Pharmacodynamic modeling of cardiovascular effects of Σb-antagonists and moxonidine : Special emphasis on tolerance and rebound effects

Abstract: Pharmacodynamic (PD) modeling was used to characterize and quantify the short- and long-term changes in cardiovascular effects following treatment with β-antagonists or imidazoline receptor agonist moxonidine, in spontaneous hypertensive rats and patients with congestive heart failure, respectively. The complex cardiovascular system is regulated by several feedback mechanisms with different time domains and gains. Both tolerance development and rebound effect are not unusual events during and after withdrawal of cardiovascular drugs. Because of complex or unknown mechanisms of action, empirical PD models are used.Acute concentration-effect relationships of l-propranolol and metoprolol were characterized by an Emax and a linear component. The two components were interpreted as a specific β-antagonist effect and a membrane-stabilizing effect at lower and higher plasma concentrations, respectively. Available empirical tolerance models were studied with respect to their interchangability and predictive performance. No reliable mechanistic information can be deduced from a model based on the fit to effect data alone without additional knowledge about physiological mechanism(s) behind the pharmacological effect. To improve PD models, knowledge of the physiology and the mechanism of drug action need to be incorporated.Chronic administration of l-propranolol resulted in a rebound effect after drug cessation. A mechanism-based model was developed which included norepinephrine, competitive receptor binding, receptor density and sensitization of the transduction system. This model was adequate even when different dosing regimens and experimental conditions were administered. No rebound effect, but rather a positive effect on heart rate and circadian rhythm was observed. Effects of moxonidine on norepinephrine, blood pressure, and heart rate were quantified by both the common empirical parallel analysis, where the drug concentration is related to the PD effects, and a sequential analysis where one PD effect is driving another. Both disease progression and circadian variation were included in the models.This thesis shows that mechanism-based modeling is feasible and can increase knowledge about the underlying biological processes involved in the pharmacological effect of the investigated drug. When the mechanism-based models can quantitatively describe the PD phenomena observed, extrapolations can be more reliable and the adequacy of the model can often be assessed from independent information on pharmacological and physiological information. For these cardiovascular agents, mechanism-based modeling has included not only a mimicking of the receptor event, but also circadian rhythms, disease progression or improvement, tolerance development and rebound effect.

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