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MDTR: a knowledge-guided interpretable representation for quantifying liver toxicity at transcriptomic level.

Inyoung Sung1, Sangseon Lee2, Dongmin Bang1,3

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.

Frontiers in Pharmacology
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

A new Multi-Dimensional Transcriptomic Ruler (MDTR) quantifies drug-induced liver injury (DILI) from gene expression data. This tool analyzes transcriptome perturbations to reveal underlying hepatotoxicity mechanisms, offering a computable approach to drug safety assessment.

Keywords:
degree of toxicitydrug-induced liver injurykernel distanceliver toxicityone-class boundarytranscriptomic signature

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Area of Science:

  • Computational biology
  • Toxicology
  • Genomics

Background:

  • Drug-induced liver injury (DILI) is a significant safety concern, traditionally studied at the patient level.
  • Analyzing cellular gene expression changes offers deeper insights into hepatotoxicity mechanisms.
  • Existing transcriptome data analysis is challenged by dose- and time-dependent expression changes and non-computable toxicity mechanisms.

Purpose of the Study:

  • To develop a novel computational method for quantifying DILI at the transcriptome level.
  • To translate complex gene expression data into interpretable biological mechanisms of hepatotoxicity.
  • To provide a user-friendly tool for assessing drug-induced toxicity from transcriptomic data.

Main Methods:

  • Proposed the Multi-Dimensional Transcriptomic Ruler (MDTR) to quantify DILI from gene expression.
  • Integrated KEGG pathways to represent and aggregate toxicity mechanisms.
  • Employed a radial basis kernel and Mahalanobis distance in a transcriptomic kernel space to measure pathway-level perturbation.
  • Visualized five hepatotoxicity mechanisms as dimensions in a radar chart.

Main Results:

  • MDTR demonstrated superior performance in measuring transcriptome data distances for dose-dependent drug perturbations compared to existing methods.
  • The method provides interpretable insights into DILI mechanisms within a metric space.
  • A publicly accessible website was developed for easy use of the MDTR tool.

Conclusions:

  • MDTR offers a robust and interpretable method for analyzing drug-induced transcriptome data to understand hepatotoxicity.
  • The tool facilitates a more computable and visual assessment of DILI mechanisms.
  • The developed platform enhances drug safety evaluation by providing accessible analysis of transcriptomic data.