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Letters to the Editor Biomarkers in Toxicology and Risk Assessment: Informing Critical Dose-Response Relationships by Swenberg et al., 2008 To the Editor: The perspective by Swenberg et al. (1), and the subsequent letters of Lutz and Gaylor (2) and Starr and Swenberg (3) have provided a recent opportunity to revisit an important debate. The letters and responses make quite a number of interesting and useful points regarding dose-response analysis. The two letters nicely frame one suggestion for an alternative way to extrapolate expected response from statistically observable data and the reply carefully frames some of the limitations of that particular alternative low-dose extrapolation approach. It is important, however, to bring out some other points. First, Swenberg et al. (1) note in their review paper and response (3) that for any kind of experimental data, there is a dose sufficiently low that the signal:noise ratio makes hypothesis testing with that kind of data unrealisticsat least as to reject any of several hypotheses that are consistent with the higher concentration data. While that may be true, it does not mean that effects below the observed range of detection do not exist. Second, it is important for readers to also consider that many of the data presented in the Swenberg et al. (1) can legitimately be viewed as being consistent with the hypothesis that mutation rates are linear at low doses for those chemicals upon which they chose to focus. The points below go into this further. The perspective by Swenberg et al. (1) briefly reviews some of the basic principles involved with the underlying science that determines the molecular dose of DNA and protein adducts, the relationships between macromolecular adducts and mutations, and the important role that mutational biomarkers have in cancer risk assessment. The paper uses a framework analysis approach to examine the mode of action of genotoxic chemicals and the default assumption that cancer can be expected to be linear at very low doses. The paper offers some interesting biological/conceptual issues in risk assessment; however, it is difficult to relate the data to the conclusion of the threshold effect for the genotoxic agents. The central theme of Swenberg et al. (1) argument is that DNA and protein adducts are biomarkers of exposure and not biomarkers of effect and that exposure biomarkers can be extrapolated to zero but effect biomarkers cannot be extrapolated to zero because there are always some endogenously induced background effects, for example, those induced by reactive oxygen species, lipid peroxidation, and other mechanisms. Swenberg et al. (1) hypothesize that “the dose-response curve for mutagenic end points may not be linear even if the associated DNA adduct response is linear” and that “mutagenic compounds may very well possess dose-response thresholds, that is, positive doses below which there is no increase in mutation frequency above that observed in unexposed control animals.” Data from three chemicals, acrylamide, ethylene oxide (EO), and methylmethane sulfonate (MMS), are used to support the 10.1021/tx800356t
Figure 1. Frequency of micronucleated PCE (fMPCE) of male CBA mice given 22 different doses (single injections, i.p.) of acrylamide: 0, 2.5, 5, 6.5, 8.5, 10, 11.5, 13.5, 15, 17.5, 20, 22.5, 25, 30, 35, 40, 45, 50, 62.5, 75, 87.5, and 100 mg/kg b.w. There were two mice in each group except for the dose groups 0 and 75 mg/kg b.w., with three mice each. Blood samples were taken 42 h after injection. Data points are mean values of fMPCE. The trend lines illustrate the slopes in two different dose regions: y ) 1.09 + 0.020x (0-15 mg/kg b.w.) and y ) 1.13 + 0.014x (17.5-100 mg/kg b.w.) adopted from ref 4.
Figure 2. Relationships between DNA adducts and micronucleus induction in mice exposed to carcinogenic doses of acrylamide. Polychromatic erythrocytes in peripheral blood (fMPCE) (9) from ref 4. DNA adducts (b) are 0.0014 µg/kg from ref 5, 1 mg/kg from ref 6, and 2.6 mg/kg from ref 7. Reprinted with permission from ref 1. Copyright 2008 American Chemical Society.
contention that genotoxic agents can have a threshold by showing that these agents have either flat (for acrylamide) or U-shaped dose-response relationships (EO and MMS) for some biomarkers of effect. In the first example, acrylamide, Swenberg et al. (1) conclude that examination of the data suggests that only doses of 6 mg/kg and higher resulted in a significant increase in micronuclei (MN) formation and that 1 or 3 mg/kg was not different from the controls; however, AbramssonZetterberg (4), the original authors of the study, conclude that acrylamide has a linear nonthreshold effect on micronuclei formation (denoted by micronucleated polychromatic erythrocytes in Figures 1 and 2) based on two independent experiments. Swenberg et al. (1) uses graphic representation (Figure 7 in Swenberg et al. and Figure 2 in this letter) to support a threshold effect for acrylamide. The purpose of showing the figure is to
This article not subject to U.S. Copyright. Published 2009 by American Chemical Society. Published on Web 12/03/2008
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Figure 3. Following is a plot of the same data set but in different Y-scales. It shows the illusionary effect of changing the Y-scale. On the basis of this observation, it is not surprising to observe a hockeystick type of graph if the slope is smaller at low doses than at high doses. A plot of the same data represented by y ) 1.13 + 0.014 × dose on two different Y-scales resulted in drastically different views.
demonstrate that exposure biomarker DNA adducts have a linear uprising curve but effect biomarkers (MN) for the three low doses are flat and thus support a threshold effect. This graphic presentation is illusionary because it has a larger Y-scale than needed. To make the point clear, the data points generated by Y ) 1.13 + 0.014 × dose for dose ) 0, 1, 2, and 3 mg/kg are plotted in Figure 3 in two different response scales. This equation is taken from Abramsson-Zetterberg (4) as given in the legend under Figure 1. In Figure 3, the upper panel (A), which has a Y-scale ranging from 1.12 to 1.18, clearly shows a linear relationship. However, as shown in the lower panel (B), the curve becomes flat when the response scale is extended slightly to range from 0 to 3, despite the fact that it represents the same set of data points. The reason for this illusionary effect is because the low-dose effect here is no more than 0.014 × 3 ) 0.042, which is very small as compared to the background effect of 1.13. It is not appropriate to establish a threshold by showingstatisticalnonsignificancealonebecauseeverydose-response function will eventually cease to be statistically different from the control group when the dose goes low enough. Endogenous production of EO through metabolism of ethylene and its role in induction of mutations (and or DNA adduct formation) has been a topic of debate in recent years. The efficiency/fidelity of DNA repair and its function in eliminating adducts formed have also been discussed. It is generally expected that there is a linear relationship for DNA adducts and mutations with an exception when chemically induced
adducts are identical to those arising endogenously. The hypothesis is that, at low doses, the biological effects of de minimus exposure below endogenous amounts will be lost in background noise, resulting in nonlinear dose-response relationships. The authors do not state explicitly what these points imply, but it appears that the purpose of highlighting them is to establish the existence of threshold due to concurrent background effects. This is depicted in Figure 9 of Swenberg et al. (1), which does not have a reference and is misleading. The bottom panel (which is the same as the first few data points of the top panel) is equivalent to EO dose conversions as compared to direct exposure to EO. Furthermore, Nivard et al. (8) acknowledge that the recessive lethal mutant frequencies were not significantly different in either nucleotide excision repair (NER)-proficient or NER-deficient females (8). The issue of linearity is distinct from the issue of which response (or biomarker) levels are significant. It is quite possible to have a completely linear dose-response, starting at the origin, where measures of response at one response unit or below cannot statistically be differentiated from the noise in the measurement method and/or sample size. The fact that there are measurements below the level of significance does not make that portion of the dose-response nonlinear. Those data are consistent with a linear dose-response connecting to the origin with the higher, statistically significant dose-response data. The presence of a nonzero background does not necessarily give rise to a situation where the applied dose-response is nonlinear. One can have a dose response that is linearly additive to the background. In the lower panel of Figure 9 from ref 1, if one extends the line from higher doses back to zero applied exposure, the value is about 1.2 × 106 Hprt, and this appears to be not significantly different from the controls. Thus, it appears that a linear regression fit to this data set, from 100 ppm down, would describe the data quite well, indicating a linear, additive-to-background, dose response. The following arguments have been used as a basis to support threshold effects in Swenberg et al. (1): (1) damaged genetic effects can be repaired, (2) there exists an exposure concentration that is not statistically significant from the background rate of endogenous formation of the chemical, and (3) the dose-response curve is either U-shaped or flat at low doses. These arguments alone are not sufficient to justify threshold effects. Because they are all statistical considerations, therefore, they should be subjected to normal statistical scrutiny. It is not appropriate to argue that a threshold must exist by mere consideration of the damage-repair mechanism. Unless the repair mechanism is always perfect (with a probability 1 of complete repair), whenever damages occur, some damages would still remain; thus, threshold cannot be guaranteed. It should also be noted that observing a U-shaped doseresponse curve does not automatically lead to the conclusion of a threshold because the chemically induced effects at low doses could be easily lost in the background noise. Although this concept is recognized by Swenberg et al. (1), their leap to the conclusion of threshold effect can not be logically supported. In fact, it is contrary to what is intended to conclude. As demonstrated in Table 1, under the assumption of randomness, if a chemically induced effect at low doses is relatively small in comparison to the background rate, then the probability of observing a U-shaped dose-response curve is high by randomness alone. Table 1 shows the probability of observing a “dip” of the dose-response curve when the frequency of background mutation defect ranges from 2 per 10+8 to 100 per 10+8 vs exposure-induced effect of 0.001 per 10+8 (i.e., actual rate of
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Table 1. Probability That Response (Mutation Frequency, 7_Me-Gua/108 Nucleotides) from the Exposure Group Is Less Than the Background Rate, Assuming That the Exposure-Induced Rate Increases by 0.001 Me-Gua/108 Nucleotides background, Me-Gua/ 108 nucleotides
probability that the rate for the exposed group is less than the background rate, under the assumption of randomness
2 20 30 100
0.397 0.4679 0.4741 0.4865
2.001 per 10+8 if the background rate is 2 per 10+8). There is almost a 40% chance of observing a “dip” when the background rate is 2 per 10+8, and additional risk due to exposure is 0.001 per 10+8. Just as Swengberg et al. (1) and Lutz and Gaylor (2) appropriately note that at a sufficiently low dose, it is difficult to statistically conclude whether or not a dose response is nonlinear and one needs to exercise caution when faced with low-dose data. Disclaimer: The views expressed in this letter are those of the authors and do not represent the policies of the U.S. Environmental Protection Agency.
References (1) Swenberg, J. A., Fryar-Tita, E., Jeong, Y., Boysen, G., Starr, T., Walker, V. E., and Albertini, R. J. (2008) Biomarkers in toxicology and risk assessment: Informing critical dose-response relationships. Chem. Res. Toxicol. 21, 253–265. (2) Lutz, W. K., and Gaylor, D. W. (2008) Letter to the Editor. Chem. Res. Toxicol. 21, 971–972. (3) Starr, T. B., and Swenberg, J. (2008) Letter to the Editor (response). Chem. Res. Toxicol. 21, 972–973. (4) Abramsson-Zetterberg, L. (2003) The dose-response relationship at very low doses of acrylamide is linear in the flow cytometer-based mouse micronucleus assay. Mutat. Res. 535, 215–222. (5) Twaddle, N. C., Churchwell, M. I., McDaniel, L. P., and Doerge, D. R. (2004) Autoclave sterilization produces acrylamide in rodent diets: Implications for toxicity testing. J. Agric. Food Chem. 52, 4344–4349. (6) Tareke, E., Twaddle, N. C., McDaniel, L. P., Churchwell, M. I., Young, J. F., and Doerge, D. R. (2006) Relationships between biomarkers of exposure and toxicokinetics in Fischer 344 rats and B6C3F1 mice administered single doses of acrylamide and glycidamide and multiple doses of acrylamide. Toxicol. Appl. Pharmacol. 217, 63–75. (7) Young, J. F., Luecke, R. H., and Doerge, D. R. (2007) Physiologically based pharmacokinetic/pharmacodynamic model for acrylamide and its metabolites in mice, rats, and humans. Chem. Res. Toxicol. 20, 388– 399. (8) Nivard, M. J. M., Czene, K., Seerback, D., and Vogel, E. W. (2003) Mutagenic activity of ethylene oxide and propylene oxide uncer XPG proficient and deficient conditions in relation to N-7 (2-hydroxyalkyl)guanine levels in Drosophila. Mutat. Res. 529, 95–107.
Nagu Keshava* Chao Chen Paul White Bob Sonawane and David Bussard National Center for Environmental Assessment Office of Research and Development U.S. Environmental Protection Agency Washington, DC, 20460 TX800356T
Response to Keshava et al. (2008) To the Editor: We are pleased to see that U.S. EPA risk assessors (1) read our perspective in Chemical Research in Toxicology (2) and correctly restated the paper’s central theme. We are disappointed with their old arguments that everything is still linear at low doses and that signal-to-noise issues make testing biological hypotheses “unrealistic”. Our concerns are not necessarily focused on low-dose linearity vs thresholds in the dose response; however, we do believe that threshold dose responses are certainly possible. More and more data are coming forward that support such possibilities. We demonstrated clearly for several chemicals that upward inflections were present in mutation data at high doses, while at low doses, the data were essentially flat at background values. This was not the case for biomarkers of exposure such as DNA adducts or globin adducts, unless there was saturation of DNA repair or detoxication. Keshava et al. (1) challenge the data that we highlighted on acrylamide (see their Figure 2), using different data from another single injection experiment described in the same original report (3). These data, depicted in their Figure 1, were obtained with only two mice per dose group and were reported only as group means, without individual values, a range, or other pertinent statistical information beyond limited results from two linear regressions. Note that the predictions from one of these regressions (see their Figure 3) are very different from the data depicted in their Figure 2 (our original Figure 7). The Abramsson-Zetterberg data that we chose to highlight were obtained using three mice per group; standard deviations, as well as two-tailed Student’s t test results also reported in their publication (3) are shown in Figure 1. These data show an essentially zero slope for the two lowest nonzero doses. Nevertheless, Keshava et al. (1) suggest that these data would also be linear if we had just scaled the y-axis so that a much smaller range of micronucleus frequency data was graphed. That is not true. As shown in Figure 1, we replotted the data using such a range but now also including the 6 mg/kg data point that was significantly positive with a slope that continues on at higher doses. It is clear that such scaling does not alter our conclusion. Furthermore, Keshava et al.’s Figure 3, which shows two different plots, with different vertical scales, of the straight line that Abramsson-Zetterberg (3) fit to the two mouse per group data over the 17.5-100 mg/kg dose range, is merely a circuitous restatement of the obvious, namely, that one can never prove the negative. Additional similar data are now in press (4) from 28 day acrylamide exposures of 10 mice per group with 11 dose groups ranging from 0.125 to 24 mg/kg/day. No significant increases in MN-RET or MN-NCE were present as compared to controls below 4 mg/kg/day. Both linear and nonlinear models fit the data, demonstrating that if one includes the high dose information, it is hard to distinguish linear from nonlinear responses. Our point is that one must look critically at the low-dose information. The concurrent controls (N ) 10) averaged 1.40 ( 0.07, as compared to historical controls (N ) 80) whose mean was 1.45 ( 0.09. The low-dose data up to 4 mg/kg are plotted in Figure 2 of Zeiger et al. (4). Their plot has a linear-linear scale that demonstrates the same conclusion that we reached previously with much larger numbers of mice per dose and doses examined. Keshava et al. (1) claim that Figure 9 from Swenberg et al. (2) is “misleading”. We take issue with this statement. This figure shows high-dose data from a report of mutagenic responses at the Hprt locus of T-cells following 4 weeks of
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Figure 1. Data from our original Figure 7 rescaled to a range from 1.05 to 1.45 on the y-axis for fMPC and up to 6 mg/kg on the x-axis to demonstrate the threshold responses for 1 and 3 mg/kg as compared to the clear upward slope of doses from 6 mg/kg and higher in Abramsson-Zetterberg (3).
inhalation exposure of mice to 0, 50, 100, or 200 ppm EO (5) and low-dose Hprt mutant frequency data plotted against EO exposure estimates based on internal dose of EO-induced biomarkers of exposure (i.e., average N7-HEG DNA adduct concentrations across three tissues, brain, lung, and spleen) following 4 weeks of exposure of mice to ethylene (6) or EO (7). These EO dose conversions are consistent with theoretical EO exposure estimates in rodents using a closed ethylene exposure system and pharmacokinetic models (8, 9). The purpose of highlighting the Hprt mutant frequency data in Figure 9 was not “to establish existence of threshold due to concurrent background effect” but simply to show that combining information from two previously published mutation studies in EO- and ethylene-exposed mice suggests that the exogenous EO-induced mutagenic responses are nonlinear. This possibility needs further investigation using large sample sizes along with sensitive methods and mutational targets with low background mutant frequencies in both mice and rats exposed directly to EO over an expansive range of exposure levels in the low-dose region. Finally, Keshava et al. (1) state that Nivard et al. (10) “acknowledge that the recessive lethal mutant frequencies were not significantly different in either nucleotide excision repair (NER)-proficient or NER-deficient females.” In the abstract of this paper, the authors state “Furthermore, at the higher dose range of EO tested, 62.5-1000 ppm, up to 20-fold enhanced mutation rates were measured in the absence of maternal nucleotide excision repair (NER) compared to repair proficient conditions.” We had discussed this paper in our Perspective, as it clearly shows nonlinear and threshold dose responses for EO and PO. Nivard et al. (10) actually plot the mutation data on the y-axis and 7-alkylguanine adducts on the x-axis to demonstrate the hockey stick dose response. It is unclear to us how Keshava et al. (1) could possibly misread this hallmark paper. Keshava et al. (1) go on to suggest in their last paragraph that threshold-like responses are the effects of “randomness alone” even though multiple independent examples have shown similar effects. They try to support this position with their Table 1, which describes “mutation frequency” using N7MeG adduct data. N7MeG is not a mutation frequency; it is a biomarker of exposure that reflects exposure at the critical molecular target. Our Perspective clearly pointed out that a DNA adduct is not a mutation, although, in some instances, an adduct may be the first step in the process of mutagenesis. However, the production
of a mutation is controlled by the cell, as illustrated in Figure 4 in our paper (2). Furthermore, to compare rigorously an exposure-induced increment in micronuclei to a corresponding background level, one needs a measure of the variability (or uncertainty) of the estimated background rate, which Keshava et al. (1) did not provide. The last section of Keshava et al.’s (1) response that equates adducts with mutations is incomprehensible. In our Perspective, we stated that more research on low-dose mutagenesis is needed. In fact, such studies continue to be completed. Comprehensive data sets on dose responses for chromosome and gene mutations in mice exposed to EMS (also known as ethyl mesilate) and ENU were recently presented at the XVII International AIDS Conference (11) and 38th European Environmental Mutagen Society Meeting (12). The data for EMS provide evidence for thresholds for induction of micronuclei and lacZ transgene mutations, in contrast to linear DNA adducts. The European Medicines Agency concluded that these studies “showed that it is possible to calculate a threshold value below which ethyl mesilate does not cause any irreversible damage (mutations) in the DNA” (http://www.emea.europa.eu/ humandocs/PDFs/EPAR/Viracept/38225608en.pdf). Biomarker data are not the only data suggesting that the present day default of low-dose linear extrapolation for cancer risks is overly conservative. Actual cancer data, not extrapolations, support this conclusion. The largest cancer bioassay in the world used 42000 trout exposed to dibenzo[a,l]pyrene (13). This study is 50 times more sensitive than conventional rodent cancer bioassays. Actual liver tumor data from this study demonstrated that the standard EPA linear model overestimated liver cancer at low doses by 3 orders of magnitude. There is a critical need to replace default approaches with science-based risk assessment. Our perspective was meant to stimulate movement in this direction. In fact, the U.S. EPA 2005 Cancer Risk Assessment Guidelines clearly state that biologically based models are preferred. We are in total agreement with this statement.
References (1) Keshava, N., Chen, C., White, P., Sonawane, B., and Bussard, D. (2009) Biomarkers in toxicology and risk assessment: Informing critical dose-response relationships by Swenberg et al., 2008. Chem. Res. Toxicol. 22, 8–10. (2) Swenberg, J. A., Fryar-Tita, E., Jeong, Y. C., Boysen, G., Starr, T., Walker, V. E., and Albertini, R. J. (2008) Biomarkers in toxicology and risk assessment: Informing critical dose-response relationships. Chem. Res. Toxicol. 21, 253–265. (3) Abramsson-Zetterberg, L. (2003) The dose-response relationship at very low doses of acrylamide is linear in the flow cytometer-based mouse micronucleus assay. Mutat. Res. 535, 215–222. (4) Zeiger, E., Recio, L., Fennell, T. R., Haseman, J. K., Snyder, R. W., and Friedman, M. (2008) Investigation of the low-dose response in the in ViVo induction of micronuclei and adducts by acrylamide. Toxicol. Sci. (5) Walker, V. E., Sisk, S. C., Upton, P. B., Wong, B. A., and Recio, L. (1997) In ViVo mutagenicity of ethylene oxide at the hprt locus in T-lymphocytes of B6C3F1 lacI transgenic mice following inhalation exposure. Mutat. Res. 392, 211–222. (6) Walker, V. E., Wu, K. Y., Upton, P. B., Ranasinghe, A., Scheller, N., Cho, M. H., Vergnes, J. S., Skopek, T. R., and Swenberg, J. A. (2000) Biomarkers of exposure and effect as indicators of potential carcinogenic risk arising from in ViVo metabolism of ethylene to ethylene oxide. Carcinogenesis 21, 1661–1669. (7) Wu, K. Y., Ranasinghe, A., Upton, P. B., Walker, V. E., and Swenberg, J. A. (1999) Molecular dosimetry of endogenous and ethylene oxideinduced N7-(2-hydroxyethyl) guanine formation in tissues of rodents. Carcinogenesis 20, 1787–1792. (8) Bolt, H. M., and Filser, J. G. (1987) Kinetics and disposition in toxicology. Example: Carcinogenic risk estimate for ethylene. Arch. Toxicol. 60, 73–76.
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(9) Csandy, G. A., Denk, B., Py¨tz, C., Kreuzer, P. E., Kessler, W., Baur, C., Gargas, M. L., and Filser, J. G. (2000) A physiological toxicokinetic model for exogenous and endogenous ethylene and ethlyene oxide in rat, mouse, and human: Formation of 2-hydroxyethyl adducts with hemoglobin and DNA. Toxicol. Appl. Pharmacol. 165, 1–26. (10) Nivard, M. J. M., Czene, K., Segerbsˇck, D., and Vogel, E. W. (2003) Mutagenic activity of ethylene oxide and propylene oxide under XPG proficient and deficient conditions in relation to N-7-(2-hydroxyalkyl)guanine levels in Drosophila. Mutat. Res. 529, 95–107. (11) Muller, L., Gocke, E., Larson, P., Lave, T., and Pfister, T. (2007) Elevated ethyl methanesulfonate (EMS) in nelfinavir mesylate (Viracept, Roche): Animal studies confirm toxicity threshold and absence of risk to patients. (12) Gocke, E., Muller, L., Pfister, T., and Lave, T. (2008) Risk assessment of genotoxic impurities: The case of EMS. p 97. (13) Williams, D. E., Bailey, G. S., Reddy, A., Hendricks, J. D., Oganesian, A., Orner, G. A., Pereira, C. B., and Swenberg, J. A. (2003) The rainbow trout (Oncorhynchus mykiss) tumor model: Recent applications in low-dose exposures to tumor initiators and promoters. Toxicol. Pathol. 31 (Suppl.), 58–61.
James A. Swenberg* Environmental Sciences and Engineering University of North CarolinasChapel Hill CB 7431 Chapel Hill, North Carolina 27599
Thomas B. Starr TBS Associates 7500 Rainwater Road Raleigh, North Carolina 27615 Richard J. Albertini Genetic Toxicology Laboratory 665 Spear Street Department of Pathology University of Vermont Burlington, Vermont 05405 Vernon E. Walker BioMosaics, Inc. 655 Spear Street Building C Burlington, Vermont 05405 TX800438G
