Antimicrobials Exposure 

Assessment Task Force (AE

Introduction and Background 
INTRODUCTION

• AEATF’s 

primary purpose is to generate exp

data to support EPA’s development of improexposure assessments for antimicrobial (biorisk analyses and associated regulatory decmaking 

• 

A secondary objective is to provide data to o

regulatory agencies (Canada PMRA, EU, BPtheir assessments and decision-makings 

October 2007 
INTRODUCTION 

AEATF overview: 

•

Represents a coordinated effort between AC

EPA 

•

Preliminary talks in 2003 

•

Development talks starting in 2004 

• Includes multinational regulatory authority inp

involvement (EPA, CDPR, PMRA, EU, OECD

October 2007 
INTRODUCTION 

AEATF overview: 

• $10 Million program (initial estimate) • 

43 participating companies representing th

antimicrobials value chain (active produceend-user formulators) 

• 

Addresses 19 different exposure scenario

October 2007 
New Antimicrobial Exposure 

Are Needed 

• 

Existing antimicrobial exposure data are very limited 

• In response to a 1986 EPA Antimicrobial Data Call-In, ACC T

(AEATF I) conducted the “CMA Antimicrobial Exposure Asses

•

The CMA study provided the first exposure data for antimicrob

many limitations (low numbers of individuals monitored, high dand only 8 activities monitored) 

• EPA currently uses the CMA study data in conjunction with Pe

Handlers Exposure Database (PHED) to conduct most risk asmost registered antimicrobial active ingredients 

• 

Paucity of existing data prompted the EPA request fo

• In more than 8 Re-registration Eligibility Decision documents 

2005, EPA has requested replacement data for the CMA stud

•

The need for additional exposure data was confirmed by the E

Advisory Panel (SAP) in January 2007 
Key Role of AEATF Data 

Development Effort

• AEATF II is a state-of-the-art exposure asse

program that will provide exceptional dregistrant and EPA decisions 

•

Cost overruns and delays threaten the scope 

viability of this program 

• AEATF II wants to continue working with the 

find workable solutions to maintain the viabilitvaluable program 
Introduction

• Antimicrobial pesticides have two majo

– Protect inanimate objects (for example material pre

in floors and walls), industrial processes or systemswater, or other chemical substances from contaminfouling, or deterioration caused by bacteria, virusesprotozoa, algae, or slime 

– Disinfect, sanitize, reduce, or mitigate growth or dev

of microbiological organisms 
Examples of Antimicrobial U
AEATF Program Scope

Use P

atter

ns Ap

plic

ation 

M

e

1. A

g

ricultu

r

al Premises a

n

d E

quip

ment 1. A

er

osol S

p

r

a

2.

F

ood Ha

n

dling/Sto

r

a

g

e Establishm

e

nts P

r

e

mises a

n

d E

quip

m

e

nt 2. High P

r

essur

3. C

omme

r

cial, I

nstit

utio

n

al & I

ndustrial P

r

e

mis

es a

n

d E

quip

m

e

nt 3. L

o

w P

r

essu

r

4. Resid

e

ntial an

d Pu

blic A

ccess Pre

mises 

4. Po

u

r Liquid 

5.

M

edi

c

al P

r

e

mises a

n

d Equip

m

e

nt 5. P

u

mp Li

quid 

6. H

u

m

a

n D

rin

kin

g W

ater Syste

ms 

6. Po

u

r Solid 

7. I

n

d

ustrial Pro

c

ess W

ater Syste

ms 

7. Plac

e Solids 

8. 

M

aterial P

r

eser

v

atives 8. Mop 

9. A

ntifo

ulant Coatin

gs 9. Wipe 

10. 

W

ood 

P

r

eser

v

atives 10. 

F

ogging 

11. Swim

ming 

P

o

ols 11. B

r

ush 

12. A

qu

atic Ar

e

as 1

2. R

oller

 1

3. Airless S

p

r

a

 1

4. Flo

od 

15. Immersion 16.

Dip 

17.

S

o

ak 

18. Impr

e

g

n

ated 

19. P

ressu

re 

T

re

October 2007 
Data Needs - Brief History:

AEATF I – CMA “Popendorf Stu

• Response to the 1986 U.S. EPA Antimicrobial Data Call-I• 1988 – 1992 sponsored by 20 companies under the ausp

CMA Biocides Panel 

• How have the data been used? 

– Exposure data from this study, combined with PHED data an

enabled continued registration and re-registration of over 12antimicrobial active ingredients and many end-use products 

• Strengths 

– Provided first generic data for antimicrobials – Generic data served as the foundation for handler unit expo

• Limitations 

– High detection limits (0.002 to 3 μg/cm2; AEATF II target 0.0– Low number of monitoring units (min = 3) – Limited number of representative antimicrobial scenarios (ap

methods) studied (n = 8) 
Data Needs

Identified by EPA

• In various Re-Registration Eligibility Decisions issued durin

and 2006, EPA has stated: 

“... the risk assessment noted deficiencies in the surrogate dinhalation exposure data available from the Chemical ManufaAssociation (CMA) data base. Therefore, the Agency is requconfirmatory data to support the uses assessed with the CMA data within this risk assessment.” 

– PHMB. September 2005. EPA739-R-05-003 – Benzisothiazoline-3-one. September 2005. EPA739-R-05-007 – Para-Tertiary-Amylphenol, Potassium Sodium Salt. January 2005. 

05-001 

– Azadioxabicyclooctane. September 2005. EPA739-R-05-010 – Chlorine Dioxide and Sodium Chlorite. August 2006. EPA738-R-06– Pine Oil. October 2, 2006. (publication number unavailable) – Aliphatic Alkyl Quaternaries (DDAC). August 2006. EPA739-R-06-0– Alkyl Dimethyl Benzyl Ammonium Chloride (ADBAC). August 2006. 

R-06-009 
EXPOSURE DATA NEEDS (c

Antimicrobial exposure-related data needbeen clearly identified and justified by OP

•

Confirmatory and new data requirements indicated in m

Registration Eligibility Decision Docu

m

e

n

t

s (RED) 

•

Need confirmed by the EPA’s Science Advisory Panel 

•

Data needs for “next generation” generic exposure dat

apparent with antimicrobial chemicals and more pronouwith most agricultural scenarios 

October 2007 
Antimicrobials Are Uniqu

•

Many of the exposure scenarios for antimicrobials are not s

AHETF (Agricultural Handlers Exposure Task Force) (e.g., mwiping, e

tc.) 

•

Even similar (shared) scenarios have different exposure pot

explained later (e.g., pour liquid) 

•

The selection process for antimicrobial surrogate active ingr

(a.i.s) to be used for generating generic data has special co

–

High reactivity and/or low concentrations of antimicrobia

that sensitive analytical methods be available for surrogachemicals. This limits the number of a.i.s 

that can be stu

•

Opportunities for conducting antimicrobial studies are limite

–

e.g., Limited access to in situ locations, limited opportun

selected surrogate chemicals are being used 

October 2007 
Quantitative Objective 

•

Primary quantitative objective is the same for both Task For

–

Reasonable (practical) estimates of the ‘middle’ and ‘upper’ 

va

normalized exposure (e.g., arithmetic mean, 95th percentile of distribution) 

–

These values are then compared to critical toxicology endpoin

given unique antimicrobial chemical (i.e., risk is function of hazexposure) 

•

Some users of these data might als

o wish to explore the rel

between exposure and the amount of AI handled (or compoas “duration of task”). While such relationships can always bexamined, the range of AaiH for most anti-microbial scenarismall to guarantee statistical power. Thus, no such objectiveformally included in the program design. 

October 2007 
AEATF Study Design Considera

• Categories of antimicrobial exposure scenar

– Indoor studies involving simple, repetitive tasks 

•

e.g., mop application, wipe application, aerosol applicati

immersion/dip/soak, spray application, liquid pouring, so

•

some of these scenario tasks are similar for both Task F

open pouring of liquids, albeit with different environmentproduct use conditions, i.e., outdoors versus indoors, larsmall quantities 

•

commonality of environmental and task conditions perm

simulated environments and semi-scripted (or synthetic) events to ensure diversity 

– Indoor studies involving more complex, multi-tasking 

•

e.g., wood treatment, metal working fluids 

• unique environmental and task conditions require in situ 

albeit with semi-scripted study designs to ensure diversi

October 2007 
Application Scenario has the Gre

Impact on Exposure

•

Many AEATF scenarios (e.g., mopping, wiping

in various indoor locations (e.g., hospitals, hotresidences), wherein repetitive tasks are perfowith a similar range of environmental conditio(surface types and configurations, temperaturhumidity, air exchange rate) 

•

The existing CMA antimicrobial exposure monstudy involving nine application methods (largrepetitive task scenarios) concluded that:

– “the hypothesis was validated that exposures for a 

applic

ation method would not vary significantly amo

use settings; therefore, the application method is a determinant of exposure than is the setting” 

October 2007 
Environmental and

Product Use Consideration

• 

Reasons for proposed semi-scripted, simulated AEATF stu

–

Existing antimicrobial exposure data have a large number of n

on inner dosimeters, despite upper-bound usage; therefore it feasible to use significantly less product to extend the range lo

–

Limiting upper-bound usage are ergonomic considerations e.g

stress, limits of endurance and the practical upper limits of areby an individual using hand-held equipment in a day. These ftogether dictate a range of ~5-fold for amount used in a particscenario. 

–

The large number of non-detects on inner dosimeters further 

to meaningfully estimate dermal exposure, the outer dosimetemust be used with an appropriate data-derived clothing penet

October 2007 
‘Sampling’ Methods

• The most recent HSRB issue is the necessity of incorporating ran

sampling in the program design. 

– This requires careful consideration of the appropriateness, practical fe

cost implications for AEATF studies. 

• Very large monitoring costs result in relatively small numbers of m

events per scenario (e.g., 15-20) 

• As much diversity as possible is needed among the set of monito• Sampling is not from an existing population of handler-days 

– Synthetic handler-days in simulated environments are used to obtain 

representation of future exposure conditions. 

– A diverse selection of volunteers are needed perform scenario-relate– Diversity is also (potentially) increased by using multiple experimenta

(e.g., buildings & monitoring dates) 

• This is viewed as a set of synthetic handler-days that represent fu

exposures under a set of diverse conditions 

October 2007 
Random Sampling 

• Issue: Should subjects (and even buildings u

simulated environments) be a probability sam

– Is there much advantage in a small random sample of su

localized population when the environments and monitoare synthetic? 

–

Random + Synthetic 

Æ Synthetic 

–

Listing activities are costly and random sampling could r

subject diversity 

– Increased time required for conduct of random sampling
Random Sampling 

• Incremental cost and time requirements versu

benefits 

•

Previous 875-series guideline cost (study phase only

ME 

•

Previous 875-series guideline total cost (includes ana

development, database development, quality assurandevelopment of SOPs, legal and program manageme= 30K per ME 

•

Revised purposive-diversity based sampling total cos

ME 

•

Random sampling based total cost = 63K per ME 
Participation/Volunteer Ra

•

Review of previous studies and task force expertise sugge

site/subject participation rates (from the entire population) extremely small 

–

For example, less than 5% response rate was observe

potential study site selection in previous CMA study. Tresponse rate was also observed in a recently conductsurvey re: information and cooperation for the brush/roscenario study. 

•

Even if a random sample of subjects were desirable, smal

rates and self-selected volunteers lessen its ability to represubject population 

• In addition, ethic

al issues arise when incentives are provid

overcome refusal to participate 

October 2007 
Purposive ‘Sampling’ is a Commonly Emp

Characteristic of Regulatory Studies

• Exposure monitoring studies are typically not de

as random samples for cost and other practical (time requirement, response rate issues). 

• Purposive-type (non-random sampling-based) s

have routinely served as the basis for applied peexposure and risk analyses (worker and consumconducted by EPA, CA EPA, PMRA, Europeanregulatory authorities and the regulated communthe past 30 years. 

• EPA 875 Series and OECD exposure monitoring

guidelines support purposive sampling study de

October 2007 
Purposive Studies: 

Role in Regulatory Agency Risk Analysis & Decision-M

• Purposive sampling has often been necessary in e

monitoring studies for regulatory purposes: 

– Precedence (purposive-type pesticide exposure monitori

• CA DPR sponsored-studies measuring worker exposure • Studies conducted in Europe • Industrial hygiene studies sponsored by OSHA, NIOSH and 

industry 

• Pesticide registrant-sponsored studies measuring worker e

• But, for large scale studies and surveys, probabilit

sampling is necessary: 

• National Human Exposure Assessment Survey (NHEXAS) • Product use and activity pattern surveys (e.g., REJV, NHAP• Dietary intake surveys (e.g., USDA’s CSFII) 

October 2007 
Re-Cap of Study Monitoring Method

and Design Elements

•

Studies will be conducted in accordance with U.S. EPA GLPs, 8

Guidelines, and regulations providing for the protection of humainvolved in research, 40 CFR Part 26 

•

Protocols are also consistent with OECD guidelines and are rev

competent authorities 

•

Key antimicrobial study design considerations: 

–

Allocation of resources to address diverse array of use patte

application methods 

–

Scenarios often involve indoor environments 

–

Some scenarios involve handling small amounts (e.g., mopp

spraying) 

–

Use of surrogate a.i.s with analytical methods that minimize 

–

Use of semi-scripted tasks in an appropriate indoor location 

means of ensuring diversity 

– Low site/participant response rate requires substantial effort 

desired diversity and sample size