AIMC Topic: Dose-Response Relationship, Drug

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Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review.

Clinical pharmacokinetics Aug 17, 2024
INTRODUCTION: In the last decade, various Machine Learning techniques have been proposed aiming to individualise the dose of anticancer drugs mostly based on a presumed drug effect or measured effect biomarkers. The aim of this scoping review was to ...
Precision Medicine Humans Neoplasms Machine Learning Antineoplastic Agents Dose-Response Relationship, Drug
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A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.

Clinical pharmacokinetics Jul 31, 2024
BACKGROUND AND OBJECTIVE: The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed fo...
Anti-Bacterial Agents Computer Simulation Female Aged Humans Machine Learning Algorithms Dose-Response Relationship, Drug Area Under Curve Models, Biological Body Weight Adult Male Middle Aged Monte Carlo Method Daptomycin
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SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations.

BMC bioinformatics Jul 30, 2024
BACKGROUND: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated n...
Lung Neoplasms Neural Networks, Computer Signal Transduction Antineoplastic Agents Dose-Response Relationship, Drug Cell Line, Tumor Humans Drug Synergism
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Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

Clinical pharmacokinetics Jul 11, 2024
INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C), as an indicator of safety and efficacy, are important for optimizing therapy.
Antitubercular Agents Models, Biological Tuberculosis Isoniazid Precision Medicine Adult Dose-Response Relationship, Drug Arylamine N-Acetyltransferase Machine Learning Algorithms Humans
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PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

European journal of medicinal chemistry Jun 25, 2024
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial in...
Humans Dose-Response Relationship, Drug Structure-Activity Relationship Molecular Structure Peptides Peptides, Cyclic Deep Learning Macrocyclic Compounds
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From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

ChemMedChem Jun 20, 2024
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effe...
Cell Proliferation Dose-Response Relationship, Drug Drug Screening Assays, Antitumor Vascular Endothelial Growth Factor Receptor-2 Protein Kinase Inhibitors Structure-Activity Relationship Molecular Structure Antineoplastic Agents Deep Learning Cell Line, Tumor Humans Molecular Docking Simulation Drug Discovery
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Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age.

International journal of clinical pharmacy Jun 11, 2024
BACKGROUND: Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiolo...
Female Male Infant Drug Monitoring Humans Child, Preschool Vancomycin Anti-Bacterial Agents Machine Learning Dose-Response Relationship, Drug Retrospective Studies
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Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data.

International journal of clinical pharmacy May 11, 2024
BACKGROUND: Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing.
Depression Dose-Response Relationship, Drug Humans Antidepressive Agents, Second-Generation Adult Artificial Intelligence Venlafaxine Hydrochloride Female Male Precision Medicine Aged Middle Aged Retrospective Studies
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Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial.

Clinical pharmacology and therapeutics May 6, 2024
Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dosage and the narrow therapeutic window. Model-informed precision dosing (MIPD) using machine learning...
Models, Biological General Practice Dose-Response Relationship, Drug Hypothyroidism Thyroxine Male Middle Aged Primary Health Care Humans Precision Medicine Aged Machine Learning Female Adult Computer Simulation
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Machine Learning: A New Approach for Dose Individualization.

Clinical pharmacology and therapeutics Sep 29, 2023
The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its earl...
Pharmaceutical Preparations Precision Medicine Dose-Response Relationship, Drug Drug Monitoring Humans Machine Learning Algorithms
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