Pharmacokinetic (PK) models are essential for optimising drug candidate selection and dosing regimens in drug development. Preclinical and population PK models benefit from integrating prior knowledge from existing compounds. While tables in scientif...
European journal of clinical pharmacology
39570408
OBJECTIVE: Limited sampling strategies are widely employed in clinical practice to minimize the number of blood samples required for the accurate area under the curve calculations, as obtaining these samples can be costly and challenging. Traditional...
CPT: pharmacometrics & systems pharmacology
39552211
Neural ordinary differential equations (NODEs) are an emerging machine learning (ML) method to model pharmacometric (PMX) data. Combining mechanism-based components to describe "known parts" and neural networks to learn "unknown parts" is a promising...
CPT: pharmacometrics & systems pharmacology
39831409
A growing number of covariate modeling methods have been proposed in the field of popPK modeling, but limited information exists on how they all compare. The objective of this study was to perform a systematic review of all popPK covariate modeling m...
Drug discovery's success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and exc...
CPT: pharmacometrics & systems pharmacology
39921335
A variety of classical machine learning (ML) approaches has been developed over the past decade aiming to individualize drug dosages based on measured plasma concentrations. However, the interpretability of these models is challenging as they do not ...
With the advancements in algorithms and increased accessibility of multi-source data, machine learning in pharmacokinetics is gaining interest. This review summarizes studies on machine learning-based pharmacokinetics analysis up to September 2024, i...
Accurate prediction of new compounds' pharmacokinetic (PK) profile in humans is crucial for drug discovery. Traditional methods, including allometric scaling and mechanistic modeling, rely on parameters from or testing, which are labor-intensive an...
A successful drug needs to combine several properties including high potency and good pharmacokinetic (PK) properties to sustain efficacious plasma concentration over time. To estimate required doses for preclinical animal efficacy models or for the ...
Quantitative structure-activity relationship (QSAR) methods have emerged as powerful tools to streamline non-clinical pharmacokinetic (PK) studies, with extensive evidence demonstrating their potential to predict key in vivo PK parameters such as cle...