Commentary: Value of information case study strongly supports use of the Threshold of Toxicological Concern (TTC).

A Value of Information (VOI) analysis can play a key role in decision-making for adopting new approach methodologies (NAMs). We applied EPA's recently developed VOI framework to the Threshold of Toxicological Concern (TTC). Obtaining/deriving a TTC value for use as a toxicity reference value (TRV) for substances with limited toxicity data was shown to provide equivalent or greater health protection, immense return on investment (ROI), greater net benefit, and substantially lower costs of delay (CoD) compared with TRVs derived from either traditional human health assessment (THHA) chronic toxicity testing in lab animals or the 5-day in vivo EPA Transcriptomic Assessment Product (ETAP). For all nine exposure scenarios examined, the TTC was more economical terms of CoD and ROI than the ETAP or the THHA; expected net benefit was similar for the TTC and ETAP with both of these more economical than the THHA The TTC ROI was immensely greater (5,000,000-fold on average) than the ROI for THHA and the ETAP ROI (100,000-fold on average). These results support the use of the TTC for substances within its domain of applicability to waive requiring certain in vivo tests, or at a minimum, as an initial screening step before conducting either the ETAP or THHA in vivo studies.

Best practices for exposure model peer review - A SciPinion advisory panel report.

The extent and rigor of peer review that a model undergoes during and after development influences the confidence of users and managers in model predictions. A process for determining the breadth and depth of peer review of exposure models was developed with input from a panel of exposure-modeling experts. This included consideration of the tiers and types of models (e.g., screening, deterministic, probabilistic, etc.). The experts recommended specific criteria be considered when evaluating the degree to which a model has been peer reviewed, including quality of documentation and the model peer review process (e.g., internal review with a regulatory agency by subject matter experts, expert review reports, formal Scientific Advisory Panels, and journal peer review). In addition, because the determination of the confidence level for an exposure model's predictions is related to the degree of evaluation the model has undergone, irrespective of peer review, the experts recommended the approach include judging the degree of model rigor using a set of specific criteria: (1) nature and quality of input data, (2) model verification, (3) model corroboration, and (4) model evaluation. Other key areas considered by the experts included recommendations for addressing model uncertainty and sensitivity, defining the model domain of applicability, and flags for when a model is used outside its domain of applicability. The findings of this expert engagement will help developers as well as users of exposure models have greater confidence in their application and yield greater transparency in the evaluation and peer review of exposure models.

Quantitative Structure Use Relationships: Highlights from a technical summit meeting.

The Quantitative Structure Use Relationship (QSUR) Summit, held on November 2-4, 2022, focused on advancing the development, refinement, and use of QSURs to support chemical substance prioritization and risk assessment and mitigation. QSURs utilize chemical structures to predict the function of a chemical within a formulated product or an industrial process. This presumed function can then be used to develop chemical use categories or other information necessary to refine exposure assessments. The invited expert meeting was attended by 38 scientists from Canada, Finland, France, the UK, and the USA, representing government, business, and academia, with expertise in exposure science, chemical engineering, risk assessment, formulation chemistry, and machine learning. Workshop discussions emphasized the importance of collection and sharing of data and quantification of relative chemical quantities to progress QSUR development. Participants proposed collaborative approaches to address key challenges, including mechanisms for aggregating information while still protecting proprietary product composition and other confidential business information. Discussions also led to proposals for applications beyond exposure and risk modeling, including sustainable formulation discovery. In addition, discussions continue to construct, conduct, and circulate case studies tied to various specific problem formulations in which QSURs supply or derive information on chemical functions, concentrations, and exposures.

The role of fit-for-purpose assays within tiered testing approaches: A case study evaluating prioritized estrogen-active compounds in an in vitro human uterotrophic assay.

Chemical risk assessment relies on toxicity tests that require significant numbers of animals, time and costs. For the >30,000 chemicals in commerce, the current scale of animal testing is insufficient to address chemical safety concerns as regulatory and product stewardship considerations evolve to require more comprehensive understanding of potential biological effects, conditions of use, and associated exposures. We demonstrate the use of a multi-level new approach methodology (NAMs) strategy for hazard- and risk-based prioritization to reduce animal testing. A Level 1/2 chemical prioritization based on estrogen receptor (ER) activity and metabolic activation using ToxCast data was used to select 112 chemicals for testing in a Level 3 human uterine cell estrogen response assay (IKA assay). The Level 3 data were coupled with quantitative in vitro to in vivo extrapolation (Q-IVIVE) to support bioactivity determination (as a surrogate for hazard) in a tissue-specific context. Assay AC50s and Q-IVIVE were used to estimate human equivalent doses (HEDs), and HEDs were compared to rodent uterotrophic assay in vivo-derived points of departure (PODs). For substances active both in vitro and in vivo, IKA assay-derived HEDs were lower or equivalent to in vivo PODs for 19/23 compounds (83%). Activity exposure relationships were calculated, and the IKA assay was as or more protective of human health than the rodent uterotrophic assay for all IKA-positive compounds. This study demonstrates the utility of biologically relevant fit-for-purpose assays and supports the use of a multi-level strategy for chemical risk assessment.

Recommendations for further revisions to improve the International Agency for Research on Cancer (IARC) Monograph program.

In 2019, the International Agency for Research on Cancer (IARC) "Preamble to the IARC Monographs" expanded guidance regarding the scientific approaches that should be employed in its monographs. These amendments to the monograph development process are an improvement but still fall short in several areas. While the revised Preamble lays out broad methods and approaches to evaluate scientific evidence, there is a lack of specificity with regard to how IARC Working Groups will conduct consistent evaluations in a standardized, objective, and transparent manner; document systematic review and evidence integration actions, and substantiate how these actions and decisions inform the ultimate classifications. Furthermore, no guidance is provided to ensure Working Groups consistently incorporate mechanistic evidence in a robust manner using a defined approach in the context of 21st century knowledge of modes of action. Nor are the conclusions of the working groups subjected to outside, independent scientific peer review. Continued improvements and modernization of the procedures for evaluating, presenting, and communicating study quality, and in the methods used to conduct and peer-review evidence-based decision making will benefit the Working Group members, the IARC Monographs Programme overall, and the international regulatory community and public who rely upon the monographs.

21st Century Approaches for Evaluating Exposures, Biological Activity, and Risks of Complex Substances: Workshop highlights.

The June 2019 workshop 21st Century Approaches for Evaluating Exposures, Biological Activity, and Risks of Complex Substances, co-organised by the International Council of Chemical Association's Long-Range Research Initiative and the European Commission's Joint Research Centre, is summarised. Focus was the need for improved approaches to evaluate the safety of complex substances. Approximately 10% and 20% of substances registered under the EU chemicals legislation are 'multi-constituent substances' and 'substances of unknown or variable compositions, complex reaction products and biological substances' (UVCBs), respectively, and UVCBs comprise approximately 25% of the U.S. Toxic Substances Control Act Inventory. Workshop participants were asked to consider how the full promise of new approach methodologies (NAMs) could be brought to bear to evaluate complex substances. Sessions focused on using NAMs for screening, biological profiling, and in complex risk evaluations; improving read-across approaches employing new data streams; and methods to evaluate exposure and dosimetry. The workshop concluded with facilitated discussions to explore actionable steps forward. Given the diversity of complex substances, no single 'correct' approach was seen as workable. The path forward should focus on 'learning by doing' by developing and openly sharing NAM-based fit-for-purpose case examples for evaluating biological activity, exposures and risks of complex substances.

Development of an adverse outcome pathway for chemically induced hepatocellular carcinoma: case study of AFB1, a human carcinogen with a mutagenic mode of action.

Adverse outcome pathways (AOPs) are frameworks starting with a molecular initiating event (MIE), followed by key events (KEs) linked by KE relationships (KERs), ultimately resulting in a specific adverse outcome. Relevant data for the pathway and each KE/KER are evaluated to assess biological plausibility, weight-of-evidence, and confidence. We aimed to describe an AOP relevant to chemicals directly inducing mutation in cancer critical gene(s), via the formation of chemical-specific pro-mutagenic DNA adduct(s), as an early critical step in tumor etiology. Such chemicals have mutagenic modes-of-action (MOA) for tumor induction. To assist with developing this AOP, Aflatoxin B1 (AFB1) was selected as a case study because it has a rich database and is considered to have a mutagenic MOA. AFB1 information was used to define specific KEs, KERs, and to inform development of a generic AOP for mutagen-induced hepatocellular carcinoma (HCC). In assessing the AFB1 information, it became clear that existing data are, in fact, not optimal and for some KEs/KERs, the definitive data are not available. In particular, while there is substantial information that AFB1 can induce mutations (based on a number of mutation assays), the definitive evidence - the ability to induce mutation in the cancer critical gene(s) in the tumor target tissue - is not available. Thus, it is necessary to consider the patterns of results in the weight-of-evidence for KEs and KERs. It was important to determine whether there was sufficient evidence that AFB1 can induce the necessary critical mutations early in the carcinogenic process, which was the case.

How well can carcinogenicity be predicted by high throughput "characteristics of carcinogens" mechanistic data?

IARC has begun using ToxCast/Tox21 data in efforts to represent key characteristics of carcinogens to organize and weigh mechanistic evidence in cancer hazard determinations and this implicit inference approach also is being considered by USEPA. To determine how well ToxCast/Tox21 data can explicitly predict cancer hazard, this approach was evaluated with statistical analyses and machine learning prediction algorithms. Substances USEPA previously classified as having cancer hazard potential were designated as positives and substances not posing a carcinogenic hazard were designated as negatives. Then ToxCast/Tox21 data were analyzed both with and without adjusting for the cytotoxicity burst effect commonly observed in such assays. Using the same assignments as IARC of ToxCast/Tox21 assays to the seven key characteristics of carcinogens, the ability to predict cancer hazard for each key characteristic, alone or in combination, was found to be no better than chance. Hence, we have little scientific confidence in IARC's inference models derived from current ToxCast/Tox21 assays for key characteristics to predict cancer. This finding supports the need for a more rigorous mode-of-action pathway-based framework to organize, evaluate, and integrate mechanistic evidence with animal toxicity, epidemiological investigations, and knowledge of exposure and dosimetry to evaluate potential carcinogenic hazards and risks to humans.

Quantitative weight of evidence to assess confidence in potential modes of action.

The evolved World Health Organization/International Programme on Chemical Safety mode of action (MOA) framework provides a structure for evaluating evidence in pathways of causally linked key events (KE) leading to adverse health effects. Although employed globally, variability in use of the MOA framework has led to different interpretations of the sufficiency of evidence in support of hypothesized MOAs. A proof of concept extension of the MOA framework is proposed for scoring confidence in the supporting data to improve scientific justification for MOA use in characterizing hazards and selecting dose-response extrapolation methods for specific chemicals. This involves selecting hypothesized MOAs, and then, for each MOA, scoring the weight of evidence (WOE) in support of causality for each KE using evolved Bradford Hill causal considerations (biological plausibility, essentiality, dose-response concordance, consistency, and analogy). This early proof of concept method is demonstrated by comparing two potential MOAs (mutagenicity and peroxisome proliferator activated receptor-alpha) for clofibrate, a rodent liver carcinogen. Quantitative confidence scoring of hypothesized MOAs is shown to be useful in characterizing the likely operative MOA. To guide method refinement and future confidence scoring for a spectrum of MOAs, areas warranting further focus and lessons learned, including the need to incorporate a narrative discussion of the weights used in the evaluation and an overall evaluation of the plausibility of the outcome, are presented.

How well can in vitro data predict in vivo effects of chemicals? Rodent carcinogenicity as a case study.

A recent research article by the National Center for Computational Toxicology (NCCT) (Kleinstreuer et al., 2013), indicated that high throughput screening (HTS) data from assays linked to hallmarks and presumed pathways of carcinogenesis could be used to predict classification of pesticides as either (a) possible, probable or likely rodent carcinogens; or (b) not likely carcinogens or evidence of non-carcinogenicity. Using independently developed software to validate the computational results, we replicated the majority of the results reported. We also found that the prediction model correlating cancer pathway bioactivity scores with in vivo carcinogenic effects in rodents was not robust. A change of classification of a single chemical in the test set was capable of changing the overall study conclusion about the statistical significance of the correlation. Furthermore, in the subset of pesticide compounds used in model validation, the accuracy of prediction was no better than chance for about three quarters of the chemicals (those with fewer than 7 positive outcomes in HTS assays representing the 11 histopathological endpoints used in model development), suggesting that the prediction model was not adequate to predict cancer hazard for most of these chemicals. Although the utility of the model for humans is also unclear because a number of the rodent responses modeled (e.g., mouse liver tumors, rat thyroid tumors, rat testicular tumors, etc.) are not considered biologically relevant to human responses, the data examined imply the need for further research with HTS assays and improved models, which might help to predict classifications of in vivo carcinogenic responses in rodents for the pesticide considered, and thus reduce the need for testing in laboratory animals.

Challenges in using the ToxRefDB as a resource for toxicity prediction modeling.

Developing and evaluating toxicity prediction models requires selection and use of datasets of known positive and negative agents for the endpoint(s) of interest. EPA's Toxicity Reference Database (ToxRefDB) is a publicly available dataset containing detailed study and effect information on more than 400 chemicals, and it has been used by EPA researchers to develop toxicity prediction models. During an initial evaluation of reproductive toxicity, however, limitations were uncovered in applying data from ToxRefDB that involved interpretation of toxicity effects and designation of toxicity endpoints, core attributes of the database that are critical to its use. These limitations for reproductive toxicity were found to be related, at least in part, to challenges faced in (1) evaluating the source of the original study data (EPA Data Evaluation Records (DERs)) for input into ToxRefDB and (2) interpretation of the biological significance of responses. These limitations of the ToxRefDB have important implications for the wider use of the database as it currently exists. Our results point to a need for improvements to the existing ToxRefDB and/or for researchers to independently evaluate, assign and verify positive or negative designations to data from ToxRefDB before use in development or validation of prediction models or testing frameworks.

Modernizing problem formulation for risk assessment necessitates articulation of mode of action.

The process of scientific hypothesis formulation affects the experimental designs, methods and interpretations applied, but to be testable, the hypotheses posed must conform to the state of scientific knowledge and available technology. An analogous situation exists in risk assessment, where the questions addressed are typically articulated in the problem formulation phase. Decades ago, regulatory agencies couched problem formulation according to the questions answerable by the science of the day. As regulatory requirements for risk assessment became codified, so too did the rudiments of problem formulation. Unfortunately, codifying problem formulation prevented it from evolving to keep pace with scientific advancements. Today's more advanced science is not always being used effectively and efficiently in risk assessment because the risk assessment problem formulation step still typically poses antiquated questions. Problem formulation needs to be improved so that modern science can inform risk considerations. Based on recent developments in the Human Relevance Framework and using well-studied example chemicals - chloroform and carbon tetrachloride - an approach is proposed for focusing problem formulation on human-relevant hypotheses. We contend that modernizing problem formulation in this way will make risk assessment more scientifically accurate, more practical, and more relevant for protecting human health and the environment.

The adverse outcome pathway for rodent liver tumor promotion by sustained activation of the aryl hydrocarbon receptor.

An Adverse Outcome Pathway (AOP) represents the existing knowledge of a biological pathway leading from initial molecular interactions of a toxicant and progressing through a series of key events (KEs), culminating with an apical adverse outcome (AO) that has to be of regulatory relevance. An AOP based on the mode of action (MOA) of rodent liver tumor promotion by dioxin-like compounds (DLCs) has been developed and the weight of evidence (WoE) of key event relationships (KERs) evaluated using evolved Bradford Hill considerations. Dioxins and DLCs are potent aryl hydrocarbon receptor (AHR) ligands that cause a range of species-specific adverse outcomes. The occurrence of KEs is necessary for inducing downstream biological responses and KEs may occur at the molecular, cellular, tissue and organ levels. The common convention is that an AOP begins with the toxicant interaction with a biological response element; for this AOP, this initial event is binding of a DLC ligand to the AHR. Data from mechanistic studies, lifetime bioassays and approximately thirty initiation-promotion studies have established dioxin and DLCs as rat liver tumor promoters. Such studies clearly show that sustained AHR activation, weeks or months in duration, is necessary to induce rodent liver tumor promotion--hence, sustained AHR activation is deemed the molecular initiating event (MIE). After this MIE, subsequent KEs are 1) changes in cellular growth homeostasis likely associated with expression changes in a number of genes and observed as development of hepatic foci and decreases in apoptosis within foci; 2) extensive liver toxicity observed as the constellation of effects called toxic hepatopathy; 3) cellular proliferation and hyperplasia in several hepatic cell types. This progression of KEs culminates in the AO, the development of hepatocellular adenomas and carcinomas and cholangiolar carcinomas. A rich data set provides both qualitative and quantitative knowledge of the progression of this AOP through KEs and the KERs. Thus, the WoE for this AOP is judged to be strong. Species-specific effects of dioxins and DLCs are well known--humans are less responsive than rodents and rodent species differ in sensitivity between strains. Consequently, application of this AOP to evaluate potential human health risks must take these differences into account.

Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes.

An adverse outcome pathway (AOP) describes the causal linkage between initial molecular events and an adverse outcome at individual or population levels. Whilst there has been considerable momentum in AOP development, far less attention has been paid to how AOPs might be practically applied for different regulatory purposes. This paper proposes a scientific confidence framework (SCF) for evaluating and applying a given AOP for different regulatory purposes ranging from prioritizing chemicals for further evaluation, to hazard prediction, and ultimately, risk assessment. The framework is illustrated using three different AOPs for several typical regulatory applications. The AOPs chosen are ones that have been recently developed and/or published, namely those for estrogenic effects, skin sensitisation, and rodent liver tumor promotion. The examples confirm how critical the data-richness of an AOP is for driving its practical application. In terms of performing risk assessment, human dosimetry methods are necessary to inform meaningful comparisons with human exposures; dosimetry is applied to effect levels based on non-testing approaches and in vitro data. Such a comparison is presented in the form of an exposure:activity ratio (EAR) to interpret biological activity in the context of exposure and to provide a basis for product stewardship and regulatory decision making.

An exposure:activity profiling method for interpreting high-throughput screening data for estrogenic activity--proof of concept.

Rapid high throughput in vitro screening (HTS) assays are now available for characterizing dose-responses in assays that have been selected for their sensitivity in detecting estrogen-related endpoints. For example, EPA's ToxCast™ program recently released endocrine assay results for more than 1800 substances and the interagency Tox21 consortium is in the process of releasing data for approximately 10,000 chemicals. But such activity measurements alone fall short for the purposes of priority setting or screening because the relevant exposure context is not considered. Here, we extend the method of exposure:activity profiling by calculating the exposure:activity ratios (EARs) using human exposure estimates and AC50 values for a range of chemicals tested in a suite of seven estrogenic assays in ToxCast™ and Tox21. To provide additional context, relative estrogenic exposure:activity quotients (REEAQ) were derived by comparing chemical-specific EARs to the EAR of the ubiquitous dietary phytoestrogen, genistein (GEN). Although the activity of a substance in HTS-endocrine assays is not a measure of health hazard or risk, understanding how such a dose compares to human exposures provides a valuable additional metric that can be used in decision-making; substances with small EARs and REEAQs would indicate low priority for further endocrine screening or testing.

Increasing Scientific Confidence in Adverse Outcome Pathways: Application of Tailored Bradford-Hill Considerations for Evaluating Weight of Evidence.

Systematic consideration of scientific support is a critical element in developing and, ultimately, using adverse outcome pathways (AOPs) for various regulatory applications. Though weight of evidence (WoE) analysis has been proposed as a basis for assessment of the maturity and level of confidence in an AOP, methodologies and tools are still being formalized. The Organization for Economic Co-operation and Development (OECD) Users' Handbook Supplement to the Guidance Document for Developing and Assessing AOPs (OECD 2014a; hereafter referred to as the OECD AOP Handbook) provides tailored Bradford-Hill (BH) considerations for systematic assessment of confidence in a given AOP. These considerations include (1) biological plausibility and (2) empirical support (dose-response, temporality, and incidence) for Key Event Relationships (KERs), and (3) essentiality of key events (KEs). Here, we test the application of these tailored BH considerations and the guidance outlined in the OECD AOP Handbook using a number of case examples to increase experience in more transparently documenting rationales for assigned levels of confidence to KEs and KERs, and to promote consistency in evaluation within and across AOPs. The major lessons learned from experience are documented, and taken together with the case examples, should contribute to better common understanding of the nature and form of documentation required to increase confidence in the application of AOPs for specific uses. Based on the tailored BH considerations and defining questions, a prototype quantitative model for assessing the WoE of an AOP using tools of multi-criteria decision analysis (MCDA) is described. The applicability of the approach is also demonstrated using the case example aromatase inhibition leading to reproductive dysfunction in fish. Following the acquisition of additional experience in the development and assessment of AOPs, further refinement of parameterization of the model through expert elicitation is recommended. Overall, the application of quantitative WoE approaches hold promise to enhance the rigor, transparency and reproducibility for AOP WoE determinations and may play an important role in delineating areas where research would have the greatest impact on improving the overall confidence in the AOP.

Interpreting estrogen screening assays in the context of potency and human exposure relative to natural exposures to phytoestrogens.

While the Environmental Protection Agency and the Organization for Economic Cooperation and Development have developed validated in vitro and in vivo screening assays to measure interaction of substances with estrogen, androgen and thyroid pathway components, to date, methods to contextualize such results in terms of potencies and actual human exposures are lacking. To place endocrine screening results in the context of potency and human exposure, we propose a method that entails (1) calculating a benchmark dose for a response measured in an endocrine screen; (2) estimating the human urinary concentration (biomonitoring equivalent, BE) expected to correspond to this dose (BEBMD ); (3) deriving the exposure:activity ratio (EAR) by comparing actual urinary values from human biomonitoring studies (e.g., National Health and Nutrition Examination Survey (NHANES)) to the BEBMD . Using OECD uterotrophic assay validation studies and NHANES results, we calculated EARs for genistein (EARGEN = 6.6 × 10(-4) ) and bisphenol A (EARBPA = 8.8 × 10(-7) ). The EARGEN is more than 700-fold greater than the EARBPA . Not only can these methods be applied to additional endocrine assays and compounds, they can contribute to weight of evidence decisions regarding the need for additional endocrine screening and testing-substances with low EARs may not warrant additional testing.

Relevance weighting of tier 1 endocrine screening endpoints by rank order.

Weight of evidence (WoE) approaches are recommended for interpreting various toxicological data, but few systematic and transparent procedures exist. A hypothesis-based WoE framework was recently published focusing on the U.S. EPA's Tier 1 Endocrine Screening Battery (ESB) as an example. The framework recommends weighting each experimental endpoint according to its relevance for deciding eight hypotheses addressed by the ESB. Here we present detailed rationale for weighting the ESB endpoints according to three rank ordered categories and an interpretive process for using the rankings to reach WoE determinations. Rank 1 was assigned to in vivo endpoints that characterize the fundamental physiological actions for androgen, estrogen, and thyroid activities. Rank 1 endpoints are specific and sensitive for the hypothesis, interpretable without ancillary data, and rarely confounded by artifacts or nonspecific activity. Rank 2 endpoints are specific and interpretable for the hypothesis but less informative than Rank 1, often due to oversensitivity, inclusion of narrowly context-dependent components of the hormonal system (e.g., in vitro endpoints), or confounding by nonspecific activity. Rank 3 endpoints are relevant for the hypothesis but only corroborative of Ranks 1 and 2 endpoints. Rank 3 includes many apical in vivo endpoints that can be affected by systemic toxicity and nonhormonal activity. Although these relevance weight rankings (WREL ) necessarily involve professional judgment, their a priori derivation enhances transparency and renders WoE determinations amenable to methodological scrutiny according to basic scientific premises, characteristics that cannot be assured by processes in which the rationale for decisions is provided post hoc.

Developing scientific confidence in HTS-derived prediction models: lessons learned from an endocrine case study.

High throughput (HTS) and high content (HCS) screening methods show great promise in changing how hazard and risk assessments are undertaken, but scientific confidence in such methods and associated prediction models needs to be established prior to regulatory use. Using a case study of HTS-derived models for predicting in vivo androgen (A), estrogen (E), thyroid (T) and steroidogenesis (S) endpoints in endocrine screening assays, we compare classification (fitting) models to cross validation (prediction) models. The more robust cross validation models (based on a set of endocrine ToxCast™ assays and guideline in vivo endocrine screening studies) have balanced accuracies from 79% to 85% for A and E, but only 23% to 50% for T and S. Thus, for E and A, HTS results appear promising for initial use in setting priorities for endocrine screening. However, continued research is needed to expand the domain of applicability and to develop more robust HTS/HCS-based prediction models prior to their use in other regulatory applications. Based on the lessons learned, we propose a framework for documenting scientific confidence in HTS assays and the prediction models derived therefrom. The documentation, transparency and the scientific rigor involved in addressing the elements in the proposed Scientific Confidence Framework could aid in discussions and decisions about the prediction accuracy needed for different applications.

Advancing human health risk assessment: integrating recent advisory committee recommendations.

Over the last dozen years, many national and international expert groups have considered specific improvements to risk assessment. Many of their stated recommendations are mutually supportive, but others appear conflicting, at least in an initial assessment. This review identifies areas of consensus and difference and recommends a practical, biology-centric course forward, which includes: (1) incorporating a clear problem formulation at the outset of the assessment with a level of complexity that is appropriate for informing the relevant risk management decision; (2) using toxicokinetics and toxicodynamic information to develop Chemical Specific Adjustment Factors (CSAF); (3) using mode of action (MOA) information and an understanding of the relevant biology as the key, central organizing principle for the risk assessment; (4) integrating MOA information into dose-response assessments using existing guidelines for non-cancer and cancer assessments; (5) using a tiered, iterative approach developed by the World Health Organization/International Programme on Chemical Safety (WHO/IPCS) as a scientifically robust, fit-for-purpose approach for risk assessment of combined exposures (chemical mixtures); and (6) applying all of this knowledge to enable interpretation of human biomonitoring data in a risk context. While scientifically based defaults will remain important and useful when data on CSAF or MOA to refine an assessment are absent or insufficient, assessments should always strive to use these data. The use of available 21st century knowledge of biological processes, clinical findings, chemical interactions, and dose-response at the molecular, cellular, organ and organism levels will minimize the need for extrapolation and reliance on default approaches.