The association between histopathologic effects and liver weight changes induced in mice and rats by chemical exposures: an analysis of the data from Toxicity Reference Database (ToxRefDB).

Absolute (ALW) and relative (RLW) liver weight changes are sensitive endpoints in repeat-dose rodent toxicity studies, and their changes are often used for quantitative assessment of health effects induced by hepatotoxic chemicals using the benchmark dose-response modeling (BMD) approach. To find biologically relevant liver weight changes to chemical exposures, we evaluated all data available for liver weight changes and associated liver histopathologic findings from the Toxicity Reference Database (ToxRefDB). Our analysis of 389 subchronic mouse and rat studies for 273 chemicals found significant differences in treatment-related ALW and RLW changes between dose groups with and without liver histopathologic changes.

Reproducibility of organ-level effects in repeat dose animal studies.

This work estimates benchmarks for new approach method (NAM) performance in predicting organ-level effects in repeat dose studies of adult animals based on variability in replicate animal studies. Treatment-related effect values from the Toxicity Reference database (v2.1) for weight, gross, or histopathological changes in the adrenal gland, liver, kidney, spleen, stomach, and thyroid were used.

ToxRefDB v2.1: update to curated study data in the Toxicity Reference Database.

The Toxicity Reference Database (ToxRefDB) contains study data from over 5,900 guideline or guideline-like studies for over 1,100 chemicals. The database includes information regarding study design, chemical treatment, dosing, treatment group parameters, treatment-related (significantly different from control) and critical (adverse) effects, guided by a controlled effect vocabulary, as well as endpoint testing status according to health effects guideline requirements. ToxRefDB v2.

Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks.

The workshop titled “Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks” was co-organized by the Evidence-based Toxicology Collaboration and the European Food Safety Authority (EFSA) and hosted by EFSA at its headquarters in Parma, Italy on October 2 and 3, 2019. The goal was to explore integration of systematic review with mechanistic evidence evaluation. Participants were invited to work on concrete products to advance the exploration of how evidence-based approaches can support the development and application of adverse outcome pathways (AOP) in chemical risk assessment.

Knowledge Organization Systems for Systematic Chemical Assessments.

Background: Although the implementation of systematic review and evidence mapping methods stands to improve the transparency and accuracy of chemical assessments, they also accentuate the challenges that assessors face in ensuring they have located and included all the evidence that is relevant to evaluating the potential health effects an exposure might be causing. This challenge of information retrieval can be characterized in terms of "semantic" and "conceptual" factors that render chemical assessments vulnerable to the streetlight effect.

Objectives: This commentary presents how controlled vocabularies, thesauruses, and ontologies contribute to overcoming the streetlight effect in information retrieval, making up the key components of Knowledge Organization Systems (KOSs) that enable more systematic access to assessment-relevant information than is currently achievable.

Python BMDS: A Python interface library and web application for the canonical EPA dose-response modeling software.

Several primary sources of publicly available, quantitative dose-response data from traditional toxicology study designs relevant to predictive toxicology applications are now available, including the redeveloped U.S. Environmental Protection Agency's Toxicity Reference Database (ToxRefDB v2.

Progress in data interoperability to support computational toxicology and chemical safety evaluation.

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21 Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis.

Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: use case in breast carcinogenesis.

Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency's ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed.

BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening.

Targeted gene lists have been used in clinical settings to specify breast tumor type, and to predict breast cancer prognosis and response to treatment. Separately, panels have been curated to predict systemic toxicity and xenoestrogen activity as a part of chemical screening strategies. However, currently available panels do not specifically target biological processes relevant to breast development and carcinogenesis.