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Colebank MJ, Oomen PA, Witzenburg CM, Grosberg A, Beard DA, Husmeier D, Olufsen MS, Chesler NC. Guidelines for mechanistic modeling and analysis in cardiovascular research. Am J Physiol Heart Circ Physiol 2024; 327:H473-H503. [PMID: 38904851 DOI: 10.1152/ajpheart.00766.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 06/07/2024] [Accepted: 06/16/2024] [Indexed: 06/22/2024]
Abstract
Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.
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Affiliation(s)
- Mitchel J Colebank
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Pim A Oomen
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Colleen M Witzenburg
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Anna Grosberg
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Daniel A Beard
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, United States
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
| | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, North Carolina, United States
| | - Naomi C Chesler
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
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2
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Zirkle J, Han X, Racz R, Samieegohar M, Chaturbedi A, Mann J, Chakravartula S, Li Z. Deep learning-enabled natural language processing to identify directional pharmacokinetic drug-drug interactions. BMC Bioinformatics 2023; 24:413. [PMID: 37914988 PMCID: PMC10619324 DOI: 10.1186/s12859-023-05520-9] [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: 03/20/2023] [Accepted: 10/04/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels. METHODS We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC. RESULTS This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences. DISCUSSION AND CONCLUSION Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more "fit for purpose" NLP methods could be developed and used to facilitate the drug development process.
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Affiliation(s)
- Joel Zirkle
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Xiaomei Han
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Mohammadreza Samieegohar
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Anik Chaturbedi
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - John Mann
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Shilpa Chakravartula
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Zhihua Li
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
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Kopańska K, Rodríguez-Belenguer P, Llopis-Lorente J, Trenor B, Saiz J, Pastor M. Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models. Arch Toxicol 2023; 97:2721-2740. [PMID: 37528229 PMCID: PMC10474996 DOI: 10.1007/s00204-023-03557-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/12/2023] [Indexed: 08/03/2023]
Abstract
In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.
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Affiliation(s)
- Karolina Kopańska
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain
| | - Pablo Rodríguez-Belenguer
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, Valencia, Spain
| | - Jordi Llopis-Lorente
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain.
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4
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Mokrov GV. Multitargeting in cardioprotection: An example of biaromatic compounds. Arch Pharm (Weinheim) 2023; 356:e2300196. [PMID: 37345968 DOI: 10.1002/ardp.202300196] [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: 04/05/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023]
Abstract
A multitarget drug design approach is actively developing in modern medicinal chemistry and pharmacology, especially with regard to multifactorial diseases such as cardiovascular diseases, cancer, and neurodegenerative diseases. A detailed study of many well-known drugs developed within the single-target approach also often reveals additional mechanisms of their real pharmacological action. One of the multitarget drug design approaches can be the identification of the basic pharmacophore models corresponding to a wide range of the required target ligands. Among such models in the group of cardioprotectors is the linked biaromatic system. This review develops the concept of a "basic pharmacophore" using the biaromatic pharmacophore of cardioprotectors as an example. It presents an analysis of possible biological targets for compounds corresponding to the biaromatic pharmacophore and an analysis of the spectrum of biological targets for the five most known and most studied cardioprotective drugs corresponding to this model, and their involvement in the biological effects of these drugs.
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Dokuchaev A, Kursanov A, Balakina-Vikulova NA, Katsnelson LB, Solovyova O. The importance of mechanical conditions in the testing of excitation abnormalities in a population of electro-mechanical models of human ventricular cardiomyocytes. Front Physiol 2023; 14:1187956. [PMID: 37362439 PMCID: PMC10285544 DOI: 10.3389/fphys.2023.1187956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Background: Populations of in silico electrophysiological models of human cardiomyocytes represent natural variability in cell activity and are thoroughly calibrated and validated using experimental data from the human heart. The models have been shown to predict the effects of drugs and their pro-arrhythmic risks. However, excitation and contraction are known to be tightly coupled in the myocardium, with mechanical loads and stretching affecting both mechanics and excitation through mechanisms of mechano-calcium-electrical feedback. However, these couplings are not currently a focus of populations of cell models. Aim: We investigated the role of cardiomyocyte mechanical activity under different mechanical conditions in the generation, calibration, and validation of a population of electro-mechanical models of human cardiomyocytes. Methods: To generate a population, we assumed 11 input parameters of ionic currents and calcium dynamics in our recently developed TP + M model as varying within a wide range. A History matching algorithm was used to generate a non-implausible parameter space by calibrating the action potential and calcium transient biomarkers against experimental data and rejecting models with excitation abnormalities. The population was further calibrated using experimental data on human myocardial force characteristics and mechanical tests involving variations in preload and afterload. Models that passed the mechanical tests were validated with additional experimental data, including the effects of drugs with high or low pro-arrhythmic risk. Results: More than 10% of the models calibrated on electrophysiological data failed mechanical tests and were rejected from the population due to excitation abnormalities at reduced preload or afterload for cell contraction. The final population of accepted models yielded action potential, calcium transient, and force/shortening outputs consistent with experimental data. In agreement with experimental and clinical data, the models demonstrated a high frequency of excitation abnormalities in simulations of Dofetilide action on the ionic currents, in contrast to Verapamil. However, Verapamil showed a high frequency of failed contractions at high concentrations. Conclusion: Our results highlight the importance of considering mechanoelectric coupling in silico cardiomyocyte models. Mechanical tests allow a more thorough assessment of the effects of interventions on cardiac function, including drug testing.
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Affiliation(s)
- Arsenii Dokuchaev
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
| | - Alexander Kursanov
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Nathalie A. Balakina-Vikulova
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Leonid B. Katsnelson
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Olga Solovyova
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
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Sobie EA. Quantitative approaches to drug safety: The 2022 PSP special issue. CPT Pharmacometrics Syst Pharmacol 2022; 11:529-531. [PMID: 35598117 PMCID: PMC9124348 DOI: 10.1002/psp4.12804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Eric A. Sobie
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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7
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Mokrov GV. Linked biaromatic compounds as cardioprotective agents. Arch Pharm (Weinheim) 2021; 355:e2100428. [PMID: 34967027 DOI: 10.1002/ardp.202100428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/02/2021] [Accepted: 12/03/2021] [Indexed: 11/08/2022]
Abstract
Cardiovascular diseases (CVDs) are widespread in the modern world, and their number is constantly growing. For a long time, CVDs have been the leading cause of morbidity and mortality worldwide. Drugs for the treatment of CVD have been developed almost since the beginning of the 20th century, and a large number of effective cardioprotective agents of various classes have been created. Nevertheless, the need for the design and development of new safe drugs for the treatment of CVD remains. Literature data indicate that a huge number of cardioprotective agents of various generations and mechanisms correspond to a single generalized pharmacophore model containing two aromatic nuclei linked by a linear linker. In this regard, we put forward a concept for the design of a new generation of cardioprotective agents with a multitarget mechanism of action within the indicated pharmacophore model. This review is devoted to a generalization of the currently known compounds with cardioprotective properties and corresponding to the pharmacophore model of biaromatic compounds linked by a linear linker. Particular attention is paid to the history of the creation of these drugs, approaches to their design, and analysis of the structure-action relationship within each class.
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Affiliation(s)
- Grigory V Mokrov
- Department of Medicinal Chemistry, FSBI "Zakusov Institute of Pharmacology", Moscow, Russia
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8
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Campana C, Dariolli R, Boutjdir M, Sobie EA. Inflammation as a Risk Factor in Cardiotoxicity: An Important Consideration for Screening During Drug Development. Front Pharmacol 2021; 12:598549. [PMID: 33953668 PMCID: PMC8091045 DOI: 10.3389/fphar.2021.598549] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 03/31/2021] [Indexed: 01/08/2023] Open
Abstract
Numerous commonly prescribed drugs, including antiarrhythmics, antihistamines, and antibiotics, carry a proarrhythmic risk and may induce dangerous arrhythmias, including the potentially fatal Torsades de Pointes. For this reason, cardiotoxicity testing has become essential in drug development and a required step in the approval of any medication for use in humans. Blockade of the hERG K+ channel and the consequent prolongation of the QT interval on the ECG have been considered the gold standard to predict the arrhythmogenic risk of drugs. In recent years, however, preclinical safety pharmacology has begun to adopt a more integrative approach that incorporates mathematical modeling and considers the effects of drugs on multiple ion channels. Despite these advances, early stage drug screening research only evaluates QT prolongation in experimental and computational models that represent healthy individuals. We suggest here that integrating disease modeling with cardiotoxicity testing can improve drug risk stratification by predicting how disease processes and additional comorbidities may influence the risks posed by specific drugs. In particular, chronic systemic inflammation, a condition associated with many diseases, affects heart function and can exacerbate medications’ cardiotoxic effects. We discuss emerging research implicating the role of inflammation in cardiac electrophysiology, and we offer a perspective on how in silico modeling of inflammation may lead to improved evaluation of the proarrhythmic risk of drugs at their early stage of development.
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Affiliation(s)
- Chiara Campana
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Rafael Dariolli
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Mohamed Boutjdir
- Cardiovascular Research Program, VA New York Harbor Healthcare System, Brooklyn, NY, United States.,Department of Medicine, Cell Biology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, NY, United States.,Department of Medicine, New York University School of Medicine, New York, NY, United States
| | - Eric A Sobie
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Lodrini AM, Barile L, Rocchetti M, Altomare C. Human Induced Pluripotent Stem Cells Derived from a Cardiac Somatic Source: Insights for an In-Vitro Cardiomyocyte Platform. Int J Mol Sci 2020; 21:ijms21020507. [PMID: 31941149 PMCID: PMC7013592 DOI: 10.3390/ijms21020507] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/10/2020] [Accepted: 01/10/2020] [Indexed: 12/24/2022] Open
Abstract
Reprogramming of adult somatic cells into induced pluripotent stem cells (iPSCs) has revolutionized the complex scientific field of disease modelling and personalized therapy. Cardiac differentiation of human iPSCs into cardiomyocytes (hiPSC-CMs) has been used in a wide range of healthy and disease models by deriving CMs from different somatic cells. Unfortunately, hiPSC-CMs have to be improved because existing protocols are not completely able to obtain mature CMs recapitulating physiological properties of human adult cardiac cells. Therefore, improvements and advances able to standardize differentiation conditions are needed. Lately, evidences of an epigenetic memory retained by the somatic cells used for deriving hiPSC-CMs has led to evaluation of different somatic sources in order to obtain more mature hiPSC-derived CMs.
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Affiliation(s)
- Alessandra Maria Lodrini
- Department of Biotechnology and Biosciences, Università degli Studi di Milano-Bicocca, Milano 20126, Italy; (A.M.L.); (M.R.)
| | - Lucio Barile
- Fondazione Cardiocentro Ticino, Lugano 6900, Switzerland;
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano 6900, Switzerland
| | - Marcella Rocchetti
- Department of Biotechnology and Biosciences, Università degli Studi di Milano-Bicocca, Milano 20126, Italy; (A.M.L.); (M.R.)
| | - Claudia Altomare
- Fondazione Cardiocentro Ticino, Lugano 6900, Switzerland;
- Correspondence:
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Li Z, Mirams GR, Yoshinaga T, Ridder BJ, Han X, Chen JE, Stockbridge NL, Wisialowski TA, Damiano B, Severi S, Morissette P, Kowey PR, Holbrook M, Smith G, Rasmusson RL, Liu M, Song Z, Qu Z, Leishman DJ, Steidl‐Nichols J, Rodriguez B, Bueno‐Orovio A, Zhou X, Passini E, Edwards AG, Morotti S, Ni H, Grandi E, Clancy CE, Vandenberg J, Hill A, Nakamura M, Singer T, Polonchuk L, Greiter‐Wilke A, Wang K, Nave S, Fullerton A, Sobie EA, Paci M, Musuamba Tshinanu F, Strauss DG. General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy. Clin Pharmacol Ther 2020; 107:102-111. [PMID: 31709525 PMCID: PMC6977398 DOI: 10.1002/cpt.1647] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 09/06/2019] [Indexed: 12/27/2022]
Abstract
This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.
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Garnett C, Johannesen L, McDowell T. Redefining Blood Pressure Assessment — The Role of the Ambulatory Blood Pressure Monitoring Study for Drug Safety. Clin Pharmacol Ther 2019; 107:147-153. [DOI: 10.1002/cpt.1690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 10/15/2019] [Indexed: 12/14/2022]
Affiliation(s)
- Christine Garnett
- Division of Cardiovascular and Renal Products Center for Drug Evaluation and Research, Food and Drug Administration Silver Spring Maryland USA
| | - Lars Johannesen
- Division of Cardiovascular and Renal Products Center for Drug Evaluation and Research, Food and Drug Administration Silver Spring Maryland USA
| | - Tzu‐Yun McDowell
- Division of Cardiovascular and Renal Products Center for Drug Evaluation and Research, Food and Drug Administration Silver Spring Maryland USA
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12
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Bai JP, Musante CJ, Petanceska S, Zhang L, Zhao L, Zhao P. American Society for Clinical Pharmacology and Therapeutics 2019 Annual Meeting Pre-Conferences. CPT Pharmacometrics Syst Pharmacol 2019; 8:333-335. [PMID: 31087531 PMCID: PMC6617844 DOI: 10.1002/psp4.12424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 05/01/2019] [Indexed: 12/13/2022] Open
Affiliation(s)
- Jane P.F. Bai
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringMarylandUSA
| | - Cynthia J. Musante
- Quantitative Systems PharmacologyEarly Clinical Development, Pfizer IncCambridgeMassachusettsUSA
| | - Suzana Petanceska
- Division of NeuroscienceNational Institute on Aging at the National Institutes of HealthBethesdaMarylandUSA
| | - Lei Zhang
- Office of Research and StandardsOffice of Generic DrugsCenter for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringMarylandUSA
| | - Liang Zhao
- Office of Research and StandardsOffice of Generic DrugsCenter for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringMarylandUSA
| | - Ping Zhao
- Bill & Melinda Gates FoundationSeattleWashingtonUSA
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