Overcoming TMF Artificial Intelligence Challenges

By Daniel Koppers and Sarah Tucker

Adapted from a Phlexglobal Innovation Tour webinar, “Streamlining TMF Documentation through Clinical Automation,” held March 31, 2021. View the webinar – available on-demand here – to gain additional insights and details around the application of machine learning technologies to deliver improved TMF health with less effort and risk.

Regulatory agencies have increased their scrutiny of the Trial Master File in inspections, considering the TMF a key performance indicator (KPI) for GCP compliance during a study. As a result, inspectors no longer just look primarily for completeness, but expect a high degree of accuracy as well. They want documents filed in the right place consistently, and to meet the proper quality standards with minimal duplication.

Document management related to the TMF, however, is time-consuming and prone to error, requiring highly experienced professionals to perform mundane and repetitive tasks such as filing and indexing documents. In short, it represents a use case that is tailor-made for artificial intelligence.

In 2019, we started training PhlexNeuron – Phlexglobal’s machine learning framework built specifically for the pharmaceutical industry – on the Trial Master File to assess what improvements in speed and accuracy we could obtain through AI-powered automation. Following is a brief synopsis of that journey, our benchmarked results to date, and lessons learned.