AIMC Topic: Pharmacokinetics

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Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.

CPT: pharmacometrics & systems pharmacology
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...

Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review.

European journal of clinical pharmacology
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...

Low-dimensional neural ordinary differential equations accounting for inter-individual variability implemented in Monolix and NONMEM.

CPT: pharmacometrics & systems pharmacology
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...

Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Molecular pharmaceutics
Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive ...

DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

Molecular pharmaceutics
After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the ev...

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.

Journal of pharmacokinetics and pharmacodynamics
The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) i...

Perspectives on the use of machine learning for ADME prediction at AstraZeneca.

Xenobiotica; the fate of foreign compounds in biological systems
A drug's pharmacokinetic (PK) profile will determine its dose and the frequency of administration as well as the likelihood of observing any adverse drug reactions.It is important to understand these PK properties as early as possible in the drug dis...

Through a computer monitor darkly: artificial intelligence in absorption, distribution, metabolism and excretion science.

Xenobiotica; the fate of foreign compounds in biological systems
Artificial Intelligence (AI) is poised or has already begun to influence absorption, distribution, metabolism and excretion (ADME) science. It is not in the area expected - that of superior modelling of ADME data to increase its predictive power. It ...

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

Molecular pharmaceutics
We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2...

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

Journal of molecular modeling
CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achie...