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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
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Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response.

Janet Piñero1, Abel Gonzalez-Perez2, Emre Guney1

  • 1Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.

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Summary

Drug targets causing side effects are more central in cell networks and less tolerant to genetic variation than other drug targets. Understanding these protein features aids in designing safer drugs.

Keywords:
adverse drug reactiondrug responsegene expressiongenomicsnetwork biologypharmacogenomics

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Area of Science:

  • Pharmacogenomics
  • Systems Biology
  • Network Pharmacology

Background:

  • Understanding drug mechanisms is key for rational drug design and managing adverse reactions.
  • Characterizing drug targets, metabolism proteins, and side effect proteins by expression, genomic tolerance, and network roles is essential.

Purpose of the Study:

  • To investigate if proteins involved in drug therapeutic and adverse effects exhibit distinct transcriptomic, genomic, and network features.
  • To differentiate properties of drug targets mediating side effects versus those not.

Main Methods:

  • Explored protein properties in global and organ-specific interactomes using multi-scale network metrics.
  • Evaluated gene expression profiles across various tissues.
  • Assessed tolerance to loss-of-function variants using data from 60,000 subjects.

Main Results:

  • Side effect-mediating drug targets are more network-central, less tolerant to loss-of-function variants, and have broader tissue expression.
  • Drug metabolizing enzymes and transporters are less network-central, more tolerant to variants, and have constrained tissue expression.

Conclusions:

  • Proteins associated with drug action and toxicity possess distinct network, genomic, and transcriptomic characteristics.
  • These findings can help prioritize drug candidates with a lower likelihood of causing adverse effects.