Classification of chemical compounds based on the correlation between \textit{in vitro} gene expression profiles

TL;DR

This study develops a novel classification method for chemical compounds using extit{in vitro} gene expression profiles, employing combinatorial optimization and simulated annealing to improve toxicology prediction without animal testing.

Contribution

Introduces a new classification approach combining gene expression data and optimization algorithms for toxicology prediction.

Findings

Successfully classified nine compounds into two groups

Utilized 1,000 RNAs for effective clustering

Enabled read-across for non-animal toxicology assessment

Abstract

Toxicity evaluation of chemical compounds has traditionally relied on animal experiments;however, the demand for non-animal-based prediction methods for toxicology of compounds is increasing worldwide. Our aim was to provide a classification method for compounds based on \textit{in vitro} gene expression profiles. The \textit{in vitro} gene expression data analyzed in the present study was obtained from our previous study. The data concerned nine compounds typically employed in chemical management.We used agglomerative hierarchical clustering to classify the compounds;however, there was a statistical difficulty to be overcome.We needed to properly extract RNAs for clustering from more than 30,000 RNAs. In order to overcome this difficulty, we introduced a combinatorial optimization problem with respect to both gene expression levels and the correlation between gene expression profiles. Then, the simulated annealing algorithm was used to obtain a good solution for the problem. As a result, the nine compounds were divided into two groups using 1,000 extracted RNAs. Our proposed methodology enables read-across, one of the frameworks for predicting toxicology, based on \textit{in vitro} gene expression profiles.