Accuracy and Precision of the Receptorial Responsiveness Method (RRM) in the Quantification of A1 Adenosine Receptor Agonists.
Int J Mol Sci
; 20(24)2019 Dec 12.
Article
en En
| MEDLINE
| ID: mdl-31842299
ABSTRACT
The receptorial responsiveness method (RRM) is a procedure that is based on a simple nonlinear regression while using a model with two variables (X, Y) and (at least) one parameter to be determined (cx). The model of RRM describes the co-action of two agonists that consume the same response capacity (due to the use of the same postreceptorial signaling in a biological system). While using RRM, uniquely, an acute increase in the concentration of an agonist (near the receptors) can be quantified (as cx), via evaluating E/c curves that were constructed with the same or another agonist in the same system. As this measurement is sensitive to the implementation of the curve fitting, the goal of the present study was to test RRM by combining different ways and setting options, namely individual vs. global fitting, ordinary vs. robust fitting, and three weighting options (no weighting vs. weighting by 1/Y2 vs. weighting by 1/SD2). During the testing, RRM was used to estimate the known concentrations of stable synthetic A1 adenosine receptor agonists in isolated, paced guinea pig left atria. The estimates were then compared to the known agonist concentrations (to assess the accuracy of RRM); furthermore, the 95% confidence limits of the best-fit values were also considered (to evaluate the precision of RRM). It was found that, although the global fitting offered the most convenient way to perform RRM, the best estimates were provided by the individual fitting without any weighting, almost irrespective of the fact whether ordinary or robust fitting was chosen.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Dinámicas no Lineales
/
Receptor de Adenosina A1
/
Agonistas del Receptor Purinérgico P1
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Int J Mol Sci
Año:
2019
Tipo del documento:
Article
País de afiliación:
Hungria