AIMC Topic: Pharmacokinetics

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Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties.

SAR and QSAR in environmental research Apr 7, 2025
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...
Quantitative Structure-Activity Relationship Cell Membrane Permeability Permeability Pharmacokinetics Machine Learning Algorithms Pharmaceutical Preparations Toxicokinetics
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Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans.

Journal of medicinal chemistry Mar 27, 2025
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...
Machine Learning Drug Discovery Humans Pharmacokinetics Pharmaceutical Preparations Models, Biological Administration, Intravenous
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An automated classification pipeline for tables in pharmacokinetic literature.

Scientific reports Mar 24, 2025
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...
Pharmacokinetics Humans Data Mining
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Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.

CPT: pharmacometrics & systems pharmacology Feb 7, 2025
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 ...
Models, Biological Fluorouracil Humans Machine Learning Pharmacokinetics Sunitinib
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Machine Learning for Prediction of Drug Concentrations: Application and Challenges.

Clinical pharmacology and therapeutics Feb 3, 2025
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...
Machine Learning Pharmacokinetics Algorithms Pharmaceutical Preparations Models, Biological Humans Neural Networks, Computer
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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 Jan 20, 2025
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...
Models, Biological Pharmacokinetics Algorithms Computer Simulation Humans Artificial Intelligence
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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 Nov 21, 2024
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...
Pharmacokinetics Humans Machine Learning Bayes Theorem Area Under Curve Models, Biological
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Low-dimensional neural ordinary differential equations accounting for inter-individual variability implemented in Monolix and NONMEM.

CPT: pharmacometrics & systems pharmacology Nov 17, 2024
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...
Neural Networks, Computer Machine Learning Humans Pharmacokinetics Software Models, Biological
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Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Molecular pharmaceutics Nov 7, 2024
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 ...
Bayes Theorem Models, Biological Humans Pharmacokinetics Drug Discovery Neural Networks, Computer Uncertainty Drug Development Animals
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DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

Molecular pharmaceutics Nov 6, 2024
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...
Models, Biological Pharmacokinetics Deep Learning Humans Pharmaceutical Preparations Animals
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