AI Startups Seek Continued Process Verification in Life Sciences


Heap of medical pills. Credit: Volodymyr Hryshchenko on Unsplash Heap of medical pills. Credit: Volodymyr Hryshchenko on Unsplash

The US Food and Drug Administration (FDA) set out guidance in 2011 for “Process Validation: General Principles and Practices.” Not only should a pharmaceutical maker consider manufacturing controls as part of the product design phase and create a process to comply with Current Good Manufacturing Practice (CGMP) regulations, but also they also need to perform continued process verification (CPV) to maintain qualification status. Specifically, they recommend:

Continued monitoring and sampling of process parameters and quality attributes at the level established during the process qualification stage until sufficient data are available to generate significant variability estimates. These estimates can provide the basis for establishing levels and frequency of routine sampling and monitoring for the particular product and process. Monitoring can then be adjusted to a statistically appropriate and representative level. Process variability should be periodically assessed and monitoring adjusted accordingly.

Variation can also be detected by the timely assessment of defect complaints, out-of-specification findings, process deviation reports, process yield variations, batch records, incoming raw material records, and adverse event reports. Production line operators and quality unit staff should be encouraged to provide feedback on process performance. We recommend that the quality unit meet periodically with production staff to evaluate data, discuss possible trends or undesirable process variation, and coordinate any correction or follow-up actions by production.

This guidance is rigorous to ensure only high-quality pharmaceuticals reach the US market. However, some in the industry argue that continuous process verification has been slow to adopt. In the years since the guidance was released, we have undergone a transformation in applied statistics with a renaissance in the fields of machine learning and neural networks (AI). Some startups have latched onto the opportunity to use machine learning to analyze variation faster, more precise and at scale.

As these startups use to AI to penetrate the pharmaceutical industry we will all be able to reap the benefits of increasingly efficient and in-control life sciences manufacturing processes.

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Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data.

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🔖 Topics: demand planning, random forest, natural language processing

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✍️ Author: Damon Purvis

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✍️ Author: Jill Jozwik

🏢 Organizations: Rittal

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🏢 Organizations: Ark Invest

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🔖 Topics: IIoT

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🏭 Vertical: Computer and Electronic

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🔖 Topics: robotics, federated learning

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Assisting Continued Process Verification with AI

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✍️ Author: Chris Lee

🔖 Topics: continued process verification

🏭 Vertical: Pharmaceutical

🏢 Organizations: Falkonry

Patterns of behavior reflected in the data from equipment sensors can give insight into these performance affecting factors. In many cases, these patterns develop before product quality is significantly affected. Putting in place analytics that can detect these patterns gives the plant operations team actionable warning before CPV limits indicate a problem. This warning can be used to limit costly production impacts. Importantly, because the CPV process itself is untouched, these kinds of pattern detection analytics can be implemented without additional filings or regulatory delay. Assisting CPV does not mean replacing or even changing CPV.

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