Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa Emax: Uchambuzi wa Uhusiano wa Dozi-Majibu ya Kifamasia× | Farmakokinetiki ya Idadi ya Watu× | |
|---|---|---|
| Nyanja | Famakometriki | Famakometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1981 | 1977 |
| Mwanzilishi≠ | Holford & Sheiner | Sheiner, Rosenberg & Marathe |
| Aina≠ | Nonlinear dose-response regression model | Nonlinear mixed-effects regression model |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | Maximum Effect Model, Hyperbolic Emax Model, Sigmoidal Emax Model, Emax Farmakodynamik Modeli | PopPK, Nonlinear Mixed-Effects Modeling, NONMEM Approach, Popülasyon Farmakokinetiği |
| Zinazohusiana | 2 | 2 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
|
|