Grouping of chemicals into mode of action classes by automated effect pattern analysis using the zebrafish embryo toxicity test

Abstract

A central element of high throughput screens for chemical effect assessment using zebrafish is the assessment and quantification of phenotypic changes. By application of an automated and more unbiased analysis of these changes using image analysis, patterns of phenotypes may be associated with the mode of action (MoA) of the exposure chemical. The aim of our study was to explore to what extent compounds can be grouped according to their anticipated toxicological or pharmacological mode of action using an automated quantitative multi-endpoint zebrafish test. Chemical-response signatures for 30 endpoints, covering phenotypic and functional features, were generated for 25 chemicals assigned to 8 broad MoA classes. Unsupervised clustering of the profiling data demonstrated that chemicals were partially grouped by their main MoA. Analysis with a supervised clustering technique such as a partial least squares discriminant analysis (PLS-DA) allowed to identify markers with a strong potential to discriminate between MoAs such as mandibular arch malformation observed for compounds interfering with retinoic acid signaling. The capacity for discriminating MoAs was also benchmarked to an available battery of in vitro toxicity data obtained from ToxCast library indicating a partially similar performance. Further, we discussed to which extent the collected dataset indicated indeed differences for compounds with presumably similar MoA or whether other factors such as toxicokinetic differences could have an important impact on the determined response patterns.

Analysis of vascular disruption in zebrafish embryos as an endpoint to predict developmental toxicity

“…This resolution was sufficient to detect the selected features (data not shown). For training of the deep learning models, approximately 3000 previously annotated images (Teixidó et al 2022 ) were used. The automated annotations were corrected by the user in the event that they were not adequately detecting visible structures.…”

Data-Driven Endpoint Selection in Data-Poor Scenarios: Bioassay Design for Shale Gas Flowback and Produced Waters

“…Each phenotype was counted only once under an ontology or pathway, and the frequency of phenotypes with the same mode of action (MoA) was the sum of the counts. MoAs were classified following effect characteristics with data from the ToxCast and PubChem …”

“…MoAs were classified following effect characteristics with data from the ToxCast and PubChem. 19 Data fusion and curation by primary key conversion from chemical name to bioactivity were performed to form the Compound TABLE and Bioassay TABLE. Gene symbols of the Bioassay TABLE were subsequently used as the primary key and fused with GO and KEGG databases to create Gene TABLE.…”

Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health

“…For the justification aspect, the grouping hypothesis was established using animal data, thus making use of the wholeorganism complexity, but the same molecular markers used in any of the big data approaches were then derived from in vitro high-throughput screening (HTS) testing to justify the addition of a new chemical to that group. [73] However, although HTS significantly reduces animal testing and increases toxicological data points for assessment, it has some limitations, which need to be considered: HTS is technologically demanding and not widely available (yet). This makes it difficult to generate the multidimensional dataset needed to allow comparisons of a new chemical with the existing databases and to ultimately justify RAx.…”