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Published on: March 14, 2019
ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery
Md Masud Rana1,2, Md Nurul Haque Mollah3, Mohammed H Albujja4
1National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
Motivation:
Understanding the genetic basis of drug-induced toxicity is crucial for drug development. In-silico analysis of toxicogenomics datasets facilitates early detection of toxicity biomarkers. However, existing tools struggle with the complex interdependencies among hierarchically structured variables, leading to inaccurate biomarker identification. To address this limitation, we developed a Hierarchical Linear Model (HLM) and implemented it in the R package ToxAssay, offering extensive functionality for comprehensive toxicity assessment.
Results:
ToxAssay outperforms existing methods by improving biomarker detection and computational efficiency. Applied to glutathione depletion-induced toxicity, it prioritized 71 key genes and identified 26 core genes with high discriminative accuracy (AUC = 0.97) and strong cross-correlation (Pearson's r = 0.88) with external datasets. Additionally, our advance outcome pathway (AOP) analysis algorithm uncovered disease outcomes linked to glutathione depletion. These findings provide precise insights into the molecular mechanisms driving drug-induced toxicity.
Availability And Implementation:
ToxAssay is available as an open-source R package at https://github.com/Fun-Gene/toxassay.
Insights
A new R package, ToxAssay, uses a Hierarchical Linear Model (HLM) to improve the identification of drug-induced toxicity biomarkers. It offers enhanced accuracy and efficiency in analyzing complex toxicogenomics data.
Area of Science:
- Toxicogenomics
- Computational Biology
- Pharmacology
Background:
- Understanding drug-induced toxicity is vital for safe drug development.
- In-silico toxicogenomics analysis aids early detection of toxicity biomarkers.
- Existing tools face challenges with complex variable interdependencies, hindering accurate biomarker identification.
Purpose of the Study:
- To develop and implement a novel Hierarchical Linear Model (HLM) for comprehensive toxicity assessment.
- To create an R package, ToxAssay, to address limitations in current toxicogenomics analysis tools.
Main Methods:
- Development of a Hierarchical Linear Model (HLM).
- Implementation of the HLM within the open-source R package ToxAssay.
- Application of ToxAssay to glutathione depletion-induced toxicity data.
Main Results:
- ToxAssay demonstrated superior biomarker detection and computational efficiency compared to existing methods.
- Identification of 71 key genes and 26 core genes with high discriminative accuracy (AUC=0.97) for glutathione depletion toxicity.
- Advanced outcome pathway (AOP) analysis revealed disease outcomes linked to glutathione depletion, providing molecular mechanism insights.
Conclusions:
- ToxAssay effectively identifies toxicity biomarkers and elucidates molecular mechanisms of drug-induced toxicity.
- The R package offers a robust solution for analyzing complex toxicogenomics datasets.
- Findings provide precise insights into glutathione depletion-induced toxicity and potential disease outcomes.
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