we found that people living close to fields were exposed year-round to pesticides in house dust. Chapter 5 studied personal exposure in the residential setting. Handwipes and urine samples were collected in parallel. Concentrations in hand wipes were overall higher during the spraying period when compared to the non-spraying period and higher in farmer families compared to non-farmer families. Chapters 6 developed a deterministic model framework (OBOmod) to assess exposure of residents living near fields. This framework describes levels in the environment from the moment pesticides are sprayed until they end up in the air and house dust. The model evaluation shows that, for most model outputs, the framework can be used in support of i) health and epidemiological studies, and ii) regulations and policy making. Chapters 7 reviewed the findings from each chapter and discussed on new evidence, comparison with previous works and future research paths. My recommendation for further improving residential exposure assessment to pesticides can be summarized in four points: 1) Perform research in areas where sideways or upward spraying techniques are used; 2) Focus more on pesticide levels in house dust, as it can be especially important in exposure of more vulnerable populations, like toddlers; 3) Improve the modelling framework, by means of more verification and making it accessible to all; 4) Studies on mixtures in relation to possible adverse health effects are needed. Particularly, for pesticides with the same mode of action and frequently detected in our samples.

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Toxicokinetics of a urinary metabolite of tebuconazole following controlled oral and dermal administration in human volunteers

Cited by 22 publications

References 18 publications

Pesticide Exposure of Residents Living Close to Agricultural Fields in the Netherlands: Protocol for an Observational Study

Pesticide Exposure of Residents Living Close to Agricultural Fields in the Netherlands: Protocol for an Observational Study

An integrated One Health framework for holistic evaluation of risks from antifungal agents in a large-scale multi-city study using post-acquisition mass spectral data mining and wastewater-based epidemiology pipelines

Measured and modelled environmental exposure of residents to pesticides

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