A mathematical model could help reduce the experimental effort needed to develop production processes for gene therapies. The digital twin, developed by a team at University College London (UCL) in collaboration with Volker Hass, PhD, at Furtwangen University in Germany aims to improve on traditional techniques for process development.
“Our plan is to use it to reduce the number of experiments needed along with the time and expense for the process development stage,” explains Frank Baganz, PhD, associate professor in fermentation and cell culture at UCL.
According to Baganz, recombinant adeno-associated viruses (rAAVs) show great promise in gene therapy. However, traditional techniques for improving rAAV titers, such as Design of Experiment (DOE) often require many expensive experiments. Instead, the team developed a mechanistic mathematical model using differential equations to describe cell growth, transfection and rAAV production.
The model was initially trained with data in the published literature., explained Baganz, adding that the results were tested against those from a reference process for rAAV production. The reference process involved triple plasmid transfection of HEK293F cells in a shake flask, and was completed after the team ran their predictive model.
The team, which discovered that the model predicted trends in viral vector formation but struggled to fit the cell density data later in the experiment, now intend to improve the model further, so it can describe experimental results more accurately. This includes an iterative process where experimental results are used to adjust the model to better fit the data.
The combination of synchronous experimental process development and digital twin core model development is among the first of its kind, says Baganz.
“Most applications have used them [digital twins] on microbial or cell culture processes, which have one-stage growth and production, whereas viral vector production is more challenging because it has three different stages – cell expansion, transfection and a third production phase.”
The post Digital Twin Focuses on Improving Viral Vector Production appeared first on GEN - Genetic Engineering and Biotechnology News.
“Our plan is to use it to reduce the number of experiments needed along with the time and expense for the process development stage,” explains Frank Baganz, PhD, associate professor in fermentation and cell culture at UCL.
According to Baganz, recombinant adeno-associated viruses (rAAVs) show great promise in gene therapy. However, traditional techniques for improving rAAV titers, such as Design of Experiment (DOE) often require many expensive experiments. Instead, the team developed a mechanistic mathematical model using differential equations to describe cell growth, transfection and rAAV production.
The model was initially trained with data in the published literature., explained Baganz, adding that the results were tested against those from a reference process for rAAV production. The reference process involved triple plasmid transfection of HEK293F cells in a shake flask, and was completed after the team ran their predictive model.
The team, which discovered that the model predicted trends in viral vector formation but struggled to fit the cell density data later in the experiment, now intend to improve the model further, so it can describe experimental results more accurately. This includes an iterative process where experimental results are used to adjust the model to better fit the data.
The combination of synchronous experimental process development and digital twin core model development is among the first of its kind, says Baganz.
“Most applications have used them [digital twins] on microbial or cell culture processes, which have one-stage growth and production, whereas viral vector production is more challenging because it has three different stages – cell expansion, transfection and a third production phase.”
The post Digital Twin Focuses on Improving Viral Vector Production appeared first on GEN - Genetic Engineering and Biotechnology News.