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Published on: February 6, 2020
Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep
Jin Komuro1, Yuta Tokuoka2, Tomohisa Seki3
1Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Insights
Researchers developed a deep learning system to screen drugs for inhibiting cardiomyocyte hypertrophy, a key factor in heart failure. Ezetimibe was identified as a promising candidate, showing effectiveness in both lab tests and mouse models.
Area of Science:
- Biomedical Engineering
- Cardiovascular Research
- Pharmacology
Background:
- Heart failure and associated mortality are rising globally.
- Cardiac hypertrophy precedes heart failure but lacks effective treatments.
- Novel therapeutic strategies are urgently needed.
Purpose of the Study:
- To develop a deep learning-based high-throughput screening system for evaluating cardiomyocyte hypertrophy.
- To identify potential drug candidates that inhibit cardiomyocyte hypertrophy.
- To validate the efficacy of identified drugs in preclinical models.
Main Methods:
- Primary rat cardiomyocytes were stimulated with angiotensin II and endothelin-1.
- A deep learning model performed instance segmentation on phase-contrast microscopy images.
- The system automatically quantified cardiomyocyte size and perimeter for unbiased evaluation.
- A library of 100 FDA-approved drugs was screened.
Main Results:
- A deep learning system was established for automated, unbiased evaluation of cardiomyocyte hypertrophy.
- Twelve out of 100 screened drugs demonstrated inhibitory effects on cardiomyocyte hypertrophy.
- Ezetimibe, a cholesterol absorption inhibitor, showed dose-dependent inhibition of hypertrophy in vitro.
- Ezetimibe treatment improved cardiac dysfunction in a mouse model of pressure overload.
Conclusions:
- The deep learning system is effective for cardiomyocyte hypertrophy evaluation and drug discovery.
- Ezetimibe shows potential as a therapeutic agent for heart failure.
- This approach facilitates the development of novel treatments for heart failure.
Abstract:
The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it. First, primary cultured cardiomyocytes from neonatal rats were stimulated by both angiotensin II and endothelin-1, and cellular images were captured using a phase-contrast microscope. Subsequently, we used a deep learning model for instance segmentation and established a system to automatically and unbiasedly evaluate the cardiomyocyte size and perimeter. Using this system, we screened 100 FDA-approved drugs library and identified 12 drugs that inhibited cardiomyocyte hypertrophy. We focused on ezetimibe, a cholesterol absorption inhibitor, that inhibited cardiomyocyte hypertrophy in a dose-dependent manner in vitro. Additionally, ezetimibe improved the cardiac dysfunction induced by pressure overload in mice. These results suggest that the deep learning-based system is useful for the evaluation of cardiomyocyte hypertrophy and drug screening, leading to the development of new treatments for heart failure.
