The analysis of dose-response curve from bioassays with quantal response: Deterministic or statistical approaches? 2016

G Mougabure-Cueto, and V Sfara
Centro de Referencia de Vectores (Ce.Re.Ve.), Ministerio de Salud de la Nación Argentina, Hospital Colonia Pabellón Rawson calle s/n (5164), Santa María de Punilla, Córdoba, Argentina. Electronic address: gmougabure@gmail.com.

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.

UI MeSH Term Description Entries
D008954 Models, Biological Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment. Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D010600 Pharmacology The study of the origin, nature, properties, and actions of drugs and their effects on living organisms. Pharmacologies
D004305 Dose-Response Relationship, Drug The relationship between the dose of an administered drug and the response of the organism to the drug. Dose Response Relationship, Drug,Dose-Response Relationships, Drug,Drug Dose-Response Relationship,Drug Dose-Response Relationships,Relationship, Drug Dose-Response,Relationships, Drug Dose-Response
D004364 Pharmaceutical Preparations Drugs intended for human or veterinary use, presented in their finished dosage form. Included here are materials used in the preparation and/or formulation of the finished dosage form. Drug,Drugs,Pharmaceutical,Pharmaceutical Preparation,Pharmaceutical Product,Pharmaceutic Preparations,Pharmaceutical Products,Pharmaceuticals,Preparations, Pharmaceutical,Preparation, Pharmaceutical,Preparations, Pharmaceutic,Product, Pharmaceutical,Products, Pharmaceutical
D014116 Toxicology The science concerned with the detection, chemical composition, and biological action of toxic substances or poisons and the treatment and prevention of toxic manifestations. Toxinology,Evidence Based Toxicology,Evidence-Based Toxicology,Based Toxicologies, Evidence,Based Toxicology, Evidence,Evidence Based Toxicologies,Evidence-Based Toxicologies,Toxicologies, Evidence Based,Toxicologies, Evidence-Based,Toxicology, Evidence Based,Toxicology, Evidence-Based
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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