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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Predictive Toxicology
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1398370
This article is part of the Research Topic Advances and Applications of Predictive Toxicology in Knowledge Discovery, Risk Assessment, and Drug Development View all 9 articles

MDTR: A Knowledge-Guided Interpretable Representation for Quantifying Liver Toxicity at Transcriptomic Level

Provisionally accepted
Inyoung Sung Inyoung Sung 1Sangseon Lee Sangseon Lee 1Dongmin Bang Dongmin Bang 1Jungseob Yi Jungseob Yi 1Sunho Lee Sunho Lee 2Sun Kim Sun Kim 1*
  • 1 Seoul National University, Seoul, Republic of Korea
  • 2 AIGENDRUG Co., Ltd., Seoul, Republic of Korea

The final, formatted version of the article will be published soon.

    Drug-induced liver injury (DILI) has been investigated at the patient level. Analysis of gene perturbation at the cellular level can help better characterize biological mechanisms of hepatotoxicity. Despite accumulating drug-induced transcriptome data such as LINCS, analyzing such transcriptome data upon drug treatment is a challenging task because the perturbation of expression is dose and time dependent. In addition, the mechanisms of drug toxicity are known only as literature information, not in a computable form. To address these challenges, we propose a Multi-Dimensional Transcriptomic Ruler (MDTR) that quantifies the degree of DILI at the transcriptome level. To translate transcriptome data to toxicity-related mechanisms, MDTR incorporates KEGG pathways as representatives of mechanisms, mapping transcriptome data to biological pathways and subsequently aggregating them for each of the five hepatotoxicity mechanisms. Given that a single mechanism involves multiple pathways, MDTR measures pathway-level perturbation by constructing a radial basis kernel-based toxicity space and measuring the Mahalanobis distance in the transcriptomic kernel space. Representing each mechanism as a dimension, MDTR is visualized in a radar chart, enabling an effective visual presentation of hepatotoxicity at transcriptomic level. In experiments with the LINCS dataset, we show that MDTR outperforms existing methods for measuring the distance of transcriptome data when describing for dose-dependent drug perturbations. In addition, MDTR shows interpretability 1 Sung et al.at the level of DILI mechanisms in terms of the distance, i.e., in a metric space. Furthermore, we provided a user-friendly and freely accessible website (http://biohealth.snu.ac.kr/software/MDTR), enabling users to easily measure DILI in drug-induced transcriptome data.

    Keywords: Drug-Induced Liver Injury, One-class boundary, kernel distance, Transcriptome, Degree of toxicity

    Received: 09 Mar 2024; Accepted: 27 Dec 2024.

    Copyright: © 2024 Sung, Lee, Bang, Yi, Lee and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sun Kim, Seoul National University, Seoul, 151-742, Republic of Korea

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.