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Hamdan A, Hooker AC, Chen X, Traschütz A, Schüle R, Synofzik M, Karlsson MO. Item performance of the scale for the assessment and rating of ataxia in rare and ultra-rare genetic ataxias. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 38769902 DOI: 10.1002/psp4.13162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/28/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
The Scale for the Assessment and Rating of Ataxia (SARA) is widely used for assessing the severity and progression of genetic cerebellar ataxias. SARA is now considered a primary end point in several ataxia treatment trials, but its underlying composite item measurement model has not yet been tested. This work aimed to evaluate the composite properties of SARA and its items using item response theory (IRT) and to demonstrate its applicability across even ultra-rare genetic ataxias. Leveraging SARA subscores data from 1932 visits from 990 patients of the Autosomal Recessive Cerebellar Ataxias (ARCA) registry, we assessed the performance of SARA using IRT methodology. The item characteristics were evaluated over the ataxia severity range of the entire ataxia population as well as the assessment validity across 115 genetic ARCA subpopulations. A unidimensional IRT model was able to describe SARA item data, indicating that SARA captures one single latent variable. All items had high discrimination values (1.5-2.9) indicating the effectiveness of the SARA in differentiating between subjects with different disease statuses. Each item contributed between 7% and 28% of the total assessment informativeness. There was no evidence for differences between the 115 genetic ARCA subpopulations in SARA applicability. These results show the good discrimination ability of SARA with all of its items adding informational value. The IRT framework provides a thorough description of SARA on the item level, and facilitates its utilization as a clinical outcome assessment in upcoming longitudinal natural history or treatment trials, across a large number of ataxias, including ultra-rare ones.
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Affiliation(s)
- Alzahra Hamdan
- Pharmacometrics Research Group, Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Andrew C Hooker
- Pharmacometrics Research Group, Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Xiaomei Chen
- Pharmacometrics Research Group, Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Andreas Traschütz
- Department of Neurodegenerative Diseases, Center for Neurology and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE) Tübingen, Tübingen, Germany
| | - Rebecca Schüle
- Department of Neurodegenerative Diseases, Center for Neurology and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Division of Neurodegenerative Diseases, Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Center for Neurology and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE) Tübingen, Tübingen, Germany
| | - Mats O Karlsson
- Pharmacometrics Research Group, Department of Pharmacy, Uppsala University, Uppsala, Sweden
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Bardo M, Huber C, Benda N, Brugger J, Fellinger T, Galaune V, Heinz J, Heinzl H, Hooker AC, Klinglmüller F, König F, Mathes T, Mittlböck M, Posch M, Ristl R, Friede T. Methods for non-proportional hazards in clinical trials: A systematic review. Stat Methods Med Res 2024:9622802241242325. [PMID: 38592333 DOI: 10.1177/09622802241242325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.
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Affiliation(s)
- Maximilian Bardo
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- Maximilian Bardo and Cynthia Huber contributed equally to this study
| | - Cynthia Huber
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- Maximilian Bardo and Cynthia Huber contributed equally to this study
| | - Norbert Benda
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - Jonas Brugger
- Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Tobias Fellinger
- Agentur für Gesundheit und Ernährungssicherheit (AGES), Vienna, Austria
| | | | - Judith Heinz
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Harald Heinzl
- Center for Medical Data Science, Section of Clinical Biometrics, Medical University of Vienna, Vienna, Austria
| | | | | | - Franz König
- Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Tim Mathes
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Martina Mittlböck
- Center for Medical Data Science, Section of Clinical Biometrics, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Robin Ristl
- Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
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3
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Fang L, Gong Y, Hooker AC, Lukacova V, Rostami-Hodjegan A, Sale M, Grosser S, Jereb R, Savic R, Peck C, Zhao L. The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA. AAPS J 2024; 26:28. [PMID: 38413548 DOI: 10.1208/s12248-024-00897-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.
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Affiliation(s)
- Lanyan Fang
- Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, Maryland, 20993, USA
| | - Yuqing Gong
- Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, Maryland, 20993, USA
| | | | | | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
- Certara Inc., Princeton, New Jersey, USA
| | - Mark Sale
- Certara Inc., Princeton, New Jersey, USA
| | - Stella Grosser
- Division of Biostatistics VIII, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rebeka Jereb
- Lek Pharmaceuticals d.d., a Sandoz Company, Ljubljana, Slovenia
| | - Rada Savic
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Carl Peck
- NDA Partners LLC., A ProPharma Group Company, Washington, District of Columbia, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Liang Zhao
- Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, Maryland, 20993, USA.
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4
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Montepiedra G, Svensson EM, Wong WK, Hooker AC. Optimizing the design of a pharmacokinetic trial to evaluate the dosing scheme of a novel tuberculosis drug in children living with or without HIV. CPT Pharmacometrics Syst Pharmacol 2024; 13:270-280. [PMID: 37946698 PMCID: PMC10864936 DOI: 10.1002/psp4.13077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023] Open
Abstract
Pharmacokinetic (PK) studies in children are usually small and have ethical constraints due to the medical complexities of drawing blood in this special population. Often, population PK models for the drug(s) of interest are available in adults, and these models can be extended to incorporate the expected deviations seen in children. As a consequence, there is increasing interest in the use of optimal design methodology to design PK sampling schemes in children that maximize information using a small sample size and limited number of sampling times per dosing period. As a case study, we use the novel tuberculosis drug delamanid, and show how applications of optimal design methodology can result in highly efficient and model-robust designs in children for estimating PK parameters using a limited number of sampling measurements. Using developed population PK models based on available data from adults living with and without HIV, and limited data on children without HIV, competing designs for children living with HIV were derived and assessed based on robustness to model uncertainty.
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Affiliation(s)
| | - Elin M. Svensson
- Department of PharmacyRadboud University Medical CenterNijmegenThe Netherlands
- Department of PharmacyUppsala UniversityUppsalaSweden
| | - Weng Kee Wong
- University of California Los AngelesLos AngelesCaliforniaUSA
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5
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Geroldinger M, Verbeeck J, Hooker AC, Thiel KE, Molenberghs G, Nyberg J, Bauer J, Laimer M, Wally V, Bathke AC, Zimmermann G. Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials. Orphanet J Rare Dis 2023; 18:391. [PMID: 38115074 PMCID: PMC10729462 DOI: 10.1186/s13023-023-02990-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/19/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians. RESULTS It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered. CONCLUSION Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.
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Affiliation(s)
- Martin Geroldinger
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Strubergasse 21, Salzburg, 5020, Austria.
- Department of Neurology, Christian Doppler Medical Centre, Full Member of European Reference Network on Rare and Complex Epilepsies EpiCARE, Paracelsus Medical University, Ignaz-Harrer Straße 79, Salzburg, 5020, Austria.
| | - Johan Verbeeck
- I-BioStat, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
| | - Andrew C Hooker
- Department of Pharmacy, Uppsala University, 751 24, Uppsala, Sweden
| | - Konstantin E Thiel
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Strubergasse 21, Salzburg, 5020, Austria
| | - Geert Molenberghs
- I-BioStat, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
- I-BioStat, KU Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium
| | - Joakim Nyberg
- Department of Pharmacy, Uppsala University, 751 24, Uppsala, Sweden
| | - Johann Bauer
- Department of Dermatology and Allergology, Paracelsus Medical University, Salzburg, 5020, Austria
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, Salzburg, 5020, Austria
| | - Martin Laimer
- Department of Dermatology and Allergology, Paracelsus Medical University, Salzburg, 5020, Austria
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, Salzburg, 5020, Austria
| | - Verena Wally
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, Salzburg, 5020, Austria
| | - Arne C Bathke
- Intelligent Data Analytics (IDA) Lab Salzburg, Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Salzburg, 5020, Austria
| | - Georg Zimmermann
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Strubergasse 21, Salzburg, 5020, Austria
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6
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Chasseloup E, Hooker AC, Karlsson MO. Generation and application of avatars in pharmacometric modelling. J Pharmacokinet Pharmacodyn 2023; 50:411-423. [PMID: 37488327 PMCID: PMC10460751 DOI: 10.1007/s10928-023-09873-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/26/2023] [Indexed: 07/26/2023]
Abstract
Simulations from population models have critical applications in drug discovery and development. Avatars or digital twins, defined as individual simulations matching clinical criteria of interest compared to observations from a real subject within a predefined margin of accuracy, may be a better option for simulations performed to inform future drug development stages in cases where an adequate model is not achievable. The aim of this work was to (1) investigate methods for generating avatars with pharmacometric models, and (2) explore the properties of the generated avatars to assess the impact of the different selection settings on the number of avatars per subject, their closeness to the individual observations, and the properties of the selected samples subset from the theoretical model parameters probability density function. Avatars were generated using different combinations of nature and number of clinical criteria, accuracy of agreement, and/or number of simulations for two examples models previously published (hemato-toxicity and integrated glucose-insulin model). The avatar distribution could be used to assess the appropriateness of the models assumed parameter distribution. Similarly it could be used to assess the models ability to properly describe the trajectories of the observations. Avatars can give nuanced information regarding the ability of a model to simulate data similar to the observations both at the population and at the individual level. Further potential applications for avatars may be as a diagnostic tool, an alternative to simulations with insurance to replicate key clinical features, and as an individual measure of model fit.
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Affiliation(s)
- Estelle Chasseloup
- Department of Pharmacy, Uppsala University, Box 580, Uppsala, 75123, Sweden
| | - Andrew C Hooker
- Department of Pharmacy, Uppsala University, Box 580, Uppsala, 75123, Sweden
| | - Mats O Karlsson
- Department of Pharmacy, Uppsala University, Box 580, Uppsala, 75123, Sweden.
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7
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Gong Y, Zhang P, Yoon M, Zhu H, Kohojkar A, Hooker AC, Ducharme MP, Gobburu J, Cellière G, Gajjar P, Li BV, Velagapudi R, Tsang YC, Schwendeman A, Polli J, Fang L, Lionberger R, Zhao L. Establishing the suitability of model-integrated evidence to demonstrate bioequivalence for long-acting injectable and implantable drug products: Summary of workshop. CPT Pharmacometrics Syst Pharmacol 2023; 12:624-630. [PMID: 36710372 DOI: 10.1002/psp4.12931] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/10/2023] [Accepted: 01/19/2023] [Indexed: 01/31/2023] Open
Abstract
On November 30, 2021, the US Food and Drug administration (FDA) and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop titled "Establishing the Suitability of Model-Integrated Evidence (MIE) to Demonstrate Bioequivalence for Long-Acting Injectable and Implantable (LAI) Drug Products." This workshop brought relevant parties from the industry, academia, and the FDA in the field of modeling and simulation to explore, identify, and recommend best practices on utilizing MIE for bioequivalence (BE) assessment of LAI products. This report summerized presentations and panel discussions for topics including challenges and opportunities in development and assessment of generic LAI products, current status of utilizing MIE, recent research progress of utilizing MIE in generic LAI products, alternative designs for BE studies of LAI products, and model validation/verification strategies associated with different types of MIE approaches.
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Affiliation(s)
- Yuqing Gong
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Peijue Zhang
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Miyoung Yoon
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Hao Zhu
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ameya Kohojkar
- Regulatory Affairs, Teva Pharmaceuticals, Fairfield, New Jersey, USA
| | | | | | - Jogarao Gobburu
- Center for Translational Medicine, School of Pharmacy, University of Maryland, College Park, Maryland, USA
| | | | | | - Bing V Li
- Office of Bioequivalence, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | | | | | - Anna Schwendeman
- Department of Pharmaceutical Sciences, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
| | - James Polli
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, College Park, Maryland, USA
| | - Lanyan Fang
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Robert Lionberger
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Liang Zhao
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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8
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Kim S, Hooker AC, Shi Y, Kim GHJ, Wong WK. Metaheuristics for pharmacometrics. CPT Pharmacometrics Syst Pharmacol 2021; 10:1297-1309. [PMID: 34562342 PMCID: PMC8592519 DOI: 10.1002/psp4.12714] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 08/06/2021] [Accepted: 09/07/2021] [Indexed: 12/22/2022] Open
Abstract
Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature-inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed-effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often-used technique to evaluate high dimensional integrals, to search for D -efficient designs for estimating parameters in nonlinear mixed-effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature-inspired metaheuristic algorithms.
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Affiliation(s)
- Seongho Kim
- Department of OncologyWayne State UniversityDetroitMichiganUSA
| | | | - Yu Shi
- Department of BiostatisticsUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Grace Hyun J. Kim
- Department of BiostatisticsUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Weng Kee Wong
- Department of BiostatisticsUniversity of California Los AngelesLos AngelesCaliforniaUSA
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9
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Bauer RJ, Hooker AC, Mentre F. Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization. CPT Pharmacometrics Syst Pharmacol 2021; 10:1452-1465. [PMID: 34559958 PMCID: PMC8674001 DOI: 10.1002/psp4.12713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 08/12/2021] [Accepted: 08/19/2021] [Indexed: 12/02/2022]
Abstract
This NONMEM tutorial shows how to evaluate and optimize clinical trial designs, using algorithms developed in design software, such as PopED and PFIM 4.0. Parameter precision and model parameter estimability is obtained by assessing the Fisher Information Matrix (FIM), providing expected model parameter uncertainty. Model parameter identifiability may be uncovered by very large standard errors or inability to invert an FIM. Because evaluation of FIM is more efficient than clinical trial simulation, more designs can be investigated, and the design of a clinical trial can be optimized. This tutorial provides simple and complex pharmacokinetic/pharmacodynamic examples on obtaining optimal sample times, doses, or best division of subjects among design groups. Robust design techniques accounting for likely variability among subjects are also shown. A design evaluator and optimizer within NONMEM allows any control stream first developed for trial design exploration to be subsequently used for estimation of parameters of simulated or clinical data, without transferring the model to another software. Conversely, a model developed in NONMEM could be used for design optimization. In addition, the $DESIGN feature can be used on any model file and dataset combination to retrospectively evaluate the model parameter uncertainty one would expect given that the model generated the data, particularly if outliers of the actual data prevent a reasonable assessment of the variance‐covariance. The NONMEM trial design feature is suitable for standard continuous data, whereas more elaborate trial designs or with noncontinuous data‐types can still be accomplished in optimal design dedicated software like PopED and PFIM.
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Affiliation(s)
- Robert J Bauer
- Pharmacometrics, R&D, ICON Clinical Research, LLC, Gaithersburg, Maryland, USA
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10
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Ryeznik Y, Sverdlov O, Svensson EM, Montepiedra G, Hooker AC, Wong WK. Pharmacometrics meets statistics-A synergy for modern drug development. CPT Pharmacometrics Syst Pharmacol 2021; 10:1134-1149. [PMID: 34318621 PMCID: PMC8520751 DOI: 10.1002/psp4.12696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 05/17/2021] [Accepted: 07/02/2021] [Indexed: 01/20/2023]
Abstract
Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.
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Affiliation(s)
- Yevgen Ryeznik
- BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, R&D Biopharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | - Oleksandr Sverdlov
- Early Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Elin M Svensson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden.,Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Grace Montepiedra
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Weng Kee Wong
- Department of Biostatistics, University of California Los Angeles, Los Angeles, California, USA
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11
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Lyauk YK, Jonker DM, Hooker AC, Lund TM, Karlsson MO. Bounded Integer Modeling of Symptom Scales Specific to Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia. AAPS J 2021; 23:33. [PMID: 33630188 PMCID: PMC7906927 DOI: 10.1208/s12248-021-00568-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 02/04/2021] [Indexed: 11/30/2022]
Abstract
The International Prostate Symptom Score (IPSS), the quality of life (QoL) score, and the benign prostatic hyperplasia impact index (BII) are three different scales commonly used to assess the severity of lower urinary tract symptoms associated with benign prostatic hyperplasia (BPH-LUTS). Based on a phase II clinical trial including 403 patients with moderate to severe BPH-LUTS, the objectives of this study were to (i) develop traditional pharmacometric and bounded integer (BI) models for the IPSS, QoL score, and BII endpoints, respectively; (ii) compare the power and type I error in detecting drug effects of BI modeling with traditional methods through simulation; and (iii) obtain quantitative translation between scores on the three abovementioned scales using a BI modeling framework. All developed models described the data adequately. Pharmacometric modeling using a continuous variable (CV) approach was overall found to be the most robust in terms of type I error and power to detect a drug effect. In most cases, BI modeling showed similar performance to the CV approach, yet severely inflated type I error was generally observed when inter-individual variability (IIV) was incorporated in the BI variance function (g()). BI modeling without IIV in g() showed greater type I error control compared to the ordered categorical approach. Lastly, a multiple-scale BI model was developed and estimated the relationship between scores on the three BPH-LUTS scales with overall low uncertainty. The current study yields greater understanding of the operating characteristics of the novel BI modeling approach and highlights areas potentially requiring further improvement.
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Affiliation(s)
- Yassine Kamal Lyauk
- Translational Medicine, Ferring Pharmaceuticals A/S, Kay Fiskers Plads, 11, Copenhagen, Denmark. .,Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Daniël M Jonker
- Translational Medicine, Ferring Pharmaceuticals A/S, Kay Fiskers Plads, 11, Copenhagen, Denmark
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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12
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Sharan S, Fang L, Lukacova V, Chen X, Hooker AC, Karlsson MO. Model-Informed Drug Development for Long-Acting Injectable Products: Summary of American College of Clinical Pharmacology Symposium. Clin Pharmacol Drug Dev 2021; 10:220-228. [PMID: 33624456 DOI: 10.1002/cpdd.928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 01/30/2021] [Indexed: 01/12/2023]
Affiliation(s)
- Satish Sharan
- Division of Quantitative Methods and Modeling (DQMM), Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Lanyan Fang
- Division of Quantitative Methods and Modeling (DQMM), Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Viera Lukacova
- Simulation Sciences, Simulations Plus, Inc., Lancaster, CA, USA
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13
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Lee J, Gong Y, Bhoopathy S, DiLiberti CE, Hooker AC, Rostami-Hodjegan A, Schmidt S, Suarez-Sharp S, Lukacova V, Fang L, Zhao L. Public Workshop Summary Report on Fiscal Year 2021 Generic Drug Regulatory Science Initiatives: Data Analysis and Model-Based Bioequivalence. Clin Pharmacol Ther 2020; 110:1190-1195. [PMID: 33236362 DOI: 10.1002/cpt.2120] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 11/14/2020] [Indexed: 12/18/2022]
Abstract
On May 4, 2020, the US Food and Drug Administration (FDA) hosted an online public workshop titled "FY 2020 Generic Drug Regulatory Science Initiatives Public Workshop" to provide an overview of the status of the science and research priorities and to solicit input on the development of Generic Drug User Fee Amendments fiscal year 2021 priorities. This report summarizes the podium presentations and the outcome of discussions along with innovative ways to overcome challenges and significant opportunities related to model-based approaches in bioequivalence assessment for breakout session 4 titled, "Data analysis and model-based bioequivalence (BE)." This session focused on the application of model-based approaches in the generic drug development, with a vision of accelerating regulatory decision making for abbreviated new drug application assessments. The session included both podium presentations and panel discussions with three topics of interest: (i) in vitro study evaluation methods and their clinical relevance, (ii) challenges in model-based BE, (iii) emerging expertise and tools in implementing new BE approaches.
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Affiliation(s)
- Jieon Lee
- Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Yuqing Gong
- Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | | | | | | | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK.,Certara, Princeton, New Jersey, USA
| | - Stephan Schmidt
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics, University of Florida, Orlando, Florida, USA
| | | | | | - Lanyan Fang
- Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Liang Zhao
- Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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14
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Lyauk YK, Jonker DM, Lund TM, Hooker AC, Karlsson MO. Item Response Theory Modeling of the International Prostate Symptom Score in Patients with Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia. AAPS J 2020; 22:115. [PMID: 32856168 PMCID: PMC7452927 DOI: 10.1208/s12248-020-00500-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 08/12/2020] [Indexed: 11/30/2022]
Abstract
Item response theory (IRT) was used to characterize the time course of lower urinary tract symptoms due to benign prostatic hyperplasia (BPH-LUTS) measured by item-level International Prostate Symptom Scores (IPSS). The Fisher information content of IPSS items was determined and the power to detect a drug effect using the IRT approach was examined. Data from 403 patients with moderate-to-severe BPH-LUTS in a placebo-controlled phase II trial studying the effect of degarelix over 6 months were used for modeling. Three pharmacometric models were developed: a model for total IPSS, a unidimensional IRT model, and a bidimensional IRT model, the latter separating voiding and storage items. The population-level time course of BPH-LUTS in all models was described by initial improvement followed by worsening. In the unidimensional IRT model, the combined information content of IPSS voiding items represented 72% of the total information content, indicating that the voiding subscore may be more sensitive to changes in BPH-LUTS compared with the storage subscore. The pharmacometric models showed considerably higher power to detect a drug effect compared with a cross-sectional and while-on-treatment analysis of covariance, respectively. Compared with the sample size required to detect a drug effect at 80% power with the total IPSS model, a reduction of 5.9% and 11.7% was obtained with the unidimensional and bidimensional IPSS IRT model, respectively. Pharmacometric IRT analysis of the IPSS within BPH-LUTS may increase the precision and efficiency of treatment effect assessment, albeit to a more limited extent compared with applications in other therapeutic areas.
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Affiliation(s)
- Yassine Kamal Lyauk
- Translational Medicine, Ferring Pharmaceuticals A/S, Kay Fiskers Plads 11, 2300, Copenhagen, Denmark. .,Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Daniël M Jonker
- Translational Medicine, Ferring Pharmaceuticals A/S, Kay Fiskers Plads 11, 2300, Copenhagen, Denmark
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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15
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Lyauk YK, Lund TM, Hooker AC, Karlsson MO, Jonker DM. Integrated Item Response Theory Modeling of Multiple Patient-Reported Outcomes Assessing Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia. AAPS J 2020; 22:98. [PMID: 32728925 PMCID: PMC7391402 DOI: 10.1208/s12248-020-00484-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 07/11/2020] [Indexed: 11/30/2022]
Abstract
In clinical trials within lower urinary tract symptoms due to benign prostatic hyperplasia (BPH-LUTS), the International Prostate Symptom Score (IPSS) is commonly the primary efficacy outcome while the Quality of Life (QoL) score and the BPH Impact Index (BII) are common secondary efficacy markers. The current study aimed to characterize BPH-LUTS progression using responses to the IPSS, the QoL, and the BII in an integrated item response theory (IRT) framework and assess the Fisher information of each scale. The power of this approach to detect a drug effect was compared with an IRT approach considering only IPSS responses. A unidimensional and a bidimensional pharmacometric IRT model, based on item-level IPSS responses in a clinical trial with 403 patients, were extended by incorporating patients’ QoL and summary BII scores over the 6-month trial period. In the developed unidimensional integrated model, the QoL score was found to be the most informative, representing 17% of the total Fisher information, while the combined information content of the seven IPSS items represented 70.6%. In the bidimensional model, “storage” and both storage and “voiding” disability drove QoL and summary BII responses, respectively. Sample size reduction of 16% to detect a drug effect at 80% power was obtained with the unidimensional integrated IRT model compared with its counterpart IPSS IRT model. This study shows that utilizing the information content across the IPSS, QoL, and BII scales in an integrated IRT framework results in a modest but meaningful increase in power to detect a drug effect.
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Affiliation(s)
- Yassine Kamal Lyauk
- Translational Medicine, Ferring Pharmaceuticals A/S, Copenhagen, Denmark. .,Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Daniël M Jonker
- Translational Medicine, Ferring Pharmaceuticals A/S, Copenhagen, Denmark
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16
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Bjugård Nyberg H, Hooker AC, Bauer RJ, Aoki Y. Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models. AAPS J 2020; 22:90. [PMID: 32617704 PMCID: PMC7373158 DOI: 10.1208/s12248-020-00471-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/09/2020] [Indexed: 11/30/2022]
Abstract
Parameter estimation of a nonlinear model based on maximizing the
likelihood using gradient-based numerical optimization methods can often fail due to
premature termination of the optimization algorithm. One reason for such failure is
that these numerical optimization methods cannot distinguish between the minimum,
maximum, and a saddle point; hence, the parameters found by these optimization
algorithms can possibly be in any of these three stationary points on the likelihood
surface. We have found that for maximization of the likelihood for nonlinear mixed
effects models used in pharmaceutical development, the optimization algorithm
Broyden–Fletcher–Goldfarb–Shanno (BFGS) often terminates in saddle points, and we
propose an algorithm, saddle-reset, to avoid the termination at saddle points, based
on the second partial derivative test. In this algorithm, we use the approximated
Hessian matrix at the point where BFGS terminates, perturb the point in the
direction of the eigenvector associated with the lowest eigenvalue, and restart the
BFGS algorithm. We have implemented this algorithm in industry standard software for
nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can
be used to avoid termination of parameter estimation at saddle points, as well as
unveil practical parameter non-identifiability. We demonstrate this using four
published pharmacometric models and two models specifically designed to be
practically non-identifiable.
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Affiliation(s)
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Robert J Bauer
- Pharmacometrics R&D, ICON CLINICAL RESEARCH LLC, Gaithersburg, Maryland, USA
| | - Yasunori Aoki
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.,National Institute of Informatics, Tokyo, Japan
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17
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Papathanasiou T, Strathe A, Overgaard RV, Lund TM, Hooker AC. Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models. AAPS J 2019; 21:95. [DOI: 10.1208/s12248-019-0365-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 07/09/2019] [Indexed: 12/30/2022]
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18
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Brekkan A, Lopez-Lazaro L, Yngman G, Plan EL, Acharya C, Hooker AC, Kankanwadi S, Karlsson MO. A Population Pharmacokinetic-Pharmacodynamic Model of Pegfilgrastim. AAPS J 2018; 20:91. [PMID: 30112626 DOI: 10.1208/s12248-018-0249-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/24/2018] [Indexed: 12/23/2022]
Abstract
Neutropenia and febrile neutropenia (FN) are serious side effects of cytotoxic chemotherapy which may be alleviated with the administration of recombinant granulocyte colony-stimulating factor (GCSF) derivatives, such as pegfilgrastim (PG) which increases absolute neutrophil count (ANC). In this work, a population pharmacokinetic-pharmacodynamic (PKPD) model was developed based on data obtained from healthy volunteers receiving multiple administrations of PG. The developed model was a bidirectional PKPD model, where PG stimulated the proliferation, maturation, and margination of neutrophils and where circulating neutrophils in turn increased the elimination of PG. Simulations from the developed model show disproportionate changes in response with changes in dose. A dose increase of 10% from the 6 mg therapeutic dose taken as a reference leads to area under the curve (AUC) increases of ~50 and ~5% for PK and PD, respectively. A full random effects covariate model showed that little of the parameter variability could be explained by sex, age, body size, and race. As a consequence, little of the secondary parameter variability (Cmax and AUC of PG and ANC) could be explained by these covariates.
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Affiliation(s)
- Ari Brekkan
- Pharmetheus, Uppsala, Sweden.,Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Gunnar Yngman
- Pharmetheus, Uppsala, Sweden.,Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | | | - Andrew C Hooker
- Pharmetheus, Uppsala, Sweden.,Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Mats O Karlsson
- Pharmetheus, Uppsala, Sweden. .,Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
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19
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Terranova N, Smith MK, Nordgren R, Comets E, Lavielle M, Harling K, Hooker AC, Sarr C, Mentré F, Yvon F, Swat MJ. The Standard Output: A Tool-Agnostic Modeling Storage Format. CPT Pharmacometrics Syst Pharmacol 2018; 7:543-546. [PMID: 30033588 PMCID: PMC6157675 DOI: 10.1002/psp4.12339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 07/05/2018] [Indexed: 11/12/2022]
Affiliation(s)
- Nadia Terranova
- Merck Institute for Pharmacometrics, Merck Serono S.A., Zurich, Switzerland "a Subsidiary of Merck KGaA, Darmstadt, Germany"
| | - Mike K Smith
- Global Clinical Pharmacology, Pfizer, Sandwich, UK
| | - Rikard Nordgren
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Emmanuelle Comets
- INSERM, IAME, UMR 1137, Paris, France.,University Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, Paris, France
| | - Marc Lavielle
- Inria Ile-de-France and Ecole Polytechnique, Paris, France
| | - Kajsa Harling
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - France Mentré
- INSERM, IAME, UMR 1137, Paris, France.,University Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, Paris, France
| | - Florent Yvon
- EMBL - European Bioinformatics Institute, Cambridge, UK.,Barcelona Supercomputing Center, Barcelona, Spain
| | - Maciej J Swat
- EMBL - European Bioinformatics Institute, Cambridge, UK.,Simcyp (a Certara company), Sheffield, UK
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20
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Ryeznik Y, Sverdlov O, Hooker AC. Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group. AAPS J 2018; 20:85. [PMID: 30027336 DOI: 10.1208/s12248-018-0242-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 06/18/2018] [Indexed: 11/30/2022]
Abstract
In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.
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Affiliation(s)
- Yevgen Ryeznik
- Department of Mathematics, Uppsala University, Room Å14133 Lägerhyddsvägen 1, Hus 1, 6 och 7, 751 06, Uppsala, Sweden. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Oleksandr Sverdlov
- Early Development Biostatistics, Novartis Institutes for Biomedical Research, East Hannover, New Jersey, USA
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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21
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Brekkan A, Jönsson S, Karlsson MO, Hooker AC. Reduced and optimized trial designs for drugs described by a target mediated drug disposition model. J Pharmacokinet Pharmacodyn 2018; 45:637-647. [PMID: 29948794 PMCID: PMC6061097 DOI: 10.1007/s10928-018-9594-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/07/2018] [Indexed: 12/01/2022]
Abstract
Monoclonal antibodies against soluble targets are often rich and include the sampling of multiple analytes over a lengthy period of time. Predictive models built on data obtained in such studies can be useful in all drug development phases. If adequate model predictions can be maintained with a reduced design (e.g. fewer samples or shorter duration) the use of such designs may be advocated. The effect of reducing and optimizing a rich design based on a published study for Omalizumab (OMA) was evaluated as an example. OMA pharmacokinetics were characterized using a target-mediated drug disposition model considering the binding of OMA to free IgE and the subsequent formation of an OMA–IgE complex. The performance of the reduced and optimized designs was evaluated with respect to: efficiency, parameter uncertainty and predictions of free target. It was possible to reduce the number of samples in the study by 30% while still maintaining an efficiency of almost 90%. A reduction in sampling duration by two-thirds resulted in an efficiency of 75%. Omission of any analyte measurement or a reduction of the number of dose levels was detrimental to the efficiency of the designs (efficiency ≤ 51%). However, other metrics were, in some cases, relatively unaffected, showing that multiple metrics may be needed to obtain balanced assessments of design performance.
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Affiliation(s)
- A Brekkan
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 75124, Uppsala, Sweden
| | - S Jönsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 75124, Uppsala, Sweden
| | - M O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 75124, Uppsala, Sweden
| | - A C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 75124, Uppsala, Sweden.
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22
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Papathanasiou T, Strathe A, Hooker AC, Lund TM, Overgaard RV. Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations. AAPS J 2018; 20:64. [PMID: 29687351 DOI: 10.1208/s12248-018-0226-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/06/2018] [Indexed: 12/26/2022]
Abstract
The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.
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Affiliation(s)
- Theodoros Papathanasiou
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Novo Nordisk A/S, Quantitative Clinical Pharmacology, Vandtårnsvej 108-110, 2860, Søborg, Denmark.
| | - Anders Strathe
- Novo Nordisk A/S, Quantitative Clinical Pharmacology, Vandtårnsvej 108-110, 2860, Søborg, Denmark
| | - Andrew C Hooker
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rune Viig Overgaard
- Novo Nordisk A/S, Quantitative Clinical Pharmacology, Vandtårnsvej 108-110, 2860, Søborg, Denmark
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23
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Pierrillas PB, Fouliard S, Chenel M, Hooker AC, Friberg LE, Karlsson MO. Correction to: Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology. AAPS J 2018; 20:55. [PMID: 29589158 DOI: 10.1208/s12248-018-0218-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The middle initial in the fifth author's name is incorrect in the original article. "Lena F. Friberg" should be "Lena E. Friberg". The original article was corrected.
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Affiliation(s)
- Philippe B Pierrillas
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Sylvain Fouliard
- Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Paris, France
| | - Marylore Chenel
- Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Paris, France
| | - Andrew C Hooker
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Lena E Friberg
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Mats O Karlsson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden.
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24
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Pierrillas PB, Fouliard S, Chenel M, Hooker AC, Friberg LF, Karlsson MO. Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology. AAPS J 2018. [DOI: 10.1208/s12248-018-0206-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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25
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Ryeznik Y, Sverdlov O, Hooker AC. Adaptive Optimal Designs for Dose-Finding Studies with Time-to-Event Outcomes. AAPS J 2017; 20:24. [PMID: 29285730 DOI: 10.1208/s12248-017-0166-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 10/28/2017] [Indexed: 11/30/2022]
Abstract
We consider optimal design problems for dose-finding studies with censored Weibull time-to-event outcomes. Locally D-optimal designs are investigated for a quadratic dose-response model for log-transformed data subject to right censoring. Two-stage adaptive D-optimal designs using maximum likelihood estimation (MLE) model updating are explored through simulation for a range of different dose-response scenarios and different amounts of censoring in the model. The adaptive optimal designs are found to be nearly as efficient as the locally D-optimal designs. A popular equal allocation design can be highly inefficient when the amount of censored data is high and when the Weibull model hazard is increasing. The issues of sample size planning/early stopping for an adaptive trial are investigated as well. The adaptive D-optimal design with early stopping can potentially reduce study size while achieving similar estimation precision as the fixed allocation design.
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Affiliation(s)
- Yevgen Ryeznik
- Department of Mathematics, Uppsala University, Room Å14133 Lägerhyddsvägen 1, Hus 1, 6 och 7, 751 06, Uppsala, Sweden. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Oleksandr Sverdlov
- Early Development Biostatistics - Translational Medicine, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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26
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Aoki Y, Röshammar D, Hamrén B, Hooker AC. Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection. J Pharmacokinet Pharmacodyn 2017; 44:581-597. [PMID: 29103208 PMCID: PMC5686275 DOI: 10.1007/s10928-017-9550-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 10/14/2017] [Indexed: 11/25/2022]
Abstract
Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.
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Affiliation(s)
- Yasunori Aoki
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
- National Institute of Informatics, Tokyo, Japan.
| | - Daniel Röshammar
- Quantitative Clinical Pharmacology, Innovative Medicines and Early Development, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
- SGS Exprimo, Mechelen, Belgium
| | - Bengt Hamrén
- Quantitative Clinical Pharmacology, Innovative Medicines and Early Development, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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27
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Duffull SB, Hooker AC. Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models. J Pharmacokinet Pharmacodyn 2017; 44:611-616. [PMID: 29064062 DOI: 10.1007/s10928-017-9552-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 10/20/2017] [Indexed: 10/18/2022]
Abstract
Optimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs that are robust to uncertainty in the prior choice of parameter values. These methods are generally based on estimating the expectation of the determinant (or a transformation of the determinant) of the information matrix over the prior distribution of the parameter values. For high dimensional models this can be computationally challenging. For nonlinear mixed-effects models the question arises as to the importance of accounting for uncertainty in the prior value of the variances of the random effects parameters. In this work we explore the influence of the variance of the random effects parameters on the optimal design. We find that the method for approximating the expectation and variance of the likelihood is of potential importance for considering the influence of random effects. The most common approximation to the likelihood, based on a first-order Taylor series approximation, yields designs that are relatively insensitive to the prior value of the variance of the random effects parameters and under these conditions it appears to be sufficient to consider uncertainty on the fixed-effects parameters only.
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Affiliation(s)
- Stephen B Duffull
- School of Pharmacy, University of Otago, 18 Frederick St, Dunedin, New Zealand.
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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28
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Strömberg EA, Hooker AC. The effect of using a robust optimality criterion in model based adaptive optimization. J Pharmacokinet Pharmacodyn 2017; 44:317-324. [PMID: 28386710 PMCID: PMC5514236 DOI: 10.1007/s10928-017-9521-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 03/22/2017] [Indexed: 11/26/2022]
Abstract
Optimizing designs using robust (global) optimality criteria has been shown to be a more flexible approach compared to using local optimality criteria. Additionally, model based adaptive optimal design (MBAOD) may be less sensitive to misspecification in the prior information available at the design stage. In this work, we investigate the influence of using a local (lnD) or a robust (ELD) optimality criterion for a MBAOD of a simulated dose optimization study, for rich and sparse sampling schedules. A stopping criterion for accurate effect prediction is constructed to determine the endpoint of the MBAOD by minimizing the expected uncertainty in the effect response of the typical individual. 50 iterations of the MBAODs were run using the MBAOD R-package, with the concentration from a one-compartment first-order absorption pharmacokinetic model driving the population effect response in a sigmoidal EMAX pharmacodynamics model. The initial cohort consisted of eight individuals in two groups and each additional cohort added two individuals receiving a dose optimized as a discrete covariate. The MBAOD designs using lnD and ELD optimality with misspecified initial model parameters were compared by evaluating the efficiency relative to an lnD-optimal design based on the true parameter values. For the explored example model, the MBAOD using ELD-optimal designs converged quicker to the theoretically optimal lnD-optimal design based on the true parameters for both sampling schedules. Thus, using a robust optimality criterion in MBAODs could reduce the number of adaptations required and improve the practicality of adaptive trials using optimal design.
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Affiliation(s)
- Eric A Strömberg
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Andrew C Hooker
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
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29
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Musuamba FT, Manolis E, Holford N, Cheung S, Friberg LE, Ogungbenro K, Posch M, Yates J, Berry S, Thomas N, Corriol-Rohou S, Bornkamp B, Bretz F, Hooker AC, Van der Graaf PH, Standing JF, Hay J, Cole S, Gigante V, Karlsson K, Dumortier T, Benda N, Serone F, Das S, Brochot A, Ehmann F, Hemmings R, Rusten IS. Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014). CPT Pharmacometrics Syst Pharmacol 2017; 6:418-429. [PMID: 28722322 PMCID: PMC5529745 DOI: 10.1002/psp4.12196] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 03/27/2017] [Accepted: 03/27/2017] [Indexed: 02/05/2023]
Abstract
Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.
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Affiliation(s)
- F T Musuamba
- EMA Modelling and Simulation Working Group, London, UK.,Federal Agency for Medicines and Health Products, Brussels, Belgium.,UMR850 INSERM, Université de Limoges, Limoges, France
| | - E Manolis
- EMA Modelling and Simulation Working Group, London, UK.,European Medicines Agency, London, UK
| | - N Holford
- Department of Pharmacology & Clinical Pharmacology, University of Auckland, Auckland, New Zealand
| | | | | | | | - M Posch
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | | | - S Berry
- Berry consultants, Austin, Texas, USA
| | | | | | | | - F Bretz
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.,Novartis, London, UK
| | | | - P H Van der Graaf
- Leiden Academic Centre for Drug Research, Leiden, The Netherlands.,Certara QSP, Canterbury, UK
| | - J F Standing
- EMA Modelling and Simulation Working Group, London, UK.,University College London, London, UK
| | - J Hay
- EMA Modelling and Simulation Working Group, London, UK.,Medicines and Healthcare Products Regulatory Agency, London, UK
| | - S Cole
- EMA Modelling and Simulation Working Group, London, UK.,Medicines and Healthcare Products Regulatory Agency, London, UK
| | - V Gigante
- EMA Modelling and Simulation Working Group, London, UK.,Agenzia Italiana del Farmaco, Roma, Italy
| | - K Karlsson
- EMA Modelling and Simulation Working Group, London, UK.,Medical Products Agency, Uppsala, Sweden
| | | | - N Benda
- EMA Modelling and Simulation Working Group, London, UK.,Bundesinstitut für Arzneimittel und Medizinprodukte, Bonn, Germany
| | - F Serone
- EMA Modelling and Simulation Working Group, London, UK.,Agenzia Italiana del Farmaco, Roma, Italy
| | - S Das
- AstraZeneca UK Limited, London, UK
| | | | - F Ehmann
- European Medicines Agency, London, UK
| | - R Hemmings
- Medicines and Healthcare Products Regulatory Agency, London, UK
| | - I Skottheim Rusten
- EMA Modelling and Simulation Working Group, London, UK.,Norvegian Medicines Agency, Oslo, Norway
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30
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Smith MK, Moodie SL, Bizzotto R, Blaudez E, Borella E, Carrara L, Chan P, Chenel M, Comets E, Gieschke R, Harling K, Harnisch L, Hartung N, Hooker AC, Karlsson MO, Kaye R, Kloft C, Kokash N, Lavielle M, Lestini G, Magni P, Mari A, Mentré F, Muselle C, Nordgren R, Nyberg HB, Parra-Guillén ZP, Pasotti L, Rode-Kristensen N, Sardu ML, Smith GR, Swat MJ, Terranova N, Yngman G, Yvon F, Holford N. Model Description Language (MDL): A Standard for Modeling and Simulation. CPT Pharmacometrics Syst Pharmacol 2017. [PMID: 28643440 PMCID: PMC5658286 DOI: 10.1002/psp4.12222] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Natallia Kokash
- Leiden University, Netherlands.,University College London, London, UK
| | | | | | | | - Andrea Mari
- CNR Institute of Neurosciences, Padova, Italy
| | | | | | | | - Henrik B Nyberg
- Uppsala Universitet, Sweden.,Mango Solutions, Chippenham, UK
| | | | | | | | - Maria L Sardu
- Merck Serono S.A., a Subsidiary of Merck KgaA, Lausanne, Switzerland
| | - Gareth R Smith
- Scientific Computing Group, Cyprotex Discovery Limited, Macclesfield, Crewe, UK
| | - Maciej J Swat
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Nadia Terranova
- Merck Serono S.A., a Subsidiary of Merck KgaA, Lausanne, Switzerland
| | | | - Florent Yvon
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Nick Holford
- Uppsala Universitet, Sweden.,University of Auckland, New Zealand
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31
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Nguyen THT, Mouksassi M, Holford N, Al‐Huniti N, Freedman I, Hooker AC, John J, Karlsson MO, Mould DR, Pérez Ruixo JJ, Plan EL, Savic R, van Hasselt JGC, Weber B, Zhou C, Comets E, Mentré F. Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics. CPT Pharmacometrics Syst Pharmacol 2017; 6:87-109. [PMID: 27884052 PMCID: PMC5321813 DOI: 10.1002/psp4.12161] [Citation(s) in RCA: 228] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 10/10/2016] [Accepted: 11/09/2016] [Indexed: 12/17/2022] Open
Abstract
This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.
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Affiliation(s)
- THT Nguyen
- INSERM, IAME, UMR 1137, Paris, France, Université Paris DiderotSorbonne Paris CitéParisFrance
| | | | - N Holford
- Department of Pharmacology and Clinical PharmacologyUniversity of AucklandAucklandNew Zealand
| | - N Al‐Huniti
- Quantitative Clinical Pharmacology, AstraZenecaWalthamMassachusettsUSA
| | - I Freedman
- Dr Immanuel Freedman Inc.HarleysvillePennsylvaniaUSA
| | - AC Hooker
- Department of Pharmaceutical BiosciencesUppsala UniversityUppsalaSweden
| | - J John
- Center for Drug Evaluation and Research, U.S. Food and Drug AdministrationWashingtonDCUSA
| | - MO Karlsson
- Department of Pharmaceutical BiosciencesUppsala UniversityUppsalaSweden
| | - DR Mould
- Projections Research Inc.PhoenixvillePennsylvaniaUSA
| | - JJ Pérez Ruixo
- The Janssen Pharmaceutical Companies of Johnson & JohnsonBelgium
| | | | - R Savic
- Department of Bioengineering and Therapeutic SciencesUniversity of California – San FranciscoSan FranciscoCaliforniaUSA
| | - JGC van Hasselt
- Division of PharmacologyLeiden Academic Centre for Drug Research, Leiden UniversityLeidenNetherlands
| | - B Weber
- Boehringer Ingelheim Pharmaceuticals, Inc.RidgefieldConnecticutUSA
| | - C Zhou
- GenentechSan FranciscoCaliforniaUSA
| | - E Comets
- INSERM, IAME, UMR 1137, Paris, France, Université Paris DiderotSorbonne Paris CitéParisFrance
- INSERM CIC 1414, Rennes, France, University Rennes‐1RennesFrance
| | - F Mentré
- INSERM, IAME, UMR 1137, Paris, France, Université Paris DiderotSorbonne Paris CitéParisFrance
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32
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Strömberg EA, Nyberg J, Hooker AC. The effect of Fisher information matrix approximation methods in population optimal design calculations. J Pharmacokinet Pharmacodyn 2016; 43:609-619. [PMID: 27804003 PMCID: PMC5110617 DOI: 10.1007/s10928-016-9499-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 10/25/2016] [Indexed: 01/04/2023]
Abstract
With the increasing popularity of optimal design in drug development it is important to understand how the approximations and implementations of the Fisher information matrix (FIM) affect the resulting optimal designs. The aim of this work was to investigate the impact on design performance when using two common approximations to the population model and the full or block-diagonal FIM implementations for optimization of sampling points. Sampling schedules for two example experiments based on population models were optimized using the FO and FOCE approximations and the full and block-diagonal FIM implementations. The number of support points was compared between the designs for each example experiment. The performance of these designs based on simulation/estimations was investigated by computing bias of the parameters as well as through the use of an empirical D-criterion confidence interval. Simulations were performed when the design was computed with the true parameter values as well as with misspecified parameter values. The FOCE approximation and the Full FIM implementation yielded designs with more support points and less clustering of sample points than designs optimized with the FO approximation and the block-diagonal implementation. The D-criterion confidence intervals showed no performance differences between the full and block diagonal FIM optimal designs when assuming true parameter values. However, the FO approximated block-reduced FIM designs had higher bias than the other designs. When assuming parameter misspecification in the design evaluation, the FO Full FIM optimal design was superior to the FO block-diagonal FIM design in both of the examples.
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Affiliation(s)
- Eric A Strömberg
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 471, 75124, Uppsala, Sweden.
| | - Joakim Nyberg
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 471, 75124, Uppsala, Sweden
| | - Andrew C Hooker
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 471, 75124, Uppsala, Sweden
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Acharya C, Hooker AC, Türkyılmaz GY, Jönsson S, Karlsson MO. A diagnostic tool for population models using non-compartmental analysis: The ncappc package for R. Comput Methods Programs Biomed 2016; 127:83-93. [PMID: 27000291 DOI: 10.1016/j.cmpb.2016.01.013] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 12/07/2015] [Accepted: 01/07/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Non-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration-time curve and peak concentration. We developed a new package in R, called ncappc, to perform (i) a NCA and (ii) simulation-based posterior predictive checks (ppc) for a population PK (PopPK) model using NCA metrics. METHODS The nca feature of ncappc package estimates the NCA metrics by NCA. The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration-time data and compares them with those estimated from the observed data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. The ncappc package also reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. RESULTS The ncappc produces two default outputs depending on the type of analysis performed, i.e., NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration-time profile of a drug and thereby evaluate model adequacy. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. CONCLUSIONS The ncappc package is a versatile and flexible tool-set written in R that successfully estimates NCA metrics from concentration-time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. ncappc is freely available on CRAN (http://cran.r-project.org/web/packages/ncappc/index.html/) and GitHub (https://github.com/cacha0227/ncappc/).
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Affiliation(s)
- Chayan Acharya
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden.
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden
| | - Gülbeyaz Yıldız Türkyılmaz
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden; Ege University, Faculty of Pharmacy, Department of Biopharmaceutics and Pharmacokinetics, 35100 İzmir, Turkey
| | - Siv Jönsson
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden
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34
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Aoki Y, Sundqvist M, Hooker AC, Gennemark P. PopED lite: An optimal design software for preclinical pharmacokinetic and pharmacodynamic studies. Comput Methods Programs Biomed 2016; 127:126-143. [PMID: 27000295 DOI: 10.1016/j.cmpb.2016.02.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Revised: 01/21/2016] [Accepted: 02/02/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Optimal experimental design approaches are seldom used in preclinical drug discovery. The objective is to develop an optimal design software tool specifically designed for preclinical applications in order to increase the efficiency of drug discovery in vivo studies. METHODS Several realistic experimental design case studies were collected and many preclinical experimental teams were consulted to determine the design goal of the software tool. The tool obtains an optimized experimental design by solving a constrained optimization problem, where each experimental design is evaluated using some function of the Fisher Information Matrix. The software was implemented in C++ using the Qt framework to assure a responsive user-software interaction through a rich graphical user interface, and at the same time, achieving the desired computational speed. In addition, a discrete global optimization algorithm was developed and implemented. RESULTS The software design goals were simplicity, speed and intuition. Based on these design goals, we have developed the publicly available software PopED lite (http://www.bluetree.me/PopED_lite). Optimization computation was on average, over 14 test problems, 30 times faster in PopED lite compared to an already existing optimal design software tool. PopED lite is now used in real drug discovery projects and a few of these case studies are presented in this paper. CONCLUSIONS PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit a short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software tool can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools.
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Affiliation(s)
- Yasunori Aoki
- Pharmacometrics Research Group, Dept. Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.
| | - Monika Sundqvist
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Pepparedsleden 1, Mölndal 431 83, Sweden
| | - Andrew C Hooker
- Pharmacometrics Research Group, Dept. Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden
| | - Peter Gennemark
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Pepparedsleden 1, Mölndal 431 83, Sweden
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35
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Ueckert S, Karlsson MO, Hooker AC. Accelerating Monte Carlo power studies through parametric power estimation. J Pharmacokinet Pharmacodyn 2016; 43:223-34. [PMID: 26934878 PMCID: PMC4791488 DOI: 10.1007/s10928-016-9468-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Accepted: 02/19/2016] [Indexed: 11/30/2022]
Abstract
Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models.
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Affiliation(s)
- Sebastian Ueckert
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden.
| | - Mats O Karlsson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden
| | - Andrew C Hooker
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden
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Aoki Y, Nordgren R, Hooker AC. Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations. AAPS J 2016; 18:505-18. [PMID: 26857397 DOI: 10.1208/s12248-016-9866-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 01/04/2016] [Indexed: 02/05/2023]
Abstract
As the importance of pharmacometric analysis increases, more and more complex mathematical models are introduced and computational error resulting from computational instability starts to become a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix. Roughly speaking, the method reparameterises the model with a linear combination of the original model parameters so that the Hessian matrix of the likelihood of the reparameterised model becomes close to an identity matrix. This approach will reduce the influence of computational error, for example rounding error, to the final computational result. We present numerical experiments demonstrating that the stabilisation of the computation using the proposed method can recover failed variance-covariance matrix computations, and reveal non-identifiability of the model parameters.
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Affiliation(s)
- Yasunori Aoki
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. .,Department of Mathematics, Uppsala University, Uppsala, Sweden.
| | - Rikard Nordgren
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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Kågedal M, Varnäs K, Hooker AC, Karlsson MO. Estimation of drug receptor occupancy when non-displaceable binding differs between brain regions – extending the simplified reference tissue model. Br J Clin Pharmacol 2015; 80:116-27. [PMID: 25406494 PMCID: PMC4500331 DOI: 10.1111/bcp.12558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Accepted: 10/28/2014] [Indexed: 11/28/2022] Open
Abstract
AIM The simplified reference tissue model (SRTM) is used for estimation of receptor occupancy assuming that the non-displaceable binding in the reference region is identical to the brain regions of interest. The aim of this work was to extend the SRTM to also account for inter-regional differences in non-displaceable concentrations, and to investigate if this model allowed estimation of receptor occupancy using white matter as reference. It was also investigated if an apparent higher affinity in caudate compared with other brain regions, could be better explained by a difference in the extent of non-displaceable binding. METHODS The analysis was based on a PET study in six healthy volunteers using the 5-HT1B receptor radioligand [(11)C]-AZ10419369. The radioligand was given intravenously as a tracer dose alone and following different oral doses of the 5-HT1B receptor antagonist AZD3783. Non-linear mixed effects models were developed where differences between regions in non-specific concentrations were accounted for. The properties of the models were also evaluated by means of simulation studies. RESULTS The estimate (95% CI) of Ki(PL) was 10.2 ng ml(-1) (5.4, 15) and 10.4 ng ml(-1) (8.1, 13.6) based on the extended SRTM with white matter as reference and based on the SRTM using cerebellum as reference, respectively. The estimate (95% CI) of Ki(PL) for caudate relative to other brain regions was 55% (48, 62%). CONCLUSIONS The extended SRTM allows consideration of white matter as reference region when no suitable grey matter region exists. AZD3783 affinity appears to be higher in the caudate compared with other brain regions.
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Affiliation(s)
- Matts Kågedal
- AstraZeneca R&DSE-151 85, Södertälje, Sweden
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - Katarina Varnäs
- Karolinska Institutet, Department of Clinical Neuroscience, Center for Psychiatric Research and Education, Karolinska HospitalS-171 76, Stockholm, Sweden
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
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Swat MJ, Moodie S, Wimalaratne SM, Kristensen NR, Lavielle M, Mari A, Magni P, Smith MK, Bizzotto R, Pasotti L, Mezzalana E, Comets E, Sarr C, Terranova N, Blaudez E, Chan P, Chard J, Chatel K, Chenel M, Edwards D, Franklin C, Giorgino T, Glont M, Girard P, Grenon P, Harling K, Hooker AC, Kaye R, Keizer R, Kloft C, Kok JN, Kokash N, Laibe C, Laveille C, Lestini G, Mentré F, Munafo A, Nordgren R, Nyberg HB, Parra-Guillen ZP, Plan E, Ribba B, Smith G, Trocóniz IF, Yvon F, Milligan PA, Harnisch L, Karlsson M, Hermjakob H, Le Novère N. Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development. CPT Pharmacometrics Syst Pharmacol 2015; 4:316-9. [PMID: 26225259 PMCID: PMC4505825 DOI: 10.1002/psp4.57] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 05/06/2015] [Indexed: 12/02/2022] Open
Abstract
The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.
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Affiliation(s)
- MJ Swat
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | | | - SM Wimalaratne
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | | | | | - A Mari
- National Research Council, Institute of Biomedical EngineeringPadova, Italy
| | - P Magni
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - MK Smith
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - R Bizzotto
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - L Pasotti
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - E Mezzalana
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - E Comets
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - C Sarr
- Advanced Quantitative Sciences (AQS), NovartisBasel, Switzerland
| | - N Terranova
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | | | - P Chan
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - J Chard
- Mango SolutionsChippenham, Wiltshire, UK
| | | | - M Chenel
- SGS Exprimo NV, Mechelen, Belgium, Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales ServierSuresnes, France
| | - D Edwards
- Simcyp (a Certara company)Sheffield, UK
| | - C Franklin
- CPMS Technology and DevelopmentSouthall, UK
| | - T Giorgino
- National Research Council, Institute of Biomedical EngineeringPadova, Italy
| | - M Glont
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - P Girard
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | - P Grenon
- CHIME, University College LondonLondon, UK
| | - K Harling
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - AC Hooker
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - R Kaye
- Mango SolutionsChippenham, Wiltshire, UK
| | - R Keizer
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - C Kloft
- Freie Universtitaet Berlin, Germany, Institute of Pharmacy, Department of Clinical Pharmacy and BiochemistryBerlin, Germany
| | - JN Kok
- Leiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeiden, The Netherlands
| | - N Kokash
- Leiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeiden, The Netherlands
| | - C Laibe
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - C Laveille
- SGS Exprimo NV, Mechelen, Belgium, Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales ServierSuresnes, France
| | - G Lestini
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - F Mentré
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - A Munafo
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | - R Nordgren
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - HB Nyberg
- Mango SolutionsChippenham, Wiltshire, UK
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - ZP Parra-Guillen
- Freie Universtitaet Berlin, Germany, Institute of Pharmacy, Department of Clinical Pharmacy and BiochemistryBerlin, Germany
| | - E Plan
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - B Ribba
- Inria Grenoble - Rhône-AlpesGrenoble, France
| | - G Smith
- Scientific Computing Group, Cyprotex Discovery LimitedMacclesfield, Crewe, UK
| | - IF Trocóniz
- Department of Pharmacy and Pharmaceutical Technology, University of NavarraPamplona, Spain
| | - F Yvon
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - PA Milligan
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - L Harnisch
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - M Karlsson
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - H Hermjakob
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - N Le Novère
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
- Babraham Institute, Babraham Research CampusCambridge, UK
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Kågedal M, Karlsson MO, Hooker AC. Improved precision of exposure-response relationships by optimal dose-selection. Examples from studies of receptor occupancy using PET and dose finding for neuropathic pain treatment. J Pharmacokinet Pharmacodyn 2015; 42:211-24. [PMID: 25792005 DOI: 10.1007/s10928-015-9410-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 03/03/2015] [Indexed: 11/30/2022]
Abstract
An understanding of the relationship between drug exposure and response is a fundamental basis for any dosing recommendation. We investigate optimal dose-selection for two different types of studies, a receptor occupancy study assessed by positron emission tomography (PET) and a dose-finding study in neuropathic pain treatment. For the PET-study, an inhibitory E-max model describes the relationship between drug exposure and displacement of a radioligand from specific receptors in the brain. The model has a mechanistic basis in the law of mass action and the affinity parameter (Ki PL ) is of primary interest. For optimization of the neuropathic pain study, the model is empirical and the exposure response curve itself is of primary interest. An alternative parameterization of the sigmoid Emax model was therefore used where the plasma concentration corresponding to the minimum relevant efficacy was estimated as a parameter. Optimal design methodology was applied using the D-optimal criterion as well as the Ds-optimal criterion where parameters of interest were defined. For the PET-study it was shown that the precision of Ki PL can be improved by inclusion of brain regions with both high and low receptor density and that the need for high doses is reduced when a brain region with low receptor density is included in the analysis. In the case of the neuropathic pain study it was shown that a Ds-optimal study design using the reparameterized Emax model can improve the precision in the minimum effective dose compared to a D-optimal design.
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Affiliation(s)
- Matts Kågedal
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden,
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Nyberg J, Bazzoli C, Ogungbenro K, Aliev A, Leonov S, Duffull S, Hooker AC, Mentré F. Methods and software tools for design evaluation in population pharmacokinetics-pharmacodynamics studies. Br J Clin Pharmacol 2015; 79:6-17. [PMID: 24548174 PMCID: PMC4294071 DOI: 10.1111/bcp.12352] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Accepted: 02/09/2014] [Indexed: 11/26/2022] Open
Abstract
Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.
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Affiliation(s)
- Joakim Nyberg
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - Caroline Bazzoli
- Laboratoire Jean Kuntzmann, Département Statistique, University of GrenobleGrenoble, France
| | - Kay Ogungbenro
- Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, University of ManchesterManchester, UK
| | - Alexander Aliev
- Institute for Systems Analysis, Russian Academy of SciencesMoscow, Russia
| | | | | | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - France Mentré
- INSERM U738 and University Paris DiderotParis, France
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Marklund M, Strömberg EA, Lærke HN, Knudsen KEB, Kamal-Eldin A, Hooker AC, Landberg R. Simultaneous pharmacokinetic modeling of alkylresorcinols and their main metabolites indicates dual absorption mechanisms and enterohepatic elimination in humans. J Nutr 2014; 144:1674-80. [PMID: 25332465 DOI: 10.3945/jn.114.196220] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Alkylresorcinols have proven to be useful biomarkers of whole-grain wheat and rye intake in many nutritional studies. To improve their utility, more knowledge regarding the fate of alkylresorcinols and their metabolites after consumption is needed. OBJECTIVE The objective of this study was to develop a combined pharmacokinetic model for plasma concentrations of alkylresorcinols and their 2 major metabolites, 3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)-propanoic acid (DHPPA). METHODS The model was established by using plasma samples collected from 3 women and 2 men after a single dose (120 g) of rye bran and validated against fasting plasma concentrations from 8 women and 7 men with controlled rye bran intake (23, 45, or 90 g/d). Alkylresorcinols in the lymph and plasma of a pig fed a single alkylresorcinol dose (1.3 mmol) were quantified to assess absorption. Human ileostomal effluent and pig bile after high and low alkylresorcinol doses were analyzed to evaluate biliary alkylresorcinol metabolite excretion. RESULTS The model contained 2 absorption compartments: 1 that transferred alkylresorcinols directly to the systematic circulation and 1 in which a proportion of absorbed alkylresorcinols was metabolized before reaching the systemic circulation. Plasma concentrations of alkylresorcinols and their metabolites depended on absorption and formation, respectively, and the mean ± SEM terminal elimination half-life of alkylresorcinols (1.9 ± 0.59 h), DHPPA (1.5 ± 0.26 h), and DHBA (1.3 ± 0.22 h) did not differ. The model accurately predicted alkylresorcinol and DHBA concentrations after repeated alkylresorcinol intake but DHPPA concentration was overpredicted, possibly because of poorly modeled enterohepatic circulation. During the 8 h following administration, <2% of the alkylresorcinol dose was recovered in the lymph. DHPPA was identified in both human ileostomal effluent and pig bile, indicating availability of DHPPA for absorption and enterohepatic circulation. CONCLUSION Intact alkylresorcinols have advantages over DHBA and DHPPA as plasma biomarkers for whole-grain wheat and rye intake because of lower susceptibility to factors other than alkylresorcinol intake.
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Affiliation(s)
- Matti Marklund
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, and
| | - Eric A Strömberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Helle N Lærke
- Department of Animal Science, Aarhus University, Tjele, Denmark
| | | | - Afaf Kamal-Eldin
- Department of Food Science, College of Food and Agriculture, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Rikard Landberg
- Department of Food Science, BioCenter, Swedish University of Agricultural Sciences, Uppsala, Sweden; and Nutritional Epidemiology Unit, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
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Steven Ernest C, Nyberg J, Karlsson MO, Hooker AC. Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model. J Pharmacokinet Pharmacodyn 2014; 41:639-54. [PMID: 25308776 DOI: 10.1007/s10928-014-9391-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 10/03/2014] [Indexed: 11/25/2022]
Abstract
D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.
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Affiliation(s)
- C Steven Ernest
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden,
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Johansson ÅM, Ueckert S, Plan EL, Hooker AC, Karlsson MO. Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7. J Pharmacokinet Pharmacodyn 2014; 41:223-38. [PMID: 24801864 DOI: 10.1007/s10928-014-9359-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 04/17/2014] [Indexed: 11/26/2022]
Abstract
NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation methods in addition to the classical methods. In this study, performance of the estimation methods available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation methods to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7. The method giving the lowest bias and highest precision across models was importance sampling, closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization. The methods relative robustness differed between models and no method showed clear superior performance. FOCE/LAPLACE was the method with the shortest runtime for all models, followed by iterative two-stage. The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.
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Affiliation(s)
- Åsa M Johansson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24 , Uppsala, Sweden
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Ueckert S, Plan EL, Ito K, Karlsson MO, Corrigan B, Hooker AC. Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling. Pharm Res 2014; 31:2152-65. [PMID: 24595495 PMCID: PMC4153970 DOI: 10.1007/s11095-014-1315-5] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Accepted: 01/23/2014] [Indexed: 11/30/2022]
Abstract
PURPOSE This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. RESULTS IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. CONCLUSION A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.
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Affiliation(s)
- Sebastian Ueckert
- Pharmacometrics Research Group Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, SE-751 24, Uppsala, Sweden,
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Marklund M, Strömberg EA, Hooker AC, Hammarlund-Udenaes M, Aman P, Landberg R, Kamal-Eldin A. Chain length of dietary alkylresorcinols affects their in vivo elimination kinetics in rats. J Nutr 2013; 143:1573-8. [PMID: 23946349 DOI: 10.3945/jn.113.178392] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Two phenolic acids, 3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)- propanoic acid (DHPPA), are the major metabolites of cereal alkylresorcinols (ARs). Like their precursors, AR metabolites have been suggested as biomarkers for intake of whole-grain wheat and rye and as such could aid the understanding of diet-disease associations. This study estimated and compared pharmacokinetic parameters of ARs and their metabolites in rats and investigated differences in metabolite formation after ingestion of different AR homologs. Rats were i.v. infused for 30 min with 2, 12, or 23 μmol/kg DHBA or DHPPA or orally given the same amounts of the AR homologs, C17:0 and C25:0. Repeated plasma samples, obtained from rats for 6 h (i.v.) or 36 h (oral), were simultaneously analyzed for ARs and their metabolites by GC-mass spectrometry. Pharmacokinetic parameters were estimated by population-based compartmental modeling and noncompartmental calculation. A 1-compartment model best described C25:0 pharmacokinetics, whereas C17:0 and AR metabolites best fitted 2-compartment models. Combined models for simultaneous prediction of AR and metabolite concentration were more complex, with less reliable estimates of pharmacokinetic parameters. Although the AUC of C17:0 was lower than that of C25:0 (P < 0.05), the total amount and composition of AR metabolites did not differ between rats given C17:0 or C25:0. The elimination half-life of ARs and their metabolites increased with length of the side chain (P-trend < 0.001) and ranged from 1.2 h (DHBA) to 8.8 h (C25:0). The formation of AR metabolites was slower than their elimination, indicating that the rate of AR metabolism and not excretion of DHBA and DHPPA determines their plasma concentrations in rats.
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Affiliation(s)
- Matti Marklund
- Department of Food Science, Swedish University of Agricultural Sciences, Uppsala, Sweden
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46
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Kågedal M, Cselényi Z, Nyberg S, Raboisson P, Ståhle L, Stenkrona P, Varnäs K, Halldin C, Hooker AC, Karlsson MO. A positron emission tomography study in healthy volunteers to estimate mGluR5 receptor occupancy of AZD2066 - estimating occupancy in the absence of a reference region. Neuroimage 2013; 82:160-9. [PMID: 23668965 DOI: 10.1016/j.neuroimage.2013.05.006] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2012] [Revised: 04/29/2013] [Accepted: 05/06/2013] [Indexed: 11/27/2022] Open
Abstract
AZD2066 is a new chemical entity pharmacologically characterized as a selective, negative allosteric modulator of the metabotropic glutamate receptor subtype 5 (mGluR5). Antagonism of mGluR5 has been implicated in relation to various diseases such as anxiety, depression, and pain disorders. To support translation from preclinical results and previous experiences with this target in man, a positron emission tomography study was performed to estimate the relationship between AZD2066 plasma concentrations and receptor occupancy in the human brain, using the mGluR5 radioligand [(11)C]-ABP688. The study involved PET scans on 4 occasions in 6 healthy volunteers. The radioligand was given as a tracer dose alone and following oral treatment with different doses of AZD2066. The analysis was based on the total volume of distribution derived from each PET-assessment. A non-linear mixed effects model was developed where ten delineated brain regions of interest from all PET scans were included in one simultaneous fit. For comparison the analysis was also performed according to a method described previously by Lassen et al. (1995). The results of the analysis showed that the total volume of distribution decreased with increasing drug concentrations in all regions with an estimated Kipl of 1170 nM. Variability between individuals and occasions in non-displaceable volume of distribution could explain most of the variability in the total volume of distribution. The Lassen approach provided a similar estimate for Kipl, but the variability was exaggerated and difficult to interpret.
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Lledó-García R, Hennig S, Nyberg J, Hooker AC, Karlsson MO. Ethically Attractive Dose-Finding Designs for Drugs With a Narrow Therapeutic Index. J Clin Pharmacol 2013; 52:29-38. [DOI: 10.1177/0091270010390041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Nyberg J, Ueckert S, Strömberg EA, Hennig S, Karlsson MO, Hooker AC. PopED: an extended, parallelized, nonlinear mixed effects models optimal design tool. Comput Methods Programs Biomed 2012; 108:789-805. [PMID: 22640817 DOI: 10.1016/j.cmpb.2012.05.005] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2011] [Revised: 05/01/2012] [Accepted: 05/03/2012] [Indexed: 06/01/2023]
Abstract
Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of the optimal design software PopED, which incorporates many of these recent advances into an easily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate design performance. Examples for; (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments.
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Affiliation(s)
- Joakim Nyberg
- The Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden
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Nyberg J, Höglund R, Bergstrand M, Karlsson MO, Hooker AC. Serial correlation in optimal design for nonlinear mixed effects models. J Pharmacokinet Pharmacodyn 2012; 39:239-49. [PMID: 22415637 DOI: 10.1007/s10928-012-9245-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2011] [Accepted: 02/26/2012] [Indexed: 10/28/2022]
Abstract
In population modeling two sources of variability are commonly included; inter individual variability and residual variability. Rich sampling optimal design (more samples than model parameters) using these models will often result in a sampling schedule where some measurements are taken at exactly the same time point, thereby maximizing the signal-to-noise ratio. This behavior is a result of not appropriately taking into account error generation mechanisms and is often clinically unappealing and may be avoided by including intrinsic variability, i.e. serially correlated residual errors. In this paper we extend previous work that investigated optimal designs of population models including serial correlation using stochastic differential equations to optimal design with the more robust, and analytic, AR(1) autocorrelation model. Further, we investigate the importance of correlation strength, design criteria and robust designs. Finally, we explore the optimal design properties when estimating parameters with and without serial correlation. In the investigated examples the designs and estimation performance differs significantly when handling serial correlation.
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Affiliation(s)
- Joakim Nyberg
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.
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Välitalo P, Kumpulainen E, Manner M, Kokki M, Lehtonen M, Hooker AC, Ranta VP, Kokki H. Plasma and cerebrospinal fluid pharmacokinetics of naproxen in children. J Clin Pharmacol 2011; 52:1516-26. [PMID: 22067196 DOI: 10.1177/0091270011418658] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The aim of this study was to characterize pediatric pharmacokinetics and central nervous system exposure of naproxen after oral administration. The pharmacokinetics of naproxen was studied in 53 healthy children aged 3 months to 12 years undergoing surgery with spinal anesthesia. Children received preoperatively a single dose of 10 mg/kg oral naproxen suspension. A single cerebrospinal fluid (CSF) sample (n = 52) was collected at the induction of anesthesia, and plasma samples (n = 270) were collected before, during, and after the operation (up to 51 hours after administration). A population pharmacokinetic model was built using the NONMEM software. Naproxen concentrations in plasma were well described by a 2-compartment model. The estimated oral clearance (CL/F) was 0.62 L/h when linearly scaled by weight to 70 kg. The apparent volume of distribution at steady state (Vss/F) was 12.5 L /70 kg. The findings are consistent with previously reported pharmacokinetic parameters for children older than 5 years. Naproxen permeated into the CSF and reached CSF concentrations that were 4 times higher than unbound plasma concentrations. Based on these data, weight can be used as a basis for naproxen dosing in children older than 3 months of age.
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Affiliation(s)
- Pyry Välitalo
- Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.
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