Literature DB >> 28073113

In vitro to in vivo extrapolation for drug-induced liver injury using a pair ranking method.

Zhichao Liu1, Hong Fang1, Jürgen Borlak2, Ruth Roberts3,4, Weida Tong1.   

Abstract

Preclinical animal toxicity studies may not accurately predict human toxicity. In light of this, in vitro systems have been developed that have the potential to supplement or even replace animal use. We examined in vitro to in vivo extrapolation (IVIVE) of gene expression data obtained from The Open Japanese Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (Open TG-GATEs) for 131 compounds given to rats for 28 days, and to human or rat hepatocytes for 24 hours. Notably, a pair ranking (PRank) method was developed to assess IVIVE potential with a PRank score based on the preservation of the order of similarity rankings of compound pairs between the platforms using a receiver operating characteristic (ROC) curve analysis to measure area under the curve (AUC). A high IVIVE potential was noted for rat primary hepatocytes when compared to rat 28-day studies (PRank score = 0.71) whereas the IVIVE potential for human primary hepatocytes compared to rat 28-day studies was lower (PRank score = 0.58), indicating that species difference plays a critical role in IVIVE. When limiting the analysis to only those drugs causing drug-induced liver injury, the IVIVE potential was slightly improved both for rats (from 0.71 to 0.76) and for humans (from 0.58 to 0.62). Similarly, PRank scores were improved when the analysis focused on specific hepatotoxic endpoints such as hepatocellular injury, or cholestatic injury. In conclusion, toxicogenomic data generated in vitro yields a ranking of drugs regarding their potential to cause toxicity which is comparable to that generated by in vivo analyses.

Entities:  

Keywords:  drug-induced liver injury (DILI); toxicogenomics; IVIVE

Mesh:

Year:  2017        PMID: 28073113     DOI: 10.14573/altex.1610201

Source DB:  PubMed          Journal:  ALTEX        ISSN: 1868-596X            Impact factor:   6.043


  4 in total

1.  Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.

Authors:  Ting Li; Weida Tong; Ruth Roberts; Zhichao Liu; Shraddha Thakkar
Journal:  Front Bioeng Biotechnol       Date:  2020-11-27

2.  Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems.

Authors:  Zhichao Liu; Brian Delavan; Ruth Roberts; Weida Tong
Journal:  Front Genet       Date:  2018-03-20       Impact factor: 4.599

3.  Toxicology Advances for 21st Century Chemical Pollution.

Authors:  Bryan W Brooks; Tara Sabo-Attwood; Kyungho Choi; Sujin Kim; Jakub Kostal; Carlie A LaLone; Laura M Langan; Luigi Margiotta-Casaluci; Jing You; Xiaowei Zhang
Journal:  One Earth       Date:  2020-04-24

Review 4.  AI-based language models powering drug discovery and development.

Authors:  Zhichao Liu; Ruth A Roberts; Madhu Lal-Nag; Xi Chen; Ruili Huang; Weida Tong
Journal:  Drug Discov Today       Date:  2021-06-30       Impact factor: 7.851

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.