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In Silico Prediction of the Point of Departure (POD) with High-Throughput Data

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Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH,volume 30)


Determining the point of departure (POD) is a critical step in chemical risk assessment . Current approaches based on chronic animal studies are costly and time-consuming while being insufficient for providing mechanistic information regarding toxicity. Driven by the desire to incorporate multiple lines of evidence relevant to human toxicology and to reduce animal use, there has been a heightened interest in utilizing transcriptional and other high-throughput assay endpoints to infer the POD . In this review, we outline common data modeling approaches utilizing gene expression profiles from animal tissues to estimate the POD in comparison with obtaining PODs based on apical endpoints . Various issues in experiment design, technology platforms, data analysis methods, and software packages are explained. Potential choices for each step are discussed. Recent development for models incorporating in vitro assay endpoints is also examined, including PODs based on in vitro assays and efforts to predict in vivo PODs with in vitro data. Future directions and potential research areas are also discussed.


  • High-throughput assays
  • Microarrays
  • Point of departure
  • Predictive modeling
  • RNAseq
  • Toxicogenomics
  • Transcriptional profiling

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Half-maximal effective concentration


Akaike information criterion


Adverse outcome pathway


Benchmark dose


A statistical lower bound of BMD


Benchmark risk




Peak plasma concentration


Environmental Protection Agency


European Union


High content imaging




In vitro-in vivo extrapolation


Kernel density mean of M-component


Key event


Key event relationship


Lowest-observed-adverse-effect level


4,4′-Methylenebis (N,Ndimethyl) benzenamine


Molecular initiating event


Mode of action


Molecular Signature Database






Point of departure


Registration, evaluation, authorization and restriction of chemical substances


Robust Multi-array Average normalization method


Reads per kilobase per million mapped reads


Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System


Target learning region






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The author would like to thank the editor and an anonymous reviewer for valuable suggestions. The opinions expressed in this paper are those of the author and do not necessarily reflect the views of the US Food and Drug Administration .

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Correspondence to Dong Wang .

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Wang, D. (2019). In Silico Prediction of the Point of Departure (POD) with High-Throughput Data. In: Hong, H. (eds) Advances in Computational Toxicology. Challenges and Advances in Computational Chemistry and Physics, vol 30. Springer, Cham.

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