Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity
Open accessAbstract
Drug-induced liver injury (DILI) is of significant concern to drug development and regulatory review because of the limited success with existing preclinical models. For developing alternative methods, a large drug list is needed with known DILI severity and toxicity. We augmented the DILIrank data set [annotated using US Food and Drug Administration (FDA) drug labeling)] with four literature datasets (N >350 drugs) to generate the largest drug list with DILI classification, called DILIst (DILI severity and toxicity). DILIst comprises 1279 drugs, of which 768 were DILI positives (increase of 65% from DILIrank), whereas 511 were DILI negatives (increase of 65%). The investigation of DILI positive-negative distribution across various therapeutic categories revealed the most and least frequent DILI categories. Thus, we consider DILIst to be an invaluable resource for the community to improve DILI research.
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The NCTR has produced drug-induced toxicity benchmark datasets by manually reading and analyzing drug labeling documents, such as DILI, DICT, and DIRIL.
“during the past two decades, the research team at the NCTR has produced several drug-induced toxicity benchmark datasets by manually reading and analyzing drug labeling documents, such as those for drug-induced liver injury (DILI) [9–11], drug-induced cardiotoxicity (DICT) [12], drug-induced renal injury list (DIRIL) [13], and others”
From Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel (2025) · cites this paper as [11]
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“…toxicity benchmark datasets by manually reading and analyzing drug labeling documents, such as those for drug-induced liver injury (DILI) [9–11], drug-induced cardiotoxicity (DICT) [12], drug-induced renal injury list (DIRIL) [13], and others, which offer large collections of manually…”
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- Authors
- Thakkar, S., Li, T., Liu, Z., Wu, L., Roberts, R., & Tong, W.
- Affiliations
- National Center for Toxicological Research (US)
- University of Birmingham (GB)
- Corresponding author
- Tong, Weida
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 25
- Issue
- 1
- Pages
- 201-208
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W2987481746
- Cited by
- 140
- References
- 5
- Citations by year
- 2026: 25 · 2025: 26 · 2024: 23 · 2023: 25 · 2022: 16 · 2021: 14 · 2020: 11
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Discovery Today (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- drug-induced liver injury, hepatotoxicity, drug safety, FDA drug labeling, therapeutic categories, preclinical safety
- MeSH terms
- Pharmaceutical Preparations, Humans, Severity of Illness Index, Chemical and Drug Induced Liver Injury
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