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| Analisis Schild× | Kinetika Michaelis-Menten× | Pemodelan Farmakodinamik Populasi× | |
|---|---|---|---|
| Bidang | Farmakologi | Farmakologi | Farmakologi |
| Keluarga | Process / pipeline | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1947 | 1913 | 1992 |
| Pencetus≠ | Henry Schild | Leonor Michaelis and Maud Menten | Lewis Sheiner and Stephen Roush |
| Tipe≠ | antagonism quantification | mechanistic model | dose-response modeling |
| Sumber perintis≠ | Schild, H. O. (1947). pA, a new scale for the measurement of drug antagonism. Journal of Physiology, 106(3), 337-357. DOI ↗ | Michaelis, L., & Menten, M. L. (1913). Die Kinetik der Invertinwirkung. Biochemische Zeitschrift, 49, 333-369. link ↗ | Dahlström, B., & Nyberg, L. (1993). Population pharmacokinetics and pharmacodynamics. Clinical Pharmacokinetics, 24(1), 45-57. link ↗ |
| Alias≠ | Schild plot, pA2 | MM kinetics, Michaelis constant, Vmax | PopPD, population PD, hierarchical PD modeling |
| Terkait≠ | 3 | 2 | 3 |
| Ringkasan≠ | Schild analysis is a quantitative method for characterizing competitive receptor antagonism developed by Henry Schild in 1947. It uses dose-response curves in the presence and absence of antagonist to estimate the antagonist affinity constant (pA2), enabling standardized comparison of antagonist potency across drugs and experimental systems. | Michaelis-Menten kinetics describes the rate of enzyme-catalyzed reactions as a function of substrate concentration. Developed by Leonor Michaelis and Maud Menten in 1913, this foundational framework models enzyme catalysis through the rapid-equilibrium approximation and enables prediction of drug metabolism rates in pharmacokinetics. | Population pharmacodynamic (PopPD) modeling integrates pharmacokinetics with individual dose-response relationships across patient populations to characterize drug efficacy and tolerability. Pioneered by Lewis Sheiner and colleagues, PopPD accounts for inter-individual variability in drug effects and enables rational dose optimization and response prediction. |
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