2024 Rome, Italy

B-06 Sam Richardson
Automating Population Pharmacokinetic Model Development using Machine Learning
Wednesday 9:20-9:40
B-07 Gilbert Koch
Learning from machine learning - how to deduce a mechanism-based pharmacometrics model for serum creatinine in preterm neonates from neural ordinary differential equations
Wednesday 9:40-10:00
C-09 Sandrine Boulet
Bayesian framework for multi-source data integration - application to Human extrapolation from preclinical studies
Thursday 11:40-12:00
C-20 Nelleke Snelder
Time-varying covariates, mediation analysis and overadjustment bias in PK/PD modelling
Thursday 16:50-17:10
D-09 Elisabeth Rouits
Incorporating clinical utility and Pharmacokinetics (PK)/Pharmacodynamics (PD) in clinical development of oncology drug candidate with a Bayesian decision analysis perspective in the context of OPTIMUS initiative
Friday 11:25-11:45