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Recent advances in probabilistic dose-response assessment to inform socioeconomic benefits analysis

Weihsueh A. Chiu, PhD(a) and Greg Paoli, MASc(b) (a) Department of Veterinary Integrative Bioscience, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA (b) Risk Sciences International, Ottawa, ON, Canada

Public Working Paper for

Risk Assessment, Economic Evaluation, and Decisions Workshop September 26-27, 2019

Hosted by Harvard Center for Risk Analysis

Version: September 6, 2019 Acknowledgments: The authors wish to thank the Workshop Organizers for their invitation to present this work, as well as members of the WHO/IPCS Workgroup on Characterizing Uncertainty in Hazard Characterisation and members of the UNEP/SETAC Life Cycle Initiative for important input into work described in this article. This work was supported, in part, by NIH grant P42 ES027704 to Texas A&M University.

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Abstract

Virtually all socioeconomic benefits analyses for chemical exposures are based on either epidemiologic data or extrapolation of experimental animal cancer bioassay data. The paucity of chemicals and health endpoints for which such data are available severely limits the ability of decision-makers to account for the impacts of chemical exposures on human health. This impact goes well beyond the relatively high profile benefit-cost analyses associated with new regulatory proposals. The development by the World Health Organization International Programme on Chemical Safety (WHO/IPCS) in 2014 of a comprehensive framework for probabilistic dose-response assessment has opened the door to a myriad of potential advances to better support decision-making. Building on the pioneering work of Evans, Hattis, and Slob from the 1990s, the WHO/IPCS framework provides both a firm conceptual foundation as well as practical implementation tools to simultaneously assess uncertainty, variability, and severity of effect as a function of exposure. Moreover, such approaches do not depend on the availability of epidemiologic data, nor are they limited to cancer endpoints. Recent work has demonstrated the broad feasibility of such approaches in order to estimate the functional relationship between exposure level and the incidence or severity of health effects. While challenges remain, such as better characterization of the relationship between endpoints observed in experimental animal (or in vitro) studies and human health effects, the WHO/IPCS framework provides a strong basis for expanding the intersection of risk assessment and economics to better support policy decisions.

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1. Introduction Socioeconomic benefits analyses are conducted to support or augment a variety of regulatory activities, ranging from particulate matter standards to drinking water maximum contaminant levels. However, in virtually all cases, the human health assessments underlying the estimation of economic benefits from reducing exposure are based on either epidemiologic data or extrapolation of experimental animal cancer bioassay data. The restriction to these very limited data types, which cover only a dozen or so agents for epidemiology and a few hundred for animal cancer bioassays, stems from the methods that have been traditionally used to conduct dose-response assessment of environmental chemicals. Specifically, in cases where suitable epidemiologic data are not available and the endpoint of concern is not cancer, risk assessments are actually more akin to “safety” assessments, where the goal is to ensure the “absence” of (any) effect. A typical output of such assessments is a “Reference Dose,” which is defined as follows.

RfD: an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. It can be derived from a NOAEL, LOAEL, or BMD, with UFs generally applied to reflect limitations of the data used. Generally used in EPA’s noncancer health assessments. (U.S. EPA, 2002)

Such assessments, while useful in some decision contexts, are not suitable for socioeconomic analyses because analyses of benefit-cost, cost effectiveness, or economic impacts necessitate a “counterfactual” paradigm wherein the occurrence (often as an expected value) of particular effects differs under different policy options. As depicted in Figure 1, this means that traditional approaches lack three key components needed to support socioeconomic analyses: a prediction of the population dose-response relationship throughout a range of exposure levels; quantitative characterization of uncertainty in order to calculate central or expected values; and defined endpoints that can be assigned economic values. Indeed, the need to better support socioeconomic analyses was cited by the National Academies in its Science and Decisions report (NAS, 2009) as one of the reasons for recommending that risk assessments redefine traditional toxicity values such as the RfD as a probabilistically-derived “risk-specific dose,” which characterizes risks as a function of dose. Interestingly, there is a long history attempts to define probabilistic approaches to dose-response assessment dating back to the 1990s (Baird et al., 1996; Slob and Pieters, 1998; Swartout et al., 1998; Evans et al., 2001; Hattis et al., 2002; Chiu and Slob, 2015), but without much success in changing risk assessment practice. The context of benefit-cost analysis in support of regulatory proposals is the most well-known and most frequently argued basis for the need for non-cancer assessment to be capable of predicting levels of risk to exposed populations below, at, or above the RfD or similar toxicity values. However, there is a much broader suite of decision-contexts which also suffers from the

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lack of capability to predict levels of risk and the benefits of risk reduction. In both private and public sector contexts, there are significant investments in the operational (as distinct from rule-making) aspects of chemical risk control. For a significant part of that very large investment, the actual level of benefits accruing to the public, workers, the environment and the corporations themselves. This impact extends from the direct (how much risk from this source?) to the many associated trade-off contexts (how much more worthwhile is this control activity as compared to the many competing possible risk control investments? What is the amount of chemical antimicrobial agent to optimize the prevention of the targeted infectious disease outcomes?). The increasingly important contexts of life cycle impact analysis (LCIA), particularly given the Environmental Footprint requirements in Europe, and chemical alternatives assessment would benefit significantly from the ability to predict relative impacts. As discussed below, some progress has been made in the context of LCIA. Chemical alternatives assessment, as currently practiced and is largely non-quantitative in describing the risk reductions expected, but may be moving slowly toward a more quantitative portrayal of the relative harms of the target chemical and its proposed alternatives (NAS, 2014). Around the same time that Science and Decisions (NAS, 2009) was released, the Harmonization Project of the World Health Organization International Programme on Chemical Safety initiated a working group to develop a framework for probabilistic hazard characterization. Although the initial impetus for the project was as a companion to the Guidance document on characterizing and communicating uncertainty in exposure assessment, published as part of a harmonization document on exposure in 2008 (WHO/IPCS, 2008), the working group soon recognized that this effort could also address the recommendations from the Science and Decisions (NAS, 2009) report. First published in 2014, with an open source journal article in 2015 and a second edition released in 2017, the resulting international guidance document presented a comprehensive framework for addressing uncertainty and variability in human health dose-response assessment of chemicals (WHO/IPCS, 2014; Chiu and Slob, 2015; WHO/IPCS, 2017). This new framework has opened the door to broader application of probabilistic approaches than earlier efforts, in part because it was developed through an extended multi-year process that included input from researchers, risk assessors, and regulators, as well as ongoing training in multiple professional fora. In this article we describe some of the recent advances spurred by the WHO/IPCS framework, reviewing recent applications as well as noting continued challenges. We begin with a brief overview of the WHO/IPCS framework, focusing on the new conceptual foundation that underlies the approach. Next we describe some of the recent advances and applications since the publication of the framework. We then discuss some challenges to its broader application. We conclude that the WHO/IPCS framework provides a firm foundation for expanding the utility of risk assessment for socioeconomic analyses and for the broader suite of decision contexts including operational cost-effectiveness and comparative risk contexts that are inevitable aspects of both private and public sector investments in chemical risk control.

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Figure 1. Toxicology-based risk assessment from an economist’s viewpoint. Traditional risk assessment approaches, such as those based on deriving points of departure (PoDs), margins of exposure (MoEs), reference doses (RfDs), and cancer slope factors (CSFs), are focused on ensuring the absence of adverse effects at some (usually imprecisely specified) level of confidence. However, economic analysis usually necessitates prediction of the occurrence of adverse effects in order to characterize societal impacts under different policy options or scenarios. Adapted from presentation by Dr. Chris Dockins, U.S. EPA National Center for Environmental Economics.

2. Overview of the WHO/IPCS framework and potential application to socioeconomic analyses The key concept underlying the WHO/IPCS approach is replacing quantities such as the RfD with a so-called target human dose.

Target Human Dose (HDMI): the estimated human dose at which effects with

magnitude M occur in the population with an incidence I. A conceptual illustration of the HDM

I is shown in Figure 2A. Specifically, this approach posits that experimental animal data can be extrapolated to dose-response curves at the individual level, with human population variation leading to a family of different dose-response curves at different percentiles of the population. The HDM

I expresses the relationship between the three quantities of human dose (HD), magnitude of effect (M), and percentile of the population (I), and can be mathematically re-expressed as needed with different variables being treated as the independent versus dependent variables.

Traditional Toxicology-Based Approaches:

Ensuring absence of effects

Risk Assessment- cancer- non-cancer

Policy Decision- standards- information

Economic Analysis- benefit-cost analysis- cost effectiveness- economic impacts

Predicting occurrence of effects

An Economist’s View of Toxicology and Risk Assessment

Traditional approaches usually lack:• Population dose-response

function prediction• Quantitative uncertainty

characterization• Defined endpoint

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Figure 2. Summary of WHO/IPCS probabilistic approach to characterize quantitative uncertainty and variability in dose-response assessment. A: Illustration of the key concept of the “human target dose,” HDMI, which replaces traditional toxicity values such as the Reference Dose (RfD). B: General approach to deriving HDMI probabilistically, in comparison to deriving a traditional deterministic RfD, along with how it addresses key needs for economic analyses. For additional details, see WHO/IPCS (2017) and Chiu and Slob (2015).

The derivation of the HDM

I along with its associated confidence interval involves the following steps, also illustrated in Figure 2B:

• Conduct benchmark dose (BMD) modeling. The BMD, introduced by Crump (1984), is the dose associated with a specific size of effect, the benchmark response (BMR), corresponding to the magnitude of effect M. The BMDM is estimated, with associated confidence interval/statistical distribution, by statistical model fitting to dose-response data. This approach contrasts with using a NOAEL as a starting point for toxicological risk assessment, the limitations of which have been recognized for decades (U.S. EPA, 1995; NAS, 2001; EFSA, 2009; WHO/IPCS, 2009; U.S. EPA, 2012). Additionally, rather than using only a single point, such as the lower confidence limit (BMDL), the entire uncertainty distribution is utilized.

• Derive probabilistic interspecies extrapolation factors. Interspecies extrapolation involves two components. The first component, denoted as the dosimetric adjustment factor (DAF), converts experimental animal exposures to “human equivalent” exposures, either through toxicokinetic modeling (Dorman et al., 2008; Schroeter et al., 2008; Teeguarden et al., 2008; Corley et al., 2012), or through generic approaches such as allometric scaling by body mass (U.S. EPA, 1994; West et al.; U.S. EPA). The second component, which is denoted UFA, accounts unknown chemical-specific interspecies differences. Preliminary default distributions were derived as part of the WHO/IPSC

Intake

Mag

nitu

de o

f res

pons

e

0

I = 99%I = 50%Different percentile human individuals

M = 10

I = 1%

Animal

Inter-species

Intra-species

HD1001

A

“Target human dose,” HDMI, defined as to estimated human dose (or intake) at which effects with magnitude M (e.g., 10) occur in the population with an incidence I (e.g., 1%). When estimated probabilistically, it includes an associated confidence interval.

B

Combine uncertainties with lognormal approximation or

Monte Carlo simulation

IHA

MIM UFUF

DAFBMDHD,´

´=

HA UFUFDAFNOAELRfD

´´

=

Human Variability Factor for Incidence I

Interspecies Factor for remaining TK and TD

differences

Benchmark Dose for Magnitude of effect M

Dosimetric Adjustment Factor for Body Size

differences

HDMI

Addresses key needs for economic analyses:• Evaluation at different incidence I defines

population dose-response function• Probability distribution characterizes uncertainty• Magnitude of response M defines endpoint

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framework based on reviewed previously published analyses of historical data (WHO/IPCS, 2014).

• Derived probabilistic human variability extrapolation factors. In the WHO/IPCS framework, the generic human variability uncertainty factor UFH is replaced with a factor that depends on the population incidence I, denoted UFH,I, which reflects TK and TD differences between individuals at the median and individuals at the Ith percentile of the population distribution (WHO/IPCS, 2014; Chiu and Slob, 2015). As with interspecies extrapolation, preliminary default distributions were derived as part of the WHO/IPSC framework based on reviewed previously published analyses of historical data (WHO/IPCS, 2014).

• Combine the components probabilistically to derive an intake-response function and its uncertainty. The integration of BMD modeling, interspecies extrapolation, and human variability extrapolation, is the HDM

I, which disaggregates “risk” into the distinct concepts magnitude of effect (M), incidence of effect (I), and uncertainty (reflected in the confidence interval). The HDM

I can furthermore be mathematically “inverted” to derive an intake-response function for a specified fraction I of the population (Chiu and Slob, 2015).

Thus, the result is family of dose-response functions where one independent variable is the usual dose, but the “response” can be either incidence (at each value for magnitude of effect) or magnitude of effect (at each value for the percentile of the population), in addition to uncertainty bound. For socioeconomic analyses either of these options may be useful. On the one hand, an “effect” can be defined as a specified value of M along with a corresponding economic value, such as a DALY or a monetized value. The incidence of this “effect” as a function of dose can then be predicted to provide the dose-response function needed to calculate marginal benefits of different policy options. Alternatively, if different levels of “M” correspond to different levels of economic value, at each dose, the population average “M” can be calculated by integrating over the population of individual dose-response functions, and this population dose-response can then be used to calculate marginal benefits.

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3. Recent advances in applying the WHO/IPCS framework 3.1. Training and web-based tools One of the key successes of the WHO/IPCS framework is the parallel development of a spreadsheet tool “APROBA” to implement the approach. Training courses have been given in numerous venues utilizing this spreadsheet tool, including the following:

• September 2015 - Eurotox meeting, Porto Portugal • December 2016 – SRA Annual meeting, San Diego, CA • December 2017 – SRA Annual meeting, Arlington, VA • April 2018 – CalEPA/Office of Environmental Health Hazard Assessment, Oakland, CA • February 2019 – CalEPA/Department of Pesticide Regulation, Sacramento, CA • December 2019 (planned) – SRA Annual meeting, Arlington, VA

Recognizing the shift towards online portals have been developed conducting a variety of analyses, two web-based tools that implement these approaches. First, the spreadsheet tool APROBA was implemented through an R “shiny” web application “APROBAweb” (https://wchiu.shinyapps.io/APROBAweb/). APROBAweb is appropriate for users new to probabilistic dose-response assessment, and also includes the results of some published results described below. Second, for more advanced users, online Bayesian benchmark dose (BBMD) system available at https://benchmarkdose.org (Shao and Shapiro, 2018) has been augmented with a Monte Carlo simulation-based implementation of the probabilistic framework described in Chiu and Slob (2015). This tool replaces two simplifying assumptions that were required in APROBA and APROBAweb:

• First, whereas APROBA assumes the BMD is distributed lognormally, BBMD utilizes Bayesian posterior samples of the BMD generated from a Bayesian model averaging approach, and thus provides a more accurate estimate of the uncertainty distribution for the BMD.

• Second, the APROBA assumes the uncertainty in the ratio between median and the Ith percentile of the population is lognormally distributed, the original data suggest that actually the log-transformed standard deviation is lognormally distributed. The Monte Carlo-based approach of the BBMD thus incorporates the original distribution.

Thus, the calculations in the BBMD system are more accurate, especially with respect to the shape of the distribution, as compared to the approximate approach. The main benefit, though, of the BBMD system is that it automatically integrates with BMD modeling, the lack of which can be a major contributor to uncertainty.

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Figure 3. Screen shots of web-based tools for conducting probabilistic dose-response analyses. A: Rshiny ”APROBAweb” tool (https://wchiu.shinyapps.io/APROBAweb/). This tool uses the lognormal approximation implemented in WHO/IPCS APROBA Excel spreadsheet. Results from Chiu et al. (2018) can be viewed and/or loaded. B: Bayesian Benchmark dose tool, which includes a probabilistic RfD module (https://benchmarkdose.org/). This tool uses Monte Carlo Simulation instead of a lognormal approximation, and can utilize posterior samples from Bayesian Benchmark Dose calculations as input.

3.2. Feasibility of broadly applying probabilistic framework to non-cancer endpoints Chiu et al. (2018) assessed the feasibility and quantitative implications of broadly applying the WHO/IPCS framework to replace traditional RfDs with probabilistic estimates of the HDM

I. After curating peer-reviewed toxicity values from U.S. government sources, they identified 1464 traditional RfDs and endpoints suitable for probabilistic estimation, consisting of 608 Chemicals, 351 of which had with multiple RfDs or endpoints. They then implemented a four-step, automated workflow that took existing RfDs and calculated corresponding HDM

I values: 1. If needed, convert to endpoint-specific RfD by removing database uncertainty factors.

This step was necessary for 317 out of 1464 RfDs because the probabilistic dose-response framework aims to make predictions related to the specific effects associated with the POD, rather than a general statement about lack of “deleterious effects.” Because the database factor is meant to cover the case where a different effect might be observed in an as yet unperformed study, it is not meaningful to include in deriving the HDM

I. 2. Assign conceptual model(s) and magnitude(s) of effect to each endpoint-specific RfD.

Each endpoint is assigned a “conceptual model” based on the type of effect (continuous or dichotomous, reflecting a stochastic process or not) associated with the POD. This choice, along with the benchmark response (BMR) for PODs that are BMDLs, determine the magnitude of effect (M). In some cases, such as when a NOAEL is reported for effects that might include both continuous and quantal endpoints, multiple conceptual models are assigned.

3. Assign uncertainty distributions for each POD and uncertainty factor. Approaches and default distributions are described previously above.

4. Combine uncertainties probabilistically using WHO/IPCS (2014) approximate lognormal approach. This approximation was previously implemented in a spreadsheet

A B

https://wchiu.shinyapps.io/APROBAweb/ https://benchmarkdose.org/

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tool “APROBA” that is available from the WHO/IPCS web page, and was reprogrammed in R for “batch” processing. The accuracy of the lognormal approximation was confirmed by Monte Carlo simulation.

Outputs of this analysis included not only the median [90% CI] for HDM

I at I=1, but also, of greater interest to benefit-cost analysis, the median [90% CI] for the incidence of as a function of dose. An example for 1,1-Dichloroethylene liver toxicity is given in Figure 4. Some key conclusions of this analysis of relevance to benefit-cost analysis are as follows:

• Traditional RfDs tend to correlate with probabilistic RfDs (defined as the lower 95% confidence bound of the HDM

I for I=1%), with differences mostly ~10-fold or less. • Estimated upper confidence bounds on incidence (“residual risk”) at exposures equal to

traditional RfDs (i.e., at HQ=1) are typically a few percent, but a quarter are >3%. These estimates increase substantially for HQ>1, with wide variation across RfDs.

• Median estimates of incidence at HQ=1 are generally very small (<0.01% in almost all cases).

• The severity of the endpoint assessed varies greatly across RfDs, ranging, for instance, from “mild irritation” to “hemorrhage” or “increased mortality.”

Figure 4. Population dose-response prediction for 1,1-Dichloroethylene liver toxicity using WHO/IPCS Framework. Incidence is shown both on log-scale (top) and natural scale (bottom)

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More importantly, however, this work demonstrated the feasibility of broadly applying probabilistic dose-response assessment to non-cancer endpoints. Moreover, the ability to make predictions of dose-response as a function of exposure can be therefore be used to support the broad range of risk management contexts, including benefit-cost analysis. 3.3. Life-cycle impact assessment Environmental LCIA aims to comprehensively assess and compare potentially adverse environmental outcomes attributable to chemical emissions and resources along full life-cycle of a product, from resource extraction to disposal (ISO International Organization for Standardization, 2006). The human health impacts component of LCIA currently assumes linearity for all dose-response models and additivity of impacts, based on the typical need for estimating the marginal, rather than absolute, impact of different options (Fantke et al., 2018). However, the current “slope” used for this evaluation is based on a linear extrapolation from the ED50, which does not address the fact that most (at least non-cancer) dose-response relationships are sigmoidal in shape (see Figure 5A). With a non-linear dose-response relationship, the slope depends on where the absolute level of exposure is, a level denoted the “working point” (Frischknecht and Jolliet, 2016). The impact of the “working point” and the marginal slope at that point can be significant. As illustrated graphically in Figure 5B-C, where the current approach is shown in the context of an underlying sigmoidal dose-response relationship. If for a given substance used in product 1 the cumulative exposure is very different than for another substance used in product 2, using a linear response from ED50 might be misleading. For example, a simple comparison between two product formulations is made, and product 1 is deemed to be “riskier” than product 2 based on the current LCIA methodology using ED50s. However, the incremental increase in incidence depends on both the dose-response shape as well as the background exposure to which the product is adding, and therefore an implicit assumption of the current LCIA approach is that the background exposure is equal to where the actual slope equals the slope to the ED50. Therefore, especially if the background cumulative exposures are very different between substances, the ED50-based calculations might be inaccurate even with respect to the relative ranking, not to mention in the actual predicted value of health impacts. A possible approach to address this issue is directly replacing the assumed linear dose-response with the estimated slope using the WHO/IPCS framework, which allows computing the value of the slope at any point, as illustrated in Figure 6 .

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A

B

C

Figure 5. Illustration of the impacts of a sigmoidal dose-response curve shape and differing background exposure levels on deriving an effect factor as slope linking health impact response to chemical exposure dose in current LCIA methods. Product 1 and Product 2 are assumed to be formulated using two different chemicals (whose dose-response curves are shown), and therefore would be expected to have different background exposures in the population.

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Figure 6. Illustration of the lognormal threshold distribution-based population effect response level as a function of population exposure dose level, indicating the exposure dose-response slope as a function of the exposure “working point” 𝑿. 𝛷 is the standard normal cumulative distribution, 𝜑 is the standard normal probability density, 𝜇% is the natural log exposure at which 50% of the population is affected, and 𝜎%' is the variance of the population sensitivity distribution.

The key remaining question, then, is at what exposure level to assign the “working point” X. LCIA is primarily interested in aggregated (over all sources and exposure pathways per substance) and cumulative (over different chemical substances and other stressors) health impacts. Thus, one logical evaluation point of X is the background exposure level, to which the new exposure is adding. For small background exposure levels and corresponding small incidence values, this approach is, however, limited by the fact that there is increasing uncertainty as to the accuracy of the assumed lognormal shape of the human population variability distribution (Pennington et al., 2002; Crump et al., 2010). Alternatively, the working point can be set via an “effective” background exposure at the equivalent background incidence rate, assuming that the response in the population occurs via the same pathways (Huijbregts et al., 2005), or at a fixed default incidence rate (e.g., 10% or 1%) in order to avoid the high uncertainty associated with the shape of the dose-response at levels much lower than measurable outcomes. Furthermore, because the output of the WHO/IPCS approach addresses both uncertainty and variability in a way that can be disaggregated into individual dose-response curves, it is also possible in principle to incorporate uncertainty and variability in the exposure information at the individual level to calculated individual-level slopes, which can then be re-aggregated to a population level.

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4. Conclusions: Remaining Challenges and Future Prospects There is considerable opportunity to fundamentally change the nature of chemical risk management. However, there are a number of challenges to be overcome before the full portential can be realized. These challenges can be considered to fall into several categories: (a) challenges in quantification; (b) institutional challenges; (c) and communication-related challenges. 4.1. Challenges in Quantification A major challenge is that the vast majority of chemicals in commerce and to which populations are exposed lack the epidemiologic or experimental animal data that have been traditionally used to develop toxicity values. However, major efforts have been undertaken in the field of toxicology to develop “new approach methods” using computational in silico methods (Wignall et al., 2018), high-throughput in vitro data (e.g., Wambaugh et al., 2015), and more detailed mechanistic frameworks such as adverse outcome pathways (AOPs) (Ankley et al., 2010). In addition, there is a long history of “data poor” approaches, such as the threshold of toxicological concern (TTC) (e.g., Kroes et al., 2005) and read-across (Patlewicz et al., 2017; Stuard and Heinonen). In the medium term, these approaches could be used to derive points of departure for use in the WHO/IPCS framework. While all of these approaches entail significant uncertainties, these uncertainties may be preferable to the alternative, which is omission of data-poor chemicals from risk assessments, an omission that essentially ascribes a value of zero to the impact of these chemicals. Another challenge will continue to be the ability to quantify uncertainty in the level of human variability at very low doses (e.g., a dose at which less than 1 person in 10,000 might have an adverse health outcome). One strategy, as discussed in the LCIA example, might be set a default level of incidence below which it is not recommended to quantify uncertainty unless a decision-maker is thought to be particularly interested in such an estimate. Additional challenges relate to the current inability to convert the incidence of predicted adverse health responses into estimates of the burden of disease (e.g., QALYs, DALYs, etc.) for a great majority of adverse health outcomes that are associated with the health response being predicted. This will be necessary to fully describe the health burden to the population and to monetize the benefit of preventing some fraction of this burden. Unfortunately, as noted by Chiu et al. (2018), critical effect endpoints from animal studies used for the construction of non-cancer dose-response curves include a wider range of severity, including alterations in clinical chemistry parameters, changes in organ or bodyweights, alterations in histology, changes in food or water consumption, and even hemorrhage and death (Figure 7). Few of these other endpoints correspond directly to a frank disease state in humans, and this disconnect inhibits the ability to assign economically meaningful values, such as DALYs or monetized units. Thus, the ability for the large range of severity of outcomes to more formally and quantitatively contribute to the determination of an appropriate level of protection and the cost-effectiveness of prevention would be a very important contribution if it were realized.

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It should be further noted that none of these quantification challenges is brought about by the particular methods or tools applied here (APROBA, BBMD, etc.). Rather, the approach described above exposes those challenges where they are otherwise left unacknowledged in the classical RfD-based approach.

Figure 7. Effect types for 1464 reference dose (RfD) derivations for different health outcomes for 608 chemicals based on analysis by (Chiu et al., 2018).

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4.2. Institutional Challenges Some of the key institutional challenges stem from the need to build capacity to describe uncertainties and to quantify the previously unquantified. While easy-to-use and freely available tools are helpful in making the analytical task much less burdensome, achieving the conceptual understanding of the quantitative components of the HDM

I and its related measures will require a sustained effort. The conceptual separation of uncertainty and variability, the concept of multiple probability distributions, Monte Carlo simulation and related techniques are not part of the standard training of a significant proportion of the relevant workforce who normally use reference values, such that continuing professional education will be needed until it becomes part of standard toxicological training and that of related disciplines. A more fundamental institutional challenge (applying to both private and public sector organizations) is the various barriers which prevent an institution from explicitly establishing quantified goals for health protection (and the level of confidence in the level of protection achieved). The transition in converting from the definition of the RfD, which includes multiple layers of ambiguity, to a statement like: “We propose to allow levels of contaminant X in drinking water up to the level where we can be 99% confident that less than 1% of the exposed population are expected to experience mild adverse effects with daily consumption.” Admitting to the degree of uncertainty and the degree to which the population is expected to be protected leads to uncomfortable conversations that are currently not part of the usual process of scrutiny of public health protection. Once again, the tools described here bring these societal judgments into the light of day. Overcoming the institutional challenges involves preparing decision-makers and communicators to transition from a binary concept of safe/unsafe to implementing explicit judgements regarding societal tolerance for non-zero risk and a significant level of uncertainty in estimating low levels of risk. 4.3. Communication Challenges Arguably, the communication burden associated with this transformation is significant. One of those challenges relates to overcoming concerns, where appropriate, that are based in expectations (public, political, media, stakeholders, workers) that current exposure limits (RfDs, etc.) delineate a “bright line” between safe and unsafe exposures. Ultimately, it may be difficult to explain why it is deemed reasonable to tolerate exposures in which a small but non-zero fraction of the population (or a fraction of the population of workers) can be predicted to experience varying degrees of harm. The explanation for this apparent “disconnect” may be that, in a sense, the current application of non-cancer risk assessment “violates” the conceptual separation between risk assessment and risk management advocated by in the foundational NRC “Red Book” (NAS, 1983). Various judgments expected of the risk management function are subsumed within the risk assessment function’s selection of adverse outcomes and the accumulation of generic and chemical-specific

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adjustment factors with no explicit requirement to achieve a level of protection that the risk manager has determined to be desirable, nor the expectation that the resulting level of public protection even be described. For this reason, a concerted effort, perhaps using Chiu et al. (2018) as a starting point, should be undertaken to understand the societal consequences of the limitations of current approaches. At one extreme, the consequences could be found to be minimal with the finding that current risk management decisions are generally appropriate and investments in public and private chemical risk control are appropriately allocated. At the other extreme, the consequences could be found to be substantial with respect to, for example, some combination of human health impacts, misallocation of financial expenditures in public and private sectors and questions of the appropriate governance of this class of risks to the public. It is also reasonable to expect that the truth lies somewhere in between those extremes. 4.4. Future prospects While this review demonstrates that the WHO/IPCS framework for probabilistic dose-response is broadly applicable and tools are already available, it may not be possible (or even desirable) to apply such fundamental change in all cases all at the same time. As such, it would be useful to consider where such application of the modified process and conceptual approach is most fruitfully applied. Examples of these considerations could include: (a) where the societal cost is predicted to be significant; (b) when applying or requiring chemical substitutions; (c) where known exposures are within an order of magnitude of existing thresholds; (d) for new chemicals only; (e ) based on ease of application; or (f) in relatively non-controversial contexts to gain more practical experience in the management of the process, the interactions with decision-makers and stakeholders during the process, and in the communication of the results of the process. Overall, although challenges remain, the WHO/IPCS framework provides a better characterization of the relationship between endpoints observed in experimental studies and human health effects, providing a strong basis for expanding the intersection of risk assessment and economics to better support policy decisions.

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