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Yıldırım C, Ural B, Odemis E, Donmazov S, Pekkan K. Computer-generated Clinical Decision-making in the Treatment of Pulmonary Atresia with Intact Ventricular Septum. Cardiovasc Eng Technol 2025; 16:222-237. [PMID: 39707136 DOI: 10.1007/s13239-024-00769-4] [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: 05/07/2024] [Accepted: 12/06/2024] [Indexed: 12/23/2024]
Abstract
PURPOSE Pulmonary atresia with intact ventricular septum is a multifactorial disease requiring complex surgeries. The treatment route is determined based on the right ventricle (RV) size, tricuspid annulus size and coronary circulation dependency of RV. Since multiple parameters influence the post-operative success, a personalized decision-making based on computed hemodynamics is hypothesized to improve the treatment efficacy. METHODS A lumped parameter cardiovascular model is developed to calculate the hemodynamics of virtual patients which are generated by statistical distribution of circulation parameters. Four cohorts each with 30 digital patients are grouped based on RV size. For each patient, biventricular and one-and-half ventricle (1.5 V) repair were applied in silico and assessed via pressure, flow and saturations computed for every organ bed. RESULTS Biventricular and 1.5 V repair yield significant increase in the pulmonary flow and oxygen saturation for all patients compared to the pre-operative state (p-values < 0.001). Approximately 30% of generated patients failed to meet the sufficient saturation and flow following biventricular repair and were directed to 1.5 V repair. However, 14% of these 1.5 V repair patients failed post-operatively, requiring Fontan completion. Based on the pre-determined hemodynamics criteria, this study implies that patients having RV sizes larger than 22 ml/m2 are likely to undergo successful biventricular repair. CONCLUSION Pending further clinical trials, computational pre-interventional planning has the potential to screen patients that would not optimally fit to the traditional pathway prior to in vivo execution by providing personalized hemodynamic outcome. Statistical approach allows in silico clinical trials, useful for diseases with low patient numbers.
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Affiliation(s)
- Canberk Yıldırım
- Department of Mechanical Engineering, Istanbul Bilgi University, Istanbul, 34060, Turkey
- Department of Mechanical Engineering, Koc University, Rumeli Feneri Campus, Sarıyer, Istanbul, 34450, Turkey
| | - Berk Ural
- Department of Mechanical Engineering, Koc University, Rumeli Feneri Campus, Sarıyer, Istanbul, 34450, Turkey
| | - Ender Odemis
- Department of Pediatric Cardiology, Koc University Hospital, Istanbul, 34010, Turkey
| | - Samir Donmazov
- Department of Mathematics, University of Kentucky, Kentucky, 40506, USA
| | - Kerem Pekkan
- Department of Mechanical Engineering, Koc University, Rumeli Feneri Campus, Sarıyer, Istanbul, 34450, Turkey.
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Adar O, Shakargy JD, Ilan Y. The Constrained Disorder Principle: Beyond Biological Allostasis. BIOLOGY 2025; 14:339. [PMID: 40282204 PMCID: PMC12025142 DOI: 10.3390/biology14040339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2025] [Revised: 03/24/2025] [Accepted: 03/24/2025] [Indexed: 04/29/2025]
Abstract
The constrained disorder principle (CDP) defines complex biological systems based on inherent variability. Allostasis refers to the physiological processes that help maintain stability in response to changing environmental demands. Allostatic load describes the cumulative wear and tear on the body resulting from prolonged exposure to stress, and it has been suggested to mediate the relationship between stress and disease. This study presents the concepts of CDP and allostasis while discussing their similarities and differences. We reviewed the current literature on the potential benefits of introducing controlled doses of biological noise into interventions, which may enhance the effectiveness of therapies. The paper highlights the promising role of variability provided by a CDP-based second-generation artificial intelligence system in improving health outcomes.
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Affiliation(s)
- Ofek Adar
- Faculty of Medicine, Hebrew University, Jerusalem 9112001, Israel; (O.A.); (J.D.S.)
- Department of Medicine, Hadassah Medical Center, Jerusalem 9112001, Israel
| | - Josef Daniel Shakargy
- Faculty of Medicine, Hebrew University, Jerusalem 9112001, Israel; (O.A.); (J.D.S.)
- Department of Medicine, Hadassah Medical Center, Jerusalem 9112001, Israel
| | - Yaron Ilan
- Faculty of Medicine, Hebrew University, Jerusalem 9112001, Israel; (O.A.); (J.D.S.)
- Department of Medicine, Hadassah Medical Center, Jerusalem 9112001, Israel
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3
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Ocana A, Pandiella A, Privat C, Bravo I, Luengo-Oroz M, Amir E, Gyorffy B. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomark Res 2025; 13:45. [PMID: 40087789 PMCID: PMC11909971 DOI: 10.1186/s40364-025-00758-2] [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: 12/03/2024] [Accepted: 03/05/2025] [Indexed: 03/17/2025] Open
Abstract
Artificial intelligence (AI) can transform drug discovery and early drug development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, and low success rates. In this review we provide an overview of how to integrate AI to the current drug discovery and development process, as it can enhance activities like target identification, drug discovery, and early clinical development. Through multiomics data analysis and network-based approaches, AI can help to identify novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold, predict protein structures with high accuracy, aiding druggability assessments and structure-based drug design. AI also facilitates virtual screening and de novo drug design, creating optimized molecular structures for specific biological properties. In early clinical development, AI supports patient recruitment by analyzing electronic health records and improves trial design through predictive modeling, protocol optimization, and adaptive strategies. Innovations like synthetic control arms and digital twins can reduce logistical and ethical challenges by simulating outcomes using real-world or virtual patient data. Despite these advancements, limitations remain. AI models may be biased if trained on unrepresentative datasets, and reliance on historical or synthetic data can lead to overfitting or lack generalizability. Ethical and regulatory issues, such as data privacy, also challenge the implementation of AI. In conclusion, in this review we provide a comprehensive overview about how to integrate AI into current processes. These efforts, although they will demand collaboration between professionals, and robust data quality, have a transformative potential to accelerate drug development.
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Affiliation(s)
- Alberto Ocana
- Experimental Therapeutics in Cancer Unit, Medical Oncology Department, Instituto de Investigación Sanitaria San Carlos (IdISSC), Hospital Clínico San Carlos and CIBERONC, Madrid, Spain.
- INTHEOS-CEU-START Catedra, Facultad de Medicina, Universidad CEU San Pablo, 28668 Boadilla del Monte, Madrid, Spain.
| | - Atanasio Pandiella
- Instituto de Biología Molecular y Celular del Cáncer, CSIC, IBSAL and CIBERONC, Salamanca, 37007, Spain
| | - Cristian Privat
- , CancerAppy, Av Ribera de Axpe, 28, Erando, 48950, Vizcaya, Spain
| | - Iván Bravo
- Facultad de Farmacia, Universidad de Castilla La Mancha, Albacete, Spain
| | | | - Eitan Amir
- Princess Margaret Cancer Center, Toronto, Canada
| | - Balazs Gyorffy
- Department of Bioinformatics, Semmelweis University, Tűzoltó U. 7-9, Budapest, 1094, Hungary
- Research Centre for Natural Sciences, Hungarian Research Network, Magyar Tudosok Korutja 2, Budapest, 1117, Hungary
- Department of Biophysics, Medical School, University of Pecs, Pecs, 7624, Hungary
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Laga Boul-Atarass I, Cepeda Franco C, Sanmartín Sierra JD, Castell Monsalve J, Padillo Ruiz J. Virtual 3D models, augmented reality systems and virtual laparoscopic simulations in complicated pancreatic surgeries: state of art, future perspectives, and challenges. Int J Surg 2025; 111:2613-2623. [PMID: 39869381 DOI: 10.1097/js9.0000000000002231] [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: 07/15/2024] [Accepted: 12/07/2024] [Indexed: 01/28/2025]
Abstract
Pancreatic surgery is considered one of the most challenging interventions by many surgeons, mainly due to retroperitoneal location and proximity to key and delicate vascular structures. These factors make pancreatic resection a demanding procedure, with successful rates far from optimal and frequent postoperative complications. Surgical planning is essential to improve patient outcomes, and in this regard, many technological advances made in the last few years have proven to be extremely useful in medical fields. This review aims to outline the potential and limitations of 3D digital and 3D printed models in pancreatic surgical planning, as well as the impact and challenges of novel technologies such as augmented/virtual reality systems or artificial intelligence to improve medical training and surgical outcomes.
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Affiliation(s)
- Imán Laga Boul-Atarass
- Department of Surgery, Virgen del Rocio University Hospital, Seville, Spain
- Oncology Surgery, Cell Therapy, and Organ Transplantation Group, Instituto de Biomedicina de Sevilla (IBiS), University of Sevilla, Seville, Spain
| | - Carmen Cepeda Franco
- Department of Surgery, Virgen del Rocio University Hospital, Seville, Spain
- Oncology Surgery, Cell Therapy, and Organ Transplantation Group, Instituto de Biomedicina de Sevilla (IBiS), University of Sevilla, Seville, Spain
| | | | | | - Javier Padillo Ruiz
- Department of Surgery, Virgen del Rocio University Hospital, Seville, Spain
- Oncology Surgery, Cell Therapy, and Organ Transplantation Group, Instituto de Biomedicina de Sevilla (IBiS), University of Sevilla, Seville, Spain
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Hurez V, Gauderat G, Soret P, Myers R, Dasika K, Sheehan R, Friedrich C, Reed M, Laigle L, Riquelme MA, Aussy A, Chadli L, Hubert S, Desvaux E, Fouliard S, Moingeon P, the PRECISESADS Clinical Consortium. Virtual patients inspired by multiomics predict the efficacy of an anti-IFNα mAb in cutaneous lupus. iScience 2025; 28:111754. [PMID: 39925417 PMCID: PMC11804754 DOI: 10.1016/j.isci.2025.111754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 09/26/2024] [Accepted: 01/03/2025] [Indexed: 02/11/2025] Open
Abstract
Lupus erythematosus is a heterogeneous autoimmune disease that requires treatments tailored to specific patient subsets. To evaluate in silico the efficacy of the anti-IFNα S95021 monoclonal antibody, we created a quantitative systems pharmacology model of cutaneous lupus and a virtual patient population, with attributes matching the diversity of actual patients. To this aim, we performed a multiomics profiling analysis of 337 lupus patients from the PRECISESADS cohort, thereby identifying four patient clusters with distinct immune dysregulation patterns, including various levels of type I interferon (IFN) pathway upregulation. Simulation of S95021 treatment in the virtual patient cohort (n = 241) predicted distinct clinical responses in patient clusters, with machine learning analysis further revealing biomarkers that distinguish predicted responders from non-responders. Combining multiomics profiling of actual patients with mechanistic mathematical modeling supports precision medicine by predicting drug responses based upon patient characteristics in a complex heterogeneous disease.
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Affiliation(s)
| | | | | | | | | | | | | | - Mike Reed
- Rosa and Co. LLC, San Carlos, CA, USA
| | | | | | - Audrey Aussy
- Servier, Research and Development, Saclay, France
| | | | | | | | | | | | - the PRECISESADS Clinical Consortium
- Rosa and Co. LLC, San Carlos, CA, USA
- Servier, Research and Development, Saclay, France
- GENYO Center for Genomics and Oncological Research, Granada, Spain
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6
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Ringeval M, Etindele Sosso FA, Cousineau M, Paré G. Advancing Health Care With Digital Twins: Meta-Review of Applications and Implementation Challenges. J Med Internet Res 2025; 27:e69544. [PMID: 39969978 PMCID: PMC11888003 DOI: 10.2196/69544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2024] [Revised: 01/20/2025] [Accepted: 01/24/2025] [Indexed: 02/20/2025] Open
Abstract
BACKGROUND Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration of DTs in health care faces challenges such as fragmented applications, ethical concerns, and barriers to adoption. OBJECTIVE This study systematically reviews the existing literature on DT applications in health care with three objectives: (1) to map primary applications, (2) to identify key challenges and limitations, and (3) to highlight gaps that can guide future research. METHODS A meta-review was conducted in a systematic fashion, adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, and included 25 literature reviews published between 2021 and 2024. The search encompassed 5 databases: PubMed, CINAHL, Web of Science, Embase, and PsycINFO. Thematic synthesis was used to categorize DT applications, stakeholders, and barriers to adoption. RESULTS A total of 3 primary DT applications in health care were identified: personalized medicine, operational efficiency, and medical research. While current applications, such as predictive diagnostics, patient-specific treatment simulations, and hospital resource optimization, remain in their early stages of development, they highlight the significant potential of DTs. Challenges include data quality, ethical issues, and socioeconomic barriers. This review also identified gaps in scalability, interoperability, and clinical validation. CONCLUSIONS DTs hold transformative potential in health care, providing individualized care, operational optimization, and accelerated research. However, their adoption is hindered by technical, ethical, and financial barriers. Addressing these issues requires interdisciplinary collaboration, standardized protocols, and inclusive implementation strategies to ensure equitable access and meaningful impact.
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Affiliation(s)
| | | | | | - Guy Paré
- HEC Montréal, Montréal, QC, Canada
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Li X, Loscalzo J, Mahmud AKMF, Aly DM, Rzhetsky A, Zitnik M, Benson M. Digital twins as global learning health and disease models for preventive and personalized medicine. Genome Med 2025; 17:11. [PMID: 39920778 PMCID: PMC11806862 DOI: 10.1186/s13073-025-01435-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 01/29/2025] [Indexed: 02/09/2025] Open
Abstract
Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.
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Affiliation(s)
- Xinxiu Li
- Medical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska Institute, Stockholm, Sweden
| | - Joseph Loscalzo
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - A K M Firoj Mahmud
- Department of Medical Biochemistry and Microbiology, Uppsala University, 75105, Uppsala, Sweden
| | - Dina Mansour Aly
- Medical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska Institute, Stockholm, Sweden
| | - Andrey Rzhetsky
- Departments of Medicine and Human Genetics, Institute for Genomics and Systems Biology, University of Chicago, Chicago, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA
| | - Mikael Benson
- Medical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska Institute, Stockholm, Sweden.
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Li L, Camps J, Rodriguez B, Grau V. Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey. IEEE Rev Biomed Eng 2025; 18:316-336. [PMID: 39453795 DOI: 10.1109/rbme.2024.3486439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2024]
Abstract
Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods for solving ECG inverse problems, their validation strategies, their clinical applications, and their future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.
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Leivaditis V, Beltsios E, Papatriantafyllou A, Grapatsas K, Mulita F, Kontodimopoulos N, Baikoussis NG, Tchabashvili L, Tasios K, Maroulis I, Dahm M, Koletsis E. Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future. Clin Pract 2025; 15:17. [PMID: 39851800 PMCID: PMC11763739 DOI: 10.3390/clinpract15010017] [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: 12/18/2024] [Revised: 01/06/2025] [Accepted: 01/08/2025] [Indexed: 01/26/2025] Open
Abstract
Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with its integration into cardiac surgery offering significant advancements in precision, efficiency, and patient outcomes. However, a comprehensive understanding of AI's applications, benefits, challenges, and future directions in cardiac surgery is needed to inform its safe and effective implementation. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, Cochrane Library, Google Scholar, and Web of Science, covering publications from January 2000 to November 2024. Studies focusing on AI applications in cardiac surgery, including risk stratification, surgical planning, intraoperative guidance, and postoperative management, were included. Data extraction and quality assessment were conducted using standardized tools, and findings were synthesized narratively. Results: A total of 121 studies were included in this review. AI demonstrated superior predictive capabilities in risk stratification, with machine learning models outperforming traditional scoring systems in mortality and complication prediction. Robotic-assisted systems enhanced surgical precision and minimized trauma, while computer vision and augmented cognition improved intraoperative guidance. Postoperative AI applications showed potential in predicting complications, supporting patient monitoring, and reducing healthcare costs. However, challenges such as data quality, validation, ethical considerations, and integration into clinical workflows remain significant barriers to widespread adoption. Conclusions: AI has the potential to revolutionize cardiac surgery by enhancing decision making, surgical accuracy, and patient outcomes. Addressing limitations related to data quality, bias, validation, and regulatory frameworks is essential for its safe and effective implementation. Future research should focus on interdisciplinary collaboration, robust testing, and the development of ethical and transparent AI systems to ensure equitable and sustainable advancements in cardiac surgery.
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Affiliation(s)
- Vasileios Leivaditis
- Department of Cardiothoracic and Vascular Surgery, WestpfalzKlinikum, 67655 Kaiserslautern, Germany; (V.L.); (A.P.); (M.D.)
| | - Eleftherios Beltsios
- Department of Anesthesiology and Intensive Care, Hannover Medical School, 30625 Hannover, Germany;
| | - Athanasios Papatriantafyllou
- Department of Cardiothoracic and Vascular Surgery, WestpfalzKlinikum, 67655 Kaiserslautern, Germany; (V.L.); (A.P.); (M.D.)
| | - Konstantinos Grapatsas
- Department of Thoracic Surgery and Thoracic Endoscopy, Ruhrlandklinik, West German Lung Center, University Hospital Essen, University Duisburg-Essen, 45141 Essen, Germany;
| | - Francesk Mulita
- Department of General Surgery, General University Hospital of Patras, 26504 Patras, Greece; (L.T.); (K.T.)
| | - Nikolaos Kontodimopoulos
- Department of Economics and Sustainable Development, Harokopio University, 17778 Athens, Greece;
| | - Nikolaos G. Baikoussis
- Department of Cardiac Surgery, Ippokrateio General Hospital of Athens, 11527 Athens, Greece;
| | - Levan Tchabashvili
- Department of General Surgery, General University Hospital of Patras, 26504 Patras, Greece; (L.T.); (K.T.)
| | - Konstantinos Tasios
- Department of General Surgery, General University Hospital of Patras, 26504 Patras, Greece; (L.T.); (K.T.)
| | - Ioannis Maroulis
- Department of General Surgery, General University Hospital of Patras, 26504 Patras, Greece; (L.T.); (K.T.)
| | - Manfred Dahm
- Department of Cardiothoracic and Vascular Surgery, WestpfalzKlinikum, 67655 Kaiserslautern, Germany; (V.L.); (A.P.); (M.D.)
| | - Efstratios Koletsis
- Department of Cardiothoracic Surgery, General University Hospital of Patras, 26504 Patras, Greece;
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Primiero CA, Janda M, Soyer HP. Skin 2.0: How Cutaneous Digital Twins Could Reshape Dermatology. J Invest Dermatol 2025; 145:18-21. [PMID: 39230536 DOI: 10.1016/j.jid.2024.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 07/01/2024] [Accepted: 07/12/2024] [Indexed: 09/05/2024]
Affiliation(s)
- Clare A Primiero
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia.
| | - Monika Janda
- Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - H Peter Soyer
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia; Dermatology Department, Princess Alexandra Hospital, Brisbane, Australia
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Berenbaum F, Buyse M. Bridging the gap: tackling the challenge of limited progressors in clinical trials aimed at slowing the transition from early preradiographic to established osteoarthritis. Ann Rheum Dis 2025; 84:5-8. [PMID: 39874234 DOI: 10.1016/j.ard.2025.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2025]
Affiliation(s)
- Francis Berenbaum
- Department of Rheumatology, Sorbonne University, INSERM CRSA, AP-HP Saint-Antoine Hospital, Paris, France.
| | - Marc Buyse
- International Drug Development Institute, Louvain-la-Neuve, Belgium; I-BioStat, Hasselt University, Hasselt, Belgium
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12
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Wu J, Koelzer VH. Towards generative digital twins in biomedical research. Comput Struct Biotechnol J 2024; 23:3481-3488. [PMID: 39435342 PMCID: PMC11491725 DOI: 10.1016/j.csbj.2024.09.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Revised: 09/30/2024] [Accepted: 09/30/2024] [Indexed: 10/23/2024] Open
Abstract
Digital twins in biomedical research, i.e. virtual replicas of biological entities such as cells, organs, or entire organisms, hold great potential to advance personalized healthcare. As all biological processes happen in space, there is a growing interest in modeling biological entities within their native context. Leveraging generative artificial intelligence (AI) and high-volume biomedical data profiled with spatial technologies, researchers can recreate spatially-resolved digital representations of a physical entity with high fidelity. In application to biomedical fields such as computational pathology, oncology, and cardiology, these generative digital twins (GDT) thus enable compelling in silico modeling for simulated interventions, facilitating the exploration of 'what if' causal scenarios for clinical diagnostics and treatments tailored to individual patients. Here, we outline recent advancements in this novel field and discuss the challenges and future research directions.
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Affiliation(s)
- Jiqing Wu
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Viktor H. Koelzer
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
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13
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Niarakis A, Laubenbacher R, An G, Ilan Y, Fisher J, Flobak Å, Reiche K, Rodríguez Martínez M, Geris L, Ladeira L, Veschini L, Blinov ML, Messina F, Fonseca LL, Ferreira S, Montagud A, Noël V, Marku M, Tsirvouli E, Torres MM, Harris LA, Sego TJ, Cockrell C, Shick AE, Balci H, Salazar A, Rian K, Hemedan AA, Esteban-Medina M, Staumont B, Hernandez-Vargas E, Martis B S, Madrid-Valiente A, Karampelesis P, Sordo Vieira L, Harlapur P, Kulesza A, Nikaein N, Garira W, Malik Sheriff RS, Thakar J, Tran VDT, Carbonell-Caballero J, Safaei S, Valencia A, Zinovyev A, Glazier JA. Immune digital twins for complex human pathologies: applications, limitations, and challenges. NPJ Syst Biol Appl 2024; 10:141. [PMID: 39616158 PMCID: PMC11608242 DOI: 10.1038/s41540-024-00450-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 09/27/2024] [Indexed: 12/06/2024] Open
Abstract
Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.
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Affiliation(s)
- Anna Niarakis
- Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France.
- Lifeware Group, Inria, Saclay-île de France, Palaiseau, France.
| | | | - Gary An
- Department of Surgery, University of Vermont Larner College of Medicine, Vermont, USA
| | - Yaron Ilan
- Faculty of Medicine Hebrew University, Hadassah Medical Center, Jerusalem, Israel
| | - Jasmin Fisher
- UCL Cancer Institute, University College London, Paul O'Gorman Building, 72 Huntley Street, London, WC1E 6BT, UK
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St Olav's University Hospital, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
- Institute of Clinical Immunology, Medical Faculty, University Hospital, University of Leipzig, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany
| | - María Rodríguez Martínez
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Liesbet Geris
- Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium
- Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Biomechanics Research Unit, GIGA Molecular and Computational Biology, University of Liège, Liège, Belgium
| | - Luiz Ladeira
- Biomechanics Research Unit, GIGA Molecular and Computational Biology, University of Liège, Liège, Belgium
| | - Lorenzo Veschini
- Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, London, UK
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, 47408, USA
| | - Michael L Blinov
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, 06030, USA
| | - Francesco Messina
- Department of Epidemiology, Preclinical Research and Advanced Diagnostic, National Institute for Infectious Diseases 'Lazzaro Spallanzani' - I.R.C.C.S., Rome, Italy
| | - Luis L Fonseca
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Sandra Ferreira
- Mathematics Department and Center of Mathematics, University of Beira Interior, Covilhã, Portugal
| | - Arnau Montagud
- Barcelona Supercomputing Center (BSC), Barcelone, Spain
- Institute for Integrative Systems Biology (I2SysBio), CSIC-UV, Valencia, Spain
| | - Vincent Noël
- Institut Curie, Université PSL, F-75005, Paris, France
- INSERM, U900, F-75005, Paris, France
- Mines ParisTech, Université PSL, F-75005, Paris, France
| | - Malvina Marku
- Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Eirini Tsirvouli
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marcella M Torres
- Department of Mathematics and Statistics, University of Richmond, Richmond, VA, USA
| | - Leonard A Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, USA
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, USA
- Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - T J Sego
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Chase Cockrell
- Department of Surgery, University of Vermont Larner College of Medicine, Vermont, USA
| | - Amanda E Shick
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
| | - Hasan Balci
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Albin Salazar
- INRIA Paris/CNRS/École Normale Supérieure/PSL Research University, Paris, France
| | - Kinza Rian
- Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
| | - Ahmed Abdelmonem Hemedan
- Bioinformatics Core Unit, Luxembourg Centre of Systems Biomedicine LCSB, Luxembourg University, Esch-sur-Alzette, Luxembourg
| | - Marina Esteban-Medina
- Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
| | - Bernard Staumont
- Biomechanics Research Unit, GIGA Molecular and Computational Biology, University of Liège, Liège, Belgium
| | - Esteban Hernandez-Vargas
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, 83844-1103, USA
| | | | | | | | | | - Pradyumna Harlapur
- Department of Bioengineering, Indian Institute of Science, Bengaluru, India
| | | | - Niloofar Nikaein
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, SE-70182, Örebro, Sweden
- X-HiDE - Exploring Inflammation in Health and Disease Consortium, Örebro University, Örebro, Sweden
| | - Winston Garira
- Multiscale Mathematical Modelling of Living Systems program (M3-LSP), Kimberley, South Africa
- Department of Mathematical Sciences, Sol Plaatje University, Kimberley, South Africa
- Private Bag X5008, Kimberley, 8300, South Africa
| | - Rahuman S Malik Sheriff
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, Cambridge, UK
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Juilee Thakar
- Department of Microbiology & Immunology and Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Van Du T Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Soroush Safaei
- Institute of Biomedical Engineering and Technology, Ghent University, Gent, Belgium
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelone, Spain
- ICREA, 23 Passeig Lluís Companys, 08010, Barcelona, Spain
| | | | - James A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, 47408, USA
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14
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D’Orsi L, Capasso B, Lamacchia G, Pizzichini P, Ferranti S, Liverani A, Fontana C, Panunzi S, De Gaetano A, Lo Presti E. Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors. Int J Mol Sci 2024; 25:11588. [PMID: 39519142 PMCID: PMC11546512 DOI: 10.3390/ijms252111588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Revised: 10/21/2024] [Accepted: 10/23/2024] [Indexed: 11/16/2024] Open
Abstract
To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms. Today, artificial intelligence (AI), machine learning (ML), and predictive computer models are more efficient at guiding decisions regarding better therapies and medical procedures. The evolution of these multiparametric and multimodal systems has led to the creation of digital twins (DTs). The goal of our review is to summarize AI applications in discovering new immunotherapies and developing predictive models for more precise immunotherapeutic decision-making. The findings from this literature review highlight that DTs, particularly predictive mathematical models, will be pivotal in advancing healthcare outcomes. Over time, DTs will indeed bring the benefits of diagnostic precision and personalized treatment to a broader spectrum of patients.
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Affiliation(s)
- Laura D’Orsi
- National Research Council of Italy, Institute for Systems Analysis and Computer Science “A. Ruberti”, BioMatLab, Via dei Taurini, 19, 00185 Rome, RM, Italy; (L.D.); (S.P.); (A.D.G.)
| | - Biagio Capasso
- Department of General Surgery, Policlinico Militare di Roma “Celio”, Piazza Celimontana, 50, 00184 Rome, RM, Italy; (B.C.); (S.F.)
| | - Giuseppe Lamacchia
- General Surgery Unit, Regina Apostolorum Hospital, Via S. Francesco d’Assisi, 50, 00041 Albano Laziale, RM, Italy; (G.L.); (A.L.)
| | - Paolo Pizzichini
- Department of Intensive Care Unit, Policlinico Militare di Roma “Celio”, Piazza Celimontana, 50, 00184 Rome, RM, Italy; (P.P.); (C.F.)
| | - Sergio Ferranti
- Department of General Surgery, Policlinico Militare di Roma “Celio”, Piazza Celimontana, 50, 00184 Rome, RM, Italy; (B.C.); (S.F.)
| | - Andrea Liverani
- General Surgery Unit, Regina Apostolorum Hospital, Via S. Francesco d’Assisi, 50, 00041 Albano Laziale, RM, Italy; (G.L.); (A.L.)
| | - Costantino Fontana
- Department of Intensive Care Unit, Policlinico Militare di Roma “Celio”, Piazza Celimontana, 50, 00184 Rome, RM, Italy; (P.P.); (C.F.)
| | - Simona Panunzi
- National Research Council of Italy, Institute for Systems Analysis and Computer Science “A. Ruberti”, BioMatLab, Via dei Taurini, 19, 00185 Rome, RM, Italy; (L.D.); (S.P.); (A.D.G.)
| | - Andrea De Gaetano
- National Research Council of Italy, Institute for Systems Analysis and Computer Science “A. Ruberti”, BioMatLab, Via dei Taurini, 19, 00185 Rome, RM, Italy; (L.D.); (S.P.); (A.D.G.)
- National Research Council of Italy, Institute for Biomedical Research and Innovation (CNR-IRIB), Via Ugo La Malfa, 153, 90146 Palermo, PA, Italy
- Department of Biomatics, Óbuda University, Bécsi Road 96/B, H-1034 Budapest, Hungary
| | - Elena Lo Presti
- National Research Council of Italy, Institute for Biomedical Research and Innovation (CNR-IRIB), Via Ugo La Malfa, 153, 90146 Palermo, PA, Italy
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15
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Visioli G, Romaniello A, Spinoglio L, Albanese GM, Iannetti L, Gagliardi OM, Lambiase A, Gharbiya M. Proliferative Vitreoretinopathy in Retinal Detachment: Perspectives on Building a Digital Twin Model Using Nintedanib. Int J Mol Sci 2024; 25:11074. [PMID: 39456855 PMCID: PMC11507981 DOI: 10.3390/ijms252011074] [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: 09/14/2024] [Revised: 10/13/2024] [Accepted: 10/14/2024] [Indexed: 10/28/2024] Open
Abstract
Proliferative vitreoretinopathy (PVR) is a pathological process characterized by the formation of fibrotic membranes that contract and lead to recurrent retinal detachment. Pars plana vitrectomy (PPV) is the primary treatment, but recurrence rates remain high, as surgery does not address the underlying molecular mechanisms driving fibrosis. Despite several proposed pharmacological interventions, no approved therapies exist, partly due to challenges in conducting preclinical and in vivo studies for ethical and safety reasons. This review explores the potential of computational models and Digital Twins, which are increasingly gaining attention in medicine. These tools could enable the development of progressively complex PVR models, from basic simulations to patient-specific Digital Twins. Nintedanib, a tyrosine kinase inhibitor targeting PDGFR, VEGFR, and FGFR, is presented as a prototype for computational models to simulate its effects on fibrotic pathways in virtual patient cohorts. Although still in its early stages, the integration of computational models and Digital Twins offers promising avenues for improving PVR management through more personalized therapeutic strategies.
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Affiliation(s)
- Giacomo Visioli
- Department of Sense Organs, Medicine and Dentistry Faculty, Sapienza University of Rome, Viale del Policlinico 155, 00161 Rome, Italy; (G.V.); (A.R.); (G.M.A.); (O.M.G.); (A.L.)
| | - Annalisa Romaniello
- Department of Sense Organs, Medicine and Dentistry Faculty, Sapienza University of Rome, Viale del Policlinico 155, 00161 Rome, Italy; (G.V.); (A.R.); (G.M.A.); (O.M.G.); (A.L.)
| | - Leonardo Spinoglio
- Department of Sense Organs, Medicine and Dentistry Faculty, Sapienza University of Rome, Viale del Policlinico 155, 00161 Rome, Italy; (G.V.); (A.R.); (G.M.A.); (O.M.G.); (A.L.)
| | - Giuseppe Maria Albanese
- Department of Sense Organs, Medicine and Dentistry Faculty, Sapienza University of Rome, Viale del Policlinico 155, 00161 Rome, Italy; (G.V.); (A.R.); (G.M.A.); (O.M.G.); (A.L.)
| | - Ludovico Iannetti
- Ophthalmology Unit, Head and Neck Department, Policlinico Umberto I University Hospital, 00161 Rome, Italy;
| | - Oscar Matteo Gagliardi
- Department of Sense Organs, Medicine and Dentistry Faculty, Sapienza University of Rome, Viale del Policlinico 155, 00161 Rome, Italy; (G.V.); (A.R.); (G.M.A.); (O.M.G.); (A.L.)
| | - Alessandro Lambiase
- Department of Sense Organs, Medicine and Dentistry Faculty, Sapienza University of Rome, Viale del Policlinico 155, 00161 Rome, Italy; (G.V.); (A.R.); (G.M.A.); (O.M.G.); (A.L.)
- Ophthalmology Unit, Head and Neck Department, Policlinico Umberto I University Hospital, 00161 Rome, Italy;
| | - Magda Gharbiya
- Department of Sense Organs, Medicine and Dentistry Faculty, Sapienza University of Rome, Viale del Policlinico 155, 00161 Rome, Italy; (G.V.); (A.R.); (G.M.A.); (O.M.G.); (A.L.)
- Ophthalmology Unit, Head and Neck Department, Policlinico Umberto I University Hospital, 00161 Rome, Italy;
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16
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Moingeon P. Harnessing the power of AI-based models to accelerate drug discovery against immune diseases. Expert Rev Clin Immunol 2024; 20:1135-1138. [PMID: 38932714 DOI: 10.1080/1744666x.2024.2373915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 06/25/2024] [Indexed: 06/28/2024]
Affiliation(s)
- Philippe Moingeon
- Servier Research Institute, Paris-Saclay University, Gif-sur-Yvette, France
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17
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Trevena W, Zhong X, Lal A, Rovati L, Cubro E, Dong Y, Schulte P, Gajic O. Model-driven engineering for digital twins: a graph model-based patient simulation application. Front Physiol 2024; 15:1424931. [PMID: 39189027 PMCID: PMC11345177 DOI: 10.3389/fphys.2024.1424931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 07/19/2024] [Indexed: 08/28/2024] Open
Abstract
INTRODUCTION Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. METHODS This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. RESULTS A short case study is presented to demonstrate the viability of the proposed simulation architecture. DISCUSSION The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.
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Affiliation(s)
- William Trevena
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Xiang Zhong
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Amos Lal
- Mayo Clinic, Rochester, MN, United States
| | | | - Edin Cubro
- Mayo Clinic, Rochester, MN, United States
| | - Yue Dong
- Mayo Clinic, Rochester, MN, United States
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18
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Wang H, Arulraj T, Ippolito A, Popel AS. From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling. NPJ Digit Med 2024; 7:189. [PMID: 39014005 PMCID: PMC11252162 DOI: 10.1038/s41746-024-01188-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024] Open
Abstract
Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alberto Ippolito
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Departments of Medicine and Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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19
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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [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: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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20
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Vallée A. Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins. J Med Internet Res 2024; 26:e50204. [PMID: 38739913 PMCID: PMC11130780 DOI: 10.2196/50204] [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: 06/22/2023] [Revised: 10/01/2023] [Accepted: 12/29/2023] [Indexed: 05/16/2024] Open
Abstract
Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.
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Affiliation(s)
- Alexandre Vallée
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
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21
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Moingeon P. [Artificial intelligence-driven drug development against autoimmune diseases]. Med Sci (Paris) 2024; 40:414-416. [PMID: 38819275 DOI: 10.1051/medsci/2024055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Affiliation(s)
- Philippe Moingeon
- Institut de recherche Servier, Université Paris-Saclay, Gif-sur-Yvette, France
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22
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Moingeon P, Garbay C, Dahan M, Fermont I, Benmakhlouf A, Gouyette A, Poitou P, Saint-Pierre A. [The revolution of AI in drug development]. Med Sci (Paris) 2024; 40:369-376. [PMID: 38651962 DOI: 10.1051/medsci/2024028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.
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23
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Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. NATURE COMPUTATIONAL SCIENCE 2024; 4:184-191. [PMID: 38532133 PMCID: PMC11102043 DOI: 10.1038/s43588-024-00607-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/12/2024] [Indexed: 03/28/2024]
Abstract
Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - B Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | | | - N Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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24
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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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Blaudin de Thé FX, Baudier C, Andrade Pereira R, Lefebvre C, Moingeon P. Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony. Drug Discov Today 2023; 28:103772. [PMID: 37717933 DOI: 10.1016/j.drudis.2023.103772] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 09/19/2023]
Abstract
High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal and external sources. Patrimony has enabled researchers to prioritize therapeutic targets based on a deep understanding of the pathophysiology of immuno-inflammatory diseases. Herein, we share our experience regarding main challenges and critical success factors faced when industrializing the platform and broadening its applications to neurological diseases. We emphasize the importance of integrating such platforms in an end-to-end drug discovery process and engaging human experts early on to ensure a transforming impact.
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Sigawi T, Ilan Y. Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems. Biomimetics (Basel) 2023; 8:359. [PMID: 37622964 PMCID: PMC10452845 DOI: 10.3390/biomimetics8040359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.
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Affiliation(s)
| | - Yaron Ilan
- Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem P.O. Box 12000, Israel;
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