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El-Nashar H, Sabry M, Tseng YT, Francis N, Latif N, Parker KH, Moore JE, Yacoub MH. Multiscale structure and function of the aortic valve apparatus. Physiol Rev 2024; 104:1487-1532. [PMID: 37732828 DOI: 10.1152/physrev.00038.2022] [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: 12/07/2022] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023] Open
Abstract
Whereas studying the aortic valve in isolation has facilitated the development of life-saving procedures and technologies, the dynamic interplay of the aortic valve and its surrounding structures is vital to preserving their function across the wide range of conditions encountered in an active lifestyle. Our view is that these structures should be viewed as an integrated functional unit, here referred to as the aortic valve apparatus (AVA). The coupling of the aortic valve and root, left ventricular outflow tract, and blood circulation is crucial for AVA's functions: unidirectional flow out of the left ventricle, coronary perfusion, reservoir function, and support of left ventricular function. In this review, we explore the multiscale biological and physical phenomena that underlie the simultaneous fulfillment of these functions. A brief overview of the tools used to investigate the AVA, such as medical imaging modalities, experimental methods, and computational modeling, specifically fluid-structure interaction (FSI) simulations, is included. Some pathologies affecting the AVA are explored, and insights are provided on treatments and interventions that aim to maintain quality of life. The concepts explained in this article support the idea of AVA being an integrated functional unit and help identify unanswered research questions. Incorporating phenomena through the molecular, micro, meso, and whole tissue scales is crucial for understanding the sophisticated normal functions and diseases of the AVA.
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Affiliation(s)
- Hussam El-Nashar
- Aswan Heart Research Centre, Magdi Yacoub Foundation, Cairo, Egypt
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Malak Sabry
- Aswan Heart Research Centre, Magdi Yacoub Foundation, Cairo, Egypt
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Yuan-Tsan Tseng
- Heart Science Centre, Magdi Yacoub Institute, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Nadine Francis
- Aswan Heart Research Centre, Magdi Yacoub Foundation, Cairo, Egypt
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Najma Latif
- Heart Science Centre, Magdi Yacoub Institute, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Kim H Parker
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - James E Moore
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Magdi H Yacoub
- Aswan Heart Research Centre, Magdi Yacoub Foundation, Cairo, Egypt
- Heart Science Centre, Magdi Yacoub Institute, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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2
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Morris PD, Anderton RA, Marshall-Goebel K, Britton JK, Lee SMC, Smith NP, van de Vosse FN, Ong KM, Newman TA, Taylor DJ, Chico T, Gunn JP, Narracott AJ, Hose DR, Halliday I. Computational modelling of cardiovascular pathophysiology to risk stratify commercial spaceflight. Nat Rev Cardiol 2024:10.1038/s41569-024-01047-5. [PMID: 39030270 DOI: 10.1038/s41569-024-01047-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/30/2024] [Indexed: 07/21/2024]
Abstract
For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data on the effects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentially either impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation-perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to risk stratify individuals and help to improve safety in commercial suborbital spaceflight.
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Affiliation(s)
- Paul D Morris
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK.
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
| | - Ryan A Anderton
- Medical Department, Spaceflight, UK Civil Aviation Authority, Gatwick, UK
| | - Karina Marshall-Goebel
- The National Aeronautics and Space Administration (NASA) Johnson Space Center, Houston, TX, USA
| | - Joseph K Britton
- Aerospace Medicine Specialist Wing, Royal Air Force (RAF) Centre of Aerospace Medicine, Henlow, UK
| | - Stuart M C Lee
- KBR, Human Health Countermeasures Element, NASA Johnson Space Center, Houston, TX, USA
| | - Nicolas P Smith
- Victoria University of Wellington, Wellington, New Zealand
- Auckland Bioengineering Institute, Auckland, New Zealand
| | - Frans N van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Karen M Ong
- Virgin Galactic Medical, Truth or Consequences, NM, USA
| | - Tom A Newman
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Daniel J Taylor
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
| | - Tim Chico
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Julian P Gunn
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Andrew J Narracott
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Insigneo Institute, University of Sheffield, Sheffield, UK
| | - D Rod Hose
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Insigneo Institute, University of Sheffield, Sheffield, UK
| | - Ian Halliday
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Insigneo Institute, University of Sheffield, Sheffield, UK
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3
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Bas TG, Duarte V. Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments. Pharmaceuticals (Basel) 2024; 17:925. [PMID: 39065775 PMCID: PMC11279612 DOI: 10.3390/ph17070925] [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: 05/16/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed.
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Affiliation(s)
- Tomas Gabriel Bas
- Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo 1781421, Chile;
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4
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Nene L, Flepisi BT, Brand SJ, Basson C, Balmith M. Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence. Clin Ther 2024:S0149-2918(24)00138-3. [PMID: 38981791 DOI: 10.1016/j.clinthera.2024.05.012] [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: 04/03/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes. METHODS A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes. FINDINGS The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs. IMPLICATIONS AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.
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Affiliation(s)
- Linda Nene
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Brian Thabile Flepisi
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Sarel Jacobus Brand
- Center of Excellence for Pharmaceutical Sciences, Department of Pharmacology, North-West University, Potchefstroom, South Africa
| | - Charlise Basson
- Department of Physiology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Marissa Balmith
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
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5
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Isenberg NM, Mertins SD, Yoon BJ, Reyes KG, Urban NM. Identifying Bayesian optimal experiments for uncertain biochemical pathway models. Sci Rep 2024; 14:15237. [PMID: 38956095 PMCID: PMC11219779 DOI: 10.1038/s41598-024-65196-w] [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: 12/22/2023] [Accepted: 06/18/2024] [Indexed: 07/04/2024] Open
Abstract
Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.
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Affiliation(s)
| | - Susan D Mertins
- Fredrick National Laboratory for Cancer Research, Fredrick, MD, 21702, USA
| | - Byung-Jun Yoon
- Texas A &M University, College Station, TX, 77843, USA
- Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Kristofer G Reyes
- University at Buffalo, Buffalo, NY, 14260, USA
- Brookhaven National Laboratory, Upton, NY, 11973, USA
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6
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Arminio M, Carbonaro D, Morbiducci U, Gallo D, Chiastra C. Fluid-structure interaction simulation of mechanical aortic valves: a narrative review exploring its role in total product life cycle. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1399729. [PMID: 39011523 PMCID: PMC11247014 DOI: 10.3389/fmedt.2024.1399729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/07/2024] [Indexed: 07/17/2024] Open
Abstract
Over the last years computer modelling and simulation has emerged as an effective tool to support the total product life cycle of cardiovascular devices, particularly in the device preclinical evaluation and post-market assessment. Computational modelling is particularly relevant for heart valve prostheses, which require an extensive assessment of their hydrodynamic performance and of risks of hemolysis and thromboembolic complications associated with mechanically-induced blood damage. These biomechanical aspects are typically evaluated through a fluid-structure interaction (FSI) approach, which enables valve fluid dynamics evaluation accounting for leaflets movement. In this context, the present narrative review focuses on the computational modelling of bileaflet mechanical aortic valves through FSI approach, aiming to foster and guide the use of simulations in device total product life cycle. The state of the art of FSI simulation of heart valve prostheses is reviewed to highlight the variety of modelling strategies adopted in the literature. Furthermore, the integration of FSI simulations in the total product life cycle of bileaflet aortic valves is discussed, with particular emphasis on the role of simulations in complementing and potentially replacing the experimental tests suggested by international standards. Simulations credibility assessment is also discussed in the light of recently published guidelines, thus paving the way for a broader inclusion of in silico evidence in regulatory submissions. The present narrative review highlights that FSI simulations can be successfully framed within the total product life cycle of bileaflet mechanical aortic valves, emphasizing that credible in silico models evaluating the performance of implantable devices can (at least) partially replace preclinical in vitro experimentation and support post-market biomechanical evaluation, leading to a reduction in both time and cost required for device development.
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Affiliation(s)
| | | | | | | | - Claudio Chiastra
- PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
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7
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Vuong TNAM, Bartolf‐Kopp M, Andelovic K, Jungst T, Farbehi N, Wise SG, Hayward C, Stevens MC, Rnjak‐Kovacina J. Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307627. [PMID: 38704690 PMCID: PMC11234431 DOI: 10.1002/advs.202307627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/12/2024] [Indexed: 05/07/2024]
Abstract
Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.
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Affiliation(s)
| | - Michael Bartolf‐Kopp
- Department of Functional Materials in Medicine and DentistryInstitute of Functional Materials and Biofabrication (IFB)KeyLab Polymers for Medicine of the Bavarian Polymer Institute (BPI)University of WürzburgPleicherwall 297070WürzburgGermany
| | - Kristina Andelovic
- Department of Functional Materials in Medicine and DentistryInstitute of Functional Materials and Biofabrication (IFB)KeyLab Polymers for Medicine of the Bavarian Polymer Institute (BPI)University of WürzburgPleicherwall 297070WürzburgGermany
| | - Tomasz Jungst
- Department of Functional Materials in Medicine and DentistryInstitute of Functional Materials and Biofabrication (IFB)KeyLab Polymers for Medicine of the Bavarian Polymer Institute (BPI)University of WürzburgPleicherwall 297070WürzburgGermany
- Department of Orthopedics, Regenerative Medicine Center UtrechtUniversity Medical Center UtrechtUtrecht3584Netherlands
| | - Nona Farbehi
- Graduate School of Biomedical EngineeringUniversity of New South WalesSydney2052Australia
- Tyree Institute of Health EngineeringUniversity of New South WalesSydneyNSW2052Australia
- Garvan Weizmann Center for Cellular GenomicsGarvan Institute of Medical ResearchSydneyNSW2010Australia
| | - Steven G. Wise
- School of Medical SciencesUniversity of SydneySydneyNSW2006Australia
| | - Christopher Hayward
- St Vincent's HospitalSydneyVictor Chang Cardiac Research InstituteSydney2010Australia
| | | | - Jelena Rnjak‐Kovacina
- Graduate School of Biomedical EngineeringUniversity of New South WalesSydney2052Australia
- Tyree Institute of Health EngineeringUniversity of New South WalesSydneyNSW2052Australia
- Australian Centre for NanoMedicine (ACN)University of New South WalesSydneyNSW2052Australia
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8
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Saldanha L, Langel Ü, Vale N. A Physiologically Based Pharmacokinetic (PBPK) Study to Assess the Adjuvanticity of Three Peptides in an Oral Vaccine. Pharmaceutics 2024; 16:780. [PMID: 38931901 PMCID: PMC11207434 DOI: 10.3390/pharmaceutics16060780] [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: 04/26/2024] [Revised: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
Following up on the first PBPK model for an oral vaccine built for alpha-tocopherol, three peptides are explored in this article to verify if they could support an oral vaccine formulation as adjuvants using the same PBPK modeling approach. A literature review was conducted to verify what peptides have been used as adjuvants in the last decades, and it was noticed that MDP derivatives have been used, with one of them even being commercially approved and used as an adjuvant when administered intravenously in oncology. The aim of this study was to build optimized models for three MDP peptides (MDP itself, MTP-PE, and murabutide) and to verify if they could act as adjuvants for an oral vaccine. Challenges faced by peptides in an oral delivery system are taken into consideration, and improvements to the formulations to achieve better results are described in a step-wise approach to reach the most-optimized model. Once simulations are performed, results are compared to determine what would be the best peptide to support as an oral adjuvant. According to our results, MTP-PE, the currently approved and commercialized peptide, could have potential to be incorporated into an oral formulation. It would be interesting to proceed with further in vivo experiments to determine the behavior of this peptide when administered orally with a proper formulation to overcome the challenges of oral delivery systems.
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Affiliation(s)
- Leonor Saldanha
- PerMed Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal;
- CINTESIS@RISE, Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal
| | - Ülo Langel
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia;
- Department of Biochemistry and Biophysics, Stockholm University, 10691 Stockholm, Sweden
| | - Nuno Vale
- PerMed Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal;
- CINTESIS@RISE, Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal
- Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto, Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal
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9
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Yao J, Crockett J, D'Souza M, A Day G, K Wilcox R, C Jones A, Mengoni M. Effect of meniscus modelling assumptions in a static tibiofemoral finite element model: importance of geometry over material. Biomech Model Mechanobiol 2024; 23:1055-1065. [PMID: 38349433 PMCID: PMC11101373 DOI: 10.1007/s10237-024-01822-w] [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: 10/16/2023] [Accepted: 01/06/2024] [Indexed: 05/18/2024]
Abstract
Finite element studies of the tibiofemoral joint have increased use in research, with attention often placed on the material models. Few studies assess the effect of meniscus modelling assumptions in image-based models on contact mechanics outcomes. This work aimed to assess the effect of modelling assumptions of the meniscus on knee contact mechanics and meniscus kinematics. A sensitivity analysis was performed using three specimen-specific tibiofemoral models and one generic knee model. The assumptions in representing the meniscus attachment on the tibia (shape of the roots and position of the attachment), the material properties of the meniscus, the shape of the meniscus and the alignment of the joint were evaluated, creating 40 model instances. The values of material parameters for the meniscus and the position of the root attachment had a small influence on the total contact area but not on the meniscus displacement or the force balance between condyles. Using 3D shapes to represent the roots instead of springs had a large influence in meniscus displacement but not in knee contact area. Changes in meniscus shape and in knee alignment had a significantly larger influence on all outcomes of interest, with differences two to six times larger than those due to material properties. The sensitivity study demonstrated the importance of meniscus shape and knee alignment on meniscus kinematics and knee contact mechanics, both being more important than the material properties or the position of the roots. It also showed that differences between knees were large, suggesting that clinical interpretations of modelling studies using single geometries should be avoided.
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Affiliation(s)
- Jiacheng Yao
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, UK
| | - John Crockett
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, UK
| | - Mathias D'Souza
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, UK
| | - Gavin A Day
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, UK
| | - Ruth K Wilcox
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, UK
| | - Alison C Jones
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, UK
| | - Marlène Mengoni
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, UK.
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10
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Achar J, Cronin MTD, Firman JW, Öberg G. A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment. Arch Toxicol 2024; 98:1727-1740. [PMID: 38555325 PMCID: PMC11106140 DOI: 10.1007/s00204-024-03721-6] [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: 11/16/2023] [Accepted: 02/29/2024] [Indexed: 04/02/2024]
Abstract
The first step in the hazard or risk assessment of chemicals should be to formulate the problem through a systematic and iterative process aimed at identifying and defining factors critical to the assessment. However, no general agreement exists on what components an in silico toxicology problem formulation (PF) should include. The present work aims to develop a PF framework relevant to the application of in silico models for chemical toxicity prediction. We modified and applied a PF framework from the general risk assessment literature to peer reviewed papers describing PFs associated with in silico toxicology models. Important gaps between the general risk assessment literature and the analyzed PF literature associated with in silico toxicology methods were identified. While the former emphasizes the need for PFs to address higher-level conceptual questions, the latter does not. There is also little consistency in the latter regarding the PF components addressed, reinforcing the need for a PF framework that enable users of in silico toxicology models to answer the central conceptual questions aimed at defining components critical to the model application. Using the developed framework, we highlight potential areas of uncertainty manifestation in in silico toxicology PF in instances where particular components are missing or implicitly described. The framework represents the next step in standardizing in silico toxicology PF component. The framework can also be used to improve the understanding of how uncertainty is apparent in an in silico toxicology PF, thus facilitating ways to address uncertainty.
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Affiliation(s)
- Jerry Achar
- Institute for Resources Environment, and Sustainability, The University of British Columbia, 2202 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Gunilla Öberg
- Institute for Resources Environment, and Sustainability, The University of British Columbia, 2202 Main Mall, Vancouver, BC, V6T 1Z4, Canada
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11
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Mann J, Meshkin H, Zirkle J, Han X, Thrasher B, Chaturbedi A, Arabidarrehdor G, Li Z. Mechanism-based organization of neural networks to emulate systems biology and pharmacology models. Sci Rep 2024; 14:12082. [PMID: 38802422 PMCID: PMC11130269 DOI: 10.1038/s41598-024-59378-9] [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: 11/22/2023] [Accepted: 04/10/2024] [Indexed: 05/29/2024] Open
Abstract
Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.
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Affiliation(s)
- John Mann
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Hamed Meshkin
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Joel Zirkle
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 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, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Bradlee Thrasher
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 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, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Ghazal Arabidarrehdor
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 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, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
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12
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McCullough JWS, Coveney PV. Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow. Sci Rep 2024; 14:11317. [PMID: 38760455 PMCID: PMC11101457 DOI: 10.1038/s41598-024-61708-w] [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: 09/15/2023] [Accepted: 05/08/2024] [Indexed: 05/19/2024] Open
Abstract
Uncertainty quantification is becoming a key tool to ensure that numerical models can be sufficiently trusted to be used in domains such as medical device design. Demonstration of how input parameters impact the quantities of interest generated by any numerical model is essential to understanding the limits of its reliability. With the lattice Boltzmann method now a widely used approach for computational fluid dynamics, building greater understanding of its numerical uncertainty characteristics will support its further use in science and industry. In this study we apply an in-depth uncertainty quantification study of the lattice Boltzmann method in a canonical bifurcating geometry that is representative of the vascular junctions present in arterial and venous domains. These campaigns examine how quantities of interest-pressure and velocity along the central axes of the bifurcation-are influenced by the algorithmic parameters of the lattice Boltzmann method and the parameters controlling the values imposed at inlet velocity and outlet pressure boundary conditions. We also conduct a similar campaign on a set of personalised vessels to further illustrate the application of these techniques. Our work provides insights into how input parameters and boundary conditions impact the velocity and pressure distributions calculated in a simulation and can guide the choices of such values when applied to vascular studies of patient specific geometries. We observe that, from an algorithmic perspective, the number of time steps and the size of the grid spacing are the most influential parameters. When considering the influence of boundary conditions, we note that the magnitude of the inlet velocity and the mean pressure applied within sinusoidal pressure outlets have the greatest impact on output quantities of interest. We also observe that, when comparing the magnitude of variation imposed in the input parameters with that observed in the output quantities, this variability is particularly magnified when the input velocity is altered. This study also demonstrates how open-source toolkits for validation, verification and uncertainty quantification can be applied to numerical models deployed on high-performance computers without the need for modifying the simulation code itself. Such an ability is key to the more widespread adoption of the analysis of uncertainty in numerical models by significantly reducing the complexity of their execution and analysis.
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Affiliation(s)
- Jon W S McCullough
- Centre for Computational Science, Department of Chemistry, University College London, London, UK
| | - Peter V Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London, UK.
- Centre for Advanced Research Computing, University College London, London, UK.
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands.
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13
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Morris MX, Fiocco D, Caneva T, Yiapanis P, Orgill DP. Current and future applications of artificial intelligence in surgery: implications for clinical practice and research. Front Surg 2024; 11:1393898. [PMID: 38783862 PMCID: PMC11111929 DOI: 10.3389/fsurg.2024.1393898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Surgeons are skilled at making complex decisions over invasive procedures that can save lives and alleviate pain and avoid complications in patients. The knowledge to make these decisions is accumulated over years of schooling and practice. Their experience is in turn shared with others, also via peer-reviewed articles, which get published in larger and larger amounts every year. In this work, we review the literature related to the use of Artificial Intelligence (AI) in surgery. We focus on what is currently available and what is likely to come in the near future in both clinical care and research. We show that AI has the potential to be a key tool to elevate the effectiveness of training and decision-making in surgery and the discovery of relevant and valid scientific knowledge in the surgical domain. We also address concerns about AI technology, including the inability for users to interpret algorithms as well as incorrect predictions. A better understanding of AI will allow surgeons to use new tools wisely for the benefit of their patients.
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Affiliation(s)
- Miranda X. Morris
- Duke University School of Medicine, Duke University Hospital, Durham, NC, United States
| | - Davide Fiocco
- Department of Artificial Intelligence, Frontiers Media SA, Lausanne, Switzerland
| | - Tommaso Caneva
- Department of Artificial Intelligence, Frontiers Media SA, Lausanne, Switzerland
| | - Paris Yiapanis
- Department of Artificial Intelligence, Frontiers Media SA, Lausanne, Switzerland
| | - Dennis P. Orgill
- Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, United States
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14
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Go N, Arsène S, Faddeenkov I, Galland T, Martis B S, Lefaudeux D, Wang Y, Etheve L, Jacob E, Monteiro C, Bosley J, Sansone C, Pasquali C, Lehr L, Kulesza A. A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials. J Allergy Clin Immunol 2024; 153:1330-1343. [PMID: 38369029 DOI: 10.1016/j.jaci.2023.12.031] [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: 05/23/2023] [Revised: 11/03/2023] [Accepted: 12/22/2023] [Indexed: 02/20/2024]
Abstract
BACKGROUND The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally. OBJECTIVE We aimed to optimize AD trial design using simulations. METHODS We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens. RESULTS We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. CONCLUSIONS This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.
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15
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Maquer G, Mueri C, Henderson A, Bischoff J, Favre P. Developing and Validating a Model of Humeral Stem Primary Stability, Intended for In Silico Clinical Trials. Ann Biomed Eng 2024; 52:1280-1296. [PMID: 38361138 DOI: 10.1007/s10439-024-03452-w] [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: 08/31/2023] [Accepted: 01/12/2024] [Indexed: 02/17/2024]
Abstract
In silico clinical trials (ISCT) can contribute to demonstrating a device's performance via credible computational models applied on virtual cohorts. Our purpose was to establish the credibility of a model for assessing the risk of humeral stem loosening in total shoulder arthroplasty, based on a twofold validation scheme involving both benchtop and clinical validation activities, for ISCT applications. A finite element model computing bone-implant micromotion (benchtop model) was quantitatively compared to a bone foam micromotion test (benchtop comparator) to ensure that the physics of the system was captured correctly. The model was expanded to a population-based approach (clinical model) and qualitatively evaluated based on its ability to replicate findings from a published clinical study (clinical comparator), namely that grit-blasted stems are at a significantly higher risk of loosening than porous-coated stems, to ensure that clinical performance of the stem can be predicted appropriately. Model form sensitivities pertaining to surgical variation and implant design were evaluated. The model replicated benchtop micromotion measurements (52.1 ± 4.3 µm), without a significant impact of the press-fit ("Press-fit": 54.0 ± 8.5 µm, "No press-fit": 56.0 ± 12.0 µm). Applied to a virtual population, the grit-blasted stems (227 ± 78µm) experienced significantly larger micromotions than porous-coated stems (162 ± 69µm), in accordance with the findings of the clinical comparator. This work provides a concrete example for evaluating the credibility of an ISCT study. By validating the modeling approach against both benchtop and clinical data, model credibility is established for an ISCT application aiming to enrich clinical data in a regulatory submission.
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Affiliation(s)
- Ghislain Maquer
- Zimmer Biomet, Sulzerallee 8, 8404, Winterthur, Switzerland.
| | | | - Adam Henderson
- Zimmer Biomet, Sulzerallee 8, 8404, Winterthur, Switzerland
| | - Jeff Bischoff
- Zimmer Biomet, 1800 West Center St., Warsaw, IN, 46580, USA
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16
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Csippa B, Sándor L, Závodszky G, Szikora I, Paál G. Comparison of Flow Reduction Efficacy of Nominal and Oversized Flow Diverters Using a Novel Measurement-assisted in Silico Method. Clin Neuroradiol 2024:10.1007/s00062-024-01404-4. [PMID: 38652163 DOI: 10.1007/s00062-024-01404-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 03/07/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE The high efficacy of flow diverters (FD) in the case of wide-neck aneurysms is well demonstrated, yet new challenges have arisen because of reported posttreatment failures and the growing number of new generation of devices. Our aim is to present a measurement-supported in silico workflow that automates the virtual deployment and subsequent hemodynamic analysis of FDs. In this work, the objective is to analyze the effects of FD deployment variability of two manufacturers on posttreatment flow reduction. METHODS The virtual deployment procedure is based on detailed mechanical calibration of the flow diverters, while the flow representation is based on hydrodynamic resistance (HR) measurements. Computational fluid dynamic simulations resulted in 5 untreated and 80 virtually treated scenarios, including 2 FD designs in nominal and oversized deployment states. The simulated aneurysmal velocity reduction (AMVR) is correlated with the HR values and deployment scenarios. RESULTS The linear HR coefficient and AMVR revealed a power-law relationship considering all 80 deployments. In nominal deployment scenarios, a significantly larger average AMVR was obtained (60.3%) for the 64-wire FDs than for 48-wire FDs (51.9%). In oversized deployments, the average AMVR was almost the same for 64-wire and 48-wire device types, 27.5% and 25.7%, respectively. CONCLUSION The applicability of our numerical workflow was demonstrated, also in large-scale hemodynamic investigations. The study revealed a robust power-law relationship between a HR coefficient and AMVR. Furthermore, the 64 wire configurations in nominal sizing produced a significantly higher posttreatment flow reduction, replicating the results of other in vitro studies.
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Affiliation(s)
- Benjamin Csippa
- Department of Hydrodynamic Systems, Faculty of Mechanical Engineering,, Budapest University of Technology and Economics, Műegyetem rkp 1-3, 1111, Budapest, Hungary.
| | - Levente Sándor
- Department of Hydrodynamic Systems, Faculty of Mechanical Engineering,, Budapest University of Technology and Economics, Műegyetem rkp 1-3, 1111, Budapest, Hungary
| | - Gábor Závodszky
- Department of Hydrodynamic Systems, Faculty of Mechanical Engineering,, Budapest University of Technology and Economics, Műegyetem rkp 1-3, 1111, Budapest, Hungary
- Faculty of Science, Informatics Institute, Computational Science Lab, University of Amsterdam, Amsterdam, The Netherlands
| | - István Szikora
- National Institute of Mental Health, Neurology, and Neurosurgery, Department of Neurointerventions, Budapest, Hungary
| | - György Paál
- Department of Hydrodynamic Systems, Faculty of Mechanical Engineering,, Budapest University of Technology and Economics, Műegyetem rkp 1-3, 1111, Budapest, Hungary
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17
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Romeo A, Kazsoki A, Musumeci T, Zelkó R. A Clinical, Pharmacological, and Formulation Evaluation of Melatonin in the Treatment of Ocular Disorders-A Systematic Review. Int J Mol Sci 2024; 25:3999. [PMID: 38612812 PMCID: PMC11011996 DOI: 10.3390/ijms25073999] [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: 02/27/2024] [Revised: 03/25/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024] Open
Abstract
Melatonin's cytoprotective properties may have therapeutic implications in treating ocular diseases like glaucoma and age-related macular degeneration. Literature data suggest that melatonin could potentially protect ocular tissues by decreasing the production of free radicals and pro-inflammatory mediators. This study aims to summarize the screened articles on melatonin's clinical, pharmacological, and formulation evaluation in treating ocular disorders. The identification of relevant studies on the topic in focus was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. The studies were searched in the following databases and web search engines: Pubmed, Scopus, Science Direct, Web of Science, Reaxys, Google Scholar, Google Patents, Espacenet, and Patentscope. The search time interval was 2013-2023, with the following keywords: melatonin AND ocular OR ophthalmic AND formulation OR insert AND disease. Our key conclusion was that using melatonin-loaded nano-delivery systems enabled the improved permeation of the molecule into intraocular tissues and assured controlled release profiles. Although preclinical studies have demonstrated the efficacy of developed formulations, a considerable gap has been observed in the clinical translation of the results. To overcome this failure, revising the preclinical experimental phase might be useful by selecting endpoints close to clinical ones.
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Affiliation(s)
- Alessia Romeo
- Department of Drug and Health Sciences, University of Catania, Via Santa Sofia 64, 95125 Catania, Italy; (A.R.); (T.M.)
| | - Adrienn Kazsoki
- University Pharmacy Department of Pharmacy Administration, Semmelweis University, Hőgyes Endre Street 7–9, 1092 Budapest, Hungary;
| | - Teresa Musumeci
- Department of Drug and Health Sciences, University of Catania, Via Santa Sofia 64, 95125 Catania, Italy; (A.R.); (T.M.)
| | - Romána Zelkó
- University Pharmacy Department of Pharmacy Administration, Semmelweis University, Hőgyes Endre Street 7–9, 1092 Budapest, Hungary;
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Paliwal A, Jain S, Kumar S, Wal P, Khandai M, Khandige PS, Sadananda V, Anwer MK, Gulati M, Behl T, Srivastava S. Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine. Expert Opin Drug Metab Toxicol 2024; 20:181-195. [PMID: 38480460 DOI: 10.1080/17425255.2024.2330666] [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: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties. AREAS COVERED The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged. EXPERT OPINION AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.
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Affiliation(s)
- Ajita Paliwal
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
| | - Smita Jain
- Department of Pharmacy, Banasthali Vidyapith, Banasthali, India
| | - Sachin Kumar
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
| | - Pranay Wal
- Department of Pharmacy, Pranveer Singh Institute of Technology, Pharmacy, Kanpur, India
| | - Madhusmruti Khandai
- Department of Pharmacy, Royal College of Pharmacy and Health Sciences, Berahmpur, India
| | - Prasanna Shama Khandige
- NGSM Institute of Pharmaceutical Sciences, Department of Pharmacology, Manglauru, NITTE (Deemed to be University), Manglauru, India
| | - Vandana Sadananda
- AB Shetty Memorial Institute of Dental Sciences, Department of Conservative Dentistry and Endodontics, NITTE (Deemed to be University), Mangaluru, India
| | - Md Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India
- ARCCIM, Health, University of Technology, Sydney, Ultimo, Australia
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Mohali, Punjab, India
| | - Shriyansh Srivastava
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
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19
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Hernandez RJ, Madhusudhan S, Zheng Y, El-Bouri WK. Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina. Invest Ophthalmol Vis Sci 2024; 65:40. [PMID: 38683566 PMCID: PMC11059806 DOI: 10.1167/iovs.65.4.40] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 05/01/2024] Open
Abstract
Purpose This study explored the relationship among microvascular parameters as delineated by optical coherence tomography angiography (OCTA) and retinal perfusion. Here, we introduce a versatile framework to examine the interplay between the retinal vascular structure and function by generating virtual vasculatures from central retinal vessels to macular capillaries. Also, we have developed a hemodynamics model that evaluates the associations between vascular morphology and retinal perfusion. Methods The generation of the vasculature is based on the distribution of four clinical parameters pertaining to the dimension and blood pressure of the central retinal vessels, constructive constrained optimization, and Voronoi diagrams. Arterial and venous trees are generated in the temporal retina and connected through three layers of capillaries at different depths in the macula. The correlations between total retinal blood flow and macular flow fraction and vascular morphology are derived as Spearman rank coefficients, and uncertainty from input parameters is quantified. Results A virtual cohort of 200 healthy vasculatures was generated. Means and standard deviations for retinal blood flow and macular flow fraction were 20.80 ± 7.86 µL/min and 15.04% ± 5.42%, respectively. Retinal blood flow was correlated with vessel area density, vessel diameter index, fractal dimension, and vessel caliber index. The macular flow fraction was not correlated with any morphological metrics. Conclusions The proposed framework is able to reproduce vascular networks in the macula that are morphologically and functionally similar to real vasculature. The framework provides quantitative insights into how macular perfusion can be affected by changes in vascular morphology delineated on OCTA.
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Affiliation(s)
- Rémi J. Hernandez
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom
- Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Savita Madhusudhan
- St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Yalin Zheng
- St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Wahbi K. El-Bouri
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom
- Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
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20
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Scott AK, Louwagie EM, Myers KM, Oyen ML. Biomechanical Modeling of Cesarean Section Scars and Scar Defects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.03.565565. [PMID: 38076933 PMCID: PMC10705231 DOI: 10.1101/2023.11.03.565565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Uterine rupture is an intrinsically biomechanical process associated with high maternal and fetal mortality. A previous Cesarean section (C-section) is the main risk factor for uterine rupture in a subsequent pregnancy due to tissue failure at the scar region. Finite element modeling of the uterus and scar tissue presents a promising method to further understand and predict uterine ruptures. Using patient dimensions of an at-term uterus, a C-section scar was modeled with an applied intrauterine pressure to study how scars affect uterine stress. The scar positioning and uterine thickness were varied, and a defect was incorporated into the scar region. The modeled stress distributions confirmed clinical observations as the increased regions of stress due to scar positioning, thinning of the uterine walls, and the presence of a defect are consistent with clinical observations of features that increase the risk of uterine rupture.
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Boverhof BJ, Redekop WK, Bos D, Starmans MPA, Birch J, Rockall A, Visser JJ. Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice. Insights Imaging 2024; 15:34. [PMID: 38315288 PMCID: PMC10844175 DOI: 10.1186/s13244-023-01599-z] [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: 08/31/2023] [Accepted: 11/14/2023] [Indexed: 02/07/2024] Open
Abstract
OBJECTIVE To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. METHODS This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. RESULTS RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. CONCLUSION The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. CRITICAL RELEVANCE STATEMENT The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. KEYPOINTS • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.
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Affiliation(s)
- Bart-Jan Boverhof
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - W Ken Redekop
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Martijn P A Starmans
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Andrea Rockall
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Jacob J Visser
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands.
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22
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Catalano C, Turgut T, Zahalka O, Götzen N, Cannata S, Gentile G, Agnese V, Gandolfo C, Pasta S. On the Material Constitutive Behavior of the Aortic Root in Patients with Transcatheter Aortic Valve Implantation. Cardiovasc Eng Technol 2024; 15:95-109. [PMID: 37985617 PMCID: PMC10884088 DOI: 10.1007/s13239-023-00699-7] [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: 05/13/2023] [Accepted: 11/06/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure used to treat patients with severe aortic valve stenosis. However, there is limited knowledge on the material properties of the aortic root in TAVI patients, and this can impact the credibility of computer simulations. This study aimed to develop a non-invasive inverse approach for estimating reliable material constituents for the aortic root and calcified valve leaflets in patients undergoing TAVI. METHODS The identification of material parameters is based on the simultaneous minimization of two cost functions, which define the difference between model predictions and cardiac-gated CT measurements of the aortic wall and valve orifice area. Validation of the inverse analysis output was performed comparing the numerical predictions with actual CT shapes and post-TAVI measures of implanted device diameter. RESULTS A good agreement of the peak systolic shape of the aortic wall was found between simulations and imaging, with similarity index in the range in the range of 83.7% to 91.5% for n.20 patients. Not any statistical difference was observed between predictions and CT measures of orifice area for the stenotic aortic valve. After TAVI simulations, the measurements of SAPIEN 3 Ultra (S3) device diameter were in agreement with those from post-TAVI angio-CT imaging. A sensitivity analysis demonstrated a modest impact on the S3 diameters when altering the elastic material property of the aortic wall in the range of inverse analysis solution. CONCLUSIONS Overall, this study demonstrates the feasibility and potential benefits of using non-invasive imaging techniques and computational modeling to estimate material properties in patients undergoing TAVI.
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Affiliation(s)
- Chiara Catalano
- Department of Engineering, Università degli Studi di Palermo, Viale delle Scienze, Palermo, Italy
| | - Tahir Turgut
- 4RealSim Services BV, Groene Dijk 2B, 3401 NJ, IJsselstein, The Netherlands
| | - Omar Zahalka
- 4RealSim Services BV, Groene Dijk 2B, 3401 NJ, IJsselstein, The Netherlands
| | - Nils Götzen
- 4RealSim Services BV, Groene Dijk 2B, 3401 NJ, IJsselstein, The Netherlands
| | - Stefano Cannata
- Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS-ISMETT, Palermo, Italy
| | - Giovanni Gentile
- Radiology Unit, Department of Diagnostic and Therapeutic Services, IRCCS-ISMETT, Palermo, Italy
| | - Valentina Agnese
- 3D printing and Virtual Reality Laboratory, Department of Research, IRCCS-ISMETT, IRCCS Mediterranean Institute for Transplantation and Advanced Specialized Therapies, Via Tricomi, 5, Palermo, Italy
| | - Caterina Gandolfo
- Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS-ISMETT, Palermo, Italy
| | - Salvatore Pasta
- Department of Engineering, Università degli Studi di Palermo, Viale delle Scienze, Palermo, Italy.
- 3D printing and Virtual Reality Laboratory, Department of Research, IRCCS-ISMETT, IRCCS Mediterranean Institute for Transplantation and Advanced Specialized Therapies, Via Tricomi, 5, Palermo, Italy.
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Arsène S, Parès Y, Tixier E, Granjeon-Noriot S, Martin B, Bruezière L, Couty C, Courcelles E, Kahoul R, Pitrat J, Go N, Monteiro C, Kleine-Schultjann J, Jemai S, Pham E, Boissel JP, Kulesza A. In Silico Clinical Trials: Is It Possible? Methods Mol Biol 2024; 2716:51-99. [PMID: 37702936 DOI: 10.1007/978-1-0716-3449-3_4] [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] [Indexed: 09/14/2023]
Abstract
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.
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Pavlović N, Milošević Sopta N, Mitrović D, Zaklan D, Tomas Petrović A, Stilinović N, Vukmirović S. Principal Component Analysis (PCA) of Molecular Descriptors for Improving Permeation through the Blood-Brain Barrier of Quercetin Analogues. Int J Mol Sci 2023; 25:192. [PMID: 38203364 PMCID: PMC10778702 DOI: 10.3390/ijms25010192] [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: 11/23/2023] [Revised: 12/13/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024] Open
Abstract
Despite its beneficial pharmacological effects in the brain, partly by modulating inositol phosphate multikinase (IPMK) activity, the therapeutic use of quercetin is limited due to its poor solubility, low oral bioavailability, and low permeability through the blood-brain barrier (BBB). We aimed to identify quercetin analogues with improved BBB permeability and preserved binding affinities towards IPMK and to identify the molecular characteristics required for them to permeate the BBB. Binding affinities of quercetin analogues towards IPMK were determined by molecular docking. Principal component analysis (PCA) was applied to identify the molecular descriptors contributing to efficient permeation through the BBB. Among 34 quercetin analogues, 19 compounds were found to form more stable complexes with IPMK, and the vast majority were found to be more lipophilic than quercetin. Using two distinct in silico techniques, insufficient BBB permeation was determined for all quercetin analogues. However, using the PCA method, the descriptors related to intrinsic solubility and lipophilicity (logP) were identified as mainly responsible for clustering four quercetin analogues (trihydroxyflavones) with the highest BBB permeability. The application of PCA revealed that quercetin analogues could be classified with respect to their structural characteristics, which may be utilized in further analogue syntheses and lead optimization of BBB-penetrating IPMK modulators as neuroprotective agents.
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Affiliation(s)
- Nebojša Pavlović
- Department of Pharmacy, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; (D.M.); (D.Z.)
| | | | - Darko Mitrović
- Department of Pharmacy, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; (D.M.); (D.Z.)
- Accelsiors CRO, Háros Street 103, 1222 Budapest, Hungary;
| | - Dragana Zaklan
- Department of Pharmacy, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; (D.M.); (D.Z.)
| | - Ana Tomas Petrović
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; (A.T.P.); (N.S.); (S.V.)
| | - Nebojša Stilinović
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; (A.T.P.); (N.S.); (S.V.)
| | - Saša Vukmirović
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; (A.T.P.); (N.S.); (S.V.)
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Aljassam Y, Caputo M, Biglino G. Surgical Patching in Congenital Heart Disease: The Role of Imaging and Modelling. Life (Basel) 2023; 13:2295. [PMID: 38137896 PMCID: PMC10745019 DOI: 10.3390/life13122295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023] Open
Abstract
In congenital heart disease, patches are not tailored to patient-specific anatomies, leading to shape mismatch with likely functional implications. The design of patches through imaging and modelling may be beneficial, as it could improve clinical outcomes and reduce the costs associated with redo procedures. Whilst attention has been paid to the material of the patches used in congenital surgery, this review outlines the current knowledge on this subject and isolated experimental work that uses modelling and imaging-derived information (including 3D printing) to inform the design of the surgical patch.
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Affiliation(s)
- Yousef Aljassam
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK;
| | - Massimo Caputo
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK;
- Cardiac Surgery, University Hospitals Bristol & Weston, NHS Foundation Trust, Bristol BS2 8HW, UK
| | - Giovanni Biglino
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK;
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Imhauser CW, Baumann AP, Liu XC, Bischoff JE, Verdonschot N, Fregly BJ, Elmasry SS, Abdollahi NN, Hume DR, Rooks NB, Schneider MTY, Zaylor W, Besier TF, Halloran JP, Shelburne KB, Erdemir A. Reproducibility in modeling and simulation of the knee: Academic, industry, and regulatory perspectives. J Orthop Res 2023; 41:2569-2578. [PMID: 37350016 DOI: 10.1002/jor.25652] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 04/23/2023] [Accepted: 05/30/2023] [Indexed: 06/24/2023]
Abstract
Stakeholders in the modeling and simulation (M&S) community organized a workshop at the 2019 Annual Meeting of the Orthopaedic Research Society (ORS) entitled "Reproducibility in Modeling and Simulation of the Knee: Academic, Industry, and Regulatory Perspectives." The goal was to discuss efforts among these stakeholders to address irreproducibility in M&S focusing on the knee joint. An academic representative from a leading orthopedic hospital in the United States described a multi-institutional, open effort funded by the National Institutes of Health to assess model reproducibility in computational knee biomechanics. A regulatory representative from the United States Food and Drug Administration indicated the necessity of standards for reproducibility to increase utility of M&S in the regulatory setting. An industry representative from a major orthopedic implant company emphasized improving reproducibility by addressing indeterminacy in personalized modeling through sensitivity analyses, thereby enhancing preclinical evaluation of joint replacement technology. Thought leaders in the M&S community stressed the importance of data sharing to minimize duplication of efforts. A survey comprised 103 attendees revealed strong support for the workshop and for increasing emphasis on computational modeling at future ORS meetings. Nearly all survey respondents (97%) considered reproducibility to be an important issue. Almost half of respondents (45%) tried and failed to reproduce the work of others. Two-thirds of respondents (67%) declared that individual laboratories are most responsible for ensuring reproducible research whereas 44% thought that journals are most responsible. Thought leaders and survey respondents emphasized that computational models must be reproducible and credible to advance knee M&S.
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Affiliation(s)
- Carl W Imhauser
- Department of Biomechanics, Hospital for Special Surgery, New York, New York, USA
| | - Andrew P Baumann
- US Food and Drug Administration, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Division of Applied Mechanics, Silver Spring, Maryland, USA
| | | | | | - Nico Verdonschot
- Department of Biomechanical Engineering, Technical Medical Institute at University of Twente, Enschede, The Netherlands
- Orthopaedic Research Lab, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Benjamin J Fregly
- Department of Mechanical Engineering, Rice University, Houston, Texas, USA
| | - Shady S Elmasry
- Department of Biomechanics, Hospital for Special Surgery, New York, New York, USA
- Department of Mechanical Design and Production, Faculty of Engineering, Cairo University, Cairo, Egypt
| | - Neda N Abdollahi
- Center for Human Machine Systems, Cleveland State University, Cleveland, Ohio, USA
- Department of Mechanical Engineering, Cleveland State University, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Donald R Hume
- Department of Mechanical and Materials Engineering, University of Denver, Denver, Colorado, USA
- Center for Orthopaedic Biomechanics, University of Denver, Denver, Colorado, USA
| | - Nynke B Rooks
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Marco T-Y Schneider
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - William Zaylor
- Center for Human Machine Systems, Cleveland State University, Cleveland, Ohio, USA
- Department of Mechanical Engineering, Cleveland State University, Cleveland, Ohio, USA
| | - Thor F Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland, New Zealand
| | - Jason P Halloran
- Applied Sciences Laboratory, Institute for Shock Physics, Washington State University, Spokane, Washington, USA
| | - Kevin B Shelburne
- Department of Mechanical and Materials Engineering, University of Denver, Denver, Colorado, USA
- Center for Orthopaedic Biomechanics, University of Denver, Denver, Colorado, USA
| | - Ahmet Erdemir
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Mandigers TJ, Ramella A, Bissacco D, Domanin M, van Herwaarden JA, Heijmen R, Luraghi G, Migliavacca F, Trimarchi S. Thoracic Stent Graft Numerical Models To Virtually Simulate Thoracic Endovascular Aortic Repair: A Scoping Review. Eur J Vasc Endovasc Surg 2023; 66:784-796. [PMID: 37330201 DOI: 10.1016/j.ejvs.2023.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/23/2023] [Accepted: 06/06/2023] [Indexed: 06/19/2023]
Abstract
OBJECTIVE Pre-procedural planning of thoracic endovascular aortic repair (TEVAR) may implement computational adjuncts to predict technical and clinical outcomes. The aim of this scoping review was to explore the currently available TEVAR procedure and stent graft modelling options. DATA SOURCES PubMed (MEDLINE), Scopus, and Web of Science were systematically searched (English language, up to 9 December 2022) for studies presenting a virtual thoracic stent graft model or TEVAR simulation. REVIEW METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) was followed. Qualitative and quantitative data were extracted, compared, grouped, and described. Quality assessment was performed using a 16 item rating rubric. RESULTS Fourteen studies were included. Among the currently available in silico simulations of TEVAR, severe heterogeneity exists in study characteristics, methodological details, and evaluated outcomes. Ten studies (71.4%) were published during the last five years. Eleven studies (78.6%) included heterogeneous clinical data to reconstruct patient specific aortic anatomy and disease (e.g., type B aortic dissection, thoracic aortic aneurysm) from computed tomography angiography imaging. Three studies (21.4%) constructed idealised aortic models with literature input. The applied numerical methods consisted of computational fluid dynamics analysing aortic haemodynamics in three studies (21.4%) and finite element analysis analysing structural mechanics in the others (78.6%), including or excluding aortic wall mechanical properties. The thoracic stent graft was modelled as two separate components (e.g., graft, nitinol) in 10 studies (71.4%), as a one component homogenised approximation (n = 3, 21.4%), or including nitinol rings only (n = 1, 7.1%). Other simulation components included the catheter for virtual TEVAR deployment and numerous outcomes (e.g., Von Mises stresses, stent graft apposition, drag forces) were evaluated. CONCLUSION This scoping review identified 14 severely heterogeneous TEVAR simulation models, mostly of intermediate quality. The review concludes there is a need for continuous collaborative efforts to improve the homogeneity, credibility, and reliability of TEVAR simulations.
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Affiliation(s)
- Tim J Mandigers
- Section of Vascular Surgery, Cardio Thoracic Vascular Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Vascular Surgery, University Medical Centre Utrecht, Utrecht, The Netherlands.
| | - Anna Ramella
- Department of Chemistry, Materials and Chemical Engineering "G. Natta", Politecnico di Milano, Milan, Italy
| | - Daniele Bissacco
- Section of Vascular Surgery, Cardio Thoracic Vascular Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Maurizio Domanin
- Section of Vascular Surgery, Cardio Thoracic Vascular Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Joost A van Herwaarden
- Department of Vascular Surgery, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Robin Heijmen
- Department of Cardiothoracic Surgery, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Giulia Luraghi
- Department of Chemistry, Materials and Chemical Engineering "G. Natta", Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Department of Chemistry, Materials and Chemical Engineering "G. Natta", Politecnico di Milano, Milan, Italy
| | - Santi Trimarchi
- Section of Vascular Surgery, Cardio Thoracic Vascular Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
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Neuer AL, Herrmann IK, Gogos A. Biochemical transformations of inorganic nanomedicines in buffers, cell cultures and organisms. NANOSCALE 2023; 15:18139-18155. [PMID: 37946534 PMCID: PMC10667590 DOI: 10.1039/d3nr03415a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/28/2023] [Indexed: 11/12/2023]
Abstract
The field of nanomedicine is rapidly evolving, with new materials and formulations being reported almost daily. In this respect, inorganic and inorganic-organic composite nanomaterials have gained significant attention. However, the use of new materials in clinical trials and their final approval as drugs has been hampered by several challenges, one of which is the complex and difficult to control nanomaterial chemistry that takes place within the body. Several reviews have summarized investigations on inorganic nanomaterial stability in model body fluids, cell cultures, and organisms, focusing on their degradation as well as the influence of corona formation. However, in addition to these aspects, various chemical reactions of nanomaterials, including phase transformation and/or the formation of new/secondary nanomaterials, have been reported. In this review, we discuss recent advances in our understanding of biochemical transformations of medically relevant inorganic (composite) nanomaterials in environments related to their applications. We provide a refined terminology for the primary reaction mechanisms involved to bridge the gaps between different disciplines involved in this research. Furthermore, we highlight suitable analytical techniques that can be harnessed to explore the described reactions. Finally, we highlight opportunities to utilize them for diagnostic and therapeutic purposes and discuss current challenges and research priorities.
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Affiliation(s)
- Anna L Neuer
- Laboratory for Particles-Biology Interactions, Department of Materials Meet Life, Swiss Federal Laboratories for Materials Science and Technology (Empa), Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
- Nanoparticle Systems Engineering Laboratory, Institute of Process Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Sonneggstrasse 3, 8092 Zurich, Switzerland
| | - Inge K Herrmann
- Laboratory for Particles-Biology Interactions, Department of Materials Meet Life, Swiss Federal Laboratories for Materials Science and Technology (Empa), Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
- Nanoparticle Systems Engineering Laboratory, Institute of Process Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Sonneggstrasse 3, 8092 Zurich, Switzerland
| | - Alexander Gogos
- Laboratory for Particles-Biology Interactions, Department of Materials Meet Life, Swiss Federal Laboratories for Materials Science and Technology (Empa), Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
- Nanoparticle Systems Engineering Laboratory, Institute of Process Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Sonneggstrasse 3, 8092 Zurich, Switzerland
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Rooks TF, Chancey VC, Baisden JL, Yoganandan N. Strain Response of an Anatomically Accurate Nonhuman Primate Finite Element Brain Model Under Sagittal Loading. Mil Med 2023; 188:634-641. [PMID: 37948230 DOI: 10.1093/milmed/usad288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 03/20/2023] [Accepted: 07/12/2023] [Indexed: 11/12/2023] Open
Abstract
INTRODUCTION Prevention and treatment of traumatic brain injuries is critical to preserving soldier brain health. Laboratory studies are commonly used to reproduce injuries, understand injury mechanisms, and develop tolerance limits; however, this approach has limitations for studying brain injury, which requires a physiological response. The nonhuman primate (NHP) has been used as an effective model for investigating brain injury for many years. Prior research using the NHP provides a valuable resource to leverage using modern analysis and modeling techniques to improve our understanding of brain injury. The objectives of the present study are to develop an anatomically accurate finite element model of the NHP and determine regional brain responses using previously collected NHP data. MATERIALS AND METHODS The finite element model was developed using a neuroimaging-based anatomical atlas of the rhesus macaque that includes both cortical and subcortical structures. Head kinematic data from 10 sagittal NHP experiments, four +Gx (rearward) and six -Gx (frontal), were used to test model stability and obtain brain strain responses from multiple severities and vectors. RESULTS For +Gx tests, the whole-brain cumulative strain damage measure exceeding a strain threshold of 0.15 (CSDM15) ranged from 0.28 to 0.89, and 95th percentile of the whole-brain maximum principal strain (MPS95) ranged from 0.21 to 0.59. For -Gx tests, whole-brain CSDM15 ranged from 0.02 to 0.66, and whole-brain MPS95 ranged from 0.08 to 0.39. CONCLUSIONS Recognizing that NHPs are the closest surrogate to humans combined with the limitations of conducting brain injury research in the laboratory, a detailed anatomically accurate finite element model of an NHP was developed and exercised using previously collected data from the Naval Biodynamics Laboratory. The presently developed model can be used to conduct additional analyses to act as pilot data for the design of newer experiments with statistical power because of the sensitivity and resources needed to conduct experiments with NHPs.
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Affiliation(s)
- Tyler F Rooks
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Valeta Carol Chancey
- Injury Biomechanics and Protection Group, U.S. Army Aeromedical Research Laboratory, Fort Novosel, AL 36362, USA
| | - Jamie L Baisden
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Narayan Yoganandan
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Convertino VA, Snider EJ, Hernandez-Torres SI, Collier JP, Eaton SK, Holmes DR, Haider CR, Salinas J. Verification and Validation of Lower Body Negative Pressure as a Non-Invasive Bioengineering Tool for Testing Technologies for Monitoring Human Hemorrhage. Bioengineering (Basel) 2023; 10:1226. [PMID: 37892956 PMCID: PMC10604311 DOI: 10.3390/bioengineering10101226] [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: 08/30/2023] [Revised: 10/02/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023] Open
Abstract
Since hemorrhage is a leading cause of preventable death in both civilian and military settings, the development of advanced decision support monitoring capabilities is necessary to promote improved clinical outcomes. The emergence of lower body negative pressure (LBNP) has provided a bioengineering technology for inducing progressive reductions in central blood volume shown to be accurate as a model for the study of the early compensatory stages of hemorrhage. In this context, the specific aim of this study was to provide for the first time a systematic technical evaluation to meet a commonly accepted engineering standard based on the FDA-recognized Standard for Assessing Credibility of Modeling through Verification and Validation (V&V) for Medical Devices (ASME standard V&V 40) specifically highlighting LBNP as a valuable resource for the safe study of hemorrhage physiology in humans. As an experimental tool, evidence is presented that LBNP is credible, repeatable, and validated as an analog for the study of human hemorrhage physiology compared to actual blood loss. The LBNP tool can promote the testing and development of advanced monitoring algorithms and evaluating wearable sensors with the goal of improving clinical outcomes during use in emergency medical settings.
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Affiliation(s)
- Victor A. Convertino
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
- Department of Emergency Medicine, University of Texas Health, San Antonio, TX 78229, USA
| | - Eric J. Snider
- Organ Support & Automation Technology Research Team, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.I.H.-T.); (J.P.C.); (S.K.E.); (J.S.)
| | - Sofia I. Hernandez-Torres
- Organ Support & Automation Technology Research Team, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.I.H.-T.); (J.P.C.); (S.K.E.); (J.S.)
| | - James P. Collier
- Organ Support & Automation Technology Research Team, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.I.H.-T.); (J.P.C.); (S.K.E.); (J.S.)
| | - Samantha K. Eaton
- Organ Support & Automation Technology Research Team, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.I.H.-T.); (J.P.C.); (S.K.E.); (J.S.)
| | - David R. Holmes
- Biomedical Analytics and Computational Engineering Laboratory, Mayo Clinic, Rochester, MN 55905, USA;
| | - Clifton R. Haider
- Special Purpose Processor Development Group, Mayo Clinic, Rochester, MN 55905, USA;
| | - Jose Salinas
- Organ Support & Automation Technology Research Team, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.I.H.-T.); (J.P.C.); (S.K.E.); (J.S.)
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Zafar I, Safder A, Imran Afridi H, Riaz S, -ur-Rehman R, Unar A, Un Nisa F, Gaafar ARZ, Bourhia M, Wondmie GF, Sharma R, Kumar D. In silico and in vitro study of bioactive compounds of Nigella sativa for targeting neuropilins in breast cancer. Front Chem 2023; 11:1273149. [PMID: 37885828 PMCID: PMC10598785 DOI: 10.3389/fchem.2023.1273149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction: Breast cancer poses a significant global challenge, prompting researchers to explore novel approaches for potential treatments. Material and Methods: For in vitro study we used thin layer chromatography (TAC) for phytochemical screening, total antioxidant capacity (TLC) assay for antioxidant capacity, and hemolytic activity test for toxicity of Neuropilins (NRPs). We performed bioinformatic analyses to predict protein structures, molecular docking, pharmacophore modeling, and virtual screening to reveal interactions with oncogenes. We conducted 200 ns Molecular Dynamics (MD) simulations and MMGBSA calculations to assess the complex dynamics and stability. Results: We identified phytochemical constituents in Nigella sativa leaves, including tannins, saponins, steroids, and cardiac glycosides, while phlobatannins and terpenoids were absent. The leaves contained 9.4% ± 0.04% alkaloids and 1.9% ± 0.05% saponins. Methanol extract exhibited the highest yield and antioxidant capacity, with Total Flavonoid Content at 127.51 ± 0.76 mg/100 g and Total Phenolic Content at 134.39 ± 0.589 mg GAE/100 g. Hemolysis testing showed varying degrees of hemolysis for different extracts. In-silico analysis indicated stable Neuropilin complexes with key signaling pathways relevant for anti-cancer therapy. Molecular docking scores at different possesses (0, C-50, C -80, C-120,C -150, C -200 ns) revealed strong hydrogen bonding in the complexes and showed -12.9, -11.6, and -11.2 binding Affinities (kcal/mol) to support their stability. Our MD simulations analysis at 200ns confirmed the stability of Neuropilin complexes with the signaling pathways protein PI3K. The calculated binding free energies using MMGBSA provided valuable quantitative information on ligand potency on different time steps. These findings highlight the potential health benefits of N. sativa leaves and their possible role in anti-cancer treatments targeting angiogenesis. Conclusion: Nigella sativa leaves have shown significant medical potential due to their bioactive compounds, which exhibit strong properties in supporting organogenic processes related to cancer. Furthermore, studies have highlighted the promising role of neuropilins in anticancer treatment, demonstrating stable interactions and potential as targeted therapy specifically for breast cancer.
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Affiliation(s)
- Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University Pakistan, Lahore, Pakistan
| | - Arfa Safder
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Punjab, Pakistan
| | - Hassan Imran Afridi
- National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro, Pakistan
| | - Sania Riaz
- Faculty of Health and Life Sciences, Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad, Pakistan
| | - Rizwan -ur-Rehman
- Faculty of Health and Life Sciences, Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad, Pakistan
| | - Ahsanullah Unar
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy
| | - Fakhar Un Nisa
- Depatment of Molecular Biology, Virtual University of Pakistan, Lahore, Pakistan
| | - Abdel-Rhman Z. Gaafar
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Department of Chemistry and Biochemistry, Faculty of Medicine and Pharmacy, Ibn Zohr University, Laayoune, Morocco
| | | | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Science, Banaras Hindu University, Varanasi, India
| | - Dileep Kumar
- UC Davis Comprehensive Cancer Center, University of California, Davis, Davis, CA, United States
- Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be) University, Pune, India
- Centre for Advanced Research in Pharmaceutical Sciences, Poona College of Pharmacy, Pune, India
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Benyó B, Paláncz B, Szlávecz Á, Szabó B, Kovács K, Chase JG. Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107633. [PMID: 37343375 DOI: 10.1016/j.cmpb.2023.107633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/21/2023] [Accepted: 05/29/2023] [Indexed: 06/23/2023]
Abstract
Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model- based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials.
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Affiliation(s)
- Balázs Benyó
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.
| | - Béla Paláncz
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Ákos Szlávecz
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Bálint Szabó
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Katalin Kovács
- Department of Informatics, Széchenyi István University, Győr, Hungary
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Ghita M, Birs IR, Copot D, Muresan CI, Neckebroek M, Ionescu CM. Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia. IEEE Trans Biomed Eng 2023; 70:2991-3002. [PMID: 37527300 DOI: 10.1109/tbme.2023.3274541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
OBJECTIVE The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only. METHODS This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS. RESULTS The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN. CONCLUSION We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information. SIGNIFICANCE Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.
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Aldieri A, Curreli C, Szyszko JA, La Mattina AA, Viceconti M. Credibility assessment of computational models according to ASME V&V40: Application to the Bologna Biomechanical Computed Tomography solution. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107727. [PMID: 37523955 DOI: 10.1016/j.cmpb.2023.107727] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND AND OBJECTIVE When a computational model aims to be adopted beyond research purposes, e.g. to inform a clinical or regulatory decision, trust must be placed in its predictive accuracy. This practically translates into the need to demonstrate its credibility. In fact, prior to its adoption for regulatory purposes, an in silico methodology should be proven credible enough for the scope. This has become especially relevant as, although evidence of the safety and efficacy of new medical products or interventions has been traditionally provided to the regulator experimentally, i.e., in vivo or ex vivo, recently the idea to inform a regulatory decision in silico has made its way in the regulatory scenario. While a harmonised technical standard is currently missing in the EU regulatory system, in 2018 the ASME issued V&V40-2018, where a risk-based framework to assess the credibility of a computational model through the performance of predefined credibility activities is provided. The credibility framework is here applied to Bologna Biomechanical Computed Tomography (BBCT) solution, which predicts the absolute risk of fracture at the femur for a subject. BBCT has recently been the object of a qualification advice request to the European Medicine Agency. METHODS The full implementation of ASME V&V40-2018 framework on BBCT is shown. Starting from BBCT proposed context of use the whole credibility plan is presented and the credibility activities (Verification, Validation, Applicability) described together with the achieved credibility levels. RESULTS BBCT risk is judged medium, and the credibility levels achieved considered acceptable. The uncertainties intrinsically present in the material properties assignment affected BBCT predictions to the highest extent. CONCLUSIONS This work provides the practical application of the ASME V&V40-2018 risk-based credibility assessment framework, which could be applied to demonstrate model credibility in any field and support future regulatory submissions and foster the adoption of In Silico Trials.
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Affiliation(s)
- Alessandra Aldieri
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Cristina Curreli
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy; Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Itlay
| | - Julia Aleksandra Szyszko
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy; Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Itlay
| | - Antonino Amedeo La Mattina
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy; Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Itlay
| | - Marco Viceconti
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy; Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Itlay
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Cortesi M, Liu D, Yee C, Marsh DJ, Ford CE. A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models. Sci Rep 2023; 13:15769. [PMID: 37737283 PMCID: PMC10517149 DOI: 10.1038/s41598-023-42486-3] [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: 05/22/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023] Open
Abstract
Computational models are becoming an increasingly valuable tool in biomedical research. Their accuracy and effectiveness, however, rely on the identification of suitable parameters and on appropriate validation of the in-silico framework. Both these steps are highly dependent on the experimental model used as a reference to acquire the data. Selecting the most appropriate experimental framework thus becomes key, together with the analysis of the effect of combining results from different experimental models, a common practice often necessary due to limited data availability. In this work, the same in-silico model of ovarian cancer cell growth and metastasis, was calibrated with datasets acquired from traditional 2D monolayers, 3D cell culture models or a combination of the two. The comparison between the parameters sets obtained in the different conditions, together with the corresponding simulated behaviours, is presented. It provides a framework for the study of the effect of the different experimental models on the development of computational systems. This work also provides a set of general guidelines for the comparative testing and selection of experimental models and protocols to be used for parameter optimization in computational models.
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Affiliation(s)
- Marilisa Cortesi
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, Australia.
- Laboratory of Cellular and Molecular Engineering, Department of Electrical Electronic and Information Engineering "G. Marconi", Alma Mater Studiorum-University of Bologna, Cesena, Italy.
| | - Dongli Liu
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, Australia
| | - Christine Yee
- Translational Oncology Group, School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW, Australia
| | - Deborah J Marsh
- Translational Oncology Group, School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW, Australia
| | - Caroline E Ford
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, Australia.
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36
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Jacob E, Perrillat-Mercerot A, Palgen JL, L'Hostis A, Ceres N, Boissel JP, Bosley J, Monteiro C, Kahoul R. Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma. BMC Bioinformatics 2023; 24:331. [PMID: 37667175 PMCID: PMC10478282 DOI: 10.1186/s12859-023-05430-w] [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: 12/15/2022] [Accepted: 07/26/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported. RESULTS We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use. CONCLUSIONS With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.
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Affiliation(s)
- Evgueni Jacob
- Novadiscovery, 1 Place Giovanni Da Verrazzano, 69009, Lyon, France.
| | | | | | - Adèle L'Hostis
- Novadiscovery, 1 Place Giovanni Da Verrazzano, 69009, Lyon, France
| | - Nicoletta Ceres
- Novadiscovery, 1 Place Giovanni Da Verrazzano, 69009, Lyon, France
| | | | - Jim Bosley
- Novadiscovery, 1 Place Giovanni Da Verrazzano, 69009, Lyon, France
| | - Claudio Monteiro
- Novadiscovery, 1 Place Giovanni Da Verrazzano, 69009, Lyon, France
| | - Riad Kahoul
- Novadiscovery, 1 Place Giovanni Da Verrazzano, 69009, Lyon, France
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37
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Uleman JF, Melis RJF, Hoekstra AG, Olde Rikkert MGM, Quax R. Exploring the potential impact of multi-factor precision interventions in Alzheimer's disease with system dynamics. J Biomed Inform 2023; 145:104462. [PMID: 37516375 DOI: 10.1016/j.jbi.2023.104462] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 06/09/2023] [Accepted: 07/26/2023] [Indexed: 07/31/2023]
Abstract
Numerous clinical trials based on a single-cause paradigm have not resulted in efficacious treatments for Alzheimer's disease (AD). Recently, prevention trials that simultaneously intervened on multiple risk factors have shown mixed results, suggesting that careful design is necessary. Moreover, intensive pilot precision medicine (PM) trial results have been promising but may not generalize to a broader population. These observations suggest that a model-based approach to multi-factor precision medicine (PM) is warranted. We systematically developed a system dynamics model (SDM) of AD for PM using data from two longitudinal studies (N=3660). This method involved a model selection procedure in identifying interaction terms between the SDM components and estimating individualized parameters. We used the SDM to explore simulated single- and double-factor interventions on 14 modifiable risk factors. We quantified the potential impact of double-factor interventions over single-factor interventions as 1.5 [95% CI: 1.5-2.6] and of SDM-based PM over a one-size-fits-all approach as 3.5 [3.1, 3.8] ADAS-cog-13 points in 12 years. Although the model remains to be validated, we tentatively conclude that multi-factor PM could come to play an important role in AD prevention.
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Affiliation(s)
- Jeroen F Uleman
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Medical Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands; Institute for Advanced Study, University of Amsterdam, Amsterdam, the Netherlands.
| | - René J F Melis
- Institute for Advanced Study, University of Amsterdam, Amsterdam, the Netherlands; Department of Geriatric Medicine, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Alfons G Hoekstra
- Computational Science Lab, Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands
| | - Marcel G M Olde Rikkert
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Medical Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Rick Quax
- Institute for Advanced Study, University of Amsterdam, Amsterdam, the Netherlands; Computational Science Lab, Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands
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Tatka LT, Smith LP, Hellerstein JL, Sauro HM. Adapting modeling and simulation credibility standards to computational systems biology. J Transl Med 2023; 21:501. [PMID: 37496031 PMCID: PMC10369698 DOI: 10.1186/s12967-023-04290-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 06/19/2023] [Indexed: 07/28/2023] Open
Abstract
Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as forms of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrower domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.
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Affiliation(s)
- Lillian T Tatka
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Lucian P Smith
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | | | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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Grancieri M, Viana ML, de Oliveira DF, Vaz Tostes MDG, Costa Ignacchiti MD, Costa AGV, Brunoro Costa NM. Yacon ( Smallanthus sonchifolius) Flour Reduces Inflammation and Had No Effects on Oxidative Stress and Endotoxemia in Wistar Rats with Induced Colorectal Carcinogenesis. Nutrients 2023; 15:3281. [PMID: 37513699 PMCID: PMC10383765 DOI: 10.3390/nu15143281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
Colorectal cancer has a high worldwide incidence. The aim of this study was to determine the effect of yacon flour (YF) on oxidative stress, inflammation, and endotoxemia in rats with induced colorectal cancer (CRC). The Wistar male rats were divided and kept for 8 weeks in four groups: S (basal diet, n = 10), Y (YF flour + basal diet, n = 10), C (CRC-induced control + basal diet, n = 12), CY (CRC-induced animals + YF, n = 12). CRC was induced by intraperitoneal injections of 1,2-dimethylhydrazine (25 mg/kg body weight). Groups Y and CY received 7.5% of the prebiotic FOS from YF. The treatment with YF increased fecal secretory immunoglobulin A levels and decreased lipopolysaccharides, tumor necrosis factor alpha and interleukin-12. However, no effect was observed on the oxidative stress by the total antioxidant capacity of plasma, anion superoxide, and nitric oxide analysis of the animals (p < 0.05). The short-chain fatty acids acetate, propionate, and butyrate showed interactions with NF-κB, TLR4, iNOS, and NADPH oxidase by in silico analysis and had a correlation (by the Person analysis) with CRC markers. The yacon flour treatment reduced the inflammation in rats with induced CRC, and could be a promising food to reduce the damages caused by colorectal cancer.
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Affiliation(s)
- Mariana Grancieri
- Department of Pharmacy and Nutrition, Center for Exact, Natural and Health Sciences, Federal University of Espirito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
| | - Mirelle Lomar Viana
- Department of Pharmacy and Nutrition, Center for Exact, Natural and Health Sciences, Federal University of Espirito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
| | - Daniela Furtado de Oliveira
- Department of Pharmacy and Nutrition, Center for Exact, Natural and Health Sciences, Federal University of Espirito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
| | - Maria das Graças Vaz Tostes
- Department of Pharmacy and Nutrition, Center for Exact, Natural and Health Sciences, Federal University of Espirito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
| | - Mariana Drummond Costa Ignacchiti
- Department of Pharmacy and Nutrition, Center for Exact, Natural and Health Sciences, Federal University of Espirito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
| | - André Gustavo Vasconcelos Costa
- Department of Pharmacy and Nutrition, Center for Exact, Natural and Health Sciences, Federal University of Espirito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
| | - Neuza Maria Brunoro Costa
- Department of Pharmacy and Nutrition, Center for Exact, Natural and Health Sciences, Federal University of Espirito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
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40
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Portugal-Cohen M, Cohen D, Kohen R, Oron M. Exploitation of alternative skin models from academia to industry: proposed functional categories to answer needs and regulation demands. Front Physiol 2023; 14:1215266. [PMID: 37334052 PMCID: PMC10272927 DOI: 10.3389/fphys.2023.1215266] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/17/2023] [Indexed: 06/20/2023] Open
Affiliation(s)
| | - Dror Cohen
- DermAb.io, Haifa, Israel
- The Myers Skin Research Laboratory, Faculty of Medicine, School of Pharmacy, Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ron Kohen
- The Myers Skin Research Laboratory, Faculty of Medicine, School of Pharmacy, Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
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Gee MM, Hornung E, Gupta S, Newton AJH, Cheng ZJ, Lytton WW, Lenhoff AM, Schwaber JS, Vadigepalli R. Unpacking the multimodal, multi-scale data of the fast and slow lanes of the cardiac vagus through computational modelling. Exp Physiol 2023:10.1113/EP090865. [PMID: 37120805 PMCID: PMC10613580 DOI: 10.1113/ep090865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 04/12/2023] [Indexed: 05/01/2023]
Abstract
NEW FINDINGS What is the topic of this review? The vagus nerve is a crucial regulator of cardiovascular homeostasis, and its activity is linked to heart health. Vagal activity originates from two brainstem nuclei: the nucleus ambiguus (fast lane) and the dorsal motor nucleus of the vagus (slow lane), nicknamed for the time scales that they require to transmit signals. What advances does it highlight? Computational models are powerful tools for organizing multi-scale, multimodal data on the fast and slow lanes in a physiologically meaningful way. A strategy is laid out for how these models can guide experiments aimed at harnessing the cardiovascular health benefits of differential activation of the fast and slow lanes. ABSTRACT The vagus nerve is a key mediator of brain-heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat-to-beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high-dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data-derived mechanistic insights have proven elusive. Elucidating insights has been complicated further by the broad distribution of the data across heart, brain and peripheral nervous system circuits. Here we lay out an integrative framework based on computational modelling for combining these disparate and multi-scale data on the two vagal control lanes of the cardiovascular system. Newly available molecular-scale data, particularly single-cell transcriptomic analyses, have augmented our understanding of the heterogeneous neuronal states underlying vagally mediated fast and slow regulation of cardiac physiology. Cellular-scale computational models built from these data sets represent building blocks that can be combined using anatomical and neural circuit connectivity, neuronal electrophysiology, and organ/organismal-scale physiology data to create multi-system, multi-scale models that enable in silico exploration of the fast versus slow lane vagal stimulation. The insights from the computational modelling and analyses will guide new experimental questions on the mechanisms regulating the fast and slow lanes of the cardiac vagus toward exploiting targeted vagal neuromodulatory activity to promote cardiovascular health.
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Affiliation(s)
- Michelle M Gee
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA
- Department of Pathology and Genomic Medicine, Daniel Baugh Institute of Functional Genomics/Computational Biology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Eden Hornung
- Department of Pathology and Genomic Medicine, Daniel Baugh Institute of Functional Genomics/Computational Biology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Suranjana Gupta
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Adam J H Newton
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Zixi Jack Cheng
- Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Abraham M Lenhoff
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA
| | - James S Schwaber
- Department of Pathology and Genomic Medicine, Daniel Baugh Institute of Functional Genomics/Computational Biology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Rajanikanth Vadigepalli
- Department of Pathology and Genomic Medicine, Daniel Baugh Institute of Functional Genomics/Computational Biology, Thomas Jefferson University, Philadelphia, PA, USA
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42
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Bao LQ, Baecker D, Mai Dung DT, Phuong Nhung N, Thi Thuan N, Nguyen PL, Phuong Dung PT, Huong TTL, Rasulev B, Casanola-Martin GM, Nam NH, Pham-The H. Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease. Molecules 2023; 28:molecules28083588. [PMID: 37110831 PMCID: PMC10142303 DOI: 10.3390/molecules28083588] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.
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Affiliation(s)
- Le-Quang Bao
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Daniel Baecker
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, Germany
| | - Do Thi Mai Dung
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen Phuong Nhung
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen Thi Thuan
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Phuong Linh Nguyen
- College of Computing & Informatics, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA
| | - Phan Thi Phuong Dung
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Tran Thi Lan Huong
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | | | - Nguyen-Hai Nam
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Hai Pham-The
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
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43
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Rathore AS, Nikita S, Thakur G, Mishra S. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol 2023; 41:497-510. [PMID: 36117026 DOI: 10.1016/j.tibtech.2022.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/08/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022]
Abstract
Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.
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Affiliation(s)
- Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
| | - Saxena Nikita
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Garima Thakur
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Somesh Mishra
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
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44
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Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls. J Cardiovasc Dev Dis 2023; 10:jcdd10030109. [PMID: 36975873 PMCID: PMC10058982 DOI: 10.3390/jcdd10030109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/15/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Introduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (E) on a Fluid–Structure Interaction (FSI) model of a patient-specific aorta. Methods: The image-based χ-method was used to compute the initial E value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the E value was assumed. Results: The influence of the uncertain E parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of E in the ascending aorta while an insignificant effect was observed in the descending tract. Conclusions: This study demonstrated the importance of the image-based methodology for inferring E, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice.
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45
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Quinn ARJ, Saxby DJ, Yang F, de Sousa ACC, Pizzolato C. A digital twin framework for robust control of robotic-biological systems. J Biomech 2023; 152:111557. [PMID: 37019066 DOI: 10.1016/j.jbiomech.2023.111557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 03/15/2023] [Accepted: 03/22/2023] [Indexed: 03/28/2023]
Abstract
Medical device regulatory standards are increasingly incorporating computational modelling and simulation to accommodate advanced manufacturing and device personalization. We present a method for robust testing of engineered soft tissue products involving a digital twin paradigm in combination with robotic systems. We developed and validated a digital twin framework for calibrating and controlling robotic-biological systems. A forward dynamics model of the robotic manipulator was developed, calibrated, and validated. After calibration, the accuracy of the digital twin in reproducing the experimental data improved in the time domain for all fourteen tested configurations and improved in frequency domain for nine configurations. We then demonstrated displacement control of a spring in lieu of a soft tissue element in a biological specimen. The simulated experiment matched the physical experiment with 0.09 mm (0.001%) root-mean-square error for a 2.9 mm (5.1%) length change. Finally, we demonstrated kinematic control of a digital twin of the knee through 70-degree passive flexion kinematics. The root-mean-square error was 2.00°, 0.57°, and 1.75° degrees for flexion, adduction, and internal rotations, respectively. The system well controlled novel mechanical elements and generated accurate kinematics in silico for a complex knee model. This calibration method could be applied to other situations where the specimen is poorly represented in the model environment (e.g., human or animal tissues), and the control system could be extended to track internal parameters such as tissue strain (e.g., control knee ligament strain). Further development of this framework can facilitate medical device testing and innovative biomechanics research.
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Affiliation(s)
- Alastair R J Quinn
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia; Advanced Design and Prototyping Technologies Institute, Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia.
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia; Advanced Design and Prototyping Technologies Institute, Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Fuwen Yang
- School of Engineering and Built Environment, Griffith University, Australia
| | - Ana C C de Sousa
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia; Advanced Design and Prototyping Technologies Institute, Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Australia; Advanced Design and Prototyping Technologies Institute, Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia
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Niklas C, Hölle T, Dugas M, Weigand MA, Larmann J. [The digital twin for perioperative medicine-An exciting look into the future of clinical research]. DIE ANAESTHESIOLOGIE 2023; 72:191-194. [PMID: 36695840 PMCID: PMC9876409 DOI: 10.1007/s00101-023-01251-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Affiliation(s)
- Christian Niklas
- Institut für Medizinische Informatik, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Deutschland.
| | - Tobias Hölle
- Klinik für Anästhesiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Deutschland
| | - Martin Dugas
- Institut für Medizinische Informatik, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Deutschland
| | - Markus A Weigand
- Klinik für Anästhesiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Deutschland
| | - Jan Larmann
- Klinik für Anästhesiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Deutschland
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47
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Pareatumbee P, Zainul-Abidin S, Yew A, Howe TS, Tan MH, Koh JSB. Reduction of geometric misfit in straight antegrade humeral nailing by evaluating the effect of entry point angulation using a three-dimensional computational analysis. Clin Biomech (Bristol, Avon) 2023; 102:105891. [PMID: 36641972 DOI: 10.1016/j.clinbiomech.2023.105891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 01/10/2023] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
BACKGROUND Straight antegrade intramedullary nails are generally inserted utilising the apex as the surgical entry point in accordance with the mechanical axis of the bone. Our objective is to optimise the bone-nail fit in intramedullary nailing by subjecting the surgical entry point to varying angulations in both the mediolateral and anterior-posterior directions via a quantitative fit assessment in each configuration to identify the optimal angulation, defined as the angulation with the lowest occurrence of thin-out to improve nail fitting within the humerus. METHODS Computed tomography (CT) scans from 10 cadaveric humeri models were used to generate three-dimensional bone models. The centreline profile of each humerus model was determined by dividing the humerus into multiple slices and identifying its respective centroid. The guidewire and nail models were then established and inserted into the humerus using the apex as the standard entry point. The bone-nail fit was measured utilising three fit quantification parameters: thin-out distance, nail protrusion volume into the cortical shell and deviation distance (top, middle, bottom) between the nail's longitudinal axis and medullary cavity centroid. FINDINGS Results revealed a statistically significant association between angulation and occurrence of thin-out (p < .001) and showed that the optimally angulated entry point resulted in decreased cortical breach across the nail insertion depth compared to the standard entry point. INTERPRETATION Our findings suggested that the current straight nail design may require further modifications to optimise the nail trajectory within the medullary canal by decreasing the bone-nail geometric mismatch to potentially maximise its working length.
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Affiliation(s)
- Pivatidevi Pareatumbee
- Singhealth-Duke NUS Musculoskeletal Sciences Academic Clinical Program, Singapore General Hospital, Singapore
| | - Suraya Zainul-Abidin
- Singhealth-Duke NUS Musculoskeletal Sciences Academic Clinical Program, Singapore General Hospital, Singapore; Department of Orthopaedic Surgery, Singapore General Hospital, Singapore; Division of Musculoskeletal Sciences, Singapore General Hospital, Singapore
| | - Andy Yew
- Division of Musculoskeletal Sciences, Singapore General Hospital, Singapore.
| | - Tet Sen Howe
- Singhealth-Duke NUS Musculoskeletal Sciences Academic Clinical Program, Singapore General Hospital, Singapore; Department of Orthopaedic Surgery, Singapore General Hospital, Singapore; Division of Musculoskeletal Sciences, Singapore General Hospital, Singapore
| | - Mann Hong Tan
- Singhealth-Duke NUS Musculoskeletal Sciences Academic Clinical Program, Singapore General Hospital, Singapore; Department of Orthopaedic Surgery, Singapore General Hospital, Singapore; Division of Musculoskeletal Sciences, Singapore General Hospital, Singapore
| | - Joyce Suang Bee Koh
- Singhealth-Duke NUS Musculoskeletal Sciences Academic Clinical Program, Singapore General Hospital, Singapore; Department of Orthopaedic Surgery, Singapore General Hospital, Singapore; Division of Musculoskeletal Sciences, Singapore General Hospital, Singapore
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48
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Derycke L, Avril S, Millon A. Patient-Specific Numerical Simulations of Endovascular Procedures in Complex Aortic Pathologies: Review and Clinical Perspectives. J Clin Med 2023; 12:jcm12030766. [PMID: 36769418 PMCID: PMC9917982 DOI: 10.3390/jcm12030766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
The endovascular technique is used in the first line treatment in many complex aortic pathologies. Its clinical outcome is mostly determined by the appropriate selection of a stent-graft for a specific patient and the operator's experience. New tools are still needed to assist practitioners with decision making before and during procedures. For this purpose, numerical simulation enables the digital reproduction of an endovascular intervention with various degrees of accuracy. In this review, we introduce the basic principles and discuss the current literature regarding the use of numerical simulation for endovascular management of complex aortic diseases. Further, we give the future direction of everyday clinical applications, showing that numerical simulation is about to revolutionize how we plan and carry out endovascular interventions.
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Affiliation(s)
- Lucie Derycke
- Department of Cardio-Vascular and Vascular Surgery, Hôpital Européen Georges Pompidou, F-75015 Paris, France
- Centre CIS, Mines Saint-Etienne, Université Jean Monnet Saint-Etienne, INSERM, SAINBIOSE U1059, F-42023 Saint-Etienne, France
| | - Stephane Avril
- Centre CIS, Mines Saint-Etienne, Université Jean Monnet Saint-Etienne, INSERM, SAINBIOSE U1059, F-42023 Saint-Etienne, France
| | - Antoine Millon
- Department of Vascular and Endovascular Surgery, Hospices Civils de Lyon, Louis Pradel University Hospital, F-69500 Bron, France
- Correspondence:
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Qureshi A, Lip GYH, Nordsletten DA, Williams SE, Aslanidi O, de Vecchi A. Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke. Front Cardiovasc Med 2023; 9:1074562. [PMID: 36733827 PMCID: PMC9887999 DOI: 10.3389/fcvm.2022.1074562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/29/2022] [Indexed: 01/18/2023] Open
Abstract
Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction-known as Virchow's triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools-such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage-have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields.
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Affiliation(s)
- Ahmed Qureshi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,*Correspondence: Ahmed Qureshi,
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom
| | - David A. Nordsletten
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, United Kingdom
| | - Oleg Aslanidi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Adelaide de Vecchi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
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50
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Ramella A, Migliavacca F, Rodriguez Matas JF, Mandigers TJ, Bissacco D, Domanin M, Trimarchi S, Luraghi G. Applicability assessment for in-silico patient-specific TEVAR procedures. J Biomech 2023; 146:111423. [PMID: 36584506 DOI: 10.1016/j.jbiomech.2022.111423] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 12/05/2022] [Accepted: 12/23/2022] [Indexed: 12/25/2022]
Abstract
Thoracic Endovascular Aortic Repair (TEVAR) is a minimally invasive technique to treat thoracic aorta pathologies and consists of placing a self-expandable stent-graft into the pathological region to restore the vessel lumen and recreate a more physiological condition. Exhaustive computational models, namely the finite element analysis, can be implemented to reproduce the clinical procedure. In this context, numerical models, if used for clinical applications, must be reliable and the simulation credibility should be proved to predict clinical procedure outcomes or to build in-silico clinical trials. This work aims first at applying a previously validated TEVAR methodology to a patient-specific case. Then, defining the TEVAR procedure performed on a patient population as the context of use, the overall applicability of the TEVAR modeling is assessed to demonstrate the reliability of the model itself following a step-by-step method based on the ASME V&V40 protocol. Validation evidence sources are identified for the specific context of use and adopted to demonstrate the applicability of the numerical procedure, thereby answering a question of interest that evaluates the deployed stent-graft configuration in the vessel.
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Affiliation(s)
- Anna Ramella
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milan, Italy
| | - Francesco Migliavacca
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milan, Italy
| | - Jose Felix Rodriguez Matas
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milan, Italy
| | - Tim J Mandigers
- Unit of Vascular Surgery, I.R.C.C.S. Fondazione Cà Granda Policlinico Milano, Via Francesco Sforza 35, Milan, Italy
| | - Daniele Bissacco
- Unit of Vascular Surgery, I.R.C.C.S. Fondazione Cà Granda Policlinico Milano, Via Francesco Sforza 35, Milan, Italy
| | - Maurizio Domanin
- Unit of Vascular Surgery, I.R.C.C.S. Fondazione Cà Granda Policlinico Milano, Via Francesco Sforza 35, Milan, Italy
| | - Santi Trimarchi
- Unit of Vascular Surgery, I.R.C.C.S. Fondazione Cà Granda Policlinico Milano, Via Francesco Sforza 35, Milan, Italy
| | - Giulia Luraghi
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milan, Italy.
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