AIMC Topic: Pharmacokinetics

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

CPT: pharmacometrics & systems pharmacology Oct 12, 2022
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...
Models, Biological Drug Interactions Pharmacokinetics Humans Machine Learning Drug Discovery
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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 Sep 23, 2022
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...
Solubility Biological Availability Pharmacokinetics Pharmaceutical Preparations Feasibility Studies Models, Biological Humans Machine Learning Computer Simulation
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Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Molecular pharmaceutics Apr 12, 2022
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...
Biological Availability Pharmacokinetics Metabolic Clearance Rate Rats Machine Learning Animals Drug Discovery Pharmaceutical Preparations Models, Biological
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Machine Learning Models for Human Pharmacokinetic Parameters with In-House Validation.

Molecular pharmaceutics Nov 10, 2021
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...
Machine Learning Models, Biological Pharmacokinetics Humans
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A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges.

Drug discovery today Sep 27, 2021
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...
Machine Learning Drug Discovery Big Data Humans Artificial Intelligence Pharmacokinetics Quantitative Structure-Activity Relationship
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A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors.

CPT: pharmacometrics & systems pharmacology Jul 26, 2021
Model-informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowled...
Pharmacokinetics Male Middle Aged Bayes Theorem Models, Biological Vancomycin Drug Dosage Calculations Aged Aged, 80 and over Adult Anti-Bacterial Agents Female Humans Machine Learning
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Machine learning models for classification tasks related to drug safety.

Molecular diversity Jun 10, 2021
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study fo...
Humans Neural Networks, Computer Pharmacokinetics ERG1 Potassium Channel Support Vector Machine Machine Learning Quantitative Structure-Activity Relationship Models, Molecular Algorithms Tissue Distribution Drug-Related Side Effects and Adverse Reactions Drug Design
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Fast screening of covariates in population models empowered by machine learning.

Journal of pharmacokinetics and pharmacodynamics May 21, 2021
One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from sup...
Models, Statistical Bayes Theorem Drug Evaluation, Preclinical Pharmacokinetics Algorithms ROC Curve Support Vector Machine Humans Neural Networks, Computer Machine Learning
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Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

Molecules (Basel, Switzerland) Apr 28, 2021
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We descr...
Computer Simulation Pharmaceutical Preparations Models, Biological Biological Availability Drug Evaluation, Preclinical Humans Pharmacokinetics Supervised Machine Learning Machine Learning Quantitative Structure-Activity Relationship Administration, Oral
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Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model.

Drug metabolism and pharmacokinetics Apr 20, 2021
We constructed machine learning-based pharmacokinetic prediction models with very high performance. The models were trained on 26138 and 16613 compounds involved in metabolic stability and cytochrome P450 inhibition, respectively. Because the compoun...
Inactivation, Metabolic Predictive Value of Tests Pharmaceutical Preparations Computer Simulation Cytochrome P-450 Enzyme System Humans Reproducibility of Results Pharmacokinetics Machine Learning Drug Discovery Quantitative Structure-Activity Relationship Metabolic Clearance Rate
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