Literature DB >> 9860894

A nonlinear isobologram model with Box-Cox transformation to both sides for chemical mixtures.

D G Chen1, J G Pounds.   

Abstract

The linear logistical isobologram is a commonly used and powerful graphical and statistical tool for analyzing the combined effects of simple chemical mixtures. In this paper a nonlinear isobologram model is proposed to analyze the joint action of chemical mixtures for quantitative dose-response relationships. This nonlinear isobologram model incorporates two additional new parameters, Ymin and Ymax, to facilitate analysis of response data that are not constrained between 0 and 1, where parameters Ymin and Ymax represent the minimal and the maximal observed toxic response. This nonlinear isobologram model for binary mixtures can be expressed as [formula: see text] In addition, a Box-Cox transformation to both sides is introduced to improve the goodness of fit and to provide a more robust model for achieving homogeneity and normality of the residuals. Finally, a confidence band is proposed for selected isobols, e.g., the median effective dose, to facilitate graphical and statistical analysis of the isobologram. The versatility of this approach is demonstrated using published data describing the toxicity of the binary mixtures of citrinin and ochratoxin as well as a new experimental data from our laboratory for mixtures of mercury and cadmium.

Entities:  

Mesh:

Substances:

Year:  1998        PMID: 9860894      PMCID: PMC1533464          DOI: 10.1289/ehp.98106s61367

Source DB:  PubMed          Journal:  Environ Health Perspect        ISSN: 0091-6765            Impact factor:   9.031


  9 in total

1.  Potency of combined estrogenic pesticides.

Authors:  K Ramamoorthy; F Wang; I C Chen; S Safe; J D Norris; D P McDonnell; K W Gaido; W P Bocchinfuso; K S Korach
Journal:  Science       Date:  1997-01-17       Impact factor: 47.728

2.  Synergy paper questioned at toxicology meeting.

Authors:  J Kaiser
Journal:  Science       Date:  1997-03-28       Impact factor: 47.728

Review 3.  What is synergy?

Authors:  M C Berenbaum
Journal:  Pharmacol Rev       Date:  1989-06       Impact factor: 25.468

Review 4.  Synergy, additivism and antagonism in immunosuppression. A critical review.

Authors:  M C Berenbaum
Journal:  Clin Exp Immunol       Date:  1977-04       Impact factor: 4.330

5.  Chemical mixtures from a public health perspective: the importance of research for informed decision making.

Authors:  K Sexton; B D Beck; E Bingham; J D Brain; D M DeMarini; R C Hertzberg; E J O'Flaherty; J G Pounds
Journal:  Toxicology       Date:  1995-12-28       Impact factor: 4.221

6.  Nonlinear statistical models for the joint action of toxins.

Authors:  C N Barton; R C Braunberg; L Friedman
Journal:  Biometrics       Date:  1993-03       Impact factor: 2.571

7.  Comparison of a class of regression equations.

Authors:  T E Jackson
Journal:  Am J Physiol       Date:  1984-03

Review 8.  Criteria for analyzing interactions between biologically active agents.

Authors:  M C Berenbaum
Journal:  Adv Cancer Res       Date:  1981       Impact factor: 6.242

Review 9.  Toxicological approaches to complex mixtures.

Authors:  J L Mauderly
Journal:  Environ Health Perspect       Date:  1993-12       Impact factor: 9.031

  9 in total
  2 in total

Review 1.  Isobologram Analysis: A Comprehensive Review of Methodology and Current Research.

Authors:  Ruo-Yue Huang; Linlin Pei; QuanJin Liu; Shiqi Chen; Haibo Dou; Gang Shu; Zhi-Xiang Yuan; Juchun Lin; Guangneng Peng; Wei Zhang; Hualin Fu
Journal:  Front Pharmacol       Date:  2019-10-29       Impact factor: 5.810

2.  Sensitivity of the immature rat uterotrophic assay to mixtures of estrogens.

Authors:  Helen Tinwell; John Ashby
Journal:  Environ Health Perspect       Date:  2004-04       Impact factor: 9.031

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.