Park, Ji Youn
(2024)
Computational Prediction of Drug-Induced Hepatotoxicity Using Transcriptomic Signatures.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Drug-Induced liver injury (DILI) poses a significant clinical and safety concern throughout the drug development process, being the main cause of approved drugs market withdrawal and acute liver failure. Drugs that can damage liver often dysfunction mitochondria and alter signaling pathways that determines the survival of cell during DILI. Recent advancements in Toxicogenomics have enabled the identification of predictive markers of DILI and mechanistic characterization of hepatotoxic chemicals at a transcription level. This study aimed to develop computational prediction models that can screen drug-induced hepatoxicity with the post-treatment transcriptomic data from rodent models. Random forest classification algorithm was employed as an effective statistical tool for model construction in which the models achieved AUCs of 0.69 to 0.80. This identified 441 DILI signature genes in acute and 765 genes in sub-chronic liver injury models in response to 119 compounds at varying doses. These genes showed distinctive expression patterns followed by the treatment of toxins at high doses, and were involved in metabolism, fatty acid oxidation and stress response pathways. 4 biomarker genes, ACOT2 and CRAT selected in models with all doses in compounds of both treatment durations were significantly upregulated, SLC23A1 and CYP39A1 were significantly downregulated after treating high dose hepatotoxins. These findings underscore the treatment-induced transcriptomic changes for mechanistic understanding and prediction of DILI.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
13 December 2024 |
| Date Type: |
Publication |
| Defense Date: |
13 November 2024 |
| Approval Date: |
13 December 2024 |
| Submission Date: |
9 December 2024 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
44 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Pharmacy > Pharmaceutical Sciences |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
transcriptomics; toxicogenomics; hepatotoxicity; drug-induced liver injury; prediction |
| Date Deposited: |
13 Dec 2024 14:20 |
| Last Modified: |
13 Dec 2024 14:20 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/47209 |
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