Digitalisation is the Future Norm of Biomanufacturing

Christopher Long, Global Marketing Leader, Digital Solutions at Cytiva explores how digitalisation is going to be the new norm in biomanufacturing. 

Digital and automation strategies are redefining biomanufacturing and enabling companies to deliver medicines to patients faster. The adoption of digital and automation strategies will better enable process optimisation – driving down the cost of goods, speeding up the time to market, eliminating waste, and reducing the risks while improving reliability and predictability.

In essence, a digital strategy is the scalpel to bring precision in accelerating drug discovery. There are three major catalysts contributing to the digitisation of the biopharma industry: the rise of in silico science offering optimisation of process performance, the need for data-driven decisions that heighten reliability, and the need for solutions to be faster and more agile.

In silico for a ‘Gold Rush’ of Data

Bioinformatics and computational biology, advances in bioprocess monitoring and analytics are changing the bioproduction process. But what is the ‘why’ for using in silico process development?

This transformation of manufacturing capabilities is being defined by greater automation, in-line probes, at-line sampling feed high throughput analytics, and robotics, because they are powerful computing hardware with flexible and connected data storage capacities. Is it any wonder then that experts are employing artificial intelligence (AI) into their systems when they can be rewarded with continuous optimisation of process performance?

For example, using scaling tools that simulate operations can provide insights, and reduce trial and error approach. Moving away from do-it-yourself, siloed formulas for bioprocess scale-up or scale-out allows for a different approach to better predict and visualise your scaling needs. Thus, in this instance in silico results are defined by a more efficiently established and verified scaling process that helps save time, ensure product quality, de-risk scaling activities, accelerate development milestones, and instill confidence of the process while reducing costs.

Ultimately, you have better control over your systems and increased efficiency results in a greater likelihood of achieving your end goal.

That is the ‘why’ for using in silico process development.

Bridging the ‘Islands of Automation’

The industry is moving at accelerating speeds towards integrating digital technologies into their process development and manufacturing because the integrated capability to synthesise, analyse, summarise your data reveals valuable insights. It can turn data into decisions.

Digital tools help biomanufacturers collect, manage, and analyse vast amounts of data from sensors, equipment, and other sources in real-time. This data can be used to identify trends, make informed decisions, and improve the overall manufacturing process.

Industry needs have moved beyond the descriptive toward the predictive and prescriptive in its analytics. The capabilities of the digital technologies must include synthesising, analysing, summarising because the data is becoming increasingly complex.

Here is the caveat: Advancing technologies like AI and machine learning need to be integrated with the process development and manufacturing. The ‘islands of automation’ have the potential to provide insights, but it is the integration of these automated systems and subsystems which hold the ‘pot of gold’ at the end of the rainbow.

These improvements in manufacturing – courtesy of digitalisation – can be translated across the entire lifecycle. Simulation and modelling can be used to create a fast, flexible, and reliable process. Advanced technologies like AI and machine learning with dashboards can enable you to make decisions quicker and with more information at your fingertips. This is because these technologies make it easier to interpret and understand vast data sets thus, decreasing potential failure points.

Why integrate it into an entire lifecycle? Digitalisation is designed to meet different demands based on different needs – a CDMO has different needs to a research company. Digital unlocks vital potential to speed up drug to market while not sacrificing quality by utilising systems with the capability of recipe and process transfer throughout the entire lifecycle from research, to process development, to clinical, to manufacturing. Thereby, providing insights to drive decisions that impact customers and patients alike.

Faster and More Agile Solutions

In silico process development can make the manufacturing workflow less time consuming, tedious, and labor-intensive than running traditional experiments. Digital solutions are helping to decrease therapeutic time to market and get lifesaving drugs to patients both faster and smarter.

Faster and more agile workflows are integral in the current climate. It is digitally connected systems and equipment in the drug development and manufacturing process that will enable and expedite this.

For patients, the ultimate recipient at the end of the workflow, the ability to improve yield and reduce time to market is smart and essential. With an integrated process development, the data-driven insights can allow for strategies to pivot where needed. Responding to changes in your pipeline or forecasting for different batch sizes or formats makes this a much more agile manufacturing process.

Increased speed from process modelling and simulation software helps optimise processes for maximum efficiency and quality, reducing waste, minimising downtime, and identifying potential problems before they occur. The same can be said for monitoring and controlled product quality throughout the manufacturing process using sensors and other monitoring tools to detect deviations from established quality parameters and taking corrective action in real-time.

We are on the verge of the ‘digital’ revolution of the industry which will enable positive change for customers and patients alike. Increasing digital integration to the biomanufacturing landscape will result in a more robust, resilient, and efficient industry. Those who embrace this digital revolution will be the ones to reap the benefits.

One challenging area faced by many in the life sciences space is supply chain optimisation. The COVID-19 pandemic legacy has continued to be top of mind in the industry. Digital tools can enable biomanufacturers to track inventory levels, monitor supplier performance, and optimise supply chain logistics in real-time. Automation tools such as robotic process automation and machine learning can automate repetitive and time-consuming tasks, freeing up human resources for more complex and strategic activities. This can help ensure that materials and components are available when needed, minimizing delays, and improving overall agility.