Modeling dermal exposure - OEESC keynote talk

  • 2,628 views
Uploaded on

This presentation was given at the Occupational and Environmental Exposure of Skin to Chemicals conference in Amsterdam, June 2013.

This presentation was given at the Occupational and Environmental Exposure of Skin to Chemicals conference in Amsterdam, June 2013.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
2,628
On Slideshare
0
From Embeds
0
Number of Embeds
36

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Typically < 5 per year for “dermal exposure” and “exposure model”

Transcript

  • 1. INSTITUTE OF OCCUPATIONAL MEDICINE . Edinburgh . UK www.iom-world.orgModeling dermal exposureJohn CherrieFor the ART ConsortiumWork partly funded by HSE and NIEHS
  • 2. Growing interest indermal exposure…Publications inScopus by year
  • 3. Summary…• A conceptual framework• Interconnections – aggregating• State of the dART• New knowledge• PPE efficacy• Liquid viscosity and stickiness• Dermal exposure data• Methodologies• Availability for calibration• The practicality of usinga Bayesian approach
  • 4. Conceptual framework…Surface contam. layer Air compartmentSource
  • 5. Conceptual framework…Surface contam. layer Air compartmentClothing outer layerSkin contamination layerSourceClothing inner layerSchneider et al. (1999) Conceptual model for assessment of dermal exposure. Occup EnvironMed vol. 56 (11) pp. 765-73.
  • 6. Updated framework…Gorman Ng M, et al. (2012) The relationship between inadvertent ingestion and dermal exposure pathways: A new integratedconceptual model and a database of dermal and oral transfer efficiencies. Ann OccupHyg 56:1000–1012.Surface contam. layer Air compartmentClothing outer layerSkin contamination layer (hands and arms)SourceClothing inner layerPerioral layerOral compartmentRPE
  • 7. Aggregating…• Aggregating exposure from differentroutes is not straightforward• Differences in measurement units• Differences in model accuracy andprecision• For dermal exposure it is not sufficientjust to model mass flux onto the skinor mass retained on skin• Additionally need information on areaexposed and concentration of substance
  • 8. The potential pitfalls ofmass estimation• Load• Different skin massloadings in each of threescenariosFrasch HF, Dotson GS, Bunge AL, et al. (2013) Analysis of finite dose dermal absorption data: Implications for dermalexposure assessment. 1–9. doi: 10.1038/jes.2013.231 4 10
  • 9. The potential pitfalls ofmass estimation• Flux• Same flux in each caseFrasch HF, Dotson GS, Bunge AL, et al. (2013) Analysis of finite dose dermal absorption data: Implications for dermalexposure assessment. 1–9. doi: 10.1038/jes.2013.23
  • 10. The potential pitfalls ofmass estimation• Percent uptake• Different proportion ofthe loading taken up ineach caseFrasch HF, Dotson GS, Bunge AL, et al. (2013) Analysis of finite dose dermal absorption data: Implications for dermalexposure assessment. 1–9. doi: 10.1038/jes.2013.23100% 25% 10%
  • 11. DREAM…• DREAM based on Schneider et al model• Based around Transfer, Deposition andEmission• Calibration reasonablysuccessful• Exposure estimated inDREAM-Unitsvan Wendel de Joode et al. (2005) Occup Environ; 62(9): 623-32.van Wendel de Joode et al. (2005) J Expo Anal Environ Epidemiol; 15(1): 111-120.
  • 12. Plans for dART• Consortium TNO, HSL and IOM• Based on three routes in Schneider et al• Aim for “proof of concept” rather than afinal comprehensive model and tool• Low-volatility liquids and powders• Include low volatility substances in volatileliquids• Spraying, brushing/rolling, mixing/loading• Hands (plus whole body?)• Efficacy of protective clothing/gloves• How to account for area and concentration?
  • 13. PPE…• Studies of exposure often show a highdegree of protection.• Creely& Cherrie found >99% reduction incontaminant inside gloves• Available biomonitoring studies suggestgloves are less effective than this• Scheepers and colleagues found…• With polyethylene gloves median reduction of51% excretion of 1-hydroxypyrene in urine• Vinyl gloves and Tyvek sleeves showed a 97%reduction in skin contamination with pyrene andbenzo(a)pyrene and a lowering in urinaryexcretion of 1-hydroxypyrene by 57%Creely KS, Cherrie JW. Ann OccupHyg 2001;45:137–43.Scheeperset al.Scan J Work, Environ Health 2009;35:212–221.
  • 14. Adaption of DREAM forPPE…• We argue that the biomonitoring data aremore reliable indicators of protection• Proposed adjustment to DREAM• No gloves = 1• Woven or permeable = 0.9 (i.e. 10% reduction)• Impermeable = 0.5• Other factors important• Replacement frequency• Wear time etc.
  • 15. Substance properties…Gorman Ng et al. (2013) Properties of Liquids and Dusts: How do They Influence Dermal Loading DuringImmersion, Deposition, and Surface Contact Exposure Pathways? Ann OccupHyg. doi: 10.1093/annhyg/mes101solids
  • 16. Substance properties…Gorman Ng et al. (2013) Properties of Liquids and Dusts: How do They Influence Dermal Loading DuringImmersion, Deposition, and Surface Contact Exposure Pathways? Ann OccupHyg. doi: 10.1093/annhyg/mes101liquids
  • 17. Possible modification toDREAM• Immersion• Low viscosity (like water) = 1• Medium viscosity (like oil) = 3• High viscosity (like tar or resin) = 9• All other pathways• Low viscosity (like water) = 1• Medium viscosity (like oil) = 1.75• High viscosity (like tar or resin) = 3
  • 18. Measurements &calibration• Issues in relation to calibration and thecomparability of the model predictions by route• Issues of availability of data for the modelcalibration• Differences betweeninterception and in situmethods need to be taken intoaccount• Model should predict both• Model should facilitate uptakeestimateSkin contamination layerInterceptionsampler
  • 19. Measurements &calibration• Issues in relation to calibration and thecomparability of the model predictions by route• Issues of availability of data for the modelcalibration• Differences betweeninterception and in situmethods need to be taken intoaccount• Model should predict both• Model should facilitate uptakeestimateSkin contamination layer
  • 20. Comparison of methods…• Removal (wipes) versus Interception(gloves) with glycerol solutions – ImmersionGorman Ng et al (in preparation) Side-by-side comparisons of three dermal sampling methods: rinses, wipes and cottongloves.
  • 21. Comparison of methods…• Removal (wipes) versus Interception(gloves) with glycerol solutions - DepositionGorman Ng et al (in preparation) Side-by-side comparisons of three dermal sampling methods: rinses, wipes and cottongloves.
  • 22. dART…• Combinemechanistic model(DREAM?)and• Exposure datausing a• Bayesian process• Will there besufficient data tosustain thisapproach?dARTdeterministicmodelDermal exposuredatabaseSimilarity module toselect dataBayesian process tocombine data andmodel outputExposure estimatesfor risk assessment
  • 23. Conclusions…• The Schneider et al conceptual model andDREAM are a good basis for model building• Dermal more complex than inhalation• Less complex than inadvertent ingestion• Modelling mass loading is not sufficient• There is new knowledge to help guide themodel development• Calibration is not straightforward• Measurement data with contextual variablesare needed for a Bayesian model
  • 24. Questions?www.OH-world.org