Propelling the biopharma industry forward with IoT and edge applications
The biopharma manufacturing industry has seen incredible growth in just the past couple years, with the pandemic spotlighting a need for the industry to move quickly and effectively to bring new drugs, vaccines and therapeutics safely to market. The result has been a drastic increase in the pace of innovation, largely driven by the ability to use technology and collaborate at global scale, turning a process that would typically take nine years into just nine months.
As we move into a world after COVID-19, the biopharma industry must understand how to maintain this pace of innovation without forfeiting precision or quality. Smart manufacturing — otherwise known as Industry 4.0 — converges IoT, software-defined infrastructure, advanced analytics and AI to create more flexible and interoperable digital manufacturing platforms. Infusing traditional manufacturing with better decision-making and analytics to be done at the edge will be key to propelling the industry forward.
Maintaining product consistency and optimizing yield
For biopharma, the two biggest pain points are maintaining product consistency and optimizing yield as batches are typically produced in thousands at a time. If there is a loss in integrity in the product at any stage of its production, that could mean the entire batch would need to be destroyed. That’s why it’s critical to be able to measure and monitor processes and parameters using certain instrumentation to extract insights, which requires significant computing power — this is where IoT and edge computing come into play.
As manufacturers embrace digital transformation, sensors are being used at almost every touch point across the production line to generate a wealth of data and valuable insights, especially if analyzed in real time. This data needs to be processed where it is being collected — at the edge — on the plant floor at machine level to enable operators to extract quick insight into what’s happening so they can make the necessary adjustments. Edge analytics in biopharma can provide real-time insights to help identify potential issues on the manufacturing line. Actioning quickly to resolve these issues can mean the difference between losing a whole batch of product, such as vaccines, versus fixing the issue and continuing production.
Moving toward personalized medicine
The biopharma manufacturing industry is moving toward more-personalized medicine, which has the potential to treat a wide range of chronic conditions or potentially reprogram the ways our bodies fight disease, aiding in treating diseases that have been previously untreatable and ultimately save lives.
In this patient-centric future, medical professionals would be able to prescribe treatments that match an individuals’ unique pathology. However, producing personalized medicine creates complex manufacturing challenges related to decreased batch size and rising costs. We’re in the early days of understanding how to shift away from a one-size-fits-all approach, but what’s clear is that the traditional approach to manufacturing will not suffice. A supply chain that uses machine learning and AI to learn and adapt could be one way for biopharma to use data to improve manufacturing and the distribution of a product. And perhaps biopharma can learn something from the consumer industry, which is already well on its way to producing individualized goods.
Advancing the pace of scientific discovery using AI
With the increasing use of AI, modern clinical, pharma, agriculture and research labs will be able to operate with greater speed and effectiveness by using data in new ways and applying sophisticated analytics. Pathologists, for example, may use AI systems in combination with their expertise to expand access to care, while pharma research scientists may use AI to suggest new drug targets and candidates from complex computer simulations.
Intel technologies for data analytics, automation and virtualized deployment of applications are helping chart a path for the future by improving workflow and efficiency. In addition, Intel technologies are supporting connecting globally distributed labs to improve collaboration and accelerate results.
With these technologies as the foundation, we may one day see R&D labs being able to leverage increased automation with cloud computing while operating on a globally distributed platform. This would also lead to more opportunities to use advanced AI to both analyze the data and modify experiments in real time. Such a future could free up time for researchers to run experiments at massive scale, or collaborate more closely with other researchers, which could in turn advance the pace of scientific discovery.
Where do we go from here?
New process control technologies and improved operational efficiencies will deliver the necessary quality, precision and cost-effectiveness to move next-gen therapeutics forward. This can only be achieved if the industry embraces the shift to smart manufacturing, particularly with the use of IoT and edge applications.
As the pandemic has made clear, almost every industry and human depends on the success of biopharma to keep communities safe and healthy. Smart manufacturing, driven by IoT and edge innovations, provides the industry an opportunity for a simplified, protected and autonomous way to lower costs, increase efficiency and enable greater access to treatments, while maintaining the necessary precision and quality.