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      Establishing Best Practices and Guidance in Population Modeling: An Experience With an Internal Population Pharmacokinetic Analysis Guidance

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          Abstract

          This tutorial describes the development of a population pharmacokinetic (Pop PK) analysis guidance within Pfizer, which strives for improved consistency and efficiency, and a more systematic approach to model building. General recommendations from the Pfizer internal guidance and a suggested workflow for Pop PK model building are discussed. A description is also provided for mechanisms by which conflicting opinions were captured and resolved across the organization to arrive at the final guidance.

          CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e51; doi: 10.1038/psp.2013.26; advance online publication 3 July 2013

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          Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models.

          Martin Bergstrand, Andrew Hooker, Johan Wallin … (2011)
          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.
          Article 0 comments Cited 445 times – based on 0 reviews     Review now
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            Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions.

            R Savic, O. Karlsson (2009)
            Empirical Bayes ("post hoc") estimates (EBEs) of etas provide modelers with diagnostics: the EBEs themselves, individual prediction (IPRED), and residual errors (individual weighted residual (IWRES)). When data are uninformative at the individual level, the EBE distribution will shrink towards zero (eta-shrinkage, quantified as 1-SD(eta (EBE))/omega), IPREDs towards the corresponding observations, and IWRES towards zero (epsilon-shrinkage, quantified as 1-SD(IWRES)). These diagnostics are widely used in pharmacokinetic (PK) pharmacodynamic (PD) modeling; we investigate here their usefulness in the presence of shrinkage. Datasets were simulated from a range of PK PD models, EBEs estimated in non-linear mixed effects modeling based on the true or a misspecified model, and desired diagnostics evaluated both qualitatively and quantitatively. Identified consequences of eta-shrinkage on EBE-based model diagnostics include non-normal and/or asymmetric distribution of EBEs with their mean values ("ETABAR") significantly different from zero, even for a correctly specified model; EBE-EBE correlations and covariate relationships may be masked, falsely induced, or the shape of the true relationship distorted. Consequences of epsilon-shrinkage included low power of IPRED and IWRES to diagnose structural and residual error model misspecification, respectively. EBE-based diagnostics should be interpreted with caution whenever substantial eta- or epsilon-shrinkage exists (usually greater than 20% to 30%). Reporting the magnitude of eta- and epsilon-shrinkage will facilitate the informed use and interpretation of EBE-based diagnostics.
            Article 0 comments Cited 175 times – based on 0 reviews     Review now
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              Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development

              D Mould, R N Upton (2012)
              Modeling is an important tool in drug development; population modeling is a complex process requiring robust underlying procedures for ensuring clean data, appropriate computing platforms, adequate resources, and effective communication. Although requiring an investment in resources, it can save time and money by providing a platform for integrating all information gathered on new therapeutic agents. This article provides a brief overview of aspects of modeling and simulation as applied to many areas in drug development.
              Article 0 comments Cited 133 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                C Cronenberger: Carol.Cronenberger@pfizer.com
                Journal
                Journal ID (nlm-ta): CPT Pharmacometrics Syst Pharmacol
                Journal ID (iso-abbrev): CPT Pharmacometrics Syst Pharmacol
                Journal ID (doi): 10.1002/(ISSN)2163-8306
                Journal ID (publisher-id): PSP4
                Title: CPT: Pharmacometrics & Systems Pharmacology
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 2163-8306
                Publication date (Electronic): 03 July 2013
                Publication date (Print): July 2013
                Volume: 2
                Issue: 7 ( doiID: 10.1002/psp4.2013.2.issue-7 )
                Electronic Location Identifier: 51
                Affiliations
                [ 1 ] Global Clinical Pharmacology, Pfizer Groton Connecticut USA
                [ 2 ] Global Clinical Pharmacology, Pfizer Sandwich UK
                [ 3 ] Global Clinical Pharmacology, Pfizer Collegeville Pennsylvania USA
                [ 4 ] Global Clinical Pharmacology, Pfizer La Jolla California USA
                Article
                Publisher ID: PSP4201326
                DOI: 10.1038/psp.2013.26
                PMC ID: 6483270
                PubMed ID: 23836283
                SO-VID: 6f188f7b-b71e-45a0-9048-daaca51998a4
                Copyright © © 2013 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/3.0/ Licence, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                Date received : 27 December 2012
                Date accepted : 02 April 2013
                Page count
                Figures: 3, Tables: 0, References: 44, Pages: 8
                Categories
                Subject: Tutorial
                Subject: Tutorial
                Custom metadata
                source-schema-version-number 2.0
                component-id PSP4201326
                cover-date July 2013
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.1 mode:remove_FC converted:06.03.2019

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