Objectives Utilizing longitudinal biomarkers to improve primary endpoint predictions is a common objective in clinical pharmacology, yet traditional joint NLME…
Read morePoster: Methodology - New Modelling Approaches
A STRUCTURED APPROACH TO PHARMACOMETRIC COVARIATE MODELING: WHY THE ESTIMAND IS THE FIRST DECISION AND THE METHOD THE SECOND
Objectives Covariates in pharmacometric models have two important objectives[1]. First, they support mechanistic understanding by helping to identify the physiological…
Read moreAN ASYMPTOTIC THEORY ON THE COMPETITIVE BINDING OF THERAPEUTIC AND ENDOGENOUS ANTIBODIES TO FCRN
Introduction: Over the last few decades physiology-based pharmacokinetic (PBPK) models have received increasingly more attention not just as an academic…
Read moreA QUANTITATIVE SYSTEMS PHARMACOLOGY (QSP) MODEL LINKING KIT SIGNALING TO MAST CELL DEGRANULATION AND DEPLETION
Objective: Mast cells (MCs) serve an important role in inflammatory and allergic reactions. KIT, a receptor tyrosine kinase, regulates MC…
Read moreTowards an Inflammation Simulation Model: Development and Validation of a Mathematical Model of the Human Inflammatory Response to Acute and Prolonged Lipopolysaccharide Exposure
Introduction: Cytokines are inflammatory mediators that play a pivotal role in the initiation and orchestration of the immune response [1]. IL-6,…
Read moreIntegrating PKPD and Agent-Based Modeling to Explore Tuberculosis Relapse after Treatment.
Introductions: The immune system’s significance in Tuberculosis (TB) drug development is often underestimated due to experimental complexities. In this study,…
Read moreA Neural Networks-assisted NLME Framework: Case Study on Modeling Platelet Counts
Objectives: Thrombocytopenia, a frequent adverse event type in cancer therapy, requires clinical platelet management to avoid high-grade thrombocytopenia and to…
Read moreSupporting pharmacometrics applications by robust parameter estimation and identifiability analysis using hybrid Neural ODEs
Introduction: Neural Ordinary Differential Equations (NODEs) [1,2] constitute a promising data-driven method for modeling dynamical systems in computational biology. Recent works…
Read moreUsing machine learning to predict longitudinal platelet counts: a methodology framework
Introduction: Conventional exposure-response (ER) models have been employed to model the dynamics of platelet changes during patient treatment, providing insights…
Read moreAddressing Confounding Bias in Exposure-Response Analysis: Evaluating Instrumental Variable Modeling Strategies
Objectives: Randomization of subjects to multiple dose levels in clinical trials enables an unbiased estimation of the dose-response relationship. However,…
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