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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Effect of Hepatic Disease on Pharmacokinetics: Active Drug, Metabolite and Fraction of Metabolized Drug01:14

Effect of Hepatic Disease on Pharmacokinetics: Active Drug, Metabolite and Fraction of Metabolized Drug

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In pharmacotherapy, monitoring drug concentrations is paramount, especially for drugs whose therapeutic effects hinge on both the active compound and its metabolite. Hepatic impairment profoundly influences drug potency by altering liver function. If the drug is more potent than its metabolite, impaired liver function amplifies drug activity due to elevated drug concentration levels. Conversely, if the metabolite holds greater potency, diminished liver function diminishes drug activity by...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Factors Influencing Drug Absorption: Disease States and Pharmacology01:25

Factors Influencing Drug Absorption: Disease States and Pharmacology

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Multiple disease states can significantly influence the oral drug absorption process by affecting blood flow and the functionality of the gastrointestinal (GI) system. Various GI diseases, including conditions that alter GI motility, such as diarrhea, decreased acid secretions (achlorhydria), and infections, have been associated with reduced drug absorption.
Substances such as alcohol and specific drugs, including antineoplastics, can also negatively impact drug absorption. For instance,...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Hypergraph-based logistic matrix factorization for metabolite-disease interaction prediction.

Yingjun Ma1, Yuanyuan Ma2

  • 1School of Applied Mathematics, Xiamen University of Technology, Xiamen 361024, China.

Bioinformatics (Oxford, England)
|September 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces HGLMF, a novel hypergraph-based method for predicting metabolite-disease interactions. HGLMF accurately identifies disease-related metabolites, advancing disease diagnosis and treatment strategies.

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Computational Biology

Background:

  • Metabolites are crucial in cell regulation and linked to complex diseases.
  • Identifying disease-related metabolites aids in diagnosis, prevention, and treatment.
  • Existing computational methods struggle with higher-order relationships.

Purpose of the Study:

  • To develop a novel computational approach for predicting metabolite-disease interactions.
  • To overcome limitations of pairwise relationship analysis in existing methods.
  • To identify novel disease-related metabolites.

Main Methods:

  • Utilized hypergraph-based logistic matrix factorization (HGLMF).
  • Extracted molecular structures, gene associations, hierarchical structures, and GO annotations.
  • Calculated kernel neighborhood similarity and fused multiple networks.
  • Built hypergraph structures for metabolites and diseases.

Main Results:

  • HGLMF accurately predicted metabolite-disease interactions.
  • The method outperformed existing state-of-the-art approaches.
  • HGLMF demonstrated capability in predicting novel metabolites and diseases.
  • Case studies confirmed the discovery of novel disease-related metabolites.

Conclusions:

  • HGLMF is an effective tool for predicting metabolite-disease interactions.
  • The approach advances the identification of biomarkers for complex diseases.
  • This method offers a promising avenue for personalized medicine and drug discovery.