Human Cardiac Drug Response Prediction
Human Cardiac Drug Response Prediction
FilamenTech’s mechanistic modeling framework has been developed through more than 20 years of computational muscle modeling research and validated across translational cardiac and pharmaceutical development settings.
MUSICO has been applied to predict molecular mechanisms, cardiac dysfunction, therapeutic response, and physiological human cardiac behavior across inherited cardiomyopathies and translational drug-development programs.
The framework integrates molecular kinetics, sarcomere mechanics, tissue dynamics, and whole-heart physiology within a unified mechanistic simulation environment.
Starting from measured ATPase kinetics and fiber mechanics data, MUSICO simulated the effects of mavacamten and disopyramide on human HCM cardiac muscle twitch. Outputs were then passed to a whole-heart finite element model to predict left ventricular pressure-volume behavior.
Rodents express predominantly α-MHC (fast myosin). Humans express predominantly β-MHC (slow myosin). A drug that reduces contractility by 30% in a rat will not produce the same 30% effect in humans because the kinetic parameters of the myosin motor are fundamentally different. Not just quantitatively, but mechanistically.
Inherited cardiomyopathies can arise from small changes in sarcomeric proteins. Using the MUSICO platform, we simulated how troponin C mutations associated with hypertrophic (HCM) and dilated (DCM) cardiomyopathies alter contraction, relaxation, and twitch behavior in cardiac muscle.
1.
Predicting the Mechanism of Mavacamten Before Experimental Confirmation
2.
Translating Cardiac Function Across Species
3.
Predicting Mutation-Specific Cardiac Dysfunction
Mavacamten demonstrated significant therapeutic effects in hypertrophic cardiomyopathy (HCM), but the molecular mechanism underlying those effects was not fully understood.
Existing experimental systems could observe functional changes but could not fully resolve which sarcomere kinetic states were responsible for the observed physiological behavior.
Using multiscale mechanistic simulations, MUSICO analyzed functional cardiac data and predicted that mavacamten stabilizes the myosin autoinhibited parked state, altering transitions between detached and force-generating myosin states.
The framework connected molecular kinetic changes to observed alterations in contraction and relaxation dynamics under physiological human cardiac conditions.
The predicted mechanism was later independently confirmed through experimental studies and incorporated into the 2024 PNAS publication describing mavacamten’s mode of action.
MUSICO simulations were used as a core analytical framework within the mechanistic analysis.
This case demonstrated that mechanistic simulations can identify clinically relevant drug mechanisms before direct experimental confirmation, supporting earlier translational decision-making and therapeutic optimization.
Traditional preclinical cardiac models frequently fail to predict human cardiac behavior because species differences in sarcomere composition and contraction kinetics are difficult to translate reliably.
MUSICO simulated cardiac twitch dynamics across mouse, rat, porcine, and human cardiac systems using species-specific physiological parameters within the same mechanistic framework.
The platform modeled how differences in myosin isoforms and sarcomere kinetics influence contraction and relaxation behavior across species.
The same mechanistic framework accurately reproduced experimentally observed cardiac twitch dynamics across species without empirical refitting.
Predictions were validated against physiological datasets under species-specific conditions.
This capability enables more reliable translation of preclinical findings into predictive human cardiac simulations and may reduce dependence on costly late-stage experimental studies.
Inherited cardiomyopathies arise from diverse sarcomere mutations that produce distinct functional abnormalities, making prediction of disease progression and therapeutic response difficult.
MUSICO simulated how specific troponin mutations alter molecular kinetics, sarcomere mechanics, and whole-heart ventricular dynamics.
The framework linked molecular-level dysfunction to physiological cardiac outcomes through integrated multiscale simulations.
The simulations predicted distinct contraction and relaxation phenotypes associated with hypertrophic and dilated cardiomyopathy mutations, including altered ventricular pressure-volume behavior.
Mechanistic mutation-specific modeling may support precision cardiology approaches and improve understanding of heterogeneous inherited cardiac diseases.
of computational muscle modeling research
across cardiac biophysics and multiscale modeling
across mouse, rat, porcine, and human physiological systems
applied in pharmaceutical therapeutic-development settings
from molecular kinetics to whole-heart function
validated against experimental and translational cardiac studies
The MUSICO framework is supported by more than 65 peer-reviewed scientific publications spanning:
Representative Journals
The framework has been continuously developed and refined through collaborations with leading academic institutions and translational cardiac researchers.
MUSICO has been applied to:
The framework is designed to support earlier and more informed therapeutic-development decisions through predictive mechanistic simulations before large-scale clinical investment.
Translational Capabilities
The MUSICO framework has evolved through collaborations involving:
Research collaborations have contributed to:
FilamenTech has received recognition through scientific, translational, and innovation initiatives, including:
These efforts support continued development of predictive multiscale cardiac modeling for inherited cardiomyopathies and therapeutic development workflows.
MUSICO builds on more than two decades of NIH-, AHA-, and EU-funded muscle mechanics, X-ray diffraction, and cardiomyopathy modeling research across Boston, Chicago, Serbia, and European collaborators.
NIH R01 AR48776-01A1
Bioengineering Analysis of Muscle Mechanics and Metabolism
PI: Srboljub M. Mijailovich
NIH R01 DC011528
Multiscale Mechanisms of Lingual Mechanical Function
PI: R. Gilbert
Co-PI: Srboljub M. Mijailovich
NIH / BioCAT P41 GM103622
Structural muscle research and X-ray diffraction
PI: T. C. Irving, IIT Chicago
Horizon 2020 / SilicoFCM
In silico clinical trial for sarcomeric mutations leading to familial cardiomyopathy
Coordinator: BioIRC, Serbia
AHA 19IPLOI34770173
Multiscale modeling and X-ray diffraction to understand heart disease
PI: T. C. Irving, IIT Chicago
NIH R01 GM144555
Analytic Tools for Fiber Diffraction of Muscle
PI: T. C. Irving, IIT Chicago
NIH / NHLBI SBIR Phase I
MUSICO validation and commercialization for HCM and DCM drug development
PI: Srboljub M. Mijailovich
NIH R01 AR48776-01A1
Bioengineering Analysis of Muscle Mechanics and Metabolism
PI: Srboljub M. Mijailovich
NIH R01 DC011528
Multiscale Mechanisms of Lingual Mechanical Function
PI: R. Gilbert
Co-PI: Srboljub M. Mijailovich
NIH / BioCAT P41 GM103622
Structural muscle research and X-ray diffraction
PI: T. C. Irving, IIT Chicago
Horizon 2020 / SilicoFCM
In silico clinical trial for sarcomeric mutations leading to familial cardiomyopathy
Coordinator: BioIRC, Serbia
AHA 19IPLOI34770173
Multiscale modeling and X-ray diffraction to understand heart disease
PI: T. C. Irving, IIT Chicago
NIH R01 GM144555
Analytic Tools for Fiber Diffraction of Muscle
PI: T. C. Irving, IIT Chicago
NIH / NHLBI SBIR Phase I
MUSICO validation and commercialization for HCM and DCM drug development
PI: Srboljub M. Mijailovich
Discover how predictive multiscale modeling is being applied to inherited cardiomyopathies, translational cardiac research, and therapeutic development.
Human cardiac prediction from data you already have.
FilamenTech develops multiscale mechanistic simulation workflows that help drug-development teams translate assay, tissue, animal, mutation, and structural data into human-relevant cardiac and muscle predictions.
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Evaluate mutation effects, drug responses, or translational hypotheses using mechanistic simulation workflows tailored to your program.
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