Introduction Constructing non-linear mixed-effects models (NLMEM) can significantly enhance our understanding of biological processes. In the population approach, NLMEM combine…
Read moreOral: Methodology - New Tools
PMx-AI Bot: Changing the way of traditional Pharmacometrics work with AI Bots
Objectives: The burgeoning influence of artificial intelligence (AI) is revolutionizing the field of pharmacometrics. This transformation is not only due…
Read moreImproving simulations by learning the true random effects’ distribution from a population using generative machine learning
Objectives: Non-linear mixed effects (NLME) modelling aims to estimate the population-level parameters and the distribution of individual-level parameters (random effects,…
Read moreDevelopment and exploration of exhaustive, stepwise, and heuristic algorithms for automated population pharmacokinetic modelling
Objectives: Automated population pharmacokinetic modelling tools are currently being investigated. The genetic algorithm (GA) has been proposed to address pharmacometric…
Read moreMultivariate Exact Discrepancy : a new tool for PK/PD model evaluation
Objectives: The criteria used to validate pharmacokinetic model can be grouped into two families. The first family comprises metrics based…
Read moreMake models great again by optimally restricting parameters to make non-identifiable models provably identifiable
Objectives: One of the goals of statistical learning is to identify the underlying parameter values in a parametric model that…
Read moreComputing Optimal Drug Dosing with Constraints on Model States in NONMEM
Objectives: Recently, we implemented the optimal drug dosing algorithm OptiDose [1] in NONMEM [2] utilizing standard commands. This allows users…
Read moreDevelopment of a tool for fully automatic model development (AMD)
Introduction/Objectives: The development of a population pharmacokinetic (PK) model is a challenging and time-consuming procedure that involves iterative manual model…
Read moreExploratory graphics (xGx): promoting the purposeful exploration of PKPD data
Introduction: As pharmacometricians, we sometimes jump into complex modeling before thoroughly exploring our data. This can happen due to tight…
Read moreSADDLE_RESET: more robust parameter estimation with a check for local practical identifiability
Objectives: In the model building process finding the parameter values that best fit the data is a crucial step. Methods based…
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