2026 PHUSE SDE in Frankfurt
On Wednesday, 29 April 2026, the Single Day Event will take place in Frankfurt. OCS Life Sciences will also participate, as we are proud to be present as a Bronze Sponsor. We have submitted two abstracts for the event.
An automation-first framework for controlled migration of clinical data
by Anton Meshcheriakov
As study portfolios grow, clinical analysis teams hit the limits of their existing environments. It creates a conundrum of migrating multiple studies - some archived and some ongoing - while maintaining both trust in the data and uninterrupted operations.
This presentation provides a practical, automation-first framework for verifying the migration of data and program assets between GxP environments. The framework is based on a layered approach to validation, allowing to manage trade-offs between the speed and assurance. Our approach combines off-the-shelf tools with a thin scripting layer, data quality checks with non-conformity review and remediation workflow.
Attendees will leave with a reusable blueprint for planning and implementing verification pipelines that reduce migration risk, accelerate study readiness and improve transparency for stakeholders across Statistics, Data Management, Quality and IT.
From Prompt to PK:
AI-Driven Development of
an Automated NCA Optimization Tool
by Ramon Jakobs
Non-compartmental analysis (NCA) is a cornerstone of pharmacokinetic (PK) analysis in clinical studies, yet terminal elimination phase selection is often performed manually, which is time consuming and introduces subjectivity and variability.
PK_NCA_Optimizer is an exploratory tool, fully developed by AI, that generates an R Shiny application to automate slope selection using an algorithm based on the Phoenix WinNonlin approach. The aim of this development project is to investigate whether generative AI can understand PK concepts and translate them into a useful analytical tool. The result is an application that, while not perfectly functional yet, illustrates the potential and challenges of using generative AI to develop analytical software for clinical research.
The presentation will review the current implementation, show how it can be applied to clinical PK datasets, and share key lessons learned on prompting, validation and reproducibility of AI-assisted tools.