Screening for regenerative therapy responders in heart failure

Satsuki Yamada, Ryounghoon Jeon, Armin Garmany, Atta Behfar, Andre Terzic

Research output: Contribution to journalArticlepeer-review


Risk of outcome variability challenges therapeutic innovation. Selection of the most suitable candidates is predicated on reliable response indicators. Especially for emergent regenerative biotherapies, determinants separating success from failure in achieving disease rescue remain largely unknown. Accordingly, (pre)clinical development programs have placed increased emphasis on the multi-dimensional decoding of repair capacity and disease resolution, attributes defining responsiveness. To attain regenerative goals for each individual, phenotype-based patient selection is poised for an upgrade guided by new insights into disease biology, translated into refined surveillance of response regulators and deep learning-amplified clinical decision support.

Original languageEnglish (US)
Pages (from-to)775-783
Number of pages9
JournalBiomarkers in Medicine
Issue number10
StatePublished - Jun 2021


  • biomarkers
  • deep learning
  • heart failure
  • heterogeneity
  • imaging
  • prediction
  • regenerative medicine
  • stem cells
  • stratification

ASJC Scopus subject areas

  • Drug Discovery
  • Clinical Biochemistry
  • Biochemistry, medical


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