Introduction: Paediatric population pharmacokinetic (PopPK) model development is complex due to physiological changes occurring throughout growth and maturation. pyDarwin, an open-source Python package, integrates machine learning with NONMEM for model selection, but its capability in handling complex paediatrics PopPK model selection remains unvalidated. A double-blinded external comparison of the pyDarwin-selected and manually-developed models is necessary […]
Author: Xiao Zhu
Determination of the pharmacodynamics of vancomycin in young infants via time-to-event analysis
Introduction: Coagulase-negative staphylococci (CoNS) are a leading cause of late-onset sepsis in young infants. Vancomycin is commonly used for the empirical treatment of CoNS. However, the therapeutic target for these bacteria is poorly defined and is currently extrapolated from data for methicillin-resistant Staphylococcus aureus (MRSA) in adults and older children (1). Further understanding of the […]
Evaluation of the fingerprint profiles of the cannabinoid-1 receptor signalling via a kinetic modelling approach
Introduction: Biased agonism (aka ligand bias) is a term that is used to describe the ability of ligands to differentially regulate multiple signalling pathways when coupled to a single receptor. Quantification of ligand bias is critical to lead compound optimisation. Signalling is affected by rapid ligand-mediated receptor internalisation. Hence, the conventional use of equilibrium models is […]
Kinetic modelling of ligand mediated internalisation
Introduction: Internalisation is by its nature kinetic. Hence, application of the standard assumption of equilibrium conditions used by pharmacologists to determine the equilibrium constant (KD) is not obvious for either the ligand-mediated internalisation pathway or other functional assays that occur over the same timeframe as internalisation. However, it is also difficult or impossible to estimate […]
Identifiability analysis of empirical models used for quantifying “biased” ligands
Background: Model identifiability is an important attribute that a model must satisfy in order to derive the meaningful interpretation of estimated parameters. In general, there are two types of identifiability: structural and deterministic. Structural identifiability is rooted in the underlying mathematical structure of model. Deterministic identifiability is concerned with the study design and its execution. […]