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Transcript of Preliminary Cumulative Risk Assessment: Organophosphorus Pesticides Presentation to the FIFRA...
Preliminary Cumulative Risk Assessment:
Organophosphorus Pesticides
Presentation to the FIFRA Scientific Advisory Panel
U.S. Environmental Protection AgencyOffice of Pesticide Programs
February 5 to 8, 2002
Session 1-2
Marcia Mulkey, DirectorOffice of Pesticide Programs
Introduction and Introduction and WelcomeWelcome
Session 1-3
Margaret J. Stasikowski, DirectorHealth Effects Division
Office of Pesticide Programs
Historical Historical Perspective Perspective and Agendaand Agenda
Session 1-4
Where We’ve Been: MilestonesWhere We’ve Been: MilestonesCommon Mechanism Guidance
January 1999
Final Cumulative GuidanceJanuary 2002
Final Aggregate GuidanceNovember 2001
Draft OP Risk AssessmentDecember 2001
Final OP Risk AssessmentJune 2002
Session 1-5
How We Got There: SAP Advice How We Got There: SAP Advice Dose-Response and HazardDose-Response and Hazard
March 1997. Common Mechanism Guidance
March 1998. OP Common Mechanism of Toxicity
September 2000. Endpoints and RPF’s: A Pilot Study
September 2001. Preliminary Hazard and Dose-Response
Session 1-6
How We Got There: SAP Advice How We Got There: SAP Advice Exposure AssessmentExposure Assessment
September 1997. Residential Scenarios
December 1997. Drinking Water
March 1998. Probabilistic for Dietary, Residential, and Common Mechanism
July 1998. Estimating Pesticide Concentrations in Drinking Water
May 1999. Statistical Methods for Acute Dietary and Drinking Water
September 1999. Residential
March 2000. Models for Dietary and Drinking Water
June 2000. Drinking Water Survey
September 2000. Residential and Dietary Models and Drinking Water
March 2001. Dietary Model
Session 1-7
How We Got There: SAP Advice How We Got There: SAP Advice Assessment Methodology and OtherAssessment Methodology and Other
March 1997. Aggregate Methodology
March 1998. Probabilistic Risk Assessment Methodology
February 1999. Aggregate Guidance
September 1999. Cumulative and Aggregate Methodology
December 1999. Cumulative Methodology
September 2000. Risk Assessment Models
December 2000. Case Study of 24 OP’s and Cumulative Assessment Methodology
Session 1-8
Key SAP RecommendationsKey SAP Recommendations
Dose-response modeling: use of exponential model and several other recommendations
Hazard Dose-Response
Dietary
Use of PDP and other monitoring data
Use of publicly-available databases and recipes
Finer division of age groups: New CSFII have more data for children
Session 1-9
Key SAP RecommendationsKey SAP Recommendations
Devote resources to surface water impacts, define higher assessment tiers and develop techniques for estimating concentration distributions for probabilistic risk assessments
Regional modeling
Shift focus to monitoring programs to support model development and evaluation
Drinking Water
Session 1-10
Key SAP RecommendationsKey SAP Recommendations
Hand-to-mouth variables
Use of uniform distribution
Residential and Non-occupational
Session 1-11
Next StepsNext Steps
Revise December 2001 Preliminary Organophosphate Risk Assessment based on: This week’s advice from the Panel;
Comments received during the 90-day public comment period
Intended completion date: June 2002
Session 1-12
This Week…This Week…
SESSION 1: Hazard Dose-Response
SESSION 2: Dietary
SESSION 3: Drinking Water
SESSION 4: Residential and Non-occupational
SESSION 5: Risk Characterization
Session 1-13
ParticipantsParticipantsKevin Costello, MAGeologyPrinceton University
Vicki Dellarco, Ph.D.GeneticsIowa State University
Elizabeth Doyle, Ph.D.ToxicologyAmerican University
Jeff Evans, BSAgronomyDelaware Valley College
Anna B. Lowit, Ph.D.Environmental ToxicologyUniversity of Tennessee
Session 1-14
ParticipantsParticipantsDavid Miller, MS and MPHEnvironmental Science
EngineeringVirginia TechEnvironmental ChemistryUniversity of Michigan
Randolph Perfetti, Ph.D.ChemistryVirginia Tech
R. Woodrow Setzer, Ph.D.Population BiologyState University of New
York at Stony Brook
William O. Smith, Ph.D.Plant PhysiologyUniversity of Kentucky
Nelson Thurman, MSSoil Science West Virginia University
Session 1-15
Session on Hazard and Session on Hazard and Relative Potency Factor Relative Potency Factor
Session 1-16
Organization of PresentationOrganization of Presentation
Introduction & Background Anna Lowit, Ph.D., OPP
Methods: Oral Toxic Potency Determination and Points of Departure R. Woodrow Setzer, Ph.D., ORD
Session 1-17
Anna B. Lowit, Ph.D.ToxicologistHealth Effects DivisionOffice of Pesticide Programs
Introduction & Introduction & BackgroundBackground
Session 1-18
Nerve Axon
Identifying a Common Mechanism: Identifying a Common Mechanism: Organophosphate PesticidesOrganophosphate Pesticides
Inhibition of Acetyl Cholinesterase
• Brain
• Peripheral Nervous System (e.g., diaphragm, muscles)
• Surrogate (RBC, Plasma)
U.S. EPA 1999 Policy Paper
Session 1-19
Timeline of Methodology DevelopmentTimeline of Methodology Development
Pilot Hazard & Dose-Response OP Case Study Presented to SAP in September 2000
Preliminary Hazard & Dose-Response: July 2001 document Presented to SAP in September 2001
Revised Preliminary Hazard & Dose-Response: December 2001 document
Session 1-20
OPs Considered in Hazard & OPs Considered in Hazard & Dose-Response AssessmentDose-Response Assessment
29 Organophosphate Pesticides Exposure through food, water, and/or
residential
Determination of relative potency of chlorethoxyphos, profenofos, and phostebupirim is on-going
Session 1-21
Relative Potency Factor MethodRelative Potency Factor Method
Relative toxic potency of each chemical was calculated in comparison to “index chemical”
Exposure equivalents of index chemical are combined in the cumulative risk assessment
Session 1-22
Toxicity Data UsedToxicity Data UsedOral, Dermal, and Inhalation Routes: Sub-chronic and chronic toxicity studies
collected
Same studies were used in July and December 2001 documents
Electronic dataset of oral ChE data is available to the public at: http://www.epa.gov/pesticides/cumulative/EPA_approach_methods.htm
Session 1-23
Key Refinements to Hazard and Key Refinements to Hazard and Dose-Response AssessmentDose-Response Assessment
Relative potency factors used in Preliminary Cumulative Risk Assessment
Method for combining ChE data
Modeling of low dose region
Measure used in potency determination
Session 1-24
RPFs Used in Preliminary CRARPFs Used in Preliminary CRA
Male RBC RPFs were proposed in July 2001 document RBC selected primarily on availability of
large database and ability to consider time course information
Males selected
Session 1-25
RPFs Used in Preliminary CRARPFs Used in Preliminary CRAFemale Brain RPFs were selected in December 2001 document Why Brain?
• Compared to RBC, tighter confidence limits on potency estimates were observed
• Target tissue
Why Female? • Sexes equally sensitive for most • Female rats more sensitive for ~5 OPs
Session 1-26
Key Refinements to Hazard and Key Refinements to Hazard and Dose-Response AssessmentDose-Response Assessment
Relative potency factors used in Preliminary Cumulative Risk Assessment
Method for combining ChE data
Modeling of low dose region
Measure used in potency determination
Session 1-27
Methods: Methods: Oral Toxic Oral Toxic
Potency Potency Determination Determination
and Points and Points of Departure of Departure
R.Woodrow Setzer, Ph.D.Mathematical StatisticianNational Health and
Environmental Effects Laboratory
Office of Research and Development
Session 1-28
OverviewOverviewReview the methods used in July draft of hazard and dose-response assessment chapter.
What issues raised in September SAP report will be addressed here?
How have those issues been addressed?
What have we done since release of the December document?
Session 1-29
SAP Report, September 2001SAP Report, September 2001Overall, SAP was supportive of the approach used by the Agency Exponential model Multiple studies and time points R software for statistical analysis.
Recommended further exploration of dose-response modeling issues which could impact the low dose region.
Comprehensive list described in appendix of preliminary CRA (III.B.3) and can be found at http://www.epa.gov/pesticides/cumulative/.
Session 1-30
July: Estimate of Potency and DRJuly: Estimate of Potency and DR
15000 500 1000
050
010
0015
0020
00
Dose
AC
hE
Act
ivit
y
exp
e m
lm Dose
y B A B
potency
Session 1-31
July: Combining EstimatesJuly: Combining EstimatesFit a model to each dataset, estimating BMD (and estimated standard error) for each dataset.
Use the global two-stage method (Davidian and Giltinan, 1995; 138-142) twice, once for each level of variability.
Potency
MRID 1 MRID 2 MRID 3
Session 1-32
July: Estimating ParametersJuly: Estimating Parameters
Use generalized least squares and assume constant coefficient of variation
Sequential approach to fitting: Fit full model to all data
If no convergence or inadequate fit, • Repeat (until good fit or # remaining doses < 3):
– set B0– refit to dataset– drop highest dose
Session 1-33
Issues from the September SAP Report:Issues from the September SAP Report:
The approach to estimating B could result in biased estimates of m (the potency measure).
The weight function underestimated the variance at low doses and overestimated it at high doses.
The dose response curves for some chemicals appeared to have a “low dose shoulder,” which the basic exponential model did not capture.
Session 1-34
Updates to MethodsUpdates to Methods
Change the way models are expressed in terms of the parameters (same model, different parameters).
Use 1/BMD as measure of potency instead of m.
Use nonlinear mixed effects method to fit model to combined data.
Session 1-35
Updates to Methods (continued)Updates to Methods (continued)Use profile likelihood to estimate a value of B consistent with the data when the standard approach does not converge.
Develop a model for low-dose shape that was inspired by saturable metabolic clearance.
Weights are proportional to means (instead of squares of means).
Session 1-36
Model ParameterizationModel Parameterization
December model
exp
e m
lm Dose
y B A B
1 e m DoseB By A P P
July model (for comparison)
(factoring out A and replacing B/A with PB )
Session 1-37
Model Parameterization (cont.)Model Parameterization (cont.)Current model (same shape, but in terms of BMD instead of m):
1log
1
1 e
B
B
m
P BMRP
DoseBMD
B By A P P
Here BMR is the benchmark response (say, a 10% reduction in mean AChE activity), and BMD is the corresponding benchmark dose.
Session 1-38
Model Parameterization (cont.)Model Parameterization (cont.)
Advantages of current model:
More stable estimation, since BMD and PB are relatively less correlated than were m and PB.
Simplifies computation of BMD and its standard error.
Session 1-39
Model Parametrization (cont.)Model Parametrization (cont.)
Parameters actually estimated: lA=ln(A)
lBMD=ln(BMD)
B=ln(PB/(1-PB))
Mainly, to assure legal parameter values (for example, A and BMD>0, 0<PB<1)
Session 1-40
Model FittingModel Fitting
Use nonlinear mixed effects models (nlme in R): Estimate a separate mean value for lA for
each sex X unit combination, and a separate value of B and lBMD for each sex.
Estimate a random effect for each parameter for each level of nesting: maximum of among studies and among datasets within studies.
Session 1-41
Model Fitting (cont.)Model Fitting (cont.)
Weights based on error variances proportional to means.
Session 1-42
Model Fitting (cont.)Model Fitting (cont.)Sometimes nlme failed to converge to estimates for this full model. Then try (in order:) Full model (Sex-specific values for B, random effects)
Single value for B, with random effects
Sex-specific values for B, no random effects
Single value for B, no random effects
Fix sex-specific values for B that are consistent with the data, and estimate other parameters given the sex-specific values
Session 1-43
Model Fitting (cont.)Model Fitting (cont.)Select PB for males and females by choosing the value that maximizes the profile likelihood: Likelihood (more usually, its natural logarithm, the log
likelihood) is a measure of the degree to which the data support a particular parameter value.
Profile likelihood for a parameter (or parameters) results when the parameters in question are fixed, and the remaining parameters estimated given those values (so, fix PB, and estimate A and BMD). The log likelihood of the resulting fit is plotted versus the parameter values. The data most support the parameter value associated with the maximum.
Session 1-44
Model Fitting (cont.)Model Fitting (cont.)Profile Likelihood (cont.) PB (males and females) were fixed in turn to each
point on an 11 X 11 grid from 0.001 to 0.999.
Log likelihood plotted for each grid point (to aid visualization, values were linearly interpolated between grid points).
The grid point with the largest log likelihood was selected as the value of PB.
Model Fitting (cont.)Model Fitting (cont.)Bright yellow (highest value) to Red (lowest value)
Circles: points not significantly different (P>0.05) from best point (likelihood ratio test)
Plus signs (P<0.05)
Session 1-46
Model Fitting (cont.)Model Fitting (cont.)
Sensitivity: How much does the choice of B effect the estimate of the BMD? Only relevant when we cannot estimate PB with the other parameters. For the same grid of values for PB, plot BMD
as a fraction of the value at the selected point.
Plot contours (in the figures, the smallest contour represents ±25%).
Session 1-47
Model Fitting (cont.): SensitivityModel Fitting (cont.): Sensitivity
Session 1-48
The D-R Shape at Low DosesThe D-R Shape at Low DosesSome of the data look as if there were a shoulder at the low-dose end of the dose-response.
A proposed explanation is saturable metabolic clearance.
Add a submodel, inspired by this mechanism, to the basic model already described, to create a low-dose shoulder.
Keep it simple!
Session 1-49
A Simple PBPK ModelA Simple PBPK Model
Two compartments: liver and everything else.
Oral dosing, assume 100% into the portal circulation.
Only consider saturable metabolic clearance and first order renal clearance.
Run to steady state.
Body (Cb)
Metabolism (Vmax,Km)
Ingestion (Dose ×BW/24)
Qb
Q1
Ca
Ve
nou
s Ar teria
lUrine (ke)
Liver (Cl)
Session 1-50
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)Solve the system of differential equations implied by the model for steady state.
The concentration of non-metabolized parent OP in the body (idose) as a function of administered oral Dose rate is:
2
max
0.5*
4
24 24;l b m e
l e b e l b
idose Dose S D
Dose S D Dose S
QQ K k VS D
BW Q k Q k QQ BW
Session 1-51
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)
This model was called the internal dose model.
The utility of this extension to the basic model does not depend on its biological interpretation. The model was treated as purely empirical, with S and D fitted to the data, along with the other parameters, A, PB, and BMD.
Session 1-52
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)Dashed lines are an example of idose plotted versus Dose.
Solid are dose-responses from combining with the basic model.
S controls shape, D controls displacement.
0 2 4 6 8 10
0
500
1000
1500
2000
Dose
0
2
4
6
8
10
Sca
led
Inte
rna
l Do
se
AC
hE A
ctiv
ity
S =20.20.001
D = 2
Session 1-53
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)
Currently difficult to estimate parameters in this model using nlme.
However, S and D can be estimated by maximizing the profile likelihood.
This mainly effects the estimate of confidence intervals for the BMD.
Session 1-54
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)Due to two programming errors, the profile likelihood plots for S and D in the December draft were in error.
The main consequence of the programming errors was to limit the number of chemicals for which BMDs could be calculated using the expanded model.
17 of 29 Chemicals have significantly improved fits using the expanded model: azinphos-methyl, bensulide, chlorpyrifos, diazinon, disulfoton, fenthion,
fosthiazate, malathion, methidathion, methyl parathion, mevinphos, phorate, phosmet, pirimiphos-methyl, terbufos, tribufos, trichlorfon
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)
Session 1-56
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)
Basic Model Expanded Model
Bensulide
Session 1-57
D-R Shape at Low Doses (cont.)D-R Shape at Low Doses (cont.)Bensulide Residual Plots
Chemical Oral Dermal Inhalation
Acephate 0.13 0.0025 0.208Azinphos-methyl 0.092Bensulide 0.003 0.0015Chlorpyrifos 0.1Chlorpyrifos-methyl 0.012Diazinon 0.024Dichlorvos 0.037 0.677Dicrotophos 1.95Dimethoate 0.33Disulfoton 1.23 0.47 6.596Ethoprop 0.049Fenamiphos 0.039 1.5 0.315Fenthion 0.35 0.015Fosthiazate 0.16Malathion 0.0003 0.015 0.003Methamidophos 1 1 1Methidathion 0.37Methyl-parathion 0.058Mevinphos 1.36Naled 0.083 0.075 0.82ODM 0.9Phorate 0.39Phosalone 0.024Phosmet 0.02Pirimiphos-methyl 0.029Terbufos 0.84Tetrachlorvinphos 0.0008 0.00075Tribufos 0.045Trichlorfon 0.014 0.0075 0.087
RPFs based on brain cholinesterase activity measured from female rats.
Tab
le o
f C
hem
icals
an
d R
PFs
Tab
le o
f C
hem
icals
an
d R
PFs
Session 1-59
Points of Departure for Index Chemical Points of Departure for Index Chemical (Methamidophos) by Route of Exposure for Brain (Methamidophos) by Route of Exposure for Brain Cholinesterase Activity Measured in Female RatsCholinesterase Activity Measured in Female Rats
Brain
0.210.39Inhalation
1.772.12Dermal
0.070.08Oral
BMDL*BMD10
Route of Administration
*BMDL is the lower 95 percent confidence interval
Session 1-60
SummarySummarySAP recommendations were considered, resulting in improvements in BMD calculations: Using profile likelihood to estimate the horizontal
asymptote results in value consistent with data
BMD is less sensitive to estimate of horizontal asymptote than was m
Reweighting
Session 1-61
Summary (cont.)Summary (cont.)Basic model was reparameterized to improve the stability of estimator.
Estimating parameters for combined datasets allows more complicated models that describe the low-dose shoulder of the dose-response.
Including the low-dose shoulder improves the fit to the data and the BMD estimate for a substantial number of chemicals.
Session 1-62
Questions Questions for the SAP for the SAP
on Hazard on Hazard and Relative and Relative
Potency Potency Factor Factor
Session 1-63
Question 1Question 1A) In September 2001, the FIFRA SAP made some specific recommendations to EPA concerning refinements of its dose response analysis of cholinesterase data on OPs such as:
the derivation of the adjustment factor "B" and modification of the decision tree for use of "B;"
a formal analysis of residuals;
minor revision to the Agency's OPCumRisk program (i.e., revision of the calculation as of the goodness of fit statistic and deletion on p- and t-values);
consideration of the appropriate measure of relative potency;
expression of inhalation exposure in the same units as the oral doses and adjustment for actual treatment durations;
consideration of the impact of individual animal data instead of summary information;
and derivation of oral doses from the actual dietary intake rates.
Session 1-64
Question 1 (continued)Question 1 (continued)
B) Several of these issues were addressed by the application of the nonlinear mixed effect model for combining cholinesterase data. In addition, EPA utilized the profile likelihood method for estimating horizontal asymptotes when they could not be estimated jointly with the other parameters.
Please comment on the use of these statistical procedures in the dose-response assessment of the organophosphate pesticides
Please comment on how the Agency addressed the recommendations listed above (I.B and III.B.3).
Session 1-65
Question 2Question 2An exponential model was utilized by EPA in the July, 2001 Preliminary Hazard and Dose-Response Assessment of the Organophosphate Pesticides. Based on the equation used in the July 2001 document, cholinesterase activity decreases linearly in the low dose region of the dose response curve. Stakeholders present at the Technical Briefing (August 2001) and also a few members of the SAP (September 2001) suggested that a flat low dose region may be a more appropriate modeling approach. In response to this issue, EPA has further investigated the shape of the low dose region of the dose-response curve.
Session 1-66
Question 2 (continued)Question 2 (continued)Two versions of the exponential model were used in the December 2001 hazard and dose-response assessment. One version, called the basic model, describes a linear low dose region and is similar to the approach used in the July 2001 document. All 29 OPs were fit to the basic model. The second version, called the expanded model, incorporates two additional variables, shape and displacement, which describe a flat low dose region of the dose-response curve. The female brain ChE data supported a flat low dose region for eight OPs (azinphos methyl, bensulide, disulfoton, malathion, methyl parathion, phorate, phosmet, and terbufos).
Please comment on the mathematical derivation of the expanded model in addition to the profile likelihood method for estimating the
shape and displacement parameters when they could not be estimated jointly with the other parameters (I.B and III.B.1).
Session 1-67