Literature DB >> 7636755

Prolactin release after remoxipride by an integrated pharmacokinetic-pharmacodynamic model with intra- and interindividual aspects.

G Movin-Osswald1, M Hammarlund-Udenaes.   

Abstract

An integrated pharmacokinetic-pharmacodynamic model is suggested for remoxipride and its effect on prolactin (PRL) release acting by preventing the inhibitory effect of dopamine D2 receptors in the anterior pituitary. The model was implemented to describe the time course of PRL plasma levels after administration of two consecutive doses of remoxipride at 5 different time intervals, 2, 8, 12, 24 and 48 hr. The model used is an indirect response model. It consists of three parts: 1) the pharmacokinetics of remoxipride; 2) a physiological substance model for PRL, incorporating the synthesis of PRL and its release into and elimination from plasma; and 3) a pharmacodynamic model describing the influence of remoxipride on the PRL release from the pool. A linear pharmacodynamic model gave the best description of the time course of PRL. The limitation in the PRL release is the amount available in the pool, which takes 24 to 48 hr to fully restore, rather than a maximal effect of remoxipride. The intra- and interindividual variability of remoxipride as well as of the PRL response was low.

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Year:  1995        PMID: 7636755

Source DB:  PubMed          Journal:  J Pharmacol Exp Ther        ISSN: 0022-3565            Impact factor:   4.030


  21 in total

Review 1.  Interchangeability and predictive performance of empirical tolerance models.

Authors:  M Gårdmark; L Brynne; M Hammarlund-Udenaes; M O Karlsson
Journal:  Clin Pharmacokinet       Date:  1999-02       Impact factor: 6.447

2.  Comparison of the agonist-antagonist interaction model and the pool model for the effect of remoxipride on prolactin.

Authors:  Guangli Ma; Lena E Friberg; Gunilla Movin-Osswald; Mats O Karlsson
Journal:  Br J Clin Pharmacol       Date:  2010-12       Impact factor: 4.335

3.  Mechanism-based pharmacokinetic-pharmacodynamic modeling-a new classification of biomarkers.

Authors:  Meindert Danhof; Gunnar Alvan; Svein G Dahl; Jochen Kuhlmann; Gilles Paintaud
Journal:  Pharm Res       Date:  2005-08-24       Impact factor: 4.200

4.  Mathematical assessment of properties of precursor-dependent indirect pharmacodynamic response models.

Authors:  Anasuya Hazra; Wojciech Krzyzanski; William J Jusko
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-10-12       Impact factor: 2.745

Review 5.  Pharmacokinetic-pharmacodynamic modelling in anaesthesia.

Authors:  Pedro L Gambús; Iñaki F Trocóniz
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

Review 6.  Characteristics of indirect pharmacodynamic models and applications to clinical drug responses.

Authors:  A Sharma; W J Jusko
Journal:  Br J Clin Pharmacol       Date:  1998-03       Impact factor: 4.335

7.  Semi-mechanistic pharmacodynamic modeling for degarelix, a novel gonadotropin releasing hormone (GnRH) blocker.

Authors:  Pravin R Jadhav; Henrik Agersø; Christoffer W Tornøe; Jogarao V S Gobburu
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-08-08       Impact factor: 2.745

Review 8.  Emerging Insights for Translational Pharmacokinetic and Pharmacokinetic-Pharmacodynamic Studies: Towards Prediction of Nose-to-Brain Transport in Humans.

Authors:  Mitchel J R Ruigrok; Elizabeth C M de Lange
Journal:  AAPS J       Date:  2015-02-19       Impact factor: 4.009

9.  Revealing the Neuroendocrine Response After Remoxipride Treatment Using Multi-Biomarker Discovery and Quantifying It by PK/PD Modeling.

Authors:  Willem J van den Brink; Yin C Wong; Berfin Gülave; Piet H van der Graaf; Elizatbeth C M de Lange
Journal:  AAPS J       Date:  2016-10-26       Impact factor: 4.009

10.  Predictions of in vivo prolactin levels from in vitro K(i) values of D(2) receptor antagonists using an agonist-antagonist interaction model.

Authors:  Klas J Petersson; An M Vermeulen; Lena E Friberg
Journal:  AAPS J       Date:  2013-02-08       Impact factor: 4.009

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