| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Polak, Sebastian
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (4/4 displayed)
- 2022Mechanistic modeling of drug products applied to the skin: A workshop summary report.citations
- 2022Mechanistic Modeling of In Vitro Skin Permeation and Extrapolation to In Vivo for Topically Applied Metronidazole Drug Products Using a Physiologically Based Pharmacokinetic Model.citations
- 2018Utilizing postmortem drug concentrations in mechanistic modeling and simulation of cardiac effects: a proof of concept study with methadone
- 2015QTc modification after risperidone administration--insight into the mechanism of action with use of the modeling and simulation at the population level approach.citations
Places of action
| Organizations | Location | People |
|---|
article
QTc modification after risperidone administration--insight into the mechanism of action with use of the modeling and simulation at the population level approach.
Abstract
Ensuring the safety of therapy is both expensive and time-consuming process, which may be supported by modeling and simulation.The objective of this study was to gain insight into the effect of risperidone administration on QT interval by in silico evaluation of the effect in the individuals with different metabolic status of CYP2D6.Evaluation was performed through the combination of empirical and mechanistic modeling with the use of the Cardiac Safety Simulator platform allowing for simulation of electrophysiological consequences of drug administration at the population level. The performance of the proposed approach was evaluated by in silico mimicking of the clinical trial conducted by Novalbos.The simulation results depict differences in ΔQT correlated with change in metabolic activity, but not as significant as observed clinically. For poor metabolizers (PMs), ΔQTc was 8.0 and 5.1 ms, for Fridericia's and Bezett's correction, respectively, in comparison to 13.9 in Novalbos's study. For intermediate metabolizers (IMs), there was 9.3 and 7.3 ms versus 4 ms observed clinically, for ultrarapid metabolizers (UMs) -4.0 and 1 ms versus 0.60 ms, for EMs -5.9 and 7.7 ms versus 6.1 ms.Simulated results underestimate changes observed in the PMs and overestimate the results for the IMs and UMs groups. EM individuals were properly predicted. The results of various QTc studies vary considerably and it is not clear which factors have a decisive influence. Nevertheless, presented differences are still more consistent with clinical results than results obtained clinically by other researchers.