Is machine learning ready for GxP production? Here's how to handle validation correctly.

Machine Learning in Pharma: Opportunities and Limitations

Machine learning has begun to find its way into the pharmaceutical industry, but many companies are still uncertain about how it can be used in production, especially under the strict GxP requirements. Although the potential is significant, particularly in predictive maintenance and quality control, it is not yet common in broader GMP production.

The biggest concern lies in how to ensure that a machine learning model is validated correctly. To be compliant with authorities like the FDA and EMA, companies must be able to prove that the models are both safe, reliable, and documented robust.

Why Validation is a Challenge

Validating machine learning models is different from traditional software validation. Machine learning models are complex and based on data that continuously changes. This means that the models also change over time, which challenges traditional validation methods. Many companies therefore find that it requires a special approach to ensure that these models function stably and within regulatory frameworks.

Another aspect is documentation. Authorities expect detailed documentation of how the model makes decisions, how the data is used, and how the model is continuously validated. This can be a significant task, especially for companies that have not previously worked with advanced data science.

How to Ensure Proper Validation of Machine Learning

In order to use machine learning in a GxP environment, it is crucial to establish a clear strategy for validation and documentation early in the process. This strategy should precisely define how the model is tested and how data is used and updated. Furthermore, the strategy must include clear guidelines for ongoing monitoring and updating of the models.

An important part of validation is risk management. Companies must conduct thorough risk assessments of the model, including identifying potential failure scenarios, consequences of failures, and how these are handled. The risk assessments must be clearly documented, and there should be a clear plan for how the risk is minimized and monitored over time.

It is also necessary to ensure that the data used is of high quality. Data integrity and reliability are key to a well-validated machine learning model. Therefore, companies must have strict procedures for data validation and ongoing data monitoring.

Finally, validation requires that companies test the models under realistic conditions and in controlled environments. The results of these tests must be carefully documented and be able to be presented to the authorities when requested.

If you would like to know more about how pharmaceutical companies can correctly validate machine learning models for use in a GxP environment, please feel free to reach out to us so we can have an informal conversation.