Cytiva

We are lucky to live in an age where many things can be forecasted. We can predict when our food delivery order will arrive, when a plane will land in real-time and when our target-date funds will mature. For biotech and biopharma companies advancing the next generation of medicines, forecasting is more challenging. They cannot know the success of their therapeutic manufacturing batch for days or weeks after it finishes.

Biopharma and biotech manufacturers face many challenges in the biopharma 4.0 era, with speed to market, access to talent, risk mitigation, and cost control being at the top of the list. In addition, biomanufacturing inefficiencies still exist today. Historically, the industry’s slower adoption rates of enterprise-wide automation can be attributed to two factors - high implementation costs and slow speed-to-market - resulting in high risks and cost associated with quality problems. Fortunately, recent advancements in digitalization can help address and overcome these challenges.

Over the last few years, the industry has been moving toward adopting digital and automation solutions in tandem with increasing single-use technology solutions, expanding capacity by using scalable and flexible solutions, and manufacturing “in region, for region” to meet growing demand for products and services. Adaptation, innovation, and collaboration are today the essential characteristics of all companies in the life sciences industry to enable fast, flexible, and reliable bioprocess development toward discovering and delivering future therapeutics.

I want to share four key technology advances that are available today to help manufacturers transition to and solve biopharma 4.0 challenges:

In-Silico process development to speed development of high performance, flexible processes;

Predictive batch, to reduce frequency of failed or underperforming batches;

Overall equipment effectiveness, to decrease customer downtime via predictive and remote maintenance; and

Adaptive plant, to reduce transfer cost where, for example, Cytiva’s virtual reality training system allows personnel to learn processes in advance.

Process development is an intense and time-consuming part of making a therapy. To build a bioprocessing operation that’s flexible and scalable for the future, companies can strengthen digital capabilities in bioprocessing through in-Silico process development. In 2021, we included German scientific software maker GoSilico in our portfolio. In-Silico simulation builds digital twins of downstream processing, thereby revealing how process parameters affect attributes. The result is a scalable and robust solution within about one week, a reduction in experiment materials and, more impactful and confident decision-making.

“Together with digitalization, especially in data management, is a bright spot for biopharma manufacturers to bring the next generation of medicines.”

Through predictive batch, biotechs can reduce the frequency of failed or underperforming batches. I am happy to share that in 2022, predictive modeling output was successfully used in two regulatory filings. Cytiva’s downstream predictive modeling software, which determines optimal purification conditions through simulation or simulated data, is used by companies to achieve better process yield, higher product purity, and faster regulatory filings. Our upstream predictive modeling software, on the other hand, was used by UMass Medical Gene Therapy Center to achieve right-first time scale up from 50L to 200L production of AAV vectors.

There is also so much potential around data sharing. Stronger integration, data collection, and analysis are crucial to create a manufacturing platform capable of core data management. To bolster our biopharma 4.0 offering, we implemented an Augmented Reality solution OptiRun* View and My Equipment*, to decrease equipment downtime and speed up repairs.  We are exploring how we can continue to help customers save time, money, and increase output by understanding what happened in every stage of a process.

Thirdly, an adaptive plant can accelerate speed to market as well as reduce costs and error. Our FlexFactory* configurable manufacturing train provides access to Current Good Manufacturing Practice (cGMP) biomanufacturing capacity by utilizing bioprocess equipment with pre-configured and pre-verified automation software. The latter mitigates risk by reducing implementation, documentation, testing, and validation effort and time and human error. In turn, it allows for flexible implementation of manufacturing execution system (MES). As manufacturers strive to build digital maturity with enterprise automation, Cytiva’s Figurate* automation software offers a robust platform to streamline manufacturing operations and capture valuable data to improve process control.

As per data in the 2023 Global Biopharma Resilience Index, companies in Asia Pacific find it hard to find mature talent and train fresh hands. Training personnel face to face creates fundamental challenges in bioprocessing. These challenges include regional barriers as well as limited access to equipment and trainers. In search of a solution, we’ve turned to virtual reality. This virtual training system is a strategic collaboration with our customers as part of their digital strategy. The big pluses for our customers are that they don’t have to shut down commercial production or maintain equipment used only for training.

Collaboration is essential to transform biopharmaceutical manufacturing in the biopharma 4.0 era. I believe that innovation together with digitalization, especially in data management, is a bright spot for biopharma manufacturers to bring the next generation of medicines. I’m excited about the adoption of digital solutions to enable flexible, reliable bioprocess development.