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Koopsen T, van Osta N, van Loon T, Meiburg R, Huberts W, Beela AS, Kirkels FP, van Klarenbosch BR, Teske AJ, Cramer MJ, Bijvoet GP, van Stipdonk A, Vernooy K, Delhaas T, Lumens J. Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination. Biomed Eng Online 2024; 23:46. [PMID: 38741182 DOI: 10.1186/s12938-024-01232-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/02/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Integration of a patient's non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019-10-07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013-11-12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient ( ICC ) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error (χ 2 ) of LV myocardial strain, strain rate, and cavity volume. RESULTS A subset of 270 parameters remained after MSM which produced high-quality DTs of all patients (χ 2 < 1.6), but minimum parameter reproducibility was poor (ICC min = 0.01). Iterative reduction yielded a reproducible (ICC min = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs (χ 2 < 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p < 0.05). CONCLUSIONS By applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work distribution was representative for the patient's underlying disease substrate. This DT technology enables patient-specific substrate characterization and can potentially be used to support clinical decision making.
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Affiliation(s)
- Tijmen Koopsen
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.
| | - Nick van Osta
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Tim van Loon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Roel Meiburg
- Group SIMBIOTX, Institut de Recherche en Informatique et en Automatique (INRIA), Paris, France
| | - Wouter Huberts
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ahmed S Beela
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Suez Canal University, Ismailia, Egypt
| | - Feddo P Kirkels
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Bas R van Klarenbosch
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Arco J Teske
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Maarten J Cramer
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Geertruida P Bijvoet
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center (MUMC), Maastricht, The Netherlands
| | - Antonius van Stipdonk
- Department of Cardiology, Maastricht University Medical Center (MUMC), Maastricht, The Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center (MUMC), Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.
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White C, Rottschäfer V, Bridge L. Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory. J Pharmacokinet Pharmacodyn 2024; 51:39-63. [PMID: 37389744 PMCID: PMC10884104 DOI: 10.1007/s10928-023-09870-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/14/2023] [Indexed: 07/01/2023]
Abstract
Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in receptor theory may be used to parameterise such interactions using timecourse data, but attention needs to be paid to the theoretical identifiability of the parameters of interest. Identifiability analysis is an often overlooked step in many bio-modelling works. In this paper we introduce structural identifiability analysis (SIA) to the field of receptor theory by applying three classical SIA methods (transfer function, Taylor Series and similarity transformation) to ligand-receptor binding models of biological importance (single ligand and Motulsky-Mahan competition binding at monomers, and a recently presented model of a single ligand binding at receptor dimers). New results are obtained which indicate the identifiable parameters for a single timecourse for Motulsky-Mahan binding and dimerised receptor binding. Importantly, we further consider combinations of experiments which may be performed to overcome issues of non-identifiability, to ensure the practical applicability of the work. The three SIA methods are demonstrated through a tutorial-style approach, using detailed calculations, which show the methods to be tractable for the low-dimensional ODE models.
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Affiliation(s)
| | - Vivi Rottschäfer
- Leiden University, Leiden, The Netherlands
- University of Amsterdam, Amsterdam, The Netherlands
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3
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Sher A, Niederer SA, Mirams GR, Kirpichnikova A, Allen R, Pathmanathan P, Gavaghan DJ, van der Graaf PH, Noble D. A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability. Bull Math Biol 2022; 84:39. [PMID: 35132487 PMCID: PMC8821410 DOI: 10.1007/s11538-021-00982-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 11/30/2021] [Indexed: 12/31/2022]
Abstract
There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models. In addition to this, there is no “gold standard” for model development and assessment in QSP. Moreover, there can be confusion over terminology such as model and parameter identifiability; complex and simple models; virtual populations; and other concepts, which leads to potential miscommunication and misapplication of methodologies within modeling communities, both the QSP community and related disciplines. This perspective article highlights the pros and cons of using simple (often identifiable) vs. complex (more physiologically detailed but often non-identifiable) models, as well as aspects of parameter identifiability, sensitivity and inference methodologies for model development and analysis. The paper distills the central themes of the issue of identifiability and optimal model size and discusses open challenges.
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Affiliation(s)
- Anna Sher
- Pfizer Worldwide Research, Development and Medical, Massachusetts, USA.
| | | | - Gary R Mirams
- Centre for Mathematical Medicine and Biology, Mathematical Sciences, University of Nottingham, Nottingham, UK
| | | | - Richard Allen
- Pfizer Worldwide Research, Development and Medical, Massachusetts, USA
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Maryland, USA
| | - David J Gavaghan
- Department of Computer Science, University of Oxford, Oxford, UK
| | | | - Denis Noble
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
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A tutorial on model informed approaches to cardiovascular safety with focus on cardiac repolarisation. J Pharmacokinet Pharmacodyn 2018; 45:365-381. [PMID: 29736890 DOI: 10.1007/s10928-018-9589-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 04/16/2018] [Indexed: 12/19/2022]
Abstract
Drugs can affect the cardiovascular (CV) system either as an intended treatment or as an unwanted side effect. In both cases, drug-induced cardiotoxicities such as arrhythmia and unfavourable hemodynamic effects can occur, and be described using mathematical models; such a model informed approach can provide valuable information during drug development and can aid decision-making. However, in order to develop informative models, it is vital to understand CV physiology. The aims of this tutorial are to present (1) key background biological and medical aspects of the CV system, (2) CV electrophysiology, (3) CV safety concepts, (4) practical aspects of development of CV models and (5) regulatory expectations with a focus on using model informed and quantitative approaches to support nonclinical and clinical drug development. In addition, we share several case studies to provide practical information on project strategy (planning, key questions, assumptions setting, and experimental design) and mathematical models development that support decision-making during drug discovery and development.
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Shebley M, Sandhu P, Emami Riedmaier A, Jamei M, Narayanan R, Patel A, Peters SA, Reddy VP, Zheng M, de Zwart L, Beneton M, Bouzom F, Chen J, Chen Y, Cleary Y, Collins C, Dickinson GL, Djebli N, Einolf HJ, Gardner I, Huth F, Kazmi F, Khalil F, Lin J, Odinecs A, Patel C, Rong H, Schuck E, Sharma P, Wu SP, Xu Y, Yamazaki S, Yoshida K, Rowland M. Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective. Clin Pharmacol Ther 2018; 104:88-110. [PMID: 29315504 PMCID: PMC6032820 DOI: 10.1002/cpt.1013] [Citation(s) in RCA: 223] [Impact Index Per Article: 37.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/05/2017] [Accepted: 01/03/2018] [Indexed: 12/15/2022]
Abstract
This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Ming Zheng
- Bristol-Myers Squibb, Princeton, NJ, USA
| | | | | | | | - Jun Chen
- Sanofi, Région de Montpellier, France
| | | | | | | | | | | | | | | | | | | | | | - Jing Lin
- Sunovion Pharmaceuticals Inc., Marlborough, MA, USA
| | | | - Chirag Patel
- Takeda Pharmaceuticals International Co., Cambridge, MA, USA
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Siripuram VK, Wright DFB, Barclay ML, Duffull SB. Deterministic identifiability of population pharmacokinetic and pharmacokinetic-pharmacodynamic models. J Pharmacokinet Pharmacodyn 2017; 44:415-423. [PMID: 28612141 DOI: 10.1007/s10928-017-9530-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Accepted: 06/07/2017] [Indexed: 01/05/2023]
Abstract
Identifiability is an important component of pharmacokinetic-pharmacodynamic (PKPD) model development. Structural identifiability is concerned with the uniqueness of the model parameters for a set of perfect input-output data and deterministic identifiability with the precision of parameter estimation given imperfect input-output data. We introduce two subcategories of deterministic identifiability, external and internal, and consider factors that distinguish between these forms. We define external deterministic identifiability as a function of externally controllable variables, i.e., the design, and internal deterministic identifiability as a function of the model and its parameter values. The concepts are explored using three common PK and PKPD models, and verified for their precision for the selected set of parameter values under optimal design.
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Affiliation(s)
- Vijay K Siripuram
- Otago Pharmacometrics Group, School of Pharmacy, University of Otago, Dunedin, New Zealand.
| | - Daniel F B Wright
- Otago Pharmacometrics Group, School of Pharmacy, University of Otago, Dunedin, New Zealand
| | - Murray L Barclay
- Departments of Gastroenterology & Clinical Pharmacology, Christchurch Hospital, Christchurch, New Zealand
| | - Stephen B Duffull
- Otago Pharmacometrics Group, School of Pharmacy, University of Otago, Dunedin, New Zealand
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7
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Marshall SF, Burghaus R, Cosson V, Cheung SYA, Chenel M, DellaPasqua O, Frey N, Hamrén B, Harnisch L, Ivanow F, Kerbusch T, Lippert J, Milligan PA, Rohou S, Staab A, Steimer JL, Tornøe C, Visser SAG. Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:93-122. [PMID: 27069774 PMCID: PMC4809625 DOI: 10.1002/psp4.12049] [Citation(s) in RCA: 215] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/19/2015] [Indexed: 12/11/2022]
Abstract
This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.
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Affiliation(s)
| | | | - R Burghaus
- Systems Pharmacology & Medicine Bayer Pharma AG Wuppertal Germany
| | - V Cosson
- Clinical Pharmacometrics F. Hoffmann-La Roche Ltd Basel Switzerland
| | - S Y A Cheung
- Quantitative Clinical Pharmacology AstraZeneca Cambridge UK
| | - M Chenel
- Institut de Recherches Internationales Servier Suresnes France
| | - O DellaPasqua
- Clinical Pharmacology Modelling & Simulation GlaxoSmithKline R&D Ltd Uxbridge UK
| | - N Frey
- Clinical Pharmacometrics F. Hoffmann-La Roche Ltd Basel Switzerland
| | - B Hamrén
- Quantitative Clinical Pharmacology AstraZeneca Gothenburg Sweden
| | | | - F Ivanow
- Global regulatory policy & Intelligence Janssen R&D High Wycombe UK
| | - T Kerbusch
- Quantitative Pharmacology & Pharmacometrics MSD Oss Netherlands
| | - J Lippert
- Systems Pharmacology & Medicine Bayer Pharma AG Wuppertal Germany
| | | | - S Rohou
- Global Regulatory Affairs & Policy AstraZeneca Paris France
| | - A Staab
- Translational Medicine & Clinical Pharmacology Boehringer Ingelheim Pharma GmbH & Co. KG Biberach Germany
| | | | - C Tornøe
- Clinical Reporting Novo Nordisk A/S Søborg Denmark
| | - S A G Visser
- Quantitative Pharmacology & Pharmacometrics Merck & Co Kenilworth USA
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8
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Model-based approaches for ivabradine development in paediatric population, part II: PK and PK/PD assessment. J Pharmacokinet Pharmacodyn 2015; 43:29-43. [DOI: 10.1007/s10928-015-9452-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 10/30/2015] [Indexed: 12/17/2022]
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9
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Collins TA, Bergenholm L, Abdulla T, Yates J, Evans N, Chappell MJ, Mettetal JT. Modeling and Simulation Approaches for Cardiovascular Function and Their Role in Safety Assessment. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225237 PMCID: PMC4394617 DOI: 10.1002/psp4.18] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Systems pharmacology modeling and pharmacokinetic-pharmacodynamic (PK/PD) analysis of drug-induced effects on cardiovascular (CV) function plays a crucial role in understanding the safety risk of new drugs. The aim of this review is to outline the current modeling and simulation (M&S) approaches to describe and translate drug-induced CV effects, with an emphasis on how this impacts drug safety assessment. Current limitations are highlighted and recommendations are made for future effort in this vital area of drug research.
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Affiliation(s)
- T A Collins
- Drug Safety and Metabolism, AstraZeneca Alderley Park, Macclesfield, UK
| | | | - T Abdulla
- School of Engineering, University of Warwick UK
| | - Jwt Yates
- Oncology, AstraZeneca Alderley Park, Macclesfield, UK
| | - N Evans
- School of Engineering, University of Warwick UK
| | | | - J T Mettetal
- Drug Safety and Metabolism, AstraZeneca Waltham, Massachusetts, USA
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