Supporting read-across using biological data

Hao Zhu

doi:10.14573/altex.1601252

Abstract

SummaryRead-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA’s ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.

Highlights

Read-across has become a primary approach to filling data gaps for chemical safety assessments
Biological similarity can be successfully applied only for specific parts of the chemical universe and only for one toxicity endpoint. This has been termed “local validity” (Patlewicz et al, 2014); 2) When biological similarity is based on a large number of bioassays, the read-across study may be successful for various types of toxicity endpoints
2.2.3 Case study: Using complex high-throughput biological data to support read-across – BioActivity-based read-across (BaBRA) using ToxCast Data As highlighted above, the advent of high-throughput screening and research initiatives such as Tox21 and ToxCast provide data on a range of targets and pathways that may be linked to toxicity (Judson et al, 2010; Betts, 2013)

Summary

Introduction

Read-across has become a primary approach to filling data gaps for chemical safety assessments. Biological similarity can be successfully applied only for specific parts of the chemical universe and only for one toxicity endpoint This has been termed “local validity” (Patlewicz et al, 2014); 2) When biological similarity is based on a large number of bioassays, the read-across study may be successful for various types of toxicity endpoints. This kind of study is so far rarely pursued because of high costs to screen the same target compounds against many (often several hundred) different bioassays (Zhu et al, 2014). While test-across deviates from traditional methods only by acknowledging the small applicability domain of proven usefulness, the HTS and omics approaches are based on what is called “big data”, i.e., curated large datasets for data-mining

The state of the art of read-across using biological data

Case study

Findings

Discussion

Conclusions