Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Emax-model: Farmacodynamische dosis-responsanalyse× | Populatie Farmacokinetiek× | |
|---|---|---|
| Vakgebied | Farmacometrie | Farmacometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1981 | 1977 |
| Grondlegger≠ | Holford & Sheiner | Sheiner, Rosenberg & Marathe |
| Type≠ | Nonlinear dose-response regression model | Nonlinear mixed-effects regression model |
| Oorspronkelijke bron≠ | Holford, N. H. G., & Sheiner, L. B. (1981). Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models. Clinical Pharmacokinetics, 6(6), 429–453. DOI ↗ | Sheiner, L. B., Rosenberg, B., & Marathe, V. V. (1977). Estimation of population characteristics of pharmacokinetic parameters from routine clinical data. Journal of Pharmacokinetics and Biopharmaceutics, 5(5), 445–479. DOI ↗ |
| Aliassen | Maximum Effect Model, Hyperbolic Emax Model, Sigmoidal Emax Model, Emax Farmakodynamik Modeli | PopPK, Nonlinear Mixed-Effects Modeling, NONMEM Approach, Popülasyon Farmakokinetiği |
| Verwant | 2 | 2 |
| Samenvatting≠ | The Emax model is a nonlinear pharmacodynamic model that describes the relationship between drug concentration and biological effect. Introduced by Holford and Sheiner in 1981, it characterizes dose-response curves using three fundamental parameters: the maximum achievable effect (Emax), the concentration producing half-maximal effect (EC50), and an optional baseline effect (E0). It remains the standard framework in clinical pharmacology and drug development for quantifying pharmacodynamic dose-response relationships. | Population Pharmacokinetics (PopPK) is a nonlinear mixed-effects modeling framework that characterizes how drugs are absorbed, distributed, metabolized, and eliminated across a patient population, estimating both typical population parameters and the magnitude of between-subject variability. Introduced by Sheiner, Rosenberg, and Marathe in 1977, it enables parameter estimation from sparse, routinely collected clinical data—making it indispensable in drug development, regulatory submissions, and individualized dosing. |
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