Advancing next-generation sequencing for optimization of rAAV production

推进下一代测序以优化 rAAV 生产

• 批准号: 10820589
• 负责人: Susanna L Lamers
• 金额: $ 29.59万
• 依托单位: BIOINFOEXPERTS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2024-09-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10820589
• 关键词: Advancing; next; generation; sequencing; optimization

1 BioInfoExperts develops pathogen-associated next-generation sequencing (NGS) analytics and software for a 2 growing number of industries that require genome characterization. In the proposed SBIR Phase I project, we 3 will develop NGS analytics for the recombinant adeno-associated viruses (rAAVs) manufacturing industry; in 4 Phase II, we will incorporate the NGS analytics into a a software-as-a-service (SaaS) where customers (rAAV 5 manufacturers) can access results from customized analytical pipelines designed explicitly for their processes. 6 There are >200 rAAV products in clinical trials and three FDA-approved rAAV-mediated gene therapies already 7 in the market. As the industry expands, the FDA is demanding more rigorous rAAV manufacturing quality control 8 (QC) methods to ensure patient safety. In fact, both the FDA and the Dark Horse Consulting group, an advisory 9 committee for the rAAV industry, have recommended NGS for rAAV QC; however, the methods for NGS 10 evaluation of rAAV data have not yet matured to the point where they are accurate or high throughput enough 11 for widespread adoption. NGS from rAAV is notoriously difficult to process, due in part to the production 12 processes and genomic structure of rAAV, combined with limitations of the sequencing platforms such as high 13 error rate, bias towards short reads, and potential for generating chimeric reads. With decades of pathogen- 14 related bioinformatics experience and a successful software-as-as-service business model, BIE plans to 15 aggressively enter this competitive market, and systematically address the issues associated with rAAV NGS 16 data. We will collaborate with Lacerta Therapeutics, a company with long experience with rAAV capsid 17 technology and scalable manufacturing platforms. In Phase I, we will assess the reliability, consistency, and 18 accuracy of NGS for quantifying production-induced mutations in rAAV vector DNA. We will generate sequence 19 data from encapsidated DNA of a self-complementary (sc)AAV vector produced in two different systems (human 20 and insect cell line). In Specific Aim 1, we will use single genome amplification (SGA) followed by Sanger 21 sequencing to generate highly accurate near-full length genomes (NFLG) of the vector DNA, which will enable 22 precise quantification of the actual production-induced mutation rate. In Specific Aim 2, we will generate data on 23 three NGS platforms: Oxford Nanopore, Pacific Bioscience Single Molecule Real Time, and Illumina. We will use 24 several different approaches for error-correction, including combining data from two or more sequencing 25 platforms, and compare results to the known mutations as identified through SGA. Our goal is to develop a 26 bioinformatics pipeline that leverages the power and efficiency of NGS while attaining the level of accuracy of 27 SGA for identifying true production induced mutations. In Phase II, we will continue to address other AAV NGS 28 quality control issues and build our SaaS in the cloud, where we will be able to quickly scale and innovate as 29 needed.

1 BioinfoExperts开发病原体相关的下一代测序（NGS）分析和软件 2越来越多的需要基因组表征的行业。在拟议的SBIR I期项目中，我们 3将开发针对重组腺相关病毒（RAAVS）制造业的NGS分析；在 第4阶段，我们将将NGS Analytics（Saav）（Raav 5个制造商）可以访问为其流程明确设计的定制分析管道的结果。 6在临床试验中有> 200种RAAV产品，已经有三个由FDA批准的RAAV介导的基因疗法 7在市场上。随着行业的扩大，FDA要求更严格的RAAV制造质量控制 8（QC）确保患者安全的方法。实际上，FDA和Dark Horse Consulting Group都是咨询 9 Raav行业委员会已向RAAV QC推荐了NGS；但是，NGS的方法 10 RAAV数据的评估尚未成熟到足够准确或高吞吐量的程度 11用于广泛采用。众所周知，来自Raav的NGS很难处理，部分原因是生产 RAAV的12个过程和基因组结构，结合了测序平台的局限性，例如高 13错误率，对简短读数的偏见以及产生嵌合读数的潜力。与数十年的病原体 - 14相关的生物信息学经验和成功的软件与服务业务模型，Bie计划 15积极进入这个竞争激烈的市场，并系统地解决与RAAV NGS相关的问题 16个数据。我们将与Lacerta Therapeutics合作，这是一家在Raav Capsid的经验丰富的公司 17技术和可扩展的制造平台。在第一阶段，我们将评估可靠性，一致性和 18 NG的准确性用于量化RAAV载体DNA中生产诱导的突变。我们将生成序列 19来自在两个不同系统中产生的自相互（SC）AAV向量的封装DNA的数据（人类） 20和昆虫细胞系）。在特定目标1中，我们将使用单个基因组扩增（SGA），然后使用Sanger 21测序以生成载体DNA的高度准确的接近长度基因组（NFLG），这将启用 22实际生产引起的突变率的精确定量。在特定的目标2中，我们将生成有关 23三个NGS平台：牛津纳米孔，太平洋生物科学单分子实时和Illumina。我们将使用 24错误校正的几种不同方法，包括结合两个或多个测序的数据 25个平台，并将结果与​​通过SGA识别的已知突变进行比较。我们的目标是开发 26利用NG的功率和效率的生物信息学管道，同时达到准确性的水平 27 SGA用于识别真正的生产引起的突变。在第二阶段，我们将继续解决其他AAV NGS 28质量控制问题并在云中建立我们的SaaS，我们将能够快速扩展和创新为 29需要。