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Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0003-1258-8297
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2019 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125Article in journal (Refereed) Epub ahead of print
Abstract [en]

AIMS: This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example.

METHODS: We assessed five model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error (PE) percentiles.

RESULTS: When IOV was lower than IIV, the accuracy was good for all approaches (50th percentile of the PE [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios.

CONCLUSIONS: Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualisation is to include IOV in the generation of the EBEs, but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

Place, publisher, year, edition, pages
2019.
Keywords [en]
NONMEM, pharmacokinetics, population analysis, therapeutic drug monitoring
National Category
Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:uu:diva-381215DOI: 10.1111/bcp.13901PubMedID: 30767254OAI: oai:DiVA.org:uu-381215DiVA, id: diva2:1302609
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-04-05
In thesis
1. Pharmacometric Approaches to Improve Dose Individualization Methods in Hemophilia A
Open this publication in new window or tab >>Pharmacometric Approaches to Improve Dose Individualization Methods in Hemophilia A
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Hemophilia A is a bleeding disorder caused by the lack of functional coagulation factor VIII (FVIII). The overall aim of this thesis was to improve dose individualization of FVIII replacement therapy in hemophilia A using pharmacometric approaches.

A population pharmacokinetic (PK) model of FVIII activity following the administration of moroctocog alfa was developed based on data from a large heterogeneous cohort of moderate to severe hemophilia A patients. Body weight, age, neutralizing anti-FVIII inhibitors, race, and analytical assay were found to be significant predictors of FVIII activity PK. In addition, large inter-individual variability (IIV) and inter-occasion variability (IOV) was identified highlighting the need for dose individualization.

High magnitudes of IOV are known to impair model-based therapeutic drug monitoring. Using a population PK model of FVIII activity, several approaches to handle IOV in Bayesian forecasting of individual PK parameters were assessed across a wide range of features. Considering IOV in Bayesian forecasting, but ignoring IOV in dose calculation, led to the most precise individualized doses, in particular, when sparse data was used.

The dose-exposure-response relationship of FVIII replacement therapy remains unclear. A parametric repeated time-to-categorical event (RTTCE) model was developed to characterize the relationship between the dose of octocog alfa, plasma FVIII activity, bleeding frequency and severity, and covariates, using data from clinical trials. The bleeding hazard was found to decrease throughout time and to be affected by plasma FVIII activity and number of previous bleeds. Unexplained IIV in the bleeding hazard was found to be large.

Bayesian forecasting based on the RTTCE model was used to predict the future occurrence of bleeds, and to contrast the predicted outcome using individual i) PK, ii) bleeding, and iii) PK, bleeding and covariate information, from data collected in clinical trials. The results support that individual bleed information can inform the optimization of prophylactic dosing regimens in severe hemophilia A patients.

In summary, the pharmacometric approaches presented provide a valuable quantitative framework to improve dose individualization in hemophilia A. Furthermore, enhanced dosing has the potential to reduce bleeding frequency and to lower the high costs associated to treatment.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 70
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 270
Keywords
Bayesian forecasting, coagulation factor VIII, dose adaptation, hemophilia/haemophilia A, inter-occasion variability, NONMEM, pharmacodynamics, pharmacokinetics, pharmacometrics, therapeutic drug monitoring
National Category
Health Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-381218 (URN)978-91-513-0631-5 (ISBN)
Public defence
2019-05-29, B41, Biomedicinskt centrum, Husargatan 3, 09:15 (English)
Opponent
Supervisors
Available from: 2019-05-06 Created: 2019-04-05 Last updated: 2019-05-06

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Abrantes, João A.Jönsson, SivKarlsson, MatsNielsen, Elisabet I.

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