Deep learning to design immune-evading viral vectors for gene therapy

深度学习设计用于基因治疗的免疫逃避病毒载体

• 批准号: 10313084
• 负责人: Nicole Thadani
• 金额: $ 6.64万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2023-05-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10313084
• 关键词: Deep; learning; design; immune; evading

Project Summary The delivery of gene therapies (DNA or RNA) using viral vectors is a promising therapeutic avenue for several indications, including genetic conditions, cancer, and neurodegenerative disease. Adeno-associated viral (AAV) vectors in particular have demonstrated efficacy and safety in several clinical trials, and have been approved for the treatment of two monogenic diseases by the FDA. However, a major bottleneck in expanding the reach of AAV-based therapies is optimizing vectors to avoid immune detection while preserving their function and tropism. AAV vector applications are significantly limited by immune targeting of viral capsids within a patient's body, resulting in accelerated clearance, loss of efficacy and dangerous hypersensitivity responses. As AAV is widely prevalent in the human population, these effects are often the result of immunological memory and thus more severe than a naïve response. Adverse effects are difficult to predict through pre-clinical experiments, increasing the risk posed to clinical trial patients. While immune recognition epitopes can sometimes be mapped to specific viral capsid residues, even high-throughput library-based strategies to ablate these epitopes are plagued by the creation of non-functional variants. To address this challenge, we propose to use a generative statistical model trained on natural sequence variation to design `smart' immune-evading AAV capsid libraries enriched in functional variants. This approach will integrate statistical models of the functional constraints on protein sequence with state-of-the-art predictors of T-cell and humoral immunogenicity to produce diverse libraries of therapeutically useful vector sequences. Data gathered from initial designed libraries will inform model optimization to iterate towards further improved viral vectors. Deimmunized, diverse vector libraries will address immune evasion at the initial stages of gene therapy development, accelerating progress towards safe and effective therapies. An algorithm that builds on observed sequences to generate diversity subject to immunogenicity constraints has broad implications for the development of all protein-based biotherapeutics, such as antibodies. This approach combines the advantages of protein therapies optimized through natural selection with precise control over desirable characteristics for human health applications.

项目摘要 使用病毒载体递送基因疗法（DNA或RNA）是一种有前途的治疗途径 用于多种适应症，包括遗传性疾病、癌症和神经退行性疾病。 特别是腺相关病毒（AAV）载体已经证明了在免疫治疗中的有效性和安全性。 几项临床试验，并已被批准用于治疗两种单基因疾病， FDA。然而，扩大基于AAV的治疗范围的主要瓶颈是 优化载体以避免免疫检测，同时保留其功能和向性。AAV 载体的应用受到患者体内病毒衣壳的免疫靶向的显著限制 体内，导致加速清除，丧失疗效和危险的超敏反应 应答由于AAV在人群中广泛流行，因此这些作用通常是AAV感染的结果。 免疫记忆，因此比幼稚反应更严重。不利影响 难以通过临床前实验预测，增加了临床试验的风险 患者虽然免疫识别表位有时可以定位到特定的病毒衣壳 残基，即使是基于文库的高通量策略，以消除这些表位的困扰， 创建非功能性变体。为了应对这一挑战，我们建议使用一个生成式 在自然序列变异上训练统计模型以设计“智能”免疫逃避AAV 富含功能变体的衣壳文库。这种方法将整合统计模型， 蛋白质序列的功能限制与最先进的预测T细胞和 体液免疫原性以产生治疗上有用的载体序列的多样文库。 从最初设计的库中收集的数据将为模型优化提供信息， 进一步改进病毒载体。去免疫化的多样化载体文库将解决免疫逃避问题 在基因治疗发展的初始阶段， 有效的治疗。一种基于观测序列生成多样性主题的算法 免疫原性的限制对所有基于蛋白质的 生物治疗剂，如抗体。这种方法结合了蛋白质疗法的优点 通过自然选择进行优化，精确控制人类所需的特征， 健康应用。

项目成果

Learning from prepandemic data to forecast viral escape.

• DOI: 10.1038/s41586-023-06617-0
• 发表时间: 2023-10
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Thadani, Nicole N.;Gurev, Sarah;Notin, Pascal;Youssef, Noor;Rollins, Nathan J.;Ritter, Daniel;Sander, Chris;Gal, Yarin;Marks, Debora S.
• 通讯作者: Marks, Debora S.