Iterative hybrid model based optimization of rAAV production.

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Claudio Müller, Gerald Siegwart, Susanne Heider, Michael Sokolov, Angela Botros, Alexandra Umprecht, Moritz von Stosch, Mariano Nicolas Cruz Bournazou
{"title":"Iterative hybrid model based optimization of rAAV production.","authors":"Claudio Müller, Gerald Siegwart, Susanne Heider, Michael Sokolov, Angela Botros, Alexandra Umprecht, Moritz von Stosch, Mariano Nicolas Cruz Bournazou","doi":"10.1002/btpr.70006","DOIUrl":null,"url":null,"abstract":"<p><p>Changes in serotype or genetic payload of recombinant adeno associated virus (rAAVs) gene therapies require adapting the transfection conditions of the upstream HEK293 cultivations. This study adopts an iterative model-based experiment design approach, where increasing data availability is leveraged to evolve models of different complexity. Initial models based on data from shaker flask runs guided the design of the first round at Ambr250 scale. With Ambr250 data becoming available, hybrid models capturing process state evolutions and historical models incorporating these evolutions to predict rAAV titer, were developed. These models were then combined into a full model approach, which was utilized within a Bayesian Optimization framework for the design of a second round of Ambr250 scale runs. The iterative approach was tested across different projects applying transfer learning to enhance the predictive power and improve the subsequent optimization. The approach was benchmarked against a statistical Design of Experiment method. The results show that the model-based experiment design consistently (and across projects) produces higher rAAV titer values than the benchmark approach (Project C: 4.4% or 7.0% increases in titer values relative to the response surface modeling approach for ELISA and ddPCR, respectively; Project D: 32.4% or 10.9% increases in titer values relative to the standard DoE-screening pick for ELISA and ddPCR, respectively), effectively optimizing the transfection mixture composition. The combination of propagation and historical models, augmented by transfer learning and an ever-increasing amount of data, enhanced the process design workflow, contributing to improved rAAV production through efficient transfection strategies.</p>","PeriodicalId":8856,"journal":{"name":"Biotechnology Progress","volume":" ","pages":"e70006"},"PeriodicalIF":2.5000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biotechnology Progress","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/btpr.70006","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Changes in serotype or genetic payload of recombinant adeno associated virus (rAAVs) gene therapies require adapting the transfection conditions of the upstream HEK293 cultivations. This study adopts an iterative model-based experiment design approach, where increasing data availability is leveraged to evolve models of different complexity. Initial models based on data from shaker flask runs guided the design of the first round at Ambr250 scale. With Ambr250 data becoming available, hybrid models capturing process state evolutions and historical models incorporating these evolutions to predict rAAV titer, were developed. These models were then combined into a full model approach, which was utilized within a Bayesian Optimization framework for the design of a second round of Ambr250 scale runs. The iterative approach was tested across different projects applying transfer learning to enhance the predictive power and improve the subsequent optimization. The approach was benchmarked against a statistical Design of Experiment method. The results show that the model-based experiment design consistently (and across projects) produces higher rAAV titer values than the benchmark approach (Project C: 4.4% or 7.0% increases in titer values relative to the response surface modeling approach for ELISA and ddPCR, respectively; Project D: 32.4% or 10.9% increases in titer values relative to the standard DoE-screening pick for ELISA and ddPCR, respectively), effectively optimizing the transfection mixture composition. The combination of propagation and historical models, augmented by transfer learning and an ever-increasing amount of data, enhanced the process design workflow, contributing to improved rAAV production through efficient transfection strategies.

基于迭代混合模型的rAAV生产优化。
重组腺相关病毒(raav)基因治疗的血清型或遗传有效载荷的变化需要适应上游HEK293培养的转染条件。本研究采用基于迭代模型的实验设计方法,利用不断增加的数据可用性来进化不同复杂性的模型。基于摇瓶运行数据的初始模型指导了Ambr250规模的第一轮设计。随着Ambr250数据的可用,捕获过程状态演变的混合模型和结合这些演变的历史模型被开发出来,以预测rAAV滴度。然后将这些模型组合成一个完整的模型方法,该方法在贝叶斯优化框架中用于第二轮Ambr250规模运行的设计。该迭代方法在不同的项目中进行了测试,应用迁移学习来增强预测能力并改进后续优化。该方法以统计实验设计方法为基准。结果表明,基于模型的实验设计始终(跨项目)比基准方法产生更高的rAAV滴度值(项目C:相对于ELISA和ddPCR的响应面建模方法,滴度值分别提高4.4%或7.0%;项目D:相对于标准的doe筛选选择(ELISA和ddPCR分别提高了32.4%和10.9%),有效优化了转染混合物的组成。传播模型和历史模型的结合,加上迁移学习和不断增加的数据量,增强了工艺设计工作流程,通过有效的转染策略有助于提高rAAV的生产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biotechnology Progress
Biotechnology Progress 工程技术-生物工程与应用微生物
CiteScore
6.50
自引率
3.40%
发文量
83
审稿时长
4 months
期刊介绍: Biotechnology Progress , an official, bimonthly publication of the American Institute of Chemical Engineers and its technological community, the Society for Biological Engineering, features peer-reviewed research articles, reviews, and descriptions of emerging techniques for the development and design of new processes, products, and devices for the biotechnology, biopharmaceutical and bioprocess industries. Widespread interest includes application of biological and engineering principles in fields such as applied cellular physiology and metabolic engineering, biocatalysis and bioreactor design, bioseparations and downstream processing, cell culture and tissue engineering, biosensors and process control, bioinformatics and systems biology, biomaterials and artificial organs, stem cell biology and genetics, and plant biology and food science. Manuscripts concerning the design of related processes, products, or devices are also encouraged. Four types of manuscripts are printed in the Journal: Research Papers, Topical or Review Papers, Letters to the Editor, and R & D Notes.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信