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Views& Commentaries :.. .. i} ;:... .%siX: views & Commentaries |T____._-----i-------.--r.......-,f,P.g....f.wRt.,4.....He .;8|S . A Toxicological Basis to Derive a Generic Interspecies Uncertainty Factor Edward J. Calabrese and Linda A. Baldwin School of Public Health, University of Massachusetts, Amherst, MA 01003 USA The use of an uncertainty factor (UF) to on experience, and a sense that it achieves account for interspecies variation in risk its goal of protecting human health. The assessment procedures for noncarcinogens present paper offers what the authors we is well known and implemented by regula- believe to be a toxicological and statistically tory agencies at the federal and state levels. defensible foundation for deriving the The approach that has been widely adopt- interspecies UF, its database requirements, ed is to assume that humans may be 10- and statistical procedures for its derivation. fold more sensitive than the animal model. In brief, the recommended interspecies UF This factor of 10 has become routinely is defined as the 95% of the population of adopted in essentially all risk assessment 95% prediction intervals (PI) for binary procedures involving animal model data interspecies comparisons based on phyloge- for extrapolation. netic relatedness. More specifically, the UF Despite the long-standing use of the is derived by determining the minimum interspecies UF of 10, only limited biologi- ratio of the estimated toxicity value and its cal and/or toxicological justification for the 95% upper or lower PI after back-transfor- interspecies UF has ever been put forth by mation from the logarithmic expression. any regulatory agency (1) or national advi- This paper presents the toxicological sory committee (e.g., National Academy of and statistical basis for this proposal and its Sciences Safe Drinking Committee). The implications for judging the reliability of adoption of the 10-fold factor appears to current regulatory interspecies UF proce- erous binary interspecies comparisons and have been based on a combination of pub- dures as well as offering a fundamentally organized them via phylogenetic related- lic health protection philosophy, prac- novel approach to deriving an interspecies ness. For example, interspecies compar- tical/intuitive toxicological insights based UF. isons were provided when the comparisons An extensive database on interspecies represented species-within-genus, genera- variation in susceptibility to toxic agents within-family, families-within-order, and twos Jlog.RlaB;2oroi~~~~~~~~~~~~~ali~~~nbrJB ~ .......].... exists in the aquatic toxicology area. The orders-within-class comparisons. For toxicity data are principally, though not example, in Figure 2 a species-within- 14 exclusively, based on acutely toxic respons- genus comparison would represent a bina- es. The data are arranged in the form of ry comparison of species 1 with species 2. binary interspecies comparisons with A genera-within-family binary comparison respect to toxicity from dozens to over 500 would be represented by a comparison of agents depending on the specific binary species 1 with species 3. The reason for comparison. A binary comparison in the organizing the comparisons in this phylo- .v... present context involves comparing the genetic manner is the assumption that responses of two species to agents that were interspecies variation in susceptibility tested in both species. For example, two would increase as the phylogenetic dis- J, species of fish (e.g., smallmouth bass and tance increased. perch) have been used to test over 500 of Table 1 provides a summary of the the same toxicants (Fig. 1). A binary com- database of phylogenetically based inter- parison of these two species would include species binary comparisons. The 95% PI more than 500 agents. These data have for each binary comparison is provided, An been organized to assess whether a mathe- along with the number of different chemi- matical relationship exists such that the cal agents tested for each binary compari- -5 0 5 10 15 LC50 of one species may be a useful predic- son. The weighted mean value indicates In Salmonifonnes Wm5 tor of the LC50 in the other species via the that in general the closer the animal use of were the smaller the Figure 1. Natural logarithms of LC50 values for regression modeling. species related, 95% Perciformes plotted against Salmoniformes The above binary comparison method- PI. The range of weighted means of 95% (orders of the same class, Osteichthyes). The ology has been used by various authors PI is from a low of 6.0 (species within solid line represents the least-squares linear (2-4) to estimate the LC50 for any new genus) to a high of 26.0 for the orders- regression of the natural logarithm of LC50 values chemical in an untested species (e.g., small- within-class grouping. for Perciformes species on the natural logarithm mouth bass) if the LC50 were known. for Slooff et al. (4) transformed the con- of values for Salmoniformes species. Each LC50 the perch. The estimate is made by calcu- cept of the 95% PI into a 95% UF. Figure circle represents the LC5, value of a specific chemical for both species. $he number of chemi- latng a prediction interval (PI) for the cals represented in the figure is 503. Data from unknown chemical. Barnthouse et al. (3) Address correspondence to E. J. Calabrese. Johnson and Finley (9). have provided 95% PI estimates for num- Received 21 June 1993; accepted 2 October 1993. 14 Environmental Health Perspectives I - - -1MLA - - - I - - 3 presents a graphic foundation of the PI as well as statistical definition and relation- ship to the UF concept. Thus, the species- within-genus 95% UF, as anticipated, is considerably smaller than the 95% UF for orders within class. The magnitude of interspecies variation in 95% PI values fol- lows fairly closely with phylogenetic relat- edness, as expected. Inconsistencies such as the similar estimates for species within genus and genera within family are likely Figure Z Interspecies comparisons based on phylogenetic relatedness. S1 represents a species for which related to issues concerning representative- data are available. S, and S2 represent a species-within-genus comparison; S1 and S3 represent a genera- ness, number of binary comparisons, and within-family comparison; S1 and S5 represent a families-within-order comparison; and S1 and S9 represent number and nature ofchemical agents test- an orders-within-class comparison. ed. The binary comparison values do not Table 1. Taxonomic extrapolation: means and weighted means calculated for the 95% and 99% prediction represent the population (or universe) of intervals (PI) for uncertainty factors calculated from regression models (3) such values but must be considered a sample Uncertainty factor of the population. No knowledge exists X variable Yvariable n 95% PI 99% P1 concerning how representative this sample Taxonomic extrapolation: species within genera of values would be of the population. For Salmo clarkii S. gairdneri 18 9 13 the sake of argument, the samples of each S. clarkii S. salar 6 6 10 phylogenetic subgroup are considered repre- S. clarkii S. trutta 8 6 8 sentative of their respective population val- S. gairdneri S. salar 10 7 11 S. gairdneri S. trutta 15 4 5 ues. Table 2 provides an estimate of upper S. salar S. trutta 7 5 8 95% (using logistic regression modeling) of Ictalurus melas I. punctatus 12 5 7 the population of 95% PI values (see Figure Lepomis cyanellus L. macrochirus 14 6 9 3 for derivation of95% PI values) according Fundulus heteroclitus F majalis 12 6 8 to phylogenetic relatedness. The unexpect- Mean 6.1 10.1 Weighted mean 6.0 7.4 edly high value from the families-within- Taxonomic extrapolation: genera within families order extrapolation group is partially incon- Oncorynchus Salmo 56 5 6 sistent with the proposed phylogenetic rela- Oncorynchus Salvelinus 13 4 5 tionship. This inconsistency is principally a Salmo Salvelinus 56 5 7 result of the low number of binary compar- Carassius Cyprinus 8 4 6 Carassius Pimephales 19 7 9 isons (N= 7) and high variability ofindivid- Cyprinus Pimephales 10 7 10 ual estimates in the families-within-order Lepomis Micropterus 30 8 11 comparison group. This value is less stable Lepomis Pomoxis 8 9 13 than the orders-within-class grouping. Cyprinodon Fundulus 12 6 8 Given the amount of data, the orders-with- Mean 6.1 8.3 Weighted mean 5.8 7.7 in-class comparison offers the most stable Taxonomic extrapolation: families within orders and reliable perspective. We propose that Centrarchidae Percidae 47 10 14 these values can be used to provide a toxico- Centrarchidae Cichlidae 6 4 6 logically and statistically based foundation Percidae Cichlidae 5 13 24 for generic interspecies UFs when normal- Salmonidae Esocidae 11 9 13 Atherinidae Cyprinodontidae 32 7 9 ized for phylogenetic relatedness. The data Mugilidae Labridae 12 55 78 suggest that four different UFs be adopted Cyprinodontidae Poecillidae 12 3 5 according to phylogenetic relatedness. The Mean 14.4 21.3 choice of 95% UFs would range from a low Weighted mean 12.6 17.9 of 10 for the species within genus to a high Taxonomic extrapolation: orders within classes within The Salmoniformes Cypriniformes 225 20 27 of 65 for the orders class. gen- Salmoniformes Siluriformes 203 39 51 era-within-family and families-within-order Salmoniformes Perciformes 443 12 16 groupings are more difficult to determine. Cypriniformes Siluriformes 111 11 15 Based on the phylogenetic relatedness con- Cypriniformes Perciformes 219 32 43 cept,
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