MDTR: a Knowledge-guided Interpretable Representation for Quantifying Liver Toxicity at Transcriptomic Level

Overview

Journal Front Pharmacol

Date 2025 Feb 10

PMID 39926256

Authors

Inyoung Sung

Sangseon Lee

Dongmin Bang

Jungseob Yi

Sunho Lee

Sun Kim

Affiliations

Soon will be listed here.

Abstract

Introduction: 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.

Methods: 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.

Results And Discussion: 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 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.

References

Kang S, Haydar G, Taniane C, Farrell G, Arias I, Lippincott-Schwartz J . AMPK Activation Prevents and Reverses Drug-Induced Mitochondrial and Hepatocyte Injury by Promoting Mitochondrial Fusion and Function. PLoS One. 2016; 11(10):e0165638. PMC: 5085033. DOI: 10.1371/journal.pone.0165638. View

Prasanna P, Renu K, Gopalakrishnan A . New molecular and biochemical insights of doxorubicin-induced hepatotoxicity. Life Sci. 2020; 250:117599. DOI: 10.1016/j.lfs.2020.117599. View

Subramanian A, Narayan R, Corsello S, Peck D, Natoli T, Lu X . A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 2017; 171(6):1437-1452.e17. PMC: 5990023. DOI: 10.1016/j.cell.2017.10.049. View

Bracken M . Why animal studies are often poor predictors of human reactions to exposure. J R Soc Med. 2009; 102(3):120-2. PMC: 2746847. DOI: 10.1258/jrsm.2008.08k033. View

DeLeve L, Kaplowitz N . Glutathione metabolism and its role in hepatotoxicity. Pharmacol Ther. 1991; 52(3):287-305. DOI: 10.1016/0163-7258(91)90029-l. View

Van Norman G . Limitations of Animal Studies for Predicting Toxicity in Clinical Trials: Is it Time to Rethink Our Current Approach?. JACC Basic Transl Sci. 2020; 4(7):845-854. PMC: 6978558. DOI: 10.1016/j.jacbts.2019.10.008. View

Steinberg G, Grahame Hardie D . New insights into activation and function of the AMPK. Nat Rev Mol Cell Biol. 2022; 24(4):255-272. DOI: 10.1038/s41580-022-00547-x. View

Rossato L, Costa V, Dallegrave E, Arbo M, Dinis-Oliveira R, Santos-Silva A . Cumulative mitoxantrone-induced haematological and hepatic adverse effects in a subchronic in vivo study. Basic Clin Pharmacol Toxicol. 2013; 114(3):254-62. DOI: 10.1111/bcpt.12143. View

Karagiannaki I, Gourlia K, Lagani V, Pantazis Y, Tsamardinos I . Learning biologically-interpretable latent representations for gene expression data: Pathway Activity Score Learning Algorithm. Mach Learn. 2023; 112(11):4257-4287. PMC: 10600308. DOI: 10.1007/s10994-022-06158-z. View

10.

Hoofnagle J, Bjornsson E . Drug-Induced Liver Injury - Types and Phenotypes. N Engl J Med. 2019; 381(3):264-273. DOI: 10.1056/NEJMra1816149. View

11.

Chapman K, Holzgrefe H, Black L, Brown M, Chellman G, Copeman C . Pharmaceutical toxicology: designing studies to reduce animal use, while maximizing human translation. Regul Toxicol Pharmacol. 2013; 66(1):88-103. DOI: 10.1016/j.yrtph.2013.03.001. View

12.

Turner M, Nedjai B, Hurst T, Pennington D . Cytokines and chemokines: At the crossroads of cell signalling and inflammatory disease. Biochim Biophys Acta. 2014; 1843(11):2563-2582. DOI: 10.1016/j.bbamcr.2014.05.014. View

13.

Wallace K, Sardao V, Oliveira P . Mitochondrial Determinants of Doxorubicin-Induced Cardiomyopathy. Circ Res. 2020; 126(7):926-941. PMC: 7121924. DOI: 10.1161/CIRCRESAHA.119.314681. View

14.

Llesuy S, Arnaiz S . Hepatotoxicity of mitoxantrone and doxorubicin. Toxicology. 1990; 63(2):187-98. DOI: 10.1016/0300-483x(90)90042-f. View

15.

Dix D, Houck K, Martin M, Richard A, Setzer R, Kavlock R . The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicol Sci. 2006; 95(1):5-12. DOI: 10.1093/toxsci/kfl103. View

16.

Andrade R, Chalasani N, Bjornsson E, Suzuki A, Kullak-Ublick G, Watkins P . Drug-induced liver injury. Nat Rev Dis Primers. 2019; 5(1):58. DOI: 10.1038/s41572-019-0105-0. View

17.

Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin A, Kim S . The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012; 483(7391):603-7. PMC: 3320027. DOI: 10.1038/nature11003. View

18.

Kaplowitz N . Drug-induced liver injury. Clin Infect Dis. 2004; 38 Suppl 2:S44-8. DOI: 10.1086/381446. View

19.

Wilke R, Lin D, Roden D, Watkins P, Flockhart D, Zineh I . Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges. Nat Rev Drug Discov. 2007; 6(11):904-16. PMC: 2763923. DOI: 10.1038/nrd2423. View

20.

Weaver R, Blomme E, Chadwick A, Copple I, Gerets H, Goldring C . Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models. Nat Rev Drug Discov. 2019; 19(2):131-148. DOI: 10.1038/s41573-019-0048-x. View