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Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group,)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
2011 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 2, 143-151 p.Article in journal (Refereed) Published
Abstract [en]

Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.

Place, publisher, year, edition, pages
2011. Vol. 13, no 2, 143-151 p.
Keyword [en]
mixed-effects modeling, model diagnostics, pcVPC, prediction correction, VPC
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
URN: urn:nbn:se:uu:diva-149187DOI: 10.1208/s12248-011-9255-zISI: 000290165700001PubMedID: 21302010OAI: oai:DiVA.org:uu-149187DiVA: diva2:404095
Available from: 2011-03-15 Created: 2011-03-15 Last updated: 2017-12-11Bibliographically 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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Bergstrand, MartinHooker, Andrew C.

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