View in EDS HTML Full Text PDF Full Text

Evaluation and Optimization of Pharmacokinetic Models for in Vitro to in Vivo Extrapolation of Estrogenic Activity for Environmental Chemicals.

Saved in:
Bibliographic Details
Title: Evaluation and Optimization of Pharmacokinetic Models for in Vitro to in Vivo Extrapolation of Estrogenic Activity for Environmental Chemicals.
Authors: Casey, Warren M.1, Xiaoqing Chang2, Allen, David G.2, Ceger, Patricia C.2, Choksi, Neepa Y.2, Jui-Hua Hsieh3, Wetmore, Barbara A.4, Ferguson, Stephen S.1, DeVito, Michael J.1, Sprankle, Catherine S.2, Kleinstreuer, Nicole C.1 nicole.kleinstreuer@nih.gov
Source: Environmental Health Perspectives. Sep2018, Vol. 126 Issue 9, p1-14. 14p. 2 Diagrams, 3 Charts, 4 Graphs.
Subject Terms: *Animal experimentation, *Carcinogens, Estrogen antagonists, Pharmacokinetics, Research funding, Rodents, Statistics, Data analysis, Government regulation, Predictive tests, Data analysis software, Statistical models, Signal peptides, Descriptive statistics, In vitro studies, In vivo studies
Abstract: BACKGROUND: To effectively incorporate in vitro data into regulatory use, confidence must be established in the quantitative extrapolation of in vitro activity to relevant end points in animals or humans. OBJECTIVE: Our goal was to evaluate and optimize in vitro to in vivo extrapolation (IVIVE) approaches using in vitro estrogen receptor (ER) activity to predict estrogenic effects measured in rodent uterotrophic studies. METHODS: We evaluated three pharmacokinetic (PK) models with varying complexities to extrapolate in vitro to in vivo dosimetry for a group of 29 ER agonists, using data from validated in vitro [U.S. Environmental Protection Agency (U.S. EPA) ToxCast? ER model] and in vivo (uterotrophic) methods. In vitro activity values were adjusted using mass-balance equations to estimate intracellular exposure via an enrichment factor (EF), and steady-state model calculations were adjusted using fraction of unbound chemical in the plasma (fu) to approximate bioavailability. Accuracy of each model-adjustment combination was assessed by comparing model predictions with lowest effect levels (LELs) from guideline uterotrophic studies. RESULTS: We found little difference in model predictive performance based on complexity or route-specific modifications. Simple adjustments, applied to account for in vitro intracellular exposure (EF) or chemical bioavailability (fu), resulted in significant improvements in the predictive performance of all models. CONCLUSION: Computational IVIVE approaches accurately estimate chemical exposure levels that elicit positive responses in the rodent uterotrophic bioassay. The simplest model had the best overall performance for predicting both oral (PPK_EF) and injection (PPK_fu) LELs from guideline uterotrophic studies, is freely available, and can be parameterized entirely using freely available in silico tools. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: 8gh
DbLabel: GreenFILE
An: 131863422
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Evaluation and Optimization of Pharmacokinetic Models for in Vitro to in Vivo Extrapolation of Estrogenic Activity for Environmental Chemicals.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Casey%2C+Warren+M%2E%22">Casey, Warren M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xiaoqing+Chang%22">Xiaoqing Chang</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Allen%2C+David+G%2E%22">Allen, David G.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ceger%2C+Patricia+C%2E%22">Ceger, Patricia C.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Choksi%2C+Neepa+Y%2E%22">Choksi, Neepa Y.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jui-Hua+Hsieh%22">Jui-Hua Hsieh</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wetmore%2C+Barbara+A%2E%22">Wetmore, Barbara A.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Ferguson%2C+Stephen+S%2E%22">Ferguson, Stephen S.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22DeVito%2C+Michael+J%2E%22">DeVito, Michael J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sprankle%2C+Catherine+S%2E%22">Sprankle, Catherine S.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Kleinstreuer%2C+Nicole+C%2E%22">Kleinstreuer, Nicole C.</searchLink><relatesTo>1</relatesTo><i> nicole.kleinstreuer@nih.gov</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Environmental+Health+Perspectives%22">Environmental Health Perspectives</searchLink>. Sep2018, Vol. 126 Issue 9, p1-14. 14p. 2 Diagrams, 3 Charts, 4 Graphs.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Animal+experimentation%22">Animal experimentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Carcinogens%22">Carcinogens</searchLink><br /><searchLink fieldCode="DE" term="%22Estrogen+antagonists%22">Estrogen antagonists</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmacokinetics%22">Pharmacokinetics</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Rodents%22">Rodents</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Government+regulation%22">Government regulation</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+tests%22">Predictive tests</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+peptides%22">Signal peptides</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22In+vitro+studies%22">In vitro studies</searchLink><br /><searchLink fieldCode="DE" term="%22In+vivo+studies%22">In vivo studies</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: BACKGROUND: To effectively incorporate in vitro data into regulatory use, confidence must be established in the quantitative extrapolation of in vitro activity to relevant end points in animals or humans. OBJECTIVE: Our goal was to evaluate and optimize in vitro to in vivo extrapolation (IVIVE) approaches using in vitro estrogen receptor (ER) activity to predict estrogenic effects measured in rodent uterotrophic studies. METHODS: We evaluated three pharmacokinetic (PK) models with varying complexities to extrapolate in vitro to in vivo dosimetry for a group of 29 ER agonists, using data from validated in vitro [U.S. Environmental Protection Agency (U.S. EPA) ToxCast? ER model] and in vivo (uterotrophic) methods. In vitro activity values were adjusted using mass-balance equations to estimate intracellular exposure via an enrichment factor (EF), and steady-state model calculations were adjusted using fraction of unbound chemical in the plasma (fu) to approximate bioavailability. Accuracy of each model-adjustment combination was assessed by comparing model predictions with lowest effect levels (LELs) from guideline uterotrophic studies. RESULTS: We found little difference in model predictive performance based on complexity or route-specific modifications. Simple adjustments, applied to account for in vitro intracellular exposure (EF) or chemical bioavailability (fu), resulted in significant improvements in the predictive performance of all models. CONCLUSION: Computational IVIVE approaches accurately estimate chemical exposure levels that elicit positive responses in the rodent uterotrophic bioassay. The simplest model had the best overall performance for predicting both oral (PPK_EF) and injection (PPK_fu) LELs from guideline uterotrophic studies, is freely available, and can be parameterized entirely using freely available in silico tools. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=131863422
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1289/EHP1655
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 1
    Subjects:
      – SubjectFull: Animal experimentation
        Type: general
      – SubjectFull: Carcinogens
        Type: general
      – SubjectFull: Estrogen antagonists
        Type: general
      – SubjectFull: Pharmacokinetics
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Rodents
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Government regulation
        Type: general
      – SubjectFull: Predictive tests
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Signal peptides
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: In vitro studies
        Type: general
      – SubjectFull: In vivo studies
        Type: general
    Titles:
      – TitleFull: Evaluation and Optimization of Pharmacokinetic Models for in Vitro to in Vivo Extrapolation of Estrogenic Activity for Environmental Chemicals.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Casey, Warren M.
      – PersonEntity:
          Name:
            NameFull: Xiaoqing Chang
      – PersonEntity:
          Name:
            NameFull: Allen, David G.
      – PersonEntity:
          Name:
            NameFull: Ceger, Patricia C.
      – PersonEntity:
          Name:
            NameFull: Choksi, Neepa Y.
      – PersonEntity:
          Name:
            NameFull: Jui-Hua Hsieh
      – PersonEntity:
          Name:
            NameFull: Wetmore, Barbara A.
      – PersonEntity:
          Name:
            NameFull: Ferguson, Stephen S.
      – PersonEntity:
          Name:
            NameFull: DeVito, Michael J.
      – PersonEntity:
          Name:
            NameFull: Sprankle, Catherine S.
      – PersonEntity:
          Name:
            NameFull: Kleinstreuer, Nicole C.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 00916765
          Numbering:
            – Type: volume
              Value: 126
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Environmental Health Perspectives
              Type: main
ResultId 1