2025/03/24 by Müller, Claudio, Siegwart, Gerald, Heider, Susanne +5
Biochemistry, Genetics and Molecular Biology · #500 Naturwissenschaften und Mathematik::570 Biowissenschaften #600 Technik #Biologie #Biologie::570 Biowissenschaften #CRISPR and Genetic Engineering #Medizin #Viral Infectious Diseases and Gene Expression in Insects #Virus-based gene therapy research #angewandte Wissenschaften::660 Chemische Verfahrenstechnik::660 Chemische Verfahrenstechnik #design of experiments #human embryonic kidney suspension cell #hybrid modeling #parallel mini-bioreactors #rAAV production
paper · doi:10.14279/depositonce-24789
openalex publication_date 2025/03/24 · openalex created_date 2025/12/10 · openalex updated_date 2026/07/01
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.