# Digital Twins from the Atom to the Rhythm

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $791,630

## Abstract

PROJECT SUMMARY: Our overarching goal is to develop a transformative integrative clinical, experimental
and in silico-based pipeline to create a digital twin technology for patient-specific prediction. Digital twin
technology holds the promise of the development and application of virtual models that replicate physiological
processes and characteristics of diseases to reveal mechanisms, simulate disease progression, identify potential
drug targets and simultaneously predict drug efficacy. While our planned approach is broadly applicable, here
we will apply digital twins to the problem of identification of cardiac drug targets and prediction of the efficacy or
cardiotoxicity of drugs in individuals. A major strength of our digital twin approach is that it incorporates data from
the atomic structure to the cardiac rhythm, allowing the inclusion of individual differences that affect individual
protein structure, cellular electrophysiology and electrocardiograms. Digital twins will allow for improved
understanding of how variation between individuals modifies disease severity and drug cardiotoxicity risks. Such
a technology is possible now due to the maturity of deep-learning based modeling and simulation approaches in
conjunction with the increasing availability of ion channel protein structures in physiologically relevant states,
and the development of patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs).
Combining these developments will allow for the realization of high throughput testing for individuals to determine
their disease- and drug-related risks. Indeed, our preliminary data indicate the promise of a new deep learning
method to extract in silico representations of individual cellular electrophysiology and Ca2+ handling to digitally
replicate the mechanistic cellular fingerprint. We will build a new digital twin framework across multiple system
scales by bringing together new methods in atomistic scale simulation with recently developed cellular level
models and deep learning networks to discover new protocols to extract needed model parameters from data
and for translating from iPSC-CM to adult cardiac myocyte electrophysiology. We will develop and test an
experimental and computational digital twin platform applied to problem of personal cardiac disease expression
and drug-induced cardiotoxicity via a combined computational-experimental approach that will allow the
construction, prediction and validation of patient-specific digital twin cardiac cells. We aim to 1) Develop cardiac
ion channel protein digital twins for structure and function prediction, 2) Develop cardiac myocyte digital twins,
and 3) Predict the patient-specific cardiac safety pharmacology of individual drugs and combined therapeutics.
We are bringing together model simulations at the level of the atom in a totally new way to include genetic
mutations spanning benign variants to ones with known arrhythmia risks (from which all other models can...

## Key facts

- **NIH application ID:** 10899977
- **Project number:** 1R01HL174001-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** COLLEEN E CLANCY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $791,630
- **Award type:** 1
- **Project period:** 2024-07-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899977

## Citation

> US National Institutes of Health, RePORTER application 10899977, Digital Twins from the Atom to the Rhythm (1R01HL174001-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10899977. Licensed CC0.

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