This year’s Population Approach Group Europe (PAGE) 2023 meeting will be held at Palexco, A Coruña, Spain, and Pumas-AI scientists will be on-site with two informative workshops!

Scientific Modeling Augmented by Machine-Learning with DeepPumas Workshop

June 26-27, 2023

DeepPumas is not just an incremental improvement, but a game-changer in pharmaceutical drug development analytics. Come experience the innovation! 

Often scientists may not know the exact biological mechanisms dictating the data signatures. For example, which risk factors drive responders to a cancer treatment or how a biomarker is related to clinical outcomes. Current data science methods require big data, and they ignore prior knowledge of the problem at hand. Pumas-AI is poised to disrupt this. DeepPumasTM enables seamless integration of domain-specific knowledge and data-science methodology, reducing dependence on data size and enabling faster decision-making. 

Here, we will learn, hands-on, how DeepPumas can automatically discover complex predictive factors to individualize predictions. Furthermore, we will learn how dynamical systems that model the longitudinal evolution of patient outcomes can be augmented by machine learning – enabling data-driven discovery of the underlying biology. Together, this enables effective use of data to rapidly develop models that predict individual outcomes from heterogeneous sources of patient data. 

Applicable across the whole chain of drug development, from lead generation, quality by design manufacturing, clinical research, and market research to individualized patient management, DeepPumas is not an incremental improvement but a game-changer. 

The workshop is split in two, where the first day is dedicated to learning the powerful Pumas software for pharmacometric modeling. During the second day, we learn about machine learning and how it can be seamlessly embedded in pharmacometric models using DeepPumas. 

REGISTER HERE 

At the end of the workshop, participants will: 

• Gain an overview of the Pumas modelling and simulation ecosystem 
• Gain an overview of the theory behind neural networks and their embedding within dynamical and statistical models. 
• Learn workflows for incorporating machine learning into scientific models 
• Use neural-embedded pharmacological models to identify predictive factors in patient data. 
• Use embedded neural networks to identify missing terms and unknown relationships in pharmacological models. 
• Gain an overview of what problems can be solved by this novel technology. 

Workflows covered: 

• Model population pharmacokinetics in Pumas 
• Model PKPD in Pumas 
• Model time-to-event in Pumas 
• Model mechanistic PKPD data using DeepPumas. 
• Automatically identify the equations that drive the drug response. 
• Identify complex relationships between covariates and patient outcomes to make personalized baseline predictions. 
• Tumor size survival analysis. 
• Use DeepPumas to automatically identify tumor size dynamics 
• Use DeepPumas to automatically Identify the effect of tumor size on survival.