Screening for regenerative therapy responders in heart failure
Satsuki Yamada1,2, Ryounghoon Jeon1, Armin Garmany1,3
1Department of Cardiovascular Medicine, Mayo Clinic, Center for Regenerative Medicine, Marriott Heart Disease Research Program, Van Cleve Cardiac Regenerative Medicine Program, Rochester, MN 55905, USA.
Biomarkers in Medicine
|June 25, 2021
Summary
Predicting patient response to regenerative therapies is crucial for innovation. New methods using deep learning and disease biology insights will improve patient selection for better therapeutic outcomes.
Area of Science:
- Regenerative medicine
- Biotherapy development
- Clinical trial design
Background:
- Therapeutic innovation faces challenges due to unpredictable patient outcomes.
- Identifying reliable indicators of patient response is essential for selecting suitable candidates for novel treatments.
- Determinants of success in regenerative biotherapies, particularly in achieving disease rescue, are not well understood.
Purpose of the Study:
- To address the challenge of outcome variability in therapeutic innovation.
- To emphasize the need for multi-dimensional decoding of repair capacity and disease resolution as responsiveness attributes.
- To upgrade phenotype-based patient selection for regenerative medicine using advanced insights and technologies.
Main Methods:
- Decoding repair capacity and disease resolution through multi-dimensional analysis.
- Integrating new insights into disease biology.
- Utilizing deep learning for enhanced clinical decision support.
Main Results:
- Current methods for patient selection are insufficient for regenerative biotherapies.
- Emphasis is shifting towards understanding individual patient responsiveness.
- Advanced computational approaches are being explored to refine patient stratification.
Conclusions:
- Improved patient selection is critical for the success of regenerative biotherapies.
- A deeper understanding of disease biology and patient response regulators is needed.
- Deep learning offers a promising avenue for optimizing clinical decision-making in regenerative medicine.
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