AI offers many possibilities in the pharmaceutical industry, including but not limited to optimizing process design and process control, smart monitoring and maintenance, and trend monitoring to drive continuous improvement.
The use of AI to support pharmaceutical manufacturing can be deployed with other advanced manufacturing technologies to achieve desired benefits. AI is an enabler for the implementation of an Industry 4.0 paradigm that could result in a well-controlled, hyper-connected, digitized ecosystem and pharmaceutical value chain for the manufacturer.
Below are examples, based on these interactions and a review of published information, that forecast how AI might be used in pharmaceutical manufacturing.
These examples are not exhaustive and the potential applications of AI in pharmaceutical manufacturing may continue evolving.
Process Design and Scale-up:
AI models such as machine learning—generated using process development data—could be leveraged to more quickly identify optimal processing parameters or scale-up processes, reducing development time and waste.
Advanced Process Control (APC):
APC allows dynamic control of the manufacturing process to achieve a desired output. AI methods can also be used to develop process controls that can predict the progression of a process by using AI in combination with real time sensor data.
APC approaches that combine an understanding of the underlying chemical, physical, and biological transformations occurring in the manufacturing process with AI techniques are expected to see increasing adoption and have already been reported by several pharmaceutical manufacturers.
Process Monitoring and Fault Detection:
AI methods can be used to monitor equipment and detect changes from normal performance that trigger maintenance activities, reducing process downtime.
AI methods can also be used to monitor product quality, including quality of packaging (e.g., vision-based quality control that uses images of packaging, labels, or glass vials that are analyzed by AI-based software to detect deviations from the requirements of a product’s given quality attribute).
Trend Monitoring:
AI can be used to examine consumer complaints and deviation reports containing large volumes of text to identify cluster problem areas and prioritize areas for continual improvement.
This offers the advantage of identifying trends in manufacturing-related deviations to support a more comprehensive root cause identification.
AI methods integrated
with process performance and process capabilitymetrics can be used to proactively monitor manufacturing operations for trends. These methods can also predict thresholds for triggering corrective and preventive action effectiveness evaluations.
