Literature DB >> 24252121

Shape and steepness of toxicological dose-response relationships of continuous endpoints.

Wout Slob1, R Woodrow Setzer.   

Abstract

A re-analysis of a large number of historical dose-response data for continuous endpoints indicates that an exponential or a Hill model with four parameters adequately describes toxicological dose-responses. No exceptions were found for the datasets considered, which related to a wide variety of endpoints and to both in vivo and in vitro studies of various types. For a given endpoint/study type dose-response shapes were found to be homogenous among chemicals in the in vitro studies considered, while a mild among-chemical variation in the steepness parameter seemed to be present in the in vivo studies. Our findings have various practical consequences. For continuous endpoints, model selection in the BMD approach is not a crucial issue. The often applied approach of using constraints on the model parameters to prevent "infinite" slopes at dose zero in fitting a model is not in line with our findings, and appears to be unjustified. Instead, more realistic ranges of parameter values could be derived from re-analyses of large numbers of historical dose-response datasets in the same endpoint and study type, which could be used as parameter constraints in future individual datasets. This approach will be particularly useful for weak datasets (e.g. few doses, much scatter). In addition, this approach may open the way to use fewer animals in future studies. In the discussion, we argue that distinctions between linear, sub/supralinear or thresholded dose-response shapes, based on visual inspection of plots, are not biologically meaningful nor useful for risk assessment.

Entities:  

Mesh:

Year:  2013        PMID: 24252121     DOI: 10.3109/10408444.2013.853726

Source DB:  PubMed          Journal:  Crit Rev Toxicol        ISSN: 1040-8444            Impact factor:   5.635


  35 in total

1.  Comparison of in vitro and in vivo clastogenic potency based on benchmark dose analysis of flow cytometric micronucleus data.

Authors:  Jeffrey C Bemis; John W Wills; Steven M Bryce; Dorothea K Torous; Stephen D Dertinger; Wout Slob
Journal:  Mutagenesis       Date:  2015-06-06       Impact factor: 3.000

2.  Pig-a gene mutation assay study design: critical assessment of 3- versus 28-day repeat-dose treatment schedules.

Authors:  Azeddine Elhajouji; Tamsanqa Tafara Hove; Oliver O'Connell; Hansjoerg Martus; Stephen D Dertinger
Journal:  Mutagenesis       Date:  2020-09-12       Impact factor: 3.000

3.  Python BMDS: A Python interface library and web application for the canonical EPA dose-response modeling software.

Authors:  Ly Ly Pham; Sean Watford; Katie Paul Friedman; Jessica Wignall; Andrew J Shapiro
Journal:  Reprod Toxicol       Date:  2019-08-12       Impact factor: 3.143

4.  Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments.

Authors:  Kan Shao; Bruce C Allen; Matthew W Wheeler
Journal:  Risk Anal       Date:  2016-12-29       Impact factor: 4.000

Review 5.  Estimating the carcinogenic potency of chemicals from the in vivo micronucleus test.

Authors:  Lya G Soeteman-Hernández; George E Johnson; Wout Slob
Journal:  Mutagenesis       Date:  2015-07-10       Impact factor: 3.000

6.  Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy.

Authors:  Stephen D Dertinger; Andrew R Kraynak; Ryan P Wheeldon; Derek T Bernacki; Steven M Bryce; Nikki Hall; Jeffrey C Bemis; Sheila M Galloway; Patricia A Escobar; George E Johnson
Journal:  Environ Mol Mutagen       Date:  2019-02-27       Impact factor: 3.216

7.  Quantitative differentiation of whole smoke solution-induced mutagenicity in the mouse lymphoma assay.

Authors:  Xiaoqing Guo; Robert H Heflich; Stacey L Dial; Mamata De; Patricia A Richter; Nan Mei
Journal:  Environ Mol Mutagen       Date:  2017-11-09       Impact factor: 3.216

Review 8.  Contributions of DNA repair and damage response pathways to the non-linear genotoxic responses of alkylating agents.

Authors:  Joanna Klapacz; Lynn H Pottenger; Bevin P Engelward; Christopher D Heinen; George E Johnson; Rebecca A Clewell; Paul L Carmichael; Yeyejide Adeleye; Melvin E Andersen
Journal:  Mutat Res Rev Mutat Res       Date:  2015-12-02       Impact factor: 5.657

9.  Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose-Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose-Response Analysis.

Authors:  Matthew W Wheeler; Walter W Piegorsch; Albert John Bailer
Journal:  Risk Anal       Date:  2018-10-25       Impact factor: 4.000

10.  Combinations of LXR and RXR agonists induce triglyceride accumulation in human HepaRG cells in a synergistic manner.

Authors:  Alexandra Lasch; Jimmy Alarcan; Alfonso Lampen; Albert Braeuning; Dajana Lichtenstein
Journal:  Arch Toxicol       Date:  2020-03-02       Impact factor: 5.153

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