Suchergebnisse

AbstractCrump's response in this issue to my critique of linear‐no‐threshold (LNT) default assumptions for noncancer and nongenotoxic cancer risks (Risk Analysis 2016; 36(3):589–604) is rebutted herein. Crump maintains that distinguishing between a low‐dose linear dose response and a threshold dose response on the basis of dose–response data is impossible even for endpoints involving increased cytotoxicity. My rebuttal relies on descriptions and specific illustrations of two well‐characterized ultrasensitive molecular switches that govern two key cytoprotective responses to cellular stress—heat shock response and antioxidant response element activation, respectively—each of which serve to suppress stress‐induced apoptotic cell death unless overwhelmed. Because detailed dose–response data for each endpoint is shown to be J‐ or inverted‐J‐shaped with high confidence, and because independent pathways can explain background rates of apoptosis, LNT assumptions for this cytotoxic endpoint are unwarranted, at least in some cases and perhaps generally.

To improve U.S. Environmental Protection Agency (EPA) dose‐response (DR) assessments for noncarcinogens and for nonlinear mode of action (MOA) carcinogens, the 2009 NRC Science and Decisions Panel recommended that the adjustment‐factor approach traditionally applied to these endpoints should be replaced by a new default assumption that both endpoints have linear‐no‐threshold (LNT) population‐wide DR relationships. The panel claimed this new approach is warranted because population DR is LNT when any new dose adds to a background dose that explains background levels of risk, and/or when there is substantial interindividual heterogeneity in susceptibility in the exposed human population. Mathematically, however, the first claim is either false or effectively meaningless and the second claim is false. Any dose‐and population‐response relationship that is statistically consistent with an LNT relationship may instead be an additive mixture of just two quasi‐threshold DR relationships, which jointly exhibit low‐dose S‐shaped, quasi‐threshold nonlinearity just below the lower end of the observed "linear" dose range. In this case, LNT extrapolation would necessarily overestimate increased risk by increasingly large relative magnitudes at diminishing values of above‐background dose. The fact that chemically‐induced apoptotic cell death occurs by unambiguously nonlinear, quasi‐threshold DR mechanisms is apparent from recent data concerning this quintessential toxicity endpoint. The 2009 NRC Science and Decisions Panel claims and recommendations that default LNT assumptions be applied to DR assessment for noncarcinogens and nonlinear MOA carcinogens are therefore not justified either mathematically or biologically.

ABSTRACTThe National Research Council 2009 "Silver Book" panel report included a recommendation that the U.S. Environmental Protection Agency (EPA) should increase all of its chemical carcinogen (CC) potency estimates by ∼7‐fold to adjust for a purported median‐vs.‐mean bias that I recently argued does not exist (Bogen KT. "Does EPA underestimate cancer risks by ignoring susceptibility differences?," Risk Analysis, 2014; 34(10):1780–1784). In this issue of the journal, my argument is critiqued for having flaws concerning: (1) intent, bias, and conservatism of EPA estimates of CC potency; (2) bias in potency estimates derived from epidemiology; and (3) human‐animal CC‐potency correlation. However, my argument remains valid, for the following reasons. (1) EPA's default approach to estimating CC risks has correctly focused on bounding average (not median) individual risk under a genotoxic mode‐of‐action (MOA) assumption, although pragmatically the approach leaves both inter‐individual variability in CC–susceptibility, and widely varying CC‐specific magnitudes of fundamental MOA uncertainty, unquantified. (2) CC risk estimates based on large epidemiology studies are not systematically biased downward due to limited sampling from broad, lognormal susceptibility distributions. (3) A good, quantitative correlation is exhibited between upper‐bounds on CC‐specific potency estimated from human vs. animal studies (n = 24, r = 0.88, p = 2 × 10−8). It is concluded that protective upper‐bound estimates of individual CC risk that account for heterogeneity in susceptibility, as well as risk comparisons informed by best predictions of average‐individual and population risk that address CC‐specific MOA uncertainty, should each be used as separate, complimentary tools to improve regulatory decisions concerning low‐level, environmental CC exposures.

A 2009 report of the National Research Council (NRC) recommended that the U.S. Environmental Protection Agency (EPA) increase its estimates of increased cancer risk from exposure to environmental agents by ∼7‐fold, due to an approximate ∼25‐fold typical ratio between the median and upper 95th percentile persons' cancer sensitivity assuming approximately lognormally distributed sensitivities. EPA inaction on this issue has raised concerns that cancer risks to environmentally exposed populations remain systematically underestimated. This concern is unwarranted, however, because EPA point estimates of cancer risk have always pertained to the average, not the median, person in each modeled exposure group. Nevertheless, EPA has yet to explain clearly how its risk characterization and risk management policies concerning individual risks from environmental chemical carcinogens do appropriately address broad variability in human cancer susceptibility that has been a focus of two major NRC reports to EPA concerning its risk assessment methods.

Average rates of total dermal uptake (Kup) from short‐term (e.g., bathing) contact with dilute aqueous organic chemicals (DAOCs) are typically estimated from steady‐state in vitro diffusion‐cell measures of chemical permeability (Kp) through skin into receptor solution. Widely used ("PCR‐vitro") methods estimate Kup by applying diffusion theory to increase Kp predictions made by a physico‐chemical regression (PCR) model that was fit to a large set of Kp measures. Here, Kup predictions for 18 DAOCs made by three PCR‐vitro models (EPA, NIOSH, and MH) were compared to previous in vivo measures obtained by methods unlikely to underestimate Kup. A new PCR model fit to all 18 measures is accurate to within approximately threefold (r = 0.91, p < 10−5), but the PCR‐vitro predictions (r > 0.63) all tend to underestimate the Kup measures by mean factors (UF, and p value for testing UF = 1) of 10 (EPA, p < 10−6), 11 (NIOSH, p < 10−8), and 6.2 (MH, p = 0.018). For all three PCR‐vitro models, log(UF) correlates negatively with molecular weight (r2 = 0.31 to 0.84, p = 0.017 to < 10−6) but not with log(vapor pressure) as an additional predictor (p > 0.05), so vapor pressure appears not to explain the significant in vivo/PCR‐vitro discrepancy. Until this discrepancy is explained, careful in vivo measures of Kup should be obtained for more chemicals, the expanded in vivo database should be compared to in vitro‐based predictions, and in vivo data should be considered in assessing aqueous dermal exposure and its uncertainty.

The U.S. Environmental Protection Agency (USEPA) guidelines for cancer risk assessment recognize that some chemical carcinogens may have a site‐specific mode of action (MOA) involving mutation and cell‐killing‐induced hyperplasia. The guidelines recommend that for such dual MOA (DMOA) carcinogens, judgment should be used to compare and assess results using separate "linear" (genotoxic) versus "nonlinear" (nongenotoxic) approaches to low‐level risk extrapolation. Because the guidelines allow this only when evidence supports reliable risk extrapolation using a validated mechanistic model, they effectively prevent addressing MOA uncertainty when data do not fully validate such a model but otherwise clearly support a DMOA. An adjustment‐factor approach is proposed to address this gap, analogous to reference‐dose procedures used for classic toxicity endpoints. By this method, even when a "nonlinear" toxicokinetic model cannot be fully validated, the effect of DMOA uncertainty on low‐dose risk can be addressed. Application of the proposed approach was illustrated for the case of risk extrapolation from bioassay data on rat nasal tumors induced by chronic lifetime exposure to naphthalene. Bioassay data, toxicokinetic data, and pharmacokinetic analyses were determined to indicate that naphthalene is almost certainly a DMOA carcinogen. Plausibility bounds on rat‐tumor‐type‐specific DMOA‐related uncertainty were obtained using a mechanistic two‐stage cancer risk model adapted to reflect the empirical link between genotoxic and cytotoxic effects of the most potent identified genotoxic naphthalene metabolites, 1,2‐ and 1,4‐naphthoquinone. Bound‐specific adjustment factors were then used to reduce naphthalene risk estimated by linear extrapolation (under the default genotoxic MOA assumption), to account for the DMOA exhibited by this compound.

The Homeland Security Act mandates the development of a national, risk‐based system to support planning for, response to, and recovery from emergency situations involving large‐scale toxic exposures. To prepare for and manage consequences effectively, planners and responders need not only to identify zones of potentially elevated individual risk but also to predict expected casualties. Emergency response support systems now define "consequences" by mapping areas in which toxic chemical concentrations do or may exceed Acute Exposure Guideline Levels (AEGLs) or similar guidelines. However, because AEGLs do not estimate expected risks, current unqualified claims that such maps support consequence management are misleading. Intentionally protective, AEGLs incorporate various safety/uncertainty factors depending on the scope and quality of chemical‐specific toxicity data. Some of these factors are irrelevant, and others need to be modified, whenever resource constraints or exposure‐scenario complexities require responders to make critical trade‐off (triage) decisions in order to minimize expected casualties. AEGL‐exceedance zones cannot consistently be aggregated, compared, or used to calculate expected casualties and so may seriously misguide emergency response triage decisions. Methods and tools well established and readily available to support environmental health protection are not yet developed for chemically‐related environmental health triage. Effective triage decisions involving chemical risks require a new assessment approach that focuses on best estimates of likely casualties, rather than on upper plausible bounds of individual risk. If risk‐based consequence management is to become a reality, federal agencies tasked with supporting emergency response must actively coordinate to foster new methods that can support effective environmental health triage.

As interest in quantitative analysis of joint uncertainty and interindividual variability (JUV) in risk grows, so does the need for related computational shortcuts. To quantify JUV in risk, Monte Carlo methods typically require nested sampling of JUV in distributed inputs, which is cumbersome and time‐consuming. Two approximation methods proposed here allow simpler and more rapid analysis. The first consists of new upper‐bound JUV estimators that involve only uncertainty or variability, not both, and so never require nested sampling to calculate. The second is a discrete‐probability‐calculus procedure that uses only the mean and one upper‐tail mean for each input in order to estimate mean and upper‐bound risk, which procedure is simpler and more intuitive than similar ones in use. Application of these methods is illustrated in an assessment of cancer risk from residential exposures to chloroform in Kanawah Valley, West Virginia. Because each of the multiple exposure pathways considered in this assessment had separate modeled sources of uncertainty and variability, the assessment illustrates a realistic case where a standard Monte Carlo approach to JUV analysis requires nested sampling. In the illustration, the first proposed method quantified JUV in cancer risk much more efficiently than corresponding nested Monte Carlo calculations. The second proposed method also nearly duplicated JUV‐related and other estimates of risk obtained using Monte Carlo methods. Both methods were thus found adequate to obtain basic risk estimates accounting for JUV in a realistically complex risk assessment. These methods make routine JUV analysis more convenient and practical.

Compounded conservatism (or "creeping safety") describes the impact of using conservative, upper‐bound estimates of the values of multiple input variates to obtain a conservative estimate of risk modeled as an increasing function of those variates. In a simple multiplicative model of risk, for example, if upper p‐fractile (100pth percentile) values are used for each of several statistically independent input variates, the resulting risk estimate will be the upper p'‐fractile of risk predicted according to that multiplicative model, where p' > p. The amount of compounded conservativism reflected by the difference between p' and p may be substantial, depending on the number of inputs, their relative uncertainties, and the value of p selected. Particular numerical examples of compounded conservatism are often cited, but an analytic approach may better serve to conceptualize and communicate its potential quantitative impact. This note briefly outlines such an approach and illustrates its application to the case of risk modeled as a product of lognormally distributed inputs.

A simple analytic solution to the dynamic version of Haber's law was derived, conditional on a specified toxic load exponent (n) and on exponential decline in environmental toxicant concentration. Such conditions are particularly relevant to assessing ecotoxicity risk posed (e.g., to juvenile salmonids) by agricultural organophosphate (OP) pesticides that are subject to degradation and/or dissipation. A dynamic Haber's law model was fit to previously published detailed data on lethality for two aquatic species induced by six agricultural OP pesticides, and more crude fits were obtained to less detailed data on five other OP and on two non‐OP pesticides, indicating that for lethality, a range of 0.5 ≤ n ≤ 1.5 may be typical for OP pesticides. The AgDRIFT® stream deposition model was next used to establish that first‐order or exponential loss, with dilution half‐times on the order of ≤0.01 days, pertains approximately to pesticide residues in streams that arise after aerial application of agricultural pesticides 100 feet upwind. The analytic model was then applied to demonstrate that pesticide concentrations deposited in downwind streams following an aerial application are effectively diluted by about 50‐ to 300‐fold from their initial concentration. Riparian ecotoxicity risk assessment models that ignore this effective dilution, and base pesticide‐specific estimates of reduced survival on the initial concentrations, are therefore unrealistically conservative.

AbstractRisks of allergic contact dermatitis (ACD) from consumer products intended for extended (nonpiercing) dermal contact are regulated by E.U. Directive EN 1811 that limits released Ni to a weekly equivalent dermal load of ≤0.5 μg/cm2. Similar approaches for thousands of known organic sensitizers are hampered by inability to quantify respective ACD‐elicitation risk levels. To help address this gap, normalized values of cumulative risk for eliciting a positive ("≥+") clinical patch test response reported in 12 studies for a total of n = 625 Ni‐sensitized patients were modeled in relation to observed ACD‐eliciting Ni loads, yielding an approximate lognormal (LN) distribution with a geometric mean and standard deviation of GMNi = 15 μg/cm2 and GSDNi = 8.0, respectively. Such data for five sensitizers (including formaldehyde and 2‐hydroxyethyl methacrylate) were also ∼LN distributed, but with a common GSD value equal to GSDNi and with heterogeneous sensitizer‐specific GM values each defining a respective ACD‐eliciting potency GMNi/GM relative to Ni. Such potencies were also estimated for nine (meth)acrylates by applying this general LN ACD‐elicitation risk model to respective sets of fewer data. ACD‐elicitation risk patterns observed for Cr(VI) (n = 417) and Cr(III) (n = 78) were fit to mixed‐LN models in which ∼30% and ∼40% of the most sensitive responders, respectively, were estimated to exhibit a LN response also governed by GSDNi. The observed common LN‐response shape parameter GSDNi may reflect a common underlying ACD mechanism and suggests a common interim approach to quantitative ACD‐elicitation risk assessment based on available clinical data.

Quantitative approaches to assessing exposure to, and associated risk from, benzene in mineral spirits solvent (MSS), used widely in parts washing and degreasing operations, have focused primarily on the respiratory pathway. The dermal contribution to total benzene uptake from such operations remains uncertain because measuring in vivo experimental dermal uptake of this volatile human carcinogen is difficult. Unprotected dermal uptake involves simultaneous sustained immersion events and transient splash/wipe events, each yielding residues subject to evaporation as well as dermal uptake. A two‐process dermal exposure framework to assess dermal uptake to normal and damaged skin was applied to estimate potential daily dermal benzene dose (Dskin) to workers who used historical or current formulations of recycled MSS in manual parts washers. Measures of evaporation and absorption of MSS dermally applied to human subjects were modeled to estimate in vivo dermal uptake of benzene in MSS. Uncertainty and interindividual variability in Dskin was characterized by Monte Carlo simulation, conditioned on uncertainty and/or variability estimated for each model input. Dermal exposures are estimated to average 33% of total (inhalation + dermal) benzene parts washing dose, with approximately equal predicted portions of dermal dose due to splash/wipe and to continuous contact with MSS. The estimated median (95th percentile) dermal and total daily benzene doses from parts washing are: 0.0069 (0.024) and 0.025 (0.18) mg/day using current, and 0.027 (0.085) and 0.098 (0.69) mg/day using historical, MSS solvents, respectively.