# Presentation: 265048

## Preliminary Cumulative Risk Assessment: Organophosphorus Pesticides

*Preliminary Cumulative Risk Assessment: *Organophosphorus Pesticides

- Presentation to the
- FIFRA Scientific Advisory Panel

- U.S. Environmental Protection Agency
- Office of Pesticide Programs
- February 5 to 8, 2002

## Marcia Mulkey, Director
Office of Pesticide Programs

- Office of Pesticide Programs

*Introduction and Welcome*

## Margaret J. Stasikowski, Director
Health Effects Division
Office of Pesticide Programs

- Health Effects Division
- Office of Pesticide Programs

*Historical Perspective **and Agenda*

## Where We’ve Been:  Milestones

*Where We’ve Been:  Milestones*

- Common Mechanism Guidance
**January 1999**

- Final Cumulative Guidance
**January 2002**

- Final Aggregate Guidance
**November 2001**

- Draft OP Risk Assessment
**December 2001**

*Final OP Risk Assessment*
***June 2002***

## How We Got There:  SAP Advice Dose-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

## How We Got There:  SAP Advice Exposure 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

## How We Got There:  SAP Advice Assessment 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

## Key SAP Recommendations

- Dose-response modeling:  use of exponential model and several other recommendations

- 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

## Key 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

## Key SAP Recommendations

- Hand-to-mouth variables
- Use of uniform distribution

## Next 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

## This Week...

## Participants

**Kevin Costello, MA**
- Geology
- Princeton University
**Vicki Dellarco, Ph.D.**
- Genetics
- Iowa State University
**Elizabeth Doyle, Ph.D.**
- Toxicology
- American University

**Jeff Evans, BS**
- Agronomy
- Delaware Valley College
**Anna B. Lowit, Ph.D.**
- Environmental Toxicology
- University of Tennessee

## Participants

**David Miller, MS and MPH**
- Environmental Science Engineering
- Virginia Tech
- Environmental Chemistry
- University of Michigan** **
**Randolph Perfetti, Ph.D.**
- Chemistry
- Virginia Tech

**R. Woodrow Setzer, Ph.D.**
- Population Biology
- State University of New York at Stony Brook
**William O. Smith, Ph.D.**
- Plant Physiology
- University of Kentucky
**Nelson Thurman, MS**
- Soil Science
- West Virginia University

## Session on Hazard and Relative Potency Factor   

## Organization of Presentation

- Introduction & Background
- Anna Lowit, Ph.D., OPP
- Methods:  Oral Toxic Potency Determination and Points of Departure
- R. Woodrow Setzer, Ph.D., ORD

## Introduction & Background

**Anna B. Lowit, Ph.D.**
- Toxicologist
- Health Effects Division
- Office of Pesticide Programs

**Introduction & Background**

## Identifying a Common Mechanism: Organophosphate Pesticides

*Identifying a Common Mechanism: Organophosphate Pesticides*

_**Inhibition of Acetyl Cholinesterase**_
- Brain
- Peripheral Nervous System (e.g., diaphragm, muscles)
- Surrogate (RBC, Plasma)

- U.S. EPA 1999 Policy Paper

## Timeline 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

## OPs Considered in Hazard & Dose-Response Assessment

- 29 Organophosphate Pesticides
- Exposure through food, water, and/or residential
- Determination of relative potency of chlorethoxyphos, profenofos, and phostebupirim is on-going

## Relative 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

**Notes:**
the  Relative Potency Factor method is the basis of the cumulative hazard assessment for Ops.  This method is similar in concept to using Toxic Equivalency Factors or TEFs if you are familiar with work with chemical mixtures.

In this method, the relative toxic potency of each chemical in a common mechanism group was calculated in comparison to a chemical selected as the index.

In the Preliminary Cumulative Risk Assessment for Ops, exposure equivalents of the index chemical will be combined to calculate risk.

It is important to note that a key underlying assumption in the using relative potency factors is dose addition at low exposure levels.  In other words at the low levels we are really exposed to from produce from the grocery store, we can predict the effect of multiple exposures using simple addition—that there are not more than additive i.e., synergistic or less than additive—less effect than predicted.

## Toxicity Data Used

- Oral, 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_

**Notes:**
Human exposure to OPs can be through a number of routes:  namely the diet—both food and water, and nonoccupational and residential exposure such as home, school, and office buildings.  Therefore, it was necessary to review the toxicology databases for the oral, dermal, and inhalation routes.

For all routes of exposure, we wanted to do a comprehensive search of all the available toxicology studies with pertinent data therefore a number of study types were collected.   

Consistent with the assertion that relative potency will be the most stable at steady state, only cholinesterase measurements taken at a minimum of 21 days on study was collected.  We have cholinesterase data that spans 21 to greater than 700 days.

For the oral database, these included subchronic and chronic studies in addition to some nonguideline studies such as range finding studies and special studies.  The nonguideline studies were only used when sufficient information was available in the study report to review the quality of the study.

## Key Refinements to Hazard and Dose-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

## RPFs 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

## RPFs Used in Preliminary CRA

**Female 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

## Key Refinements to Hazard and Dose-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

## Methods:  Oral Toxic Potency Determination and Points of Departure 

**Methods: **** Oral Toxic Potency Determination and Points ****of Departure **

**R.Woodrow Setzer, Ph.D.**
- Mathematical Statistician
- National Health and
- 	Environmental Effects Laboratory
- Office of Research and Development

## Overview

- Review 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?

## SAP Report, September 2001

- Overall, 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/._

## July:  Estimate of Potency and DR

- 1500

- 0

- 500

- 1000

- 0

- 500

- 1000

- 1500

- 2000

- Dose

**AChE Activity**

*potency*

## July: Combining Estimates

- Fit 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.

## July: 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 B************0***
- refit to dataset
- drop highest dose

## 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.

**Notes:**
Some Problems

How using “issues” instead of problems
Also say issues with what???

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.

## Updates 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.

**Notes:**
Question....what does ‘parameterize’ mean?  
Make sure you define nonlinear mixed effect model
Bullet 3—should it be model or models?

## 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).

**Notes:**
I like bullet #1—normal english!

Develop a model for low-dose shape that is consistent with saturable metabolic clearance.
	Be careful with this---make sure you are not claiming this to be a biologically based model.

## Model Parameterization

**December model**

**July model (for comparison)**

- (factoring out *A* and replacing *B/A* with *P**B* )

## Model Parameterization (cont.)

- Current model (same shape, but in terms of *BMD* instead of *m)*:

- Here BMR is the benchmark response (say, a 10% reduction in mean AChE activity), and BMD is the corresponding benchmark dose.

## Model Parameterization (cont.)

- Advantages of current model:
- More stable estimation, since BMD and *P**B* are relatively less correlated than were *m* and *P**B*.
- Simplifies computation of BMD and its standard error.

## Model Parametrization (cont.)

- Parameters actually estimated:
*lA*=ln(*A*)
*lBMD*=ln(*BMD*)
*B*=ln(*P**B*/(1-*P**B*))
- Mainly, to assure legal parameter values (for example, *A* and *BMD*>0, 0<*P**B*<1)

## Model 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.

**Notes:**
I would split into two slides and explain this in english—Don’t forget the biologists and neuro people on the panel.
Define R.

## Model Fitting (cont.)

- Weights based on error variances proportional to means.

## 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

**Notes:**
Sometimes nlme failed to converge to estimates for this full model (which model—the reparameterized one—be clear).  
Then, try (in the following order):

Why do a single B before sex specific ones...explain.

Make sure you have defined somewhere what ‘random effects’ are
The last bullet is the profile likelihood plots...right?

Given that....what??? 

## Model Fitting (cont.)

- Select *P**B* 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 *P**B*, 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.

## Model Fitting (cont.)

- Profile Likelihood (cont.)
*P**B* (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 *P**B*.

## 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)

**Notes:**
Spend some time here.  Explain:
1) the axes
2) the grid (missing values)
3) the color scheme.

## Model Fitting (cont.)

- Sensitivity: How much does the choice of *B* effect the estimate of the BMD?  Only relevant when we cannot estimate *P**B* with the other parameters.
- For the same grid of values for *P**B*, plot BMD as a fraction of the value at the selected point.
- Plot contours (in the figures, the smallest contour represents ±25%).

**Notes:**
Sensitivity: how much does the choice of B effect the estimate of the BMD?

Can you add a bullet reminding people that these graphs were only used when B could

How about selection instead of ‘choice’

## Model Fitting (cont.): Sensitivity

*Model Fitting (cont.): Sensitivity*

## The D-R Shape at Low Doses

- Some 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!*

## A 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.

## 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:

**Notes:**
Is unmetabolized a word? Non-metabolized?

This could be confusing...a lot of ops need activation...do you mean ‘parent compound’

S and D are functions of the PK parameters, but could as easily be estimated quantities.
	What does this mean?

Ed Gray has asked where the 0.5, 4, and quadratic came from.  Will you discuss this?

## 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, P**B**,* and *BMD*.

## 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.

**Notes:**
Talk about the dashed curves first: “internal dose” as a function of Dose.  Combining this with the basic model gives the solid curves

## 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.

**Notes:**

I think you need a slide either before or after this one---saying there was error(s) in the December code.—how that impacted the Dec RPFs
	What has changed since Dec. 

This is key to the public---be transparent and open....lots of bullets and refer to the handout.  
Make a bullet saying revised RPFs released following SAP review.

## 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.)*

**Notes:**
Talking points:
1) scales depend on dose scale
2) color: white is high, red is low
3) circles, not significantly different from maximum (confidence contours) based on likelihood ratio test.

## D-R Shape at Low Doses (cont.)

*D-R Shape at Low Doses (cont.)*

- Basic Model

- Expanded Model

- Bensulide

**Notes:**
Talking point:  Point out the dotted lines = different MRIDs

## D-R Shape at Low Doses (cont.)

- Bensulide Residual Plots

**Notes:**
Talking points:
1) residuals are scaled so that the variance should be constant across levels of inhibition
2) should be centered about zero
3) Note the excess in positive residuals from the basic model around 0.1, not present in the plot for the expanded model.

## Table of Chemicals and RPFs

*Table of Chemicals and RPFs*

## Points of Departure for Index Chemical (Methamidophos) by Route of Exposure for Brain Cholinesterase Activity Measured in Female Rats

- Brain

- 0.21

- 0.39

- Inhalation

- 1.77

- 2.12

- Dermal

- 0.07

- 0.08

- Oral

- BMDL*

- BMD10

- Route of Administration

- *BMDL is the lower 95 percent confidence interval

## Summary

- SAP 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

## 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.

## Questions for the SAP on Hazard and Relative Potency Factor 

**Questions for the SAP on Hazard and Relative Potency Factor **

## Question 1

- A) 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.

## 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).*

## Question 2

- An 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.

## 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).*

## Slide 67