Chapter 9
A Pair Ranking (PRank) Method
for Assessing Assay Transferability
Among the Toxicogenomics Testing
Systems
Zhichao Liu, Brian Delavan, Liyuan Zhu, Ruth Robert and Weida Tong
Abstract The use of animal models for risk assessment is not a reliable and satisfying paradigm. Accompanying the strategic planned shift by regulatory agencies,
more and more advocating campaigns such as the 3Rs in Europe and Tox21/ToxCast
in the USA were proposed to develop in silico and in vitro approaches to eliminate
animal use. To effectively implement non-animal models in risk assessment, novel
approaches are urgently needed for investigating the concordance between testing
systems to facilitate the selection of the fit-for-purpose assay. In this chapter, we
introduce a Pair Ranking (PRank) method for the quantitative evaluation of assay
transferability among the different toxicogenomics (TGx) testing systems. First, we
will summarize the critical issues of TGx related to its success in risk assessment.
Second, we will elucidate the application of proposed PRank method for addressing key questions in TGx. Finally, we will suggest some potential use of the PRank
method for advancing risk assessment.
Z. Liu (B) · B. Delavan · L. Zhu · W. Tong
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research,
US Food and Drug Administration (FDA), Jefferson, AR 72079, USA
e-mail: zhichao.liu@fda.hhs.gov
B. Delavan
e-mail: bsdelavan@ualr.edu
L. Zhu
e-mail: Liyuan.Zhu@fda.hhs.gov
W. Tong
e-mail: Weida.Tong@fda.hhs.gov
B. Delavan
Department of Biosciences, University of Little Rock, Little Rock, AR, USA
R. Robert
ApconiX, BioHub at Alderley Park, Alderley Edge, UK
e-mail: Ruth.Roberts@apconix.com
Department of Biosciences, University of Birmingham, Birmingham, UK
© 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_9
159
A Pair Ranking (PRank) Method
for Assessing Assay Transferability
Among the Toxicogenomics Testing
Systems
Zhichao Liu, Brian Delavan, Liyuan Zhu, Ruth Robert and Weida Tong
Abstract The use of animal models for risk assessment is not a reliable and satisfying paradigm. Accompanying the strategic planned shift by regulatory agencies,
more and more advocating campaigns such as the 3Rs in Europe and Tox21/ToxCast
in the USA were proposed to develop in silico and in vitro approaches to eliminate
animal use. To effectively implement non-animal models in risk assessment, novel
approaches are urgently needed for investigating the concordance between testing
systems to facilitate the selection of the fit-for-purpose assay. In this chapter, we
introduce a Pair Ranking (PRank) method for the quantitative evaluation of assay
transferability among the different toxicogenomics (TGx) testing systems. First, we
will summarize the critical issues of TGx related to its success in risk assessment.
Second, we will elucidate the application of proposed PRank method for addressing key questions in TGx. Finally, we will suggest some potential use of the PRank
method for advancing risk assessment.
Z. Liu (B) · B. Delavan · L. Zhu · W. Tong
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research,
US Food and Drug Administration (FDA), Jefferson, AR 72079, USA
e-mail: zhichao.liu@fda.hhs.gov
B. Delavan
e-mail: bsdelavan@ualr.edu
L. Zhu
e-mail: Liyuan.Zhu@fda.hhs.gov
W. Tong
e-mail: Weida.Tong@fda.hhs.gov
B. Delavan
Department of Biosciences, University of Little Rock, Little Rock, AR, USA
R. Robert
ApconiX, BioHub at Alderley Park, Alderley Edge, UK
e-mail: Ruth.Roberts@apconix.com
Department of Biosciences, University of Birmingham, Birmingham, UK
© 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_9
159