Increasing Efficiency and Cost-Effectiveness by Automating the Authoring of the Development Safety Update Report.

Area of Science:

• Pharmacovigilance and Drug Safety
• Regulatory Affairs
• Health Informatics

Background:

• Development Safety Update Reports (DSURs) are crucial for reviewing investigational drug safety data annually.
• Manual DSUR creation is resource-intensive, demanding extensive stakeholder communication and collaboration.

Purpose of the Study:

• To enhance the efficiency and cost-effectiveness of DSUR generation through an electronic authoring platform.
• To explore the application of Natural Language Processing (NLP) for automating DSUR content population.

Main Methods:

• Developed requirements for a DSUR authoring tool and mapped data sources to a company DSUR template.
• Utilized NLP to extract and auto-populate relevant data from source documents into the DSUR template.
• Analyzed time and cost savings for DSURs generated between 2017-2019 using the automation tool.

Main Results:

• The automation tool fully or partially automated 21 out of 31 DSUR sections.
• Achieved an approximate 25% time saving per report, equating to a cost saving of US$4550 per report.

Conclusions:

• The DSUR automation tool reduced manual production time and costs, enhancing report quality, compliance, and consistency.
• This technology facilitates timely generation of larger DSUR volumes without additional resources or quality compromise.
• The tool's principles can be applied to automate other regulatory submission documents requiring cross-functional input.