IdopNetwork as a genomic predictor of drug response

Jincan Che, Yuebo Jin, Claudia Gragnoli, Shing Tung Yau, Rongling Wu

Research output: Contribution to journalReview articlepeer-review

Abstract

Despite being challenging, elucidating the systematic control mechanisms of multifactorial drug responses is crucial for pharmacogenomic research. We describe a new form of statistical mechanics to reconstruct informative, dynamic, omnidirectional, and personalized networks (idopNetworks), which cover all pharmacogenomic factors and their interconnections, interdependence, and mechanistic roles. IdopNetworks can characterize how cell–cell crosstalk is mediated by genes and proteins to shape body–drug interactions and identify key roadmaps of information flow and propagation for determining drug efficacy and toxicity. We argue that idopNetworks could potentially provide insight into the genomic machinery of drug responses and provide scientific guidance for the design of drugs whose potency is maximized at lower doses.

Original languageEnglish (US)
Article number104252
JournalDrug Discovery Today
Volume30
Issue number1
DOIs
StatePublished - Jan 2025

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Drug Discovery

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