| 1. |
|
|
| 2. |
- Ma, Guangli, et al.
(författare)
-
Comparison of the agonist-antagonist interaction model and the pool model for the effect of remoxipride on prolactin
- 2010
-
Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 70:6, s. 815-824
-
Tidskriftsartikel (refereegranskat)abstract
- AIMS The tolerance to the prolactin response following administration of antipsychotic drugs has been modelled as a depletion of a prolactin pool (pool model) and a model where the tolerance is explained by a feedback loop including the dopamine interaction of prolactin release (agonist-antagonist interaction model, (AAI model)). The AAI model was superior to the pool model when analyzing data from clinical trials of risperidone and paliperidone. Here we evaluated the two models using the remoxipride data, designed to challenge the short-term prolactin response, from which the original pool model was built. METHODS The remoxipride data were collected from a study where eight healthy male subjects received two remoxipride infusions on five occasions. The intervals between the first and second dose on each occasion were 2, 8, 12, 24 and 48 h, respectively. The pool and AAI models were fitted using NONMEM. RESULTS According to the objective function values the pool model with a circadian rhythm function fitted the data slightly better, while the AAI model was better in describing the circadian rhythm of prolactin. Visual predictive checks revealed that the models predicted the prolactin profiles equally well. CONCLUSIONS According to the analysis performed here, a previous analysis of several clinical studies and literature reports on prolactin concentrations, it appears that the dopamine feedback mechanism included in the AAI model is better than the storage depletion mechanism in the pool model to estimate the bio-rhythm of prolactin time-course and the tolerance development across different populations, drugs, treatment schedules and time.
|
|
| 3. |
- Nothnagel, Axel, et al.
(författare)
-
Strategic Plan of the IVS for the Period 2016-2025
- 2016
-
Ingår i: IVS 2016 General Meeting Proceedings "New Horizons with VGOS". Edited by Dirk Behrend, Karen D. Baver, and Kyla L. Armstrong, NASA/CP-2016-219016, 2016. ; :NASA/CP-2016-219016, s. 3-12
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)
|
|
| 4. |
- Plan, Elodie L, et al.
(författare)
-
Transient Lower Esophageal Sphincter Relaxations PKPD Modeling : Count Model and Repeated Time-To-Event Model
- 2011
-
Ingår i: Journal of Pharmacology and Experimental Therapeutics. - : American Society for Pharmacology & Experimental Therapeutics (ASPET). - 0022-3565 .- 1521-0103.
-
Tidskriftsartikel (refereegranskat)abstract
- Transient lower esophageal sphincter relaxation (TLESR) is the major mechanism for gastro-esophageal reflux. Characterization of candidate compounds for reduction of TLESRs are traditionally done through summary exposure and response measures and would benefit from model-based analyses of exposure-TLESR events relationships. PKPD modeling approaches treating TLESR either as count data or as repeated time-to-event (RTTE) data were developed and compared in terms of ability to characterize system and drug characteristics. Vehicle data comprising 294 TLESR events were collected from 9 dogs. Compound (WIN55251-2) data containing 66 TLESR events, as well as plasma concentrations, were obtained from 4 dogs. Each experiment lasted for 45min and was initiated with a meal. Counts in equispaced 5-min intervals and 1-min intervals were modeled based on a Poisson probability distribution model. TLESR events were analyzed with the RTTE model. PK was connected to PD models with a 1-compartment model. Vehicle data were described by a baseline and a surge function; the surge peak was determined around 9.69min by all approaches and its width of 5min (1-min count and RTTE) or 10min (5-min count). TLESRs inhibition by WIN55251-2 was described by an Imax model, with an IC50 of on average 2.39nmol.L-1. Modeling approaches utilizing count or RTTE data linked to a dynamic PKPD representation of exposure is superior to using summary PK and PD measures. Differences in terms of predictions and power to detect a significant drug effect are illustrated with a simulation-based investigation, and a range of diagnostics for such modeling approaches is presented.
|
|
