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


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


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


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

Session 1-56


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

preliminary cumulative risk assessment: organophosphorus pesticides presentation to the fifra...

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