AbbVie And Amgen's Blueprint To Digitalize Biomanufacturing

By Tyler Menichiello, Chief Editor, Bioprocess Online

It’s an understatement to say that it’s been a difficult time in biotech. Between the funding cuts and layoffs (lamented daily on the r/biotech subreddit), and broad restructuring across the space (most recently illustrated by Takeda axing its cell therapy portfolio), industry morale could use a boost. However, despite general market conditions, I left last month’s BioProcess International (BPI) conference in Boston with a sense of optimism. Not because things are necessarily looking up from a funding point of view (unfortunately, that will take time), but because the brightest minds and innovators are still working tirelessly to plow ahead and find ways to deliver advanced therapies to patients.

One exciting area of innovation, and a theme that threaded through most sessions at this year’s BPI, is digitalization. By no means is the digital revolution new in biotech, but my impression from this show is that companies like AbbVie and Amgen, who made strategic investments in technology more than a decade ago, are finally seeing a return on their efforts.

Representatives from each company gave plenary keynotes at this year’s BPI. Below are my key takeaways from both of these sessions.

Amgen On Leveraging Data, Building Digital Skills In The Workforce

The opening keynote was delivered by Amgen’s VP of site operations, Thomas Seewoester, Ph.D., titled “From Pipettes To Prompts: Accelerating Biomanufacturing At Exponential Velocity.” He spoke about the company’s approach to flexible manufacturing (through its hybrid, modular FleXBatch platform) and how it’s embracing AI to streamline operations for efficiency.

“We are not just changing tools, we are changing the tempo of biomanufacturing,” Seewoester said.

This tempo change is in no small part thanks to today’s information technology, according to Seewoester. He outlined the five “digital accelerators” that are enabling this transformation:

• Processing Power
• Bandwidth
• Data Storage
• Data Generation
• AI

Advances in these areas have allowed companies like Amgen to revolutionize biomanufacturing and move with agility and high velocity towards commercialization. At the intersection of all five accelerators is data — but it’s how companies utilize these data that matters.

“Data is no longer the new oil,” Seewoester said. “Wisdom is.”

This statement seems to represent an evolution in the company’s mindset from last year, when its SVP of process development, Jerry Murry, Ph.D., said at the Bioprocessing Summit that data is the new oil. It also exemplifies a broader shift in the industry away from focusing on the value of data itself and more towards the valuable insights that tools like AI can extract from the data. (I discussed this shift with independent AI consultant Kat Kozyrytska at BPI, which you can watch here.)

That AI will unlock new, actionable insights from data is certainly not new — it’s been a trend in discovery for a couple years now. However, my impression from this year’s BPI is that companies (mainly Big Pharma with its deep pockets and resources) are increasingly applying this utility to drive advancement and speed in manufacturing.

At Amgen, this increased velocity is being achieved through three dimensions, according to Seewoester: Hardware, Software, and Mindset.

 “Machines act as teammates,” he said, and operators are the orchestrators. Seewoester describes Amgen’s progressive mindset as figuring out where to apply these technologies to have the greatest impact.

Towards the end of his keynote, Seewoester talked about how this technological evolution will impact the workforce. Digital skills — knowing how to build something with data and AI — will increasingly become a prerequisite in biotech. He put the onus on companies to shape these “builders of tomorrow,” saying that they are not just found; these employees need to be built up and retained.

To do this, Seewoester said, companies need to focus on training new hires to be adaptable, curious, and digitally savvy. In the future of biotech, the right combination of these attributes may ultimately outweigh credentials when it comes to entering the workforce.

AbbVie’s Lab-In-A-Loop Accelerating CMC

AbbVie’s VP of biologics product development, Kartik Subramanian, Ph.D., gave a plenary keynote titled “From Scale to Speed: Building the Next Era of Biologics Innovation.” In this keynote, Subramanian explained how AbbVie is moving towards a CMC model that prioritizes agility and robustness at the onset, as opposed to its historical model of iterative improvement in the long-term. Humira (one of the best-selling drugs of all time) represents the latter approach.

“It’s a story of continuous, iterative improvement over 20 years,” said Subramanian. The company scaled Humira’s manufacturing over five different sites globally during that time, reaching a peak productivity of roughly one metric ton of product per year and delivering approximately 1,400 batches.

“We improved the productivity per batch by about 35% iteratively over time,” he said. It took approximately 10 years to reach a steady state of productivity, with a focus on ensuring product quality over speed.

Skyrizi, on the other hand, has had a much more compressed timeline — achieving steady-state production over just four years, according to Subramanian. AbbVie achieved this through a deeper understanding of processes, established platforms, and making step changes in development, rather than the slow, incremental changes that helped scale Humira’s production.

The company’s current focus is to accelerate its productivity even more by developing an effective “lab-in-a-loop model” that integrates automation, machine learning (ML), advanced analytics, and human insight to optimize workflows in tandem. “The way I see [lab-in-a-loop], is it’s a combination of using laboratory experiments along with digital technologies that can work together in a continuous, adaptive cycle,” Subramanian explains. The goal, he said, is to advance beyond a sequential workflow to one resembling a “Formula One pit stop,” to develop a CMC model that can sustain the developmental speed, complexity, and volume of new assets.

AbbVie’s lab-in-a-loop is being built on the following pillars:

• Integrated workflows — moving away from sequential steps and iterative design to cross-functional work and modeling
• Smart Experimentation — Automating experiments and doing unsupervised perturbations to generate novel data that feeds into ML models
•  Deep Analytics — Collecting more data per sample or experiment to inform these models
• Adaptive Modeling — Utilizing techniques like Bayesian optimization to predict better performance; Subramanian describes this approach to modeling as “self-driving” in nature and uses the analogy of a car’s GPS that automatically reroutes to reduce travel time
• Digitalizing Workflows — Converting manual workflows into electronic and computerized systems to achieve greater connectivity and reduce the time scientists spend on data collection

Most importantly, at the center of this loop is human insight and decision-making, said Subramanian. The technologies enabling this digital revolution are tools, after all, not replacements for human creativity and problem-solving. The current objective is to use these tools to break down silos and ease the burden of manual workflows, taking organizations like AbbVie one step closer to operating at the speed of a Formula One pit crew. The long-term goal, however, is to culminate these technologies into true, digital twins that can predict and anticipate variability at launch to even further reduce the time it takes to deliver life-saving therapies to patients.