Retrospective mining of toxicology data to discover multispecies and chemical class effects: Anemia as a case study

Abstract

Predictive toxicity models rely on large amounts of accurate in vivo data. Here, we analyze the quality of in vivo data from the U.S. EPA Toxicity Reference Database (ToxRefDB), using chemical-induced anemia as an example. Considerations include variation in experimental conditions, changes in terminology over time, distinguishing negative from missing results, observer and diagnostic bias, and data transcription errors. Within ToxRefDB, we use hematological data on 658 chemicals tested in one or more of 1738 studies (subchronic rat or chronic rat, mouse, or dog). Anemia was reported most frequently in the rat subchronic studies, followed by chronic studies in dog, rat, and then mouse. Concordance between studies for a positive finding of anemia (same chemical, different laboratories) ranged from 90% (rat subchronic predicting rat chronic) to 40% (mouse chronic predicting rat chronic). Concordance increased with manual curation by 20% on average. We identified 49 chemicals that showed an anemia phenotype in at least two species. These included 14 aniline moiety-containing compounds that were further analyzed for their potential to be metabolically transformed into substituted anilines, which are known anemia-causing chemicals. This analysis should help inform future use of in vivo databases for model development.

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

“…It is, therefore, often a challenge to conclude on a similar toxicological hazard of the grouped compounds mainly based on apical findings. In vivo data inherit a certain variability, because of, e.g., small differences in the study design of the animal studies [e.g., species, strains, dose selection, dose spacing, route (Judson et al 2017;Escher et al 2019)] or inter-individual variability of the tested species. A better understanding of the mechanism(s) that causes an adverse outcome will, therefore, be helpful to conclude on similarity and by this, strengthen the read-across hypothesis.…”

“…The in vivo data showed some predominately shared toxic effects, but also a number of individual effects at several dose levels. This finding could be the result of differences in tested strains or species, study design (e.g., selection of doses and dose spacing), scope of examination or testing in different laboratories/years (Escher et al 2019;Judson et al 2017). As in vivo data do not directly indicate the underlying MoA, it is often a challenge to define categories based solely on apical in vivo findings.…”

“…In many read-across cases, it is difficult to prove similar toxicokinetic and toxicodynamic properties within the grouped compounds, e.g., because of a sparse in vivo data matrix. It is also often a challenge to conclude on a similar adverse toxicological effect pattern, as the apical findings might vary with regard to type, severity and lowest observed adverse effect level (LOAEL) within the grouped compounds (Judson et al 2017). The apical findings in vivo do in the majority of cases not allow for a deep insight into the mechanisms underlying the observed adverse outcomes.…”

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

“…The liver is a key organ in terms of toxicology and crucial in interpreting repeated dose toxicity ( 25 – 27 ). Obviously the liver has a vital physiological role and is prone to toxicity due to high, first-pass, blood flow which increases the likelihood of toxicants reaching a significant concentration.…”

Moving towards a holistic approach for human health risk assessment – Is the current approach fit for purpose?

“…Read‐across requires a similarity assessment of the grouped compounds in terms of toxicokinetic and dynamic properties. It is often a challenge to reach a conclusion on the similar adverse toxicological effect pattern, as the apical findings might vary in the type, severity and lowest observed adverse effect level within the grouped compounds (Judson et al., ). Another difficulty is that, apical findings from in vivo data often do not enable a mechanistic understanding of the observed adverse outcomes.…”