One important aim in population pharmacokinetics (PK) and pharmacodynamics (PD) is identification and quantification of the relationships between the parameter and covariates to improve the predictive performance of the population PK/PD modeling. Several new mathematical methods have been developed in pharmacokinetics in recent year which indicated that the machine learning-base methods are an appealing tool […]
Abstracts
Assessing the Impact of Confounded Time-Averaged Exposure in Logistic Regression Exposure-Response Analyses
Objectives: Exposure-response (ER) analyses are an integral part of model-informed drug development, playing a key role in evaluating the risk-to-benefit ratio for dose selection, justification, and confirmation. In the context of logistic regression analyses with binary endpoints (such as objective response rate for efficacy or treatment-emergent adverse events [TEAEs] for safety), the selection and derivation […]
A workflow for resolving model internal consistency in use-reuse settings (aka repairing unstable models)
On the whole, model building for data analysis is a well-defined process with relatively few issues relating to a model’s internal consistency. The concept of model internal consistency is defined as a property of the model that is in some way inconsistent within itself rather than an inconsistency resulting from the interaction of the model […]
Hepatic Protein Signatures of Chronic Polypharmacy, Monotherapy, and Deprescribing in Aged Mice
Introduction. Polypharmacy is common in old age and is associated with adverse geriatric outcomes. Deprescribing medications may alleviate some outcomes. The liver is a key metabolic organ and is affected by drugs and ageing. However, the molecular effects of chronic monotherapy, polypharmacy, and deprescribing in the ageing liver remains uncharacterised. Aims. In aged mice, to […]
Analytical and non-analytical variation may lead to inappropriate antimicrobial dosing in neonates: an in silico study
Background. Therapeutic drug monitoring (TDM) of aminoglycosides and vancomycin is used to prevent oto- and nephrotoxicity in neonates. Analytical and non-analytical factors potentially influence dosing recommendations. Aim. To determine the impact of analytical variation (imprecision and bias) and non-analytical factors (accuracy of drug administration time, use of non-trough concentrations, biological variation and dosing errors) on […]
PyDarwin Designer, a prototype, user friendly interface to pyDarwin for machine learning based model selection.
pyDarwin(1) is a Python package with a command line API, designed for machine learning model selection in NONMEM and NLME. It doesn’t have a graphical user interface (GUI). Instead, it requires users to manually create three files: a template file, a tokens file, and an options file. The tokens and options files are formatted in […]
An Evaluation of Three Semi-mechanistic PK/PD Models for Predictive Performance of Long-term HbA1c via Short-term Glycemic Changes for a Glucagon Receptor Antagonist in Type 2 Diabetes Patients
Glycated hemoglobin (HbA1c) is the gold standard for assessing long-term glucose control in diabetes. Ability to predict long-term HbA1c using glucose response from clinical trials of shorter duration is much desired, but uncertainty exists in using short-term glucose biomarker changes to predict long-term HbA1c changes in Type 2 diabetes (T2D) trials. Three common semi-mechanistic PK/PD […]
Effect of ciprofloxacin against Pseudomonas aeruginosa with different resistance mechanisms can be predicted with mechanism-based mathematical modelling where using PK/PD indices fails
Introduction: Pseudomonas aeruginosa has a large armamentarium of mutational resistance mechanisms enabling resistance emergence during therapy against almost all antibiotics. PK/PD indices are based on minimum inhibitory concentrations (MICs) and link bacterial response to antibiotic exposure. The index relevant for fluoroquinolone antibiotics is the ratio of free drug area under the concentration-time curve to MIC […]
Machine learning for enhanced survival prediction from tumour growth inhibition data
Background: The two-stage modelling approach is often used to characterise the link between overall survival (OS) and tumour growth inhibition metrics (TGI) derived from longitudinal tumour size data. This study evaluated the predictive performance of a two-stage TGI-OS model using survival machine learning (ML) methods in comparison to standard Cox proportional hazards (Cox PH) regression. […]
Methodologically appropriate evaluation of continuous BMI as a clinical predictor of chemoimmunotherapy efficacy in advanced non-small cell lung cancer
Background: Multiple studies have indicated that obese and overweight patients may experience favourable survival outcomes during treatment with an immune checkpoint inhibitor (ICI). However, most prior studies evaluated ICI-treated patients without a control cohort, and utilised categorised BMI with inconsistent body mass index (BMI) cut point definitions. Methods: This secondary analysis pooled individual patient data […]