CONTINUOUS PHARMACEUTICAL MANUFACTURING CASE STUDY
What are the disadvantages of batch manufacturing?
In the pharmaceutical industry, batch manufacturing is the tried, tested and trusted approach. It involves manufacturing drugs in multiple steps. After each step in the process, production typically stops, and samples are taken and then tested offline. Only when the quality requirements have been met, the product is released for further processing. This makes batch manufacturing slow – it can take hours, days, or weeks, depending on the product.
What is continuous manufacturing?
In contrast to batch manufacturing, continuous manufacturing processes produce drug products such as tablets continuously, without having to wait for each batch to finish before beginning a new one. The materials, either dry bulk or fluids that are being processed in a continuous flow, undergoing chemical reactions, or subject to various types of treatments. Unlike batch processing, continuous manufacturing processes operate 24/7. They are stopped only due to infrequent maintenance breaks, for instance bi-annually or annually.
Benefits of continuous manufacturing for pharmaceutics
The move towards the use of continuous manufacturing has been slow. That’s because pharma is a highly regulated and science-based industry with a large installed base of batch manufacturing equipment.
But times are changing. Today more and more drug manufacturers are internally assessing and developing continuous manufacturing processes. That’s because the rapidly evolving field of advanced data analytics is making it easier for pharmaceutical manufacturers to move towards continuous manufacturing processes.
A key driver for the industry to switch to continuous manufacturing is the FDA – the Food and Drug Administration in the US. The FDA believes continuous manufacturing provides pharmaceutical industry many benefits including:
- Continuous manufacturing improves product quality
- It reduces medicine shortages
- increases efficiencies
- It shortens production times
- Reduces manufacturing costs
- Minimizes the risks of human errors
- It improves quality
What enablers do you need for continuous manufacturing?
Continuous manufacturing presents new questions for how to ensure quality and regulatory compliance. The primary question is, how to maintain consistent process control and quality when there are no check points between batches? This requires two enablers in place: real-time monitoring and advanced analytics.
The primary enabler is real-time monitoring. Therefore, implementing a real-time process monitoring approach is an element of the control strategy for any drug manufacturing process. For continuous manufacturing processes, process monitoring generates real-time information on process parameters and attributes of input materials, in-process materials, and final products for the duration of the manufacture. This information can enable high detectability of short-term disturbances and process deviations, active process control, and more accurate material diversion.
The other primary enabler is advanced analytics, which is essential for quality control, and it must integrate into the manufacturing process.
Since continuous manufacturing will generate more data in real-time, a scalable analytics platform – that can ingest data from diverse data types – will become essential.
Historically, pharmaceutical manufacturers have regularly relied on analytics for reporting purposes. However, few manufacturers have taken the next step toward more predictive and prescriptive analytics that can impact ongoing continuous processes.
The reason for not having used advanced analytics is not, however, lack of data. In fact, most drug manufacturers have been collecting data from disparate devices, systems, and networks for years. The real challenge has been how to deliver advanced analytics based on massive datasets quickly enough to impact production processes in real-time.
To successfully predict a future outcome, an analytics platform must be able to take full advantage of structured and unstructured data from various devices, sensors, and business systems. Now, thanks to advanced industrial IoT platform and advanced analytics platforms manufacturers can exact more value from more disparate data sources faster than ever before.
Continuous pharmaceutical manufacturing case study
To ensure a smooth transition from batch manufacturing to continuous manufacturing, pharmaceutical companies need to develop a detailed plan and strategy for data analytics.
A global pharmaceutical manufacturer turned to Elisa Smart Factory team for help in understanding what they need from analytics perspective.
The manufacturer had been collecting extensive amounts of data for the past ten years, but the data was in silos and the customer didn’t have the means to integrate it. Consequently, they asked Elisa’s team of data scientists to turn the massive datasets into actionable insights that would help the customer embark on the transition process.
Elisa analytics team applied the latest Machine Learning (ML) technologies and delivered full project report in just three months. This included Azure-based data pipeline configuration, validated Machine Learning models, and sample dashboards.
Once these are implemented in production environment, the pharmaceutical manufacturer can:
- Optimize their bioreactor process to reach higher yield
- Easily detect anomalies with help of a modelling framework
- Effortlessly monitor a large amount of process parameters
Learn how to start the transition to continuous manufacturing
Learn how Elisa Smart Factory enabled the global pharmaceutical company to leverage advanced analytics to begin the transition project from batches to continuous manufacturing.