Maintaining sufficient volumes of high-quality seed culture is a major challenge, especially as more automated production platforms come online. These platforms typically demand 10-100L of cell culture with =99% viability each week, which at this scale is labor-intensive and difficult to automate. Here we are introducing a fully-automated platform to resolve these issues and bring reliability and scalability to traditional cell culture work.

Next-Generation biologics for biotherapeutics and drug development requires advanced and complex characterization assays. We present a novel device that enables complete sample preparation, including thermal heating, centrifugation, filtration, viscosity independent ML/AI integrated liquid handling, vacuum washing, and microfluidic electrophoresis procedures. This device enables higher complexity microfluidic characterization assays, particularly protein-based assays for biotherapeutic development to run independently, efficiently, and reproducibly.

Improving protein production efficiency and increasing capacity to produce large screening sets is crucial to accelerate early-stage discovery and lead optimization. This talk will highlight the integration of Design of Experiments (DOE) methodologies with automated liquid handling to systematically optimize a transient Chinese Hamster Ovary (CHO) expression system. Key factors explored include plasmid DNA usage, transfection reagent, and transfection and feeding strategies. The automated DOE workflow leads to improved cell viability, increased protein production titer, and decreased transfection DNA usage without compromising product quality, while maintaining culture scale and compatibility with high-throughput automated protein production.

• What challenges do we face? 
•  What improvements do you apply? 
•  What might address future needs?

Join a group to share and experience and hear what others have learned:

Think Tank 1: Workflow vs technology development?

Think Tank 2: When and how to go from benchtop to R&D?

Think Tank 3: Doing more with less: how do you test new methods and workflows without blowing up your annual budget?

Think Tank 4: Tearing down silos: how do you foster cross-functional collaborations to innovate and improve?

5:10 Think Tank Report Outs: Listen and Learn​

During the Think Tank Table discussions, we shared our experiences and working solutions for end-to-end protein production workflows. Now as a collective community, let’s hear from the table facilitators as they share key discussion points and strategies and provide a wrap-up of their table’s discussion. What can we take away and apply?

High-throughput protein production remains a time-consuming, expensive bottleneck that relies heavily on trial-and-error experiments. Here we present a holistic, automation-friendly solution to maximize the efficiency, by leveraging synthetic biology and AI techniques. It involves the application of a massive library for multiplexed expression improvement and AI-aided optimization, and chromatography-free purification via synthetic organelles in vivo. This integrated pipeline de-risks discovery by making protein production a predictable, scalable, and automated engineering task.

Production of multi-specific antibodies is often complicated by product related impurities, such as mispaired species and half molecules. Without repeated productions and process optimization, it is hard to predict the impurities and downstream process. We will share an analytics platform that is implemented at harvest, prior to affinity chromatography consisting of HT MS, cGE, and analytical HPLC, to help quantitate impurities and guide purification strategies.

Mapping the human intracellular chemoproteome is a major frontier for drug discovery.  I will describe an optimized high-throughput protein expression and purification workflow that seamlessly integrates with our next-generation DEL screening platform. By systematically identifying and eliminating key noise sources through reengineered methodologies, we have dramatically improved DEL data quality. We are now approaching 1,000 human proteins profiled — an unprecedented scale that reveals clearer protein–small-molecule interaction patterns and reduces hit-set artifacts common in small DEL experiments. I will conclude with perspectives on how large, high-quality experimental datasets are essential for training truly predictive ML/AI models in drug discovery.

Protein Sciences at Aera Therapeutics has developed a robust multi-production platform that encompasses multiple expression systems, utilization of complex chromatography over automated 4-step and 2-step preparative purifications, and automated small-scale productions to support an internally built yeast display platform.

Mammalian cell transfection enables the rapid generation of diverse large-molecule modalities for drug discovery. We have built an automated ecosystem to produce highly pure, complex antibodies suitable for cell-based functional assays and developability assessments. Although automation of protein production has grown exponentially in recent years, automated QC and characterization platforms remain challenging. Here, we present a high-throughput workflow for protein quantification and purity evaluation that accelerates hit-to-lead progression.