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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Related Experiment Video

Updated: Mar 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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A physiome interoperability roadmap for personalized drug development.

Simon Thomas1, Katherine Wolstencroft2, Bernard de Bono3

  • 1Cyprotex Discovery Ltd , 15 Beech Lane, Macclesfield SK10 2DR , UK.

Interface Focus
|April 7, 2016
PubMed
Summary
This summary is machine-generated.

Personalized medicine development is advanced by simulation models that integrate inter-individual variability data. The Human Physiome Project is crucial for standardizing data to improve model development and personalized therapies.

Keywords:
data and model resourceshuman physiomeinteroperabilitypersonalized medicinepharmacogenomicsphysiologically based pharmacokinetic modelling

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Area of Science:

  • Pharmacology and Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Inter-individual variability in drug disposition, toxicity, and response necessitates tailored therapies.
  • '-Omics' data and other molecular information are available but challenging to integrate into simulation models.
  • Standardized data annotation is essential for effective model development.

Purpose of the Study:

  • To review the current state and challenges of personalized medicine.
  • To discuss the role of physiome modeling in drug discovery and development.
  • To identify data resources and potential contributions of the physiome community to personalized drug development.

Main Methods:

  • Review of current personalized medicine practices and challenges.
  • Analysis of available data resources for model development.
  • Exploration of physiome modeling's contribution to physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling.
  • Proposal of a roadmap for the physiome community.

Main Results:

  • Simulation models incorporating inter-individual variability can aid in developing personalized therapies.
  • The Human Physiome Project enhances data usability for model development.
  • Physiome modeling offers potential contributions to PBPK/PD modeling for personalized drug development.
  • Standardized data and improved integration are key to advancing personalized medicine.

Conclusions:

  • Advancing personalized medicine requires overcoming data integration challenges.
  • The Human Physiome Project plays a vital role in standardizing data for improved model development.
  • A roadmap is proposed to guide the physiome community in supporting personalized medicine through enhanced data usability and modeling integration.