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Handling data below the limit of quantification in mixed effect models.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
2009 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 11, no 2, 371-380 p.Article in journal (Refereed) Published
Abstract [en]

The purpose of this study is to investigate the impact of observations below the limit of quantification (BQL) occurring in three distinctly different ways and assess the best method for prevention of bias in parameter estimates and for illustrating model fit using visual predictive checks (VPCs). Three typical ways in which BQL can occur in a model was investigated with simulations from three different models and different levels of the limit of quantification (LOQ). Model A was used to represent a case with BQL observations in an absorption phase of a PK model whereas model B represented a case with BQL observations in the elimination phase. The third model, C, an indirect response model illustrated a case where the variable of interest in some cases decreases below the LOQ before returning towards baseline. Different approaches for handling of BQL data were compared with estimation of the full dataset for 100 simulated datasets following models A, B, and C. An improved standard for VPCs was suggested to better evaluate simulation properties both for data above and below LOQ. Omission of BQL data was associated with substantial bias in parameter estimates for all tested models even for seemingly small amounts of censored data. Best performance was seen when the likelihood of being below LOQ was incorporated into the model. In the tested examples this method generated overall unbiased parameter estimates. Results following substitution of BQL observations with LOQ/2 were in some cases shown to introduce bias and were always suboptimal to the best method. The new standard VPCs was found to identify model misfit more clearly than VPCs of data above LOQ only.

Place, publisher, year, edition, pages
2009. Vol. 11, no 2, 371-380 p.
Keyword [en]
Likelihood, limit-of-quantification, NONMEM, visual-predictive-check
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-122151DOI: 10.1208/s12248-009-9112-5ISI: 000269940800017PubMedID: 19452283OAI: oai:DiVA.org:uu-122151DiVA: diva2:308504
Available from: 2010-04-06 Created: 2010-04-06 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Application of Mixed-Effect Modeling to Improve Mechanistic Understanding and Predictability of Oral Absorption
Open this publication in new window or tab >>Application of Mixed-Effect Modeling to Improve Mechanistic Understanding and Predictability of Oral Absorption
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Several sophisticated techniques to study in vivo GI transit and regional absorption of pharmaceuticals are available and increasingly used. Examples of such methods are Magnetic Marker Monitoring (MMM) and local drug administration with remotely operated capsules. Another approach is the paracetamol and sulfapyridine double marker method which utilizes observed plasma concentrations of the two substances as markers for GI transit. Common for all of these methods is that they generate multiple types of observations e.g. tablet GI position, drug release and plasma concentrations of one or more substances. This thesis is based on the hypothesis that application of mechanistic nonlinear mixed-effect models could facilitate a better understanding of the interrelationship between such variables and result improved predictions of the processes involved in oral absorption.

Mechanistic modeling approaches have been developed for application to data from MMM studies, paracetamol and sulfapyridine double marker studies and for linking in vitro and in vivo drug release. Models for integrating information about tablet GI transit, in vivo drug release and drug plasma concentrations measured in MMM studies was outlined and utilized to describe drug release and absorption properties along the GI tract for felodipine and the investigational drug AZD0837. A mechanistic link between in vitro and in vivo drug release was established by estimation of the mechanical stress in different regions of the GI tract in a unit equivalent to rotation speed in the in vitro experimental setup. The effect of atropine and erythromycin on gastric emptying and small intestinal transit was characterized with a semi-mechanistic model applied to double marker studies in fed and fasting dogs.

The work with modeling of in vivo drug absorption has highlighted the need for, and led to, further development of mixed-effect modeling methodology with respect to model diagnostics and the handling of censored observations.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 89 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 142
Keyword
Absorption, magnetic marker monitoring, drug release, IVIVC, pharmacometrics, NONMEM, model diagnostics, pcVPC, pvcVPC, visual predictive check, VPC, BQL, paracetamol, sulfapyridine.
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
urn:nbn:se:uu:diva-149314 (URN)978-91-554-8030-1 (ISBN)
Public defence
2011-04-29, B41, Uppsala Biomedicinska Centrum (BMC), Husargatan 3, Uppsala, Sweden, 09:15 (English)
Opponent
Supervisors
Available from: 2011-04-07 Created: 2011-03-17 Last updated: 2011-05-04Bibliographically approved

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Karlsson, Mats O.

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