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Models of Lung Transplant Research: a consensus statement from the National Heart, Lung, and Blood Institute workshop
Lung transplantation, a cure for a number of end-stage lung diseases, continues to have the worst long-term outcomes when compared with other solid organ transplants. Preclinical modeling of the most common and serious lung transplantation complications are essential to better understand and mitigat...
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Published in: | JCI insight 2017-05, Vol.2 (9) |
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Main Authors: | , , , , , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Lung transplantation, a cure for a number of end-stage lung diseases, continues to have the worst long-term outcomes when compared with other solid organ transplants. Preclinical modeling of the most common and serious lung transplantation complications are essential to better understand and mitigate the pathophysiological processes that lead to these complications. Various animal and in vitro models of lung transplant complications now exist and each of these models has unique strengths. However, significant issues, such as the required technical expertise as well as the robustness and clinical usefulness of these models, remain to be overcome or clarified. The National Heart, Lung, and Blood Institute (NHLBI) convened a workshop in March 2016 to review the state of preclinical science addressing the three most important complications of lung transplantation: primary graft dysfunction (PGD), acute rejection (AR), and chronic lung allograft dysfunction (CLAD). In addition, the participants of the workshop were tasked to make consensus recommendations on the best use of these complimentary models to close our knowledge gaps in PGD, AR, and CLAD. Their reviews and recommendations are summarized in this report. Furthermore, the participants outlined opportunities to collaborate and directions to accelerate research using these preclinical models. |
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ISSN: | 2379-3708 2379-3708 |
DOI: | 10.1172/jci.insight.93121 |