Amgen is piloting a process using artificial intelligence (AI) that has the potential to greatly enhance its ability to trend and find patterns in manufacturing deviations and to prevent their recurrence.
The AI tool will replace a manual, labor-intensive process with one that can look across large data sets and find correlations between obscure signals and events which the previous system could have missed.
Agile Software Development
In creating the AI tool, the team used agile software development. Agile development is an iterative approach under which requirements and solutions evolve through the collaborative effort of cross-functional teams and their end users.
It involves an incremental development strategy, meaning each successive version of the product is usable and each builds upon the previous version by adding user-visible functionality.
Natural Language Processing
Because the deviation reports — which Amgen calls non-conformance (NC) reports — are documents containing mostly text, the project team decided to employ an AI tool called natural language processing (NLP) to examine them.
NLP provides a way for computers to analyze, understand, and derive meaning from human language, including not only words but also concepts and how they are linked together to create meaning.
At the second Xavier Health AI Summit in Cincinnati, Ohio, Amgen Quality Data Sciences Executive Director Dan Weese and Director Mark DiMartino presented the strategy, approaches, and lessons learned from their implementation of AI tools in Amgen’s quality processes, with a specific focus on the NLP project used to analyze and trend manufacturing NCs. (A review of the first Xavier AI Summit a year earlier is available here.)
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Using Data Science In Quality Operations
Weese provided an overview of the role data science plays in Amgen’s quality operations. He noted that as a large company manufacturing, purifying, and packaging biotech drugs, a huge amount of diverse data is generated, not all of which is digitized.
The focus of his team’s efforts, Weese said, is the application of data science specifically in quality operations, using a data science process (see Figure 1).
The process begins with a question, after which the team:
- Gets the data
- Explores, models, and visualizes it
- Interprets it for presentation to management
An important component of the process is the creation of a “data lake.” A data lake is a combination of all relevant data, including raw data and transformed data, which is used for various tasks including reporting, visualization, analytics, and machine learning.
The data lake can include structured data from:
- relational databases
- semi-structured data such as CSV files
- unstructured data such as emails, documents, and PDFs
- Binary data
Weese characterized the data lake as a core technology platform needed to advance data management and analytics capabilities across functions. “We have to have the ability for people to easily input data and get data.”
“Those people need ‘the knowledge of a subject matter expert, the insight of a mathematician, and the discipline of a computer scientist,’ Weese said. ‘Who has all of those skills combined? Probably no one. So we have to work with these different skill sets and people.’”
He noted the data lake could be large, and the company needs the right tools to analyze and visualize it.
“We have a race at the company right now to see whether [software platforms] Tableau or Spotfire are the best tools to visualize data. Then we need to be able to apply some of the data science tools such as jupyter, python, and RStudio. These are free and downloadable. So this is not a discussion about the coding — it is about how we use it to get business results.”
The next step is finding people who will access, visualize, and analyze the data to unlock value. Those people need “the knowledge of a subject matter expert [SME], the insight of a mathematician, and the discipline of a computer scientist,” Weese said. “Who has all of those skills combined? Probably no one. So we have to work with these different skill sets and people.”
Regarding the role of data scientists in quality, he said the company believes quality system vigilance provides opportunities for applying data science to core quality function responsibilities, as outlined in the FDA’s 2006 guidance on quality systems.
Included in the quality system recommendations from the FDA is “continually monitoring trends and improving systems.”
This can be achieved by:
- Monitoring data and information
- Identifying and resolving problems
- Anticipating and preventing problems
Amgen Quality Data Sciences Areas of Focus
Weese presented eight areas Amgen is exploring for attention by data scientists (see Figure 2).
Beginning at the top of the illustration and moving clockwise, Weese began with a brief discussion of trending NCs. “Finding weak signals hidden in deviations is difficult. We can do simple things like Pareto charts, bucketing, and statistical process control charting.”
“In our case, quality owns environmental health and safety. There are a lot of safety incidents. Maybe we can use AI there. We need to keep track of and trend complaints. Then there are supplier quality trending and dashboards of metrics for our process owners.”
GMP Intelligence — that is, can we keep an eye on GMP warning letters and form 483s from agencies and find out what is trending?
Predictive Disposition — can we predict when we will be ready to disposition any one lot?
“Continuous product quality, which Xavier has a workstream on, gets into real-time release, which companies like us are very interested in.”
In addition to the continuous product quality work stream, Xavier also has a team of FDA officials and industry professionals that has taken a major step toward forging the use of AI by providing a Good Machine Learning Practices (GmLP) document for the evaluation and use of continuously learning systems.
In the second part in this series, we will cover:
- Amgen’s current NC trending process
- Designing and piloting a new process
- Q&A focusing on GMP documentation aspects
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