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Related Concept Videos

Therapeutic Drug Monitoring: Affecting Factors01:29

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Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
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Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
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Related Experiment Video

Updated: Dec 10, 2025

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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TargetDB: A target information aggregation tool and tractability predictor.

Stephane De Cesco1, John B Davis1, Paul E Brennan1

  • 1Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom.

Plos One
|September 3, 2020
PubMed
Summary
This summary is machine-generated.

Identifying new therapeutic protein targets is crucial. TargetDB aggregates public data, providing a scoring system and machine learning to assess target tractability, streamlining drug discovery.

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Area of Science:

  • Biotechnology
  • Drug Discovery
  • Bioinformatics

Background:

  • Identifying novel therapeutic protein targets is essential for drug discovery.
  • Current methods like literature searches are time-consuming and lack uniformity.
  • The rapid increase in available data necessitates efficient target evaluation tools.

Purpose of the Study:

  • To develop TargetDB, a tool for aggregating and analyzing public data on potential therapeutic protein targets.
  • To create a scoring system for evaluating desirable attributes of therapeutic targets.
  • To implement a machine learning model for categorizing target tractability.

Main Methods:

  • Developed TargetDB to aggregate public information including disease links, safety data, 3D structures, and ligandability.
  • Implemented a target scoring system based on key therapeutic attributes.
  • Built a machine learning classification system to predict target tractability (promising or challenging).

Main Results:

  • TargetDB successfully aggregates diverse public data into an easily analyzable format.
  • The scoring system and machine learning model provide quantitative and qualitative assessments of targets.
  • Test cases demonstrate the utility of TargetDB in evaluating potential drug targets.

Conclusions:

  • TargetDB offers a streamlined and uniform approach to identifying and evaluating therapeutic protein targets.
  • The tool aids researchers in making informed decisions for drug discovery programs.
  • TargetDB enhances the efficiency and consistency of the target identification process.