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Inhalt:
Given the narrow therapeutic window and the large inter-individual variability (IIV) associated with certain drugs, employing a fixed dosing strategy may result in unsafe or ineffective therapy. Model-informed precision dosing (MIPD) can improve efficacy and safety for these drugs by individualising doses based on patient-specific data with a suitable model. Traditionally, pharmacokinetic (PK)/pharmacodynamic (PD) models developed from clinical data, thus depending on the study design and population, have been applied in MIPD. While quantitative systems pharmacology (QSP) models promise better extrapolation capabilities, their complexity often prevents straightforward parameter estimation within MIPD. Model reduction approaches can help develop mechanism-based PK/PD models suitable for MIPD that retain the clinically relevant dynamics from QSP models. Yet, these methods often fail to achieve a level of reduction that renders the models as manageable as classical PK/PD models. Moreover, existing reduction approaches often rely on a single reference parameter set, neglecting that, to predict individual outcomes accurately, the reduced model should approximate the full model well across variability. [...]
Anmerkung:
Dissertation Universität Potsdam 2024
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe Falkenhagen, Undine Statistical approaches to model reduction in systems pharmacology Potsdam, 2024
Sprache:
Englisch
Schlagwort(e):
Hochschulschrift
DOI:
10.25932/publishup-66918
URN:
urn:nbn:de:kobv:517-opus4-669187
Mehr zum Autor:
Röblitz, Susanna 1979-
Mehr zum Autor:
Lehr, Thorsten 1977-
