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.
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.