PAGE 2026 – AI/ML Workshop

Course Description

Artificial Intelligence and Machine Learning (AI/ML) are rapidly transforming pharmacometrics, offering new tools for model development, covariate selection, and data analysis. Organized by the AI/ML Special Interest Group (SIG) under ISoP, this interactive satellite workshop provides both an overview and hands-on experience with AI-assisted coding tools and open-source ML frameworks in R and Julia, alongside a panel discussion of regulatory considerations and best practices.

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GenAI Coding Assistants

Hands-on demonstration of AI coding tools (GitHub Copilot) to accelerate pharmacometric workflows

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Live Coding Exercises

Open-source exercises in R and Julia covering Neural ODEs, ML-based covariate selection, and Explainable AI

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Regulatory Panel

Discussion with regulatory and industry experts on Use Cases and Regulatory Considerations of AI in Pharmacometrics and Clinical Pharmacology

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Networking

Connect and build community with peers working at the intersection of AI/ML and pharmacometrics

Prerequisites for the Hands-on Session

Participants must bring their own laptop
A GitHub account is required for the hands-on exercises
Basic familiarity with R or Julia is helpful but not required

Agenda

A full day of talks, hands-on coding sessions, and expert discussions. The program is outlined below and will be updated as the event approaches.

Morning

08:30 – 08:45

Welcome & Round of Introductions Opening

Presentation of the AI/ML SIG under ISoP and the structure of the workshop, followed by a round of introductions from participants and instructors.

👤 Jane Knöchel

Session 1: GenAI Coding Assistants

08:45 – 09:00

Presentation — GitHub Copilot Lecture

Overview of GenAI coding assistants and their application in pharmacometric workflows, focusing on GitHub Copilot.

👤 Elba Raimundez

09:00 – 10:00

Live Coding: GitHub Copilot Hands-on

Interactive exercises demonstrating typical pharmacometric tasks such as data QC and exploratory analyis. A shared PK dataset will be used across sessions.

👤 Elba Raimundez

10:00 – 10:20

☕ Coffee Break & Networking

Session 2: Open-source AI/ML in Pharmacometrics — Julia & R

10:20 – 10:35

Presentation: Deep Compartment Models and NeuralODEs: gaining insights from AI models Lecture

Introduction to Deep Compartment Models and (hybrid) NeuralODEs in the context of pharmacometrics. We discuss how to leverage such approaches alongside Explainable AI methods (SHAP) to gain valuable insights from learned effects to accelerate downstream analyses.

👤 Alexander Janssen

10:35 – 11:35

Live Coding: Deep Compartment Models, NeuralODEs, and model interpretation (Julia) Hands-on

How to set up and fit deep compartment models and (hybrid) NeuralODEs to PK data using the DeepCompartmentModels.jl package. Next, we probe the developed models to interpret learned effects which can be a usefull tool to support covariate selection.

👤 Alexander Janssen

11:35 – 11:45

Short Break

11:45 – 12:00

Presentation: Machine Learning as Your Analytical Toolkit: uncovering what your data is telling you Lecture

Introduction to ML techniques for pharmacometric data exploration and interpretation: gradient-boosted trees (xgboost) as a versatile tool for rapid pattern detection, anomaly identification, and hypothesis generation, SHAP values for interrogating model drivers, and UMAP with clustering for identifying patient subpopulations.

👤 Victoria Ponce

12:00 – 13:00

Live Coding: Machine Learning Toolkit in Action (R) Hands-on

Walkthrough on how to use xgboost to explore and make sense of a pharmacometric dataset, flagging anomalies, exploring covariate effects, and uncovering structure, then interpret findings with SHAP plots and use UMAP with clustering to see whether meaningful subgroups emerge.

👤 Victoria Ponce

Lunch

13:00 – 14:00

Light Lunch & Networking

Afternoon

Session 3: Panel Discussion on Use Cases and Regulatory Considerations of AI in Pharmacometrics and Clinical Pharmacology

14:00 – 14:10

Panel Format Introduction Panel

Overview of the panel discussion format and presenter introductions.

👤 Ali Farnoud

14:10 – 14:30

FDA Perspective Invited Talk

Title TBD

👤 TBD

14:30 – 14:50

EMA Perspective Invited Talk

Title TBD

👤 Flora Musuamba

14:50 – 15:10

Industry Perspective Invited Talk

Title TBD

👤 James Lu

15:10 – 15:30

☕ Coffee Break & Networking

15:30 – 16:00

Panel Discussion Panel

Moderated discussion.

👥 Moderators: Victoria Ponce, Ali Farnoud | Panelists: TBD, Flora Musuamba, James Lu

16:00 – 16:15

Audience Q&A Panel

Open questions from participants.

👥 Panelists: TBD, Flora Musuamba, James Lu

16:15 – 16:30

Closing & Participant Reflection Closing

Key takeaways, community resources, and a short survey on what participants learned and enjoyed most.

👤 Elba Raimundez

Panelists

Expert panelists from regulatory agencies and industry will share perspectives on AI/ML use cases and regulatory considerations in pharmacometrics and clinical pharmacology.