ArticleJournal of pharmacokinetics and pharmacodynamics2012
Mechanism-based PK-PD model for the prolactin biological system response following an acute dopamine inhibition challenge: quantitative extrapolation to humans.
Article in Journal of pharmacokinetics and pharmacodynamics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 46 citations in OpenAlex.
- Quantification of the endogenous growth hormone and prolactin lowering effects of a somatostatin-dopamine chimera using population PK/PD modeling.Journal of pharmacokinetics and pharmacodynamics · 2020Trial
- Intranasal drug delivery to the brain for neurodegenerative diseases: Current efforts and challenges in delivery platforms and modeling.Advanced drug delivery reviews · 2026Review
- Scaling Pharmacodynamics from Rats to Humans to Support Erythropoietin and Romiplostim Combination Therapy to Treat Erythropoietin-Resistant Anemia.Pharmaceutics · 2023Article
- Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine.Journal of pharmacokinetics and pharmacodynamics · 2020Review
- Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of DBritish journal of pharmacology · 2018Article
- Pharmacokinetic and Pharmacodynamic Properties of Rosmarinic Acid in Rat Cholestatic Liver Injury.Molecules (Basel, Switzerland) · 2018Article
- Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- Access to the CNS: Biomarker Strategies for Dopaminergic Treatments.Pharmaceutical research · 2018Review
- Impact of mathematical pharmacology on practice and theory: four case studies.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- Modeling of prolactin response following dopamine DPharmacology research & perspectives · 2017Article
- Modeling the Effect of the Selective S1PPharmaceutical research · 2017Article
- A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.Pharmaceutical research · 2017Article
- Revealing the Neuroendocrine Response After Remoxipride Treatment Using Multi-Biomarker Discovery and Quantifying It by PK/PD Modeling.The AAPS journal · 2017Article
- Article
- Understanding the Behavior of Systems Pharmacology Models Using Mathematical Analysis of Differential Equations: Prolactin Modeling as a Case Study.CPT: pharmacometrics & systems pharmacology · 2016Article
- Kinetics of drug action in disease states: towards physiology-based pharmacodynamic (PBPD) models.Journal of pharmacokinetics and pharmacodynamics · 2015Article
- Emerging Insights for Translational Pharmacokinetic and Pharmacokinetic-Pharmacodynamic Studies: Towards Prediction of Nose-to-Brain Transport in Humans.The AAPS journal · 2015Review
- White paper: landscape on technical and conceptual requirements and competence framework in drug/disease modeling and simulation.CPT: pharmacometrics & systems pharmacology · 2013Article
- The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects.Fluids and barriers of the CNS · 2013Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this investigation was to develop a mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) model for the biological system prolactin response following a dopamine inhibition challenge using remoxipride as a paradigm compound. After assessment of baseline variation in prolactin concentrations, the prolactin response of remoxipride was measured following (1) single intravenous doses of 4, 8 and 16 mg/kg and (2) following double dosing of 3.8 mg/kg with different time intervals. The mechanistic PK-PD model consisted of: (i) a PK model for remoxipride concentrations in brain extracellular fluid; (ii) a pool model incorporating prolactin synthesis, storage in lactotrophs, release into- and elimination from plasma; (iii) a positive feedback component interconnecting prolactin plasma concentrations and prolactin synthesis; and (iv) a dopamine antagonism component interconnecting remoxipride brain extracellular fluid concentrations and stimulation of prolactin release. The most important findings were that the free brain concentration drives the prolactin release into plasma and that the positive feedback on prolactin synthesis in the lactotrophs, in contrast to the negative feedback in the previous models on the PK-PD correlation of remoxipride. An external validation was performed using a dataset obtained in rats following intranasal administration of 4, 8, or 16 mg/kg remoxipride. Following simulation of human remoxipride brain extracellular fluid concentrations, pharmacodynamic extrapolation from rat to humans was performed, using allometric scaling in combination with independent information on the values of biological system specific parameters as prior knowledge. The PK-PD model successfully predicted the system prolactin response in humans, indicating that positive feedback on prolactin synthesis and allometric scaling thereof could be a new feature in describing complex homeostatic mechanisms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.