"New statistics" in regulatory toxicology?

Replacing concurrent controls with virtual control groups in rat toxicity studies

Virtual control groups (VCGs) in nonclinical toxicity represent the concept of using appropriate historical control data for replacing concurrent control group animals. Historical control data collected from standardized studies can serve as base for constructing VCGs and legacy study reports can be used as a benchmark to evaluate the VCG performance. Replacing concurrent controls of legacy studies with VCGs should ideally reproduce the results of these studies. Based on three four-week rat oral toxicity legacy studies with varying degrees of toxicity findings we developed a concept to evaluate VCG performance on different levels: the ability of VCGs to (i) reproduce statistically significant deviations from the concurrent control, (ii) reproduce test substance-related effects, and (iii) reproduce the conclusion of the toxicity study in terms of threshold dose, target organs, toxicological biomarkers (clinical pathology) and reversibility. Although VCGs have shown a low to moderate ability to reproduce statistical results, the general study conclusions remained unchanged. Our results provide a first indication that carefully selected historical control data can be used to replace concurrent control without impairing the general study conclusion. Additionally, the developed procedures and workflows lay the foundation for the future validation of virtual controls for a use in regulatory toxicology.

Using R in Regulatory Toxicology

Statistical analyses are an essential part of regulatory toxicological evaluations. While projects would be ideally monitored by both toxicologists and statisticians, this is often not possible in practice. Hence, toxicologists should be trained in some common statistical approaches but also need a tool for statistical evaluations. Due to transparency needed in regulatory processes and standard tests that can be evaluated with template approaches, the freely available open-source statistical software R may be suitable. R is a well-established software in the statistical community. The principal input method is via software code, which is both benefit and weakness of the tool. It is increasingly used by regulating authorities globally and can be easily extended by software packages, e.g., for new statistical functions and features. This manuscript outlines how R can be used in regulatory toxicology, allowing toxicologists to perform all regulatory required data evaluations in a single software solution. Practical applications are shown in case studies on simulated and experimental data. The examples cover a) Dunnett testing of treatment groups against a common control and in relation to a biological relevance threshold, assessing the test's assumptions and plotting the results; b) dose-response analysis and benchmark dose derivation for chronic kidney inflammation as a function of Pyridine; and c) graphical/exploratory data analysis of previously published developmental neurotoxicity data for Chlorpyrifos.

A new conceptional model for deriving average dermal absorption estimates from studies with multiple tested concentrations for non-dietary risk assessment of pesticides

Dermal absorption values are used to translate external dermal exposure into potential systemic exposure for non-dietary risk assessment of pesticides. While the Environmental Protection Agency of the United States of America (US EPA) derives a common dermal absorption factor for active substances covering all related products, the European Food Safety Authority (EFSA) requests specific product-based estimates for individual concentrations covering the intended use rates. The latter poses challenges, because it disconnects exposure dose from applied dose in absorption studies, which may not be suitable in scenarios where concentration is not relevant. We analyzed the EFSA dermal absorption database, collected 33 human in vitro studies from CropLife Europe (CLE) companies, where ≥3 in-use dilution concentrations were tested, and 15 dermal absorption triple pack datasets. This shows that absolute dermal absorption correlates with absolute applied dose on a decadic logarithm-scale, which is concordant with the toxicological axiom that risk is driven by exposure dose. This method is radically different from the current European approach focused on concentrations and offers new insights into the relationship of internal and external exposure doses when utilizing data from in vitro studies. A single average dermal absorption value can be simply derived from studies with multiple tested concentrations, by calculating the y-intercept of a linear model on a decadic logarithm scale while assuming a slope of 1. This simplifies risk assessment and frees resources to explore exposure refinements. It also serves as a basis to harmonize dermal absorption estimation globally for use in exposure-driven risk assessments.

Benchmark dose modelling in regulatory ecotoxicology, a potential tool in pest management

For several authorities, benchmark dose (BMD) methodology has become the recommended approach by which to derive reference values for risk assessment. However, in practice, the BMD approach is not standard use in risk assessment for pesticides where the no observed adverse effect level, lowest observed adverse effect level and effective dose (ED₅₀ or ED_x ) prevail. Regression-based BMD and the benchmark dose lower confidence limit (BMDL) have several advantages, such as utilizing more information from the generated data and being less dependent on tested dose levels. However, the BMD approach requires some degree of expert knowledge for defining an appropriate risk level for estimating the BMD and using more sophisticated statistical methods to calculate BMD and BMDL. The BMD approach is one way to move away from p value-based binary decision-making towards putting the weight on effect sizes. We review the advantages and disadvantages of focusing on the BMD approach for risk assessment of pesticides. Further, we discuss potential applications in efficacy trials for pest management purposes. © 2021 Society of Chemical Industry.

Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language

Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.

Using historical control data in bioassays for regulatory toxicology

Historical control data (HCD) consist of pooled control group responses from bioassays. These data must be collected and are often used or reported in regulatory toxicology studies for multiple purposes: as quality assurance for the test system, to help identify toxicological effects and their effect-size relevance and to address the statistical multiple comparison problem. The current manuscript reviews the various classical and potential new approaches for using HCD. Issues in current practice are identified and recommendations for improved use and discussion are provided. Furthermore, stakeholders are invited to discuss whether it is necessary to consider uncertainty when using HCD formally and statistically in toxicological discussions and whether binary inclusion/exclusion criteria for HCD should be revised to a tiered information contribution to assessments. Overall, the critical value of HCD in toxicological bioassays is highlighted when used in a weight-of-evidence assessment.

Metribuzin-induced non-adverse liver changes result in rodent-specific non-adverse thyroid effects via uridine 5'-diphospho-glucuronosyltransferase (UDPGT, UGT) modulation

Metribuzin is a herbicide that inhibits photosynthesis and has been used for over 40 years. Its main target organ is the liver and to some extent the kidney in rats, dogs, and rabbits. Metribuzin shows a specific thyroxine (T4) profile in rat studies with T4 increases at low doses and T4 decreases at higher doses. Only the T4 decreases occur together with histopathological changes in the thyroid and weight changes of liver and thyroid. A set of experiments was conducted to investigate metribuzin's endocrine disruptor potential according to European guidance and regulations. The results indicate that a liver enzyme modulation, i.e. of the uridine 5'-diphospho-glucuronosyltransferase (UDPGT, UGT), is most likely responsible for both increased and decreased plasma thyroxine level and for thyroid histopathological observations. Animals with high T4 levels show low UGT activity, while animals with low T4 levels show high UGT activity. A causal relationship was inferred, since other potentially human-relevant mode of action (MOA) pathways were excluded in dedicated studies, i.e. inhibition of deiodinases (DIO), inhibition of thyroid peroxidase (TPO) or of the sodium importer system (NIS). This liver metabolism-associated MOA is considered not relevant for human hazard assessment, due to species differences in thyroid homeostasis between humans and rats and, more importantly, based on experimental data showing that metribuzin affects UGT activity in rat but not in human hepatocytes. Further, we discuss whether or not increased T4 levels in the rat, in the absence of histopathological changes, should be considered as adverse and therefore used as an appropriate hazard model for humans. Based on a weight of evidence approach, metribuzin should not be classified as an endocrine disruptor with regard to the thyroid modality.

Expanding the toxicologist's statistical toolbox: Using effect size estimation and dose-response modelling for holistic assessments instead of generic testing

It is tempting to base (eco-)toxicological assay evaluation solely on statistical significance tests. The approach is stringent, objective and facilitates binary decisions. However, tests according to null hypothesis statistical testing (NHST) are thought experiments that rely heavily on assumptions. The generic and unreflected application of statistical tests has been called "mindless" by Gigerenzer. While statistical tests have an appropriate application domain, the present work investigates how unreflected testing may affect toxicological assessments. Dunnett multiple-comparison and Williams trend testing and their compatibility intervals are compared with dose-response-modelling in case studies, where data do not follow textbook behavior, nor behave as expected from a toxicological point of view. In such cases, toxicological assessments based only on p-values may be biased and biological evaluations based on plausibility may be prioritized. If confidence in a negative assay outcome cannot be established, further data may be needed for a robust toxicological assessment.

Compounded conservatism in European re-entry worker risk assessment of pesticides

We review the risk parameters and drivers in the current European Union (EU) worker risk assessment for pesticides, for example considering crop maintenance, crop inspection or harvesting activities, and show that the current approach is very conservative due to multiple worst-case default assumptions. As a case study, we compare generic exposure model estimates with measured worker re-entry exposure values which shows that external cumulative exposure is overpredicted by about 50-fold on average. For this exercise, data from 16 good laboratory practice (GLP)-compliant worker exposure studies in 6 crops were evaluated with a total number of 184 workers. As generic overprediction does not allow efficient risk management or realistic risk communication, we investigate how external exposure can be better predicted within the generic model, and outline options for possible improvements in the current methodology. We show that simply using averages achieves more meaningful exposure estimates, while still being conservative, with an average exposure overprediction of about 9-fold. Overall, EU risk assessment includes several numerically unaccounted "hidden safety factors", which means that workers are well protected; but simultaneously risk assessments are biased towards failing due to compounded conservatism. This should be considered for further global or regional guidance developments and performing more exposure-relevant risk assessment.

bmd: an R package for benchmark dose estimation

The benchmark dose (BMD) methodology is used to derive a hazard characterization measure for risk assessment in toxicology or ecotoxicology. The present paper's objective is to introduce the R extension package bmd, which facilitates the estimation of BMD and the benchmark dose lower limit for a wide range of dose-response models via the popular package drc. It allows using the most current statistical methods for BMD estimation, including model averaging. The package bmd can be used for BMD estimation for binomial, continuous, and count data in a simple set up or from complex hierarchical designs and is introduced using four examples. While there are other stand-alone software solutions available to estimate BMDs, the package bmd facilitates easy estimation within the established and flexible statistical environment R. It allows the rapid implementation of available, novel, and future statistical methods and the integration of other statistical analyses.

Community Approaches for Integrating Environmental Exposures into Human Models of Disease

BACKGROUND

A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist.

OBJECTIVES

Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement.

DISCUSSION

There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215.