This week on Data in Biotech, we’re joined by Amy Gamber, VP of Manufacturing at Atara Biotherapeutics, an allogeneic T-cell immunotherapy company developing off-the-shelf treatments to help achieve faster patient outcomes.

As a treatment that sits at the cutting edge of options available for cancer and autoimmune disease, host Ross Katz explores how Atara is able to deliver personalized medicine that can be with the patient inside a three-day window.  

Amy is clear-eyed about what works well in this field and what doesn’t. We gain insight into the complexity of developing this type of cell therapy and the subsequent production challenges of manufacturing at scale. We also cover the manufacturing process, corresponding data problems Amy encounters on a day-to-day basis in her role as VP of Manufacturing, and the strategies she employs to overcome them.

Amy discusses the importance of continuous quality monitoring and the need to introduce it from an early stage to see how a program changes through the development phases. She highlights the importance of data as a tool for the ‘detective work’ needed to understand where problems arise during manufacturing.

Finally, she and Ross close the episode by discussing the future of cell therapy manufacturing, a world where modeling enables predictive QC, the possibilities of AI, and the need to standardize data. 

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

Chapter Markers

[1:56] Introduction to Amy and the manufacturing process at Atara, including the importance of cryo storage to facilitate faster patient treatment.  

[6:37] Amy and Ross discuss the challenge of donor variability in cell therapy manufacturing and how to manage it.

[12:38] Ross asks about scaling cell therapy production and the different considerations for small batch versus commercial-scale manufacturing.

[15:47] Amy discusses the importance of continuous quality monitoring, highlighting the value of tracking metrics to ensure quality control and identify improvement opportunities

[18:46] Ross moves the focus to automating data collection, as he and Amy emphasize the need for more efficient data access and analysis for timely decision-making.

[20:50] Ross and Amy explore the data challenges biotechnology companies face, including the problem with manual data processes, creating feedback loops, and regulatory compliance.

[25:16] Amy explains how Atara addressed manufacturing efficiency challenges, the importance of ‘detective work’ to understand problem causes, and the process of solving them. 

[33:28] Ross and Amy examine how to use data to gather meaningful manufacturing insights, particularly identifying true signals when analyzing small datasets.

[36:33] Ross talks about predictive QC measures as the solution to the point Amy makes about being able to guarantee product quality from the outset.

[37:31] Amy gives her perspective on the future of biotech manufacturing, the role of AI, predictive modeling, and the need for standardization in the industry.

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