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Exploring group size for statistical analysis of real‐time signalling experiments
Author(s) -
Yang Liang,
Zhu Xiao,
Finlay David B.,
Glass Michelle,
Duffull Stephen B.
Publication year - 2021
Publication title -
british journal of pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.432
H-Index - 211
eISSN - 1476-5381
pISSN - 0007-1188
DOI - 10.1111/bph.15572
Subject(s) - kinetic energy , statistics , biological system , design of experiments , mathematics , coefficient of variation , group (periodic table) , computer science , chemistry , physics , biology , organic chemistry , quantum mechanics
Background and Purpose Classical pharmacological bioassays generally use observed effects from a concentration series, at a single equilibrium time point to construct a concentration–effect curve, representing one experiment. However, if the full kinetic profile of the effect data for each concentration was evaluated simultaneously, then the analysis would be more powerful. In this work, we explore if more precise parameters can be achieved by using the full kinetic method. Experimental Approach We used a simulation estimation study to explore the influence of kinetic analysis on the precision of the E max model parameter estimates ( E max and C 50 ). We compared a full kinetic approach in which all effect versus time data from a theoretical real‐time signalling experiment were analysed simultaneously with a ‘reference’ approach. The theoretical real‐time signalling experiment was based on a previously published CB 2 receptor‐binding experiment. Key Results The reference method with a group size ( n ) of 5 provided highly precise parameter estimates (coefficient of variation [CV] 3.4% for E max and 0.72% for C 50 ). A full kinetic method provided more precise estimates than the reference with equal or smaller group sizes. Note that group size ‘ n ’ here refers to the number of technical replicates rather than the number of biological replicates. Conclusion and Implications A full kinetic method can yield more precise parameter estimates than the equilibrium method. Such an approach may be more useful for researchers.

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