How Is AI Transforming Biomanufacturing and Biopharma Workforce Training?

*Full transcript available below

Sigma Mostafa, PhD, chief scientific officer at KBI Biopharma, discusses key strategies and trends shaping the future of biomanufacturing in an interview with BioPharm International® during CPHI Frankfurt. Dr. Mostafa exhibits a keen focus on digital transformation and artificial intelligence (AI). Drawing on more than 15 years of experience at KBI Biopharma and prior roles at top companies like Eli Lilly and Co. and Merck, known as MSD outside of the United States and Canada, Dr. Mostafa provides insights into optimizing processes, overcoming supply chain hurdles, and preparing the workforce for the AI era.

Efficient supply chain management remains pivotal, and Dr. Mostafa notes the value of "building true partnership with the vendors" to secure long-term supply continuity and reduce costs. New technologies such as moving from columns to membranes, especially for early-stage human processes, offer significant cost benefits for smaller firms and streamline operations.

How is AI-driven digital transformation revolutionizing biomanufacturing processes?

One of the most compelling insights centered on automation, data, and AI. “Being able to track all the analytical data from batches—and being able to predict what is going to happen with the batch—through digital twins, through various other technologies, are game changers,” she emphasizes. This notion captures the transformative role AI and high-throughput automation now play, not just in experiment speed but in overall manufacturing efficiency and regulatory validation.

Dr. Mostafa also identified current challenges, particularly around integrating AI in good manufacturing practice-regulated settings. Data quality, data formats, and skills gaps present ongoing hurdles, leading KBI Biopharma to emphasize the need for training and internal champions to drive successful digital adoption, according to Dr. Mostafa. She points out that universities and tech giants now offer valuable resources to equip staff, reinforcing that “each company now has a responsibility to upscale the training of their staff.”

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About the speaker

Sigma Mostafa, PhD, Chief Scientific Officer, KBI Biopharma

Dr. Mostafa oversees analytical services, process development, and cell line development at KBI Biopharma. Over her 13-year tenure, she has driven the company’s rapid growth, building KBI’s North Carolina operations into a world-class contract development and manufacturing organization with advanced automation and scaling its development programs tenfold. Previously, she served as senior vice-president and site head, leading KBI’s largest site through unprecedented expansion and a successful FDA pre-licensure inspection.

Transcript

Editor's note: This transcript is a direct, unedited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

I'm Sigma Mostafa. I'm chief scientific officer at KBI biopharma. In this role, I'm responsible for the vision of our new offerings, as well as for our cell line and process development and analytical services overall. Prior to coming to KBI, 15 years back, I was with Eli Lilly and Merck for several years

That is a very important question, and it really comes down to demand planning, essentially right, being able to plan as far ahead as possible and building true partnership with the vendors, which is what we have been doing in terms of being able to provide long term supply chain requirements, as well as looking at different locations the vendors may have for same product, and being able to access the products from from locations that that are going to be favorable to us. In case of a cdmo, of course, the materials cost goes to client, but we want to make sure we do the best we can, both in terms of cost and in terms of timeline impact of these supply chain challenges. So we have been proactively improving our supply chain processes and building partnership with the vendors to stay ahead of this challenge.

Some of the process optimization trends we are seeing are in terms of moving more towards membrane technology for capture. We know protein a resin, is extremely costly, and being able to move from columns to membranes for first in human processes, especially, can be quite a bit of a cost savings for for smaller companies. And we also are seeing lot of use of in what we call n minus one per fusion, in terms of getting to higher cell density in the seed reactor and being able to run the production reactor for a shorter period of time, so you can reduce the production process from 14 or 16 days to perhaps half the time, which has an impact on how many batches you can run in manufacturing. In addition to that, obviously, all the automations and high throughput technologies that are continue to come in the bio manufacturing sector is having significant impact, both in terms of being able to do experiments faster, but also in terms of batch release and batch validation.

So in terms of manufacturing efficiency, being able to track the manufacturing batches, being able to track all the analytical data from batches, and being able to predict what is going to happen with the batch, through digital twins, through various other technologies, are Game changers. That is where we see gaining efficiency. That is where for commercial batches, we can improve success rate. We also see some challenges. Being able to bring in AI and digital technology in the GMP realm, in a regulatory setting, can be challenging. So then the guidance and being able to create that precedence is there is lot of focus. Now, what we also see challenges on is the data quality and the data formats. Lot of companies have spent significant amount of money to build their own systems for digital transformation and with mixed success. So it is important to build small successes and demonstrate to the organization that this this can create efficiency and to understand how the data structure will work out, so that historical data can be taken advantage of as well when we are implementing AI into manufacturing. One advantage, of course, is being able to look at current runs. But if you can look at historical data and put your current batch in perspective against that, that is significantly more powerful, and that's where lot more data crunching and having the proper format of data is needed.

Great question. This skill gap is a real challenge, and the trainings are also not very well defined, right? Obviously, training and retraining of our employees is going to be needed. What will also be needed is to find a segment of the employee population who wants to become experts and who can be the champions within the organizations, rather than having a separate AI training group, which may be needed as well, but having embedded technical folks who are also very savvy in digital solutions is what is needed to overall bring up the skill set of the organization,

We are seeing very good programs being created in a lot of universities that have bio manufacturing masters programs where trainings are being given, not just on hands on manufacturing, but on a lot of these digitalization tools. We also see that a lot of the larger tech companies are getting into the health sector, and they are creating lot of training opportunities. There are certainly online, lot of training available. So some of it has to be self training for people, but each company now has a responsibility to to, you know, up upscale the training of their staff.