A computational system for Bayesian benchmark dose estimation of genomic data in BBMD

Countdown to 2027 - maximising use of NAMs in food safety assessment: closing the gap for regulatory assessments in Europe

Safety assessments of regulated food products in the European Union (EU) largely rely on experimental animal studies. Currently, the European Commission is developing a roadmap to phase out animal testing for chemical safety assessment across all relevant pieces of legislation, including foods, while the ambition of the European Food Safety Authority (EFSA) is that by 2027, new scientific developments, i.e., new approach/non-animal methods (NAMs), will be integrated into assessments leading to "the minimisation of animal testing". However, considering recent requests that have been made to conduct new animal studies for some regulated products, significant progress is required to minimise further and ultimately replace animal testing in the food safety environment. To advance this, we review several NAMs amenable for use in food safety assessment and reflect on their presence in EU food safety regulation and sectoral guidance. For many years, proposals to incorporate NAMs into food safety assessments have been made with questionable regulatory impact. Therefore, we present several amendments which could be made to the EU food regulatory system and current strategies towards phasing out animal testing which, if taken up, could lead to a tangible difference in the extent of animal testing within the food safety environment. Recognising that research may be required for some of these NAMs to enhance regulatory uptake, we propose potential follow-up projects that complement recent research & innovation (R&I) needs published by EFSA which food safety stakeholders could coordinate or participate in.

The use of canonical dose-response models for benchmark dose analysis of continuous toxicological data

The benchmark dose (BMD) approach employs dose-response modeling to determine the dose associated with a small change in response relative to the background response. Here, we introduce a conceptual framework for modeling continuous data that is based on key risk assessment principles and requirements. Based on this framework, we define a class of dose-response models sharing the same four biologically interpretable model parameters, while exhibiting five common properties that are essential from a risk assessment perspective: such models are denoted as "canonical" models. The first two canonical properties are straightforward: property 1. The models should predict positive values only (as measurements of continuous endpoints are typically positive) and property 2. the outcomes should not depend on the measurement unit. Canonical property 3 reflects the observation that toxicological dose-response data related to different subgroups (e.g. species, sexes, and exposure durations) are typically (at least approximately) parallel on a log-dose scale, which is at the same time an implicit assumption in defining fundamental toxicological concepts, such as extrapolation factors, relative potency factors (RPFs), and relative sensitivity factors (RSFs). Property 4 is needed to enable comparisons of the sensitivity of endpoints differing in maximum response. A fifth canonical property reflects our view that choices regarding the dose-response model expression, the assumed distribution for the within-group variation, and the benchmark response (BMR) that is being used should be internally consistent. The canonical models that we discuss are suitable to fit parallel dose-response curves to combined datasets related to different subgroups (e.g. species, sexes, and exposure durations). Doing so provides a tool to check canonical property 3 of the particular data analyzed. We provide a review of empirical evidence indicating that this property has general validity, which is highly fortunate, as this legitimizes the use of extrapolation factors and RPFs in risk assessment. We then evaluate to what extent the approaches in current BMD guidance by European Food Safety Authority (EFSA) or U.S. Environmental Protection Agency (US-EPA) comply with the principles of canonical dose-response modeling, concluding that this is only partly the case. The latter can have unfavorable and sometimes far-reaching consequences. For instance, some of the recommended non-canonical models result in different BMDs when changing the measurement unit (e.g. µg to mg). As another example, the BMD tool recently developed by EFSA implements covariate analysis in such a way that canonical property 3 cannot possibly be represented by any of the models. As another disadvantage, non-canonical models preclude the effective development and use of prior distributions in a Bayesian approach. Finally, we argue that a concomitant but important advantage of only using canonical models is that BMD methodology will be more transparent, so that risk assessors will be better able to understand it, and BMDs with high societal impact can be more easily defended. The present paper may be a helpful tool for toxicologists and risk assessors to critically follow the developments in BMD methodology at the conceptual level.

Bioinformatic workflows for deriving transcriptomic points of departure: current status, data gaps, and research priorities

There is a pressing need to increase the efficiency and reliability of toxicological safety assessment for protecting human health and the environment. Although conventional toxicology tests rely on measuring apical changes in vertebrate models, there is increasing interest in the use of molecular information from animal and in vitro studies to inform safety assessment. One promising and pragmatic application of molecular information involves the derivation of transcriptomic points of departure (tPODs). Transcriptomic analyses provide a snapshot of global molecular changes that reflect cellular responses to stressors and progression toward disease. A tPOD identifies the dose level below which a concerted change in gene expression is not expected in a biological system in response to a chemical. A common approach to derive such a tPOD consists of modeling the dose-response behavior for each gene independently and then aggregating the gene-level data into a single tPOD. Although different implementations of this approach are possible, as discussed in this manuscript, research strongly supports the overall idea that reference doses produced using tPODs are health protective. An advantage of this approach is that tPODs can be generated in shorter term studies (e.g. days) compared with apical endpoints from conventional tests (e.g. 90-d subchronic rodent tests). Moreover, research strongly supports the idea that reference doses produced using tPODs are health protective. Given the potential application of tPODs in regulatory toxicology testing, rigorous and reproducible wet and dry laboratory methodologies for their derivation are required. This review summarizes the current state of the science regarding the study design and bioinformatics workflows for tPOD derivation. We identify standards of practice and sources of variability in tPOD generation, data gaps, and areas of uncertainty. We provide recommendations for research to address barriers and promote adoption in regulatory decision making.

The Role of Simulation Science in Public Health at the Agency for Toxic Substances and Disease Registry: An Overview and Analysis of the Last Decade

Environmental exposures are ubiquitous and play a significant, and sometimes understated, role in public health as they can lead to the development of various chronic and infectious diseases. In an ideal world, there would be sufficient experimental data to determine the health effects of exposure to priority environmental contaminants. However, this is not the case, as emerging chemicals are continuously added to this list, furthering the data gaps. Recently, simulation science has evolved and can provide appropriate solutions using a multitude of computational methods and tools. In its quest to protect communities across the country from environmental health threats, ATSDR employs a variety of simulation science tools such as Physiologically Based Pharmacokinetic (PBPK) modeling, Quantitative Structure-Activity Relationship (QSAR) modeling, and benchmark dose (BMD) modeling, among others. ATSDR's use of such tools has enabled the agency to evaluate exposures in a timely, efficient, and effective manner. ATSDR's work in simulation science has also had a notable impact beyond the agency, as evidenced by external researchers' widespread appraisal and adaptation of the agency's methodology. ATSDR continues to advance simulation science tools and their applications by collaborating with researchers within and outside the agency, including other federal/state agencies, NGOs, the private sector, and academia.

A Bayesian benchmark concentration analysis for urinary fluoride and intelligence in adults in Guizhou, China

Environmental fluoride exposure has been linked to numerous cases of fluorosis worldwide. Previous studies have indicated that long-term exposure to fluoride can result in intellectual damage among children. However, a comprehensive health risk assessment of fluorosis-induced intellectual damage is still pending. In this research, we utilized the Bayesian Benchmark Dose Analysis System (BBMD) to investigate the dose-response relationship between urinary fluoride (U-F) concentration and Raven scores in adults from Nayong, Guizhou, China. Our research findings indecate a dose-response relationship between the concentration of U-F and intelligence scores in adults. As the benchmark response (BMR) increased, both the benchmark concentration (BMCs) and the lower bound of the credible interval (BMCLs) increased. Specifically, BMCs for the association between U-F and IQ score were determined to be 0.18 mg/L (BMCL1 = 0.08 mg/L), 0.91 mg/L (BMCL5 = 0.40 mg/L), 1.83 mg/L (BMCL10 = 0.83 mg/L) when using BMRs of 1 %, 5 %, and 10 %. These results indicate that U-F can serve as an effective biomarker for monitoring the loss of IQ in population. We propose three interim targets for public policy in preventing interllectual harm from fluoride exposure.

Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

Quantitative Integration of Mode of Action Information in Dose-Response Modeling and POD Estimation for Nonmutagenic Carcinogens: A Case Study of TCDD

BACKGROUND

Traditional dose-response assessment applies different low-dose extrapolation methods for cancer and noncancer effects and assumes that all carcinogens are mutagenic unless strong evidence suggests otherwise. Additionally, primarily focusing on one critical effect, dose-response modeling utilizes limited mode of action (MOA) data to inform low-dose risk.

OBJECTIVE

We aimed to build a dose-response modeling framework that continuously extends the curve into the low-dose region via a quantitative integration of MOA information and to estimate MOA-based points of departure (PODs) for nonmutagenic carcinogens.

METHODS

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) was used as an example to demonstrate the new dose-response modeling framework. There were three major steps included: a) identifying and extracting key quantifiable events (KQEs), b) calculating essential doses that sequentially activate KQEs using the benchmark dose (BMD) methodology, and c) characterizing pathway dose-response relationship for MOA-based POD estimation.

RESULTS

We identified and extracted six KQEs and corresponding essential events composing the MOA of TCDD-induced liver tumors. With the essential doses estimated from the BMD method using various settings, three link functions were applied to model the pathway dose-response relationship. Given a toxicologically plausible definition of adversity, an MOA-based POD was derived from the pathway dose-response curve. The estimated MOA-based PODs were generally comparable with traditional PODs and can be further used to calculate reference doses (RfDs).

CONCLUSIONS

The proposed framework quantitatively integrated mechanistic information in the modeling process and provided a promising strategy to harmonize cancer and noncancer dose-response assessment through pathway dose-response modeling. However, the framework can also be limited by data availability and the understanding of the underlying mechanism. https://doi.org/10.1289/EHP12677.

A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126

A main function of dose-response assessment is to estimate a "safe" dose in the target population to support chemical risk assessment. Typically, a "safe" dose is developed differently for cancer and noncancer effects based on a 2-step procedure, ie, point of departure (POD) derivation and low-dose extrapolation. However, the current dose-response assessment framework is criticized for its dichotomized strategy without integrating the mode of action (MOA) information. The objective of this study was, based on our previous work, to develop a MOA-based probabilistic dose-response framework that quantitatively synthesizes a biological pathway in a dose-response modeling process to estimate the risk of chemicals that have carcinogenic potential. 3,3',4,4',5-Pentachlorobiphenyl (PCB-126) was exemplified to demonstrate our proposed approach. There were 4 major steps in the new modeling framework, including (1) key quantifiable events (KQEs) identification and extraction, (2) essential dose calculation, (3) MOA-based POD derivation, and (4) MOA-based probabilistic reference dose (RfD) estimation. Compared with reported PODs and traditional RfDs, the MOA-based estimates derived from our approach were comparable and plausible. One key feature of our approach was the use of overall MOA information to build the dose-response relationship on the entire dose continuum including the low-dose region. On the other hand, by adjusting uncertainty and variability in a probabilistic manner, the MOA-based probabilistic RfDs can provide useful insights of health protection for the specific proportion of population. Moreover, the proposed framework had important potential to be generalized to assess different types of chemicals other than nonmutagenic carcinogens, highlighting its utility to improve current chemical risk assessment.

The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics

High-throughput toxicogenomics as an advanced toolbox of Tox21 plays an increasingly important role in facilitating the toxicity assessment of environmental chemicals. However, toxicogenomic dose-response analyses are typically challenged by limited data, which may result in significant uncertainties in parameter and benchmark dose (BMD) estimation. Integrating historical data via prior distribution using a Bayesian method is a useful but not-well-studied strategy. The objective of this study is to evaluate the effectiveness of informative priors in genomic dose-response modeling and BMD estimation. Specifically, we aim to identify plausible informative priors and evaluate their effects on BMD estimates at both gene and pathway levels. A general informative prior and eight time-specific (from 3 h to 29 d) informative priors for seven commonly used continuous dose-response models were derived. Results suggest that the derived informative priors are sensitive to the specific data sets used for elicitation. Real data-based simulations indicate that BMD estimation with the time-specific informative priors can achieve increased or equivalent accuracy, significantly decreased uncertainty, and a slightly enhanced correlation with the points of departure estimated from apical end points than the counterparts with noninformative priors. Overall, our study systematically examined the effects of historical data-based informative priors on BMD estimates, highlighting the benefits of plausible information priors in advancing the practice of toxicogenomics.

Using Prior Toxicological Data to Support Dose-Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models

The benchmark dose (BMD) methodology has significantly advanced the practice of dose-response analysis and created substantial opportunities to enhance the plausibility of BMD estimation by synthesizing dose-response information from different sources. Particularly, integrating existing toxicological information via prior distribution in a Bayesian framework is a promising but not well-studied strategy. The study objective is to identify a plausible way to incorporate toxicological information through informative prior to support BMD estimation using dichotomous data. There are four steps in this study: determine appropriate types of distribution for parameters in common dose-response models, estimate the parameters of the determined distributions, investigate the impact of alternative strategies of prior implementation, and derive endpoint-specific priors to examine how prior-eliciting data affect priors and BMD estimates. A plausible distribution was estimated for each parameter in the common dichotomous dose-response models using a general database. Alternative strategies for implementing informative prior have a limited impact on BMD estimation, but using informative prior can significantly reduce uncertainty in BMD estimation. Endpoint-specific informative priors are substantially different from the general one, highlighting the necessity for guidance on prior elicitation. The study developed a practical way to employ informative prior and laid a foundation for advanced Bayesian BMD modeling.