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Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
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Limits and Variability in Drug Databases: Lessons Learnt from Drug Comparisons.

Jean-Baptiste Lamy1, Hélène Berthelot1, Madeleine Favre2,3

  • 1LIMICS, Université Paris 13, Inserm, Sorbonne Université, 93017 Bobigny, France.

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|June 24, 2020
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Summary
This summary is machine-generated.

Drug databases vary in content. We propose a taxonomy to classify these differences and introduce a tool for visually comparing drug data across multiple sources.

Keywords:
Computer GraphicsDrug databasesNonverbal Communication

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Area of Science:

  • Pharmacology
  • Bioinformatics
  • Data Science

Background:

  • Multiple drug databases are available for researchers.
  • Discrepancies and variability exist across these drug information sources.
  • Understanding data inconsistencies is crucial for accurate drug research.

Purpose of the Study:

  • To propose a classification system for observed variability in drug databases.
  • To present a novel tool for the visual comparison of drug properties across different databases.
  • To aid researchers in identifying and understanding data discrepancies.

Main Methods:

  • Developed a taxonomy to categorize observed data variability.
  • Created a software tool for visual data comparison.
  • Utilized drug properties as the basis for comparison across sources.

Main Results:

  • A structured taxonomy for drug database variability was established.
  • The presented tool enables effective visual investigation of data discrepancies.
  • The tool facilitates the identification of differences in drug property representation.

Conclusions:

  • The proposed taxonomy and tool address the challenge of data variability in drug databases.
  • Visual comparison is an effective method for understanding drug data inconsistencies.
  • This work supports more reliable data integration and analysis in drug discovery and research.