Network Model of Fibrotic and Immune Signaling in Fibroblasts can Predict Therapeutic Efficacy in Different Fibrotic Diseases

Introduction: Fibrotic disease is used to describe a wide array of conditions that manifest in multiple organs, including the lung, heart, and liver, among others. Though many of these diseases result in the stiffening of tissue, or fibrosis, their etiology is complex, and the mechanisms by which fibrotic tissue accumulates is poorly understood. Some diseases, such as sarcoidosis, are understood to have a strong immunologic component, others, such as idiopathic pulmonary fibrosis (IPF), are understood to be more fibrotic in their progression. Due to this, the potential success for therapeutic intervention is disease-specific. There exists an urgent need for strategies to better identify therapeutics, both novel and repurposed, that are efficacious in the treatment of these diseases. We hypothesize that by building a in silico fibroblast network model that can respond to varying levels of environmental, fibrotic, and inflammatory stresses, we can identify novel pathways that attenuate fibroblast responses to these signals in different disease contexts. This model would be tunable to the relative fibrotic and immunologic components of a specific disease to effectively predict therapeutic efficacy.

Materials and

Methods: A computational model of intracellular signaling was constructed using logic-based ordinary differential equations to represent molecular signaling networks in fibroblasts (Fig. 1a). Simulated fibroblasts respond to fibrotic (i.e., TGF-β1, matrix stiffness) and immunologic (i.e., TNF-α, IL-1β) signals to modulate the surrounding environment through secretion of cytokines, matrix proteins, and by modulating cell phenotypic changes. This model was developed using the Netflux toolkit in MATLAB. Subsequent in vitro studies were performed to validate this model. Fibroblasts were cultured on a matrix of collagen type 1 to match the stiffness of healthy tissues. Cultures were then stimulated with pro-fibrotic (TGF-β1 (20 ng/mL)) or pro-immunologic (TNF-α (50 ng/mL), IL-1β (50 ng/mL)) cytokines. Fibroblast RNA transcription and protein production were quantified.

Results, Conclusions, and Discussions: Fibroblast responses to stimuli was quantified via ELISA or immunocytochemistry and compared to in silico model results. IL-6, IL-8, and pro-collagen 1a1 (Col1a1) showed good agreement with model prediction (Fig 1C-E). In silico fibroblast responses were marked significant if perturbed state resulted in a change in expression greater than 10% of baseline expression. Overall, 80% of validation experiments agreed with model-predicted outcomes (Fig 1B). We then tested the model’s accuracy in predicting the efficacy of therapeutic interventions in diseased contexts. Fibroblast activation inputs were scaled to be either pro-fibrotic or pro-immunologic, to match the disease states of IPF and sarcoidosis, respectively. Pirfenidone, an approved treatment for IPF that inhibits the TGF-β1 activation cascade, was then added to the model. Under pro-fibrotic conditions, pirfenidone inhibited fibroblast activation and matrix remodeling, while under pro-inflammatory conditions, pirfenidone had no impact on fibroblast activation. This matches clinical trial results where pirfenidone was given to patients with progressive fibrotic sarcoidosis and had no significant impact on disease progression.

By simulating intracellular biochemical signaling networks, our model can predict heterogeneous fibroblast responses to different stimuli, including pro-inflammatory, pro-fibrotic, or gradients of both stimuli. Hence, our model can be used to make predictions of fibroblast phenotypic responses in different disease contexts. Through in silico experimentation, we can make alterations to these signaling networks to determine which nodes can be targeted to attenuate specific pathways in various fibrotic disease contexts. These predictions can be used to aid in identification of novel therapeutic targets, informing the repurposing of already approved therapeutics for new disease indications, or better strategize the selection of target disease indications in clinical trials. Future simulations will model cellular responses to other anti-fibrotic therapeutics in disease-specific contexts (similar the results outlining pirfenidone’s impact in IPF and sarcoidosis), while predicting cellular responses to these same treatments in expanded fibrotic conditions, such as systemic sclerosis-associated interstitial lung disease (SSc-ILD).

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