An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library

In vitro–in vivo extrapolation (IVIVE) analyses translating high-throughput screening (HTS) data to human relevance have been limited. This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration–response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (<i>C</i>_max/AC₅₀), analogous to decision-making methods for clinical drug–drug interactions. Fraction unbound in plasma (<i>f</i>_up) and intrinsic hepatic clearance (CL_int) parameters were estimated in silico and incorporated in a three-compartment toxicokinetic (TK) model to first predict <i>C</i>_max for in vivo corroboration using therapeutic scenarios. Toward lower exposure scenarios, 36 compounds of 3925 unique chemicals with curated activity in the HTS data using high-quality dose–response model fits and ≥40% efficacy gave “possible” human in vivo interaction likelihoods lower than median human exposures predicted in the United States Environmental Protection Agency’s ExpoCast program. A publicly available web application has been designed to provide all Tox21−ToxCast dose-likelihood predictions. Overall, this approach provides an intuitive framework to relate in vitro toxicology data rapidly and quantitatively to exposures using either in vitro or in silico derived TK parameters and can be thought of as an important step toward estimating plausible biological interactions in a high-throughput risk-assessment framework

A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics

<div><p>Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in <i>in vitro</i> model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of “sentinel genes” adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.</p></div

S1500+ gene selection workflow.

<p>To compile the S1500+ gene set, a combination of modular data-driven algorithms as well as manual crowd-sourced knowledge-based gene nominations was used to optimize for pathway coverage and the ability to extrapolate to the whole transcriptome.</p

Pathway coverage.

<p>Pathway coverage.</p

Summary of gene and pathway level extrapolation performance of the S1500 and S1500+ gene sets using independent test set.

<p>Summary of gene and pathway level extrapolation performance of the S1500 and S1500+ gene sets using independent test set.</p

Dimension reduction plot.

<p>X-axis shows the percentage of the total principal components (eigengenes) and the Y-axis shows percentage of variability captured. The red line represents the expected relationship given statistically independent gene expression, whereas the blue curve shows the observed relationship.</p