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Computationally derived points of fragility of a human cascade are consistent with current therapeutic strategies
Deyan Luan1, Michael Zai, Jeffrey D Varner
1Department of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, USA.
Abstract:
The role that mechanistic mathematical modeling and systems biology will play in molecular medicine and clinical development remains uncertain. In this study, mathematical modeling and sensitivity analysis were used to explore the working hypothesis that mechanistic models of human cascades, despite model uncertainty, can be computationally screened for points of fragility, and that these sensitive mechanisms could serve as therapeutic targets. We tested our working hypothesis by screening a model of the well-studied coagulation cascade, developed and validated from literature. The predicted sensitive mechanisms were then compared with the treatment literature. The model, composed of 92 proteins and 148 protein-protein interactions, was validated using 21 published datasets generated from two different quiescent in vitro coagulation models. Simulated platelet activation and thrombin generation profiles in the presence and absence of natural anticoagulants were consistent with measured values, with a mean correlation of 0.87 across all trials. Overall state sensitivity coefficients, which measure the robustness or fragility of a given mechanism, were calculated using a Monte Carlo strategy. In the absence of anticoagulants, fluid and surface phase factor X/activated factor X (fX/FXa) activity and thrombin-mediated platelet activation were found to be fragile, while fIX/FIXa and fVIII/FVIIIa activation and activity were robust. Both anti-fX/FXa and direct thrombin inhibitors are important classes of anticoagulants; for example, anti-fX/FXa inhibitors have FDA approval for the prevention of venous thromboembolism following surgical intervention and as an initial treatment for deep venous thrombosis and pulmonary embolism. Both in vitro and in vivo experimental evidence is reviewed supporting the prediction that fIX/FIXa activity is robust. When taken together, these results support our working hypothesis that computationally derived points of fragility of human relevant cascades could be used as a rational basis for target selection despite model uncertainty.
Insights
Mechanistic mathematical models can identify fragile points in human biological systems, like the coagulation cascade, for potential therapeutic targets. This approach aids drug discovery by predicting sensitive mechanisms despite model uncertainties.
Area of Science:
- Systems biology
- Computational biology
- Pharmacology
Background:
- The utility of mechanistic mathematical modeling in molecular medicine and clinical development is not yet fully established.
- Systems biology approaches are increasingly important for understanding complex biological processes.
Purpose of the Study:
- To investigate if mechanistic models, despite inherent uncertainties, can computationally identify fragile points in human biological cascades.
- To explore the potential of these identified fragile mechanisms as therapeutic targets.
Main Methods:
- Developed and validated a mechanistic mathematical model of the human coagulation cascade (92 proteins, 148 interactions).
- Used sensitivity analysis (Monte Carlo strategy) to calculate state sensitivity coefficients, assessing mechanism robustness or fragility.
- Validated the model using 21 published datasets from in vitro coagulation studies.
Main Results:
- The model accurately simulated platelet activation and thrombin generation (mean correlation of 0.87).
- Identified factor X/activated factor X (fX/FXa) activity and thrombin-mediated platelet activation as fragile mechanisms in the absence of anticoagulants.
- Identified factor IX/activated factor IX (fIX/FIXa) and factor VIII/activated factor VIII (fVIII/FVIIIa) activation and activity as robust mechanisms.
Conclusions:
- Computationally identified points of fragility in human cascades can serve as a rational basis for therapeutic target selection.
- This approach holds promise for drug discovery in molecular medicine, even with model uncertainty.
