PumasAI will present new AI in pharmacometrics research at ACoP 2026, including a workshop examining machine learning in drug development and four studies covering pharmacokinetics, exposure-response analysis and AI-enabled modelling.
The American Conference on Pharmacometrics (ACoP) brings together researchers working across pharmacometrics and model-informed drug development (MIDD).
PumasAI’s scientific programme will focus on both established pharmacometric methods and emerging applications of artificial intelligence and scientific machine learning.
AI in pharmacometrics takes centre stage
Dr Niklas Korsbo will lead a full-day pre-conference workshop titled AI in Pharmacometrics: A Strategic Vision for the Next Five Years.
The session will examine the strategic and technical foundations of machine learning in pharmacometrics. It will also consider how these technologies could support scientific decision-making and future modelling strategies.
“ACoP brings together many of the people defining what comes next for pharmacometrics and model-informed drug development,” said Dr Vijay Ivaturi, Co-Founder and CEO of PumasAI.
“This year, we are especially excited to contribute to the conversation around how AI and Scientific Machine Learning can help scientists translate increasingly sophisticated models into better, faster, and more confident drug development decisions.”
Four pharmacometric studies scheduled for ACoP 2026
Alongside the workshop, PumasAI researchers will present four scientific posters covering population pharmacokinetics, exposure-response analysis and AI-enabled modelling.
One study examines the population pharmacokinetics of taletrectinib in healthy participants and patients with ROS1-positive non-small cell lung cancer.
A second presentation examines DeepNLME and its ability to autonomously develop pharmacokinetic functions for mosunetuzumab compared with expert-specified approaches.
The remaining studies focus on an oral meloxicam formulation, MR-107A-02. Researchers will present population pharmacokinetic findings alongside an exposure-response analysis of pain relief in post-surgery patients.
Scientific machine learning and drug development
The research programme reflects growing interest in combining computational methods with established pharmacometric approaches during drug development.
Importantly, AI in pharmacometrics could support scientists working with increasingly complex datasets and modelling workflows. However, the scientific performance and appropriate use of individual approaches require evaluation within their intended applications.
“The research we are bringing to ACoP spans the work pharmacometricians are doing today and emerging approaches that could change how that work is done tomorrow,” said Ivaturi.
PumasAI focuses on model-informed drug development
PumasAI develops modelling and simulation software and provides scientific services across clinical pharmacology, pharmacometrics and model-informed drug development.
Its technology includes the Pumas modelling and simulation platform, which supports quantitative workflows across drug development.
Further information about the company’s ACoP programme is available through the PumasAI ACoP 2026 event page.
