Toxicology and Industrial Health, Vol. 8, No.3, 1992 17..

ON REFERENCE DOSE (RfD) AND ITSUNDERLYING TOXICITY DATA BASE

MICHAEL L. DOURSON, LINDA A. KNAUF, ANDJEFFREY C. SWARTOUT

Environmental Criteria and Assessment OfficeOffice of Research and Development

Environmental Protection AgencyCincinnati, Ohio

The toxicity data of pesticides were summarized and comparedamongst different animal species and types of bioassays. Thesecomparisons showed the expected inter-species and inter-bioas-say variability. After quantitative and statistical analysis of thesedata, it was concluded that, on the average, a 2-year dog bioas-say detected toxic responses at similar doses as a 2-year ratstudy, and that both of these bioassays detected toxic responsesat lower doses than either a rat 2-generation bioassay, a rat de-velopmental toxicity study, or a 2-year mouse bioassay. Al-though these chronic dog and rat bioassays were found to detecttoxic responses at lower doses than the other studies listed, thisanalysis does not reflect the seriousness of the effects that werecompared. Within the confines of this analysis, then, it appearsthat a 2-year dog and rat study, reproductive and developmentalbioassays are a sufficient data base on which to estimate highconfidence Reference Doses (RfDs) , and furthermore, that anadditional uncertainty factor is needed to estimate RfDs to ac-count for this inter-species and inter-bioassay variability whenfewer than this number of bioassays are available.

1. Address all correspondence to: Michael L. Dourson. Environmental Criteria and Assessment Office.Office of Research and Development. Environmental Protection Agency. 26 Martin Luther King Drive.

Cincinnati. OH 45268. Tel: (513) 569-7533.2, Abbreviations: ADI, acceptable daily intake; DG, dog; DV. developmental toxicity; LOAEL. lo\\'est

obseT\'ed adverse effect level; MS, mouse; NOAEL. no observed adverse effect level; RE. reproductive; RfD.Reference Dose; RT, rat.

3. Key words: Reference Dose (RfD), Acceptable Daily Intake (ADI). uncertainty factor. safety factor.pesticides. inter-species comparison of toxic response.

4. Although the research (or other work) described in this article has been founded wholly or in part bythe United States Environmental Protection Agency, it has not been subjected to the Agency's required peerand administrative review and. therefore. does not necessarily reflect the view of the Agency and no officialendorsement should Qe inferred.

Toxicology and Industrial Health. 8:3. pp. 171-189Copyright 1992 Princeton Scientific Publishing Co.. Inc.

ISSN :0748-2337

172 Dourson, Knauf, and Swartout

INTRODUCTION

Questions regarding the appropriateness and sufficiency of toxicity data that willprovide a basis for meaningful extrapolation from animal to man have remaineddespite much work on this issue (see for example Roloff, 1987). For noncancerextrapolation, one common method has been the derivation of an AcceptableDaily Intake (ADI). The use of the ADI was initiated by the World HealthOrganization in 1961 (Lu, 1988) based, in part, on previous work by Lehmanand Fitzhugh (1954). The toxicological basis of the ADI has always dependedon professional judgment. For example, Clegg (1978) stated that the typicalminimum requirements for a data base pennitting an estimation of the ADI arethe following: two 9O-day bioassays in different species, metabolic studies, re-production studies of at least two generations in at least one species, teratologystudies in at least one species different from that used in the reproductive studies,carcinogenicity studies, and special studies related to known toxic properties ofrelated compounds. Vettorazzi (1984) discuss a similar list of studies but cau-tioned that the type of infonnation required for the assessment of the toxicityof a chemical is a complex matter, and that the interpretation of the design andsuitability of a study may be controversial.

The U.S. Environmental Protection Agency (EPA) has suggested the oral Ref-erence Dose (RfD) or inhalation Reference Concentration J as a basis of extrap-olation for noncancer toxicity (Barnes and Dourson, 1988; larabek et al., 1989;EPA, 1989a, 1991a). The RfD is similar in origin and scope to the ADI. Recentdeliberations by the EP A on the toxicological basis of Rills have focused on asomewhat reduced array of tests compared with Clegg (1978) or Vettorazzi(1984). For a high confidence Rill, the EPA (1989a) nonnally suggests at leastthe following bioassays: two adequate mammalian chronic toxicity studies indifferent species, one adequate mammalian 2-generation reproductive toxicitystudy, and two adequate mammalian developmental toxicity studies in differentspecies. For a low-confidence RfD, the EPA suggests at least a single subchronicbioassay.One scientific question in relationship to these various bioassays is how muchtoxicity data is needed in order to generate high confidence in the resulting value(ADI or RfD). It is known, for example, that any single toxicity study cannotadequately address all possible adverse outcomes and that the toxic endpointsvary among different species; these facts argue for more than one type of studyand for testing more than one species. Furthermore, reproductive and devel-opmental studies are unique in terms of the types of effects they are able to

'The EPA defines the oral RfD as "an estimate (with uncertainty spanning perhaps an order of magnitude)of a daily exposure to the human population (including sensitive subgroups) that is likely to be withoutappreciable risk of deleterious effects during a lifetime (EPA, 1991a)". An inhalation RfC is the equivalentto the oral Rffi, although dosimetry adjustments are incorporated into the analysis.

Toxicology and Industrial Health, Vol. 8, No.3, 1992 7;3

detect and are, therefore, considered essential. A second scientific question ishow, in the absence of such multiple studies, is the scientific uncertaintycfromthe first question addressed in the resulting extrapolation. When toxicity dataon only one experimental animal species are available for the estimation of anADJ, the U.S. Food and Drug Administration has used an extra two-fold un-certainty factor to account for the expected inter-species variability in toxicresP9nse (Shibko, 1981). The EPA is suggesting the use of a similar uncertaintyfactor for "incomplete" data bases in estimating Rills. Are such factors scien-

tifically supportable?The purpose of this manuscript is to present and quantitatively analyze toxicitydata on various endP9ints and species in order to focus further discussions onthe scientific aspects of both of these questions.

METHODS

Definitions used throughout this paper (see Appendix A) are consistent withthe parlance of the EPA (1991b). These definitions are for illustration only;other terms, are used in different organizations and countries.

The toxicity data for 69 pesticides were obtained from the EPA's IntegratedRisk Information System (IRIS) (EPA, 1991b) (Table 1). The chemicals selectedhad the most complete toxicity data base possible in 1- to 2-year rat (RT), 1- to2-year dog (DO) and 1- to 2-year mouse (MS) bioassays, and rat reproductive(RE) and rat developmental toxicity (DV) studies. A total of 296 studies wereincluded in the analysis. Several 9O-day studies were included in the data basebecause they were shown to detect toxic responses at lower doses than an existing1- to 2-year study in the same species for the same chemical (as determined bya comparison of 9O-day with 1- to 2-year no observed adverse effect levels[NOAELs]). In most of the studies included in the data base, both a NOAELand a lowest observed adverse effect level (LOAEL) were determined. However,the LOAELs do not necessarily refer to the same effect across species.

This comparison of disparate effects is considered appropriate since the purposeof this paper is to compare the relevance of different species and endpoints inorder to evaluate their usefulness in determining an Rffi. Moreover, the deter-mination of both the NOAEL and LOAEL is important since, in a well-con-ducted toxicity study, the NOAEL and LOAEL indentify the region of theexperimental threshold. All studies were based on the oral route of exposure.Mg/kg/day doses were estimated for dietary and drinking water exposurethrough the use of standard default consumption assumptions for different speciesand/or strains.The data were examined by using ratios of the NOAELs and were analyzedstatistically in two sets. Three ratios were used in the first data set: 1) 1- to 2-

1'14 Dourson. Knauf. and Swartout

TABLE 1Highest No-Observed-Adverse-Effect Levels (NOAELS) For 69 Pesticides

(mg/kg/day)UData Type~

CASNumber DC RE MS DVRTChemical

7.5" 1.

10

O.

2..

2()iI

20150255

1(xx)300

150010503030

62476-59-915972~60-833089-61-167485-29-474115-25-576578-14-83337-71-1114-26-143121-43-368359-37-517804-35-21563~255285-14-85234-68-4133-~-418t)7-45-6101-21-364902- 72-368085-85-8(ij215-27-81596-84-539515-41-81918-00-062-73-755290-64-7951-51-7127-39-485..00.7330-54-12439-10-3145.73-3759-94-42224-92-659756-ro-469409-94-5133-07-339148-24-81071-83-635554-44-081335-37-736734-19-783558-50-7

77501~3-483055-99-657837-19-1

AcifluourfenAlachlorAmitrazAndroApolloAssureAsulamBaygonBayletonBaythroidBenomylCarbofuranCarbosulfanCarboxinChlorambenChlorathalonilChlorprophamChlorsulfuronCyhalothrinCyromazineDaminozideDanitolDicambaDichlorvosDimethipinDiphenamidDiphenylamineDiquatDiuronDodineEndothallEPTCFenamiphosFluridoneFluvalinateFolpetFosetyl-alGlyphosateImazalilImazaquinIprodioneIsoxabenLactofenLondaxMetalaxyl

0.25"0.33

.'

1.25"2,5«1'"

5"2.54

12.50.5

J2.5"15"..1

250"..11.5

50'"62.501

10.15

75"'"2.5

52.1O.~7.532.5c1.7c0.6251.25'2"

150.25

755

1025020

1.2525

4.210

, S

206.25

2.7515'l.S'

25

75"S

2536102.52.5

125"

12.5'2.52.55

1057.5

t8.7S:2

375

25

100

125

25

15

5

JCX)

6

3

110"50"'"3"..1

100'52.51.5

150"..10.05

125"0.5"2

10"3.1'0.221.25"7"

125"..15Q,581

10"10031"l()"

500"50"5

253512.5

225o:d

ISO"

250.5

50IS""1.25

25"25"-"1010'"125"25"6.25"-"

-7515

"~~

12 100

5

-10

1000.3

1251010

1000i(xx)

10500~)

32050

3005(J

5101.5d

32.5 j$'

34.5

3(X)

10

4()d

1(XX)'125

125

2.5

355,1

62.5.1

3(XX)

~

ISO1870'. 14

226

25'

2.5

2.5'

2.5

Z

Toxicology and Industrial Health, Vol. 8.. No. 3. /992 75

TABLE 1 Continued

Data TypebCAS

Number MSRT DG DVChemical

0.1cZ,S

0.25,5" .:

ISIS""6,

12.5

~5:'0.57.5"

1.67.5

1501201575

2.2520

360154025

101

40060

1(xx)2030

720250

2912.5

15025

1253080

950-37-8'16752-77-55121845-221087-64-9300-76-519044-88-319666-30-942874-03-31910-42-552645-53-1732-4-6918-02-17~98-800207-90-178587-05-074051-80-288671-89-05902-51-2961-11-528249-77-623564-05-858138-08-250471-44-8

0/1s

IS5

1()11lSc~2

2.5:0.:2

2.52.50.455

lQd1

15"

1.873

.25S2

20;c 5'

52318d2.5

12.56.25

4d

50"5Qd2S35

162d9.28

12.5d16.5100.d80.33°

72.9".d

1537.51837.5

1.2S2.523.11.2503.138

SO102.$

MethidathionMethomylMetolachlorMetribuzinNaledOryzalinOxadiazonOxyfluorfenParaquatPermethrinPhosmetPicloramPropanilPropiconazoleSaveySethoxydimSysthaneTerbacilTetrachlorovinphosThiobencarbThiophanate-MethylTridiphaneVinclozolin

22.85gc.1

~

14~ n.9

'Source: EPA. 1991b. NOAELs represented actual doses administered or calculated from dietary exposures.Doses have not been adjusted for either length of exposure or fraction of life span. Please note that data inIRIS are updated on a monthly basis; thus the information in this table may be out of date. Please check IRISor call IRIS User Support (513/569-7572) for more information. ."RT = Chronic Rat. DG = Chronic Dog. RE = Reproductive Rat. MS = Mouse. DV = DevelopmentalToxicity"Value is from a study that was rated as having poorer study quality. These were deleted to form Subset II(RT. DG and RE data only)."Value is from a study that produced a NOAEL. but no LOAEL. These were deleted to form Subset I (RT.DG and RE data only).

year rat to 1- to 2-year dog (RT/DG), 2) reproductive rat to 1- to 2-year rat(RE/RT), and 2) 1- to 2-year dog to reproductive rat (DG/RE):cCalculationsmade directly from the empirical data were used to determine the probabilitiesthat the ratios were greater than 10k or less than 10-k, for k = 0.5,1.0, and 1.5.Figure 1 shows the frequency histograms of the logs (base 10) of these first ratios.Note that the values on the horizontal axis correspond to the values of k.1 For

'We are not assuming any distribution with these histograms. They are constructed to reflect the probabilitiesof interest in this analysis. The intervals. therefore. are as follows: (X:;; -1.5). (-1.5 < X:;; -1..0).(-1.0 < X s -0.5). (-0.5 < X :;; 0.0). (0.0 s X < 0.5). (0.5 s X < 1.0). (1.0 s X < f.5). (1.5 :;; X).Values of X = 0 were evenly divided between the two intervals containing thi" number.

l76 Dourson, Knauf, and Swartout

FREQUENCY

c-1.5-1.6-1.0-0.60.0 0.5 1.0 1.5 )1.5

~ LOG10(RT 100)

20 C: RE)RTD: RT)RE

13

~,5 4

:~..0(-1.5 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 >1.5

~ LOG10(RE/RT)

FREQUENCY

4

0

6

2

8

4

0

FIGURE 1. Frequency histograms are shown of ratios of NOAELs for chronic rat (RT) to chronicdog (DG). reproductive rat (RE) to chronic rat. and chronic dog (DG) to reproductive rat (RE).Letter designations A through F correspond to Table 2.

3

2

2

1

1

2

2

1

1

Toxicology and Industrial Health '01. 8, No.3, 1992 177

example. in the right tail of the top figure, the probability that rat is greaterthan dog by a factor of 101 is 7/67 (-0.10). This probability is calculated bytaking the frequency rate of the data points to the right of 10'. The probabilitythat dog is greater than rat by 10' is shown to be 3/67 (- 0.04) by the left tailof the top figure. This second probability is calculated by taking the frequencyrate of the data points to the left of 10-1.

These probabilities were used to estimate the percent of the time that one animalNOAEL is greater than another animal NOAEL by a given amount. Theseestimations are primarily of importance in determining the potential impact ofmissing studies on the Rill within the framework of the EPA's current procedure,at least for these pesticides. The analysis can also be used to estimate the relativesensitivity of endpoints. Specifically, for any predefined data base, the frequencywith which a missing study or combination of studies might provide a lowerNOAEL value than what is actually available can be determined. For example,if the predefined data base includes RT, DG, and RE, but only the RT and REstudies are present, one can estimate the probability that the missing DG studywould provide a lower NOAEL value than the available data. In a sense, thisprovides an estimate of how often the most sensitive endpoint would be missed,given a less than "ideal" set of data.

The calculations described above were repeated for two subsets of the first setof data (69 chemicals, 201 studies). The first subset (SSl) consisted of only studiesthat produced both a NOAEL and LOAEL (65 chemicals, 157 studies). Thesecond subset (SS2) consisted of only studies that exhibited a better measure ofstudy quality (52 chemicals, 144 studies). These subsets were examined to de-termine if the probabilities would change due to the absence of a LOAEL ordue to better study quality.

In addition, a second data set was formed by combining the first data set withchronic mouse and developmental toxicity data. This second set of data ratioswas examined for the purpose of analyzing how much the probabilities areaffected by the number of studies in the predefined data base or by the choiceof studies in the predefined data base.

Finally. the nonparametric Wilcoxon Signed Rank Test was used to look forstatistically significant differences between the NOAEL values for specific pairsof species.

RESULTS

Table 2 shows the probabilities that one animal NOAEL is greater than anotheranimal NOAEL by a factor of 10*, for all ratios. For example, in event A.. 28%of the time, the rat NOAEL (RT) was larger than 3.16 (1Q!'5) times the dogNOAEL (DG) for the same chemical.. or in event E.. 3% of the time.. the dog

'78 Dourson, Knauf, and Swartout

TABLE 2Probabilities for All Ratios

Event: NOAEL. ?; NOAEL2*10li

Probability of Event

0.030.010.060.00'0.000.100.000.170.000.060.00o.~0.000.230.020.190'.000.100.00o.~

o.o.0.-o.o.o.o.o.0.'o.o.o.o.0,o.o.Q.O.O.o.

0.100.040.17O.~0.030.300.000.500.000.2S0.000.190.030.440.030.480.020.37O.~0.22

ABe,DEFGHIJKLMN0pQRST

Reproductive Rat. MS = Mouse. DV - DevelopmentalRT = Chronic Rat. DG - Chronic Dog. RE

Toxicity

NOAEL (DG) was larger than 10 times (k = 1) the reproductive rat NOAEL(RE) for the same chemical.Notice the events A (RT > DG) and B (DG > RT) are mutually exclusive. asare C (RE > RT) and D (RT > RE). and also E (DO> RE) and F (RE >DG). Thus. only cenain combinations of events A through F can occur. Forexample. events A. C. and F can all occur simultaneously. These three eventsmight combine to form the mathematically true statement. RE > RT > DG.In contrast. events A. C. and E cannot occur simultaneously.The probabilities of Table 2 can be combined to estimate the probability ofNOAEL overestimation. Table 3 presents these results for the first set of ratios.For example. suppose only the rat and reproductive rat studies are available.How likely is it that the missing dog NOAEL would have been smaller than thechronic rat or reproductive rat NOAELs'! The probabilities that apply from

2819311511460361034413350566056S13501638

Tox;cology and /ndu.5tr;al Health, Vol. 8. No.3. /992 79

TABLE 3Combined Probabilities for the First Set of Ratios

RTDGRERT.DGRT.REDG.RERT. DG, RE

Auf)BVEcuFonEAnFBncNone

P(A) + P(D) - P(A ID)P( D)PCB) + P(E) - P(BIE)P(E)P(C) + P(F) - p(qF)P(F)

P(DI£)P(£)P(AIF>P(F)P( BIc)P( C)

0.370.240.620.060.180.120.00

0.16O.{K)0.39

~OO0.040.020.00

0:030.010.130.000.000.000.00

-Events which must occur if the missing studies are to undercut the lowest NOAEL a\OaiJableU - Union

= intersection

Table 2 are the probabilities of events A. (RT > 00), and F. (RE > DO); thefollowing scenarios could occur:

DataAvailable

Event F(RE > DO)

Order ofNOAELs

SmallestNOAEL

Event A(RT > DO)

DGRERTMin(RT. RE)

TrueTrueFalseFalse

TrueFalseTrueFalse

RT > DG; RE > DGRT > DG ~ RERE > DG ~ RTDG ~ RT; DG ~ RE

Thus. both events A and F must occur simultaneously in order for the missingdog NOAEL to be the smallest. This probability is found by taking the inter-section of events A and F. In this case, Table 3 indicates that an 18% chanceexists that the missing dog NOAEL is three-fold (i.e., K = 0.5) or more lowerthan both the rat and the reproductive rat NOAELs; a 4% chance exists that itis 10-fold (i.e., K = 1.0) or more lower: and a 0% chance exists that it is 30-fold (i.e.. K = 1.5) or more lower.

Suppose only the rat study is present (Table 3). How likely is it that the missingdog and/or reproductive rat NOAELs would have been less than the ratNOAEL? The probabilities that apply from Table 2 are the probabilities ofevents A (RT > DG). and D (RT > RE); the following scenarios could occur:

DataAvailable

SmallestNOAEL

Event A(RT> DO)

Min(DG. REDGRERT

RTRTRTRT

TrueTrueFalseFalse

TrueFalseTrueFalse

RT > DG: RT > RERE ~ RT > DGDG 2: RT > REDG 2: RT: RE ~ RT

~T.RERT.RERT.RERT.RE

180 Dourson, Knauf, and Swartout

Thus, if events A and D occur simultaneously, or if either event occurs by itself,then the rat NOAEL is not the smallest of the three NOAELs. This combinedprobability is found by taking the union of the two events A and D. In this case.Table 3 indicates that a 37% chance exists that either the missing dog NOAELand/or the missing reproductive rat NOAEL is three-fold or more lower thanthe rat NOAEL; a 16% chance exists that either one or both is to-fold or morelower; and a 3% chance exists that either one or both is 3D-fold lower.

Tables 2 and 3 show the results of the calculations for the first set of ratios.listing the probabilities when only certain studies are present out of the prede-fined three study data base. It can be seen that the reproductive rat study is notas sensitive as the chronic rat and dog studies. For example. when the repro-ductive rat study alone is present. a 39% chance exists that the two missingstudies would have provided at least a 10-fold lower NOAEL as compared with16% and 6% chances for the rat study only and dog study only. respectively(Table 3).

Also, any combination of two of the three studies is superior to having only onestudy available; the best two-study combination is the chronic rat and dog com-bination. From this data base, there is a zero percent chance that a 10-fold lowerNOAEL will be provided by the missing study with a dog and rat combination.a 4% chance with a rat and reproductive rat combination, and a 2% chance witha dog and reproductive rat combination (Table 3). Finally, the probabilities fortwo-study combinations are highest when estimating the NOAEL within half anorder of magnitude (i.e., K = 0.5). Table 3 shows a 6% chance of a three-foldlower NOAEL being provided by the missing study with a dog and rat combi-nation, and 18% chance with a rat and reproductive rat combination. and a 12o/cchance with a dog and reproductive rat combination.

Tables 4 and 5 show the same analysis as Tables 2 and 3, respectively, for thefirst set of ratios and for the two subsets of these ratios as previously describedin "Methods," Few differences exist among the calculated probabilities for eachcombination of events occurring in each of the subsets when compared with eachother or to the primary ratios, Tables 4 and 5 also indicate that having only areproductive rat study is the worst scenario for the purposes of calculating anRfD, and that having the chronic rat and dog studies available is the best two-study combination. These relationships remain constant across the subsets.

Chronic mouse and developmental toxicity data are a15O listed in Table 1, andthe probabilities for events G through T that relate to these data and theircombinations are given in Table 2. It may be noted from Table 1 that the valueof these mouse and developmental toxicity NOAELs are generally larger com-pared with those of DG, RT. and RE. For example, in Table 2 the mouseNOAEL is larger than 10 times the dog NOAEL 500ft, of the time (event H).

Toxicology and Industrial Health, Vol. 8, No.3. 1992 181

TABLE 4Probabilities Across Subsets

Event: NOAEL) ~ NOAEL2*10k

Probability of Event

k = 0.5 k = 1.0 k = 1.5

0.28

0.1~0.370.150.110.46

0.250.170.320.160.130.37

,0.22b.241).340.110.10'0.45

0.10

O.~0.,17

O:!/$0.030.30

0.040.020.130.050.03'0.18

o.~O.()J.0.140.05d.()20.31

0.030.01O.{W)

0.00

0.00

0..10

0.000.020.050.000.000.05

O.():2

0,020.050.000,000.10

ABCDEF

DB = Complete data base: SSI = studies with no LOAEL removed: SS2 = studies with poorer study qualityremoved

and the developmental toxicity NOAEL is larger than 10 times the reproductiverat NOAEL 37% of the time (event R).

Table 6 shows that as the number of study types required in the predefined database increases, the probabilities relating to overestimating the NOAELs willeither increase or remain the same, but they will not decrease. This happensbecause a new study in the data base presents a new opportunity to undercutthe "lowest" NOAEL. For example, in line 3 of Table 6, a three-study data baseis defined as chronic rat, dog, and reproductive rat studies. If dog is the onlystudy available, then the union of events B (DG > RT) and E (DG > RE)

TABLESCombined Probabilities Across Subsets

Probability of Events Occurring

k = 0.5 k = 1.0 k = 1.5StudyPresent

EventsOccurring* SSI SS2 DB 55] SS2DB 551 SS2 DB

AUDBEUCUFDnEAnFBnCNone

0...370.240,62

Q.~0".18'0.12().OO

0.370.230.630.080.080.130.00

o.o.Clto~

0:'cr

'0.

0J

0.160.06

9.3?O.~0:04"0.020.00

0;090.050.300:00,0.01

Q.03

0:00

0.13

0.070.41

O.~0.02O.OS0.00

0.030.01

,Ct,,!3"

.~~

'O~OO'

~OO0.""

0.000.020.10

;JO...W,0.00

:9.~0.:00:

o.~O.~0.130.000.000.02..00

RTDGRERT.DGRT. REDG.RERT, DG, RE

DB = Complete data base; SS! = studies with no LOAEL removed; SS2 = studies with poorer study qualityremoved-Events which must occur if the missing studies are to undercut the lowest NOAEL available. n = intersection;U = union

32266902081100

182 Dollrson, KnOll/' and Swartout

TABLE 6Combined Probabilities for Various Predefined Data Base Combinations

Probability of Events Occur-

rin~Study

PresentStudies in the Pre-defined Data Base

EventsOccurringLine k = 0.5 k = 1.0 k = 2.5

0.000.190.240.240.270.27

0.004.J.280.370.390.400.45

0.000.000.020.060.060.08

J).(KJ

O.U~,9r°3c0.18'0.180.20

0.000:100.21O.2..~

0;000.040.060.060.090.09

0.000;10'0.160.180.16

~.4~1~OO'0.000.020;00Q~OO0.02

0.000

~O.OO'00.00

'b.04

0.0..

0.04

0.00

O:~-O.OS

0.07

0.000.010.010.010.010.01

0.000.030.030.050.030.05

(1.000.000.000.009.000.00

;0.000.000.000.000.000.00

0.00f).OJO.OJ0.01

2

3

4

5

6

7

8-

t'

10

11

12

13

14

15

16

17

J8

19

20,

21

22

23

25262728

DO DO NoneDO DO. RT BDO DO. RT. RE B U EDO DO. RT. RE. MS B U E U GDO DO. RT. RE.DV BuEuMDO DO. RT. RE. MS, DV B U E U GUM

RT RT NoneRT RT. DO ART RT. DO, RE A U D 'i

RT RT. DO, RE. DV A U DUORT RT, DO. RE. MS A U D U IRT RT.DO,RE.MS.DV AUDU~W~

DO. RT DO, RT NoneDO. RT DO. RT. MS In GDO. RT DO. RT. DV 0 n M ' "DO. RT DO. RT. RE D n E if::, 1DO. RT DO. RT. RE, MS (D nf£)u (J.n ,V)."DO. RT DO. RT. RE. DV (D n £)",", (0 ~M)

RT. RE RE. RE NoneRT.RE RT.RE.DVOnQ;RT. RE RT. RE, MS , InK ,. ':ij' """,RT. RE RT. RE. DO A rI F i L "

RT. RE RT. RE. DO. MS (A n 1) u un K)RT. RE RT. RE. DO. DV (~n 1) U (0 n'fJ)RT. MS RT.MS Nonei: ,. t ':~'1",f1'

RT.MS RT.;\tS.RE DnCRT. ~IS RT. MS. DO A nH ~ ~

RT. ~1S RT. MS. RE. DO (A n H)V (D n L)

U = Union. n = inlc~clion

yields the probability of undercutting the dog NOAEL; for a K value of 1.0,this probability is a.O6. In line 5 of Table 6, the predefined data base is comprisedof the four study types: rat. dog, reproductive rat, and developmental. Again,if dog is the only study present, the probability that the missing developmentalNOAEL (i.e., the new study) would be less than the dog NOAEL. event Mfrom Table 2 (OG > OV). must be added in with events Band E to figure theprobability of undercutting the dog NOAEL; for a K value of .1.U. this combinedprobability is O.()9 as compared to O.()6 with a three study data base.

Toxicology and Industrial Health, Vol. 8, No.3, 1992 183

Table 6 also reveals that the choice of study type is important to the estimationof the smallest possible NOAEL. Lines 26 and 27 of Table 5 are examples oftwo different predefined three-study data bases with rat and mouse studies pres-ent for each case. In line 27, the rat and mouse studies are available from thethree-study data base of rat, mouse, and dog. The probability that the missingdog study would yield at least a three-fold (i.e., K = 0.5) lower NOAEL is 0.21.However, if the rat a,nd mouse studies are available from the three study database of rat, mouse, and reproductive rat (line 26), then the probability that themissing reproductive rat study would yield at least a three-fold lower NOAELis only 0.10.

Hypothesis TestingHypothsis tests were performed using the Wilcoxon Signed Rank Test to findand estimate differences in NOAEL values between pairs of species. The as-sumed model was:

loglO(NOASLI) - loglO(NOAEL1) 9i.c~,

where. C is some constant value. The corresponding hypothesis test was:

Ho: C = Ovs. H.: C;':iQ.

Furthermore, by looking at differences in the logs of the NOAELs and estimatingC (Hollander and Wolfe, 1973), we can find the relationship of the ratios of theNOAELs, with 95% confidence intervals from:

NOAEL,fNOAEL2 = lOC.

We looked at seven relationships with an overallp = 0.05; therefore, signficancelevel for each test was p = 0.00357. Results were as follows:

Ratio N

465262313057(jJ

1(1.'

1.582.452..683.87j:017.91'8.tiJ

Reject Ho:

NoYesYesYesYesYesYes

RTIREIREIMSIMSIDVIDVI

From these tests. it was concluded that:

, REDG ? RT < MS

DV

DG'RT'DGRTDG'RT'DG

~84 Dourson, Knauf, and Swartout

DISCUSSION

From a practical point of view, the development of credible dose-response as-sessments such as an Rill or an ADI should allow for scientific distinction amongpossible minimum toxicity data bases in the number of studies as well as thenecessary study types. This issue has plagued previous efforts because one couldalways object that the most sensitive species was not tested or that the mostsensitive effect was not monitored. The data presented in this paper allow forsome discussion and perhaps limited resolution of this issue. Minimum datarequirements and study sensitivity, and dose-response assessment will be dis-cussed in turn.

Minimum Data Requirements and Study SensitivityA primary objective of this research was to define a data set that would providereasonable assurance that a sensitive effect was not missed in the determinationof an Rill (or ADI). This could be viewed as an impossible task because nomatter how many data are in hand, that many more are lacking. However, froma practical standpoint, only a few well-defined standard animal bioassays areregularly available. Therefore, we attempted to determine which bioassays areessential.

Quantitatively speaking, a four study base should be adequate within the scopeof the data analyzed here. Good chronic dog and rat studies, and reproductiveand developmental studies together, cover nearly all of the uncertainty in thisset of pesticide data. The chronic mouse data add little, if anx, quantitativeimpact on the probabilities listed in Table 6 (e.g., compare lines 3 & 4, 5 & 6,9 & 11, 13 & 14, 16 & 17, 19 & 21, 22 & 23). Several bioassays have a largeimpact on the probabilities listed in Table 6, for example, the chronic dog study(e.g., compare lines 7 & 8, 19 & 22, 25 & 27). Other types of studies haveconsistent and yet smaller quantitative impact in Table 6, for example, devel-opmental toxicity study (e.g., compare lines 3 & 5, 4 & 6, 9 & 10, 11 & 12,13 & 15, 16 & 18, 22 & 24).

The data also allow for some conclusions regarding the relative sensitivity of thestudies examined. In the context of this discussion, "sensitivity" refers to theability of a given bioassay to detect any adverse effect, it does not necessarilyrefer to the innate ability of a particular substance to cause damage in any givenspecies or organ system, nor to the seriousness of the different toxicity evokedamong studies. Thus. the distinction between true physiological sensitivity andrelative measurement sensitivity cannot be made here.

The data show a general ordering of increasing sensitivity of DV, MS. RE, RT,DG. The DG:RT difference would almost certainly be magnified (with DG beingmore sensitive) if the DG study design were similar to the RT with respect tothe number of animals and relative duration. RT studies generally use about 5-

Toxicology and Industrial Health, Vol. 8, No.3, 1992 185

10 times as many animals per dose group as DG studies, and study duration isgenerally a much greater proportion of life span for RT than DG (about six-fold for 2-year studies). (Note that NOAELs used in these comparisons werenot adjusted for life span differences; see Table 1.)

The trend in sensitivity could be ascribed to differences in genetics, or thephenomenon may be an artifact of animal size. The latter possibility could occurin perhaps one of two ways. The toxicity of these substances, in general, maybe proportional to a fractional power of body weight, as are a number of phys-iological processes, rather than to body weight. An apparent negative correlationof body-weight-based toxicity as measured by NOAEL and animal size wouldthen result when the toxicities were actually equivalent. (That is, animals oflarger body weight would tend to have smaller NOAELs. See Dourson andStara, 1983, for further discussion.) A second artifact of animal size arises fromthe technical aspects of animal experimentation. Quite simply, the experimentercan investigate more endpoints and obtain more samples, more easily, fromlarger animals, resulting in a greater likelihood of detecting treatment-relatedeffects.

The lower sensitivities exhibited. by the RE and DV studies, as evidenced bythe RT:RE and RT:DV differences observed, may not be artifacts of body sizebecause RE and DV are primarily rat studies. The RE and DV endpoints, asmeasured, are less sensitive than those measured in chronic general:'toxicitystudies. This may be due in part to the older nature of the RE and DV studiesused here when compared to the relatively recent guidelines for analyzing thesedata for risk assessment proposes (EPA, 1986, 1989b).

Dose-Response AssessmentThe impact of these results on current dose-response assessments, such as theEPA approach for deriving Rills, depends in part on how one interprets thenature of uncertainty factors. The most common interpretation is essentiallyempirical. Under this interpretation uncertainty factors are viewed as necessaryloose upper-bound values because of missing data. They are not average values,as discussed by Lewis et al. (1990).

Within this interpretation, one attempts to establish a human reference dosesufficiently below the expected population threshold dose for any adverse effectwith a given set of data. It follows that, having identified a set of necessary datathat have a significant quantitative impact on the uncertainty of the health hazardassessment, one must divide by an uncertainty factor to estimate the value ofthe missing data set.

Should this adjustment always be in one direction (that is, downward)? Theuncertainty factors currently used by the EPA (Barnes and Dourson, 1988) todervie RfDs are: UFH (sensitive humans), UF A (animal to human, UFs (sub-

186 Dourson, Knauf, and S,vartout

chronic to chronic). and UFL (LOAEL to NOAEL). The unidirectional natureof UFL and UFH is obvious; zero expectation exists that an increase in dose ratewill provide a NOAEL in the first case and protection for sensitive subpopu-lations in the second case. The case for UFs is less unambiguous but conceptuallysound. In general, a much greater expectation exists that continued exposure ata given dose rate will produce rather than alleviate effects. A number of mech-anisms can be conceived by which effects. not evident after subchronic exposure.may arise following chronic exposure. Repeated insult may result in the accu-mulation of unrepaired damage not measurable in the short-term. or in thegradual compromise of homeostatic mechanisms, for example.

The argument for the directionality of UF A is less specific, and perhaps not ascompelling as that for the other uncertainty factors. One could argue that theassumption that humans. in general. are inherently more sensitive than the mostsensitive laboratory animal species is unreasonable. This argument is particularlycogent when a surfeit of data exist for many species. yet the magnitude of U FAdoes not change. It has been suggested previously (Dourson and Stara. 1983)that UF A. in part. is covering the possibility that the wrong scaling factor forinterspecies dose conversion was selected (power of body weight equal to 1instead of less than 1). However, as U FA is always constant (10). this reasoningis most applicable when the species is unknown (which of course is never the

case).Another possible interpretation of U F A is that it accounts for all aspects of therelative insensitivity of laboratory animal experimentation. with respect to real-world human exposure. not already accounted for by other uncertainty factors.This UFo then. would allow for all the measurement error. lack of statisticalpower. design limitations. and other aspects of uncertainty inherent in the con-duct of toxicological studies (such as the inability to measure subjective humanendpoints). We would have a higher expectation that this combination of un-certainties would result in a higher value for an experimentally-determined an-imal threshold than an actual human threshold.Within this interpretation of uncertainty factors. the results presented in thispaper suggest two general conclusions. The first conclusion is that the choice inboth the number of species and types of studies in a dose-response assessmentcan affect the outcome of the specific probabilities of missing relevant data. andmore generally. one needs more than a single bioassay to ensure a relativelyhigh confidence in any given RfD or ADJ. This conclusion confirms what hasbeen the general thinking of toxicologists for years (for example. Clegg. 1978).This analysis merely expresses the concept in a quantitative fashion. However.a second conclusion is that one may not have to consider very many studies.given that we have looked at all the species and endpoints generally availablefur this class of compounds. and the data summarized in Table 6 show that theprobabilities tend to approach a plateau as the number of studies in the studv

Toxicology and industrial Health, Vol. 8, No.3, i992 187

base increases. One implication of these two conclusions is that when one ormore studies is missing from a predefined "complete" data set, uncertainty isencountered that must be expressed. Whether this uncertainty should be ex-pressed quantitatively (as in the use of an additional uncertainty factor) or qual-itatively remains a subject of further research and debates, but in the interimthe EPA has used such a factor to account for missing studies (EPA. 1991b).

The limitations of this analysis are several, and some cannot be addressed easily.For example, more meaningful estimates of study sensitivity might be obtainedby focusing more on the effect levels than on the no-effect levels. Also, an idealanalysis would include silmilar experimental protocols for the same bioassay fordifferent chemicals-we obtain data for different chemicals from a diverse groupof laboratories and throughout a number of years. In addition, the increasingsensitivity from MS to RT to perhaps DO should provoke thought as to thelikely biological basis.

Because the data set used in this analysis is not from a random sample of allchemicals. but a collection of readily-available, relatively complete data setsderived from oral exposure (all of which happen to be pesticides), one may notbe able to conclude much about other, even larger, categories (solvents, forexample) or other routes of exposure. However, in a world of many chemicalsand limited toxicity data, generalizations are needed that can be applied to manysubstances of environmental concern. Research based on sound toxicologicalprinciples is continuing to focus towards this end.

ACKNOWLEDGMENT

The authors acknowledge the comments and guidance of Drs. Ellen J. O'Fla-herty, George Ghali, and Reto Engler in the preparation of this manuscript.

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CLEGG. D.J. (1978). Toxicological basis of the ADI-Present and future considera-tions. In: Pesticide Reviews (H. Frehse and H. Geissbuhler. eds.) Pergamon Press.Oxford. England.

DOURSON. M.L. and STARA. J.F. (1983). Regulatory history and experimental sup-port of uncertainty (safety) factors. Reg. Toxicol. Pharamcol. 3:224-238.

HEYWOOD, R. (1981). Target organ toxIcity. Toxicol. Leu. 8:349-358.HEYWOOD, R. (1983). Target organ toxicity II. Toxicol. Leu. 18:83-88.HOLLANDER. M. and WOLFE, D.A. (1973). Nonpara~etric Statistical Methods,

John Wiley and Sons. Inc.. New York.JARABEK. A.M., MENACHE. M.G., OVERTON. J.H.. DOURSON. M.L.. and

MILLER, F.J. (1989). Inhalation Reference Dose (Rffi): An application of inter-

188 Dourson, Knauf, and Swartout

pecies dosimetry modeling for risk assessment of insoluble particles. Health Physics.57:177-183.

LEHMAN, A.J. and FITZHUGH, O.G. (1954). 100-Fold margin of safety. Assoc.Food Drug. Off. U.S.Q. Bull. 18:33-35.

LEWIS, S.C., LYNCH, J.R., and NIKIFOROV, A.I. (1990). A new approach to de-riving community exposure guidelines from no-observed-adverse-effect levels. Reg.Toxicol. Pharmacol. 11:314-330.

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VETTORAZZI, G. (1984). Pesticide residues in food in the context of the internationalactivities of the Joint FAO/WHO Meeting on Pesticide Residues and the CodexCommittee on Pesticide Residues. Environmental Health Criteria 104. Principles forthe Toxicological Assessment of Pesticide Residues in Food. International Programmeon Chemical Safety, World Health Organization, Geneva. Switzerland.

APPENDIX ADEFINmONS

Critical effect-The first adverse effect, or its known precursor, that occurs asthe dose rate increases.

Lowest-observed-adverse-effect level (LOAEL)- The lowest exposure level atwhich there are statistically or biologically significant increases in frequency orseverity of adverse effects between the exposed population and its appropriatecontrol group.

Toxicology and Industrial Health, Vol. 8, No.3. 1992 189

No-observed-adverse-effect level (NOAEL)-An exposure level at which thereare no statistically or biologically significant increases in the frequency or severityof adverse effects between the exposed population and its appropriate control;some effects may be produced at this level, but they are considered neitheradverse nor precursors to specific adverse effects. In an experiment with severalNOAELs. the regulatory focus is primarily on the highest one. leading to thecommon usage of the term NOAEL as the highest exposure \\'ithout adverseeffect.

Reference dose (Rm)-An estimate (with uncertainty spanning perhaps an or-der of magnitude) of a daily exposure to the human population (including sen-sitive subgroups) that is likely to be without appreciable risk of deleterious effectsduring a lifetime.

Uncertainty factor (UF)-One of several, generally 10-fold factors, used in op-erationally deriving the dose (RfD) from experimental data. UFs are intendedto account for (1) the variation in sensitivity among the members of the humanpopulation; (2) the uncertainty in exrapolating animal data to the case of humans;(3) the uncertainty in extrapolating from data obtained in a study that is of less-than-lifetime exposure; (4) the uncertainty in using LOAEL data rather thanNOAEL data; and (5) the inability of any single study to adequately address allpossible adverse outcomes in man.