Systems pharmacology model to control cardiac fibrosis

Jeff SaucermanIMAG MSM March 2019

Signaling complexity hinders targeting of fibrosis after myocardial infarction

Objective: Develop computational models that predict and mechanistically explain therapeutic strategies to control myofibroblast activation and fibrosis

Travers et al Circ Res 2016Stuart et al JMCC 2016

Every year, ~1M Americans have a myocardial infarction

Reconstruction of the fibroblast signaling network

89 nodes, 129 reactions reconstructed from 177 papers2+ citations per reaction, emphasis on cardiac fibroblasts

Cytokines TGFβBeta-adrenergic

Integrin

Zeigler+ J Mol Cell Cardiol 2016

From network structure to dynamics: logic-based differential equations

• Proteins/genes in units of normalized activity, not concentration• Reactions use normalized-Hill functions and AND/OR logic gates.• Default parameter values: EC50,(0.5), Hill coeff (1.4), rxn weight (1) time

constant (signaling: 10 s, transcription: 1 h, translation: 10 h)Kraeutler+ BMC Sys Bio 2010

𝑓𝑓𝑎𝑎𝑎𝑎𝑎𝑎 𝐴𝐴 =𝐴𝐴𝑛𝑛

𝐸𝐸𝐶𝐶50𝑛𝑛 + 𝐴𝐴𝑛𝑛𝑑𝑑𝐶𝐶𝑑𝑑𝑑𝑑

=1𝜏𝜏𝐶𝐶

(𝑊𝑊𝐴𝐴𝐶𝐶 𝑓𝑓𝑎𝑎𝑎𝑎𝑎𝑎 𝐴𝐴 𝑌𝑌𝑀𝑀𝐴𝐴𝑀𝑀,𝐶𝐶 − 𝐶𝐶)

𝐴𝐴𝐴𝐴𝐴𝐴 𝑋𝑋,𝑌𝑌 = 𝑓𝑓 𝑋𝑋 𝑓𝑓 𝑌𝑌𝑂𝑂𝑂𝑂 𝑋𝑋,𝑌𝑌 = 𝑓𝑓 𝑋𝑋 + 𝑓𝑓 𝑌𝑌 − 𝑓𝑓 𝑋𝑋 𝑓𝑓 𝑌𝑌

Z

ANDX Y

Z

ORX Y

Predicted network response to TGFβTGFβ

Logic-based differential equation model

ET1IL6

Angela Zeigler

latent TGFβ

Methods: Kraeutler et al, BMC Sys Bio 2010

Model accurately predicts 66 of 82 input-output relationships

other outputs

Cardiac fibroblast validation data from 34 papers not used for model buildingValidation accuracy = 80%; see poster

Zeigler+ J Mol Cell Cardiol 2016

TGFβRi: SD208

Predicted and validated role for TGFβautocrine loop in mechano-induced αSMA

3D culture of cardiac fibroblasts in either mechanically restrained or floating collagen gelsGreen: αSMA; purple: DAPI

Will Richardson

Zeigler+ J Mol Cell Cardiol 2016

TGFβ stretch

αSMA

Applying the network model to predict matrix remodeling dynamics after

myocardial infarction9 model inputs driven by experimentally measured cytokine/hormone dynamics after myocardial infarction

Post-MI experimental data from rator human hearts, from infarct zone where available

0 1 2 3 4 5 6 7 8Time (Weeks)

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Angiotensin II

Idealized CurveScheiffer 1994White 2001

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TGFBIdealized CurveHerskowitz 1995Hao 1999

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IL6Idealized CurveHerskowitz 1995Deten 2002

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IL1Idealized CurveHerskowitz 1995Deten 2002

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TNFaIdealized CurveHerskowitz 1995Heba 2001

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NEIdealized CurveWhite 2001

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ET1Idealized CurveWhite 2001

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BNP

Idealized CurveWhite 2001

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PDGFIdealized CurveZhao 2011

Experimental validation of predicted collagen mRNA and protein outputs after

myocardial infarction

Experimental data from rat infarcts:Deten 2001Zimmerman 2001Fomovsky 2010

Network model coupled to single ODE model with experimentally measured time-dependent MMP activity and fibroblast proliferation

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(FoldChange)

Collagen I mRNAModel PredictionRat Infarct Deten 2001Rat Infarct Zimmerman 2001

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Collagen III mRNAModel PredictionRat Infarct Deten 2001Rat Infarct Zimmerman 2001

Simulating FDA approved drugs targeting fibroblast signaling network

25 FDA approved drugs that uniquely target 24 network nodes

Competitive inhibitors/activators

Non-competitive inhibitors/activators

shift [X] adjust reaction weights Dose: 80% of saturation

Virtual screen for drugs that modulate fibroblasts after myocardial infarction

Time post-MI:

Fraccarollo et al Hypertension 2015

Chu et al Card Res 2012

Leuder+ Circ HF ‘15

Network mechanisms contributing to fibrosis induced by arsenic trioxide

Ani Chandrabhatla

Chu et al Card Res 2012

Network mechanisms contributing to fibrosis attenuated by Entresto

Ani Chandrabhatla

Entresto

Rats 4 weeks post-MI Trivedi+ JAHA 2018

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Entresto

Model prediction after MI

Model prediction 1 week after MI

Integrated agent-based and signaling network model of fibrosis

4

Michaela Rikardw. Peirce-Cottler, Holmes

Summary• Developed and validated a large-scale model of cardiac

fibroblast signaling• Validated model prediction that stretch-mediated

myofibroblast differentiation involves a TGFβ autocrine loop• Framework for predicting in vivo signaling dynamics and

effects of drugs after myocardial infarction• Predicted pro- and anti-fibrotic FDA approved drugs (e.g.

arsenic trioxide, nitrates, Entresto)

AcknowledgementsSaucerman LabAni ChandrabhatlaBryan ChunSteve ChristiansenDeborah FrankMonika GrabowskaAli KhalilimeybodiCody NarcisoAnders NelsonBethany WissmannLaura WooAngela Zeigler

CollaboratorsJeff Holmes (UVA)Shayn Peirce-Cottler (UVA)Michaela Rikard (UVA)Will Richardson (Clemson)

Funding:

Posters on automated validation, fibrosis gene therapies

Grad student and post-doc positions availablehttp://bme.virginia.edu/saucerman

@sauce_lab