Analysis of asymmetry of agonist concentration–effect curves

Graaf, Pieter H. Van der; Schoemaker, Rik C.

doi:10.1016/s1056-8719(99)00026-x

Cited by 33 publications

(13 citation statements)

References 20 publications

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“…Thus, we propose the following methodology when using the operational model: first, to assess the asymmetry of experimental E/[A] curves by comparing the fit of empirical models including asymmetry [3,8]; and, second, to use the operational model expressed by Eqn 15 or 22 depending on whether the curve is asymmetric or symmetric, respectively.…”

Section: Discussionmentioning

confidence: 99%

Have many estimates of efficacy and affinity been misled? Revisiting the operational model of agonism

Roche

Graaf

Giraldo

2016

Drug Discovery Today

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Section: Discussionmentioning

confidence: 99%

Have many estimates of efficacy and affinity been misled? Revisiting the operational model of agonism

Roche

Graaf

Giraldo

2016

Drug Discovery Today

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“…However, a basic assumption therein that is not always verified is whether the resulting curve is symmetrical (Van der Graff and Schoemaker, 1999). In some instances the curves are asymmetrical and this can affect conclusions derived from the analysis.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an asymmetry parameter for curve-fitting in single-chemical and mixture toxicity assessment

Dawson¹,

Genco²,

Bensinger³

et al. 2012

Toxicology

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In mixture toxicity, concentration-effect data are often used to generate conclusions on combined effect. While models of combined effect are available for such assessments, proper fitting of the data is critical to obtaining accurate conclusions. In this study an asymmetry parameter (s) was evaluated for data-fitting and compared with our previous approach. Inhibition of bioluminescence was assessed with Vibrio fischeri at 15, 30 and 45-minutes of exposure with seven or eight concentrations and a control (each duplicated) for each single-chemical (A or B) and mixture (A:B). Concentration-effect data were fitted to sigmoid curves using the four-parameter logistic function (4PL) and the five-parameter logistic minus one-parameter (5PL-1P) function. For the 4PL, parameters included minimum effect, maximum effect, EC50 and slope, while for the 5PL-1P the minimum effect parameter was removed and an asymmetry parameter was added. A total of 72 mixture toxicity data sets were evaluated, representing 432 single-chemical and 216 mixture curves. Mean coefficients of determination (r2) for all 648 curves showed that the 5PL-1P gave better fitting (0.9982 ± 0.0018) than the 4PL (0.9973 ± 0.0030). For both functions, the sum-of-squares of the residuals (SS-Res) was determined for each curve. The 5-parameter rational regression best described the relationship between the decrease in sum-of-squares of the residuals (i.e., 4PL: SS-Res – 5PL-1P: SS-Res) and log s, with fitting improved the most at low values of s (s < 0.8). This held even when curves with r2 values ≤ 0.9970 were removed from the analyses. Subsequent review of the combined effects obtained via the 4PL and the 5PL-1P functions resulted in a change in the interpretation of combined effect in 39/216 (18%) cases.

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“…The data were simulated from the new 5PL function in Equation (3). The current 5PL function in Equation (2) is linked to the re-parameterized 5PL function in Equation (3) To have a broader comparison between the two 5PL forms, simulations on different parameter combinations were conducted.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…Studies show that the similarity test is sensitive to model inadequacy and the distribution of the F-probability is biased upward when one fails to take asymmetry into account [2]. Subtle deviations from symmetrical behavior may be an indicator of receptor heterogeneity and detection of such complexity may provide crucial information about the pharmacological pathway at an early stage of assay characterization study [3]. Ignoring the asymmetry can also lead to inaccurate estimates of important parameters, poor characterization of the pharmacological pathway and the mechanism, and poor inference with respect to drug-receptor interactions [4].…”

Section: Introductionmentioning

confidence: 99%

Re‐parameterization of five‐parameter logistic function

Liao

Liu

2009

Journal of Chemometrics

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a Recently, the five-parameter logistic (5PL) function has seen increased use as a model for bioassay dose-response curves. The 5PL function takes curve asymmetry into consideration to overcome some drawbacks of the fourparameter logistic (4PL) function. These drawbacks include biased estimates of important model parameters, poor characterization of pharmacological pathways and the mechanisms, and inaccurate inference of drug-receptor interactions. However, currently used 5PL functional form does not have the same practical useful parameter interpretation as the 4PL function. In this paper, we re-parameterize the 5PL function to preserve the practical useful parameter interpretation of the 4PL. Comparing to the currently used 5PL, the re-parameterized 5PL function preserves practical useful parameter interpretation of 4PL and has better statistical properties in terms of bias and mean squared error (MSE).

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Analysis of asymmetry of agonist concentration–effect curves

Cited by 33 publications

References 20 publications

Have many estimates of efficacy and affinity been misled? Revisiting the operational model of agonism

Have many estimates of efficacy and affinity been misled? Revisiting the operational model of agonism

Evaluation of an asymmetry parameter for curve-fitting in single-chemical and mixture toxicity assessment

Re‐parameterization of five‐parameter logistic function

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