AIMC Topic: Pharmacokinetics

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Transfer learning empowers accurate pharmacokinetics prediction of small samples.

Drug discovery today
Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. R...

Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling.

Drug metabolism and pharmacokinetics
Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. ...

A pharmacokinetic-pharmacodynamic model based on the SSA-1DCNN-Attention network and the semicompartment method.

Biotechnology & genetic engineering reviews
To solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the...

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Molecular pharmaceutics
Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to...

Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage.

Journal of chemical information and modeling
Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roc...

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.

CPT: pharmacometrics & systems pharmacology
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific dat...

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

Molecular pharmaceutics
While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) ma...

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Molecular pharmaceutics
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in anim...

Machine Learning Models for Human Pharmacokinetic Parameters with In-House Validation.

Molecular pharmaceutics
Prior to clinical development, a comprehensive pharmacokinetic characterization of a novel drug is required to understand its exposure at the site of action and elimination. Accordingly, assays and animal pharmacokinetic studies are regularly employ...

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges.

Drug discovery today
Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly av...