非人类灵长类动物用于丝状病毒药物和疫苗评估的最新情况。
An update on nonhuman primate usage for drug and vaccine evaluation against filoviruses.
发表日期:2024 Aug 01
作者:
Marc-Antoine de La Vega, Ara Xiii, Christopher S Massey, Jessica R Spengler, Gary P Kobinger, Courtney B Woolsey
来源:
Expert Opinion on Drug Discovery
摘要:
由于非人类灵长类动物(NHP)忠实地再现了人类疾病,因此被认为是评估抗埃博拉病毒和其他丝状病毒药物的黄金标准。长期目标是通过更合乎道德的替代方案来减少对 NHP 的依赖。计算机模拟和类器官模型有可能通过提供准确的、以人为基础的系统来模拟疾病过程和药物反应,从而彻底改变药物测试,而无需考虑与动物测试相关的伦理问题。然而,由于这些新兴技术仍处于发展初期,目前需要 NHP 模型来对丝状病毒疫苗和药物进行后期评估,因为它们为新医疗对策的有效性和安全性提供了重要的见解。在这篇综述中,作者介绍了可用的 NHP 模型并检查相应模型中所有具有医学意义的丝状病毒的药物发现的现有文献。需要有意识地转向无动物模型,以与动物研究的 3R 保持一致。短期内,可以通过增强可复制性和发布负面数据来细化和减少 NHP 模型的使用。替代是一个渐进的过渡,首先是选择和优化更好的小动物模型;推进类器官系统,并使用计算机模型来准确预测免疫结果。
Due to their faithful recapitulation of human disease, nonhumanprimates (NHPs) are considered the gold standard for evaluating drugs against Ebolavirus and other filoviruses. The long-term goal is to reduce the reliance on NHPswith more ethical alternatives. In silico simulations and organoidmodels have the potential to revolutionize drug testing by providing accurate,human-based systems that mimic disease processes and drug responses without theethical concerns associated with animal testing. However, as these emergingtechnologies are still in their developmental infancy, NHP models are presentlyneeded for late-stage evaluation of filovirus vaccines and drugs, as theyprovide critical insights into the efficacy and safety of new medicalcountermeasures.In this review, the authors introduce available NHP models andexamine the existing literature on drug discovery for all medically significantfiloviruses in corresponding models.A deliberate shift towards animal-free models is desired to alignwith the 3Rs of animal research. In the short term, the use of NHP models canbe refined and reduced by enhancing replicability and publishingnegative data. Replacement involves a gradual transition, beginning withthe selection and optimization of better small animal models; advancingorganoid systems, and using in silico models to accurately predictimmunological outcomes.