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      Good Practices in Model‐Informed Drug Discovery and Development: Practice, Application, and Documentation

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          Abstract

          This document was developed to enable greater consistency in the practice, application, and documentation of Model‐Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of “good practice” recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

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          Most cited references47

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

          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.
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            Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions.

            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.
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              Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development

              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.
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                Author and article information

                Journal
                CPT Pharmacometrics Syst Pharmacol
                CPT Pharmacometrics Syst Pharmacol
                10.1002/(ISSN)2163-8306
                PSP4
                CPT: Pharmacometrics & Systems Pharmacology
                John Wiley and Sons Inc. (Hoboken )
                2163-8306
                14 March 2016
                March 2016
                : 5
                : 3 ( doiID: 10.1002/psp4.v5.3 )
                : 93-122
                Affiliations
                [ 1 ] PharmacometricsPfizer Ltd SandwichUK
                [ 2 ] Systems Pharmacology & MedicineBayer Pharma AG WuppertalGermany
                [ 3 ] Clinical PharmacometricsF. Hoffmann‐La Roche Ltd BaselSwitzerland
                [ 4 ] Quantitative Clinical PharmacologyAstraZeneca CambridgeUK
                [ 5 ] Institut de Recherches InternationalesServier SuresnesFrance
                [ 6 ] Clinical Pharmacology Modelling & SimulationGlaxoSmithKline R&D Ltd UxbridgeUK
                [ 7 ] Quantitative Clinical PharmacologyAstraZeneca GothenburgSweden
                [ 8 ] Global regulatory policy & IntelligenceJanssen R&D High WycombeUK
                [ 9 ] Quantitative Pharmacology & PharmacometricsMSD OssNetherlands
                [ 10 ] Global Regulatory Affairs & PolicyAstraZeneca ParisFrance
                [ 11 ] Translational Medicine & Clinical PharmacologyBoehringer Ingelheim Pharma GmbH & Co. KG BiberachGermany
                [ 12 ] PharmacometricsNovartis BaselCH
                [ 13 ] Clinical ReportingNovo Nordisk A/S SøborgDenmark
                [ 14 ] Quantitative Pharmacology & PharmacometricsMerck & Co KenilworthUSA
                Author notes
                [*] [* ]Correspondence: S Marshall ( scott.marshall@ 123456pfizer.com )
                Article
                PSP412049
                10.1002/psp4.12049
                4809625
                27069774
                7e072b06-2123-42ac-86e7-fb6fde63ad23
                © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, 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
                : 30 July 2015
                : 19 October 2015
                Page count
                Pages: 30
                Categories
                White Paper
                White Paper
                Custom metadata
                2.0
                psp412049
                March 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.8.5 mode:remove_FC converted:28.03.2016

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