The analysis of dose-response curve from bioassays with quantal response: Deterministic or statistical approaches?
- PMID: 26952004
- DOI: 10.1016/j.toxlet.2016.03.001
The analysis of dose-response curve from bioassays with quantal response: Deterministic or statistical approaches?
Abstract
Dose-response relations can be obtained from systems at any structural level of biological matter, from the molecular to the organismic level. There are two types of approaches for analyzing dose-response curves: a deterministic approach, based on the law of mass action, and a statistical approach, based on the assumed probabilities distribution of phenotypic characters. Models based on the law of mass action have been proposed to analyze dose-response relations across the entire range of biological systems. The purpose of this paper is to discuss the principles that determine the dose-response relations. Dose-response curves of simple systems are the result of chemical interactions between reacting molecules, and therefore are supported by the law of mass action. In consequence, the shape of these curves is perfectly sustained by physicochemical features. However, dose-response curves of bioassays with quantal response are not explained by the simple collision of molecules but by phenotypic variations among individuals and can be interpreted as individual tolerances. The expression of tolerance is the result of many genetic and environmental factors and thus can be considered a random variable. In consequence, the shape of its associated dose-response curve has no physicochemical bearings; instead, they are originated from random biological variations. Due to the randomness of tolerance there is no reason to use deterministic equations for its analysis; on the contrary, statistical models are the appropriate tools for analyzing these dose-response relations.
Keywords: Deterministic methods; Dose-response curve; Probit method; Quantal response; Tolerance distribution.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Similar articles
-
The mass-action law based algorithms for quantitative econo-green bio-research.Integr Biol (Camb). 2011 May;3(5):548-59. doi: 10.1039/c0ib00130a. Epub 2011 Mar 14. Integr Biol (Camb). 2011. PMID: 21403972
-
The distribution of the maximum likelihood estimator in up-and-down experiments for quantal dose-response data.J Biopharm Stat. 1999 Aug;9(3):499-519. doi: 10.1081/BIP-100101190. J Biopharm Stat. 1999. PMID: 10473034
-
Conditioning on certain random events associated with statistical variability in PK/PD.J Pharmacokinet Pharmacodyn. 2005 Apr;32(2):213-43. doi: 10.1007/s10928-005-0090-7. Epub 2005 Nov 7. J Pharmacokinet Pharmacodyn. 2005. PMID: 16283533 Review.
-
[Model and inference for quantal response pharmacological assay].Nihon Yakurigaku Zasshi. 2000 Jul;116(1):29-35. doi: 10.1254/fpj.116.29. Nihon Yakurigaku Zasshi. 2000. PMID: 10976450 Review. Japanese.
-
[The role of the biometrician in dosage finding].Arzneimittelforschung. 1977 Feb;27(2A):257-64. Arzneimittelforschung. 1977. PMID: 577172 German.
Cited by
-
Phenotypic plasticity, canalisation and developmental stability of Triatoma infestans wings: effects of a sublethal application of a pyrethroid insecticide.Parasit Vectors. 2021 Jul 6;14(1):355. doi: 10.1186/s13071-021-04857-5. Parasit Vectors. 2021. PMID: 34229739 Free PMC article.
-
Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes.Front Digit Health. 2020 Dec 3;2:569178. doi: 10.3389/fdgth.2020.569178. eCollection 2020. Front Digit Health. 2020. PMID: 34713042 Free PMC article.
-
Order Through Disorder: The Characteristic Variability of Systems.Front Cell Dev Biol. 2020 Mar 20;8:186. doi: 10.3389/fcell.2020.00186. eCollection 2020. Front Cell Dev Biol. 2020. PMID: 32266266 Free PMC article. Review.
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical