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逝者模型:降低像异种移植这样高风险临床试验风险的新模式。

The decedent model: A new paradigm for de-risking high stakes clinical trials like xenotransplantation.

机构信息

NYU Langone Transplant Institute, New York, NY, USA.

NYU Langone Transplant Institute, New York, NY, USA.

出版信息

Am J Transplant. 2024 Apr;24(4):526-532. doi: 10.1016/j.ajt.2024.01.035. Epub 2024 Feb 8.

Abstract

The first 2 living recipients of pig hearts died unexpectedly within 2 months, despite both recipients receiving what over 30 years of nonhuman primate (NHP) research would suggest were the optimal gene edits and immunosuppression to ensure success. These results prompt us to question how faithfully data from the NHP model translate into human outcomes. Before attempting any further heart xenotransplants in living humans, it is highly advisable to gain a more comprehensive understanding of why the promising preclinical NHP data did not accurately predict outcomes in humans. It is also unlikely that additional NHP data will provide more information that would de-risk a xenoheart clinical trial because these cases were based on the best practices from the most successful NHP results to date. Although imperfect, the decedent model offers a complementary avenue to determine appropriate treatment regimens to control the human immune response to xenografts and better understand the biologic differences between humans and NHP that could lead to such starkly contrasting outcomes. Herein, we explore the potential benefits and drawbacks of the decedent model and contrast it to the advantages and disadvantages of the extensive body of data generated in the NHP xenoheart transplantation model.

摘要

首例接受猪心脏移植的两名活体受者在不到 2 个月的时间内意外死亡,尽管两名受者都接受了 30 多年的非人类灵长类动物(NHP)研究表明是最佳的基因编辑和免疫抑制措施,以确保成功。这些结果促使我们质疑从 NHP 模型获得的数据在多大程度上准确预测了人类的结果。在尝试对活体人类进行任何进一步的异种心脏移植之前,强烈建议更全面地了解为什么有希望的临床前 NHP 数据不能准确预测人类的结果。此外,额外的 NHP 数据不太可能提供更多的信息来降低异种心脏临床试验的风险,因为这些案例是基于迄今为止最成功的 NHP 结果的最佳实践。尽管不完美,但尸检模型提供了一种补充途径,可以确定适当的治疗方案来控制人体对异种移植物的免疫反应,并更好地了解人类和 NHP 之间可能导致如此截然不同结果的生物学差异。在此,我们探讨了尸检模型的潜在优势和缺点,并将其与在 NHP 异种心脏移植模型中产生的大量数据的优势和缺点进行了对比。

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