Umfang:
x, 138 Seiten
,
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Inhalt:
While patients are known to respond differently to drug therapies, current clinical practice often still follows a standardized dosage regimen for all patients. For drugs with a narrow range of both effective and safe concentrations, this approach may lead to a high incidence of adverse events or subtherapeutic dosing in the presence of high patient variability. Model-informedprecision dosing (MIPD) is a quantitative approach towards dose individualization based on mathematical modeling of dose-response relationships integrating therapeutic drug/biomarker monitoring (TDM) data. MIPD may considerably improve the efficacy and safety of many drug therapies. Current MIPD approaches, however, rely either on pre-calculated dosing tables or on simple point predictions of the therapy outcome. These approaches lack a quantification of uncertainties and the ability to account for effects that are delayed. In addition, the underlying models are not improved while applied to patient data. Therefore, current approaches are not well suited for ...
Anmerkung:
Dissertation Universität Potsdam 2021
Weitere Ausg.:
Erscheint auch als Online-Ausgabe Maier, Corinna Sabrina Bayesian data assimilation and reinforcement learning for model-informed precision dosing in oncology Potsdam, 2021
Sprache:
Englisch
Schlagwort(e):
Arzneimitteldosis
;
Datenassimilation
;
Operante Konditionierung
;
Hochschulschrift
Mehr zum Autor:
Röblitz, Susanna 1979-
