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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...
72
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

136
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
136
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

74
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.
74
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

105
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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Convert-Pheno: A software toolkit for the interconversion of standard data models for phenotypic data.

Manuel Rueda1, Ivo C Leist1, Ivo G Gut1

  • 1Centro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028 Barcelona, Spain; Universitat de Barcelona (UB), Barcelona, Spain.

Journal of Biomedical Informatics
|November 30, 2023
PubMed
Summary

Convert-Pheno is an open-source toolkit that harmonizes diverse phenotypic data standards, improving data sharing for precision medicine and public health research. This software facilitates interconversion between common data models, enhancing biomedical data integration and accessibility.

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Area of Science:

  • Biomedical Informatics
  • Health Data Standards
  • Precision Medicine

Background:

  • Efficient sharing and integration of phenotypic data are essential for advancing biomedical research, precision medicine, and public health.
  • Harmonization of variable names and values using common standards is critical but hindered by diverse data models across research centers.

Purpose of the Study:

  • To present Convert-Pheno, an open-source software toolkit designed to enable the interconversion of various common data models for phenotypic data.
  • To facilitate seamless data sharing and integration across different research institutions by addressing the challenge of diverse data standards.

Main Methods:

  • Developed an open-source software toolkit named Convert-Pheno.
  • Implemented functionalities for interconverting phenotypic data between multiple common data models, including Beacon v2 Models, CDISC-ODM, OMOP-CDM, Phenopackets v2, and REDCap.
  • Created comprehensive documentation covering deployment and installation procedures.

Main Results:

  • The Convert-Pheno toolkit successfully enables the interconversion of specified common data models for phenotypic data.
  • The software provides a solution for harmonizing diverse data standards, thereby improving data interoperability.
  • Detailed documentation is available to support the deployment and utilization of the toolkit.

Conclusions:

  • Convert-Pheno enhances the efficient sharing and integration of phenotypic data by enabling interconversion between diverse data models.
  • The toolkit supports advancements in precision medicine and public health by promoting data harmonization and accessibility.
  • The open-source nature and comprehensive documentation of Convert-Pheno facilitate its adoption and contribution to the research community.