Chapter 15
In Silico Prediction of the Point
of Departure (POD)
with High-Throughput Data
Dong Wang
Abstract Determining the point of departure (POD) is a critical step in chemical risk
assessment. Current approaches based on chronic animal studies are costly and timeconsuming 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.
Keywords High-throughput assays · Microarrays · Point of departure · Predictive
modeling · RNAseq · Toxicogenomics · Transcriptional profiling
Abbreviations
AC 50
Half-maximal effective concentration
AIC
Akaike information criterion
AOP
Adverse outcome pathway
BMD
Benchmark dose
BMDL
A statistical lower bound of BMD
D. Wang (B)
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research,
US Food and Drug Administration (FDA), Jefferson, AR, USA
e-mail: dong.wang@fda.hhs.gov
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_15
299
In Silico Prediction of the Point
of Departure (POD)
with High-Throughput Data
Dong Wang
Abstract Determining the point of departure (POD) is a critical step in chemical risk
assessment. Current approaches based on chronic animal studies are costly and timeconsuming 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.
Keywords High-throughput assays · Microarrays · Point of departure · Predictive
modeling · RNAseq · Toxicogenomics · Transcriptional profiling
Abbreviations
AC 50
Half-maximal effective concentration
AIC
Akaike information criterion
AOP
Adverse outcome pathway
BMD
Benchmark dose
BMDL
A statistical lower bound of BMD
D. Wang (B)
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research,
US Food and Drug Administration (FDA), Jefferson, AR, USA
e-mail: dong.wang@fda.hhs.gov
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_15
299