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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

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WASHINGTON, D.C. 20460 awl r 

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"5,1*@66 

MEMORANDUM 

DP Barcode 306327 

August 24,2005 

PC Code 112701 

SUBJ: EFED Evaluation of "A Probabilistic Assessment of the Risk of Brodifacoum to 

Nontarget Predators and Scavengers," by The Cadmus Group, September 10, 

2004; Submitted by Syngenta Crop Protection, Inc. 

TO: Kelly White 

Chemical Review Manager 

Special Review & Reregistration Division 7,508C 

FROM: Mary Frankenbeny, Statistician '-' 

William Erickson, 11 ' Biologist 2 

Environmental Fate & Effects Division 75 

THRU: 

Branch Chiefs, ERE32 & ERE33 

Environmental Fate & Effects Division 7507C 

Attached is the Environmental Fate & Effects Division's evaluation of the Cadrnus I probabilistic assessment for brodifacourn, submitted by Syngenta Crop Protection for the 

brodifacoum registrants. It includes a more comprehensive review of the document performed 

by the Syracuse Environmental Research Associates and Syracuse Research Corporation for 

EFED and OPP. Please let us know if you have any questions you would like to discuss further 

with regaid to these documents. 

Text Searchable File
OPP Evaluation of Cadmus/Brodifacoum Repistrants (C/BR) Probabilistic Risk 

Assessment Model for Brodifacoum 

Executive Summary 

Brodifacoum is a second-generation anticoagulant rodenticide primarily used to control Norway 

and roof rats and house mice. In 1998, EPA issued a Reregistration Eligibility Decision (RED) 

for the rodenticide cluster, which included an assessment of human and ecological risks of 

brodifacoum and other rodenticides. However, the RED noted that EPA had received recent 

wildlife incident reports and that the Agency would continue to evaluate the risks of labeled uses 

of brodifacoum to nontarget birds and mammals. In 2001 (updated in 2004), EPA completed a 

comparative assessment of nine rodenticides used in the United States and concluded that 

brodifacoum labeled uses pose high potential primary and secondary risks to birds and nontarget 

mammals. 

The 1998 RED and 2004 comparative assessment evaluated risks based on a lines of evidence 

and comparative-analysis model approach. In an attempt to estimate the probability and 

magnitude of potential ecological effects of brodifacoum, four rodenticide registrants (Bell 

Laboratories, Inc., LiphaTech, Reckitt Beneckiser, and Syngenta Crop Protection) contracted The 

Cadmus Group, Inc. to conduct a probabilistic ecological risk assessment. The Cadmus Group 

used a dietary dose model to develop distributions to estimate daily dose to nontarget predator 

species as a function of the body weight and food ingestion rate of animals, the concentration of 

residue in food, and the fraction of food in the diet containing brodifacoum. They also used an 

uptake-depuration model "to estimate the cumulative dose over time." Effects data used to 

develop the distributions were taken from published and unpublished sources. Finally, exposure 

and effects distributions were combined to estimate the probability of mortality to nontarget 

predator organisms. In their risk assessment, C/BR claimed low secondary risk of brodifacouminduced mortality to coyote, red fox, and red-tailed hawk and inferred,the same conclusion to 

other species of birds and mammals with similar sensitivity and diet. They also claimed that the 

secondary risk from brodifacoum was only slightly higher for the kit fox and great homed owl. 

Primary exposure of nontarget organisms was not addressed nor were risks to scavengers 

addressed explicitly. In characterizing the ecological risk of brodifacoum, C/BR acknowledged 

that risk estimates were limited by the lack of data and were subject to a number of uncertainties 

and assumptions. 

After reviewing the C/BR probabilistic risk assessment of brodifacoum, EPA has concluded that 

the probabilistic risk assessment does not provide sufficient evidence to alter EPA's risk 

conclusions in the deterministic risk assessment. Similar to EPA's assessment, the C/BR 

assessment identifies information and data gaps that lead to major uncertainties in quantitatively 

assessing risks from secondary exposure to predators. The uncertainties identified in the C/BR 

report suggest that risks could range from a minimal likelihood of mortality to a high likelihood 

of significant mortality, depending on the extent to which predators consume rodenticideexposed prey. Data that would help reduce the uncertainties in the risk assessments include 

information on local baiting practices, dietary composition and foragibg behavior of birds and 
mammals when bait andlor dead and dying animals supplement the natural food supply, toxicity 

data for predatory and,scavenging species, concentrations of brodifacoum in target rodents and 

nontarget birds and mammals, and information on the retention, storage, and elimination of 

I brodifacourn in nontarget birds and mammals. Because data are very limited for quantifjring 

exposure under expected and typical use patterns, there is a large amount of uncertainty in the 

estimation of risk. As acknowledged in the C/BR assessment, risk depends strongly on local 

conditions and the foraging behavior and habitat use of predators and scavengers. Because of the 

spatial and temporal variability in all of these considerations, it becomes extremely difficult to 

quantify exposure and risk on a national scale. 

1 Problem Formulation 

General Conceptual Model 

Although it was included in the initial drafl of the problem formulation for the C/BR 

probabilistic assessment, primary exposure to nontarget animals that ingest bait was omitted in 

the final assessment. According to the C/BR assessment, primary exposure was excluded 

because (1) secondary exposure was of greater concern to regulators, (2) a different model would 

be needed to assess primary exposure, and (3) risk reduction for non-bait feeders is mostly a 

matter of bait station design. EPA has received data indicating that primary exposure to 

nontarget organisms does occur, and therefore, it is a concern for the Agency. Whether it is less 

or greater than secondary exposure is not as important as the fact that it does occur and should be 

addressed in the risk assessment. The need for another exposure model to address primary 

exposure is also not a reason to avoid assessing it. Thirdly, the Agency does not agree with the 

assertion that primary exposure is largely a function of bait station design, and thus, that it was 

appropriate to exclude pkimary exposure from this assessment. 

Although the probabilistic assessment was designed to estimate probability and magnitude of 

potential ecological impacts, the scope of the C/BR assessment was limited. The initial problem 

formulation indicated that three scenarios would be considered: urban, suburban, and rural. 

Those scenarios were not addressed in the CB/R assessment, nor were any aspects of spatial or 

temporal variability. 

The C/BR assessment of risks to predators examined exposure from ingestion only and, for the 

most part, ingestion of only Norway rats. Exposure from ingestion otroof rats, target and 

nontarget mice, birds, and invertebrates was not assessed. For exampie, residues might be higher 

or lower in mice than in Norway rats, and there are predators (e.g., kektrels, weasels) in use areas 

that might prey heavily on mice but take few, if any, rats. 

I 

I 

Many predators and scavengers feed on those dead or dying birds that eat bait. For example, redtailed hawks will prey on other birds (e.g., morning doves and starlings) which could be exposed 

to bait directly through the consumption of insects. Some of the studies in the source cited in the 
CBR report (e.g., U.S. EPA 1993) indicate that birds may comprise 10% or more of the hawk's 

diet. Canids (e.g., foxes and coyotes) will eat dead birds that have eaten bait, and one study 

found that birds comprised more than 48% of the diet (by frequency) of the coyote. This source 

of exposure needs to be considered in the risk assessment. 

As acknowledged by CBR, critical exposure data are lacking, which makes it difficult to 

quantifL risk. For example, CBR states that PT (the proportion of rodents in the diet that have 

been exposed to brodifacoum) is crucial to the model, but that "We were unable tofind any data 

regarding the proportion of rodents in the environment or in a predator's diet that may be 

exposed to rodenticides." As a result, the CBR assessment relies on a series of assumptions, I - which are not well documented and which lead to a large amount of uncertainty in the risk 

conclusions. CBR also states that PD (the proportion of rodents in the diet) and PT ". . . are 

largely determined by the behavior of the focal species and the characteristics of the habitat," 

but these factors are not considered in the model. Instead the assessment relied on LD50 values 

and average predator diets to derive risk curves for selected predators. 

2 Effects Analysis 

Quality of Data 

Mammalian LD50 data were obtained for the dog, feral pig, guinea pig, mouse, rabbit, rat, sheep, 

and wallaby. Avian LD50 data were obtained for California quail, harrier hawk, chicken (two 

studies), Japanese quail, mallard, pheasant (two studies), and pukeko. The effects data were 

reviewed for quality only as it relates to the statistical aspects of the study. Methodological 

aspects of these studies were not examined. For example, the length of the study and 

supplemental vitamin K in the basal diet, both of which have been shown to affect toxicity of 

brodifacourn in laboratory tests, were not reported. Several of the avian tests were only available 

as sketchily described studies reported by Godfiey (1 986)'. Godfrey dosed different New 

Zealand bird species in 13 tests, but states that many of the results were inconclusive because of 

the small number of birds in several groups, problems in maintaining some species in cages, and 

the high interspecific and intraspecific variations known to occur with anticoagulant poisoning. 

The author goes on to call the results "inconsistent" and "inconclusive" and says his trials support 

the view that larger test groups are needed (e.g., only 3 test groups of 4 birds per group were used 

for the harrier hawk). "Many of the birds had superjkial wounds cauJed by contact with the 

cages and aviaries. Other birds did not adapt well to captivity and their feeding was irregular. 

Thus it is likely that several were under considerable stress which may have confounded the 

results obtained." CBR explained that they teased data from the stud) and did not use results 

that were not appropriate. Such data were not used to calculate risk q40tients in EPA's 

deterministic assessment and should not be relied upon in other than a qualitative manner in a 

higher-tier assessment unless the uncertainty is accounted for. 

'Godfrey, M.E.R. 1986. An evaluation of the acute-oral toxidity of brodifacoum to birds. 

Proc. Vertebr. Pest Conf. 12:78-8 1. 
Sensitivity of Species 

CBR derived species sensitivity distributions (SSDs) separately for birds and mammals. They 

defined "low sensitivity" and "high sensitivity" species as those with LD5Os at the 90th percentile 

and 10th percentile, respectiv-ely, of the distribution. (p. 19). 

EPA is concerned not only with a large portion of the data used to generate the SSDs, but also 

with how the SSDs are used to generate risk probabilities. Whereas it is typical to use the 5th 

and 95th percentiles as the extremes of the SSD, CBR instead used the 10th and 90th percentiles 

as indicators of "high" and "low" sensitivity, as described above. Because LD50 values for three 

of the nine bird species used by CBR fall below the 10th percentile dose-response curve, the 

10th percentile is not representative of a highly sensitive species. The risk assessment should 

note that data for other species not used also fall below the 10th percentile. For example, 

Godfiey (1986) reports LD5Os <0.75 mglkg for the Canada goose and black-backed gull. CBR 

did not use or even cite those values because they were reported as "<". The values do provide 

information on the toxicity of brodifacoum, however, and should have been discussed in the risk 

characterization. Additionally, the available dietary (LC50) data and their potential impact on the 

species distributions should be discussed'in the risk characterization, even if they were not used 

to generate the SSDs. 

Representativeness of the Data 

EPA questions how representative the SSDs are for nontarget animals that may be exposed to 

brodifacoum in North America. Much of the data were generated in New Zealand in the 1970s 

and consisted of LD5Os for sheep, wallaby, European rabbit, Asian harrier hawk, and other 

species not occurring in North America. As discussed by Calow and Forbes (2003)', the 

fundamental assumption of representativeness of SSDs is violated if the data are from a small 

and biased set of species not present in the community or communities under threat. They also 

note that "We rarely have enough information on the species sensitivities in particular 

communities to place much confidence in the precise risk probabilities generated by the SSD 

approach." Uncertainty, however, should be acknowledged and can be addressed, at least to 

some extent, using SSDs. Failing to do so is a weakness of model development and not of the 

SSD approach and probabilistic methods. 

Canids as Surrogates 

CBR uses 3 surrogate species for mammalian predators and scavengers, and all are canids 

(coyote, kit fox, red fox). The only basis for using 3 canids as surrogates for all mammalian 

predators and scavengers seems to be that some dietary data were readily available (p. 15). 

'Calow, P. and V.E. Forbes. 2003. Does ecotoxicology info& ecological risk 

assessment? Environ. Sci. Technol. April 1,2003, pp. 147A-151A. 

4 
In their risk conclusions (p. 45), CIBR states that "By inference, the same conclusion applies to 

other species of birds and mammals with similar dietary composition and metabolism." Using 

other species besides canids (e.g., felid, mustelid) would have been more appropriate unless the 

assessment was intended solely to address risks to canids. Other generalist mammalian predators 

suitable for this assessment include the bobcat (Lynx rufus), long-tailed weasel (MustelaJFenata), 

raccoon (Procyon lotor), striped skunk (Mephitis mephitis), and opossum (Didelphis virginiana). 

These species all have the ability to use a variety of habitat types but, nevertheless, they all differ 

considerably in their ecology and life history, and these differences are borne out in their 

differential responses to fragmented landscape^.^ EPA also notes that any of the surrogate 

canids might well ingest bait in addition to preying and scavenging on poisoned target and 

nontarget animals. 

3 Exposure Analysis 

Limitations of the Residue Data-Pulsed Baiting 

CIBR states that "The availability offleld data on residues in rodents greatly simplzj?ed the 

exposure analysis."(p. 17), "Our exposure analysis was based on residue data from carcasses, 

which we considered to overestimate the residue concentrations in live rodents captured by 

predators." (p. 15), and "Use of carcass residues overestimated exposure to predators, because 

brodifacoum concentrations in live rats were lower on average than concentrations in carcasses - 

(Table 4)." (pp. 26-27). 

These statements imply that adequate residue data are available to characterize residues in dead 

and dying target species exposed to brodifacoum, and secondly, to determine whether or not they 

overestimate residues found in live rodents. The residue data used in the assessment come 

entirely from three field studies conducted on farms in the U.K. in the, early 1980s4 and almost all 

are for the Norway rat. In addition, most of the residue data were obtained from rats which were 

exposed to 'pulsed-baiting' applications, which differs markedly from the 'saturation' or 

3Gehring, T.M. and R.K. Swihart. 2003. Body size, niche breadth, and ecologically 

scaled responses to habitat fragmentation: mammalian predators in an agricultural landscape. 

Biol. Conservation 109:283-295 

4Edwards, P.J. and H. Swaine. 1983. Hazard to non-target animals from the use of 

'Klerat' bait on farms in the UK for control of the common rat Rattus nowegicus. Report no. 

RJ0305B. ICI Plant Protection Division, Berkshire, UK. 3 1 pp. 

Edwards, P.J., H. Swaine, and S.H. Kennedy. 1984. Hazard to non-target animals from 

'pulsed' baiting with 'Klerat' pelleted bait around farms buildings. Report no. RJ0369B. ICI 

Plant Protection Division, Berkshire, UK. 53 pp. 

Edwards, P.J., H. Swaine, J.M. Coulson and S.H. Kennedy. 1984. Hazard to non-target 

animals from 'pulsed' baiting with wax block baits around farm buildings. Report no. RJ0375B. 

ICI Plant Protection Division, Berkshire, UK. 57 pp. I 
'sustained' baiting done in the U.S. and on several of the farms in the U.K. Kaukeinen (1993)' 

states that "Pulsed baiting is a technique ofplacing limited amounts of rodenticide at timed 

intervals, rather than maintaining a surplus. This may extend the time to control and increase 

labor (which may be unacceptable for some use patterns), but the method can reduce the 

quantity of toxicant placed in the environment at any given time." 

In the pulsed baiting in the U.K. studies, 1-2 oz. (20-50 g) of bait was placed at 15- to 30-ft (5-10 

m) intervals, and bait was replenished only at 7-day intervals. In contrast, product labels in the 

U. S. direct applicators to apply 4-16 oz of. bait at 15- to 30-foot intervals and to maintain an 

uninterrupted supply of fresh bait until signs of rat activity cease. A rat can ingest a lethal dose 

in a single feeding but, on average, it does not die until 6 days later and will continue to eat bait. 

By applying smaller amounts of bait and replenishing only at 7-day intervals, the technique of 

pulsed baiting results in individual rats consuming lesser amounts of bait before dying, thereby 

reducing the total amount of bait needed for control (Dubock 1982)6. In contrast, saturation 

baiting allows individual rats to feed on bait continually until they die, thereby increasing the 

amount of brodifacoum ingested and increasing residue levels in the body. Regarding the residue 

data collected in these U.K. studies, Kaukeinen (1993) states that "Pulsed baitingproduced more 

carcasses containing lower residues than typical, sustained baiting." 

CIBR claims (p. 26) that the residue data of Edwards, et al. were analyzed for differences among 

the baiting practices (pulsed vs saturation) and that no significant differences were found. 

Therefore, the rat data were pooled along with residue data for 4 house mice, 4 wood mice, and 1 

vole subjected to pulsed-baiting. EPA has examined the residue data for the rats analyzed in the 

three Edwards studies. Of 138 rats analyzed, 11 1 were from farms which were pulse-baited at 7- 

day intervals and 27 from farms which were saturation-baited. However, data from one farm 

needs to be excluded, because other anticoagulant rodenticides (coumatetralyl, warfarin) were 

used concurrently with brodifacoum but were not accounted for in the residue analyses. 

Excluding those, residue data are available for 22 rats subjected to saturation baiting. The mean 

residue level in the 11 1 pulsed-baited rats was 1.6 ppm, versus 3.4 ppm in the 22 saturationbaited rats. These values are significantly different (t = -2.52, p<0.01) and clearly should not be 

pooled. Even if the data were not significantly different, there is no justification for using residue 

data from rats exposed to pulsed-baiting applications. Pooling the pulse-baited rats with the 

saturation-baited rats reduces the mean residue level fi-om 3.4 ppm to 1.9 ppm. This, in turn, 

leads to predictions of lower exposure and therefore lower risks to predators and scavengers. 

'Kaukeinen, D.E. 1993. Nontarget organism evaluations for rodenticides. Pages 352-363 

in K.D. Racke and A.R. Leslie (eds), Pesticides in Urban Environments: Fate and Significance. 

Developed from a symposium sponsored by the Division of Agrochemicals at the 203rd National 

meeting of the American Chemical Society, San Francisco, April 5-10, 1992 

6Dubock, A. 1982. Pulsed baiting -- a new slow acting 

rodenticides. Proc. Vertebr. Pest Conf. 10: 123- 136. 

6 
Omitting the pulsed-baiting studies from the assessment would leave field data from only 22 rats 

exposed with any similarity to baiting practices in the U.S. Consistent with the comments EPA 

has made repeatedly in communications with the registrants, this is a very minimal dataset 

contributing to uncertainty in the assessment. 

Residue Reporting Data 

The data from 13 rats captured alive and analyzed for brodifacourn residue in Edwards et al. 

(1984) was incorrectly reported as being "0.05 ppm (mgkg)," which was the Limit of Detection 

for brodifacoum. By omitting the "<" and reporting the residue level as "0.05 mag" for each of 

those rats, the assessment implies that a low level of residue was detected. This is probably not 

the case. In fact, if no residue was detected in 5 rats, it may indicate that the baiting program was 

not very efficacious or that the rats had not yet eaten the bait. 

Exposure Model 

The CIBR dietary dose model is specified as follows: 

DD=FIRxPDxPTxC 

where 

DD = 

FIR = 

PD = 

PT = 

C - - 

where 

FMR = 

GE = 

AE = 

M = 

Wt = 

Daily dose to the nontarget predator 

food ingestion rate of the predator 

% rodents in diet of the predator 

% rodents exposed to rodenticide 

concentration of brodifacoum in exposed rodents 

FIR=FMR/(GE x AE x M x Wt) 

field metabolic rate '(function of log(a), b, and Wt) of tbe predator 

gross energy content of food (rodent) 

assimilation efficiency 

moisture content of food 

body weight 

Limiting Assumptions for PD and PT 

Two important components of the exposure model are the proportion of rodents in a predator's 

diet (PD), and the proportion of exposed rodents in the predator's diet, (PT), Regarding PD and 

PT, CIBR states (p. 17) that "Both variables are largely determined by the behavior of the focal 

species and the characteristics of the habitat." and PT is ". . . assumdd equal to the fraction of 

time spent in the treated area." I 
Behavior and habitat are not addressed in the model. Instead, C/BR uses field data on dietary 

composition as reported in the literature. This may be a problem since most of the dietary studies 

in the literature were conducted in wild areas (e.g., boreal forest, prairie, fields, woodlots, etc.) 

devoid of commensal rats and mice, and thus commensal rodents comprised little if any of the 

diet. Secondly, when a rodenticide bait is applied, the food supply is altered in two ways. The 

bait provides a supplemental food source for target and nontarget primary consumers, which in 

turn provides a source of dead and dying food for predators and scavengers. Opportunistic 

species, including the five surrogate species used in the model, may drastically alter their 

foraging behavior and food habits to exploit an abundant, even if ephemeral, food source. C/BR 

does acknowledge this on p. 24 where they state that "manypredators, including theJive focal 

species we modeled, are opportunistic, and their diet reJects available food supply." Although 

the assessment acknowledges that exposed rodents are more susceptible to predation than 

unexposed rodents, the model did not account for this in the predator's diet composition. 

Incorporating feeding behavior and other aspects affecting it into the model may be more 

difficult than ignoring it, but it can be done. In their submission of February 23,2004, C/BR 

states that FIR and PD could be adjusted to vary seasonally to reflect changing feeding habits of a 

predator species; the distribution of C [Concentration in exposed rodents] could be adjusted to 

represent increased predation on moribund rodents, changes in dosing regimens, and changes in 

the concentration in rodents as a function of time; PT could fluctuate cyclically to represent 

different baiting practices; and time dependencies could be incorporated." 

C/BR states in the Results (p. 38) that ". . . risk increased sharply with increasing PT." and "In 

I situations where a high percentage of rodents are exposed to brodijiacoum, risk of seconda y 

~ poisoning may become high even for predators with a relatively low proportion of rodents in 

their diet." Moreover, in their submission of February 23, 20047, CBR states that ". . . we will 

run the model using different assumptions based on indirect estimates and expert opinion 

(including PT=1 as the worst case)." For some reason, higher-exposure scenarios were not 

presented in the assessment. In the Summary Table on p. 3, however, CIBR presents estimated 

mortality rates for the five surrogate species, assuming median sensitivity and only 1% exposed 

rodents in the diet. Results of exposure scenarios using PT values above 2.5% (e.g., 15%) were 

not included in the isk assessment even though the C/BR acknowledged that they "compared 

results of the daily dose model using PT values ranging from 1 % to 15%." 

In contrast to its stated results at the end of the document, C/BR writes at the beginning of the 

report that "The risk of brodijiacoum-induced mortality is low in the coyote, red fox, and redtailed hawk, and by inference the same conclusion applies to other species of birds and mammals 

with similar dieta y composition and metabolism. Risk is slightly greater for species with a 

higher percentage of rodents in their diet, such as the kit fox and great horned owl." "Slightly" 

7Methods and Procedures For Assessing the Risk of Rodenticide Baits Containing 

Brodifacoum to Non-Target Animals, W. Warren-Hicks and J. Giddings, The Cadmus Group, ~ February 23,2004,5 pp. 
kit fox means that there is a 10% probability that mortality exceeds 5% under those 

exceeds 7% for the great horned owl. Apparently, mortality could be as high as 

of the range of such a sensitive parameter as PT to an unsupported 

appropriate. As suggested below, assumptions concerning values for PT 

that brodifacoum is being used in an effective baiting program 

rodents may be exposed. 

of Norway rats and house mice that are exposed to a 

is likely to be much smaller than one in ten." 

The value o /f one in ten is unsupported and seems to postulate an ineffective baiting program - 

only a small proportion of the target organisms will beexposed. Assuming that 

used effectively - i.e., a large proportion of the target rodents are killed by 

it follows that a large proportion of the target rodent population is 

is reenforced by the Edwards studies (Edwards and Swaine 1983; 

which also suggest that substantial nybers of nontarget species 

effective baiting program, particularly if saturation baiting methods 

are used. I 

As a prelim'nary illustration, Edwards et al. (1984b, cited in footnote 4) provide data on a 

relatively s ccessful baiting program - i.e., p. 20, Table 8, Farm A. I$ this example, control of 

rats is esti ated at 88%. In other words, it seems safe to assume that at least 88% of the targeted 

rats were e posed - i.e., PT = 0.88, rather than 1%, for effective contyol of a rodent infestation. 

As discuss d by Edwards et al. (1984b), the control rate of 88% is probably an underestimate 

because of einfestation. A more careful review of data associated with successful baiting 

programs ould probably result in better estimates of PT and possible error ranges associated 

with PT. E A asserts that plausible estimates of PT (which should encompass both target and 

nontarget c r dents) are substantially higher than the 0.01 to 0.15 values used by C/BR. 

- 

further describes PT as the portion of consumed rodents that are exposed to 

the daily dose model, PT is implemented as a binomial distribution, where a 

that none of the rodents consumed contain brodifacoum and a value 1 

the rodents consumed contain brodifacourn. All or nothing is an extreme 

scenario. 1 

(p. 25), "Because a single rat would represent a substantialfiaction of the 

intermediate values of [daily] DPT would be 

be either entirely exposed or entirely unexposed." 

be explored more fully, especially for target And nontarget mice which 

The distribution of FIR should be presented together with the 

I 
The consequence of the "all or nothing" modeling approach for determining daily doses (given 

the small P* value used by the authors), is that a high percentage of the exposure days yield a 

dose of 0, ahd a small percentage yield an extremely high dose. The likelihood of exposure, or 

equivalently the average percentage of days in which exposure is non-zero is determined by PT, 

which the aithors have claimed to be very small. 

The authors of the report appended to this review suggest the alternative approach of applying PT 

to individu 1 1 prey meals rather than to total daily events, such that the conceptual model 

simulates ''4rey consumption events" rather than "total daily events." As they note, this gives a 

that at least one meal is contaminated, so there will be fewer days with doses of 0. 

'all or nothing' case that is modeled right now, it is less likely that ALL of the 

per day]. . . [would or would not be] contaminated on any given day." 

on pages 35-39 of the Appendix, for very low values of PT (e.g., PT < 20%) 

of PT (e.g., PT >go%), the two approaches yield essentially the same result, 

This is because the frequency of daily consumption of contaminated prey is 

cases. However, for intermediate estimates of PT (e.g., 30% < PT < 

be significant. While the mean daily dose (DD) is approximately the 

daily estimates are 0 for the individual prey method. (See Figures 1 

of this alternative is the bioaccumulation potential of brodifacoum. The 

approaches (i.e., All or Nothing vs. Individual Prey methods) may be 

for elimination kinetics is moderate to long (> 10 days), as is the case 

the frequency of consecutive days of non-zero exposure, the more 

to accumulate in the body of the predator or scavenger. 

Distribution of 10,000 Exposure Events 

CIBR calcu ated a summary statistic from the time series of body burdens. They suggested that 

the 90-day aximum is an appropriate metric. For each individual, 100 sets of time series were 

generated, ith each time series yielding an estimate of the 90-day maximum. Therefore, for 100 

individuals, there a were 100 sets of time series of 90-day maxima. 

At this point, 

represents 

\ 

1. 

the 

The 

risk 

there are two possible approaches to generating the distribution of doses that 

fne combined sources of variability in the model: 

Summary Statistic Approach. For each individual, calculate a summary statistic from 

:. 00 sets of 90-day maximum. For example, calculate the mean 90-day maximum, or 

calc~late an upper percentile (e.g., %ile), or even the maximum of the maximum. 

choice clearly has important implications for the overall distribution of doses and 

; to the population. 

10 
2. All 90-day Maxima Approach. Use all of the 100 values of 90-day maxima for all 100 

individuals, yielding a data set of 100 x 100 or 10,000 values to define the exposure 

(dose) distribution. 

Section 3.4.4 of the report describes the approach, stating that "ne mean of the 100 simulation 

outcomes was calculated, and the 100 means @om 100 individuals) were plotted as reverse 

cumulative frequency distributions. " The risk characterization (Section 5. I), however, states, 

"For each cumulative dose model run, the full set of 10,000 dose estimates (1 00 estimates for 

each of 100 individuals) was combined into a single exposure distribution reflecting both withinindividual and among-individual variation. " 

While the statements are conflicting and the input files were not included in materials provided, 

it appears from code and from certain descriptive statistics that the CBR assessment elected 

Option 1, with the summary statistic being the arithmetic mean of the 90-day maxima. This 

yielded a data set of 100 values of mean 90-day maxima. 

EPA maintains that the difference between the distribution of 100 means of 90-day maxima, and 

the distribution of 10,000 values of 90-day maxima is a large one. The former could greatly 

underestimate the upper tail compared with the latter. Figures 7 and 8 of the Appendix provide 

an example for coyote. For this example, setting PT = 0.025, halflife = 5 days, and SD of DPD = 

5%, the effect of decreasing variability by representing individuals by the mean 90-day max is to 

decrease the dose at the 5% exceedance level by 40%. 

90-day Time Period for Exposure 

The C/BR assessment claims that "In each of the 100 inner loop iterations, a sequence of 90 

independent daily doses was simulated ... " 

The choice of a 90-day period appears to be arbitrary. C/BR elected to select the first 90-day 

period for each time series of daily doses. All individuals began the time series with a body 

burden of 0 mglkg. The concept of first order kinetics implies that it may take some time for the 

chemical to achieve steady state. This time to steady state is influenced by both the choice of 

depuration halflife (t %) as well as the frequency of daily doses. It should be noted that for some 

scenarios, the frequency will be very low because PT is set to a low percentage, and because, as 

mentioned above, it is applied as an "all or nothing" approach. 

The difference setween the 90-day maximum during the first 90 days and the maximum during 

the last 90 days of a 365 day period is demonstrated in Figures 9-1 1 of the Appendix. These 

authors make the following observations: 

When the dosing regimen is frequent, the assumption about t % becomes critical; and 

The maximum 90-day body burden is unlikely to be capturedby the first 90-days of the 
time series, especially for longer halflife scenarios. 

Kinetic model 

C/BRYs kinetic model is limited to one compartment and does not consider available metabolism 

studies (See detailed comments in the Appendix.) C/BR postulates a single compartment kinetic 

model for brodifacoum, claiming that "Depuration kinetics of brodifacoum are complex, as 

described in Sections 2.2.3 and 3.3.2.2. Studies in rats suggest that depuration follows a 

biphasic curve (Kaukeinen et al. 2000). The initial phase of the curve is steep, with a halflife on 

the order of a few days, reflecting a pool of rapidly eliminated brodifacoum in the body. The 

secondphase of the depuration curve is much slower, with a halJlife on the order of many 

months, reflecting a much smallerpool of tightly bound brodifacoum in the liver. However, the 

toxicokinetics of brodifacoum, particularly the rates of exchange between blood, liver, and other 

tissues, have not been quantiJied even in the rat. Many mechanistic and quantitative 

assumptions would beneeded to simulate the biphasic depuration process, and the results would 

reflect large uncertainties in these assumptions. ..." 

The latter two statements give the impression that the kinetics of brodifacoum are poorly 

understood and cannot be used for the type of analysis being considered by CBR. Information 

on the pharmacokinetics of brodifacoum is available in 16 studies on seven species. These 

studies are discussed in the Rodenticide Cluster RED' and in EPA's comparative risk assessment 

for the rodenticides. 

C/BRYs discussion of a "pool of rapidly eliminated brodifacoum" (p. 21) appears to be a 

misinterpretation of the biphasic pattern in the concentration of brodifacoum in the body. The 

first and more rapid phase is associated with redistribution from blood to tissue, not excretion. 

The C/BR report chose a range of halflives from 2 days to 200 days with the implication that the 

"real" halflives are somewhere in this region. In terms of the body burden of the prey species, 

EPA believes that the shorter halflives of 2 days to several days reflect the blood data and 

redistribution from blood to tissue, but do not reflect body burden. The upper range of 200 days 

is presumably based on liver halflives. As reviewed in EPA's rodenticide risk assessment, 

reported liver halflives are in the range of about 74 to 350 days. 

The assessment then went on to discuss the use of the first order model and a sensitivity analysis 

using the range of values (2 to 200 days). As discussed in detail in the Appendix, the sensitivity 

analysis may lead to the impression that the kinetics have minimal impact on the assessment of 

risk. Basing most of the analyses on a 50-day halflife in a simple one compartment model, 

however, will probably underestimate risk. 

I 

in ere gist ration Eligibility Decision (RED): Rodenticide ~lusker. July, 1998. EPA738-R98-007. 307 
As illustrated in the Appendix, the kinetics of brodifacoum can be described by a simple flowlimited physiologically based pharmacokinetic (PBPK) model that is optimized with 

concentration data in liver, kidney, and carcass. Such a model accounted for the bulk of the 

brodifacoum residue data in the rat. 

Field and Incident Data 

I 

Relevant information that could be used as data inputs for the exposure model is available. For 

example, there, is an ongoing field project involving the San Joaquin kit fox9, one of C/BR's 

surrogate species, that could have provided more relevant and usehl information than the few 

dietary studies C/BR gleaned from the open literature. Also in California, researchers associated 

with the Santa Monica Mountains National Recreation Area, Department of Natural Resources 

Conservation, and the University of California at Los Angeles have studied the ecology and 

behavior of coyotes (also a surrogate species) and bobcats relative to development in a 

fragmented habitat in southern California for several years''. They captured and radiocollared 

numerous coyotes, bobcats, and mountain lions and determined home ranges in relation to human 

development. Many of these individuals were exposed to anticoagulant rodenticides, including 

brodifacoum. 

In addition, the C/BR risk characterization could include an explanation of the incident data that 

are available on brodifacoum. This data was summarized in EPAYs rodenticide risk assessment 

as well as incident data reported in publications cited in the C/BR report (e.g., Edwards and 

9Dr. B. Cypher, Research Ecologist for The Endangered Species Recovery Program 

(ESRP), coordinates several of ESRP's research projects on San Joaquin kit foxes. The ESRP is 

a cooperative research program on biodiversity conservation'in central California, administered 

by California State Unviersity, Stanislaus Foundation. Its mission is to facilitate endangered 

species recovery and resolve conservation conflicts through scientific'ally based recovery 

planning and implementation. The program was established in 1992 kt the request and with the 

support of the U.S. Fish and Wildlife Service and the U.S. Bureau of eclamation. Over the past 

decade, ESRP has grown into a cooperative research program working R with local, State, and 

Federal agencies, non-governmental organizations, corporations, andprivate land owners. 

1°e.g., see S.P.D. Riley et al. 2002. Effects of Urbanization a d h Habitat Fragmentation 

on Bobcats and Coyotes in Southern California. Conservation Biol. 17:566-576 and in prep 
Swaine 1983; Edwards et al. 1983a,b) and in the open literat~re"~'~"~~'~ 

4 Risk Characterization 

Implementation 

While employing a Bayesian hierarchical framework is an interesting and potentially useful 

innovation, the use of such a model is not a substitute for limited, incomplete, and inconsistent 

data. Further, the model is implemented using certain assumptions that have the potential to 

severely limit the range of possible predicted outcomes. 

On page 35 the authors state that "hierarchical models reduce the effect of incomplete data sets, 

small numbers of tests, inconsistent information on effects among species, and other issues that 

lend uncertainty to the effects models." While EPA agrees that this can be a helpful approach in 

such situations, the extent to which the limitations of incomplete data can be addressed by such a 

modeling approach is not clear in general. In the specific case of the C/BR assessment where the 

range of species selected is limited, where only certain studies within species have been selected, 

where available metabolism data have not been used, and where assupptions on behavioral 

parameters have not been explored to the full range of their implications for exposure, the benefit 

of such an approach cannot be expected to overcome the limitations of the data. 

Furthermore, the selection of the 10th percentile to constitute a "sensitive" species, especially 

among those particular species assembled, does not necessarily constitute a conservative 

approach, but rather, appears to limit severely the resulting overall effects distribution when 

combined with the median and 90th percentile species estimates. As pointed out in the ,- 

SERAISRC report appended to this document, the "bounds shown in Figures 15 and 16 do not 

appear to represent the variability that exists, even within these very limited data sets, and should 

not be used to define what the dose-response relationship would be for a hypothetical 

sensitive nontarget species. The bounds do not include even 80% of the dose-response curves 

used. For birds, out of 9 curves, only 3 are completely contained within the 80% confidence 

bounds. For mammals, ... Of the 8 curves shown, only 1 is completely contained within the 80% 

"Stone, W.B., J.C. Okonlewski, and J.R. Stedelin. 2003. Anticoagulant rodenticides and 

raptors: recent findings from New York, 1998-2001. Bull. Environ. Contam. Toxicol. 70:34-40 

12Stone, W.B., J.C. Okonlewski, and J.R. Stedelin. 1999. Poisoning of wildlife with 

anticoagulant rodenticides in New York. J. Wildl. Diseases 3 5: 187-1 93 

I3Hosea, R.C. 2000. Exposure of non-target wildlife to anticoagulant rodenticides in 

California. Proc. Vertebr. Pest Conf. 19:236-244 I 

I 

14S.P. D. Riley et al. 2002. Effects of Urbanization and at Fragmentation on 

Bobcats and Coyotes in Southern California. Conservation 
bounds." (p. 10) An examination of these figures therefore indicates that even with the limited 

data chosen, this implementation of the hierarchical approach appears to restrict, rather than 

encompass or expand, the range of outcomes expected. 

Finally, factors such as distribution assumptions for both the model parameters of the overall 

distribution and for the individual species dose-response curves should have been discussed in 

the C/BR assessment. Although the assumptions for the 'super' distribution the parameter 

values may be typical, their impact should be explored, especially given the results in the figures 

mentioned above. Additionally, while the impact of using a logistic model rather than the probit 

in analyzing the dose-response data might not be large with respect to the LD50, it may 

underestimate mortality at the lower end of the curves. 

Results and Conclusions 

The C/BR exposure characterization is based on a series of unsupported assumptions that 

contribute to a highly uncertain l'isk estimate. C/BR provides exceedence probabilities (Table 14, 

p. 70) assuming high (10th percentile), median (50th percentile), and low (90th percentile) 

sensitivity and either 1 % or 2.5% exposed rodents in the diet. For the kit fox, assuming high 

sensitivity and 2.5% exposed rodents in the diet, there is a 10% probability that mortality exceeds 

67% and a 50% probability that mortality exceeds 25%. CIBR categorizes that risk as "slightly 

greater" than the "low" risk identified for other surrogate species. Even assuming only 1% 

exposed rodents in the diet and high sensitivity, there is a 10% probability that mortality exceeds 

23%. Exceedences for the great horned owl are nearly as high. Exceedences are even higher as 

I the percentage of exposed rodents increases in the diet. ~ 

I 

On p. 3 1 C/BR states that "Circumstances in which PT is greater will result in pvoportionally 

greater daily doses. " While in the model this does not necessarily correlate directly with 

proportional estimates of mortality, there is some indication from Table 14 that mortality may 

increase at a higher rate, depending on species and assumption of sensitivity. Nevertheless, it is 

clear that a 6-fold increase in PT from 0.025 to only 0.15 would result in a substantially higher 

probability that mortality will be close to 70%. Alternatively, the same change in PT would 

result in a 50% probability of far greater than 25% mortality for kit fox under the circumstances 

cited above. I. 

Finally, field data would appear to support these higher assumptions tor exposure. Low-to-no 

predictions of risk in C/BR's assessment are not substantiated in the field, where field studies 

and incident reports clearly indicate that risks to nontarget species are not only plausible but are 

well-documented. 

Uncertainty 

I 

Although the CBR assessment acknowledges the limitations and undertainties associated with 

the assumptions inherent in their assessment of brodifacoum risk to &avengers and predators, a 

quantitative uncertainty analysis or bounding analysis to improve the 

15 

risk characterization is not 
provided. Had the variables that C/BR summarized been addressed (or followed through) in the 

problem formulation stage of the effort, a more useful product for risk managers might have been 

developed. These include limitations of scope from restricting the assessment to secondary 

exposure with one target species, failing to consider spatialltemporal variations such as habitat 

and season, and a more detailed explanation of PT and PD. 

Finally, as previously emphasized in this evaluation, a major issue in the C/BR assessment is the 

lack of quantifiable exposure data. Data which can provide a more solid foundation for 

estimating exposure would be extremely useful to reduce uncertainty in risk estimates. Where 

data are limited, however, methods are available in the implementation of the model for 

addressing uncertainty. A major deficiency with the current model which severely limits its 

utility is its failure to address the important variables mentioned above and to account adequately 

for uncertainty in the parameters it does address. 
SERA TR-46-2- 1 0- 1 e 

Submitted to: 

Gail V. Brooks (7507C) 

and Gail Maske-Love 

U.S. Environmental Protection Agency 

1801 Bell Street 

Room 1 102 1 -L - Crystal Mall Building #2 

Arlington, VA 22202 

U.S. EPA Contract No. 68-W-02-035, WA 2-10 

Period of Performance: May 1,2004 to April 30,2005 (Option Period 2) 

Probabilistic Risk Assessment Support for EFED 

Contracting Officer: 

Mr. Robert Minjack (3803R) 

U. S. Environmental Protection Agency 

1200 Pennsylvania Avenue, NW 

Ronald Reagan Building 

Washington, DC 20460 

Submitted by: 

Syracuse Environmental Research Associates, Inc. 

5 100 Highbridge St., 42C 

Fayetteville, New York 13066-0950 

Telephone: (3 15) 637-9560 

Fax: (3 15) 637-0445 

E-Mail: SERA-INC@msn.com 

Home Page: www.sera-inc.com 

Prepared by: 

Philip E. Goodrum2, Maxine E. Dakins3, Maria Mastriano2, Patrick Durkinl 

Syracuse Environmental Research Associates1 

Syracuse Research Corporation2 

University of Idaho3 

January 4,2005 
Table of Contents 

1 . Introduction ............................................................... 3 

Acronyms and Abbreviations ............................................... 4 

2 TECHNICALISSUES . ...................................................... 5 

2.0. General Comments ................................................... 5 

2.1. Conceptual Model .................................................. -5 

2.2. Exposure Assessment ................................................. 7 

2.3. Toxicity Assessment ................................................ 10 

2.4. Risk Characterization ................................................ 11 

Appendix A . Original Comments from Maxine Dakins. PhD .......................... 13 

Appendix B . Original Comments from Patrick Durkin. PhD .......................... 15 

Appendix C . Original Comments from Philip E . Goodnun. PhD ....................... 33 

1 . Conceptual Model (Figure 1. p . 77; Section 2.2) ............................ 33 

2 . PT as All or Nothing ................................................. -34 

3 . Monte Carlo Analysis ................................................ -40 

Step 1 . Outer loop. Interindividual Variability .......................... 40 

Step 2 . Inner Loop 1. Time Series of Daily Doses ...................... -44 

Step 3 . Inner Loop 1. Calculate Summary Statistics ...................... 48 

Step 4 Inner . Loop 2. Time Series of Cumulative Doses (Body Burdens) ..... 49 

Step 5 . Inner Loop 2. Summary Statistics .............................. 49 

Appendix D . Original Comments from Maria Mastriano MS ......................... 55 

ATTACHMENTS 

Attachment 1: A Physiologically Based Pharrnacokinetic Model for Brodifacoum 
1. Introduction 

U.S. EPA requested a peer review of the ecological risk assessment on Brodifacoum submitted 

by Syngenta Crop Protection, Inc. in September 2004. This review focuses primarily on the 

scientific merits of the analysis. In order to better understand how the analysis was conducted, 

we referred to electronic files provided with the report (summarized below). These files were 

used to both rerun selected analyses, and to modify the models to more fully explore the 

consequences of the assumptions presented by the report authors. We did not attempt to perform 

a hll QAIQC of all of the steps in the analysis. 

This report synthesizes all of the comments into general issues, as well as issues specific to the 

conceptual model, exposure assessment, toxicity assessment, and risk characterization. Detailed 

comments from each individual are included in Appendices. The following questions posed by 

Mary Frankenberry helped to guide our review: 

1. Are the assumptions in the analysis well supported? 

2. If alternative assumptions could have been made, what were the consequences of the 

approaches used on the overall risk estimates? Did the approaches used tend to increase 

or decrease risk? 

3. Should additional questions be posed, or should additional materials be requested of 

Syngenta, in order to improve the clarity and transparency of the analysis? 

The following materials were submitted to the Agency and were considered in this review: 

1. Risk assessment report entitled, "A Probabilistic Assessment of the Risk of Brodifacoum 

to Non-target Predators and Scavengers. Prepared by The Cadmus Group, Inc., Jeffrey 

Giddings and William Warren-Hicks, on behalf of Syngenta Crop Protection. September 

10,2004. Syngenta Number T001270-04." Hereafter, this reported is referenced as 

Cadmus (2004). The file name is 1270-04 Assessment Publication.pdf. 

2. Conceptual Model Overview - MS Power Point files 

EPA Model Demo 060ct04.ppt 

Wash talk February 2004.ppt 

Monte Carlo Analysis - MS Excel files ' 

CD001-01 .xls 

DD00 1 .xls 

Exposure Runs.xls 

GHO-CDOO1-001 .xls 

Page 3 of 60 I 

~ 
Bayesian Analysis - Winbugs analysis files 

winbugs input-bird data-FINAL.txt 

winbugs input-mammal data-FINAL.txt 

5. SAS files 

bird dose-response plot FINAL.sas 

final risk code.sas 

generate bird data - FINAL.sas 

generate mammal data - FINAL.sas 

mammal-response plot FNAL.sas 

It should be noted that these files were sufficient to evaluate some, but not all of the analyses 

presented. Specifically, only selected Excel files were provided to rerun Crystal Ball simulations 

to reconstruct the exposure assessment. For example, the file CD001-01 .xls provided selected 

summary statistics from the 2-D Monte Carlo simulation for coyote; runs for other receptors were 

not available. The SAS files provide some insights into how the exposure and toxicity 

distributions were combined, however, specific files that are referenced in input file commands 

were not provided. Therefore, the risk calculations could not be easily reproduced. I 

The references cited in the Cadmus report should have been submitted with the document. Some 

key references were obtained during the review and these references were useful. If additional 

citations had been readily available, they would have made the review process more efficient and 

the quality of the review would have been enhanced. 

A number of acronyms and abbreviations appear in the Cadmus report and in our comments. 

The following list of the more frequently used terms is provided here for convenience. 

Acronyms and Abbreviations 

PD = % rodents in diet I 

PT = % rodents exposed to rodenticide 

FIR = food ingestion rate 

log(a), b = allometric scaling factors 

Wt = body weight 

GE = gross energy content of food 

AE = assimilation efficiency 

M = moisture content of food 

FMR = field metabolic rate (function of log(a), b, and Wt) 

DPD = daily PD 

DPT = daily PT 

C = concentration in exposed rodents 

t , = halftime for first order elimination kinetics 

Page 4 of 60 
2. TECHNICAL ISSUES 

This section summarizes the main comments on the brodifacoum risk assessment. Detailed 

comments from each of the reviewers are included as appendices. 

I 2.0. General Comments 

The overall approach used to characterize risks to birds and mammals reflects an attempt to 

develop refined estimates of risk by incorporating variability and uncertainty in exposure and 

dose response. The objective is commendable and has the potential to provide usehl 

information for the risk management of brodifacoum and other rodenticides. However, the 

analysis presented by Cadmus (2004) is an incomplete and superficial assessment that ignores 

relevant data, is based on an inappropriate pharrnacokinetic model, and includes questionable 

assumptions that, in most cases, can be expected to substantially underestimate exposure and 

risk. The conclusions reached in the Cadrnus (2004) submission are of little use in assessing 

risks posed by brodifacoum used in effective rodent control programs. 

Key shortcomings of the analysis are identified in the following sections. Table 1 summarizes 

the consequences of the approach that was used in terms of the potential impact on the overall 

risk estimate. This effect is characterized qualitatively (i.e., an alternative approach would tend 

to yield higher or lower estimates of risk). In cases where we demonstrate an alternative 

assumption or modeling approach, the effect on the risk estimate is expressed quantitatively in 

the more detailed comments. Table 1 also serves as a cross reference to specific comments in 

each Appendix. 

The results of the analysis lead the authors of the Cadmus report to conclude that risks to 

terrestrial receptors are low, and unlikely to exceed levels of concern for acute effects. This 

conclusion seems to be based on a large number of unsupported assertions all of which lead to 

lower estimates of risk. These sort of broad and unsupported statemJnts detract from the sense 

of objectivity that a risk assessment should have. The basic assumption appears to be that the 

exposure of rodents to the rodenticide is negligible to low (PT=O.Ol tp 0.15). Thus, the risks to 

other organisms consuming the rodents will probably be negligible to, low as well. This 

conclusion, however, is of little use in assessing risks posed by brodifacoum used in effective 

rodent control programs. 

2.1. Conceptual Model 

1. The conceptual model excludes potential routes of exposure ahd does not carry forward 

several of the pathways that were identified. l 

a. Direct ingestion of bait by predators/scavengers is identifi i, d as a potential (primary) 

route of exposure, but it is not included in the assessment. A relatively simple 

exposure model can be used to address this exposure path ay (see Appendix B, 

Comment 2; Appendix D, Figure 1). 

Page 5 of 60 1 I 

I 
b. The conceptual model identifies rodents as the target species for baiting. All 

nontarget animals other than rodents (e.g., rabbits, birds, larger mammals, etc) are 

apparently excluded. However, these are also potential prey species for at least some 

of the predators considered in the assessment. For example, several studies 

demonstrate that rabbits comprise more than 25% (by volume) of the stomach 

contents of red fox (see Appendix D). Furthermore, these are species that could 

easily consume the bait directly, or via diet. For example, coyote may be a predator 

of both birds and fox. Also, it is unclear if the information used to define PD is 

intended to be inclusive of all small rodents (e.g., mice, voles, squirrels), or if it is 

specific to rats. To the extent possible, all target and nontarget prey species (feeders 

and carcasses) should be included. 

c. Invertebrates such as beetles and ants that are consumed by some of the predators are 

excluded from the dietary model as a source of brodifacoum exposure. Since 

invertebrates do comprise a non-negligible portion of the total diet, especially during 

the summer, we question whether this is truly a minor source of uncertainty in the 

overall exposure model. The major uncertainty stems not so much fkom the estimate 

of the percent of diet comprised of invertebrates ("PD-invertebrates"), but rather fi-om 

the lack of information available to determine the fraction of invertebrates that are 

consumed and contaminated (i.e., "PT-invertebrates"). This PT term likely varies as 

a function of baiting practice. It could easily be explored in a series of alternative, 

plausible scenarios, similar to what was done with the PT term for rodents. 

2. The assessment does not adequately describe the underlying assumptions concerning how 

the bait is used and applied and does not address uncertainties associated with baitins 

practices. 

a. The assessment attempts to model exposure under the assumption that baiting 

practices are performed according to the directions on the label. The use 

characterization should be more fully described. Very different exposure scenarios 

may be reflected by the assumptions regarding baiting. A successful bait scenario 

would result in a higher number of carcasses over a short period of time. A less 

successful bait scenario would result in a lower number of carcasses in the short term, 

but possibly a prolonged period of exposure for predators. 

b. It was assumed the amount of product used was consistent with the directions on the 

label. This assumption is questionable and should be explored through sensitivity 

analysis. 

c. It was assumed that indoor use would not lead to an increase in exposure to nontarget 

species. This assumption is questionable and should be explored through sensitivity 

analysis. 

Page 6 of 60 I 
2.2. Exposure Assessment 

1. The selection of PT (the proportion of rodents exposed to brodifacoum) appears to be 

arbitrary, incorrect, and biased. 

a. PT is varied across a vary narrow range, with a maximum of 15%. This is unlikely to 

reflect the extent of contamination associated with successful baiting practices. The 

full range (up to PT = 100%) should be illustrated, especially since this variable is 

defined exclusively based on judgement rather than available data. 

b. As used in the Cadmus report, PT appears to be a composite parameter reflecting 

exposures to both target rodents as well as nontarget organisms. It is likely that the 

analysis would have been clearer if these two factors were explicitly distinguished. 

Field data are available indicating that PT can exceed 80% for target rodents. The 

Cadmus report appears to assume that PT for nontarget rodents is much lower and 

, that the consumption of target rodents will be low. The report, however, does not 

adequately discuss or justify these assumptions. 

c. The Cadmus report states that: neproportion of Norway rats and house mice that 

are exposed to a brodifacoum baitingprogram is unknown, but is likely to be much 

smaller than one in ten (Cadmus, 2004, p. 27 of 114). The value of one in ten is 

unsupported. This seems to explicitly postulate an ineffective baiting.program - i.e., 

only a small proportion of the target organisms will be exposed. . 

d. The argument in the Cadmus report that the true PT value in the field is at or below 

1% seems without merit and at very least is open to debate. This assumption could be 

construed to be servingPthe interests of a particular group. 

The selection of values for PD (proportion of rodents in the diet of predators) and the 

estimates of the variability of PD appears to be overly restrictive. 

a. The use of only PD (proportion of rodents in the diet) as an input for dose ignores 

both direct consumption of the bait by nontarget species as well as the consumption of 

other types of animals (e.g., birds and insects) that may contain toxicologically 

significant amounts of brodifacoum. The importance of both direct consumption of 

bait and the consumption on non-rodent organisms is amply demonstrated 

b. The variability in daily percent diet from rodents (PD) is qepresented by a mean PD 

and an assumed standard deviation. The parameters are assumed to be independent, 

suggesting that individuals with very low percentages of rodents in diet exhibit the 

same variability in diet as individuals with very high percentages of rodents. This 

seems like a less plausible assumption than the use of a fiked CV, which ensures that 

higher mean PD is also associated with higher variability jn PD. 

I 

Page 7 of 60 I 
3. The probabilistic approach does not address potential sources of variability and 

uncertainty. 

a. PT is modeled as all or nothing for any given day. An alternative, and perhaps more 

plausible scenario is that PT should be applied to individual prey meals. The result 

will be a time course of more frequent daily doses instead of the long periods of no 

exposure that are currently simulated when PT is set to a low value (e.g., 3%). 

b. The correlation between the log(a) and b parameters of the FMR equation is ignored. 

This is a source of uncertainty that could result in an underestimate of the 95th 

percentile FMR by as much as 10%. 

' c. Total daily consumption is modeled instead of individual prey meals. This does not 

account for gorging behavior (i.e., exceedance of average daily FIR), which is likely 

for opportunistic feeders like the fox and coyote. A simple prey meal consumption 

scenario could be incorporated into the daily dose inner loop by estimating the body 

weight of each prey item consumed. Data on common prey items such as rats, mice, 

meadow voles, and rabbits are readily available. 

d. A lognormal distribution is used to describe variability in DPD. Alternative plausible 

probability distributions should be explored as part of the uncertainty analysis, 

especially if DPD is identified from a sensitivity analysis as a major source of 

variability in the model output (i.e., distribution of daily bddy burdens). 

e. Seasonal variability in dietary habits will result in periods Of the year in which rodents 

comprise a greater percentage of the diet. Data on seasonal variability should be 

taken into consideration in defining the appropriate input distribution for PD. 

f. Variability in exposure and effects is expected as a hctioh of developmental status. 

Risks to kits, for example, may be greater than risk to adul! foxes. 

The reliance on monitored concentrations of brodifacoum in rdt carcasses limits the 

ability of the analysis to reflect variations in baiting practices ahd may underestimate risk. 

l a. The Cadmus report states that: Earlier drafts of the analyszls plan had assumed that 

submodels would need to be developed to estimate concenflations in food. This 

would have been preferable to the approach taken in the ddcument and would have 

allowed the analysis to assess differing risks among differe h t baiting practices. The 

modeling effort could have been validated with the monitofing data that are available 

with specific baiting practices. 

b. The reliance on monitoring data may have modestly simpliked the analysis but this 

approach ignores temporal relationships that may lead to ad underestimate of risk. In 

other words, the number of rodents (target and nontarget) that are exposed to 

Page 8 of 60 
brodifacoum and the concentrations of brodifacoum in these organisms is likely to 

vary over time. The failure to consider this may miss periods of peak exposure in 

predators. 

5. The simulation approach focuses on summary statistics that are not conservative. 

The first inner loop is used to develop a sequence of daily doses for each of 100 

individuals. Each sequence is then fit to a lognormal distribution defined by a mean and 

standard deviation. Then, the parameters of the lognormal are characterized by 

distributions - a beta is used to represent variability in the mean daily dose, and a gamma 

is used to represent the coefficient of variation (CV) daily dose. Next, a set of parameters 

(mean, SD = CVImean) is determined, and a new time course of daily doses is simulated 

to estimate daily body burdens of brodifacoum. 

a. The first summary statistic calculated is the maximum body burden during the first 

90-day period for one individual. Depending on the choice for the half-life value used 

to represent elimination and metabolism, bioaccumulation may occur such that body 

burdens associated with the first 90-day window of exposure will be much lower than 

body burdens later in the year. The maximum 1-year body burden would be a more 

appropriate metric than the maximum of the first 90 days. 

b. The second summary statistic calculated is the arithmetic mean of the maximum 90- 

day body burdens estimated from 100 consecutive simulations of the individual 

described above. This could be interpreted as either an alternative realization of the 

dosing for one individual, or an attempt to model 100 different individuals with the 

same general daily patterns. This step is unnecessary, and not protective. It 

effectively dampens the variability in peak exposures. Instead, each individual 

simulation should be used to generate a plausible maximum. 

c. The third summary statistic involves generating an empirical probability distribution 

based on 100 separate estimates of the mean of the maximum 90-day body burdens 

described in (b) above. It is unclear why only 100 individuals are simulated, and there 

was no attempt to demonstrate that 100 individuals is sufficient to achieve stability in 

the characterization of the upper tail (> gofh percentiles) of the distribution. 

6. The justification for and documentation of the distributions used in the Monte Carlo 

simulation are incomplete. For example, probability distributions used in the analysis are 

not fully described in the text. At a minimum, the following information should be given: 

the type of probability distribution, the parameter values, summary statistics if the 

parameter values are not obvious to most readers, and a graphic (probability density 

function andlor cumulative distribution function). 

a. All choices of probability distributions should be justified or at leasted discussed and 

summarized in a table and with graphics. I 

Page 9 of 60 
b. It is unclear if unbounded distributions like the lognormal and gamma, which are used 

in this analysis, are actually truncated to reflect the constraints of the variable. For 

example, the lognormal PDF for percent diet comprised of rodents should be 

truncated at 100%. 

2.3. Toxicity Assessment 

1. Use of the full dose-response curve in the risk calculation is an improvement over the use 

of species sensitivity distributions. However, some of the methods and results associated 

with the aggregation of toxicity data need further explanation. 

a. Definitions of sensitive species is subjective. It is unclear why a sensitive species was 

defined as the loth percentile and not the 5th percentile or 2.5th percentile. Defining the 

bounds as the 5th and 95th would lead to 90% confidence while using the 2.5th and 

97.5th would lead to a 95% confidence of including the dose response curves of 

nontarget species. A 80% confidence seems inappropriately low in this instance. 

b. The bounds shown in Figures 15 and 16 do not appear to represent the variability that 

exists, even within these very limited data sets, and should not be used to define what 

the dose-response relationship would be for a hypothetical sensitive nontarget species. 

The bounds do not include even 80% of the dose-response curves used. For birds, out 

of 9 curves, only 3 are completely contained within the 80% confidence bounds. For 

mammals, one curve lies left of the lower bound, two are partially left of it, two are 

completely right of the upper bound, and two are partially right of it. Of the 8 curves 

shown, only 1 is completely contained within the 80% bounds. 

c. For the Bayesian approach, discussion and justification should be provided for the 

assumption that both alpha and beta are normally distributed. If it was simply done 

for convenience, it would be helpful to have a sensitivity analysis on that assumption. 

This is particularly important given that the 80% confidence b~unds on the 

doselresponse curves did not appear to cover 80% of the curves. 

d. The Cadrnus report should justifL or at least discuss the basis for using the logistic 

transformation (over the more commonly used probit transformation) in the doseresponse assessment. 

2. The discussion and application of the single compartment kinetic model is based on a 

superficial and incomplete review of the available data and an incorrect assessment of the 

available data on the pharmacokinetics of brodifacoum. The use of the single 

compartment kinetic model is unnecessary, inappropriate, and pimits the assessment of 

the adverse effects that may occur. (See detailed comments inAppendix.B.) 

Page 10 of 60 
a. Information on the pharmacokinetics of brodifacoum are available in 16 studies on 

seven species. None of this information is meaningfully discussed in the Cadmus 

document. 

b. Cadmus (2004) report states that there is apool of rapidly eliminated brodifacoum 

(Cadmus, 2004, p. 21 of 114, lines 1 to 2). This appears to be a misinterpretation of 

the classical two compartment model. There is a biphasic pattern in the concentration 

of a compound of brodifacoum. The first and more rapid phase is associated with 

redistribution, not excretion. 

c. In the same paragraph, the Cadmus report states that ... the toxicokinetics of 

brodifacoum, particularly the rates of exchange between blood, liver, and other 

tissues, have not been quantified even in the rat. This is also largely incorrect and 

certainly misleading. One of the 16 available studies (Batten and Bratt 1990) was 

consulted in the current review. The study appears to be reasonably well done and is 

not difficult to interpret. 

d. Also in the same paragraph, the Cadmus (2004) report states that many mechanistic 

and quantitative assumptions would be need to model the kinetics of brodifacoum. 

This is also incorrect. As illustrated in Attachment 1, a reasonable albeit exploratory 

PBPK model can be developed using only the residue data from Batten and Bratt 

(1990). The kinetics of brodifacoum can be described by a simple flow-limited PBPK 

model that can be optimized with the concentration data in liver, kidney, and carcass 

(which account for the bulk of the brodifacoum in the rat). 

e. The Cadmus (2004) report chooses a range of halftimes from 2 days to 200 days with 

the implication that the "real" halftimes are somewhere in this region. In terms of the 

body burden of the prey species, this is not correct. The shorter halftimes of 2 days to 

several days reflect the blood data and redistribution from, blood to tissue. This has 

nothing to do with body burden. The upper range of 200 days is presumably based on 

liver halftimes. As reviewed by Erickson and Urban (2004), reported liver halftimes 

are in the range of about 74 to 350 days. 

f. The same paragraph goes on to discuss the use of the first, order model and a 

sensitivity analyses using a range of values. The value of sensitivity analyses on a 

structurally incorrect model is minimal and leads to the mis-impression that the 

kinetics have minimal impact on the assessment of risk. ~ 

2.4. Risk Characterization 

1. The approach to risk characterization taken in the Cadmus report involves comparing 

single dose gavage LD,, doses to peak body burdens from intermittent dietary exposures I over a prolonged period - e.g., 90 days in most of the simulations conducted by Cadmus 

(2004). The underlying assumption in this approach is that tdxicity is based on peak body 

Page 11 of 60 
burden. This assumption should be justified. The Cadmus report should have discussed 

and possibly considered the use of time-weighted average (TWA) body burdens (e.g., 

Haber's Law) rather than peak body burdens. This is important for intermittent exposures 

to slowly eliminated compounds. 

The Cadmus (2004) report should have attempted to reconcile their analysis with the 

rather clear and compelling body of experience with brodifacoum reflected in field 

studies that clearly indicates that risks to nontarget species are not only plausible but are 

well-documented. An attempt to use field data as a check of conclusions should always 

be part of a PRA, particularly one in which many of the assumptions are not wellsupported. 

The risk estimates appear to be based on a distribution from the exposure assessment that 

reflects the mean 90-day maximums across 100 individuals, rather than all 10,000 

estimates of the 90-day maxima that were calculated. If true, the approach used could 

greatly underestimate the upper tail (> 90th percentile) of the risk distribution. An 

example given in Appendix C (Comment 15) illustrates the difference between the two 

approaches. The approach using the mean 90-day maxima yields a distribution with a 

40% lower dose at the 5% exceedance level. 

The sensitivity analysis of the probabilistic model is incomplete. The uncertainty analysis 

does not adequately address alternative, plausible modeling approaches and input 

assumptions. 

A Tier 1 analysis (point estimate approach) is not presented along with the probabilistic 

analysis. 

Page 12 of 60 
Appendix A. Original Comments from Maxine Dakins, I PhD 

1. They laid out a conceptual model in Figure 1 but did not include all possible pathways of 

exposure and they did not carry forward several of the pathways they did identify. 

a. Specifically, they identify Nontarget Bait Feeders and Nontarget Carcasses as important 

components of the conceptual model but exclude them in the calculations. 

b. They did not include, in concept or in the calculations, the pathway of Direct Ingestion of 

Bait by nontarget organisms. 

c. In addition, they made assumptions regarding use that affected their parameterization of their 

model. Specifically, they assumed the product was always distributed according to the 

directions on the label (limiting direct outdoor exposure to bait) and they assumed that 

indoor use would not lead to an increase in exposure to nontarget species. Neither of these 

assumptions seems to me to be beyond debate. They should, at the least, be explored through 

sensitivity analysis. 

2. Their use of a 90-day period for simulation seems insufficient. The authors state on page 46 that 

"Extending the simulation for longer periods (180 or 360 d) resulted in a 2- to 3-fold increase in 

maximum cumulative exposure". Given this, it is unclear why the authors decided not to run all 

simulations for 360 days or longer. In addition, it is unclear why the authors "did not carry these 

exposure estimates forward to determine the effect of the simulation period on risk". 

3. Their exploration of the percentage of exposed rodents in the diet (PT) through sensitivity 

analysis seems appropriate given the lack of data. . 

a. However, their limitation of this variable to less than 15% appyars to be without justification. 

In the absence of limiting data, the sensitivity analysis of PT should include a full range of 

values up to 100%. The authors state "In the immediate vicinity of a structure where baiting 

is in progress, PT may approach 1 ". ~ 

b. They present a sensitivity analysis of this variable only for coypte which is the fourth most 

sensitive to brodifacoum of the five surrogate non-target species they explore. The results of 

their analysis for coyote (Figure 24) do not even include their highest value of PT=0.15. A 

full sensitivity analysis should be performed and included for all surrogate non-target 

species, particularly those known to be sensitive to brodifacoum, such as the great horned 

owl and kit fox. , 

I 

c. Finally, their argument that the true PT value in the field is at Ar below 1% seems without 

merit and open to debate. This assumption could be construed o be serving the interests of a 

' particular group. 

4. In the effects analysis, their use of Bayesian hierarchical interesting and innovative. 

This analysis, unlike the species sensitivity distribution lead to an entire 

execution of this approach here raises some concerns. 

estimated dose-response curve for a hypothetical However, the 

Page 13 of 60 Dakins 
a. It is unclear why a sensitive species was defined as the 10th perLentile and not the 5th 

percentile or 2.5th percentile. Defining the bounds as the 5th and 95th would lead to 90% 

I confidence while using the 2.5th and 97.5th would lead to a 95% confidence of including the 

dose response curves of nontarget species. A 80% confidence seems inappropriately low in 

this instance. 

b. Depending on the applicability of the species where dose response data exists to the species 

likely exposed in the field, a conservative approach should be used and carried out through 

the definition of a high confidence level in a) 

c. Most troubling, the bounds shown in Figures 15 and 16 do not appear to include even 80% of 

the dose-response curves used. For example, for birds, out of 8 dose response curves 

(ignoring the 1986 pheasant data), two curves lie left of the lower bound and three lie 

partially right the upper bound. The pheasant curve violates both bounds. Therefore, out of 9 

curves, only 3 are completely contained within the 80% confidence bounds. For mammals, 

one curve lies left of the lower bound, two are partially left of it, two are completely right of 

the upper bound, and two are partially right of it. Of the 8 curves shown, only 1 is completely 

contained within the 80% bounds. These bounds, as shown, do not appear to represent the 

variability in dose response curves that exist, even within these very limited data sets, and 

should not be used to define what the dose-response relationship would be for a hypothetical 

sensitive nontarget species. 

d. For the Bayesian approach, they assume that both alpha and beta are normally distributed 

without any discussion andlor justification of this assumption. It might be a reasonable 

assumption but I would like to hear their justification for it. If it was simply done for 

convenience, it would be helpful to have a sensitivity analysis on that assumption. This is 

particularly important given that their 80% confidence bounds on the doselresponse curves 

did not appear to cover 80% of the curves. 

5. As far as information EPA should request from Cadmus, I believe they should ask for the full 

sensitivity analysis on the variable PT for all levels and all species. I would guess Cadmus did all 

of them but there's no guarantee. They don't even report all levels for Coyote (as noted in 3b 

above). 

6. As far as studiesldata, they report none on the pathways that they disregard. So, for Direct 

Ingestion of Bait, what is the literature? Also for Ingestion of Nontarget Species and Carcasses? 

They decide to eliminate these pathways without appropriate justification, certainly without 

discussing the literature. 

Page 14 of 60 Dakins 
Appendix B. Original Comments from Patrick Durkin, PhD 

I have reviewed the report by Cadmus (2004). I have focused my review on how the available 

information on the pharrnacokinetics and toxicokinetics of brodifacoum are incorporated (or not 

incorporated) into the analysis presented by Cadmus (2004). 

Most specific comments are identified by page and paragraph number (e.g., p. 1,72 for page 1, 

paragraph 2). The Cadmus (2004) document has two differing sets of page numbers, one at the 

top of each page and another at the bottom. All page numbers cited below refer to the page 

I number specified on the bottom of the page. Paragraphs are counted from the top of each page 

with any initial partial paragraph designated as 71. Line numbers are identified only when 

necessary and always refer to the line in the paragraph that is being referenced. Quotations from 

the Cadmus (2004) report are given in italics as needed to clarify comments. 

In any draft that is being sent out for external review, the use of line numbering should be 

considered. 

p. 12,72 and 3 

These are the only paragraphs that mention the large number of incident data that are available on 

brodifacoum. The report goes on to present an analysis suggesting no substantial risks to 

nontarget species. The analysis should have attempted to reconcile the incident data summarized 

by Erickson and Urban (2004) as well as incident data reported in publications cited in the 

Cadmus (2004) report (e.g., Edwards and Swaine 1983; Edwards et al. 1983a,b). 

p. 13,73 to 5 

Mortality is used as the sole endpoint of concern. The estimates of wlortality are based on 

standard laboratory studies. As discussed by Erickson and Urban (2d04), there is some evidence 

that wildlife may be at greater risk because of the possibility that expbsures to brodifacoum at 

doses that are sublethal in laboratory studies could cause adverse effects (including mortality) in 

wildlife secondary to injuries (e.g., bruising) that would otherwise be/ sublethal. This is 

acknowledged in the Cadmus (2004) report: 

Lethal poisoning, as well as direct sublethal effects that could increase the 

likelihood of individual mortality (e.g., hemorrhaging), are 04greatest concern. 

P. 13,75, line 3 ff , 

The analysis by Cadmus (2004), however, focuses solely on lethalityland does not present a 

consideration of the sublethal adverse effects that could be associate d , with hemorrhaging in 

wildlife. I 

p. 14, Last 7 

While not a serious issue, general statements asserting that water an air concentrations are 

unimportant are not well supported. While I do not think that either Later or air concentrations 

Page 15 of 60 Durkin 
are important relative to dietary intake, an at least preliminary exposure assessment to 

demonstrate this would have been appropriate. 

P. 15,8 1 

This paragraph is filled with assertions that are unsupported and appear to be unsupportable. The 

statement that secondary exposure to predators and scavengers appears to be of greater concern 

to regulators than risk ofprimary exposure simply does not make any sense and I doubt that it 

reflects the position of EFED. Similarly, the statement that a fundamentally different exposure 

model would be required is incorrect. The statement that invertebrate feeders are considered 

unimportant is not supported and does not appear to be supportable. The significance of 

invertebrate bait feeders to birds is demonstrated by incident data (see Thorsen et al. 2000 as 

cited in Erickson and Urban 2004, p. 65). These sort of broad and unsupported statements 

detract from the sense of objectivity that a risk assessment should have. 

p. 15,74, last sentence 

The decision to use monitoring rather than modeling may underestimate risk and perhaps 

substantially. This ignores temporal relationships in brodifacoum concentrations in prey that are 

plausible. The implication that complex behavioral simulations would be need is incorrect. 

Relatively simple models could be employed that might lead to more credible assessments of 

risk. 

p. 15, Last 7 

While a relatively minor point, the Cadmus (2004) report repeatedly uses the phrase cumulative 

dose - which in common usage would be the total of all the doses that are consumed over some 

period of time - when they appear to be referring to body burden - the total amount of the 

chemical in the organism at any given time. 

p. 15, 7 1 and2 

The discussion of Kaukeinen et al. (2000) is unclear. What other toxic effects are caused? The 

liver binding with consequent interference in clotting factors appears to be the mechanism of 

concern. 

p. 18 and elsewhere 

Use either non-target or nontarget consistently. The latter is preferable. I 

P. 19, TI 4 

Red-tailed hawks will prey on other birds (e.g., morning doves and starlings) which could be 

exposed to bait either directly or through the consumption of insects. some of the studies in the 

source cited in the Cadmus (2004) report (e.g., U.S. EPA 1993) indiclte that birds may comprise 

10% or more of the hawks diet (see Volume II of U.S. EPA 1993, pp. A-9 1 ff). Ignoring this 

source of exposure will bias the analysis to an underestimate of risk. 0 ther paragraphs on this 

and the following page do talk about the consumption of invertebratee and birds but this is not 

addressed in the model. 

Page 16 of 60 I Durkin 
p. 2077 3 

Earlier dvafts of the analysis plan had assumed that submodels would need to be developed to 

estimate concentrations in food. This would have been preferable to the approach taken in the 

document. The reliance on monitoring data may have modestly simplified the analysis. This 

approach, however, ignores temporal relationships that may lead to an underestimate of risk. In 

I other words, the number of rodents (target and nontarget) that are exposed to brodifacoum and 

the concentrations of brodifacoum in these organisms is likely to vary over time. The failure to 

consider this may miss periods of peak exposure in predators. 

P. 20, a 4 

I understand what PT is supposed to be but I do not see here or elsewhere how PT is set in any 

objective way. 

P. 20, a s 

The use of the maximum concentration or maximum body burden should be justified in some 

way. For brodifacoum, it is plausible that concentrations in the liver could be a more reasonable 

measure of exposures that would be toxicologically significant. Also, the analysis would be 

enhanced by considering or at least discussing the issue of delayed death. 

p. 20, Last 7 to p. 21 71 

Depuration kinetics of brodifacoum are complex, as described in Sections 2.2.3 

and 3.3.2.2. Studies in rats suggest that depuration follows a biphasic curve 

(Kaukeinen et al. 2000). The initial phase of the curve is steep, with a halflife on 

the order of a few days, reflecting a pool of rapidly eliminated brodifacoum in the 

body. The secondphase of the depuration curve is much slower, with a hawife on 

the order of many months, reflecting a much smallerpool of tightly bound 

brodifacoum in the liver. However, the toxicokinetics of brodifacoum, 

I particularly the rates of exchange between blood, liver, and other tissues, have 

not been quantzjied even in the rat. Many mechanistic and quantitative 

assumptions would be needed to simulate the biphasic depuration process, and 

the results would reflect large uncertainties in these assumptions. ... 

This is a very important paragraph and virtually nothing in this paragraph is correct. 

Neither Section 2.2.3 nor 3.3.2.2 describe kinetics (complex or simple) in any meaningfbl way. 

Information on the pharmacokinetics of brodifacoum are available in 16 studies on seven species: I rats (Bachmann and Sullivan 1983; Batten and Bratt 1990; Belleville 199 1 ; Bratt and Hudson 

1979; Hawkins 1991; Parrnar et al. 1987), possum (Eason et al. 1996), rabbits (Breckenridge et 

al. 1985), sheep (Lass et al. 1985), dogs (Robben et al. 1998; Woodyet al. 1992), horses 

(Boermans et al. 1991; McConnico et al. 1997) and humans (Bruno et al. 2000; Hollinger and 

Pastoor 1993; Weitzel et al. 1990). Additional information may also be available in Eason et al. 

2002. None of these studies are mentioned in the discussion of the purportedly complex kinetics. 

The one reference that is cited (Kaukeinen et al. 2000) is &om a conference proceeding on risk I 

I 

I 

Page 17 of 60 I Durkin 
benefit considerations of anticoagulants. While this paper was not available for the current 

review, it seems reasonable to assume that the paper does not involve a critical review of kinetic 

studies on brodifacoum. 

Given to the failure of the document to clearly describe the kinetic data on brodifacoum, the 

reviews by Erickson and Urban (2004) and WHO (1995) were consulted and the study by Batten 

and Bratt (1990) was examined. The statement by Cadmus (2004) concerning apool of rapidly 

eliminated brodifacoum appears to be a misinterpretation of the classical two compartment 

model which is described in most basic texts on pharmacokinetics (e.g., O'Flaherty 1981; Gibaldi 

and Perrier 1982). Adopting the nomenclature of Gibaldi and Perrier (1 982) for the twocompartment open model, there is a biphasic pattern in the concentration of a compound in the 

central compartment. The first and more rapid phase is associated with redistribution from the 

central to the peripheral compartment. The terminal phase is associated with excretion. The 

, influence that the peripheral compartment has on total body burden will depend on the apparent 

volume of distribution (VD). As VD approaches zero, the two-compartment open model 

approaches a simple one-compartment model. Thus, the very short halftimes in blood (analogous 

to the central compartment) represent redistribution to tissue and not excretion. The longer halftimes reported in liver (analogous to the peripheral compartment) reflect retention. As discussed 

further below, the short halftimes in blood have little to do with body burden and are not relevant 

to the type of model proposed by Cadmus (2004). 

Based on a review of Batten and Bratt (1 990), the statement that ... the toxicokinetics of 

brodifacoum, particularly the rates of exchange between blood, liver, and other tissues, have not 

been quantzfied even in the rat ... is incorrect. This statement leaves the impression that the 

kinetics of brodifacoum are poorly understood and cannot be used for the type of analysis being 

considered by Cadmus (2004). The study by Batten and Bratt (1990) appears to be reasonably 

well done and is not difficult to interpret. Blood concentrations are problematic because 

brodifacoum is rapidly transported from blood to tissues, including the liver. Thus, unless very 

high doses are used, concentrations of brodifacoum in blood will rapidly fall below detectable 

concentrations. If very high doses are used, toxicity (including fundamental changes in the blood 

compartment due to hemorrhaging) would complicate the kinetic analysis. As illustrated in 

Attachment 1, these problems can be readily addressed with a physiologically based 

pharmacokinetic model rather than reliance on an inappropriate single compartment classical 

pharmacokinetic model. This has the added benefit of being able to consider prey in terms of 

total body burden (the relevant measure for prey) and liver concentrations as a potentially useful 

index for exposure in the nontarget predator species. 

The assertion made by Cadmus (2004) that many mechanistic and quantitative assumptions 

would be need to model the kinetics of brodifacoum is also incorrect. As also illustrated in 

Attachment 1, a reasonable albeit exploratory PBPK model can be developed using only the 

residue data from Batten and Bratt (1 990). The kinetics of brodifacoum can be described 

reasonably well (3 = 0.92, p = 0.015) by a simple flow-limited PBPK model that can be 

Page 18 of 60 Durkin 
optimized with the concentration data in liver, kidney, and carcass (which account for the bulk of 

the brodifacoum in the rat). 

The above quoted paragraph goes on to discuss the use of the first order model and the conduct 

of sensitivity analyses using a range of values. The value of sensitivity analyses on a structurally 

incorrect model is minimal. In addition, Cadmus (2004) chose a range of halftimes from 2 days 

to 200 days with the implication that the "real" halftimes are somewhere in this region. In terms 

of the body burden of the prey species, the shorter halftimes of 2 days to several days reflect the 

blood data and redistribution from blood to tissue. This has nothing to do with body burden. 

The upper range of 200 days is presumably based on liver halftimes. As reviewed by Erickson 

and Urban (2004), reported liver halftimes are in the range of about 74 to 350 days. While these 

halftimes are not directly relevant (i.e., are not whole-body halftimes) they are more applicable 

than the blood halftimes. Basing most of the analyses on a 50-day halftime in a simple one 

compartment model is inappropriate, incorrect, and will underestimate risk. 

p. 21, last 8, last bullet to p. 22,71 

It is unclear why toxicity studies involving dietary exposure were not considered. Simply citing a 

review and a technical report from the Canadian Wildlife Service (Mineau et al. 1994,2001) is 

not compelling. Cadmus (2004) note: "LC,, values can be converted to LD,# ifassumptions are 

made about food ingestion during the test, but we considered that the uncertainty introduced by 

those assumptions would outweigh the loss of infomation caused by excluding LC,, data." This 

reservation may not be relevant because most guideline feeding studies in birds will report food 

consumption values. For those that do not, there are ample data on food consumption of quail 

and mallards from other guideline studies. At very least, Cadmus (2004) should have discussed 

the feeding studies in terms of study quality. 

Nonetheless, I would agree that gavage studies will generally lead to more conservative (e.g., 

lower) estimates of the LD,, than would transformations of dietary LC,, values. In reviewing the 

data from Batten and Bratt (1990), however, I note a very rapid but transit spike in fecal 

excretion on Day 1 in rats dosed at 0.15 mglkg bw (see Batten and Bratt 1990, Table 8, p. 33). 

This is worrisome in terms of the risk assessment because it could be related to a pharmacologic 

effect associated with gavage dosing that might not be seen in more gradual dietary exposures. 

Additional studies would need to be reviewed to further explore this issue. At a minimum, 

however, Cadmus (2004) should have presented a quantitative comparison of the gavage and 

dietary toxicity studies and should have more hlly discussed which set of data would be most 

appropriate. This is particularly important for brodifacourn because the anticipated exposures 

will approximate dietary studies more closely than gavage studies. 

P. 22, T[1 

The authors should justify or at least discuss the basis for using the logistic transformation (over 

the more commonly used probit transformation) in the dose-response assessment. As discussed 

by Finney (1972), the probit model does provide a very simple and plausible underlying 

assumption in the analysis of quanta1 response data (the log-normal distribution of individial 

I 

Page 19 of 60 I I Durkin 
tolerances). I am less certain that the logistic transformation of quanta1 data provides a similarly 

simple and reasonable assumption. 

P. 22, 74 

The approach to risk characterization taken by Cadrnus (2004) involves comparing single dose 

gavage LD,, doses to peak body burdens fi-om intermittent dietary exposures over a prolonged 

period - e.g., 90 days in most of the simulations conducted by Cadmus (2004). The underlying 

assumption in this approach is that toxicity is based on peak body burden. Cadmus (2004) 

should have discussed this assumption more critically. For some toxicants, such as fast acting 

agents that are rapidly excreted and do not cause cumulative damage, peak body burden or peak 

target organ burden may be the most appropriate exposure index. It is not clear that this is the 

case for brodifacoum. As reviewed by Erickson and Urban (2004), brodifacoum causes delayed 

mortality. The delayed mortality could be associated with turnover times for K-dependant 

clotting factors (II, VII, IX, and X). While modeling this mechanistically may not be feasible, the 

Cadmus (2004) should have discussed and possibly considered the use of TWA body burdens 

(e.g., Haber's Law) rather than peak body burdens. If a PBPK model were used rather than a 

simple one-compartment model, TWA concentrations in the liver might well be the most 

relevant measure of exposure. Again, the review by Erickson and Urban (2004) provides a 

summary of some multiple dose studies that could be useful in assessing the whether peak ' 

concentrations or some averaging of concentrations would be the most appropriate measure of 

exposure. 

P. 23, 83 

The comment on density dependence is reasonable. I concur with the implicit assumption that 

incorporating this consideration directly into the model is probably not necessary at this time. 

The description of the key issues for "Stage 2" is correct but the approach taken in the analysis by 

Cadmus (2004) is questionable on both counts as detailed in my comments above. 

p. 24, description of Equation 1 

Equation 1 contains two key terms, PD (proportion of the diet that consists of all rodents) and PT 

(proportion of rodents in the diet that are treated). 

The use of PD ignores other sources (non-rodents) that may be significant for some of the 

nontarget species. I discuss this in other comments. 

PT appears to be a composite term reflecting the proportion of target rodents that are exposed to 

brodifacoum (termed in my comments here as PT,) and the fraction of the target rodents that are 

consumed by the nontarget predator (PT,). In other words, 

PT = PTI x PT, 

In an effective baiting program, PT, is likely to be quite high and this is acknowledged by 

Cadmus (2004) near the end of their report (p. 41,72). As I discuss below, information is 

Page 20 of 60 Durkin 
available that could be used to set PT, reasonably objectively. PT, is much more problematic 

and difficult to set objectively. Based on professional judgement, Cadmus (2004) appears to 

think that PT, is low and hence they suggest that the composite PT will be very low. I am not 

convinced that they are correct. 

By not clearly distinguishing what I am terming PT, and PT,, they give the impression that the 

low values of PT are based on PD (proportion of the diet that consists of rodents). In other 

words, they appear to suggest that because only a small proportion of target rodents are 

consumed by a predator (PD is small), the proportion of rodents that will be exposed to 

brodifacoum (PT) is also small. This, of course, makes no sense and I doubt that these authors 

are seriously asserting this. However, as I note below, the discussion of the relationship of PD to 

PT is muddled and it would be clearer if the two factors influencing PT were separately 

discussed. 

In making this distinction, I am concerned that the Cadmus (2004) report does not sufficiently 

consider the proportion of nontarget rodents that may be exposed. Based on rather rapid review 

of the Edwards studies (Edwards id Swaine 1983; Edwards et al. 1983a,b), I think that a case 

could be made that exposures to nontarget rodents may be high. Thus, even though the 

consumption of target rodents may indeed be low in some instances (i.e., PT, is small), the 

nontarget predator will be exposed to brodifacoum by the consumption of nontarget rodents. In 

other words, the equation that I give above, PT = PT, x PT,, seems to reflect how the 

professional judgement expressed in the Cadmus (2004) report lead to a low value for PT but 

this approach may be overly simplistic and may have mislead their judgement in not considering 

exposures to nontarget rodents. 

P. 26972 

I do not object to the use of a lognormal distribution for body weights for wildlife species. It 

would have been appropriate for the authors to provide some references for this assumption 

(which should not be difficult to find). 

p. 26,14 to p. 28,al 

The use of only PD (proportion of rodents in the diet) as an input for dose ignores both direct 

consumption of the bait by nontarget species as well as the consumption of other types of animals 

(e.g., birds and insects) that may contain toxicologically significant amounts of brodifacourn. 

The importance of both direct consumption of bait and the consumption on non-rodent organisms 

is amply demonstrated in Erickson and Urban (2004). The sole focus on rodents will 

underestimate risk and this underestimate could be substantial, partichlarly for some species of 

birds - e.g., the discussion of Godfiey (1985) in Erickson and Urban (2004), p. 64. 

I 

I did not have time to review all of the selected PD values. I did look at the PD value used for 

the coyote in some detail. The Cadmus (2004) report appears to have used a value of 0.1985 

(Table 7) as the mean of a beta distribution. The PD value appears td be consistent with cited 

references for the consumption of all rodents by coyote. As noted in he discussion and detailed i 

Page 21 of 60 Durkin 
in Table 17, there is considerable variability among the different studies as well as differences in 

the ways in which data are reported. All things considered, the value of about 0.2 seems to 

reflect the sources cited. 

I have a residual concern, however, with what may not be considered in selecting the mean of 

about 0.2. The field studies used by Cadmus (2004) - i.e., the U.S. EPA Wildlife Exposure 

Factors Handbook and the CalIEcotox Exposure Tables - may not be representative of the 

consumption of rodents by wildlife in areas with atypically high rodent populations - i.e., the 

type of rodent infestations that might trigger the application of brodifacoum. I am not sure how 

or if this could be handled quantitatively in a reasonably simple model. This would require a 

more detailed review of the cited studies as well as other studies that may be available. 

P. 28972 

In the studies where Norway rats and house mice were reported (7 observations 

out of approximately 160 listed in Appendix A), they comprised only 0.3% to 

17.8% of the rodents in the diet. 

This appears to be source of the 1% to 15% assumption for PT, discussed further below. As 

clearly indicated in this sentence, however, the range of 0.3% to 17.8%refers to the propokion of 

the two species of target rodents in the diet of nontarget predator species. As the model 

developed by Cadmus (2004) clearly states - i.e., Equation 1 on p. 24 and the discussion on pp. 

26-27 - the proportion of rodents in the diet is the PD term and not the PT term. 

The Cadmus (2004) report appears to be suggesting that because specific rodents comprise only a 

small proportion of the diet in some nontarget predator species (PD), only a small proportion of 

the rodents will be exposed to brodifacoum (PT). This further suggests that only target rodents 

will be exposed to brodifacoum. 

As I have noted above, PD cannot be used as a basis for PT and Cadmus (2004) should have 

explicitly discussed PT as a composite of PT, and PT, as defined above. This sort of discussion 

could have lead the authors of the Cadmus (2004) report and would certainly lead me to 

considering higher values for any composite PT because of the consumption on nontarget rodents 

by predators. 

P. 28,73 

The proportion of Norway rats and house mice that are exposed to a brodifacoum 

baitingprogram is unknown, but is likely to be much smaller thin one in ten. 

The value of one in ten is unsupported. This seems to explicitly postulate an ineffective baiting 

program - i.e., only a small proportion of the target organisms will be exposed. I assume that 

brodifacoum can be and typically is used effectively - i.e., a large propgrtion of the target rodents 

are killed by exposure to brodifacoum - and thus a large proportion of the target rodent 

Page 22 of 60 Durkin 
population is exposed. This assumption is reenforced by a very casual reading of the Edwards 

studies (Edwards and Swaine 1983; Edwards et al. 1983, 1984a,b) which also suggest that 

substantial numbers of nontarget species are likely to be exposed in an effective baiting program, 

particularly if saturation baiting methods are used. This is discussed further below. 

P. 28,lT3 

In the absence of data to support any particular value of PT, we compared results 

of the daily dose model using PT values ranging from 1% to 15%. The dieta y 

composition data reviewed above suggest that even the lowest of these values is 

probably an overestimate. 

As noted above, this statement is taking data on PD (which seems in this case to be treated as the I proportion of target rodents in the diet of some nontarget predators) and using these data to 

estimate a composite PT that should include both target and nontarget rodents. 

As discussed subsequently by Cadmus (2004), PT is a very sensitive parameter. To base the 

estimate of the range of PT on an unsupported assumption is both an error and is unnecessary. 

As suggested above, assumptions concerning values PT could be based on the assumption that 

brodifacoum is being used in an effective baiting program and that both target and nontarget 

rodents may be exposed. 

As a preliminary illustration, Edwards et al. (1 984b) provide data on a relatively successful 

baiting program - i.e., p. 20, Table 8, Farm A. In this example, control of rats is estimated at 

88%. In other words, it seems safe to assume that at least 88% of the rats were exposed - i.e., PT 

= 0.88. As discussed by Edwards et al. (1984b), the control rate of 88% is probably an 

underestimate because of reinfestation. A more careful review of data associated with successful 

baiting programs would probably result in better estimates of PT and possible error ranges 

associated with PT. While somewhat speculative, it seems reasonable to assert that plausible 

estimates of PT (which would encompass both target and nontarget rodents) would be higher ' 

and perhaps substantially higher than 0.01 to 0.15 as used by Cadmus (2004). 

Plausible estimates of PT could probably be constructed from either the available field studies or 

from some reasonable assumption based on a plausible definition of an effective baiting program. 

The range of PT values used by Cadmus (2004) seems implausible. 

P. 28974 

The use of DPT as an all or none variable does not bother me greatly for some receptors like the 

coyote. For the coyote, it seems likely that a small mammal such as a rat would be completely 

consumed. For other receptors, such as a red-tailed hawk, I am somewhat concerned that this 

approach is less realistic and could possibly underestimate risk. Some of the smaller predators 

such as the hawk might not consume the entire rat but could selectivelybegin to feed on internal 

organs such as the liver which would likely have higher concentrations bf brodifacoum than 

average values in the carcass (see Attachment 1). Concern that some cakcasses may be only 

Page 23 of 60 1 Durkin 

I 
partially consumed is reenforced by observations of partially consumed carcasses in studies of 

baiting applications (Edwards and Swaine 1983; Edwards et al. 1983a,b). 

P. 29,112 

As I have noted above, I think that the Cadmus (2004) report should have at least explored a rat 

feeding model that could have been compared to the monitoring data. In discussing the 

monitoring data of Edwards (Edwards and Swaine 1983; Edwards et al. 1983a,b), the Cadmus 

(2004) report states that: A total of 144 rats found dead above ground were analyzed for 

brodifacoum residues. For clarity, the Cadmus (2004) report should state that these studies did 

not assay all of the rats that were found. Only the first two dead rats found at each farm were 

sampled. It is unclear what, if any, impact this would have on the distribution fitting. 

p. 32,al and 72 

See my previous comments on the use of a single compartment model for brodifacoum and the 

selection of halftimes. This model is structurally unsound. I suggest that the model introduces 

error rather than uncertainty. As noted above, the selection of the range of halftimes from 2 days 

to 200 days is incorrect and seems to reflect a misunderstanding of the underlying kinetics. 

P. 32,114 

In each of the 100 inner loop iterations, a sequence of 90 independent daily doses 

was simulated, ... 

While I have not examined the EXCEL files in detail, I doubt that any of the simulations 

involved 90 daily doses unless the authors are counting days in which the animal is unexposed as 

a zero dose. Typically, the 90-day simulation period would involve far fewer than 90 exposures 

to brodifacoum and this is why the halftimes appeared to be relatively insensitive. As I note 

above, the low PT values used in the simulations are based on a very poorly supported 

assumption and probably grossly underestimate exposures to nontarget species. 

The utility of the inner loop is unclear and the use of averages of maximum values will tend to 

dampen risk. This is addressed in greater detail in the comments by ?hi1 Goodrum and I concur 

with these comments. I 

Also, the text does not seem consistent in the number of repetitions of the inner loop. On p. 30 

and here on p. 33, 1000 repetitions are specified. On p. 32 (74) the number of iterations in the 

inner loop is specified as 100. I may be missing something here but other readers may be , 

similarly confused. This should be clarified in the document. 

P. 33, a3 ~ 

I did not focus my review on the handling and derivations of the FIR. I did note, however, that 

the means of the estimated FIR'S were near the upper limit of the me a , sured FIR'S. While this is 

also noted by Cadmus (2004) in this paragraph, it should be discussed and explained. Given how 

I think the FIR values were derived, I am not sure why this occurred. 

Page 24 of 60 Durkin 
P. 34,85 

i%e daily dose model was therefore very sensitive to the assumed value of Pi". 

Based on the considerations presented in Section 3.2.2, we believe that in most 

cases PT is likely to be less than 0.01 fewer than 1 % of all rodents in the diet 

have been exposed to brodfacoum), the lowest value used in our simulations. 

See my detailed comments on p. 28. 

P. 35273 

Here the inner loop appears to be 100 again. As for the use of means from the inner loop and the 

dampening of risk, see the comments from Phil Goodrum. 

P. 35,14 

I have commented at length above on the inappropriate and unnecessary use of the first-order 

model. 

I 

As for the influence of halftimes on body burden (again, not the cumulative dose) if the firstorder model is used, the arithmetic is well-characterized (e.g., Goldsteih et al. 1974) and should 

be discussed in order to allow for a clearer interpretation of the apparent insensitivity of the 

halftime assumptions. Assuming first-order kinetics for a single expos;ure, the concentration in a 

system at time T after exposure (C,), can be calculated as: I 

To simplify notation for considering multiple exposures, At is defined s a the interval between 

exposures and p is defined as The peak concentration immediately after the n" exposure 

(C,) can be calculated as: ~ 

c, = co x (1-p") + (1-p). I (Eq. 2) 

As n -m,p -0, so the peak concentration after an infinite number of exposures is: 

For multiple exposures, the concentration at time T can be calculated ak: 

I 

CT = c* - k(T- [(n- l)At]) 

(1 - P) I 

I 

Where n is the number of exposures that have occurred up to time T and At is the interval 

between exposures. ~ 

Page 25 of 60 Durkin 
What appears to be going on in the modeling effort by Cadmus (2004) is the At (the interval 

between doses) is typically relatively long. This decreases the value ofp which in turn decreases 

the value of C,. All of this occurs because of the low (and implausibly low) values of PT that 

were selected by Cadmus (2004). 

P. 36973 

I comment above on the influence of T as well as At on the results. A more realistic and 

plausible selection of PT would lead to a much greater impact of the extension of the simulation 

period. This follows from the kinetics discussed above. 

In considering the duration of the exposure, it is also worth noting again the use of TWA 

concentrations may be more appropriate than peak concentrations and the TWA concentrations 

would be more sensitive than peak concentrations even with low values of PT. 

P. 37371 

The toxicity of brodifacoum is relatively well-studied in mammals and birds. The discussion 

presented in the Cadmus (2004) document is superficial. In terms of extrapolating among 

species, the analysis should have at least explored the possibility of systematic (allometric) 

differences among species. I have quickly scanned the reviews by Erickson and Urban (2004) 

and WHO (1 995) and I am not under the impression that systematic differences are pronounced. 

Nonetheless, this issue should at least be addressed in a serious risk assessment. 

As I have also noted above, the decision to exclude dietary exposure studies is not well 

supported. The dietary studies should have been quantitatively assesded along with the gavage 

studies. This analysis could have supported a more objective decision on which data sets to 

include or exclude. As done in the Cadmus (2004) document, the decision seems arbitrary. 

p. 37, last 7 to p. 40 

I will defer to Dr. Dakins on most aspects of the dose-response assessment. As I have noted 

above, however, the Cadmus (2004) report should at least discuss thebasis for selecting the logit 

transformation over the probit transformation. 

p. 4 1 ff. Section 5.2 Results 

Given the flaws in the methods used in the Cadmus (2004) report, the utility of the results seems 

minimal. 

The analysis is driven by the low values of PT, defined as the proportion of the rodents exposed 

to brodifacoum and set at values of 0.01 to 0.15. A baiting program in which only 1% to 15% of 

the target species are exposed to brodifacourn would be ineffective. I would agree that an 

ineffective baiting program, which is what Cadmus (2004) appears to model, will not, by 

definition, pose much of a risk to rats and other target species and, by extension, this ineffective 

baiting program might not pose much of a risk to nontarget species. $the rats do not eat the 

brodifacoum, other species which eat the rats will not be adversely affected. If there is negligible 

Page 26 of 60 Durkin 
exposure there will be negligible risk regardless of the sophistication of the dose-response 

modeling. As I have noted above, however, these values for PT are highly questionable. These 

very low values of PT combined with the unnecessary, 'improperly parameterized, and 

inappropriate use of the simple first-order model lead to results that are not useful to the risk 

manager or anyone else seeking to understand the risks that might be posed by brodifacoum and 

how these risks might be reduced. 

Notwithstanding these limitations, the Cadmus (2004) report should have at very least attempted 

to reconcile their analysis with the rather clear and compelling body of experience with 

brodifacoum that clearly indicates that risks to nontarget species are not only plausible but are 

well-documented. An attempt to use field data should always be part of a PRA and might have 

lead the authors to reexamine their model and assumptions. 

p. 42, Section 6: Assumptions, Uncertainties, and Limitations 

Many of the points covered in this section are important issues that should be, but were not, 

addressed. Simply acknowledging limitations does not improve the results. 

In other cases, the Cadmus (2004) report appears to simply dismiss important issues. For 

example, the authors state that: 

Prima y exposure to non-target bait feeders could not readil~ be incorporated 

into the models we used for seconda y exposure. Prima y exposure was 

considered of less concern than seconda y exposure, and mou;e manageable 

through reJinement of bait station design andplacement. (p. $2,74) 

I do not understand why primary exposure could not be incorporated into the model, particularly 

a model as simple as the one used in the Cadmus (2004) report. The review by Erickson and 

Urban (2004) clearly indicates that this is an important source of exposure and this is also 

indicated in the studies by Edwards (Edwards and Swaine 1983; Edyards et al. 1983a,b) that the 

authors of the Cadmus (2004) report had clearly reviewed in some del;ail. 

Similarly, as I have noted above, secondary exposures via the consumption of contaminated 

invertebrates is also clearly important but is not considered in the model in the Cadmus (2004) 

i report. While this is acknowledged (p. 42,75) in this 'section, the ac&owledgment does not 

improve the risk assessment. 

P. 42,T6 

I am not sure why Cadmus (2004) did not include misuse in the risk assessment. An elaboration 

of the potential effects of misuse can be instructive and misuse will occur. 

This paragraph clearly describes how Cadmus (2004) elected not to handle exposures in a way 

that considered the potential relationship of time on exposure. As I hLd noted above, I think that 

Page 27 of 60 Durkin 
the original plan, which did address the time issue, would have lead to a more realistic analysis. 

I have examined the studies by Edwards (Edwards and Swaine 1983; Edwards et al. 1983a,b) and 

these studies do not appear to provide any useful information that would assist in determining 

how important this limitation is. 

P. 43,76 

7aze effects analysis was based on data for acute mortality, not sublethal or 

chronic effects (see Section 2.2.3). 

This is an important consideration and deserves more than a one sentence paragraph in 

addressing limitations. See my comments above on p. 13,73 to 5. 

p. 43, Last 7 

I have not looked at the data on concentrations of brodifacoum in live-trapped rats versus 

carcasses. I will do so if time permits. 

P. 44975 

As I have noted above, the approach used to estimate PD is based on data that may not be 

representative of diets in areas with rodent infestations. 

P. 45,72 

This is a relatively clear discussion of the factors that could effect PT, although the second point 

seems a bit muddled. 

I 

P. 45,83 

Following a reasonably lucid paragraph in which the plausibility of PT approaching unity is 

clearly acknowledged, the assertion that professional judgment leads td a PT of 0.01 is not 

credible. I 

p. 45, last 7 

This is a clear and correct admission of limitations associated with the /use of the monitoring data 

on the concentrations of brodifacoum in rodent carcasses. As I have noted above, limitations in 

some of the monitoring studies could have been more hlly discussed - i.e., selecting only the 

first two rats found at each site in the studies by Edwards. I 

P. 46, 71 

I have commented above on the use of kinetics in the "cumulative dosy" model. In discussion 

limitations, the Cadmus (2004) report notes: 

~ 

Although the complexities of brodifacoum toxicokinetics were Lot represented in 

the model, we considered development of a full toxicokinetic mbdel beyond the 

scope of the risk assessment and also beyond the extent of available data. 

Page 28 of 60 

~ 

Durkin 
Again, these authors are suggesting that the data are either too complex andlor unavailable. It is 

true that developing afull toxicokinetic model - i.e., a model that would incorporate both 

pharmacokinetics and the mechanism of action - is probably not required for a useful PRA and 

the available data on the mechanism of action at the molecular level may be insufficient to model 

quantitatively. This, however, does not justify the superficial and fkndamentally incorrect 

approach taken in the Cadmus (2004) report. The reported appears to misinterpret the available 

information on halftimes - i.e., the difference between redistribution and excretion - and 

essentially ignores 16 studies on seven species. As illustrated in Attachment 1, a very casual 

effort has resulted in simple PBPK model that adequately describes the kinetics of brodifacoum 

in the rat (the target species) and which could be readily adapted to nontarget species such as the 

coyote. Again, acknowledging limitations that are unnecessary or that reflect an incomplete 

analysis does not improve the analysis or excuse the limitations. 

P. 46773 

Give the other problems with this analysis, I do not have a major quarrel with limiting the 

simulation period to 90-days. It would have enhanced the analysis if the report had attempted to 

justify the period in some more rigorous way. 

p. 47,72, Section 6.5 Data Gaps 

This is a relatively standard and reasonably clear discussion of data gaps with one exception: 

However, data are still insufficient to parameterize even a simple two 

compartment (body and liver) kinetic model for the rat. 

I have covered this adequately above - 16 studies covering seven species are essentially ignored. 

Furthermore, a "simple two compartment (body and liver) kinetic model" does not make any 

sense at all in terms of classical kinetic analysis. There is very little purpose in even trying to 

derive a classical multi-compartment kinetic model for any species. The advantages of PBPK 

models over classical kinetic models (e.g., species scaling) are clear (e.g. Bruckner et al. 2004). 

In terms of the sufficiency of the data, a cursory examination of one study (Batten and Bratt 

1990) can be used to develop a simple PBPK model for bulk of brodifacoum in a mammal - i.e., 

liver, kidney, and carcass (Attachment 1). 

p. 47 to 48, Section 7 Conclusions 

The Cadmus (2004) report concludes that: 

The risk of brodifacoum -induced mortality in coyote, red fox,, and red-tailed 

hawk is low, even ifthese species are all assumed to be highld sensitive to 

brodifacoum. 

Within the context of the assumptions used in the Cadmus (2004) repbrt, this conclusion is 

supportable but should be reword: I 

Page 29 of 60 I 

1 Durkin 
Under the conditions modeled - i.e., brodifacoum is ineffective in killing 

rodents because most of the rodents are not exposed to substantial 

quantifies of brodifacoum - the risks of brodifacoum to nontarget species 

consuming the rodents will be low. 

The Cadmus (2004) report goes on to address once again the dependence of their conclusion on 

PT. This has been adequately discussed in my comments above. 

If the assumption is made that the exposure of rodents to the rodenticide is negligibleto low 

(PT=O.Ol to 0.15), the risks to other organisms consuming the rodents will probably be 

negligible to low as well. This conclusion is of little use in assessing risks posed by brodifacoum 

used in effective rodent control programs. I 

I 

REFERENCES 

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brodifacoum in warfarin-sensitive rats. Pharmacol. 27: 28 1-288. 

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administration of single oral doses. Unpubl. report submitted to EPA by ICI Americas, Inc., Wilmington, 

DE. 65 pp. 

Belleville, M. 1991. Compared hepatic kinetics of brodifacoum and difethialone in the rat, after oral 

administration of 0.06 mgkg once weekly for 4 consecutive weeks. Unpubl. report submitted to EPA by 

LiphaTech, Inc., Milwaukee, WI. 39 pp. 

Boermans HJ, Johnstone I, Black WD, Murphy M. 1991. Clinical signs, laboratory changes and 

toxicokinetics of brodifacoum in the horse. Can J Vet Res. 55(1):21-7. 

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Breckenridge, A.M., S. Cholerton, J.A.D. Hart, B.K. Park, and A.K. Scott. 1985. A study of the 

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Bruno GR, Howland MA, McMeeking A, Hoffman RS. 2000. Long-acting anticoagulant overdose: 

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- 
Eason, C.T., G.R. Wright, and D. Batcheler. 1996. Anticoagulant effects and the persistence of 

brodifacoum in possums (Trichosurus vulpecula). New Zealand J. Agric. Research 39:397-400. 

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birds and mammals in New Zealand. Ecotoxicology. 11(1):35-48 -not sure if kinetics are here. 

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Comparative Approach. EFED report dated January 15,2004. 

\ 

Edwards PJ, Swaine H. 1983. Brodifacoum: Hazard to non-target animals from the use of "Klerat" bait 

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Edwards PJ, Swaine H, Coulson JM, Kennedy SH, Richards CGJ. 1984a. Brodifacoum: Hazard to 

non-target animals from "pulsed" baiting with wax block baits around farm buildings. Report No. RJ 

0375B. ICI Plant Protection Division, Jealotts Hill, UK. 

Edwards PJ, Swaine H, Kennedy SH. 1984b. Brodifacoum: Hazard to non-target animals from "pulsed" 

baiting with "Klerat" pelleted bait around farm buildings. Report No. RJ 0369B. ICI Plant Protection 

Division, Jealotts Hill, UK. 

Finney DJ. 1971. Probit Analysis. New York: Cambridge University Press. 333 p. 

Gibaldi M; Perrier D. (Eds). 1982. Pharmacokinetics: Second Edition Revised and Expanded. Marcel 

Dekker, Inc., New York, NY. 494 p. 

Goldstein A, Aronow L; Kaman SM. 1974. Principles of Drug Action: The Basis of Pharmacology. 2nd 

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Haddad,S; Pelekis M; Krishnan K. 1996. A methodology for solving phy$iologically based 

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Hawkins, D.R., R.R. Brodie, D. Clarke, and C. Brindley. 1991. ~etermination of the residues and 

I half-life of the rodenticides brodifacoum, bromadiolone, and flocoumafen in the livers of rats during 200 

days after a single oral dose of each at a dose level of 0.2 mglkg. Unpubl. kport submitted to EPA by 

LiphaTech, Inc. Milwaukee, WI. 78 pages. 

Hollinger BR, Pastoor TP. 1993. Case management and plasma half-life ih a case of brodifacoum 

poisoning. Arch Intern Med. 53(16): 1925-8. I 

Kaukeinen DE, Spraggins CW, Hobson JF. 2000. Risk-benefit considerations in evaluating commensal 

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Laas, F.J., D.A. Forss, and M.E.R. Godfrey. 1985. Retention of brodifacolm in sheep tissues and 

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O'Flaherty EJ. 198 1. Toxicants and Drugs: Kinetics and Dynamics. John Wiley and Sons, New York, 

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Pannar, G., H. Bratt, R. Moore, and P.L. Batten. 1987. Evidence for a common binding site in vivo for 

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Page 32 of 60 Durkin 
Appendix C. Original Comments from Philip E. Goodrum, PhD 

My comments focus on the conceptual modeling approach and the Monte Carlo analysis. 

1. Conceptual Model (Figure 1, p. 77; Section 2.2) 

The conceptual model accounts for secondary exposures to mammals and birds via the consumption of 

target and non-target rodents. Three reasons are given for excluding primary exposures to non-target 

receptors (p. 12): 

a. "Risks of secondary exposure ... appears to be of greater concern to regulators than risk of 

primary exposure.. ." 

Comment 1: 

What is the basis for this statement? A citation is needed. Ignoring an exposure pathway implies that 

exposures are underestimated in the risk assessment. It is difficult to understand how Agency guidance 

would support this assumption. In fact, the statement conflicts with the Introduction (p. 9), in which the 

authors cite Erickson and Urban (2002), "..EPA concluded that brodifacoum poses a greater primary and 

secondary hazard to birds and rnmals than the other active ingredients examined." I 

b. "A fundamentally different exposure model would be required ... because a different approach 

would be needed to estimate the proportion of brodifacoum-containing food in the diet." 

Comment 2: I 

This statement suggests that the model required to estimate exposure fromlbait would be too complicated 

to develop. This is a weak argument. A simple model is offered below. Currently, the modeling 

approach to address secondary exposures involves an estimate of the propdrtion of the diet comprised of 

rodents (PD) on a daily basis. This is multiplied by the food ingestion ratd, which is a function of the 

field metabolic rate (FMR). A third variable (PT) accounts for the fractiod of the animal's diet that is 

taken from the treated area. In short, secondary exposures are a fraction of the total daily ingestion rate 

and the time spent in the treated area. Equation 1 (p. 2i) specifies the daily dose (DD): 

A second source of exposure (i.e., primary exposure to bait) could be accounted for in a similar fashion. 

Presumably, rodenticide baiting practices are based on an understanding of feeding behavior and habitat 

preferences of small mammals. As noted on p. 14, factors to consider wodld include the application 

method, baiting station design, and the accessibility of the bait to non-target vertebrates. The authors 

indicate that data could not be obtained on these factors, so the frequency of encounters with 

brodifacoum-containing food were represented by the variable PT. A simdlifying assumption could be 

that non-target animals are exposed to bait only when they are in the treated area. If PT is defined by 

binomial with probability (p), then a random value selected from PT will be 0 or 1. If PT = 1, then the 

animal may also be exposed to bait. The number of exposure events could be described by a Poisson 

distribution. The average rate of exposure events would be based on information from baiting practices; 

in the absence of data, different scenarios could be explored, similar to ho& the authors explored the 

uncertainty in DD as a function of different estimates for PT. The daily ddse from bait (DDb) would be: 

~ 

I 

Page 33 of 60 ~ I Goodrum 
where EV is the number of exposure events (characterized by a Poisson), Mb is the average dietary mass 

provided by the bait, and Cb is the average concentration of brodifacoum in the bait. If PT = 0 (i.e., there 

is no contribution of diet fi-om the treated area), then EV = 0. Presumably, information on baiting 

practices is available to define Mb and Cb. According to the authors (Section 2.2.1, p. 1 I), brodifacoum 

bait may be consumed by non-target animals that feed on pelleted or wax block baits. If pellets or wax 

blocks provide energy, this consumption may need to be considered part of the overall diet of the nontarget animal. If the contribution to diet from bait consumption is non-trivial, then the Mb could be 

connected to the FIR and PD terms by the following constraint: 

Mb 2 FIR(1 -PD) 

That is, the contribution of bait to diet cannot exceed the balance of the FIR after accounting for the 

consumption of rodents. Alternatively, both primary and secondary sources can be weighted so that FIR 

is not exceeded: 

If (FIR x PD + Mb) > FIR, then the total daily dose (DDt) is constrained by FIR: 

FIR 

DD, = (DDt DD,) x I 

7 FIR x PD + hdb 

I have limited knowledge of baiting practices; the point of this comment is that a plausible conceptual 

approach for quantifying primary exposures to bait can be developed with a? approach that is no more 

complex than the method proposed for secondary exposures. I 

c. "Risk reduction for non-target bait feeders is essentially a matter of bait station design and 

placement." 

I 

Comment 3: 

Does this imply that bait stations could be located in areas that are unavailable to non-target vertebrates? 

According to the authors, usage scenarios (Section 2.3 .l) include applying qait outdoors (around 

buildings, transport vehicles, and in sewers). In rural areas, outdoor use is extensive and bait would be 

readily available to non-target vertebrates as a source of primary exposure. 

2. PT as All or Nothing 

Section 3.2.2.5 describes PT as the portion of consumed rodents that are exhosed to brodifacoum. In the 

daily dose model, this is implemented as a binomial distribution, where a value of 0 indicates that non of , 

the rodents consumed contain brodifacoum and a value 1 indicates that all of the rodents consumed 

contain brodifacoum. All or nothing is an unnecessarily extreme scenario. 

Page 34 of 60 Goodrum 
The authors state (p. 25), "Because a single rat would represent a substantial fraction of the daily food 

intake of an individual predator, intermediate values of DPT would be unlikely; that is, the day's rodent 

intake would be either entirely exposed or entirely unexposed." 

Comment 4: 

This assumption needs to be explored more fully. The distribution of FIR needs to be presented together 

with the distribution of body weights for rodents. 

The consequence of the "all or nothing" modeling approach for determining daily doses is that a high 

percentage of the days yield a dose of 0, and a small percentage yield an extremely high dose. The 

likelihood of exposure, or equivalently, the average percentage of days in which exposure is non-zero is 

determined by PT. The authors vary PT from 1 % to 15%. 

As an aside (Comment 5), varying PT from 1% to 15% is not sufficient in my opinion - when exploring 

what-if scenarios in the absence of robust data (i.e., representative, high quality, high quantity), a broader 

range should be presented to risk managers. The authors may believe that 2.5% is a high estimate for PT, 

but they need to demonstrate the full range (1% to 100%) for others who may have a different opinion in 

the absence of data. 

An alternative approach is to apply the PT to individual prey meals rather thkn to total daily events. That 

is, the conceptual model can modified to simulate prey consumption events rather than total daily events. 

The product, FIR x PD, defines the mass of rodents consumed on 1 day. Th'ls product can then be 

divided by the average body weight of the rodents consumed to determine th b number of individual prey 

items that the predator needs to consume. Then apply the PT to each meal. This gives a greater chance I that at least one meal is contaminated, so there will be less days with 0 doses. Also, unlike the "all or 

nothing" case that is modeled right now, it is less likely that ALL of the items contaminated on any 

given day. Finally, by modeling prey meals rather than average daily events! additional sources of 

variability may be introduced regarding the prey availability and selection. i 

I 

The difference in approaches is illustrated in Figures 1,2, and 3 for PT values of 0.025, 0.50, and 1.0, 

respectively. For very low values of PT (e.g., PT < 20%) [illustrated by Fighe 11 and very high values 

of PT (e.g., PT >go%) [illustrated by Figure 21, the two approaches yield esdentially the same result, as 

one would expect. This is because the frequency of daily consumption of contaminated prey are 

represented by extreme cases. However, for intermediate estimates of PT (e.g., 30% < PT < 

70%)[illustrated by Figure 21 the difference can be significant. Table 1 summarizes the percentiles of the 

daily doses for PT = 0.50. While the mean DD is approximately the same, lkss than 25% of the daily 

estimates are 0 for the prey method. The key variable is the bioaccumulatioh potential of brodifacoum. 

The difference in modeling approaches (i.e., All or Nothing vs. Individual piey methods) may be 

important if the half-life for elimination kinetics is moderate to long (> 10 dais). The greater the 

frequency of consecutive days of non-zero exposure, the more brodifacoum Lan be expected to 

accumulate in the body of the predator or scavenger. ~ 

~ 

Page 35 of 60 i Goodrum 
Farecast: DDall or nothiq 

2,900Trids Frequency Chart 2470 Cisplayed 

Fa-ecast: DDprey method 

2,900TriB Frequency Chart 2452 Cisplayed 1 

W'hJ-W 

Figure 1. Comparison of approaches used to estimated daily dose togreat homed owl 

when the proportion of rodents contaminated (PT) is set to 0.025. Average prey weight 

is assumed to be 50g. The "All or Nothing" approach (top panel) indicates that on any 

day, PT determines if all or none of the prey are contaminated. with the "prey method" 

(bottom panel) PT determines if any individual prey meal is contamiLated; each day may 

result in one or more meals, some of which may be contaminated. 

Page 36 of 60 Goodrum 
Fcrecast: Dall or nothiq 

2,9DOTrids Frequency Chart 241 5 Ilsplayed 

Fcrecas t: DD prey nxthod 

Frequency Chart 2423 Displayed 

Figure 2. Comparison of approaches used to estimated daily dose to great homed 

owl when the proportion of rodents contaminated (PT) is set to 0.50. Average prey ~ weight is assumed to be 50g. The "All or Nothing" approach (top panel) indicates 

that on any day, PT determines if all or none of the prey are contamihated. With 

the "prey method" (bottom panel) PT determines if any individual pAey meal is 

contaminated; each day may result in one or more meals, some of which may be 

contaminated. 

Page 37 of 60 Goodrum 
2,500Trids Frequency Chart 2447 Cisplayed 

.m 

.OR 

a 

.I 

I .I

9 .m 

(A 

9 

0 ,i .m 

.m 

0. m 0.l461 0 .m 0.4382 0.9343 

mwld 

Fcrecas t: CD prey mdhod 

Frequency Chart 2447 Cisplayed / 

0. QXX) 0.M1 0.2321 0.4382 0.9343 

m-csy 

Figure 3. Comparison of approaches used to estimated daily dose to &eat homed owl 

when the proportion of rodents contaminated (PT) is set to 1 .O. Avera e prey weight is 

assumed to be 50g. The "All or Nothing" approach (top panel) indicates that on any day, 

PT determines if all or none of the prey are contaminated. With the bbpiey method" 

(bottom panel) PT determines if any individual prey meal is contamina ed; each day may 

result in one or more meals, some of which may be contaminated. t 

~ 

I 

Page 38 of 60 Goodrum 

~ 
Table 1. Comparison of distributions of daily dose (mglkg-day) for 

two methods of applying PT variable. All or nothing indicates prey 

consumed on any day are either all contaminated or none are 

contaminated. Prey method refers to the simulation of individual prey 

meals. Example is for the great homed owl, PT = 50%, 2500 iterations 

of the daily dose model. Corresponds to Figure 2. 

Summary 

Statistics 

Mean - 

I Standard Dev 

Percentile 

All or Nothing per Day Individual Prey Meals 

Page 39 of 60 Goodrum 
3. Monte Carlo Analysis 

The use of the 2-D Monte Carlo loop to characterize inter- and intra-individual variability is an 

interesting and potentially informative approach. The report does not adequately describe the 

assumptions; without having the Excel worksheets, I would not have been able to confirm what was 

actually done. The power point slides provide a clearer presentation of the daily dose and cumulative 

dose models. The SAS code was not accompanied by the input files that were used, which also made it 

difficult to understand exactly how the output from the exposure assessment was used to calculate risks. 

In order to evaluate the assumptions in the exposure assessment, I found it necessary to reconstruct the 

conceptual diagram. Figure 4 presents the entire Monte Carlo methodology (for exposure) as a series of 

five steps. Comments on the approach are included in the figure (see items in bullets under each step), 

and summarized below. 

I 

Step I. Outer loop, Interindividual Variability. 

The outer loop is used to characterize inter-individual variability in feeding behavior (percent 

contribution to diet of rodents) and ingestion rate. 

Comment 6: 

The terms log(a) and b are the intercept and slope of an allometric regression equation (Equation 3, p. ' 

22). In the form of the regression equation, the relationship between body weight and FMR is: 

FMR=~W~~ 

log (FMR) = log(a) + b x log(Wt) i 

or more generally 

In this analysis, the parameters of the regression equation (Po, P,) are described by independent normal 

distributions. Typically, for simple linear regression, there is a correlation between the parameters that 

can be determined relatively easily. This correlation should be incorporated into the Monte Carlo 

simulation, as described in detail below. The consequence of ignoring the correlation depends on the 

direction of the correlation as well as the magnitude of the standard errors of the parameter estimates. In 

general, if the correlation is positive, then assumifig independence will tend to yield underestimates of 

the response variable (Y) at the upper tail of the distribution. The opposite id1 true if the correlation is 

negative. Either way, ignoring the correlation unnecessarily constrains the variability in FMR. 

Page 40 of 60 Goodrum 
Step Specifics 

1. Outer Loop - select PD - beta = mean for DPD 

parameters to define ifh PT - constant = pt estimate 

Individual FIR - f [log(a); b; Wt; GE; AE; MI = pt estimate 

I 

log(a) - normal 

b - normal 

Wt - lognormal 

GE, AE, M = pt estimates 

Characterizes inter-individual variability based on: PD, and FMR (log(a); b; Wt) 

Ignores correlation between log(a) and b - if correlation is positive, will tend to 

underestimate the variability in FMR 

2. Inner Loop 1 - develop DPD - lognormal (mean from Step 1, SD) time series of 1000 daily DPT - binomial (p = Pi from Step 1) = 0,l 

doses (DD) for ifh FIR - point estimate 

Individual. Repeat for n C - beta 

= 100 individuals 

Characterizes intra-individual variability in daily dose based on PD, PT, and C. 

PD mean is from Step 1; standard deviation (SD) is a constant. Introduces consistency for an 

individual (mean PD), with some day-to-day variability. By fixing SD, daily variability is 

assumed to be independent of mean PD. Results in extreme cases in coefficlient of variation 

(CV =SD/mean); may underestimate daily variability for individuals that are high-end 

consumers. 

Binomial for PT defines the entire day rather than each prey meal. If DPT = Il, all prey items 

are contaminated, otherwise none are contaminated. With p = very low probabilrty, most days 

are 0 dose. For days in which prey are contaminated, this will yield very highdose (all prey 

are contaminated). 

PD =% rodents in diet; PT = % rodents exposed to rodenticide; FIR = food ingestion rate; log(a), b = 

allometric scaling factors; Wt = body weight; GE =gross energy content of food; AE = assimilation 

efficiency; M = moisture content of food; FMR = field metabolic rate (function of log(a), b, and Wt); 

DPD = daily PD; DPT = daily PT, C = concentration in exposed rodents; t % = half-life for first order 

elimination kinetics 

Figure 4. Conceptual modeling approach for the 2-D Monte Carlo &alysis. 

Page 41 of 60 I Goodrum 
Step 

3. lnner Loop 1 Summary 

Stats - for n=100 

individuals, fit PDFs to 

mean DD and CV DD 

Specifics 

Mean DD - beta 

CV DD - gamma 

Defines inter-individual variability in 2-parameter lognormal distribution of DD. Mean = mean DD, SD = mean DD x CV DD. 

Mean DD and CV DD are assumed to be independent for an individual; same as 

assumption that mean DD and SD DD are independent. 

4. lnner Loop 2 - develop DD - lognormal (mean DD, CV DD) time series of daily body t W - constant = pt estimate burdens for ifh Individual; 

repeat for n = I00 time Body burden - f (DD; t W ) 

series; repeat for n = 100 

individuals 

~ For ith Individual, represents collective variability in DPD, DPT, and C. 

For inter-individual variability, represents collective variability in mean PD and FMR. 

I 

Assumes no variability in toxicokinetics among individuals. I 

5. Inner Loop 2 Summary 90-day max for each time seribs 

Stats - calculate summary 100 estimates of 90-day max for an 

stats for each individual individual > calculate mean 90-day max 

and across all individuals 100 estimates of mean 90-day max for 

population > generate EDF, mean, 90th 

percentile 

90-day rnax is taken from the Is' 90-days of the time series, which does not allow time for 

steady state. Time to steady state depends on choice oft H. May underestimate~he 

maximum daily body burden. I 

I 

Mean of 9Oday man is not a conservative choice. May be much lower than upper percentile 

or maximum of the 90-day rnax estimates for an individual. 

EDF based on mean of 90-day rnax values has N = 100 points representing interirjdividual 

variability. How stable is the upper tail? Should N be >> 100? 

Figure 4 (cont'd). Conceptual modeling approach for the 2-D Monte barlo 

analysis. 

PD =% rodents in diet; PT = % rodents exposed to rodenticide; FIR = food ingestion rat; log(a), b = allometric scaling factors; Wt = body weight; GE = gross energy content of food; AE = aksimilation 

efficiency; M = moisture content of food; FMR = field metabolic rate (function of log(a), b and Wt); 

DPD = daily PD; DPT = daily PT, C = concentration in exposed rodents; t % = half-life fol first order 

elimination kinetics I 

I 

Page 42 of 60 

~ 

I 

I 

Goodrum 

~ 
The regression statistics should be reviewed from either the Wildlife Exposure Factors Handbook, or the 

original study values themselves. The authors should clarify whether their intent in modeling the 

parameters of the regression equation (Po, PI) with distributions is to characterize variability or 

uncertainty. 1 believe it is better described as a source of parameter uncertainty, in which case the outer 

loop of the 2-D MCA describes both uncertainty and variability. This is not a critical flaw so much as a 

point of clarification for the risk characterization. 

The steps for incorporating the correlation are presented below. Suppose that we wish to simulate a 

simple linear regression of the form: 

Let bo and b, be the usual least squares estimates of Po and PI, respectively. Then, under the usual 

assumptions regarding the regression model, bo and b, have a joint normal distribution. The key is that bo 

and b, are correlated: 

Without getting into the theory to support this statement, the following general approach can be used to 

give estimates of Y that are consistent with the idea that Po and p, are correlated parameters. First, define 

terms as follows: 

Po = intercept of the regression line (log(a) in FMR equation)^ 

ppo = mean of Po 

opo = standard error of Po 

PI = slope of the regression line (b in FMR equation) 

ppl = mean of PI 

opl = standard error of PI 

P = correlation coefficient for (Po, PI) 

1. Generate standard normal deviate, Z, - N(0, 1) 

2. Generate standard normal deviate, Z, - N(0, 1) 

3. Calculate log(a) 

log(a) = Ppo + Opo Z1 

4. Calculate b I 

b = Pp1 + op1 [(P Zl) + (1 - P~)~.~ 221 

Assuming independence will yield the same result as setting the correlation to 0. For example, setting p 

= 0, yields b = ppl + opl Z2, which is the approach currently being implementid. 

For this analysis, the mean and standard errors for log(a) and b are in the Cadmus (2004) 

report on Table 1 (p. 55). The SE for each focal species is actually to the mean, so the 

consequence of ignoring the correlation is relatively minor. An 

calculation for coyote using log(a) - N(0.412,0.058), b - 

Page 43 of 60 Goodrum 

-- I 
3600), and correlations ranging from +1 to -1. Ignoring the correlations results in differences in the tails 

of the distribution of FMR of about *lo% (see Figure 5). 

Step 2. Inner Loop 1, Time Series of Daily Doses 

The 2-D MCA analysis is best described as an approach that involves one outer loop and two sets of 

inner loops. The first inner loop is used to define a time series of daily doses for 100 individuals. The 

second inner loop is used to develop estimates of body burdens over time for each individual, 

corresponding to the variability in daily doses. In the Cadmus (2004) report, these loops are described as 

the Daily Dose model and Cumulative Dose model, respectively. 

It is a good idea to vary daily doses for an individual, but some of the assumptions should be revisited. 

Comments below are on the conceptual approach to modeling daily exposures; as well as on specific 

assumptions regarding choices of inputs to the model. 

Comment 7: 

Section 3.1 (Overview of the Exposure Model) indicates that the analysis approach addresses four 

questions: (1) How much food does the receptor eat?; (2) What fraction of the food consists of rodents?; 

(3) What fraction of those rodents have been exposed to brodifacoum baits?; and (4) What is the 

concentration of brodifacoum in rodents exposed to baits? 

The approach introduces a somewhat artificial constraint on feeding behavior. If an individual coyote is 

determined to have an average food ingestion rate (FIR), this implies that there is also day-to-day 

variability in'ingestion rates. The model only accounts for daily variabilityin the fraction of the diet that 

is comprised of rodents (DPD), the concentration in the food consumed (c):, and the chance of 

encountering contaminated prey (DPT). DPT is addressed in Comment 4 above. The authors make the 

point (p. 24) that diets for opportunistic predators reflect the available food^ supply. In simple 

homogeneous environments, diets are similar among individuals; in complex habitats where a wider 

variety of food can be found, inter-individual variability may be greater. This concept likely applies to 

intra-individual (day-to-day) variability as well. I 

I 

I 

Page 44 of 60 I Goodrum 

I 
rho 95th%Ie 

-1 11700(-11%) 

0 13143 

+I 14800 (+13%) 

Page 45 of 60 Goodrum 

Figure 5. Distributions of FMR for coyote based on different assumptions regarding the correlation 

between log(a) and b in the regression equation. Top panel shows the full distribution, while the middle 

and bottom panels show the effect on the tails. 

I 

Page 46 of 60 Goodrum 

----- 
With a fixed estimate of FIR for all 1000 days simulated, there is no opportunity for an individual to 

engage in gorging. Two factors could be considered to introduce this source of variability: (1) Describe 

FIR with a probability distribution in which the mean is characterized by the point estimate from the 

outer loop, and the standard deviation is an assumption, similar to the approach used for DPD. 

Essentially, create a variable called DFIR, and (2) Ask an additional question of the exposure model: In 

order to achieve the DFIR, how many rodents (whole animals) must be consumed? It seems unrealistic 

to assume that a predator will eat only a fraction of its prey because it has satisfied the daily energy 

requirement. The concept of FIR should be interpreted as an average, with variability dependent on 

opportunity. The consequence of excluding this source of variability is to underestimate variability in 

daily dose. 

Comment 8: 

On p.21, the authors acknowledge the same basic model could be applied both deterministically or 

probabilistically. The results of the (Tier 1) deterministic (point estimate) analysis should always be 

presented in the same report as a more refined analysis. This information should be added to the Cadmus 

report if it is revised. 

Comment 9: 

The variability in daily dose is described as an uncertain constant by using different estimates for the 

standard deviation. Daily variability is assumed to follow a lognormal distributibn with mean equal to 

the time invariant estimate of PD from the outer loop, and standard deviation equal to 3%, 5%, or 10% 

(pp. 24-25). This assumes that variance is independent of the mean. Regardless of whether an individual 

is a high or low consumer of rodents, their day-to-day variability is constant. A potential improvement 

would be to specify the coefficient of variation (CV = SDImean) rather than the SD. In this way, the 

variability is in lock step with the mean. Curiously, the authors elected to use this approach in another 

part of the exposure model. In Step 3 (described below), distributions are fit to the time series of DD 

estimates. The mean DD is described by a beta and the CV DD is described by a gamma. This 

parameters are later multiplied to determine the corresponding SD of a lognormal distribution. 

The consequence of modeling daily variability in PD with SD rather than CV is to potentially 

underestimate variability in individuals whose diet is comprised of a high percentage of rodents, and 

overestimate variability for individuals whose diet is comprised of a low percentage of rodents. 

Information on CV would come from studies of individual predators over time. In the absence of this 

information, plausible estimates of CV could be explored as a source of uncertainty in the exposure 

model. 

Comment 10: 

Also, the choice of fixed values for SD does not introduce much daily variability. It is difficult to see 

this without referring to the Excel sheets. The authors should include an additional table that provides 

not just the mean PD or the parameters of the beta distribution, but also selected percentiles and a 

graphic. Most readers will not be able to interpret the alpha/beta/scale parameters, but a graph would 

improve the transparency. 

For example, Table 7 (p. 63) gives the mean PDs for each of the receptor species. The full beta 

distributiod and selected percentiles should be provided. Referring to the Excel sheet for the Great 

Horned Owl (GHO-DD001 .xls), cell B 17 is where the beta distribution is specified. This beta is 

parameterized as: alpha = 1.04 beta = 0.47, scale = 97.73. The mean of this distribution is approximately 

~ 

Page 47 of 60 I Goodrum 
67% and the standard deviation is 28%. The interquartile range (2Sh, 75th percentiles) is (36%, 92%). 

Figure 6 provides additional information. 

Figure 6. Beta distribution used to characterize 

interindividual variability in PD for great homed owl. 

If this distribution is representative, this particular predator clearly exhibits great individual variability in 

rodent consumption. It is difficult to believe that daily variability for one individual is constrained to a 

standard deviation of 3% to 10%. A better approach would be to explore the idea that daily variability 

may be as great as 50% of the long-term average food preference. So, for a symmetric distribution, we 

would choose to model the mean PD h 50% of the mean PD. For the average horned owl with 67% of 

diet comprised of rodents, the standard deviation would be h 33%, so on any given day PD may range 

from 33% to 100%. This approach is equivalent to setting CV equal to 0.5. 

Comment 11 : 

The distribution of daily doses (DPD) was characterized by a lognormal distributions. The authors state 

(p. 25), "The lognormal distribution was selected .... because it includes only positive values and seems a 

reasonable choice. No data were available for direct assessment of possible distributions". The 

sensitivity analysis was performed only on the parameters of the SD for the lognormal. Further 

exploration should be done on the significance of the choice of the lognormal. Perhaps a more plausible 

choice in the absence of any data is a uniform distribution with (min, max) equal to h x%, constrained to 

the plausible bounds for PD (0%, 100%). Uniform distributions, in contrast to the lognormal, will select 

values from the high end of the range with more fi-equency. 

Comment 12: 

The authors should be sure to define any unbounded distribution like the lognormal with a maximum 

truncated at 100% for variables like PD that represent a percentage. I did not see this constraint included 

in the assumption cells of the Excel sheets. This will result in values that are greater than loo%, 

although given the relatively low SD for DPD, this situation is unlikely to have occurred very often. 

Step 3. Inner Loop I, Calculate Summa y Statistics 

For each of 100 individuals simulated with the DD model, the time series is described by a mean and CV. 

These parameters are fit to beta and gamma distributions, respectively (sed Section 3.3.2). The time 

I 

I 

Page 48 of 60 I Goodrum 

I I 
series are observed to follow approximately lognormal distributions. All of these assumptions are 

appropriate and well described. 

Comment 13: 

The only possible concern is that 100 individuals is not a sufficient sample size to character the mean and 

CV. This source of uncertainty could be explored by comparing the results to a single simulation with 

many more individuals, or by rerunning multiple sets of 100 and comparing the summary statistics for the 

fitted distributions. This is not a major source of uncertainty in my opinion, given that they are focusing 

on mean and variance rather than upper percentiles. 

Step 4. Inner Loop 2, Time Series of Cumulative Doses (Body Burdens) 

The cumulative dose model describes the depuration process with a first order model. The rate constant 

(k), or alternatively the half-life of brodifacourn (t %) is assumed to be an uncertain constant. 

Simulations were rerun with half-life values ranging from 5 days to 200 days to examine the effect on the 

exposure and risk distributions. 

The model was implemented by simulating a time series of daily doses for an individual, and calculating 

the corresponding body burden on each day. 

Step 5. Inner Loop 2, Summary Statistics 

I 

The next step is to calculate a summary statistic fi-om the time series of bod$ burdens. The authors 

suggest that 90-day maximum is an appropriate metric. For each individual! 100 sets of time series are 

generated, with each time series yielding an estimate of the 90-day maximu&. Therefore, for 100 

individuals, there are 100 sets of time series of 90-day maxima. 

Comment 14: 

At this point, the report gets very confusing. The reader needs to know the bottom line - what is the 

distribution of exposure (dose) values that is used on the calculation of the distribution of risks? There 

are two possible approaches to generating the distribution of doses that reprksents the combined sources 

of variability in the model: 

1. Summary Statistic Approach. For each individual, calculate a summary statistic from the 100 

sets of 90-day maximum. For example, calculate the mean 90-day daximum. Or, calculate an 

upper percentile (e.g., 95" %ile), or even the maximum of the maximum. The choice clearly has 

important implications for the overall distribution of doses and risks it0 the population. 

2. All 90-day Maxima Approach. Use all of the 100 values of 90-da4 maxima for all 100 

individuals, yielding a data set of 100 x 100 or 10,000 values to defi$e the exposure (dose) 

distribution. 

The Cadmus (2004) report elected Option I, with the summary statistic beind the arithmetic mean of the 

90-day maxima. This yields a data set of 100 values of mean 90-day maximal. 1 

Section 3.4.4 describes the approach, stating that "The mean of the 100 simuiation outcomes was 

calculated, and the 100 means (from 100 individuals) were plotted as reverse cumulative frequency 

Page 49 of 60 Goodrum 
distributions." But the risk characterization (Section 5.1) states, "For each cumulative dose model run, 

the full set of 10,000 dose estimates (100 estimates for each of 100 individuals) was combined into a 

single exposure distribution reflecting both within-individual and among-individual variation." 

This latter statement is at best confusing, and possibly misleading. One might interpret that the earlier 

graphic referred to in Section 3.4.4 was for illustration only, and that 'when it came time to calculate risk, 

all 10,000 values were used as a single exposure distribution (perhaps expressed as an empirical 

distribution function). Indeed, Table 8 (p. 64) provides two summary statistics of the distribution of 

mean 90-day maxima - the mean of the means and the 90" percentile of the means. The latter statistic is 

described as the 10% exceedance value, which implies that the EDF that is used to calculate risk is based 

on the distribution of mean 90-day maxima across 100 individuals, rather than all 10,000 estimates of the 

90-day maxima that were calculated. The SAS code that reads input files (which were not included in 

materials provided) includes loop definitions for 100 values, which further confirms the approach that 

was used. 

Comment 15: 

After establishing what was actually done, the next question is - was it a reasonable approach? There are 

few important observations that can be made. 

The difference between the distribution of 100 values of mean 90-day maxima, and the' distribution of 

10,000 values of 90-day maxima are not the same. Figures 7 and 8 provide an example for coyote. For 

this example, setting PT = 0.025, half-life = 5 days, and SD of DPD = 5% (units are incorrect on the 

figures), the effect of decreasing variability by representing individuals by the mean 90-day max is to 

decrease the dose at the 5% exceedance level by 40%. 

Page 50 of 60 ~ Goodrum 

I 

I 
Coyote, PT = 0.025, halflife = 5 d, DPD SD = 5 mglkgday 

I 

Coyote, PT = 0.025, halflife = 5 dl DPD SD = 5 mglkg-day 

I 

A, 

I 1 0.20 

, 

- - - -+ -- All 90-day maxs I 

0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5 

90-day Maximum (mglkg) 

Mean 90-day maxs 

5% Exceedance Level 

I Figure 7. Comparison of two different approaches for characterizilig 

variability in dose for coyotes. The approach used by Cadmus (20d4) is to 

define the distribution of doses based on the mean of the 90-day maxima 

calculated for each of 100 individuals. At the 5% exceedance levell this 

yields a difference of approximately 1.4 lower than the alternative ?fusing 

all estimates of 90-day maxima (see Fig 8). I 

\..., Difference = x 1.4 

0 .o I 

0.1 0.2 0.3 0.4 0.5 

I 

90day Maximum (mglkg) I 

I 

Figure 8. Same information as Figure 7, but focusing on the low end of 

the exceendance probability curve. The difference between approacLs is 

greater with increasing protectiveness (lower exceedance probabilitiks). I 

Page 51 of 60 Goodrum 
Comment 16: 

The choice of summary statistics is not well supported. Why'choose the mean of the 90-day maxima 

instead of an upper percentile? What difference does this decision make? Given the importance of this 

choice of approaches, it should be quantified more fully in the risk assessment. 

Comment 17: 

The 90-day period is an arbitrary choice. Cadmus elected to select the first 90-day period for each time 

series of daily doses. All individuals begin the time series with a body burden of 0 mglkg. The concept 

of first order kinetics implies that it may take some time for the chemical to achieve steady state. This 

time to steady state is influenced by both the choice of depuration half life (t %) as well as the frequency 

of daily doses. It should be noted that for some scenarios, the frequency will be very low because PT is 

set to a low percentage, and because it is applied as an "all or nothing" approach (see Comment 4). 

The difference between the 90-day maximum during the first 90 days and the maximum during the last 

90 days of a 365 day period is demonstrated in Figures 9-1 1. The following observations can be made: 

When the dosing regimen is frequent, the assumption about t ?4. becomes critical; and 

The maximum 90-day body burden is unlikely to be captured by the first 90-days of the time 

series, especially for longer half-life scenarios. 

Comment 18: 

The conclusions do not provide a comprehensive summary of the sources of uncertainty that were 

considered by the authors, much less the additional sources of uncertainty described above. The 

statement that risk of brodifacoum-induced mortality in the predator species studies is low, is unlikely to 

be true. Numerous examples of assumptions have been identified to illustrate that the analysis presented 

by Cadmus (2004) tends to underestimate exposure. Considered colle~tive~~, a reanalysis is likely to 

yield estimates of exposure that are greater by at least an order of magnitude. 

I 

Page 52 of 60 I 

I 

Goodrum 
Julian Day 

Figure 9. Example of a random 1-year time series of daily doses for a hypothetical 

coyote based on PT = 0.025 and SD DPD = 5%. 

+- half-life = 5 d - half-life = I00 d 

0.40 

0.35 

0.30 

r 

CI) 

E 0.25 V 

E 

0.20 

5 * 0.15 h 

D 2 0.10 

0.05 

0.00 

0 100 200 300 400 

Julian Day I J 

Figure 10. Comparison of the body burdens using the same dose time series (Fig 9) 

but two different estimates of depration half-life. In both cases, steady state is not 

achieved due to the low frequency of high doses. Assuming the peak occurred 

during the first 90 days, it would have been captured in this example by( both 

approaches, although the peak is higher for the scenario with slower deduration 

kinetics. I 

Page 53 of 60 Goodrum 
I 

received a lower maximum doses, but more frequent moderate doses. 

-o- half-life = 5 d - half-life = 100 d 

0.40 

0 

I 

100 200 300 400 

Julian Day 

Figure 13. Comparison of daily body burdens corresponding to the time series of 

doses given in Fig. 12. In this case, the difference in maximum body bydens for the 

two scenarios is about 3.5, with the maximum body burden for t K = 100 days 

occurring near the end of the year. 

Page 54 of 60 Goodrum 
Appendix D. Original Comments from Maria Mastriano, MS 

Potential Model Weaknesses 

1. The conceptual model doesn't include all the potential pathways for total daily dose. 

a. The food ingestion pathway is the only pathway considered. 

b. Rodents are the only portion of the food ingestion pathway considered. This is not 

conservative. The scientific literature does not support this decision. Other food items 

comprise a large portion of the surrogate species' diet (e.g., rabbit, birds, invertebrates, 

plants, etc.)(Please see attached tables.) 

c. No direct exposure 

The rationale given to exclude the direct exposure pathway is not compelling. This 

decision in the conceptual model could cause the risk assessment to seriously 

underestimate risk. 

2. Why were 3 mammals with very similar feeding behaviors chosen as the surrogate species? 

Perhaps it would be more appropriate to include predators/scavengkrs with more diverse eating 

habits. 

3. The model does not cover a scenario for heavy doses of pesticide use- such as application in the 

I 

open field. This was labeled a misuse of the product and was not considered in this risk 

assessment. Although it may be a misuse of the product (in terms o'f label instructions), it is 

I plausible that this type of application occurs. Therefore, regardless)of whether it is a misuse or 

not, it is still a potential use, and probably should be included in the risk assessment. 

4. The assessment does not include population modeling. The output of the risk model is individual 

survival and the, "Effects on population abundance were not estimated, but must be inferred from 

the mortality rate." Population abundance was a major goal of the risk assessment and is a 

concern to EPA. Some attempt to model the effects on population Ahould be included in the risk 

assessment. 

5. Seasonal variability and development stage variability was not included in the model. 

a. Data for seasonal variabilify is available in the open literatbe. Although not as abundant 

as composite data, seasonal data does exist. (Please see attached table 3-a study by 

Korschgen, 1959 which is included in EPA Wildlife ~xposure Factors Handbook, 1993 .) 

As the red fox, kit fox, and coyote are opportunistic feededs and their diets vary 

considerably with season, it would be better to include this seasonal variability 

component some where in the model. 

I 

b. Age (developmental stage) variability could play an impo&ant role in individual 

mortality and population abundance. Kits may be at a higder risk threshold both prior to 

I weaning and post-weaning. This should be accounted for ib the risk assessment. 

Concentration-whole body vs. liver I 

Why was 90 days chosen? Which 90 days is represented? Was this an arbitrary decision? Is it 

missing peak risk? 

First order depuration model I 

~ 

Page 55 of 60 I 

I Mastriano 
Assumptions for the distributions might need modification-some assumptions don't appear to be 

based in solid reasoning. For example, the PT was assumed to be low because of the variety and 

abundance of small and medium sized rodents. Variety isn't relevant here; whether there is a 

variety of rodents or not, it is still potential rodent prey. Secondly, abundance of rodents doesn't 

mean that.. ."the proportion of exposed rates and mice in the diet of most individuals is likely to 

be quite small (pg 28)." What if all the rodents are all poisoned? Also, for Norway rats, how 

was the 1 in 10 proportion derived? 
1 I 

1 1 

Non-target Bait Feeder Non-target Target Plants2 Mist.' 

(other than rodent)' Rodent Rodent (e.g. soil) 

1 Surrogate 

Cor~surne?,~ )' Species 4 --Imq* m*m,mmw-, - :: 

Bold arrows represent major routes of brodifacoum to the surrogate specles. 

The conceptual model in the draft risk assessment does not capture all the potential pathways for total 

daily dose in the surrogate species. Excluding these dietary pathways is not a conservative decision and 

not supported by the scientific literature. As evidenced in the attached tables, 3 of the 4 studies presente 

in the Wildlife Exposure Factors Handbook (EPA, 1993) illustrate that rabbits, birds, invertebrates, and 

plants are major dietary components (either all year or seasonally). Further, the draft figure includes 

detail that is irrelevant (e.g. the distinction made between a target rodent and target carcass, etc.) to the 

risk analysis, as explained in the draft's corresponding text to the figure. Above is a revised conceptual 

model. It includes all major dietary exposure pathways, and only categories that are relevant to the risk 

assessment. 

1. Non-target bait feeders (other than rodents) which comprise a portion of the surrogate species' diet 

and potential total daily dose (e.g. rabbits, birds, invertebrates, etc.) 

2. Plants, consumers, and misc. food items are likely to be a minor, howevef, perhaps not negligible 

source of total daily dose. 

3. The consumer category is defined as non-rodent prey that consume otherbait feeders (e.g., foxes 

consumed by other foxes and coyotes, scavenger birds (e.g. crow) by fox e s, etc.) 

Page 57 of 60 Mastriano 
RED FOX 

- Birds 

Plants 

Insects 

32.8 

30.5 

10.9 

12 

10.4 

0.9 

Page 60 of 60 

(Vulpes fulva) in Union 

County, Illinois. 

Transactions of the Illinois 

State Academy of Science 

631359-365. 

Mastriano 

House Mouse 

Fox Squirrel 

Red Fox 

Eastern Mole 

Pine vole 

Undetermined Mammal 

Eastern Chipmunk 

Bog Lemming 

Undetermined Rodent 

Other 

Birds 

Reptiles 

l nvertebrates 

Plants 

Nc 

4.7 

2.4 

28.2 

0.6 

2.9 

8.2 

1.2 

0.6 

2.4 I 33.7 

25.3 

2.4 

31.8 

97.1 

15.9 

2.5 

2.3 

1 

0.9 

0.8 

0.7. 

0.2 

0.2 

T 

11.6 

10.3 

0.3 

2.9 

18.6 

0.2 
SERA TR-46-2- 10- 1 e-Att-01 

Submitted to: 

Gail V. Brooks (7507C) 

and Gail Maske-Love 

U.S. Environmental Protection Agency 

1801 Bell Street 

Room 1 102 1 -L - Crystal Mall Building #2 

Arlington, VA 22202 

U.S. EPA Contract No. 68-W-02-035, WA 2-1 0 

Period of Performance: May 1,2004 to April 30,2005 (Option Period 2) 

Probabilistic Risk Assessment Support for EFED 

Contracting Officer: 

Mr. Robert Minjack (3803R) 

U. S. Environmental Protection Agency 

1200 Pennsylvania Avenue, NW 

Ronald Reagan Building 

Washington, DC 20460 

Prepared by: 

Patrick Durkin 

Syracuse Environmental Research Associates, Inc. 

5 100 Highbridge St., 42C 

Fayetteville, New York 13066-0950 

Telephone: (3 15) 637-9560 

Fax: (3 15) 637-0445 

E-Mail: SERA-INC@msn.com 

Home Page: www.sera-inc.com 

January 2,2005 I 
Table of Contents 

1 . Introduction ................................................................. 1 

2 . MaterialsandMethods ....................................................... 1 

2.1. Pharmacokinetic Study in Rats ......................................... 1 

2.2. Model Structure ........................................ : ............ 2 

2.3. Parameter Estimates .................................................. 3 

3 . Results .................................................................... 4 

4 . Discussion ................................................................. 5 

5 . References ................................................................. 7 

Appendix: Model Source Code in ACSL 

LIST OF FIGURES 

Figure 1 : Overview of PBPK Model for Brodifacoum ........................... Figure-1 

Figure 2: Comparison of observed and modeled concentration in the liver. kidney. 

and carcass for the mid dose group .............................. Figure-2 

Figure 3: Comparison of observed and modeled concentration in the liver ........... Figure-3 

Figure 4: Comparison of observed and modeled concentration in the kidney ......... Figure-4 

LIST OF TABLES 

Tables 1: Physiological Parameters Used in PBPK Models ....................... Tables-1 

Table 2: Selected Properties of Brodifacoum .................................. Tables-2 

Table 3: Estimated first-order rate constants ................................... Tables-2 

NOTE: Tables and Figures are placed after the appendix 
1. INTRODUCTION 

This brief report presents an exploratory and preliminary physiologically based pharmacokinetic 

(PBPK) model for brodifacoum. This exercise is motivated by the review of a probabilistic risk 

assessment on brodifacoum (Cadmus 2004) that was submitted to the Environmental Fate and 

Effects Division (EFED). The Cadrnus (2004) report states that ... the toxicokinetics of 

brodifacoum, particularly the rates of exchange between blood, liver, and other tissues, have not 

been quantzfied even in the rat and bases their probabilistic risk assessment on a classical single 

compartment kinetic model. The nature of the available pharmacokinetic studies, however, were 

not detailed. 

In order to obtain a better understanding of the limitations in the pharmacokinetic studies on 

brodifacoum, two reviews on brodifacoum (Erickson and Urban 2004; WHO 1995) were 

consulted and a preliminary literature search was conducted on TOXLINE 

(http://toxnet.nlm.nih.nov/cgi-bin/sis/htmlaen?TOXLINE). Information was identified on the 

pharmacokinetics of brodifacoum in 16 studies on seven species: rats (Bachmann and Sullivan 

1983; Batten and Bratt 1990; Belleville 199 1 ; Bratt and Hudson 1979; Hawkins 199 1 ; Parmar et 

al. 1987), possum (Eason et al. 1996), rabbits (Breckenridge et al. 1985), sheep (Lass et al. 1985), 

dogs (Robben et al. 1998; Woody et al. 1992), horses (Boermans et al. 1991; McConnico et al. 

1997) and humans (Bruno et al. 2000; Hollinger and Pastoor 1993; Weitzel et al. 1990). 

Based on the summary of most of these studies in Erickson and Urban (2004), full copies of two 

studies were obtained: Batten and Bratt (1990) and Lass et al. (1985). The study in sheep by Lass 

et al. (1985) did not contain many experimental details and was conducted using only two dose 

levels. While this study might be useful in assessing the species scalability of a PBPK model for 

brodifacoum, it did not appear to be suitable for the development of a PBPK model, and this 

study was not pursued further. The study by Batten and Bratt (1990) is a relatively standard 

pharmacokinetic study submitted to support the registration of brodifacoum. As detailed below, 

the study provided sufficient information for the development of a preliminary PBPK model for 

brodifacoum. 

2. MATERIALS AND METHODS 

2.1. Pharmacokinetic Study in Rats 

The study by Batten and Bratt (1990) involved groups of male rats (about 7 weeks old and 

weighing between about 180 and 220 g). The rats were administered C14-brodifacoum by gavage 

at doses of 0.02 mg/kg, 0.15 mglkg, or 0.35 mglkg. Animals were observed and assays of 

brodifacoum were made for up to 104 days. 

Concentrations of brodifacourn were determined (by serial sacrifice) in the liver, kidney, salivary 

glands, and pancreas in all three dose groups. Concentrations of brodifacoum were determined 

in the blood and fat in the high-dose group and the carcass in the mid-dose group. Attempts were 

made to determine the concentration of brodifacoum in the blood of 4rnals in the low-dose and 

I 

1 I 
mid-dose groups but the concentrations were below the limit of detection. Even at the high-dose 

group, concentrations in the blood could not be detected after Day 8. Measurements of the 

proportion of the dose excreted in urine and feces were made by 24-hour collections before 

sacrifice at various times in the low-dose and mid-dose groups. Except for values in the middose groups at Day 1, amounts in the urine were below the limit of detection. In the low-dose 

and mid-dose groups, the majority of the brodifacoum was excreted in the feces. In both groups, 

there was a very high rate of fecal excretion at Day 1. This is not discussed by Batten and Bratt 

(1990) but may simply be an artifact of gavage administration. 

As discussed by Batten and Bratt (1990), the two lower doses were considered non-toxic. No 

adverse effects were noted and no effect was seen on clotting times or body weight gain. In the 

high-dose group, clotting time was increased from about 24 hours to 96 hours after dosing. Also 

in the high-dose group, body weight gain was depressed, some of the rats died, and other rats 

evidenced signs of internal hemorrhaging. All of these effects in the high-dose group were 

attributed to brodifacoum. 

Details of the observations from Batten and Bratt (1 990) that were used or considered for 

quantitative analyses are included in the worksheet titled Bat ten et a1 1987 Data in the 

EXCEL workbook, Bat ten and Brat 199 0 Data. xls. A copy of this workbook is 

included with the current report. Batten and Bratt (1 990) report data in nanomoles/gram of tissue 

(equivalent to pmoleskg tissue). In the developement of the PBPK model, all concentrations 

were transformed to units of mg/kg or mg/L. Details of these conversions are given in the 

workbook. 

2.2. Model Structure 

An overview of the PBPK model for brodifacoum is given in Figure 1 and the source code for 

the model is given in Appendix 1. 

Brodifacoum was modeled with a standard blood flow-limited system: 

In Equation 1, the change in the amount of a substance in an organ per unit time (dAddt) can be 

defined equivalently as product of the volume of the organ (V,) and the change in the 

concentration of a substance in an organ per unit time (dColdt). The a~nount entering the organ at 

time t is simply the rate of blood flow to the organ (Q, in Wday) and the concentration in the 

afferent or arterial blood (C, in mg/L). The concentration in the efferent or venous blood is 

calculated as the concentration in the arterial blood divided by the organ-blood partition 

coefficient (Po). This type of flow-limited transport assumes that movement across the cell 

membrane is very rapid relative to the rate of blood flow. This assumption is generally 
applicable to lipid soluble, unionized, low molecular weight compounds - i.e., compounds that 

are likely to be rapidly absorbed across cell membranes (Gibaldi and Perrier 1982). 

Excretion from the kidney and liver is modeled as a first-order process. Excretion from the liver 

is directed to the gut lumen and excretion from the kidney is directed to the urine. The gut tissue 

is modeled as a standard flow-limited compartment with absorption from gut lumen and stomach 

lumen. Absorption from the gut lumen and stomach lumen is treated as a first-order process. 

Movement from the stomach lumen to the gut lumen and from the gut lumen to the feces is also 

modeled as a first order process. While enterohepatic recirculation is not discussed in the study 

by Batten and Bratt (1990), enterohepatic recirculation seems plausible for a highly lipophilic 

compound like brodifacoum and was incorporated as resorption from the gut lumen to gut tissue 

with subsequent transfer back to the liver. 

Preliminary attempts to optimize liver tissue clearly suggested that the standard flow-limited 

model did not well describe the biphasic pattern over time in the concentration of brodifacoum in 

the liver. Thus, as a first approximation, a standard deep compartment was added to the liver in 

which transport to and from the deep compartment was modeled as a first-order process - i.e., the 

parameters k,,, and k, ,. I 

2.3. Parameter Estimates 

The physiological parameters used in the PBPK models are summarized in Table 1. Most 

parameters, including the organ volume and flow, are taken from ILSI (1994); the other values 

are taken from Davies and Morris (1 993). The fractional weight and fractional flow for the 

carcass was set to 1 minus the sum of the fractional weights of the other organs. In other words, 

the fractional organ weights (including the blood) always sum to unity. Similarly, the proportion 

of cardiac output is always set so that the sum of the fractional flows equals unity. 

As noted above, transport from the stomach lumen to the lumen of the gastrointestinal tract is 

explicitly modeled. To avoid having to optimize this parameter, the lalue was set to the 

reciprocal of the transit time for the stomach, as reported by Davies and Morris (1993). 

Similarly, rather than optimizing the first-order coefficient for fecal excretion, the value was set 

to the reciprocal of the GI transit time in rats, also from Davies and Morris (1993). Neither of 

these estimates required adjustment. 

The pharrnacokinetic models were developed, solved, and optimizedusing ModelMaker 4 

software (Chenvell Scientific Limited, 2000) with Gear's method for numerical integration. 

Although Batten and Bratt (1990) provide individual animal data, mdan values were used in this 

preliminary analysis. Initial model parameters were set by eye fit. Final estimates prior to 

optimization were set by simulated annealing. Model optimization vrias performed using a 

standard Levenberg-Marquardt algorithm with an error weighting of 10.2. This weighting factor 

was based on a cursory examination of the variability of the individual animal data. In a more 

detailed analysis, the individual animal data would be used. The mokel was also ported to ACSL 

(Aegis Technologies 2004) and the source code for the model in ~~dendix 1 is given in ACSL. 
The ModelMaker source code is in the file Rat Model with PK Data .mod which 

accompanies this report. 

Measured tissue:plasma partition coefficients for all tissues included in the model were not 

located for brodifacoum. As an exploratory exercise, the tissue:plasma partition coefficients 

were estimated using the method of Poulin and Krishnan (1 995). This method is implemented in 

the EXCEL workbook partition Coefficients from Poulin and Krishnan 

19 9 5 . xl s which accompanies this report. The input values needed for this algorithm as well 

as the estimated values using this algorithm are surnrnarized'in Table 2. 

The model was optimized using data from Batten and Bratt (1990) on average concentrations in 

the liver, kidney, and carcass. Liver and kidney data were included because these are excretory 

organs and data on concentrations of brodifacoum in these organs are available for all three dose 

levels. Except for the concentrations in the kidney in the low-dose group, the data on liver and 

kidney included detectable concentrations covering or nearly covering the 104 day duration of the 

study. Data on concentrations in the carcass are only available for the mid-dose group. These 

data were included in the optimization because the carcass comprises about 87% of the mass in 

the model. Thus, even though concentrations in the carcass are low relative to the kidney and 

liver, this organ was included because it may contain an appreciable fraction of the total body 

burden. 

3. RESULTS 

3.1. Model Optimization 

The parameter estimates for the PBPK model for brodifacoum are summarized in Table 2 

(tissue:blood partition coefficients) and Table 3 (first-order rate constants). Preliminary attempts 

to fit the model using the parameter estimates from Poulin and Krishnan (1995) were 

unsuccessful. Subsequently, these partition coefficients were treated as adjustable parameters 

and were optimized. Taking this approach, the PBPK model provided a reasonable fit to the data 

(? = 0.92,~ = 0.015). 

Figure 2 illustrates the model fit to the observed values at the mid-dose group for concentrations 

of brodifacourn in liver, kidney, and carcass. Visual inspection suggests that the data are fit 

reasonably well. Nonetheless, the model does not account for a spike in kidney concentration at 

Day 1 and the liver concentrations tend to be somewhat underestimatkd by the model after about 

30 days. As illustrated in Figure 3 - liver concentrations for all three dose groups - the 

underestimates of liver concentration are seen in all three dose group$. In addition, it is apparent 

that the spike in liver concentrations was not well-modeled using a classical deep compartment. 

Concentrations in the kidney (Figure 4) are also somewhat underestifiated at the high-dose group 

for the two observations after Day 30. This underestimate is not app+ent in the mid-dose group. 

There are relatively few observations of kidney concentrations in the low-dose group and these 

are scattered. 

I 
4. DISCUSSION 

4.1. Model Optimization 

The major purpose of this exercise was to determine if the available data on the 

pharmacokinetics of brodifacoum might be sufficient for the developement of a PBPK model 

that would be useful in assessing potential exposures and risks to target and nontarget animals. 

The current analysis suggests that the development of such a model is feasible. 

This is not to suggest that the current model is adequate or directly useful. It is not. The 

development of a mature PBPK model would require the inclusion of all data not only from the 

study by Batten and Bratt (1990) but also from the other studies that are available. 

All of the studies will have limitations. The study by Batten and Bratt (1990), for example, 

involved relatively few and sporadic collections of 24-hour urine and feces samples. This would 

require an optimization of urine and fecal excretion rates which are generally more difficult to fit 

than cumulative urinary and fecal excretion data. Similarly, the study in sheep by Lass et al. 

(1985) provides very little quantitative information on fecal excretion rates and residue data only 

in carcass, liver, and omental fat. Nonetheless, such data could be used to assess the scalability 

of a PBPK model developed with data from rats. Species scalability is one of the major benefits 

in the use of PBPK models and a major criterion in the evaluation of PBPK models (e.g., 

Bruckner et al. 2004; Durkin et al. 2004). As noted above, data are available on the kinetics of 

brodifacoum in 16 studies on seven species. Thus, brodifacoum appears to be a compound for 

which a PBPK model could be developed and well evaluated. 

The inclusion of additional data could and probably would lead to a different model than that 

presented in this report, both in terms of the value of the model parameters and the structure of 

the model. For example, as noted in Section 2.2, a classical deep compartment was used in an 

attempt to model the transient peaks noted in the concentrations of brodifacoum in the liver. 

While this improved the model fit, the improvement was not substantial, the peaks were not wellmodeled, and this may have contributed to the underestimates in liver concentrations noted at 

later time periods at all three doses (Figure 3). This sort of problem is not uncommon during the 

development of PBPK models and indicates that the model may not be properly structured. 

Summaries of the study by Parmar et al. (1987) suggest that the pattern in liver concentrations 

might be better modeled as receptor binding. Explicit receptor binding or the development of 

empirical models that reflect receptor binding are preferable in PBPK models over the use of 

classical deep compartments (e.g., Durkin et al. 2004). A receptor binding model component 

could be considered in a further elaboration of the exploratory model. 

4.2. Utility 

If a PBPK model were developed and properly validated, it would be usehl in assessing several 

issues associated with the potential risks in the use of brodifacoum for rodent control. It is likely 

that an at least crude toxicodynamic component, could be added. As noted by Batten and Bratt 

(1990), the coagulation times in rats were associated with concentrations in liver of greater than 
about 3 nmollg tissue. These clotting times returned to normal when liver concentrations 

dropped below 3 nmollg tissue but the liver concentrations remained only marginall; below toxic 

levels - i.e., about 1.35 to 2.5 nmollg liver tissue from Days 8 to 84. Given the slow excretion of 

brodifacoum and the availability of some multiple dose studies on brodifacoum (Erickson and 

Urban 2004), it may be possible to model both cumulative effects and the delayed mortality 

associated with brodifacoum exposures. 

Apart from toxicodynamic considerations, the development of a mature PBPK model for 

brodifacoum could be useful for assessing the potential benefits of different baiting practices. 

This may be a very practical concern for brodifacoum. As summarized by Erickson and Urban 

(2004), there is ample data suggesting that brodifacoum will impact both target and nontarget 

rodents. A PBPK model could be used to assess the potential benefits of different baiting 

practices in risk mitigation. 
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AEgis Technologies Group, Inc. 2004. AcslXtreme Version 1.4. User's Guide and 

Documentation. Available at: www.acslXtreme.com. 

Bachrnann, K.A. and T.J. Sullivan. 1983. Dispositional and pharmacodynarnic characteristics of 

brodifacoum in warfarin-sensitive rats. Pharmacol. 27: 28 1-28 8. 

Batten, P. and H. Bratt. 1990. Brodifacoum: Elimination from the tissues of rats following 

administration of single oral doses. Unpubl. report submitted to EPA by ICI Americas, Inc., 

Wilmington, DE. 65 pp. 

Belleville, M. 1991. Compared hepatic kinetics of brodifacoum and difethialone in the rat, after 

oral administration of 0.06 mglkg once weekly for 4 consecutive weeks. Unpubl. report 

submitted to EPA by LiphaTech, Inc., Milwaukee, WI. 39 pp. 

Boermans HJ, Johnstone I, Black WD, Murphy M. 1991. Clinical signs, laboratory changes and 

toxicokinetics of brodifacoum in the horse. Can J Vet Res. 55(1):21-7. 

I Bratt, H. and P. Hudson. 1979. Brodifacoum: absorption, excretion and tissue retention in the 

rat. Unpubl. report submitted to EPA by ICI Americas, Inc. 30 pp. 

Breckenridge, A.M., S. Cholerton, J.A.D. Hart, B.K. Park, and A.K. Scott. 1985. A study of the 

relationship between the pharmacokinetics and pharmacodynamics of 4-hydroxycoumarin 

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84: 81-91. 

Bruckner JV; Keys DA; Fisher JW. 2004. The Acute Exposure Guideline Level (AEGL) 

program: applications of physiologically based pharmacokinetic modeling. J Toxic01 Environ 

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Bruno GR, Howland MA, McMeeking A, Hoffman RS. 2000. Long-acting anticoagulant 

overdose: brodifacoum kinetics and optimal vitamin K dosing. Ann Emerg Med. 36(3): 262-7. 

Cadmus. 2004. Brodifacoum (PP581): Assessment. Prepared by J. Giddings and W. WarrenHicks. Cadmus Group, Inc., Chapel Hill, NC. Prepared for Syngenta Crop Protection, Inc. 

Report dated September 10,2004. 

Chenvell Scientific Limited. 2000. ModelMaker User Manual. Version 4. Oxford Science 

Park, Oxford OX4 4GA, United Kingdom. Available at: www.modelkinetix.com 

Davies B; Morris T. 1993. Physiological parameters in laboratory anirhals and humans. 

Pharma. Res. 10, 1093. I 
Durkin P; Diamond G; Hertzberg R. 2004. Application of PBPK Model for 2,4-D to Estimates 

of Risk in Backpack Applicators. Environmental Toxicology and Pharmacology. 16: 73-91. 

Eason, C.T., G.R. Wright, and D. Batcheler. 1996. Anticoagulant effects and the persistence of 

brodifacoum in possums (Trichosurus vulpecula). New Zealand J. Agric. Research 39:397-400. 

Eason CT, Murphy EC, Wright GR, Spurr EB. 2002. Assessment of risks of brodifacourn to 

non-target birds and mammals in New Zealand. Ecotoxicology. 11(1):35-48 - not sure if 

kinetics are here. 

Erickson W; Urban D. 2004. Potential Risks of Nine Rodenticides to Birds and nontarget 

Mammals: a Comparative Approach. EFED report dated January 15,2004. 

Edwards PJ, Swaine H. 1983. Brodifacoum: Hazard to non-target animals from the use of 

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F' Compartment 

1 Urine 1 

QLm c* I Liver 

"" \I1 el Lumen 

1 Lumen 1 

Figure 2: Overview of PBPK Model for Brodifacoum 

Figure- 1 

1 Feces 1 
Mid Dose 

- Li~r 

- - - - + Kidney 

-- - Carcass 

Figure 2: Comparison of observed and modeled concentrations in the liver, kidney, and carcass 

for the mid-dose group. 
Concentration in Liver 

10.00 

- - - - + Low Dose 

- - Mid Dose 

-@High Dose 

Figure 3: Comparison of observed and modeled concentrations in the liver 
Concentration in Kidney 

1 .ooo 

111111111111111!11 I I I I I I I I 

0 10 20 30 40 50 60 70 80 90 100 110 

Days 

- - - - +Low Dose 

- - rn Mid Dose 

-0 High Dose 

Figure 4: Comparison of observed and modeled concentrations in the kidney 
~lood 0.054 0.0720 NIA NIA 

Carcass 0.8751 0.8528 0.685 0.53 

Gut 

Kidney 

Liver 

Other 

Body Weight 

Cardiac outputa 

GI transit (ke) 

0.027 0.0368 

0.0073 0.0055 

0.0366 0.0329 

Rats 

0.2 

0.235 BW,$ 75 

101.2 

800 (0.00125) 

1.8 

Dogs 

10 

2.936 

4227 

770 (0.0013) 

1.87 

units 

kg 

L bloodhnin 

L bloodday 

minutes (min-') 

day-' 

Stomach transit 32 (0.03) 96 (0.01) minutes (mir-') 

(keld 

43.2 14.4 day-' 

I 

"Taken fi-om ILSI (1 994) unless otherwise specified. I 

bDavies and Morris (1993), Table 11, p. 1094 I 

" Carcass value not specified in ILSI. The value of 0.1 is set judgmentally based on values in other tissue. 

Davies and Morris (1 993), Table 111, p. 1094 or Table V, p. 1095. Here and elskwhere, ke signifies first-order 

excretion rate. The ke values in days-' calculated as the ke in min' x 1440 mudday. 

Tables- 1 
Molecular weight 

(glmole) 

KOlw 3 16,000,000 Epi-Suite 

Tissue:Blood Partition coefficients (P ,issue:Blood) 

Calculated ' Optimized Units 

Liver 21.17 35.8 Llkg 

Kidney 18.21 6.4 

Gut 18.24 0.15 

' Calculated from KO, using the method of Poulin and Krishnan (1 995) 

I 

Parameter Value I Units 

Gut ka 

Stomach ka 

Kidney k, 

Liver k, 

Liver k,,, 
! Brodifacoum vla. ACSL Model Source Code 

! PBPK model for brodifacoum in rats. 

! Optimized in ModelMaker. See Rat Model vOlb Opt 10b.mod 

! All time units are in Days 

PROGRAM 

INITIAL 

!Rate constants for deep liver compartment. 

CONSTANT Liver-k12 = 0.02034 

CONSTANT Liver-k21 = 0.00001279 

!Tissue Partition Coefficients 

CONSTANT PCarcass = 0.6732 

CONSTANT PGut = 0.2174 

CONSTANT PKidney = 2.196 

CONSTANT PLiver = 14.04 

!First order rate constants. These will not change with species. 

CONSTANT kastomach = 0.27048 

CONSTANT keKidney = 0.3338 

CONSTANT keLiver = 0.2705 

CONSTANT kaGut = 0.01729 

I ***A******* parameters for Rats *XX*****XX*** 

CONSTANT RatBW = 0.2 !Body Weight, kg 

CONSTANT RatQ = 101.2 !Cardiac output, L/day 

!The next two parameters are fixed, not optimized. 

CONSTANT Rat-keGutLumen = 1.8 

CONSTANT Rat-keStomLumen = 43.2 

! Blood flow as a proportion of cardiac volume 

CONSTANT RatFFCarc = 0.685 

CONSTANT RatFFGut = 0.153 

CONSTANT RatFFKidn = 0.141 

CONSTANT RatFFLivr = 0.021 

! Fractional Organ Weights 

CONSTANT RatFWBld - - 0.054 

CONSTANT RatFWCarc - - 0.8751 

CONSTANT RatFWGut - - 0.027 

CONSTANT RatFWKidn - - 0.0073 

CONSTANT RatFWLivr - - 0.0366 

! Experimental 

! High-dose group in rats from Batten and Bratt 1987 for illustration. 

CONSTANT RatDoseRate = 0.35 ! mg/kg bw 

END ! 

CONSTANT TSTOP = 110 ! Days 

CONSTANT nPoints = 1100 ! Number of points to plot 

! Initial Calculated Values 

RatDose = RatDoseRate * RatBW 

RatBldVol = RatBW*RatFWBld 

RatCarcFlow = RatQeRatFFCarc 

RatCarcVol = RatBWXRatFWCarc 

RatGutFlow = RatQ*RatFFGut 

RatGutVol = RatBW*RatFWGut 

RatKidnFlow = RatQ*RatFFKidn 

RatKidnVol = ~atBW*RatFWKidn 

RatLivrFlow = RatQ*RatFFLivr 

RatLivrVol = ~atBW*RatFWLivr 

INITIAL 

Appendix - 1 
DYNAMIC 

ALGORITHM IALG = 2 !Use Gear integration algorithm 

CINTERVAL CINT = 0.1 !Communication interval 

DERIVATIVE 

I************ FLOWS ******************* 

Gut2Feces = Rat-keGutLumen * RatGutLumen 

I RatLivrIn = Liver-k12 * RatLiver 

RatLivrOut = Liver-k21 * LiverDeep 

Lumen2Gut = kaGut * RatGutLumen 

RatBld2Carc = RatCarcFlow * RatBloodConc 

RatBld2Gut = RatGutFlow * RatBloodConc 

RatBld2Kidn = RatKidnFlow*RatBloodConc 

RatBld2Livr = RatLivrFlow * RatBloodConc 

RatCarc2Bld = RatCarcF~ow*RatCarcConc/PCarcass 

RatGut2Livr = RatGutFlow * RatGut/PGut 

RatKidn2Bld = RatKidnFlow*RatKidnConc/PKidney 

RatKidn2Urine = keKidney * RatKidney 

RatLivr2Bld = (RatLivrFlow+RatGutFlow)*RatLivrConc/PLiver 

RatLivr2Gut = keLiver * RatLiver 

RatStom2Gut = kastomach * RatStomachLumen 

RatStom2RatGutLumen = Rat-keStomLumen * RatStomachLumen !*A********* COMPARTMENTS ************* 

LiverDeep = INTEG(RatLivr1n-RatLivrOut, 0) 

RatBlood = INT~G(-RatBld2Gut-RatBld2Livr+RatLivr2B1 & 

-RatBld2Kidn+RatKidn2Bld-RatBld2Carc & 

+RatCarc2Bld, 0) I 

Ratcarcass = INTEG(RatBld2Carc-RatCarc2Bld, 0) 

RatFeces = INTEG(Gut2Feces, 0) 

RatGut = 1NTEG(RatStom2Gut+Lumen2Gut+RatBld2Gut-RatGutLvr, 0) 

RatGutLumen = INTEG(-Gut2Feces-Lumen2Gut+RatLiyr2Gut+RatStomRatGutLumen, 0) 

RatKidney = INTEG(-RatKidn2Urine+RatBld2Kidn-RatKidnBd, 0) 

RatLiver = 1N~~G(-~at~ivr2Gut+~atGut2Livr+RatBld2Livr & 

-RatLivr2Bld-RatLivrIn+RatLivrOut, 0) 

RatStomachLumen = INTEG(-RatStom2Gut, RatDose) 

RatUrine = INTEG(RatKidn2Urine, 0) 

!************ VARIABLES *************** 

BodyBurden = RatBlood+RatCarcass+RatGut+RatKidney+RatLiver & 

+~at~tomach~umen+~at~u~~umen+~iver~ee~ 

MassBallance = (BodyBurden+RatFeces+RatUrine)/RatBW 

! Variables calculated with each integration step. 

RatBloodConc = RatBlood/RatBldVol 

RatCarcConc = RatCarcass/RatCarcVo1 

RatGutConc = RatGut/RatGutVol 

RatKidnConc = RatKidney/RatKidnVol 

RatLivrConc = (LiverDeep+RatLiver)/RatLivrVol 

END ! DERIVATIVE 

I TERMT (T .GE. TSTOP, 'Model reached TSTOP') I 

I 

END ! DYNAMIC 

TERMINAL 

! Unused at this time. 

END ! TERMINAL 

END ! PROGRAM 

Appendix - 2