Predicting In Vivo Anti-Hepatofibrotic Drug Efficacy Based on In Vitro High-Content Analysis
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Zheng-2011-Predicting in vivo a.pdf
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Author(s)
Zheng, Baixue
Tan, Looling
Mo, Xuejun
Yu, Weimiao
Wang, Yan
Tucker-Kellogg, Lisa
Welsch, Roy E.
So, Peter T. C.
Yu, Hanry
Date Issued
November 2011
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Zheng, Baixue et al. “Predicting In Vivo Anti-Hepatofibrotic Drug Efficacy Based on In Vitro High-Content Analysis.” Ed. Maria A. Deli. PLoS ONE 6.11 (2011): e26230. Web. 15 Feb. 2012.
Version
Final published version
Abstract
Background/Aims
Many anti-fibrotic drugs with high in vitro efficacies fail to produce significant effects in vivo. The aim of this work is to use a statistical approach to design a numerical predictor that correlates better with in vivo outcomes.
Methods
High-content analysis (HCA) was performed with 49 drugs on hepatic stellate cells (HSCs) LX-2 stained with 10 fibrotic markers. ~0.3 billion feature values from all cells in >150,000 images were quantified to reflect the drug effects. A systematic literature search on the in vivo effects of all 49 drugs on hepatofibrotic rats yields 28 papers with histological scores. The in vivo and in vitro datasets were used to compute a single efficacy predictor (Epredict).
Results
We used in vivo data from one context (CCl4 rats with drug treatments) to optimize the computation of Epredict. This optimized relationship was independently validated using in vivo data from two different contexts (treatment of DMN rats and prevention of CCl4 induction). A linear in vitro-in vivo correlation was consistently observed in all the three contexts. We used Epredict values to cluster drugs according to efficacy; and found that high-efficacy drugs tended to target proliferation, apoptosis and contractility of HSCs.
Conclusions
The Epredict statistic, based on a prioritized combination of in vitro features, provides a better correlation between in vitro and in vivo drug response than any of the traditional in vitro markers considered.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Engineering Systems Division
Sloan School of Management
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0026230
