# High throughput platform for simultaneous multiparametric assessment of cardiac physiology for heart failure drug development

> **NIH NIH R33** · STANFORD UNIVERSITY · 2024 · $444,849

## Abstract

Heart failure affects 2-3% of the US population and remains the single largest cause of mortality.
Despite the large unmet need, heart failure drug development is notoriously difficult, and few first-in-
class drugs have been approved in the past decade. Human induced pluripotent stem cell (hiPSC)-
based models of heart disease are widely considered to hold tremendous potential for the development
of heart failure drugs because they faithfully model disease phenotypes and reflect individual patient
genetics. However, their utility for drug screening is limited because the technology for assessing
disease modifying effects are too cumbersome and low throughput for large-scale screens.
Visualizing disease-modifying activity of genes and drugs for heart failure requires kinetic read outs of
cardiomyocyte function that correlate calcium (Ca2+) cycling with contractile force and resting tension
to reveal systolic and diastolic heart dysfunction. The lack of off-the-shelf solutions for simultaneous
measurement of these parameters is a critical gap that has hindered the pace of basic research into
disease mechanisms and drug development. Although modern high content imaging systems can
acquire the requisite fast kinetic datasets, the major roadblock is that available data analysis tools lack
key capabilities, are too low throughput, and/or require substantial coding expertise to implement in
large-scale genetic studies and drug discovery.
Resolving this roadblock will be transformative by placing powerful tools in the hands of scientists
without coding expertise, enabling them to develop gene and drug screens using iPSC and adult
cardiomyocyte models of heart failure. We will deliver an integrated toolbox of software, reagents, and
standardized protocols for contemporaneous measurement of intracellular and subcellular Ca2+
dynamics with contractile force and resting tension that can be overlaid with subcellular feature
detection – all compatible with 384-well plate format – to model systolic and diastolic heart function.
The software will have a user-friendly graphical user interface to fully automate measurements of Ca2+-
contractility force curves (in absolute µM and nN terms) from beating cardiomyocytes. A key feature
will be individual, cell-by-cell analysis that will increase dynamic range and allow the recognition of
cellular heterogeneity in the preparations making possible realistic culture models with multiple cell
types. We will also develop a toolkit of viral vectors to deliver genetically encoded sensors of absolute
intracellular Ca2+ concentrations in cardiomyocyte populations without generating stable cell lines. The
integrated platform will be validated and benchmarked against current software in pilot drug and gene
screens that demonstrate ability to quantify disease-modifying activities for systolic and diastolic heart
disease. Quantitative performance measures will evaluate assay readiness.

## Key facts

- **NIH application ID:** 10916423
- **Project number:** 5R33HL167258-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** MARK MERCOLA
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $444,849
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916423

## Citation

> US National Institutes of Health, RePORTER application 10916423, High throughput platform for simultaneous multiparametric assessment of cardiac physiology for heart failure drug development (5R33HL167258-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10916423. Licensed CC0.

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