Pharmaceutical studies and practices

The paper develops a PET-informed PBPK model using micro- and conventional dose data from non-human primates to predict human dolutegravir pharmacokinetics and inform first-in-human dose selection.

This work evaluates stochastic differential equations (SDEs) in NONMEM as a diagnostic tool for detecting population pharmacokinetic model misspecification through simulation and estimation scenarios.

This paper evaluates heavy-tailed residual distributions for outlier handling in population PK modeling, finding that the Student's t distribution provides more stable inference than exponential-tail alternatives under extreme contamination.

This study develops a model-informed Deep Q-Network to automate infliximab dosing in pediatric Crohn's disease, achieving high target attainment with minimal overtreatment.

This study evaluates Bayesian modeling strategies for population pharmacokinetics, finding that Students t-based M3 censoring improves accuracy and precision in handling outliers and censored data.

This paper introduces Neural Networks with Stochastic Gates for automated covariate selection in population pharmacokinetics, demonstrating improved efficiency and accuracy compared to traditional methods.

This paper develops a hybrid population PK-ML model to improve infliximab concentration predictions in pediatric and young adult Crohn's disease patients, showing better accuracy than Bayesian estimation alone.