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Lapp MM, Lin G, Komin A, Andrews L, Knudson M, Mossman L, Raimondi G, Arciero JC. Modeling the Potential of Treg-Based Therapies for Transplant Rejection: Effect of Dose, Timing, and Accumulation Site. Transpl Int 2022; 35:10297. [PMID: 35479106 PMCID: PMC9035492 DOI: 10.3389/ti.2022.10297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/17/2022] [Indexed: 02/04/2023]
Abstract
Introduction: The adoptive transfer of regulatory T cells (Tregs) has emerged as a method to promote graft tolerance. Clinical trials have demonstrated the safety of adoptive transfer and are now assessing their therapeutic efficacy. Strategies that generate large numbers of antigen specific Tregs are even more efficacious. However, the combinations of factors that influence the outcome of adoptive transfer are too numerous to be tested experimentally. Here, mathematical modeling is used to predict the most impactful treatment scenarios. Methods: We adapted our mathematical model of murine heart transplant rejection to simulate Treg adoptive transfer and to correlate therapeutic efficacy with Treg dose and timing, frequency of administration, and distribution of injected cells. Results: The model predicts that Tregs directly accumulating to the graft are more protective than Tregs localizing to draining lymph nodes. Inhibiting antigen-presenting cell maturation and effector functions at the graft site was more effective at modulating rejection than inhibition of T cell activation in lymphoid tissues. These complex dynamics define non-intuitive relationships between graft survival and timing and frequency of adoptive transfer. Conclusion: This work provides the framework for better understanding the impact of Treg adoptive transfer and will guide experimental design to improve interventions.
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Affiliation(s)
- Maya M. Lapp
- Department of Mathematics, The College of Wooster, Wooster, OH, United States
| | - Guang Lin
- Department of Mathematics, Purdue University, West Lafayette, IN, United States
| | - Alexander Komin
- Department of Plastic and Reconstructive Surgery, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Leah Andrews
- Department of Mathematics, St. Olaf College, Northfield, MN, United States
| | - Mei Knudson
- Department of Mathematics, Carleton College, Northfield, MN, United States
| | - Lauren Mossman
- Department of Mathematics, St. Olaf College, Northfield, MN, United States
| | - Giorgio Raimondi
- Department of Plastic and Reconstructive Surgery, Johns Hopkins School of Medicine, Baltimore, MD, United States,*Correspondence: Giorgio Raimondi, ; Julia C. Arciero,
| | - Julia C. Arciero
- Department of Mathematical Sciences, Indiana University-Purdue University of Indianapolis, Indianapolis, IN, United States,*Correspondence: Giorgio Raimondi, ; Julia C. Arciero,
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Fribourg M. A case for the reuse and adaptation of mechanistic computational models to study transplant immunology. Am J Transplant 2020; 20:355-361. [PMID: 31562790 PMCID: PMC6984985 DOI: 10.1111/ajt.15623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 02/06/2023]
Abstract
Computational mechanistic models constitute powerful tools for summarizing our knowledge in quantitative terms, providing mechanistic understanding, and generating new hypotheses. The present review emphasizes the advantages of reusing publicly available computational models as a way to capitalize on existing knowledge, reduce the number of parameters that need to be adjusted to experimental data, and facilitate hypothesis generation. Finally, it includes a step-by-step example of the reuse and adaptation of an existing model of immune responses to tuberculosis, tumor growth, and blood pathogens, to study donor-specific antibody (DSA) responses. This review aims to illustrate the benefit of leveraging the currently available computational models in immunology to accelerate the study of alloimmune responses, and to encourage modelers to share their models to further advance our understanding of transplant immunology.
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Affiliation(s)
- Miguel Fribourg
- Translational Transplant Research Center, Department of Medicine, and Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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