# Research 

# | **Article**

Methods for evaluating risks from exposure to toxic substances for noncancer health end points (such as birth defects, respiratory effects, and hepatotoxicity) are based on the theory that there is a threshold below which there is negligible risk of adverse health effects from environmental exposures. At the U.S. Environmental Protection Agency (U.S. EPA), this negligible risk is quantified through the use of reference doses (RfDs) and reference concentrations (RfCs). The RfD or RfC is defined as an estimate of daily or continuous exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime (U.S. EPA 1999a). The value of the RfD or RfC is derived by determining a point of departure divided by uncertainty factors(UFs), which are used to account for uncertainties in the available studies, such as limitations in the database, variability within humans, and differences in species response (i.e., animal-to-human extrapolation). The point of departure in environmental health risk assessment is meant to represent the lowest dose within the range of experimental data. In past practices, the point of departurewas exclusively based on a no-observedadverse-effect level (NOAEL) or a lowestobserved-adverse-affect level (LOAEL), derived from animal or epidemiologic studies. The NOAEL is the highest dose for which there areno observed statistically or biologically significant increases in the frequency or severity of adverse effects between the exposed population and its control. Similarly, the LOAEL is the lowest dose at which there are statistically orbiologically significant increases in the frequency or severity of adverse effects between the exposed population and its appropriatecontrol group. The NOAEL/LOAEL structure, however, does not provide sufficientinformation to quantify the equivalent risk levels from exposure at the RfD/RfC because there is no estimated risk at the NOAEL or LOAEL itself. Several authors have criticized the use of the NOAEL because of its sensitivity to sample size, the high sampling variability from experiment to experiment, and the inability to use all of the available dose– response data (Barnes et al. 1995; Crump 1984; Gaylor et al. 1998; Leisenring and Ryan 1992; U.S. EPA 2000a). Leisenring and Ryan (1992) have shown that average risk levelsassociated with the NOAEL may be substantial. The true risk of exposure at the NOAEL can vary from zero to > 20%, depending on the end point, spacing of doses, and numbers of animals used (Leisenring and Ryan 1992). In many cases, an adverse effect may not be detected in a critical effect study because of insufficient statistical power. Although the NOAEL/LOAEL structure does not provide sufficient information to quantify risk levels from exposure (Gaylor and Kodell 2000), the resulting RfDs andRfCs are assumed to be equivalent to negligible or de minimis risks. As a point of comparison, the U.S. EPA has defined 1 in 1,000,000 excess cancer risk as a de minimis risk level for cancer (Caldwell et al. 1998; Clean Air Act Amendments 1990; Fiori and Meyerhoff 2002; U.S. EPA 1991), although regulatory actions are sometimes limited to instances where risk exceeds 1 in 100,000. Over the past several years, the U.S. EPA has been in the process of developing the benchmark dose (BMD), which is derived from modeling the exposure–response data, as an alternative to the NOAEL/LOAEL as thepoint of departure for noncancer risk assessments. The BMD is the dose that corresponds to a specified level of increased response [the benchmark response (BMR)] compared with background. This dose is calculated by fitting a mathematical model to the dose–response data, which can be continuous or quantal. The BMD method has several advantages overthe NOAEL/LOAEL method, including making better use of dose–response information and reflecting sample size more appropriately (Barnes et al. 1995; Crump 1995). In singlechemical hazard assessments, the BMD allows for consideration of the dose–responseover the entire exposure range, and furthermore, when a dose derived from benchmark modeling is used as the point of departure,actual risk levels can be calculated as an alternative to the hazard index (which is typically based on comparisons of human exposures with an RfD or RfC). 

# Assessment of Potential Risk Levels Associated with U.S. Environmental Protection Agency Reference Values

Rosemary Castorina 1,2 

### and Tracey J. Woodruff2

**1 Center for Children’s Environmental Health Research, School of Public Health, University of California, Berkeley, California, U**  **SA;**

**2 U.S. Environmental Protection Agency, Washington, DC, USA** 

Address correspondence to T.J. Woodruff, Public Health and Environmental Policy Team, National Center for Environmental Economics, U.S. EPA, 75 Hawthorne St., MC SPE-1, San Francisco, CA 94105 USA. Telephone: (415) 947-4277. Fax: (415) 947- 3519. E-mail: Woodruff.Tracey@epamail.epa.gov

We thank D. Axelrad, V.J. Cogliano, J. Gift, J. Jinot, and T. McKone for their expert technical
assistance and advice.

This work was supported in part by grants from the
U.S. EPA and the National Institute of Environmental Health Sciences (R826709 and ES09605). The views expressed in this article are those of the authors and do not necessarily reflect those of the U.S. EPA.

The authors declare they have no conflict of interest. Received 2 January 2003; accepted 12 May 2003. 

### The U.S. Environmental Protection Agency (U.S. EPA) generally uses reference doses (RfDs) or reference concentrations (RfCs) to assess risks from exposure to toxic substances for noncancerhealth end points. RfDs and RfCs are supposed to represent lifetime inhalation or ingestion exposure with minimal appreciable risk, but they do not include information about the estimated risk from exposures equal to the RfD/RfC. We used results from benchmark dose modeling approaches recently adopted for use in developing RfDs/RfCs to estimate the risk levels associated with exposures at the RfD/RfC. We searched the U.S. EPA Integrated Risk Information System (IRIS) database and identified 11 chemicals with oral RfDs and 12 chemicals with inhalation RfCs that used benchmark dose modeling. For assessments with sufficient model information, we found that 16 of 21 (76%) of the dose–response models were linear or supralinear. We estimated the risk from exposures at the established RfDs and RfCs for these chemicals using a linear dose–response curve to characterize risk below the observed data. Risk estimates ranged from 1 in 10,000 to 5 in 1,000 for exposures at the RfDs, and from 1 in 10,000 to 3 in 1,000 for exposures at the RfCs. Risk estimates for exposures at the RfD/RfC values derived from sublinear dose–response curvesranged from 3 in 1,000,000,000 to 8 in 10,000. Twenty-four percent of reference values corresponded to estimated risk levels greater than 1 in 1,000; 10 of 14 assessments had points of departure greater than the no-observed-adverse-effect levels. For policy development regarding management of cancer risks, the U.S. EPA often uses 1 in 1,000,000 as a de minimis risk level. Although noncancer outcomes may in some instances be reversible and considered less severe thancancer, our findings call into question the assumption that established RfD and RfC values represent negligibly small risk levels. Key words: benchmark dose, noncancer, risk assessment. Environ Health Perspect 111:1318–1325 (2003). doi:10.1289/ehp.6185 available via http://dx.doi.org/ [Online 12 May 2003]



### 1318 VOLUME 111 | NUMBER 10 | August 2003 • 

### Environmental Health Perspectives

### In 2000, the U.S. EPA published a draft guidance document on the application of the BMD approach in determining the point of departure for all types of health effects data (U.S. EPA 2000a). Although a BMR of 10% has most often been used by the U.S. EPA in its assessments, it is anticipated that 5% or 1% would be a more appropriate response level for some end points and designs. Furthermore, inthese draft guidelines, a lower statistical confidence limit on the BMD (BMDL) is specifically proposed as a replacement for theNOAEL/LOAEL in setting the point of departure, which is used to determine acceptable levels of human exposure to environmental toxicants. A lower confidence limit is placed on the BMD to obtain a dose (BMDL) that assures with high confidence (e.g., 95%) that the BMR is not exceeded (U.S. EPA 2000a). In addition to ensuring an added measure ofprotection, this process rewards better experimental design and procedures that provide more precise estimates of the BMD. Most new and revised RfDs and RfCs in the U.S. EPA Integrated Risk Information System (IRIS) assessments are based on BMDmodeling. Certain health end points, however, are not amenable to modeling, and the NOAEL/LOAEL approach will continue to be used in some cases (U.S. EPA 2000a). For this article, we have reviewed and synthesizedcurrently available risk assessment information on the chemicals for which U.S. EPA reference values are based on BMD modeling. We estimate the equivalent risk levels expected from hypothetical human exposures at established RfD and RfC values using theBMD dose–response information to investigate whether these levels represent negligible risks, and to underscore some of the potential strengths of using benchmark modeling in environmental health risk assessment.



## Methods

We searched the U.S. EPA IRIS database (U.S. EPA 2000b) to identify the chemicals for which current regulatory reference values reliedon BMD modeling. We identified 11 chemicals with RfDs based on oral BMD values, and 12 chemicals with RfCs based on benchmark concentration (BMC) values. The BMD and

Environmental Health Perspectives • VOLUME 111 | NUMBER 10 | August 2003 

### 1319 Table 1. Risk assessment information for chemicals with oral RfDs derived from benchmark modeling. a

NOAEL LOAEL BMD 10 BMDL 10 BMD 05 BMDL 05 RfD Chemical (mg/kg/day) (mg/kg/day) (mg/kg/day) (mg/kg/day) (mg/kg/day) (mg/kg/day) BMD/BMDL UF (mg/kg/day)

Quantal end point

Beryllium and compounds (U.S. EPA 1998a) 0.1 — 1.4 0.46 b

— — 3.0 300 0.002 Chloroform c (U.S. EPA 2001b) — 15 (12.9 ADJ ) 1.7 (1.5ADJ ) 1.2 (1.0 ADJ ) b

— — 1.4 100 0.01 1,1-Dichloroethylene (U.S. EPA 2002a) 9 14 6.6 4.6 b

— — 1.4 100 0.05 1,3-Dichloropropene (U.S. EPA 2000c) 2.5 — 5.1 3.4 b

— — 1.5 100 0.03 Hexachlorocyclopentadiene (U.S. EPA 2001d) — 10 (7 ADJ ) 11 6 b

— — 1.8 1,000 0.006 Continuous end point

Benzene d (U.S. EPA 2003) — 1.2 2.2 1.2 b

— — 1.8 300 0.004 EGBE e (U.S. EPA 1999b) — 59 — — 7.7 HED 5.1 HED b 1.5 10 0.5 Methylmercury (U.S. EPA 2001a) — — — — 1.4–2.6E–03 0.9–1.5E–03 b 1.6 10 0.0001 Naphthalene f (U.S. EPA 1998i) 100 (71 ADJ ) b 200 (143 ADJ ) 171(122 ADJ ) 130 (93 ADJ ) — — — 1.3 3,000 0.02
Phenol g (U.S. EPA 2002c) 60 — 157 93 b

— — 1.7 300 0.3 Tributyltin oxide (U.S. EPA 1997a) 0.025 — 0.05 0.034 b

— — 1.5 100 0.0003

Abbreviations: ADJ, adjusted for duration of exposure; BMD 10, BMD that equals a BMR of 10%; BMDL 10, lower confidence limit on the BMD 10; BMD 05, BMD that equals a BMR of 5%;
BMDL05, lower confidence limit on BMD 05; C max, peak blood concentration; EGBE, ethylene glycol monobutyl ether; HED, human dose of an agent that is believed to induce the s ame magnitude of toxic effect as the experimental animal species concentration or dose (this adjustment may incorporate toxicokinet ic information on the particular agent, if available, or use a default procedure, such as assuming that daily oral doses experienced for a lifetime are proportional to body weight rais ed to the 0.75 power).

a The reported number of significant figures is not standardized in IRIS. bPoint of departure. cRfD derived from the LOAEL (1,000-fold UF) = 0.01 mg/kg/day; BMD-based RfD (100-fold UF) = 0.01 mg/kg/day. d Oral BMDL[ADJ] was derived from the BMCL[ADJ] (8.2 mg/m 3 ) by route-to-route extrapolation with the assumptions that inhalation absorption was 50% and oral absorption was 100% in the dose range near the BMC; BMDL (adjusted for continuous exposure) = (8.2 mg/m 3 × 20 m 3 /day × 0.5)/70 kg = 1.2 mg/kg/day; former RfD derived from the LOAEL (1,000-fold UF) =
0.001. eHED was calculated as follows: using C max for 2-butoxyacetic acid as the dose metric, the BMDL05 was determined to be 64 μM; physiologically based pharmacokinetic modeling was used to “back-calculate” a human equivalent dose of 5.1 mg/kg/day, assuming that rats and humans receive their entire dose of E GBE from drinking water over a 12-hr period each day.

f RfDs were based on the NOAEL and BMD 10 values adjusted for duration (e.g., BMD × 5/7 days); the RfD in IRIS is derived from the NOAEL (3,000-fold UF) = 0.02 mg/kg/day; prospective RfD derived from BMD (3,000-fold UF) = 0.03 mg/kg/day. gBMD was based on a benchmark response of a 1-standard-deviation change from the control mean.

Table 2. End points and UFs for chemicals with oral RfDs derived from benchmark modeling.

Composite UF a Chemical Reference End point: quantal or continuous UF Interspecies Intraspecies Subchronic Database ELE

Beryllium and compounds U.S. EPA 1998a Quantal (small intestinal lesions) 300 10 10 — 3 — Chloroform U.S. EPA 2001b Quantal (fatty cyst formation in liver and elevated 100 10 10 — — — serum glutamate-pyruvate transaminase) 1,1-Dichloroethylene U.S. EPA 2002a Quantal [liver toxicity (fatty change)] 100 10 10 — — — 1,3-Dichloropropene U.S. EPA 2000c Quantal (chronic irritation of stomach) 100 10 10 — — — Hexachlorocyclopentadiene U.S. EPA 2001d Quantal (chronic irritation of stomach) 1,000 10 10 3 b 3 b

— Benzene U.S. EPA 2003 Continuous (decreased lymphocyte count) 300 — 10 3 3 3 EGBE U.S. EPA 1999b Continuous (changes in mean corpuscular 10 — 10 — — — volume) Methylmercury U.S. EPA 2001a Continuous (developmental neuropsychologic 10 — 10 — — — impairment) Naphthalene U.S. EPA 1998i Continuous (decreased mean terminal body 3,000 10 10 10 3 — weight in males) Phenol U.S. EPA 2002c Continuous (decreased maternal body 300 10 10 — 3 — weight gain) Tributyltin oxide U.S. EPA 1997a Continuous (immunosuppression; 100 10 10 — — — decrease in IgE titer)

Abbreviations: EGBE, ethylene glycol monobutyl ether; ELE, effect level extrapolation factor.

a Interspecies extrapolation, intraspecies differences (human variability), subchronic-to-chronic extrapolation, database deficie ncies, and ELE. bUFs assigned a value of 10 1/2 were rounded to 3. 

### Article 

## | Reference values and risk

### BMC values and other risk assessment information for these chemicals are presented in Tables 1–4 (U.S. EPA 1995a, 1995b, 1995c, 1995d, 1997a, 1998a, 1998c, 1998e, 1998g, 1999b, 2000c, 2001a, 2001b, 2001d, 2002a, 2002c, 2002e, 2003). We have included risk assessment information for naphthalene, a chemical with an IRIS assessment containing an established RfD based on a NOAEL (RfD = 0.02 mg/kg/day) as well as a prospective RfD based on BMD modeling (RfD = 0.03 mg/kg/day) (U.S. EPA 1998i). We determined whether the principal study identified for each chemical’s IRIS assessment was derived from quantal (dichotomous) or continuous critical effect data (Tables 2 and 4). For compounds with BMD or BMC values based on quantal data, the BMR is expressed in terms of a percent increase in risk of adverseoutcome compared with background. For compounds with BMD or BMC values based on continuous data, the BMR may be expressed as a percent change in mean response compared with control (e.g., immunosuppression with tributyltin oxide) or in terms of a 1 standard deviation change from the control mean response (e.g., decreased lymphocyte count with benzene). The BMR is a response level used to define a BMD, which is used as the point of departure, from which an RfD or RfC can be developed. The BMR is typically set at the lower end of the range of responses (e.g., 10% or 5% change) that can be detected experimentally. This can help to avoid uncertainties associated with low-dose extrapolation using models that may not reflect biologic realities (Crump 1995).Using the benchmark modeling information described above from the IRIS assessments of 19 chemicals, we estimated the equivalent risk levels expected from hypothetical human exposures at the established RfDs and RfCs. We also evaluated whether each of the modelsused for BMD calculations was linear, sublinear, or supralinear in the observed dose range (Table 5) [National Research Council (NRC) 2000; U.S. EPA 1995a, 1995b, 1995c, 1995d, 1997b, 1998b, 1998c, 1998d, 1998f, 1998h,



### 1320 VOLUME 111 | NUMBER 10 | August 2003 • 

### Environmental Health Perspectives Table 3. Risk assessment information for chemicals with RfCs derived from benchmark modeling. a

BMC/ Chemical NOAEL LOAEL BMC 10 BMCL 10 BMCL 10[ADJ] BMCL 10[HEC] BMC 05 BMCL 05 BMCL UF RfC

Quantal end point Antimony trioxide b (U.S. EPA 1995b) 0.51 (0.42 HEC ) — 1.43 0.87 0.16 0.074 c

— — 1.6 300 0.0002 1,3-Butadiene (U.S. EPA 2002e) — 2.5 2.25 1.98 c

— — — — 1.1 1,000 0.002 1,1-Dichloroethylene (U.S. EPA 2002a) 99.2 (17.7 HEC ) 297.8 (53.2 HEC ) 59.95 38.9 — 6.9 c

— — 1.5 30 0.2 1,3-Dichloropropene (U.S. EPA 2000c) 3.7 ADJ 14.9 ADJ 5.91 ADJ — 3.66 0.72 c

— — 1.6 30 0.02 Methyl methacrylate (U.S. EPA 1998e) 102.4 (18.2 ADJ ) — 178.7 143 25.6 7.2 c

— 104.6 1.2 10 0.7 Methylene diphenyl diisocyanate 0.2 (0.036ADJ ) 1.0 (0.18ADJ ) 0.22 ADJ — 0.14 0.06 c

— — 1.6 100 0.0006 (U.S. EPA 1998g) Phosphoric acid (U.S. EPA 1995d) 50 (2.7 ADJ ) 180 150 100 5.4 3.4c 112 64 1.5 300 0.01 1,1,1,2-Tetrafluoroethane (U.S. EPA 1995a) 7,450 HEC 37,250 HEC — 46,000 8,200 8,200 c

— —— 100 80 Continuous end point

Benzene d (U.S. EPA 2003) — 8.7 ADJ 43.77 23 8.2 c

— — — 1.9 300 0.03 Carbon disulfide e (U.S. EPA 1995c) 15.9 (5.7 ADJ ) 39.2 (14.0 ADJ ) — 55.1 19.7 c

— — —— 30 0.7 Chromium VI (particulates) (U.S. EPA — 0.05 0.036 0.016 0.034c

— — — 2.3 300 0.0001

1998c) EGBE (U.S. EPA 1999b) — 150 HEC — —— — 530 HEC 380 HEC c 1.4 30 13

Abbreviations: ADJ, dose that has been adjusted for duration of exposure; BMC 10, BMC that equals a BMR of 10%; BMCL 10, lower confidence limit on the BMC 10; BMC 05, BMC that equals a BMR of 5%; BMCL05, lower confidence limit on BMC 05; HEC, human equivalent concentration [the human concentration (for inhalation exposure) of an agent that is believed to inducethe same magnitude of toxic effect as the experimental animal species concentration or dose; this adjustment may incorporate toxico kinetic information on the particular agent, if available, or use a default procedure, such as assuming that daily oral doses experienced for a lifetime are proportional to body weight rais ed to the 0.75 power]; MVh, human ambient default minute volume; MVho, human occupational default minute volume; RDDR, regional deposited dose ratio (the ratio of the regional deposite d dose calculated for a give exposure in the animal species of interest to the regional deposited dose of the same exposure in a human; this ratio is used to adjust the exposure e ffect level for interspecies dosimetric differences to derive a human equivalent concentration for particles).

a The reported number of significant figures is not standardized in IRIS. bPoint of departure is the BMCL 10[HEC] (BMCL 10[HEC] = BMC 10[ADJ] × RDDR = 0.074 mg/m 3

. cPoint of departure. dBMC was based on a benchmark response of a 1-standard-deviation change from the control mean; the BMCL is the statistical lower bound e stimate on the dose corresponding to a 1-standarddeviation change from control. eBMC is based on an 8-hr time-weighted average occupational exposure; its point of departure is BMCL 10[HEC] = 55.1 mg/m 3 × (MVho/MVh × 5/7 days) = 19.7 mg/m 3 . MVho = 10 m 3 /day; MVh = 20 m 3 /day.

Table 4. End points and UFs for chemicals with RfCs derived from benchmark modeling.

Composite UF a Chemical Reference End point: quantal or continuous UF Interspecies Intraspecies Subchronic Database ELE

Antimony trioxide U.S. EPA 1995b Quantal (pulmonary toxicity, chronic 300 3 10 3 3 — interstitial inflammation) 1,3-Butadiene U.S. EPA 2002e Quantal (ovarian atrophy) 1,000 3 10 — 3 10 1,1-Dichloroethylene U.S. EPA 2002a Quantal [liver toxicity (fatty change)] 30 3 10 — — — 1,3-Dichloropropene U.S. EPA 2000c Quantal (hyperplasia of nasal epithelium) 30 3 10 — — — Methyl methacrylate U.S. EPA 1998e Quantal (degeneration/atrophy of 10 3 3 — — — olfactory epithelium) Methylene diphenyl diisocyanate U.S. EPA 1998g Quantal (hyperplasia of the olfactory epithelium) 100 3 10 — 3 — Phosphoric acid U.S. EPA 1995d Quantal (bronchiolar fibrosis) 300 3 10 10 — — 1,1,1,2-Tetrafluoroethane U.S. EPA 1995a Quantal (Leydig cell hyperplasia) 100 3 10 — 3 — Benzene U.S. EPA 2003 Continuous (decreased lymphocyte count) 300 — 10 3 3 3 Carbon disulfide U.S. EPA 1995c Continuous (peripheral nervous system 30 — 3 — 10 — dysfunction) Chromium VI (particulates) U.S. EPA 1998c Continuous (lactate dehydrogenase 300 3 10 10 — — in bronchioalveolar lavage fluid) EGBE U.S. EPA 1999b Continuous (changes in red blood cell count) 30 — 10 — — 3

Abbreviations: EGBE, ethylene glycol monobutyl ether; ELE, effect level extrapolation factor.

a Interspecies extrapolation, intraspecies differences (human variability), subchronic-to-chronic extrapolation, database deficien cies, and ELE. 

### Article 

## | Castorina and Woodruff

### 1998j, 1999c, 2000d, 2001c, 2001e, 2002b, 2002d]). To estimate the risk level of the derived RfDs and RfCs, which have a linear or supralinear dose–response curve at the BMD/BMC, we assumed that the dose– response curves for these compounds are linear at doses below the BMD or BMC (Figure 1). For two assessments, sufficient information was not available to determine the shape of the dose–response curve (carbon disulfide and 1,1,1,2-tetrafluoroethane), and we assumed linearity [this assumption did not overly bias our results because linearity or supralinearity was the shape of the dose–response curve in approximately three-quarters (16 of 21) of cases in which the shape of the dose–responsecurve was discernable]. This assumption of linearity at the relevant part of the dose–response curve is necessary to extrapolate equivalent risk levels from U.S. EPA reference values derived from BMD modeling, and it is consistent withmethods proposed in the U.S. EPA draft cancer risk assessment guidelines (U.S. EPA 1999d). Such risk-level extrapolation is not possible using the NOAEL/LOAEL approach. Methods for risk-level estimation varied between reference values based on quantal end points and those based on continuous end points. For BMD/BMC values derived from quantal critical effect data, we estimated riskfrom exposure at concentrations equal to established RfD and RfC values by extrapolating linearly from the point represented by the BMR at the BMDL/BMCL to the established RfD and RfC values (Figure 1). We divided the risk at the BMR by the composite UF forthose BMD models that were linear or supralinear. For example, to estimate risk from exposure to chloroform’s RfC, we divided the estimated risk level at the point of departure (1 in 10 for BMR = 10%) by the composite UF of 100, to arrive at a risk of 1 in 1,000. For BMD/BMC values derived from continuous critical effect data (normally distributed), a change in response of 1 standard deviation from control is considered roughly equivalent to a 10% increase in risk of adverse response from exposure (e.g., benzene’s BMR = change of 1 standard deviation in lymphocyte count compared with control mean) (U.S. EPA 2000a, 2003). Therefore, when data quality and distribution allowed, we treated the dose that resulted in a 1-standard-deviation change from control as equivalent to BMD 10

### /BMC 10 

### (BMD that equals a BMR of 10%/BMC that equals a BMR of 10%) values derived fromquantal data. For assessments based on sublinear dose–response curves, we estimated risk ofexposure at the RfD/RfC dose levels by extrapolating the BMD model response function to the RfD/RfC (i.e., using the BMD model, we estimated risk by putting the exposure equal to the RfD/RfC in the model). For all chemicals in our assessment group with adequate data, we calculated the ratio of the central estimate (BMD or BMC) to the lower statistical confidence limit on the benchmark dose (BMDL) or concentration (BMCL) (Tables 1 and 3). This ratio (e.g., BMD/BMDL) provides a metric to compare the relative impact on estimated risk levels resulting from the selection of the BMD/BMC versus the BMDL/BMCL as the point of departure. Finally, among the studies thatidentified NOAELs, we compared the modeled BMDL and BMCL values with the empirical NOAELs as a means to investigate how using BMDL/BMCL values versus NOAELs compares with previous RfD/RfC methods based on the NOAEL approach.

Environmental Health Perspectives • VOLUME 111 | NUMBER 10 | August 2003 

### 1321 BMR a

Risk at RfD

RfD BMDL

or POD BMD

Dose (mg/kg/day)Risk level

Composite

UF Central estimate × × × ×

× Lower confidence

limit on BMD

Figure 1. Linear extrapolation of risk from exposure
at the RfD when the RfD is derived from benchmark modeling of quantal dose–response data. Assuming the relationship between risk of adverse outcome and dose is linear at low doses, we estimated riskfrom exposure at concentrations equal to established RfD and RfC values by extrapolating from the point represented by the BMR at the BMDL or BMCL to established RfD and RfC values. POD, point of departure.

a For quantal data, the BMR represents a percentage increase in risk compared with controls.

Table 5. Fitted BMD models and dose–response curve characterizations for 23 RfD/RfC assessments.

Shape of Chemical Reference value End point Fitted models a dose–response curve

Antimony trioxide (U.S. EPA 1995b) RfC Quantal Linear and Weibull b Linear EGBE (U.S. EPA 1999b, 1999c; NTP 1993, 1998) RfC Continuous Power model ( k = 0.95) Linear Methylene diphenyl diisocyanate (U.S. EPA 1998g, 1998h) RfC Quantal Polynomial regression ( β i = 0) c Linear Methylmercury (U.S. EPA 2001a; NRC 2000) RfD Continuous Power model ( k = 1) d Linear Phenol (U.S. EPA 2002c, 2002d) RfD Continuous Polynomial ( β 2 ≅ 0) e Linear Phosphoric acid (U.S. EPA 1995d) RfC Quantal Linear and Weibull b Linear Tributyltin oxide (U.S. EPA 1997a, 1997b) RfD Continuous Polynomial mean response ( β i = 0) c Linear Benzene (U.S. EPA 2003) RfD Continuous Log-linear Supralinear Benzene RfC Continuous Log-linear Supralinear Beryllium (U.S. EPA 1998a, 1998b) RfD Quantal Exponential polynomial Supralinear 1,3-Butadiene (U.S. EPA 2002e) RfC Quantal Weibull Supralinear Chloroform (U.S. EPA 2001b, 2001c) RfD Quantal Quantal-linear Supralinear 1,1-Dichloroethylene (U.S. EPA 2002a, 2002b) RfD Quantal Gamma Supralinear 1,1-Dichloroethylene RfC Quantal Quantal-linear Supralinear f EGBE (U.S. EPA 1999b, 1999c; NTP 1993, 1998) RfD Continuous Power model ( k = 0.66) Supralinear Naphthalene (U.S. EPA 1998i, 1998j) RfD Continuous Polynomial and power Supralinearg Chromium VI (particulates) (U.S. EPA 1998c, 1998d) RfC Continuous Polynomial mean response Sublinear 1,3-Dichloropropene (U.S. EPA 2000c, 2000d) RfD Quantal Gamma Sublinear 1,3-Dichloropropene RfC Quantal Gamma Sublinear Hexachlorocyclopentadiene (U.S. EPA 2001d, 2001e) RfD Quantal Log-logistic Sublinear Methyl methacrylate (U.S. EPA 1998e, 1998f) RfC Quantal Polynomial mean response Sublinear Carbon disulfide (U.S. EPA 1995c) RfC Continuous Weibull and polynomial NA h 1,1,1,2-Tetrafluoroethane (U.S. EPA 1995a) RfC Quantal Weibull and polynomial (multistage) NA h

Abbreviations: EGBE, ethylene glycol monobutyl ether; NA, not available.

aModels were defined for an observed range of experimental data. bBMCs were obtained using both Weibull and linear models; the models gave similar goodness of fit to the data and BMC estimates. cLinear dose–response curve because assessment is based on a polynomial model with β i = 0 for i > 1. dModel restricted to not allow supralinear forms. eEssentially linear dose–response curve because squared coefficient term of second-degree polynomial model is insignificantly small ( β 2 ≅ 0). f Shape of dose–response curve determined supralinear based on visual inspection; because slope parameter is small, curve approaches linear at low doses. gShape of dose–response curve determined supralinear based on visual inspection; BMD model response function was not available. hShape of dose–response curve information was not available; assumed linear or supralinear. 

### Article 

## | Reference values and risk

## Results
We found that 13 out of 23 (57%) of the BMD and BMC values were derived fromquantal versus continuous data. A 10% additional risk or 10% change from control mean response was selected as the BMR for 17 of the 23 assessments, and a 5% BMR was selected for 3 of the remaining 6 assessments (Tables 6 and 7) [National Toxicology Program (NTP) 1993; U.S. EPA 1995a, 1995b, 1995c, 1995d, 1997a, 1998a, 1998c, 1998e, 1998g, 1998i, 1999b, 2000c, 2001a, 2001b, 2001d, 2002a, 2002e]. The BMR values for benzene’s BMC and BMD and for phenol’s BMD were basedon a 1-standard-deviation change in acute lymphocyte count and maternal body weight, respectively, compared with the control mean (U.S. EPA 2002c, 2003). Of the 21 BMD and BMC values for which sufficient dose–response information was available, we found that 16 (76%) werederived from dose–response data fitted to linear or supralinear models in the observed dose range (Table 5) (NRC 2000; NTP 1993, 1998; U.S. EPA 1995b, 1995d, 1997b, 1998b, 1998c, 1998d, 1998f, 1998h, 1998j, 1999c, 2000c, 2001c, 2001e, 2002b, 2002d, 2002e, 2003). Seven assessments were based on linear models (two linear models; three polynomial models with β i 

### ≅ 0 for i > 1; two power models with k ≅ 1), nine were based on supralinear models, and five were based on sublinear models. Sufficient information wasnot available to determine the shape of the fitted model for two assessments, carbon disulfide and 1,1,1,2-tetrafluoroethane (i.e., the response function and model parameters were not provided) (U.S. EPA 1995a, 1995c). We calculated 17 RfD and RfC equivalent risk levels (for 14 compounds) assuming linear dose–response curves. These risk estimates ranged from 1 in 10,000 to 5 in 1,000 for the oral route of exposure for compounds with RfDs based on BMD values, and from 1 in10,000 to 3 in 1,000 for inhalation for compounds with RfCs based on BMC values (Tables 6 and 7). Figures 2 and 3 present the RfD and RfC equivalent risk estimates on a



### 1322 VOLUME 111 | NUMBER 10 | August 2003 • 

### Environmental Health Perspectives Table 6. Estimated risk levels from exposure at the RfD.

Point of departure RfD Chemical (mg/kg/day) BMR UF (mg/kg/day) Risk estimate End point: quantal or continuous

Beryllium and compounds (U.S. EPA 1998a) 0.46 10% 300 0.002 1 in 10,000 Quantal (small intestinal lesions) Chloroform (U.S. EPA 2001b) 1.2 10% 100 0.01 1 in 1,000 Quantal (fatty cyst formation in liver and elevated serum glutamate-pyruvate transaminase) 1,1-Dichloroethylene (U.S. EPA 2002a) 4.6 10% 100 0.05 1 in 1,000 Quantal [liver toxicity (fatty change)] 1,3-Dichloropropene (U.S. EPA 2000c) 3.4 10% 100 0.03 1 in 100,000 a Quantal (chronic irritation of stomach) Hexachlorocyclopentadiene (U.S. EPA 2001d) 6.0 10% 1,000 0.006 3 in 10 9 a Quantal (chronic irritation of stomach)
Benzene b (U.S. EPA 2003) 1.2 1 SD 300 0.004 3 in 10,000 Continuous (decreased lymphocyte count) EGBE (U.S. EPA 1999b; NTP 1993) 5.1 5% 10 0.5 2 in 1,000 Continuous (changes in mean corpuscular volume) Methylmercuryc (U.S. EPA 2001a) 0.0009 5% 10 0.0001 5 in 1,000 Continuous (developmental neuropsychological impairment) Naphthalene d (U.S. EPA 1998i) 93 10% 3,000 0.03 — Continuous (decreased mean terminal body weight) Phenol (U.S. EPA 2002c) 93 1 SD 300 0.3 3 in 10,000 Continuous (decreased maternal body weight gain) Tributyltin oxide (U.S. EPA 1997a) 0.03 10% 100 0.0003 1 in 10,000 Continuous (immunosuppression—decrease in IgE titer)

EGBE, ethylene glycol monobutyl ether.

a Because IRIS assessment is based on sublinear dose–response curve, linearity was not assumed for low dose extrapolation, and ri sk estimate was derived from the BMD model response function. bBMR is expressed in terms of the standard deviation (in the absence of a clear definition for an adverse effect for this continu ous end point, a default BMR of 1-standard-deviation change from the control mean was selected). This default definition of a BMR for continuous end points correspond s to an excess risk of approximately 10% for the proportion of individuals below the second percentile (or above the 98th percentile) of the control distribution for normally dist ributed effects. Benzene’s oral BMDL [ADJ] was derived from the BMCL [ADJ] (8.2 mg/m 3 ) by route-to-route extrapolation with the assumptions that inhalation absorption was 50% and oral absorption was 100% in the d ose range near the BMC. cBMR represents a 5% increased risk of neuropsychologic impairment compared to background. dInsufficient data available to estimate risk level (i.e., the response function, underlying distribution of the end point, or mean response and standard deviation of the treatment group and controls were not provided). Naphth alene’s RfD in IRIS derived from the NOAEL (3,000-fold uncertainty factor) = 0.02 mg/kg/day; Tthe prospective RfD derived from the BMD (3,000-fold uncertainty factor) = 0.03 mg/kg/da y.

Table 7. Estimated risk levels from exposure at the RfC.

Point of departure RfC Chemical (mg/m 3 ) BMR UF (mg/m 3 ) Risk estimate End point: quantal or continuous

Antimony trioxide (U.S. EPA 1995b) 0.074 10% 300 0.0002 3 in 10,000 Quantal (pulmonary toxicity, chronic interstitial inflammation) 1,3-Butadiene (U.S. EPA 2002e) 1.98 10% 1,000 0.002 1 in 10,000 Quantal (ovarian atrophy) 1,1-Dichloroethylene (U.S. EPA 2002a) 6.9 10% 30 0.2 3 in 1,000 Quantal [liver toxicity (fatty change)] 1,3-Dichloropropene (U.S. EPA 2002c) 0.72 10% 30 0.02 3 in 10 6 a Quantal (hyperplasia of nasal epithelium) Methyl methacrylate (U.S. EPA 1998e) 7.2 10% 10 0.7 8 in 10,000 a Quantal (degeneration/atropy of olfactory epithelium) Methylene diphenyl diisocyanate (U.S. EPA 1998g) 0.06 10% 100 0.0006 1 in 1,000 Quantal (hyperplasia of the olfactory epithelium) Phosphoric acid (U.S. EPA 1995d) 3.4 10% 300 0.01 3 in 10,000 Quantal (bronchiolar fibrosis) 1,1,1,2-Tetrafluoroethane (U.S. EPA 1995a) 8,200 10% 100 80 1 in 1,000 Quantal (Leydig cell hyperplasia)
Benzene b (U.S. EPA 2003) 8.2 1 SD 300 0.03 3 in 10,000 Continuous (decreased lymphocyte count) Carbon disulfide c (U.S. EPA 1995c) 19.7 10% 30 0.7 3 in 1,000 Continuous (peripheral nervous system dysfunction) Chromium VI (particulates) d (U.S. EPA 1998c) 0.034 10% 300 0.0001 — Continuous (lactate dehydrogenase in bronchioalveolar lavage fluid) EGBE (U.S. EPA 1999b; NTP 1993) 380 5% 30 13 2 in 1,000 Continuous (changes in red blood cell count)

EGBE, ethylene glycol monobutyl ether.

a Because IRIS assessment is based on sublinear dose–response curve, linearity was not assumed for low dose extrapolation, and ri sk estimate was derived from the BMC model response function. bBMC was based on a BMR of 1-standard-deviation change from the control mean (in the absence of a clear definition for an adverse effect for this continuous end point, a default BMR of 1-standard-deviation change from the control mean was selected). This default definition of a BMR for co ntinuous end points corresponds to an excess risk of approximately 10% for the proportion of individuals below the second percentile (or above the 98th percentile) of the control d istribution for normally distributed effects. c A 10% relative change was selected as an appropriate BMR for the nerve conduction velocity measurements because this level is about equal to a difference of 1 standard deviation from the control, and because a change of about 10% would likely raise concern in a clinical setting. dInsufficient data available to estimate risk level (i.e., the response function, underlying distribution of the end point, or mean response and standard deviation of the treatment group and controls were not provided). 

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## | Castorina and Woodruff

### logarithmic scale for these chemicals with linear or supralinear dose–response curves at theBMD/BMC. For four RfD and RfC equivalent risk levels (for 3 compounds), we used the sublinear dose–response model to calculate risk at the RfD or RfC. These risk estimates ranged from 3 in 1,000,000,000 to 1 in 100,000 from the oral route of exposure for compounds with RfDs based on BMD values, and from 3 in 1,000,000 to 8 in 10,000 from inhalation for compounds with RfCs based on BMC values (Tables 6 and 7). Five of 21 reference values (24%) reviewed for this assessment corresponded to estimated risk levels greater than 1 in 1,000. Insufficient information was available to estimate risk from exposure at two reference values that were based on continuous response data, chromium VI particulates and naphthalene(i.e., the response function, underlying distribution of the end point, or mean response and standard deviation of the treatment group and controls were not provided). Figure 4 presents the distribution of estimated risk levels from human exposures at established RfD and RfCvalues for compounds with a linear or supralinear dose–response curve at the BMD. Risk estimates for four assessments derived from sublinear dose–response curves are presented in Tables 6 and 7. Among the chemicals for which the RfD was based on a BMD, the BMD/BMDL ratio ranged from 1.3 to 3.0. Among the chemicals for which the RfC was based on a BMC, the BMC/BMCL ratio ranged from 1.1 to 2.3(Tables 1 and 3). Thus, using the central estimate of the BMD or BMC (maximum likelihood estimate) instead of the lower statistical confidence limit (BMDL or BMCL) as the point of departure would result in a 1- to 3-fold difference in the estimated risk levels (Figures 2 and 3). The effect level extrapolation factor (ELE)is an UF analogous to the LOAEL-toNOAEL extrapolation factor. ELEs wereapplied in the assessments of three compounds, 1,3-butadiene (RfC), benzene (RfC and RfD), and ethylene glycol monobutyl ether (RfC) (Tables 2 and 4) (U.S. EPA 1999b, 2002e, 2003). Thus, no ELE factor was assigned for 16 of 17 assessments that were based on a BMR of 10%.When we compared the points of departure (i.e., BMDL/BMCL values) with theNOAELs, we found that the points of departure were higher than the NOAELs in 10 of the 14 studies with identified NOAELs (Figure 5). The BMDL values were up to 4.6 times higher than the empirical NOAELs (range, 0.5–4.6), and the BMCL values were up to 3.9 times higher than the empirical NOAELs (range, 0.2–3.9) (Tables 1 and 3).

Environmental Health Perspectives • VOLUME 111 | NUMBER 10 | August 2003 

### 1323 1/10

1/100

1/1,000

1/10,000

1/100,000Risk level (log scale)

Beryllium Chloroform Dichloroethylene Tributyltin oxide Benzene Phenol EGBE Methyl

mercury

Quantal data Continuous data
Chemical Risk level based on BMDL

Risk level based on BMD

1/10

1/100

1/1,000

1/10,000

1/100,000Risk level (log scale)

Butadiene Antimony trioxide Phosphoric acid Methylene diphenyl diisocyante Tetrafluoroethane a Carbon disulfide a

Quantal data Continuous data

Chemical Risk level based on BMCL

Risk level based on BMC

Dichloroethylene Benzene EGBE Figure 2. Estimated risk levels from exposure at the RfD based on the BMD and the BMDL derived from linear or supralinear dose–response curves. EGBE, ethylene glycol monobutyl ether.

Figure 3. Estimated risk levels from exposure at the RfC based on the BMC and the BMCL derived from linear or supralinear dose–response curves. EGBE, ethylene glycol monobutyl ether.

aBMC central estimate not provided by the U.S. EPA. Figure 4. Distribution of estimated risk levels fromhuman exposures at established RfD and RfC values for reference doses derived from linear or supralinear dose–response curves.

a Risk estimates for assessments based on sublinear dose–response curves are not included.

Figure 5. Ratio of the points of departure (PODs) toempirically derived NOAELs. PODs based on benchmark modeling.

6

5

4

3

2

1

0No. of chemical assessments

1/10,000 Estimated risk level a 3/10,000 1/1,000 2–3/1,000 5/1,000

6

5

4

3

2

1

0No. of chemicals

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 POD < NOAEL POD > NOAEL 

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### Environmental Health Perspectives 

## Discussion

To determine whether U.S. EPA reference values represent negligibly small risk levels, we reviewed and synthesized currently available risk assessment information on chemicals for which established RfD and RfC values are based on BMD modeling. For RfDs and RfCs derived from linear or supralinear dose– response curves, our risk estimates ranged from 1 in 10,000 to 5 in 1,000 for the oral route of exposure, and from 1 in 10,000 to 3 in 1,000 for inhalation. Risk estimates for RfDs and RfCs derived from sublinear dose–response curves ranged from 3 in 1,000,000,000 to 8 in 10,000. Twenty-four percent of reference values reviewed for thisassessment corresponded to estimated risk levels greater than 1 in 1,000. The estimated risk of exposure to 1,1-dichloroethylene at its RfC, for example, corresponded to a 3-in-1,000 risk of adverse effect [liver toxicity (fatty change)]. The BMD methodology is the first step in the development of continuous risk functions that can be used to estimate risks at different exposures rather than using an RfD/RfCapproach, which has limited use in the decision-making process. For example, BMD and BMC values that are based on the same level of adverse response (e.g., BMR = 10%) can be used to rank the potential hazard of exposure to multiple toxicants. Further application of BMD models, such as has been done here, can be used for estimating adverse noncancer health outcomes from different exposures for other risk-ranking exercises, regulatory policy development, and cost/benefit analyses. The U.S. EPA used a variety of fitted models to calculate the BMD/BMC values found in IRIS (e.g., K power, linear, quantallinear, exponential polynomial, and Weibull).To compare RfD and RfC equivalent risk levels, we assumed that the dose–response curves for the chemicals in our assessment group are linear at doses below the point of departure. This assumption could have resulted in both underestimates and overestimates of risk. In the case of a supralinear dose–response curve at low doses, for example, this assumption may have resulted in an underestimate of risk. Among the chemicals we reviewed, 9 of the 21 assessments with sufficient information to determine the shape of the dose–response curve were based on supralinear functions. In the case of a sublinear dose–response, this assumption may have resulted in a marked overestimate of risk. For the 5 assessments based on sublinear dose–response curves, therefore, we did not assume linearity for low dose extrapolation and risk estimation.We believe that the assumption of linearity in the relevant part of the dose–response curve is justified and useful to compare risk levels among this group of compounds. Seventy-six percent of BMD and BMC values considered in this assessment were derived from dose–response data fitted to linear or supralinear models. Furthermore, the range of extrapolation for the RfD/RfC calculations was not large among this group, with most based on points of departure extrapolated to 2orders of magnitude or less (7 were extrapolated to 1 order of magnitude, 13 were extrapolated to 2 orders of magnitude, and 3 were extrapolated to 3 orders of magnitude). The average and median composite UFs among the compounds in our assessment are 340 and 100, respectively. This implies that even if thedose–response curve for a particular compound is not strictly linear at much lower doses, we could expect the potential impact on the risk estimate to be relatively small.Current U.S. EPA methodology for reference value derivation assumes that the established RfDs/RfCs represent negligibly small risk levels. For assessments that have linear dose–response curves, the extrapolation from the point of departure is typically 2 orders of magnitude or less. Therefore, for the RfD/RfC values to represent risk levels that are below regulatory concern, the dose–response curve would have to drop off sharply after the point of departure. This assumption seems unlikely, especially given our finding that a large number of the assessments we reviewed (9 of 21) were based on supralinear dose–response functions.Although this supralinearity may carry significant implications for risk assessment, more research is needed to determine whether these dose–response relationships remain supralinearat very low doses. On the other hand, assessments based on dose–response curves that are not monotonic may have sublinear or stepwise relationships below the observed data. Using the BMDL or BMCL as the point ofdeparture in the risk assessment of noncarcinogenic compounds rather than the BMD or BMC central estimate is generally characterizedas a conservative assumption (in the health-protective sense). We found that using the central estimate of the BMD (maximum likelihood estimate) instead of the lower bound estimate as the point of departure results in a 1- to 3-fold difference in the risk estimates. According to the U.S. EPA draft BMD guidelines (U.S. EPA 2000a), a lower confidence limit is placed on the BMD to obtain a dose (BMDL) that assures with high confidence (e.g., 95%) that the BMR is not exceeded. This process of using the BMDL rewards better experimentaldesign and procedures that provide more precise estimates of the BMD, resulting in tighter confidence intervals and thus BMDLs that are closer to the central estimate. Our results suggest that the current practice ofusing the statistical lower bound estimate versus the maximum likelihood estimate as the point of departure is reasonable and does not substantially bias the risk estimate. For carcinogens, the U.S. EPA typically develops a linear estimate of the slope of the dose–response curve, under the assumption that the curve is linear at low doses. This allows for quantification of risk at any given level of exposure. The U.S. EPA has defined

1 in 1,000,000 excess cancer risk as a de minimis risk level for cancer (Caldwell et al. 1998; Clean Air Act Amendments 1990; Fiori and Meyerhoff 2002; U.S. EPA 1991), although regulatory actions are sometimes limited to instances where risk exceeds 1 in 100,000. Among compounds in IRIS with RfDs and RfCs based on BMD modeling, however, we found risk estimates as great as 5 in 1,000. Although noncancer outcomes may in some instances be reversible and considered less severe, this finding calls into question theassumption that noncancer RfD and RfC values represent “acceptable levels” of exposure. In addition, some of the noncancer health end points considered in this assessment are severe and irreversible events, for example, ovarian atrophy (1,3-butadiene) and developmentalneuropsychologic impairment (methylmercury), highlighting the need for a renewed discussion within the public health communityabout what should be considered an “acceptable level” of risk from exposure to toxicants with noncancer health end points. Most of the BMDLs and BMCLs used as points of departure in IRIS are based on 10% BMRs, many with values higher than the empirically derived NOAELs. This research should help inform discussions about whether this level of BMR is adequately protective ofthe public health, and whether human exposures at concentrations equal to the resulting reference values do in fact represent negligibly small risk levels.

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