ReviewThe AAPS journal2008
A flexible nonlinear feedback system that captures diverse patterns of adaptation and rebound.
Review in The AAPS journal, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Pharmacokinetic-Pharmacodynamic Modeling and Simulation of Merigolix, a Nonpeptide Gonadotropin-Releasing Hormone Antagonist.Clinical pharmacokinetics · 2026Article
- In Vitro-In Silico Models to Elucidate Mechanisms of Bile Acid Disposition and Cellular Aerobics in Human Hepatocytes.The AAPS journal · 2025Article
- Feedback control indirect response models.Journal of pharmacokinetics and pharmacodynamics · 2016Article
- Challenges of a mechanistic feedback model describing nicotinic acid-induced changes in non-esterified fatty acids in rats.Journal of pharmacokinetics and pharmacodynamics · 2013Article
- Feedback modeling of non-esterified fatty acids in rats after nicotinic acid infusions.Journal of pharmacokinetics and pharmacodynamics · 2011Article
- Turnover modeling of non-esterified fatty acids in rats after multiple intravenous infusions of nicotinic acid.Dose-response : a publication of International Hormesis Society · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An important approach to modeling tolerance and adaptation employs feedback mechanisms in which the response to the drug generates a counter-regulating action which affects the response. In this paper we analyze a family of nonlinear feedback models which has recently proved effective in modeling tolerance phenomena such as have been observed with SSRI's. We use dynamical systems methods to exhibit typical properties of the response-time course of these nonlinear models, such as overshoot and rebound, establish quantitive bounds and explore how these properties depend on the system and drug parameters. Our analysis is anchored in three specific in vivo data sets which involve different levels of pharmacokinetic complexity. Initial estimates for system (k(in), k(out), k(tol)) and drug (EC(50)/IC(50), E(max)/I(max), n) parameters are obtained on the basis of specific properties of the response-time course, identified in the context of exploratory (graphical) data analysis. Our analysis and the application of its results to the three concrete examples demonstrates the flexibility and potential of this family of feedback models.
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