I ,@ "4** UNITED STATES ENVIRONMENTAL PROTECTIONAGENCY 

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### MEMORANDUM DP Barcode 306327 August 24,2005 PC Code 112701 SUBJ: EFED Evaluation of "A ProbabilisticAssessment ofthe Risk ofBrodifacoum to Nontarget Predators and Scavengers," by The Cadmus Group, September 10, 2004; Submittedby SyngentaCrop Protection, Inc. TO: Kelly White Chemical ReviewManager SpecialReview & 

### Reregistration Division 7,508C FROM: Mary Frankenbeny, Statistician

# '-' 

# 11 ' 

### William Erickson,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 evaluationofthe Cadrnus I 

### probabilistic assessmentfor brodifacourn, submitted by SyngentaCrop Protection for the brodifacoum registrants. It includes a more comprehensive review ofthe document performed by the Syracuse Environmental Research Associates and Syracuse Research Corporation for EFED and OPP. Please let us know ifyou have any questionsyou would like to discuss further with regaid to these documents.

### OPP Evaluation of Cadmus/BrodifacoumRepistrants (C/BR) Probabilistic Risk Assessment Model for Brodifacoum
Executive Summary
Brodifacoum is a second-generation anticoagulantrodenticide primarilyused to controlNorway and roof rats and house mice. In 1998,EPA issued a Reregistration EligibilityDecision (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 continueto evaluatethe risks of labeled uses of brodifacoum to nontarget birds and mammals. In 2001 (updated in 2004), EPA completed a comparative assessment ofnine rodenticidesused in the United States and concluded that brodifacoum labeled uses pose high potential primary and secondaryrisks to birds and nontarget mammals. The 1998RED 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 ofpotential ecological effects ofbrodifacoum, 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 distributionsto estimate daily dose to nontarget predator species as a function ofthe body weight and food ingestion rate of animals, the concentration of residue in food, and the fraction of food in the diet containingbrodifacoum. They also used an uptake-depurationmodel "to estimate the cumulative dose over time." Effects data used to develop the distributionswere taken from published and unpublished sources. Finally, exposure and effects distributionswere combined to estimatethe probabilityofmortality to nontarget predator organisms. 

### In their risk assessment, C/BR claimed low secondaryrisk of brodifacouminduced mortalityto coyote, red fox, and red-tailed hawk and inferred,thesame conclusionto other species ofbirds and mammals with similar sensitivityand diet. They also claimed that the secondary risk from brodifacoum was only slightlyhigher 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 limitedby the lack of data and were subjectto 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. Similarto EPA's assessment,the C/BR assessment identifies information and data gaps that lead to major uncertaintiesin quantitatively assessing risks from secondary exposureto 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 rodenticideexposedprey. Data that would help reduce the uncertainties in the risk assessments include information on local baiting practices, dietary composition and foragibgbehavior ofbirds and

### mammals when bait andlor dead and dying animals supplement the natural food supply, toxicity data for predatory and,scavengingspecies, concentrationsof brodifacoum in target rodents and nontarget birds and mammals, and information on the retention, storage, and eliminationof

I 

### brodifacourn in nontarget birds and mammals. Because data are very limited for quantifjring exposureunder expected and typical use patterns, there is a large amount ofuncertainty in the estimation ofrisk. As acknowledged in the C/BR assessment, risk depends stronglyon local conditions and the foraging behavior and habitat use ofpredators and scavengers. Because of the spatial and temporal variability in all ofthese considerations, it becomes extremely difficultto quantify exposure and risk on a national scale. 

### 1 Problem Formulation General Conceptual Model 

### Although it was included in the initial drafl ofthe problem formulation for the C/BR probabilistic assessment, primary exposureto nontarget animals that ingest bait was omitted in the final assessment. According to the C/BR assessment, primary exposurewas excluded because (1) 

### secondary exposurewas of greater concern to regulators, (2) a differentmodel would be needed to assess primary exposure, and (3) 

### risk reduction for non-bait feeders is mostly a matter ofbait station design. EPA has received data indicatingthat primary exposureto nontarget organisms does occur, and therefore, it is a concern for the Agency. Whether it is less or greater than secondary exposureis not as important as the fact that it does occur and shouldbe addressed in the risk assessment. The need for another exposuremodel to address primary exposure is also not a reason to avoid assessingit. Thirdly, the Agency does not agree with the assertionthat primary exposure is largely a function of bait station design, and thus, that it was appropriateto excludepkimary 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 scenarioswould be considered: urban, suburban, and rural. Those scenarioswere not addressed in the CB/R assessment, nor were any aspects of spatial or temporal variability. The C/BR assessment ofrisks to predators examined exposure from ingestion only and, for the most part, ingestion of onlyNorway rats. Exposure from ingestion otroofrats, target and nontargetmice, birds, and invertebrateswas 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 scavengersfeed on those dead or dyingbirds that eat bait. For example, redtailed hawks will prey on other birds (e.g., morning doves and starlings) which couldbe exposed to bait directlythrough the consumptionof insects. Some of the studies in the source cited in the

### CBRreport (e.g., U.S. EPA 1993)indicatethat birds may comprise 10% or more ofthe 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) ofthe coyote. This source of exposureneeds to be consideredin the risk assessment. As acknowledged by CBR, critical exposure data are lacking, which makes it difficultto quantifLrisk. For example, CBR states that 

### PT (the proportion ofrodents in the diet that have been exposedto brodifacoum) is crucialto the model, but that "Wewere unable tofind any data regarding the proportion of rodents in the environment or in apredator'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, feralpig, guineapig, 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 ofthe study. Methodological aspects ofthese studies were not examined. For example, the length ofthe 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 sketchilydescribed studies reported by Godfiey (1986)'. Godfrey dosed differentNew Zealand bird species in 13tests, but states that many of the results were inconclusivebecause of the small number ofbirds in several groups, problems in maintaining some species in cages, and the high interspecific and intraspecific variationsknown to occur with anticoagulant poisoning. The author goes on to call the results "inconsistent" and "inconclusive" and sayshis trials support the view that larger test groups are needed (e.g., only 3 test groups of4 birds per group were used for the harrier hawk). "Manyof 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." CBRexplainedthat they teased data from the stud) and did not use results that were not appropriate. Such data were not used to calculaterisk q40tients in EPA's deterministic assessment and should not be relied upon in other than 

### a qualitativemanner in a higher-tier assessment unless the uncertaintyis accounted for.
'Godfrey, M.E.R. 1986. An evaluation ofthe acute-oraltoxidity ofbrodifacoum to birds. Proc. Vertebr. Pest Conf. 12:78-81.

### Sensitivity of 

### Species 

### CBRderived 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 ofthe dataused 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 ofthe SSD, CBRinstead used the 10th and 90th percentiles as indicators of "high" and "low" sensitivity, as described above. Because LD50 values for three ofthe nine bird speciesused by CBRfall below the 10thpercentile dose-response curve, the 10thpercentile is not representative of a highly sensitive species. The risk assessment should note that data for other species not used also fall below the 10thpercentile. 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 questionshow representativethe SSDs are for nontarget animals that may be exposed to brodifacoum in North America. Much ofthe 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 occurringin North America. As discussed by Calow and Forbes (2003)', the fundamental assumption ofrepresentativenessof 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 inparticular communities to place much confidence in theprecise 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 

### CBRuses 3 surrogate species formammalian predators and scavengers, and all are canids (coyote, kit fox, red fox). The only basis for using 3 canids as surrogatesfor 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 

### "Byinference, the same conclusion applies to other species ofbirds 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 solelyto address risks to canids. Other generalistmammalian predators suitable for this assessment include the bobcat 

### (Lynxrufus), long-tailed weasel 

### (MustelaJFenata), raccoon 

### (Procyon lotor), striped skunk 

### (Mephitis mephitis), and opossum 

### (Didelphisvirginiana). These species all have the abilityto use a variety ofhabitat types but, nevertheless,they all differ considerablyin their ecology and life history, and these differences are borne out in their differential responses to fragmented 

### landscape^.^ EPA also notes that any ofthe surrogate canids might well ingest bait in additionto preying and scavenging on poisoned target and nontarget animals. 

### 3 

### Exposure Analysis
Limitations 

### of the Residue Data-Pulsed Baiting CIBR states that 

### "Theavailability offleld data on residues in rodents greatly simplzj?ed the exposure analysis."(p. 17), 

### "Ourexposure 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 

### "Useofcarcass 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 availableto characterizeresidues indead 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 ofthe 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 agriculturallandscape. 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 ofthe 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 usepatterns), but the method can reduce the quantity oftoxicant placed in the environment at any given time."
In the pulsed baiting in the U.K. studies, 1-2 oz. (20-50 g) ofbait 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 applicatorsto apply 4-16 oz of. bait at 15- to 30-foot intervals and to maintain an uninterrupted supply of fresh bait until signs ofrat 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 ofbait and replenishing only at 7-day intervals, the technique of pulsed baiting results in individual rats consuming lesser amounts ofbait before dying, thereby reducing the total amount ofbait needed for control (Dubock 1982)6.

### In contrast, saturation baiting allows individual rats to feed on bait continually until they die, thereby increasingthe amount ofbrodifacoum 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 differenceswere 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, 111were 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 anticoagulantrodenticides (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 1pulsed-baited rats was 1.6ppm, 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 nojustification 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-om3.4 ppm to 1.9ppm. This, in turn, leads to predictions of lower exposure and therefore lower risks to predators and scavengers.

'Kaukeinen, D.E. 1993. Nontarget organism evaluationsfor 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 ofthe 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 similarityto baiting practices in the 

### U.S. Consistent with the comments 

### EPA has made repeatedlyin communications with the registrants,this is a very minimal dataset contributingto uncertaintyin the assessment. Residue Reporting Data The data from 13 rats captured alive and analyzed for brodifacourn residue in Edwards et al. (1984) was incorrectlyreported as being "0.05 ppm (mgkg)," which was the Limit ofDetection for brodifacoum. By omitting the 

### "<" and reporting the residue level as "0.05mag"for each of those rats, the assessment implies that a low level ofresidue was detected. This is probably not the case. 

### In fact, ifno 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 ofthe predator %

### rodents in diet ofthe predator %

### rodents exposedto rodenticide concentrationof brodifacoum in exposed rodents FIR=FMR/(GE

### x AE 

### x M x Wt)

field metabolic rate '(function of log(a), b, and Wt) oftbe predator gross energy content of food (rodent) assimilation efficiency moisture content of food body weight Limiting Assumptions for PD and PT
Two important components ofthe exposuremodel are the proportion ofrodents 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 "Bothvariables 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 ofthe dietary studies in the literature were conductedin 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 supplyis altered in two ways. The bait provides a 

### supplementalfood 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 speciesused 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 susceptibleto predation than unexposed rodents, the model did not account for this in the predator's diet composition. Incorporatingfeeding behavior and other aspects affecting it into the model may be more difficult than ignoring it, but it can be done. In their submissionofFebruary 23,2004, C/BR states that FIR and PD could be adjusted to vary seasonallyto reflect changing feeding habits of a predator species; the distribution of C [Concentrationin exposed rodents] could be adjusted to represent increased predation on moribund rodents, changes in dosing regimens, and changes in the concentrationin rodents as a function oftime; PT could fluctuate cyclically to represent different baiting practices; and time dependencies couldbe incorporated."
C/BR states in the Results (p. 

### 38) that 

### ".

. . risk increased sharply with increasingPT." and "In

I 

### situations where a high percentage ofrodents are exposed to brodijiacoum, risk ofseconda y 

# ~ poisoning may become high even for predators with a relatively low proportion of rodents in their diet." Moreover, in their submissionof February 23, 20047,CBR states that 

### ".

. . 

### we will run the model using different assumptions based on indirect estimates and expert opinion (includingPT=1 as the worst case)." For somereason, higher-exposure scenarioswere 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 sensitivityand only 1% exposed rodents in the diet. Results of exposure scenariosusing PT values above 2.5% (e.g., 15%) were not included in the iskassessment 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 ofthe report that "Therisk ofbrodijiacoum-inducedmortality is low in the coyote, red fox, and redtailed hawk, and by inference the same conclusion applies to other species ofbirds 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"

7Methodsand Procedures For Assessing the Risk of Rodenticide Baits Containing Brodifacoum toNon-Target Animals, W. Warren-Hicks and J. 

### Giddings, The Cadmus Group, 

# ~ February 23,2004,5 pp.

### kit fox means that there is a 10%probabilitythat mortality exceeds 5% under those exceeds 7% for the great horned owl. Apparently, mortality could be as high as

ofthe range of such a sensitiveparameter 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 ofthe target organisms will beexposed. Assuming that used effectively -

### i.e., a large proportion ofthe target rodents are killed by it followsthat a largeproportion of the target rodent population is

is reenforced by the Edwards studies (Edwards and Swaine 1983; which also suggest that substantialnybersofnontarget 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 ccessfulbaiting 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% ofthe 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 carefulreview of data associated with successful baiting programs ould probably result in better estimates ofPT and possible error ranges associated with PT. E 

### A asserts that plausible estimates ofPT (which should encompass both target and nontarget r dents) are substantiallyhigher than the 0.01 to 0.15 values used by C/BR. 

# c

- 

### further describesPT 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 ofthe 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 ofthe intermediate values 

### of[daily] 

### DPT would be be either entirely exposed or entirely unexposed." 

### be explored more fully, especiallyfor target And nontarget mice which The distribution ofFIR should be presented together with the

I

### The consequenceofthe "all or nothing" modeling approach for determining daily doses (given the small 

### P* value used by the authors), is that a high percentage ofthe exposure days yield a dose of 0, ahd a small percentageyield an extremely high dose. The likelihood of exposure, or equivalently the average percentage of days in which exposureis non-zero is determined by PT, which the aithorshave claimed to be very small.
The authors ofthe 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 likelythat 

### 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 essentiallythe same result,

This is because the frequency of daily consumptionof contaminated prey is

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

### <PT 

### < be significant. While the mean daily dose (DD) is approximatelythe daily estimates are 0 for the individualprey method. (See Figures 1

of this alternativeis 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 (> 

### 10days), as is the case the frequency of consecutivedays of non-zero exposure,the more to accumulate in the body ofthe predator or scavenger.



### Distribution of 10,000 ExposureEvents 

### CIBR calcu ated a summary statistic from the time series ofbody burdens. They suggested that the 90-day aximum is an appropriate metric. For each individual, 100 sets oftime series were generated, ith each time series yielding an estimate of the 90-day maximum. Therefore, for 100 individuals, there were 100 sets of time series of 90-day maxima. 

# a

At this point, represents
\ 1. 

### the The risk there are two possible approaches to generating the distribution of doses that fne combined sources of variabilityin the model: 

### Summary StatisticApproach. For each individual, calculate a summary statistic from 

### :. 00 sets of 90-daymaximum. For example, calculate the mean 90-daymaximum, 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.

102. 

### 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 100or 10,000values to define the exposure (dose) distribution. Section 3.4.4 ofthe report describesthe approach, statingthat "ne mean ofthe 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 (100 estimates for each of 100 individuals) was combined into a single exposure distribution reflecting both withinindividualand among-individual variation. " 

### While the statements are conflicting and the input files were not included in materialsprovided, it appears fromcode and from certain descriptive statisticsthat the 

### CBRassessment elected Option 1, with the summary statistic being the 

### arithmetic mean ofthe 90-day maxima. This yielded a data set of 100values ofmean 90-day maxima.
EPA maintains that the differencebetween the distribution of 100means of 90-day maxima, and the distribution of 10,000 values of 90-day maxima is a large one. The former could greatly underestimatethe upper tail compared with the latter. Figures 

### 7 and 8 of the Appendix provide an example for coyote. For this example, settingPT = 

### 0.025, halflife = 

### 5 days, and SD of DPD = 5%, 

### the effect of decreasing variabilityby representing individualsby the mean 90-daymax 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 ofthe 100 inner loop iterations, a sequence of90 independent daily doses was simulated



### ... " 

### The choice of a 90-day period appears to be arbitrary. 

### C/BR electedto select the first 90-day period for each time series of daily doses. All individualsbegan 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 chemicalto achieve steady state. This time to steady state is influencedby both the choice of depurationhalflife (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 differencesetween the 90-day maximum during the first 90 days and the maximum during the last 90 days of a 365 dayperiod is demonstrated in Figures 9-1 1 ofthe Appendix. These authors make the following observations:
When the dosingregimen is frequent, the assumption about t %

### becomes critical; and The maximum 90-daybody burden is unlikely to be capturedby the first 90-days of the

### time series, especially for longerhalflife scenarios. 

### Kinetic model


### C/BRYs kinetic model is limited to one compartment and does not consider availablemetabolism studies (See detailed comments in the Appendix.) C/BR postulates a single compartmentkinetic model for brodifacoum, claimingthat "Depuration kinetics ofbrodifacoum 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 ofthe curve is steep, with a halflife on the order of a few days, reflecting apool ofrapidly eliminated brodifacoum in the body. The secondphase ofthe depuration curve is much slower, with a halJlifeon the order of many months, reflecting a much smallerpool of tightly bound brodifacoum in the liver. However, the toxicokinetics ofbrodifacoum,particularly the rates ofexchange between blood, liver, and other tissues, have not been quantiJiedeven 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 impressionthat the kinetics of brodifacoum are poorly understood and cannot be used for the type of analysis being considered by CBR. Information on the pharmacokineticsofbrodifacoum is availablein 16 studies on seven species. These studies are discussed in the Rodenticide Cluster

### RED' and in EPA's comparativerisk assessment for the rodenticides.
C/BRYs discussion of a "poolofrapidly eliminated brodifacoum" (p. 21) appears to be a misinterpretation ofthe biphasic pattern in the concentration of brodifacoum in the body. The first and more rapid phase is associated with redistribution fromblood to tissue, not excretion.
The C/BR report chose a range ofhalflives from 2 days to 200 days with the implicationthat the "real" halflives are somewherein this region. In terms ofthe body burden ofthe 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 ofthe first order model and a sensitivityanalysis using the range of values (2 to 200 days). As discussed in detail in the Appendix, the sensitivity analysis may lead to the impressionthat the kinetics have minimal impact on the assessment of risk. Basing most ofthe analyses on a 50-dayhalflife in a simple one compartment model, however, will probably underestimate risk.

I 

### in ere gist ration EligibilityDecision (RED): Rodenticide ~lusker. July, 1998.EPA738-R98-007. 307

### As illustrated in the Appendix, the kinetics of brodifacoum can be describedby a simple flowlimited physiologicallybased pharmacokinetic (PBPK) model that is optimizedwith concentration data in liver, kidney, and carcass. Such a model accounted for the bulk ofthe 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 SantaMonica MountainsNational RecreationArea, Department ofNatural 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 ofthese individualswere exposed to anticoagulant rodenticides,including brodifacoum.


### In addition, the C/BR risk characterizationcould include an explanationofthe 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 SpeciesRecovery Program (ESRP), coordinates several ofESRP's research projects on San Joaquinkit foxes. The ESRP is a cooperative researchprogram on biodiversity conservation'incentral California, administered by California State Unviersity, StanislausFoundation. Its mission is to facilitate endangered speciesrecovery and resolve conservation conflictsthrough scientific'ally based recovery planning and implementation. The program was established in 1992

### kt the request and with the support ofthe U.S. Fish and Wildlife Service and the U.S. Bureau of eclamation. Over the past 

# R decade, ESRP has grown into a cooperative research program working with local, State, and Federal agencies, non-governmental organizations,corporations, andprivate land owners. 

# h 1°e.g., see S.P.D. Riley et al. 2002. 

### Effects ofUrbanization a d 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 frameworkis an interesting and potentiallyuseful 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 ofpossible predicted outcomes. On page 35 the authors state that 

### "hierarchical models reduce the effect ofincomplete data sets, small numbers oftests, 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 approachis 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 specieshave been selected, where available metabolism data have not been used, and where assupptions on behavioral parametershave not been explored to the full range oftheir implications for exposure,the benefit of such an approach cannot be expected to overcome the limitations ofthe data. Furthermore, the selection ofthe 10thpercentile 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 distributionwhen combined with the median and 90th percentile species estimates. As pointed out in the ,- 

### SERAISRCreport appended to this document, the "bounds shown in Figures 15 and 16 do not appear to represent the variabilitythat 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% ofthe dose-response curves used. For birds, out of 9 curves, only 3 are completely contained within the 80% confidence bounds. For mammals,



## ...Ofthe 8 curves shown, only 1is completelycontained 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 35: 187-1 93 I3Hosea,R.C. 2000. Exposure of non-target wildlife to anticoagulant rodenticidesin 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 examinationofthese figures therefore indicates that even with the limited data chosen, this implementationofthe 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 ofthe 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 ofusing 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 characterizationis based on a series of unsupported assumptionsthat contributeto a highly uncertain l'isk estimate. C/BR provides exceedence probabilities (Table 14, p. 70) assuming high (10th percentile),median (50thpercentile), 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%probabilitythat mortality exceeds 67% and a 50% probabilitythat 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%probabilitythat mortality exceeds 23%. Exceedences for the great horned owl are nearly as high. Exceedences are even higher as I 

### the percentage of exposed rodents increasesin 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 14that mortalitymay increase at a higherrate, 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 substantiallyhigher probabilitythat 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 assumptionstor exposure. Low-to-no predictions ofrisk in C/BR's assessment are not substantiatedin 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 assumptionsinherent 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 summarizedbeen addressed (or followed through) in the problem formulation stage ofthe effort, a more useful product for risk managers might havebeen developed. These include limitations of scope from restricting the assessment to secondary exposure with one target species, failing to consider spatialltemporalvariations such as habitat and season, and a more detailed explanationofPT 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 foundationfor estimating exposurewould be extremely useful to reduce uncertaintyin risk estimates. Where data are limited, however, methods are availablein the implementationofthe model for addressinguncertainty. 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 uncertaintyin 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 ProtectionAgency 1801Bell Street Room 1102 1 

### -L 

### - Crystal Mall Building

### #2 Arlington, VA 22202
U.S. EPA Contract No. 68-W-02-035,WA 2-10 Period ofPerformance: May 1,2004to April 30,2005 (Option Period 2) Probabilistic Risk Assessment Support for EFED Contracting Officer: Mr. Robert Minjack (3803R) U.S. Environmental ProtectionAgency 1200 Pennsylvania Avenue, NW Ronald Reagan Building Washington, DC 20460



### Submittedby: Syracuse EnvironmentalResearch Associates, Inc. 

### 5100Highbridge St., 42C Fayetteville, New York 13066-0950 Telephone: (3 15) 637-9560 Fax: (315) 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 January4,2005Table of Contents



# . ............................................................... 1 Introduction 3 Acronyms and Abbreviations



### ............................................... 4



# . ...................................................... 2 TECHNICALISSUES 5 2.0. General Comments



### ................................................... 5 2.1. ConceptualModel



### .................................................. -5 2.2. ExposureAssessment



### ................................................. 7 2.3. ToxicityAssessment



### ................................................ 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; Section2.2)



### ............................ 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 ofDaily 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 fileswere used to both rerun selected analyses, and to modify the models to more fully explore the consequences ofthe 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 questionsposed by Mary Frankenberry helped to guide our review:
1. Are the assumptionsin the analysis well supported? 2. If alternativeassumptions 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 ProbabilisticAssessment of the Risk ofBrodifacoum 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 1270-04Assessment 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-mammaldata-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 ofthe 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.xlsprovided selected summary statistics from the 2-D Monte Carlo simulationfor 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 calculationscould 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 citationshad 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 frequentlyused 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 = assimilationefficiency M = moisture content of food FMR = field metabolic rate (function of log(a), b, and Wt) DPD = daily PD DPT = daily PT C = concentrationin exposedrodents t 

# , = halftime for first order eliminationkinetics

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 ofrisk by incorporating variability and uncertaintyin 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 inappropriatepharrnacokinetic model, and includes questionable assumptions that, in most cases, can be expected to substantiallyunderestimate exposure and risk. The conclusionsreached in the Cadrnus (2004) submission are of little use in assessing risks posed by brodifacoum used in effectiverodent controlprograms. Key shortcomings of the analysis are identified in the following sections. Table 1 summarizes the consequences ofthe approach that was used in terms ofthe 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 ofrisk). 

### In cases where we demonstrate an alternative assumptionor 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 ofrisk. These sort ofbroad and unsupported statemJnts detract from the sense of objectivitythat a risk assessment should have. The basic assumption appears to be that the exposure ofrodents to the rodenticideis negligibleto 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 controlprograms. 2.1. 

### Conceptual Model
1. The conceptual model excludes potential routes of exposure ahd does not carry forward several ofthe pathways that were identified. l

a. Direct ingestion ofbait 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 exposuremodel can be used to address this exposurepath ay (see Appendix 

### B, Comment 2; Appendix D, Figure 1). Page 5 of 60 

# 1

I Ib. The conceptualmodel identifiesrodents 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 potentialprey species for at least some of the predators considered in the assessment. For example, several studies demonstratethat rabbits comprisemore than 25% (by volume) of the stomach contents of red fox (seeAppendix 

### D). Furthermore, these are speciesthat could easily consume the bait directly, or via diet. For example, coyote may be a predator ofboth birds and fox. Also, it is unclear if the informationused to define PD is intended to be inclusive of all smallrodents (e.g., mice, voles, squirrels), or if it is specificto 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 dietarymodel as a source ofbrodifacoum exposure. Since invertebratesdo comprise a non-negligible portion of the total diet, especiallyduring the summer, we questionwhether this is truly a minor source of uncertainty in the overall exposure model. The major uncertainty stems not so much fkom the estimate ofthe percent of diet comprised of invertebrates ("PD-invertebrates"), but rather fi-om the lack of information availableto determine the fraction of invertebratesthat are consumed and contaminated (i.e., "PT-invertebrates"). This PT term likely varies as a function ofbaiting practice. It could easilybe explored in a series of alternative, plausible scenarios, similarto 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 attemptsto model exposure under the assumption that baiting practices are performed according to the directions on the label. The use characterizationshould be more fully described. Very different exposure scenarios may be reflected by the assumptionsregarding 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 forpredators.
b. It was assumed the amount of product used was consistent with the directions on the label. This assumption is questionableand should be explored through sensitivity analysis.

c. 

### It was assumed that indoor use would not lead to an increase in exposureto nontarget species. This assumptionis questionableand should be explored through sensitivity analysis.

Page 6 of 60 

# I2.2. 

### Exposure Assessment
1. The selection ofPT (the proportion ofrodents 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 exclusivelybased onjudgement rather than available data.
b. As used in the Cadmus report, PT appears to be a composite parameter reflecting exposuresto both target rodents as well as nontarget organisms. It is likely that the analysis would have been clearer if these two factors were explicitlydistinguished. Field data are available indicatingthat 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 oftarget rodents will be low. The report, however, does not adequately discuss orjustify these assumptions.

c. The Cadmus report states that: 

### neproportion ofNorway 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 ineffectivebaiting.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 orbelow 1% seems without merit and at very least is open to debate. This assumptioncould be construed to be servingPthe interests of a particular group. The selection of values for PD (proportion ofrodents in the diet ofpredators) and the estimates of the variabilityof PD appears to be overly restrictive.

a. The use of onlyPD (proportion ofrodents 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 containtoxicologically significant amounts ofbrodifacoum. The importance of both direct consumption of bait and the consumption on non-rodent organisms is amply demonstrated b. The variabilityin dailypercent diet from rodents (PD) is qepresentedby a mean PD and an assumed standard deviation. Theparameters are assumed to be independent, suggesting that individuals with very low percentages of rodents in diet exhibit the same variabilityin diet as individuals with very high percentages ofrodents. This seems like a less plausible assumptionthan the use of a fiked CV, which ensures that higher mean PD is also associated with higher variabilityjn PD.

I Page 7 of 60 I3. 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 individualprey meals. The result will be a time course ofmore frequent daily doses instead of the long periods ofno exposurethat are currently simulated when PT is set to a low value (e.g., 3%).
b. The correlationbetween the log(a) and b parameters of the 

### FMR equation is ignored. This is a source ofuncertainty that could result in an underestimate ofthe 95th percentile

### FMR by as much as 

### 10%.

'c. Total daily consumptionismodeled instead of individual prey meals. This does not account for gorging behavior (i.e., exceedance of average daily

### FIR), which is likely for opportunisticfeeders 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 variabilityin DPD. Alternative plausible probability distributions should be explored as part of the uncertainty analysis, especiallyifDPD is identified from a sensitivityanalysis as a major source of variability in the model output (i.e., distribution of dailybddy burdens). e. Seasonal variabilityin dietaryhabits will result in periods Ofthe year in which rodents comprise a greater percentage of the diet. Data on seasonal variability should be taken into considerationin defining the appropriate input distributionfor PD. f. Variability in exposure and effects is expected as a hctiohof developmental status. Risks to kits, for example, may be greater than risk to adul! foxes.
The reliance on monitored concentrations ofbrodifacoum in rdt carcasses limits the ability of the analysis to reflect variations in baitingpractices ahd may underestimate risk. 

# l a. The Cadmus report states that: 

### Earlier drafts ofthe analyszlsplan had assumed that submodels would need to be developed to estimate concenflationsin food. This would have been preferableto 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 monitofingdata that are available with specificbaiting 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 ofrodents (target and nontarget) that are exposed to Page 8 of 60brodifacoum and the concentrationsofbrodifacoum in these organismsis likelyto vary over time. The failureto consider this may miss periods ofpeak exposure in predators.
5. The simulation approach focuses on summary statisticsthat are not conservative.
The first inner loop is used to develop a sequence of daily doses for each of 100 individuals. Each sequenceis then fit to a lognormal distributiondefined by a mean and standard deviation. Then, the parameters of the lognormal are characterized by distributions

### - a beta is used to represent variabilityin the mean daily dose, and a gamma is used to represent the coefficient of variation (CV) daily dose. Next, a set ofparameters (mean, 

### SD = CVImean) is determined, and a new time course of daily doses is simulated to estimate daily body burdens ofbrodifacoum.

a. The first summary statistic calculated is the maximum body burden during the first 90-dayperiod for one individual. Depending on the choice for the half-lifevalue used to represent eliminationand 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- daybody burdens estimated from 100consecutive 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 100different individuals with the same general daily patterns. This step is unnecessary, and not protective. It effectively dampens the variabilityin 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-daybody burdens described in (b) above. It is unclear why only 100individuals are simulated, and there was no attempt to demonstrate that 100 individualsis sufficient to achieve stabilityin the characterization of the upper tail (> gofh percentiles) of the distribution.
6. The justification for and documentation of the distributionsused in the Monte Carlo simulation are incomplete. For example, probability distributionsused 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 (probabilitydensity function andlor cumulative distributionfunction).

a. All choices ofprobability distributions should bejustified or at leasted discussed and summarized in a table and with graphics. I Page 9 of 60b. It is unclear if unbounded distributions like the lognormal and gamma, which are used in this analysis, are actually truncated to reflect the constraints ofthe variable. For example, the lognormal PDF for percent diet comprised ofrodents should be truncated at 100%.
2.3. ToxicityAssessment
1. Use of the full dose-response curve in the risk calculationis an improvement over the use of species sensitivity distributions. However, some of the methods and results associated with the aggregation oftoxicity 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 shownin Figures 15 and 16 do not appear to represent the variabilitythat exists, even within these very limited data sets, and should not be used to definewhat the dose-response relationship would be for a hypothetical sensitivenontarget 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% confidencebounds. For mammals, one curve lies left of the lowerbound, two are partially left of it, two are completelyright of the upper bound, and two are partiallyright of it. Of the 8 curves shown, only 1 is completelycontained within the 80% bounds.
c. For the Bayesian approach, discussion andjustification should be provided for the assumptionthat both alpha and beta are normallydistributed. If it was simplydone for convenience, it would be helpful to have a sensitivityanalysis on that assumption. This isparticularlyimportant given that the 80% confidenceb~unds on the doselresponse curves did not appear to cover 80% of the curves. d. The Cadrnusreport shouldjustifL 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 isbased on a superficialand incompletereview ofthe available data and an incorrect assessment ofthe available data on the pharmacokinetics ofbrodifacoum. The use of the single compartment kinetic model is unnecessary, inappropriate, and pimits the assessment of the adverse effects that may occur. (See detailed commentsinAppendix.B.)

Page 10 of 60a. Information on the pharmacokinetics ofbrodifacoum are available in 16 studies on seven species. None of this information is meaningfully discussed in the Cadmus document.
b. Cadmus (2004)report statesthat there is 

### apool ofrapidly eliminated brodifacoum (Cadmus, 2004,p. 21 of 114, lines 1to 2). This appears to be a misinterpretation of the classicaltwo compartment model. There is a biphasic pattern in the concentration of a compound ofbrodifacoum. The first and more rapid phase is associated with redistribution,not excretion.

c. In the sameparagraph, the Cadmus report statesthat



### ...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 currentreview. The study appears to be reasonablywell done and is not difficultto interpret.
d. Also in the sameparagraph, the Cadmus (2004) report statesthat 

### many mechanistic and quantitative assumptions would be need to model the kinetics ofbrodifacoum. This is also incorrect. As illustratedin Attachment 1, a reasonable albeit exploratory PBPK model can be developed using only the residue data from Batten and Bratt (1990). The kinetics ofbrodifacoum can be describedby 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 ofhalftimes from 2 days to 200 days with the implicationthat the "real" halftimes are somewhere in this region. In terms of the body burden ofthe prey species, this is not correct. The shorterhalftimes of 2 days to several days reflect the blood data and redistributionfrom, 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 sensitivityanalyses using a range of values. The value of sensitivity analyses on a structurallyincorrect 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 simulationsconductedby Cadmus (2004). The underlying assumptionin this approach is that tdxicity is based on peak body Page 11 of 60burden. This assumption should bejustified. The Cadmusreport 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 100individuals, rather than all 10,000 estimates ofthe 90-day maxima that were calculated. If true, the approach used could greatlyunderestimate 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 ofthe probabilisticmodel 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 60Appendix A. Original Comments from Maxine Dakins, I PhD
1. They laid out a conceptualmodel 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 componentsof the conceptualmodel 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 accordingto the directions on the label (limiting direct outdoor exposureto bait) and they assumed that indoor use would not lead to an increase in exposureto 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 simulationsfor 360 days or longer. In addition, it is unclear why the authors "did not carry these exposure estimates forward to determinethe 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 withoutjustification. 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 structurewhere baiting is in progress, PT may approach 1

### ". 

# ~ b. They present a sensitivity analysis ofthis 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 sensitiveto 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 couldbe construed o be servingthe 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-responsecurve for a hypothetical However, the

Page 13 of 60 Dakinsa. It is unclear why a sensitive species was defined as the 10thperLentile 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 inappropriatelylow 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 shouldbe 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 curvesused. For example, for birds, out of 8 dose response curves (ignoringthe 1986pheasant data), two curves lie left of the lowerbound and three lie partially right the upper bound. The pheasant curve violates both bounds. Therefore, out of 9 curves, only 3 are completely containedwithin the 80% confidencebounds. 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 andlorjustification 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 ofNontarget Species and Carcasses? They decide to eliminate these pathways without appropriate justification, certainlywithout 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 presentedby Cadmus (2004). Most specific comments are identified by page and paragraph number (e.g.,p. 1,72for 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 ofthe page. Paragraphs are counted from the top of each page with any initial partialparagraph 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 shouldbe considered.
p. 12,72and 

### 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 attemptedto 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. 13,73to 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 greaterrisk because of the possibility that expbsuresto brodifacoum at doses that are sublethal in laboratory studies could cause adverse effects (including mortality) in wildlife secondaryto injuries (e.g., bruising) that would otherwisebe/ 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 ofthe sublethal adverse effects that couldbe associate 

# d
, with hemorrhagingin 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 

### Durkinare 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 assertionsthat are unsupported and appear to be unsupportable. The statement that 

### secondary exposure to predators and scavengers appears to be ofgreater concern to regulators than risk ofprimary exposure simplydoes 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 invertebratefeeders 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 ofbroad and unsupported statements detract from the sense of objectivitythat a risk assessment should have.
p. 15,74,last sentence The decisionto use monitoringrather than modeling may underestimate risk and perhaps substantially. This ignores temporal relationshipsin brodifacoum concentrationsin prey that are plausible. The implicationthat 

### 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-tailedhawks will prey on other birds (e.g.,morning doves and starlings) which could be exposed to bait either directlyor 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 ofU.S. EPA 1993,pp. A-9 1 ff). Ignoring this source of exposure will bias the analysis to an underestimate ofrisk. 

# 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 Durkinp. 2077 3 

### Earlier dvafts ofthe 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 simplifiedthe analysis. This approach, however, ignores temporal relationships that may lead to an underestimate ofrisk. 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 PTis supposedto be but I do not see here or elsewhere how 

### PTis 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 concentrationsin the liver could be a more reasonable measure of exposures that would be toxicologically significant. Also, the analysiswould be enhanced by considering or at least discussing the issue of delayed death.
p. 20, Last

## 7to p. 21 71 

### Depuration kinetics ofbrodifacoum 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 ofthe curve is steep, with a halflife on the order of a few days, reflecting apool of rapidly eliminated brodifacoum in the body. Thesecondphase ofthe depuration curve is much slower, with a hawife on the order ofmany months, reflecting a much smallerpool oftightly bound brodifacoum in the liver. However, the toxicokinetics ofbrodifacoum, 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 meaningfblway. Information on the pharmacokinetics ofbrodifacoum are available in 16 studies on seven species: I rats (Bachmann and Sullivan 1983;Batten and Bratt 1990; Belleville 1991;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 

### Durkinbenefit considerations of anticoagulants. While this paper was not available for the current review, it seems reasonableto assume that the paper does not involve a criticalreview 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 ofrapidly eliminated brodifacoum appears to be a misinterpretationofthe classicaltwo 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 (1982) 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 redistributionfrom 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-compartmentmodel. Thus, the very short halftimes in blood (analogous to the central compartment) represent redistributionto 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 ofmodel proposed by Cadmus (2004).
Based on a review of Batten and Bratt (1990), 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 impressionthat the kinetics ofbrodifacoum 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, concentrationsofbrodifacoum 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 illustratedin 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 ofbeing able to consider prey in terms of total body burden (therelevant measure for prey) and liver concentrations as a potentiallyuseful index for exposurein the nontarget predator species. The assertion made by Cadmus (2004) that 

### many mechanistic and quantitative assumptions would be need to model the kinetics ofbrodifacoum is also incorrect. As also illustratedin Attachment 1, a reasonable albeit exploratoryPBPK model can be developed using only the residue data from Batten and Bratt (1 990). The kinetics ofbrodifacoum 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 

### Durkinoptimized with the concentrationdata 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 ofthe first order model and the conduct of sensitivityanalyses using a range of values. The value of sensitivityanalyses on a structurally incorrect model is minimal. 

### In addition, Cadmus (2004) chose a range ofhalftimes from 2 days to 200 days with the implicationthat 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 redistributionfrom 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 directlyrelevant (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 technicalreport from the Canadian Wildlife Service (Mineau et al. 1994,2001) is not compelling. Cadmus (2004) note: 

### "LC,, values can be converted toLD,# ifassumptions are made about food ingestion during the test, but we considered that the uncertainty introduced by those assumptions would outweigh the loss ofinfomation caused by excludingLC,, data." This reservation may not be relevantbecause 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 dietaryLC,, 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 dosingthat might not be seen in more gradual dietaryexposures. Additional studieswould need to be reviewed to further explore this issue. At a minimum, however, Cadmus (2004) should have presented a quantitativecomparison of the gavage and dietary toxicity studies and should have more hllydiscussed which set of data would be most appropriate. This is particularlyimportant for brodifacourn because the anticipated exposures will approximate dietary studiesmore closelythan 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 plausibleunderlying assumption in the analysis of quanta1response data (the log-normal distribution of individial

I Page 19of 60 I I Durkintolerances). I am less certain that the logistic transformationof quanta1data provides a similarly simple and reasonable assumption.
P. 22, 74 The approach to risk characterizationtaken by Cadrnus (2004) involves comparing single dose gavage 

### LD,, doses to peak body burdens fi-omintermittent dietary exposuresover 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 discussedthis assumptionmore critically. For some toxicants, such as fast acting agents that are rapidly excreted and do not cause cumulativedamage, 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) shouldhave 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 concentrationsin 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 studiesthat could be useful in assessing the whether peak ' concentrationsor some averaging of concentrationswould 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 questionableon both counts as detailed in my comments above.
p. 24, description of Equation 1 Equation 1contains two key terms, PD (proportionof the diet that consists of all rodents) and PT (proportionofrodents in the diet that are treated).
The use ofPD ignores other sources (non-rodents)that may be significant for some of the nontarget species. I discuss this in other comments.
PTappears to be a compositeterm reflecting the proportion of target rodents that are exposed to brodifacoum (termed in my comments here as PT,) and the fraction ofthe 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 oftheir report (p. 41,72). As I discussbelow, information is
Page 20 of 60 Durkinavailable that could be used to set PT, reasonably objectively. PT, is much more problematic and difficultto set objectively. Based onprofessional 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 distinguishingwhat I 

### am terming PT, and PT,, they give the impressionthat the low values ofPTare based onPD (proportionofthe diet that consists ofrodents). 

### In other words, they appear to suggest that because only a small proportion oftarget 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 assertingthis. However, as I note below, the discussion of the relationship ofPDto 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 ofthe Edwards studies (Edwards id Swaine 1983; Edwards et al. 1983a,b), I think that a case couldbe made that exposuresto nontarget rodents may be high. Thus, even though the consumptionof 

### target rodentsmay 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,, seemsto reflect how the professional judgement expressed in the Cadmus (2004) report lead to a low value forPT but this approach may be overly simplistic and may have mislead theirjudgement 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 difficultto 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 consumptionof 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 ofbrodifacourn. The importance ofboth direct consumptionofbait 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 couldbe 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 PDvalue 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 

### Durkinin 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 studiesused by Cadmus (2004)
-i.e., the 

### U.S. EPA Wildlife Exposure Factors Handbook and the CalIEcotox Exposure Tables
-may not be representativeof the consumption of rodents by wildlife in areas with atypically high rodent populations -i.e., the type of rodent infestationsthat might trigger the application ofbrodifacoum. 

### I am not sure how or ifthis could be handled quantitatively in a reasonably simple model. This would require a more detailedreview of the cited studies as well as other studiesthat 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 forPT, discussed further below. As clearlyindicated 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 ofnontarget predator species. As the model developed by Cadmus (2004) clearly states -i.e., Equation 1on p. 24 and the discussionon pp. 26-27
-the proportion ofrodents 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 smallproportion 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 PTand Cadmus (2004) should have explicitly discussedPTas a composite ofPT, 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 ofNorway 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 smallproportion 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 exposureto brodifacoum
- and thus a large proportion of the target rodent
Page 22 of 60 

### Durkinpopulation is exposed. This assumptionis reenforced by a very casual reading ofthe Edwards studies (Edwards and Swaine 1983; Edwards et al. 1983, 1984a,b)which also suggest that substantialnumbers ofnontarget species are likelytobe exposedin an effective baiting program, particularlyif 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 ofthe 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 caseto be treated as the I proportion of

### target rodents in the diet of somenontarget predators) and using these data to estimate a composite

### PT that should include both target and nontarget rodents.
As discussed subsequentlyby Cadmus (2004),

### PT is a very sensitiveparameter. 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 couldbe 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. (1984b) provide data on a relatively successful baiting program -i.e., p. 20, Table 8, Farm A. 

### In this example, control ofrats is estimated at 88%. 

### In other words, it seems safe to assume that at least 88% ofthe 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 ofreinfestation. 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 seemsreasonable 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 assumptionbased on a plausible definition of an effective baiting program. The range of

### PT values used by Cadmus (2004) seems implausible.
P. 28974 The use ofDPT 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-tailedhawk, 

### I am somewhat concerned that this approach is less realistic and could possiblyunderestimate risk. Some ofthe smallerpredators such as the hawk might not consume the entirerat but could selectivelybeginto feed on internal organs such as the liver which would likelyhave higher concentrationsbf brodifacoum than average values in the carcass (see Attachment 1). Concern that some cakcasses maybe only
Page 23 of 60 

# 1 Durkin
Ipartially 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 distributionfitting.
p. 32,al and 72 See my previous comments on the use of a single compartment model for brodifacoum and the selection ofhalftimes. This model is structurallyunsound. I suggest that the model introduces error rather than uncertainty. As noted above, the selection of the range ofhalftimes from 2 days to 200 days is incorrect and seems to reflect a misunderstanding ofthe underlying kinetics.
P. 32,114 

### In each of the 100 

### inner loop iterations, a sequence of90 

### 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 countingdays in which the animal is unexposed as a

### zero dose. Typically, the 90-day simulationperiod 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 PTvalues used in the simulations arebased on a very poorly supported assumption and probably grosslyunderestimate exposures to nontarget species. The utility of the inner loop is unclear and the use of averages ofmaximum 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 consistentin 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

, similarlyconfused. 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 DurkinP. 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% 

### ofall 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 appearsto be 100 again. As for the use ofmeans 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 ofthe first-order model.

I 

### As for the influence ofhalftimes 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 

# a To simplify notation for considering multiple exposures,

### At is defined s the intervalbetween exposures and p is defined as The peak concentration immediatelyafter 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 infinitenumber of exposures is:

For multiple exposures, the concentration at time 

### Tcan be calculated ak:

I 

### CT = 

# c* - k(T- [(n- l)At]) 

### (1 

### -

## P) I I Where n is the number of exposuresthat have occurred up to time 

### Tand 

### At is the interval between exposures. 

# ~

Page 25 of 60 DurkinWhat 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 ofpwhich in 

### turn decreases the value of 

### C,. All of this occurs because of the low (and implausiblylow) values of

### PT that were selectedby Cadmus (2004).
P. 36973 

### I comment above on the influence of 

### T as well asAt on the results. A more realistic and plausible selection of

### PT would lead to a much greater impact of the extension ofthe simulation period. This follows from the kinetics discussed above.


### In considering the duration ofthe exposure,it is also worth noting again the use of TWA concentrationsmay be more appropriatethan peak concentrations and the TWA concentrations would be more sensitivethan peak concentrationseven with low values of

### PT.
P. 37371 The toxicity ofbrodifacoum is relatively well-studied in mammals and birds. The discussion presented in the Cadmus (2004) document is superficial. 

### In terms of extrapolatingamong 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 quantitativelyassesded 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 

## 7to p. 40 

### I will defer to Dr. Dakins on most aspects ofthe 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 ofthe results seems minimal. The analysis is drivenby the low values of

### PT, defined as the proportion ofthe 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 specieswhich eat the rats will not be adversely affected. If there is negligible
Page 26 of 60 Durkinexposure there will be negligible risk regardless of the sophisticationof the dose-response modeling. As 

### I have noted above, however, these values for 

### PTare highly questionable. These very low values ofPTcombined with the unnecessary, 'improperlyparameterized, and inappropriateuse of the simple first-ordermodel lead to results that are not useful to the risk manager or anyone else seekingto 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 plausiblebut 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 improvethe 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 seconday 

### exposure, and mou;emanageable through reJinement ofbait 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 studiesby Edwards (Edwards and Swaine 1983;Edyards et al. 1983a,b)that the authors of the Cadmus (2004) report had clearlyreviewed 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 improvethe 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 ofmisuse can be instructive and misuse will occur.

This paragraph clearly describeshow Cadmus (2004) elected not to handle exposures in a way that consideredthe potential relationship oftime on exposure. As 

### I hLd noted above, 

### I think that
Page 27 of 60 

### Durkinthe original plan, which did addressthe time issue, would have lead to a more realistic analysis. 

### I have examinedthe 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. 13,73to 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 estimatePD 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 ofthe factors that could effectPT, although the second point seems a bit muddled.

I P. 45,83 Following a reasonably lucid paragraph in which the plausibility ofPT approaching unity is clearly acknowledged,the assertion that

### professional judgment leads td a PTof 0.01 is not credible.
I p. 45, last

## 7 This is a clear and correct admission of limitations associated with the /useofthe monitoring data on the concentrations of brodifacoum in rodent carcasses. As I have noted above, limitations in some ofthe 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 ofkinetics in the 

### "cumulative dosy" model. In discussion limitations, the Cadmus (2004) report notes: 

# ~ Although the complexities ofbrodifacoum toxicokinetics were Lot represented in the model, we considered development ofa full toxicokinetic mbdel beyond the scope ofthe risk assessment and also beyond the extent of available data.

Page 28 of 60 

# ~ DurkinAgain, these authors are suggesting that the data are either too complex andlorunavailable. It is true that developing afull toxicokinetic model -i.e., a model that would incorporate both pharmacokineticsand 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 insufficientto model quantitatively. This, however, does notjustify 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 essentiallyignores 16 studies on seven species. As illustrated in Attachment 1, a very casual effort has resulted in simplePBPK model that adequately describesthe kinetics ofbrodifacoum in the rat (the target species) and which could be readily adapted to nontarget species such as the coyote. Again, acknowledging limitationsthat are unnecessary or that reflect an incomplete analysis does not improve the analysis or excuse the limitations.
P. 46773 Give the otherproblems 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 adequatelyabove - 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 ofPBPK models over classical kinetic models (e.g., species scaling) are clear (e.g. Bruckner et al. 2004). 

### In terms of the sufficiencyofthe data, a cursory examinationof 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 ofbrodifacoum -induced mortality in coyote, red fox,, and red-tailed hawk is low, even ifthesespecies are all assumed to be highld sensitive to brodifacoum.
Within the context ofthe 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 assumptionis made that the exposure of rodents to the rodenticideisnegligibleto 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 effectiverodent controlprograms. I

I REFERENCES Bachmann, K.A. and T.J. Sullivan. 1983. Dispositional and pharmacodynamic characteristicsof brodifacoumin warfarin-sensitive rats. Pharmacol. 27: 28 1-288. 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 ofbrodifacoum and difethialone in the rat, after oral administrationof 0.06 mgkg once weekly for 4 consecutiveweeks. Unpubl. report submitted to EPAby LiphaTech,Inc., Milwaukee,WI. 39 pp. Boermans HJ, Johnstone I, Black WD, Murphy M. 1991. Clinical signs, laboratory changes and toxicokinetics ofbrodifacoumin the horse. Can J Vet Res. 55(1):21-7. 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 pharmacokineticsand pharmacodynamics of 4-hydroxycoumarin anticoagulants, warfarin, difenacoum, and brodifacoum in the rabbit. British J. Pharmacol. 84: 8 1-91. BrucknerJV; Keys DA; Fisher JW. 2004. The Acute Exposure GuidelineLevel (AEGL) program: applications ofphysiologically based pharmacokinetic modeling. J Toxic01Environ Health A. 67(8-10): 621-34.
Bruno GR, Howland MA, McMeeking A, Hoffman RS. 2000. Long-acting anticoagulantoverdose: brodifacoum kinetics and optimal vitamin K dosing. Ann Emerg Med. 36(3)1: 262-7. Cadmus. 2004. Brodifacoum (PP581): Assessment. Prepared by J. Giddings and W. Warren-Hicks. Cadmus Group, Inc., Chapel Hill, NC. Prepared for Syngenta Crop Protectioh,Inc. Report dated September 10,2004.

I Page 30of 60 I 

### Durkin I
I



### -Eason, C.T., G.R. Wright, and D. Batcheler. 1996. Anticoagulant effects and the persistence of brodifacoumin possums (Trichosurus vulpecula). New Zealand J. Agric. Research 39:397-400. Eason CT, Murphy EC, Wright GR, Spurr EB. 2002. Assessment ofrisks ofbrodifacoum 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 Rodenticidesto Birds and nontarget Mammals: a Comparative Approach. EFED report dated January 15,2004.

\ Edwards PJ, SwaineH. 1983. Brodifacoum: Hazard to non-target animals from the use of "Klerat" bait on farms in the UK for control of the common rat Rattus nowegicus. ReportNo. RJ0305B. ICI Plant Protection Division, Jealotts Hill, UK. EdwardsPJ, 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. ReportNo. 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 farmbuildings. Report No. RJ 0369B. ICI Plant Protection Division, Jealotts Hill, UK. Finney DJ. 1971. Probit Analysis. New York: Cambridge University Press. 333 p. GibaldiM; 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 ed. John Wiley and Sons, New York, NY. 854 p. I Haddad,S; Pelekis M; Krishnan K. 1996. A methodology for solvingphy$iologically based pharrnacokinetic models without the use of simulation software. Toxicology Letters. 85: 113-126. 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 considerationsin evaluatingcommensal anticoagulant rodenticide impacts to wildlife. Proceedings of the ~ertebrale Pest Conference 19:245-256. 

# ~

I Laas, F.J., D.A. Forss, and M.E.R. Godfrey. 1985. Retention ofbrodifacolm in sheep tissues and excretion in faeces. New Zealand J. Agric. Research 28:357-359.

Page 31 

### of 60 

### DurkinMcConnicoRS, Copedge K, Bischoff KL. 1997. Brodifacoum toxicosis in two horses. J Am Vet Med Assoc. 21l(7): 882-6. O'Flaherty EJ. 198 1. Toxicants and Drugs: Kinetics and Dynamics. John Wiley and Sons, New York, NY. 398 pp. Pannar, G., H. Bratt, R. Moore, and P.L. Batten. 1987. Evidence for a common binding site in vivo for the retention of anticoagulants in rat liver. Human Toxicol. 6:43 1432. Robben, J.H., E.A. Kujpers, and H.C. Mout. 1998. Plasma superwarfarin levels and vitamin K1 treatment in dogs with anticoagulant rodenticide poisoning. Veterin. Quart. 20:24-27. Weitzel, J.N., J.A. Sadowski, B.C. Furie, R. Moroose, H. Kim, M.E. Mount, M.J. Murphy, and B. Furie. I 1990. Surreptitious ingestion of a long-actingvitamin K antagonistlrodenticide,brodifacoum: clinical and metabolic studies of three cases. Blood 76:2555-2559.
WHO (World Health Organization ). 1995.Anticoagulant rodenticides.Environmental Health Criteria 175. World Health Organization,Geneva. Available at: http://www.inchem.ora/documents/ehc/ehc/ehc 175.htm Winn MJ, Cholerton S, Park BK. 1988. An investigationof the pharmacological response to vitamin K1 in the rabbit. Br J Pharmacol. 94(4): 1077-84. Woody, B.J., M.J. Murphy, A.C. Ray, and R.A. Green. 1992. Coagulopathic effects and therapy of brodifacoum toxicosis in dogs. J. Veterin. Internal Medicine 6:23-28.

Page 32 of 60 Durkin

### Appendix C. Original Comments from Philip 

### E. Goodrum, PhD My comments focus on the conceptualmodeling approach and the Monte Carlo analysis. 1. ConceptualModel (Figure 1, p. 77; Section 2.2) The conceptualmodel accounts for secondary exposuresto 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 concernto regulators than risk of primary exposure..

### ."
Comment 1: What is the basis for this statement? A citation is needed. Ignoring an exposurepathway 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 greaterprimary and secondary hazard to birds and rnmals than the other active ingredients examined."
I b. "A fundamentally different exposuremodel 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 offeredbelow. Currently,the modeling approachto 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 ingestionratd, 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 understandingof feedingbehavior and habitat preferences of small mammals. As noted on p. 14, factors to considerwodld 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 frequencyof 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 couldbe describedby a Poisson distribution. The average rate of exposure events would be based on informationfrombaiting practices; in the absence of data, different scenarios could be explored, similar to ho& the authors explored the uncertaintyin DD as a function of different estimates for PT. The daily ddse from bait (DDb) would be: 

# ~
I Page 33 of 60 

## ~I 

### Goodrumwhere 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 concentrationofbrodifacoum in the bait. If PT = 0 (i.e., there is no contribution of diet fi-omthe treated area), then EV = 0. Presumably, information on baiting practices is availableto define Mb and Cb. According to the authors (Section 2.2.1, p. 1I), brodifacoum bait may be consumed by non-target animals that feed on pelleted or wax block baits. Ifpellets 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 frombait consumption isnon-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 contributionofbait to diet cannot exceed the balance of the FIR after accounting for the consumption ofrodents. Alternatively, both primary and secondary sources canbe weighted so that FIR isnot exceeded:
If (FIR x PD 

### +Mb) >FIR, then the total daily dose (DDt) is constrained by FIR: 

### FIR DD, = 

### (DDtDD,) x I 7 

### FIR x 

### PD 

# + hdb
I have limited knowledge ofbaiting practices; the point of this comment is that a plausible conceptual approach for quantifyingprimary 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 ofbait 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 ofprimary 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 unnecessarilyextreme scenario.

Page 34 of 60 GoodrumThe authors state (p. 25), "Because a singlerat would represent a substantial fraction of the daily food intake of an individual predator, intermediate values ofDPT 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 ofbody weights for rodents. The consequenceof 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 extremelyhigh 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 shouldbe 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 conceptualmodel can modified to simulate prey consumption events rather than total daily events. The product, FIR x PD, defines the mass ofrodents consumed on 1 day. Th'ls product can then be divided by the averagebody weight of the rodents consumed to determineth 

# 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 differencein approachesis illustrated in Figures 1,2,and 3 for PT values of 0.025, 0.50, and 1.0, respectively. For very low values ofPT (e.g., PT <20%) [illustratedby Fighe 11 and very high values of PT (e.g., PT >go%) [illustratedby Figure 21, the two approachesyield esdentiallythe same result, as one would expect. This is because the frequency of daily consumption of contaminated prey are representedby extreme cases. However, for intermediate estimates of PT (e.g., 30% <PT < 70%)[illustratedby 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% ofthe 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 importantif the half-life for eliminationkinetics is moderate to long (> 10 dais). The greater the frequency of consecutive days ofnon-zero exposure,the more brodifacoumLan be expectedto accumulate in the body of the predator or scavenger. 

# ~



# ~ Page 35 of 60 

### i GoodrumFarecast: DDall

### or nothiq 2,900Trids Frequency

### Chart 2470 Cisplayed

Fa-ecast:DDpreymethod 2,900TriB FrequencyChart 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) indicatesthat on any day, PT determines if all or none ofthe 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 maybe contaminated. Page 36 of 60 GoodrumFcrecast: Dall

### or nothiq 2,9DOTrids FrequencyChart 241 5 Ilsplayed

Fcrecas t: DD prey nxthod FrequencyChart 2423 Displayed

Figure 2. Comparison of approaches used to estimated daily dose to great homed owl when the proportion ofrodents 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" (bottompanel) PT determines if any individualpAey meal is contaminated; each day may result in one or more meals, some of which may be contaminated.

Page 37 of 60 

### Goodrum2,500Trids FrequencyChart 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

Fcrecast: 

### CD preymdhod FrequencyChart 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 ofrodents contaminated (PT) is set to 1 

### .O. Avera e prey weight is assumed to be 

### 50g. The "All or Nothing" approach (top panel) indicatesthat on any day, PT determinesif all or none of the prey are contaminated. With the bbpiey method" (bottom panel) PT determinesif any individualprey meal is contamina ed; each day may result in one or more meals, some ofwhich may be contaminated. 

# t
~

I Page 38 of 60 Goodrum 

# ~Table 1. 

### Comparison of distributions of daily dose (mglkg-day) for two methods of applyingPT 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. Correspondsto Figure 2. 

### Summary Statistics Mean
- 

# I Standard Dev

Percentile 

### All or Nothing per Day Individual Prey Meals

Page 39 of 60 Goodrum3. 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 confirmwhat 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 calculaterisks.
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 Carlomethodology (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 characterizeinter-individual variability in feedingbehavior (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 relationshipbetween 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 describedby independent normal distributions. Typically, for simple linear regression, there is a correlationbetween 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 ofthe parameter estimates. In general, ifthe correlationis positive, then assumifig independence will tend to yield underestimatesof the response variable (Y) at the upper tail of the distribution. The oppositeid1true if the correlationis 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 parametersto 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. Introducesconsistency 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 coefficlientof 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 independentfor 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 = I00time Body burden

# - f (DD; t 

### W ) series; repeatfor 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 oftH. 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 uppertail? 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 scalingfactors; Wt = body weight; GE = gross energy content of food; AE = aksimilation efficiency; M = moisturecontent 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-lifefolfirst 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 boand b, be the usual least squares estimates of

## Po and PI,respectively. Then, under the usual assumptions regarding the regression model, boand 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 followinggeneral approach can be used to give estimates of Y that are consistent with the idea that 

## Po and 

### p, are correlatedparameters. 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 standardnormal deviate,Z, 

## -N(0, 1) 2. Generate standard normal deviate,Z, 

# -N(0, 1) 3. Calculatelog(a) log(a) = Ppo + Opo Z1 4. Calculateb I b = Pp1 + op1 [(P Zl) + (1 - P~)~.~ 221 Assuming independence will yield the same result as setting the correlationto 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

-- I3600), 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, TimeSeries ofDaily Doses The 2-D MCA analysis isbest described as an approach that involves one outer loop and two sets of inner loops. The first inner loop isused to define a time series of daily doses for 100 individuals. The second inner loop is used to develop estimates ofbody 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. Commentsbelow are on the conceptual approachto modeling daily exposures; as well as on specific assumptions regarding choices of inputs to the model. Comment7: 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 ofrodents?; (3) What fraction of those rodents have been exposed to brodifacoum baits?; and (4) What is the concentrationofbrodifacoumin rodents exposed to baits? The approach introduces a somewhat artificial constraint on feedingbehavior. 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 ofrodents (DPD), the concentration in the food consumed (c):, and the chance of encountering contaminated prey (DPT). DPT is addressed in Comment 4 above. The authorsmake 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 canbe found, inter-individual variabilitymay be greater. This concept likely applies to intra-individual(day-to-day) variability as well. I

I I Page 44 of 60 I Goodrum 

### Irho 95th%Ie -1 11700(-11%) 0 13143 +I 14800 (+13%)

Page 45 of 60 GoodrumFigure 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 1000days 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 characterizedby the point estimate from the outer loop, and the standard deviation is an assumption, similar to the approachused for DPD. Essentially, create a variable called DFIR, and (2) Ask an additional question of the exposuremodel: 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 shouldbe 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 samebasic model could be applied both deterministicallyor 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 shouldbe addedto the Cadmus report if it is revised. Comment 9: The variability in daily dose is described as an uncertain constantby using different estimates for the standard deviation. Daily variability is assumed to follow a lognormal distributibn with mean equal to the time invariant estimate ofPD from the outer loop, and standarddeviation equal to 3%, 5%, or 10% (pp. 24-25). This assumes that variance is independent of the mean. Regardlessof whether an individual is a high or low consumer ofrodents, their day-to-dayvariability 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 exposuremodel. In Step 3 (described below), distributions are fit to the time series of DD estimates. The mean DD is describedby a beta and the CV DD is describedby a gamma. This parameters are later multipliedto determine the correspondingSD 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 individualswhose diet is comprised of a lowpercentage 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 introducemuch daily variability. It is difficultto 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/scaleparameters, 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 17is where the beta distributionis specified. This beta is parameterized as: alpha = 1.04 beta = 0.47, scale = 97.73. The mean of this distributionis approximately 

# ~ Page 47 

### of 60 I 

### Goodrum67% and the standard deviation is 28%. The interquartile range (2Sh,75th percentiles) is (36%, 92%). Figure 6 provides additional information.

Figure 

### 6. Beta distributionused to characterize interindividual variabilityin 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 constrainedto a standard deviation of 3% to 10%. A better approachwould 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% ofthe mean PD. For the average horned owl with 67% of diet comprised ofrodents, the standard deviation would be h 33%, so on any given day PD may range from 33% to 100%. This approachis equivalentto setting CV equal to 0.5. Comment 11: The distribution of daily doses (DPD) was characterizedby 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 ofpossible distributions". The sensitivity analysis was performed only on the parameters ofthe 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%, constrainedto 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 occurredvery often. Step 3. Inner Loop I, Calculate Summa y Statistics
For each of 100 individuals simulated with the DD model, the time series is describedby amean 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 Iseries are observed to follow approximately lognormal distributions. All of these assumptions are appropriate and well described. Comment 13: The only possible concern is that 100individuals is not a sufficient sample size to characterthe mean and CV. This source of uncertainty could be exploredby 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, TimeSeries of CumulativeDoses (BodyBurdens) The cumulative dose model describesthe depuration process with a first order model. The rate constant (k), or alternatively the half-life ofbrodifacourn (t %) is assumed to be an uncertain constant. Simulationswere rerun with half-life values ranging from 5 daysto 200 days to examine the effect on the exposure and risk distributions. The model was implemented by simulatinga 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-omthe time series ofbod$ 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 ofrisks? There are two possible approaches to generating the distribution of doses that reprksentsthe 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 clearlyhas 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-da4maxima 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 statisticbeind the arithmeticmean 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 simuiationoutcomes was calculated,and the 100means (from 100individuals)were plotted asreverse cumulative frequency
Page 49 of 60 

### Goodrumdistributions." 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 100individuals) was combined into a single exposure distribution reflecting both within-individualand among-individual variation."
This latter statementis 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 10,000values 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 100individuals, 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 ofmean 90-day maxima, and the' distribution of 10,000values 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 variabilityby representing individualsby the mean 90-day max is to decrease the dose at the 5% exceedance level by 40%.

Page 50 of 60 

# ~ Goodrum

I ICoyote, 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 variabilityin dose for coyotes. The approach used by Cadmus 

### (20d4) is to definethe distribution of dosesbased on the mean ofthe 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) II Figure 

### 8. Same information as Figure 7, but focusing on the low end of the exceendanceprobability curve. The differencebetween approacLs is greater with increasing protectiveness (lower exceedanceprobabilitiks). I Page 51 of 60 GoodrumComment 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 decisionmake? Given the importance of this choice of approaches,it shouldbe quantifiedmore fully in the risk assessment. Comment 17: The 90-dayperiod is an arbitrary choice. Cadmus elected to select the first 90-day period for each time series of daily doses. All individualsbegin 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 influencedby both the choice of depuration half life (t %) as well as the frequency of daily doses. It shouldbe 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 Comment4). 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 dosingregimen 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 consideredby the authors,much less the additional sources of uncertainty described above. The statement that risk ofbrodifacoum-induced mortality in the predator species studies is low, is unlikely to be true. Numerous examples of assumptionshave 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 greaterby at least an order ofmagnitude.

I Page 52 of 60 I

I GoodrumJulian Day Figure 9. Example of a random 1-yeartime series of daily doses for a hypothetical coyote based on PT = 0.025 and SD DPD =5%.



## +- half-life = 5 d 

## - half-life = I00d
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 ofhigh 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 GoodrumI

received a lowermaximum 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 dailybody 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 = 100days 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 conceptualmodel doesn't include all the potential pathways for total daily dose. a. The food ingestion pathway is the onlypathway considered. b. Rodents are the only portion of the food ingestionpathway 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 exposurepathway is not compelling. This decision in the conceptualmodel 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 includepredators/scavengkrs with more diverse eating habits.
3. The model does not cover a scenario for heavy doses of pesticide use- such as applicationin the I open field. This was labeled a misuse of the product and was not consideredin 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)ofwhether 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 mortalityrate." 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 variabilitywas 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 opportunisticfeededs 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&antrole 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 isrepresented? Was this an arbitrary decision? Is it missing peak risk? First order depurationmodel 

# I


# ~ Page 55 of 60 I I 

### MastrianoAssumptions for the distributionsmight 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 individualsis likely to be quite small (pg 28)." What if all the rodents are all poisoned? Also, for Norway rats, how was the 1in 10proportion 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 conceptualmodel in the draft risk assessment does not capture all the potential pathways for total daily dose in the surrogate species. Excluding these dietarypathways is not a conservative decision and not supportedby the scientific literature. As evidenced in the attached tables, 3 of the 4 studiespresente 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 distinctionmade 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 exposurepathways, 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 s, etc.) 

# e

Page 57 of 60 

### Mastriano

### RED FOX

Birds 

### - Plants Insects 32.8 30.5 10.9 12 10.4 0.9Page 60 of 60 (Vulpes fulva) in Union County, Illinois. Transactions of the Illinois StateAcademy 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 lnvertebrates 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

0.2SERA TR-46-2- 10-1 e-Att-01

Submitted to: 

### Gail V. 

### Brooks (7507C)



### and Gail Maske-Love U.S. Environmental ProtectionAgency 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 ofPerformance: May 1,2004to April 30,2005 (Option Period 2) Probabilistic Risk Assessment Support for EFED Contracting Officer: Mr. Robert Minjack (3803R) 

### U.S. Environmental ProtectionAgency 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: (315) 637-9560 Fax: 

### (3 15) 637-0445 E-Mail: SERA-INC@msn.com Home Page: www.sera-inc.com
January2,2005 ITable 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 ofPBPK 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 ParametersUsed in 

### PBPK Models Tables-1



### .................................. Table 2: Selected Properties ofBrodifacoum Tables-2



### ................................... Table 3: Estimated first-order rate constants Tables-2 

### NOTE: Tables and Figures are placed after the appendix

### 1. INTRODUCTION
This briefreport presents an exploratoryand preliminaryphysiologically based pharmacokinetic (PBPK) model for brodifacoum. This exercise is motivated by the review of a probabilisticrisk assessment on brodifacoum (Cadmus 2004) that was submitted to the Environmental Fate and Effects Division (EFED). The Cadrnus (2004) report statesthat



### ... the toxicokinetics of brodifacoum, particularly the rates ofexchange 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 ofbrodifacoum in 16 studies on seven species: rats (Bachmann and Sullivan 1983; Batten and Bratt 1990; Belleville 1991;Bratt and Hudson 1979;Hawkins 1991;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 ofmost of these studies in Erickson and Urban (2004), full copies oftwo studies were obtained: Batten and Bratt (1990) and Lass et al. (1985). The studyin sheep by Lass et al. (1985) did not contain many experimental details and was conducted using onlytwo dose levels. While this studymight be useful in assessing the species scalabilityof a PBPK model for brodifacoum, it did not appearto be suitable for the development of a PBPK model, and this study was not pursued further. The studyby Batten and Bratt (1990) is a relatively standard pharmacokinetic study submitted to support the registration ofbrodifacoum. As detailedbelow, the studyprovided 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 104days. Concentrations ofbrodifacourn were determined (by serial sacrifice) in the liver, kidney, salivary glands, and pancreas in all three dose groups. Concentrations ofbrodifacoum 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 ofbrodifacoum in the blood of 4rnalsin the low-dose and I 1 Imid-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 feceswere 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 simplybe 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 clottingtimes 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 internalhemorrhaging. All ofthese effects in the high-dose group were attributed to brodifacoum.
Details ofthe observationsfrom Batten and Bratt (1 990) that were used or considered for quantitative analyses are included in the worksheet titled Bat ten et a1 1987 Datain the EXCEL workbook, Bat ten and Brat 1990 Data.xls. A copy of this workbook is included with the current report. Batten and Bratt (1 990) report data in nanomoles/gram oftissue (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 changein the amount of a substance in an organ per unit time (dAddt) can be defined equivalently as product ofthe volume of the organ 

### (V,) and the changein the concentration of a substancein an organ per unit time (dColdt). The a~nount entering the organ at time t is simply the rate ofblood 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 concentrationin the arterial blood divided by the organ-bloodpartition coefficient (Po). This type of flow-limitedtransport assumes that movement across the cell membraneis very rapid relative to the rate ofblood flow. This assumption is generallyapplicableto lipid soluble,unionized, low molecularweight 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-limitedcompartmentwith 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 stomachlumen 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 seemsplausible 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 ofbrodifacoum 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 compartmentwas 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 (1993). The fractional weight and fractional flow for the carcass was set to 1minus 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 gastrointestinaltract is explicitlymodeled. To avoid having to optimize this parameter, the laluewas set to the reciprocal of the transit time for the stomach, as reported by Davies and Morris (1993). Similarly,rather than optimizing the first-ordercoefficient 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 estimatesrequired adjustment.
Thepharrnacokinetic models were developed, solved, and optimizedusing ModelMaker 4 software (Chenvell ScientificLimited, 2000) with Gear's method fornumerical 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 optimizationvrias 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 datawould be used. The mokel was alsoported 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:plasmapartition coefficientsfor 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 ofPoulin and Krishnan (1 995). This method is implemented in the EXCEL workbook partition Coefficients from Poulin and Krishnan 1995 

# . xls 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 optimizedusing 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 ofbrodifacoum 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 detectableconcentrations covering or nearly covering the 104day duration of the study. Data on concentrationsin the carcass are only available for the mid-dose group. These data were included in the optimizationbecause 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:bloodpartition coefficients) and Table 3 (first-order rate constants). Preliminaryattempts to fit the model using the parameter estimatesfrom Poulin and Krishnan (1995) were unsuccessful. Subsequently, these partition coefficients were treated as adjustableparameters 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 ofbrodifacourn 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 concentrationstend to be somewhatunderestimatkdby the model after about 30 days. As illustrated in Figure 3 -liver concentrations for all threedose groups -the underestimates of liver concentrationare 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 (Figure4) are also somewhatunderestifiated at the high-dose group forthe two observations after Day 30. This underestimate is not app+ent in the mid-dose group. There are relativelyfew observations of kidney concentrations in the low-dose group and these are scattered. 

## I4. 

### DISCUSSION
4.1. 

### Model Optimization The major purpose ofthis exercise was to determine if the available data on the pharmacokinetics ofbrodifacoum 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 suggeststhat 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 studiesthat are available.
All of the studies will have limitations. The studyby Batten and Bratt (1990), for example, involvedrelatively 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 cumulativeurinary 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 scalabilityis one ofthe major benefits in the use of PBPK models and a major criterionin 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 ofbrodifacoum 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 concentrationsnoted at later time periods at all three doses (Figure 

### 3). This sort ofproblem is not uncommon during the development ofPBPK models and indicatesthat the model may not be properly structured. Summaries of the studyby Parmar et al. (1987) suggest that the pattern in liver concentrations might be better modeled as receptorbinding. Explicit receptorbinding or the development of empirical models that reflect receptor binding arepreferable 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 ofbrodifacoum for rodent control. It is likely that an at least crude toxicodynamic component, could be added. As noted by Batten and Bratt (1990), the coagulationtimes in rats were associated with concentrations in liver of greater thanabout 

### 3 nmollg tissue. These clotting times returned to normal when liver concentrations dropped below 

### 3 nmollg tissue but the liver concentrations remained onlymarginall; 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 cumulativeeffects 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 baitingpractices. This may be a very practical concern for brodifacoum. As summarizedby Erickson and Urban (2004), there is ample data suggestingthat brodifacoum will impact both target and nontarget rodents. A PBPK model could be used to assess the potentialbenefits of different baiting practices in risk mitigation.5. 

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### 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 ofbrodifacoum and difethialone in the rat, after oral administrationof 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 ofbrodifacoum 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 ofthe relationship between the pharmacokinetics and pharmacodynamics of4-hydroxycoumarin anticoagulants, warfarin, difenacoum, and brodifacoum in the rabbit. British J. Pharmacol. ' 84: 81-91.
Bruckner JV; Keys DA; Fisher JW. 2004. The Acute Exposure Guideline Level (AEGL) program: applications ofphysiologically based pharmacokinetic modeling. J Toxic01Environ Health A. 67(8-10): 621-34.
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 ScientificLimited. 2000. ModelMaker User Manual. Version 4. Oxford Science Park, Oxford 

### OX4 4GA, United Kingdom. Available at: www.modelkinetix.com Davies B; Morris T. 1993. Physiologicalparametersin laboratoryanirhals and humans. Pharma. Res. 10, 1093. IDurkin P; Diamond G; Hertzberg R. 2004. Application ofPBPK 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, SpurrEB. 2002. Assessment of risks ofbrodifacourn 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 ofNine 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 "Klerat"bait on farms in the 

### UK for control of the common rat Rattus nowegicus. Report No. RJ0305B. ICI Plant ProtectionDivision, Jealotts Hill, 

### UK. Edwards PJ, SwaineH, 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, SwaineH, Kennedy 

### SH. 1984b.Brodifacoum: Hazard to non-target animals from "pulsed"baiting with "Klerat"pelleted bait around farm buildings. ~edort No. 

### RJ 0369B. ICI Plant Protection Division, Jealotts Hill, 

### UK. Finney DJ. 1971. Probit Analysis. New York: CambridgeUniversity Press. 333 p. Gibaldi M; Perrier D. (Eds). 1982. Pharmacokinetics: Second Edition Revised and Expanded. Marcel Dekker, Inc., New York, 

### NY. 494 p. Haddad,S; Pelekis 

### M; Krishnan 

### K. 1996. 

### A methodology for solvingphysiologically based pharmacokinetic models without the use of simulation software. ToxicologyLetters. 85: 113-126. Hawkins, D.R., R.R. Brodie, D. Clarke, and C. Brindley. 1991. Determination of the residues and half-life ofthe rodenticidesbrodifacoum, 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 mag. Unpubl. report submitted to EPA by LiphaTech, Inc. Milwaukee, WI. 78 pages. Hollinger BR, Pastoor TP. 1993. Case management and plasma half-life in a case of brodifacoum poisoning. Arch Intern Med. 53(16):1925-8. ,ILSI (International Life Sciences Institute), 1994. Physiological Parameter Values for PBPK Models. Report prepared under a cooperative agreement with U.S. EPA, Office of Health and Environmental Assessment. December 1994. Available from ILSI Risk Science Institute via an e-mailrequest to rsi@ilsi.org.
~aukeinen DE, Spraggins CW, Hobson 

### JF. 2000. Risk-benefit considerationsin evaluating commensal anticoagulant rodenticide impactsto wildlife. Proceedings ofthe Vertebrate Pest Conference 19:245-256. Kim, C.S., et al., 1995. Development of a physiologically based phannacokinetic model for 2,4-dichlorophenoxyacetic acid (2,4-D) dosimetry in discrete areas ofrabbit brain. Neurotoxicol. Teratol. 17, 1 11. Laas, F.J., D.A. Forss, and M.E.R. Godfiey. 1985. Retention ofbrodifacoum in sheep tissues and excretion in faeces. New Zealand J. Agric. Research 28:357-359.
McConnico RS, Copedge 

### K, Bischoff 

### KL. 1997. Brodifacoum toxicosis in two horses. J 

### Am Vet Med Assoc. 21l(7): 882-6. Parmar, G., 

### H. Bratt, R. Moore, and P.L. Batten. 1987. Evidence for a common binding site in vivo for the retention of anticoagulants in rat liver. Human Toxicol. 6:431432. Poulin P; Krishnan K. 1995. An algorithm for predicting tissue:blood partition coefficients of organic chemicals fiom n-octano1:water partition coefficient data. J. Toxicol. Environ. Health. 46, 117. Robben, J.H., E.A. Kujpers, and H.C. Mout. 1998. Plasma superwarfarin levels and vitamin K1 treatment in dogs with anticoagulant rodenticide poisoning. Veterin. Quart. 20:24-27. Weitzel, J.N., J.A. Sadowski, B.C. Fwie, R. Moroose, H. Kim, M.E. Mount, M.J. Murphy, and B. Furie. 1990. Surreptitious ingestion of a long-acting vitamin K antagonistlrodenticide, brodifacourn: clinical and metabolic studies of three cases. Blood 76:2555-25 59.
WHO (World Health Organization ). 1995.Anticoagulant rodenticides. Environmental Health Criteria 175.World Health Organization, Geneva. Available at: http 

### ://www.inchem.orddocuments/ehc/ehc/ehc 1 75. htm Winn MJ, Cholerton S, Park BK. 1988. An investigation

### ofthe pharmacological responseto vitamin K1 in the rabbit. Br J Pharrnacol. 94(4):1077-84. Woody, B.J., M.J. Murphy, A.C. Ray, and R.A. Green. 1992. Coagulopathic effects and therapy ofbrodifacoum toxicosis in dogs. J. Veterin. Internal Medicine 6:23-28. ICompartment 

# F' 1 Urine 

# 1

QLm 

## c* 

# Liver 

# I

"" 

# \I1 

# el

Lumen


# 1 Lumen 

# 1

Figure 2: Overview of

### PBPK Model for Brodifacoum

Figure- 1 

# 1 Feces

# 1Mid 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 concentrationsin the liverConcentration 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 concentrationsin 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-omILSI (1 994) unless otherwise specified. I bDaviesand Morris (1993), Table 11, p. 1094 I " Carcass value not specified in ILSI. The value of 0.1 is setjudgmentally based on values in other tissue. Davies and Morris (1 993), Table 111,p. 1094or TableV, p. 1095. Here and elskwhere,ke signifies first-order excretion rate. The ke values in days-' calculated as the ke in min' x 1440mudday.

Tables-1Molecular weight (glmole) 

### KOlw 3 16,000,000 Epi-Suite Tissue:BloodPartition 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 (1995)

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 

### - 1DYNAMIC 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