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Review
. 2021 Jun;15(10):775-783.
doi: 10.2217/bmm-2020-0683. Epub 2021 Jun 25.

Screening for regenerative therapy responders in heart failure

Satsuki Yamada  1   2 , Ryounghoon Jeon  1 , Armin Garmany  1   3 , Atta Behfar  1   4 , Andre Terzic  1   5
Affiliations
Review

Screening for regenerative therapy responders in heart failure

Satsuki Yamada et al. Biomark Med. 2021 Jun.

Abstract

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.

Keywords: biomarkers; deep learning; heart failure; heterogeneity; imaging; prediction; regenerative medicine; stem cells; stratification.

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Conflict of interest statement

Financial & competing interests disclosure

The authors are supported by the National Institutes of Health (R01 HL134664), Regenerative Medicine Minnesota, Marriott Family Foundation, Van Cleve Cardiac Regenerative Medicine Program, Michael S and Mary Sue Shannon Family, Center for Regenerative Medicine, Center for Biomedical Discovery and Medical Scientist Training Program at Mayo Clinic. A Terzic holds the Marriott Family Professorship in Cardiovascular Diseases Research, and is Michael S and Mary Sue Shannon Director of the Mayo Clinic Center for Regenerative Medicine. S Yamada, A Behfar and A Terzic are inventors on regenerative sciences related intellectual property disclosed to Mayo Clinic. Previously, Mayo Clinic has administered research grants from Celyad. Mayo Clinic, A Behfar and A Terzic have interests in Rion LLC. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Figures

Figure 1.
Figure 1.. Heterogeneous response puzzle.
Unpredictable refractoriness to therapeutic interventions is a major challenge in healthcare. Mixed results have not fully characterized multi-factorial causes. Biological diversity among individuals and nonuniform care delivery refute a ‘one size fits for all’ paradigm. Personalized approaches are required to overcome heterogeneous outcomes in practice.
Figure 2.
Figure 2.. Disease complexity and diagnostic toolkit.
Left: body health is supported by molecular, cellular, organ and systems well-being. Multi-level compensatory mechanisms engage to maintain integrative homeostasis. Harm exceeding intrinsic safeguard triggers progressive disease manifestation, and ultimately refractory state leading in extremis to cardiovascular collapse. Right: vulnerability, stress load and response readouts are useful in assessing health versus disease. In clinical cardiology, diagnostic armamentarium has expanded from blood extracted biomarkers to imaging and signal detection modalities, covering all levels of the disease hierarchy.
Figure 3.
Figure 3.. Disease management evolution: from reactive to proactive.
Current practice targeting advanced disease is suboptimal offering limited options. Emerging paradigms are poised to ensure optimized and customized treatment solutions.
Figure 4.
Figure 4.. Artificial intelligence supported clinical practice.
With increasing overflow of new knowledge, clinical decision making benefits from AI-empowered algorithms. AI: Artificial intelligence.
Figure 5.
Figure 5.. Iterative optimization of individual outcome.
The diagnosis–therapy–delivery triad is increasingly transformed by reliance on biomarker-guided personalized care, targeted cures and AI-empowered clinical decision making. AI: Artificial intelligence.
See this image and copyright information in PMC

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