Precursor-dependent indirect pharmacodynamic response model for tolerance and rebound phenomena

doi:10.1021/js980171q

Journal of Pharmaceutical Sciences

Volume 87, Issue 12, December 1998, Pages 1577-1584

https://doi.org/10.1021/js980171q Get rights and content

Abstract

A precursor-dependent model of indirect pharmacody-namic response which can describe tolerance and rebound was characterized in terms of the effects of changes in the fundamental properties of the drug on its response profiles. The model extends previous models by considering inhibition or stimulation of production of the response variable dependent on the amount of precursor which may accumulate or deplete after administration of some drugs. Standardized pharmacokinetic and pharmacodynamic parameters were used for generating dose, plasma concentration, and response–time profiles using computer simulations. The peak response (R_max) and the time of its occurrence (T_Rmax) were dependent on the dose, degree of maximum inhibition (I_max) or stimulation (S_max), and drug concentrations causing 50% inhibition (IC₅₀) or stimulation (SC₅₀). The maximum rebound (RB_max) and the time of its occurrence (TRB_max) after a single bolus dose were also dependent on these factors, but were of lesser magnitude and showed relatively later occurrence. Interestingly, values of area between the baseline and effect curve (ABEC) and area between the baseline and rebound curve (ABRC) were equal for each set of conditions for each model, but the latter is reduced when there is a second pathway for loss of precursor. Tolerance occurs because of diverse mechanisms, and the response patterns demonstrated may be helpful in describing tolerance and rebound phenomena for drugs which affect precursor pools.

References and Notes (18)

M.C. Houston et al.
Beta-adrenergic blocker withdrawal syndromes in hypertension and other cardiovascular diseases
Am. Heart J.
(1988)
D.Z. D'Argenio et al.
A program package for simulation and parameter estimation in pharmacokinetic systems
Comp. Programs Biomed.
(1979)
W. Krzyzanski et al.
Integrated functions for four basic models of indirect pharmacodynamic response
J. Pharm. Sci.
(1998)
Y.-N. Sun et al.
Transit compartments versus gamma distribution function to model signal transduction processes in pharmacodynamics
J. Pharm. Sci.
(1998)
D.R. Sibley et al.
Molecular mechanisms of receptor desensitization using the β-adrenergic receptor-coupled adenylate cyclase system as a model
Nature
(1985)
J.J. Lima et al.
Drug- or hormone-induced adaptation: model of adrenergic hypersensitivity
J. Pharmacokin. Biopharm.
(1989)
E.J. Ariens et al.
Molecular pharmacology: The mode of action of biologically active compounds
A.C. Guyton
Blood pressure control-special role of the kidneys and body fluids
Science
(1991)
G. Movin-Osswald et al.
Prolactin release after remoxipride by an integrated pharmacokinetic-pharmacodynamic model with intra- and inter-individual aspects
J. Pharmacol. Exp. Ther.
(1995)

There are more references available in the full text version of this article.

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Citation Excerpt :
In their most basic form these models are based on the concept of a physiological indirect response or turnover model as originally proposed by Levy to describe the time course of the anticoagulant effect of warfarin (Nagashima et al., 1969). Jusko et al. have subsequently formalized the concept of using various forms of a turnover model to characterize time-dependencies in the pharmacodynamics of a wide range of drugs (Dayneka et al., 1993; Jusko, 1995; Jusko and Ko, 1994; Sharma et al., 1998). Here, the drug effect is being characterized as an enhancing or inhibiting effect on either the zero order rate constant for input in the system or the first order rate constant for elimination from the system (Dayneka et al., 1993).
Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity.
Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior.
Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders).
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^†: Supported in part by Grant No. 24211 from the National Institute of General Medical Science, National Institutes of Health.

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Research ArticlesPrecursor-dependent indirect pharmacodynamic response model for tolerance and rebound phenomena†

Abstract

Am. Heart J.

Comp. Programs Biomed.

J. Pharm. Sci.

J. Pharm. Sci.

Molecular mechanisms of receptor desensitization using the β-adrenergic receptor-coupled adenylate cyclase system as a model

Nature

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Blood pressure control-special role of the kidneys and body fluids

Science

Prolactin release after remoxipride by an integrated pharmacokinetic-pharmacodynamic model with intra- and inter-individual aspects

J. Pharmacol. Exp. Ther.

Research Articles
Precursor-dependent indirect pharmacodynamic response model for tolerance and rebound phenomena†