Systems Pharmacology Modelling for Translating Animal Models of Neuroinflammation

点击翻译按钮获取中文摘要

Chronic neuroinflammation has emerged as a major contributing factor to aging and several neuro-degenerative diseases for which treatment options remain limited or non-existent, including Alzheimer's disease and multiple sclerosis. Although some genetic and extrinsic risk factors have been identified, definitive causes and cures remain elusive. Experimental animal models are a critical tool for testing hypotheses of disease initiation/progression, generating potential targets for prevention and intervention, and evaluating the concentration-effect relationships for new compounds, existing drugs (i.e., repurposing), and innovative drug combinations. However, it is clear that no one animal model can completely recapitulate the pathophysiology, neuro-cognitive symptoms, heterogeneity in drug responses, and concentration-effect relationships associated with chronic inflammation and neurodegeneration in humans. Our goals are to develop a hybrid modelling strategy to evaluate the various animal models available for chronic neuro-inflammation, identify a combination of experimental and computational platforms to best translate preclinical testing to humans, and to use the final models to evaluate current targets for drug development. Our hybrid modelling strategy will combine quantitative systems pharmacology (QSP) modelling with machine learning (ML) algorithms to bridge the molecular determinants of disease progression and drug response in animals to human clinical outcomes. This is an emerging paradigm that has shown promise in other therapeutic areas by leveraging the modeling of known biophysical processes (QSP) and top-down ML approaches for bridging knowledge gaps and generating new testable hypotheses. We intend for the QSP/ML platform to be modular in nature such that it could be calibrated to multiple neurodegenerative disorders. Although there are several QSP models that have been developed for neuroscience R&D [6], they do not account for the neuroinflammatory components that may underly the failure of many formerly promising compounds for multiple neurodegenerative disorders. We have shown how multiple modelling approaches, such as Boolean network analysis and ordinary differential equations, can be used in a strategic manner to link drug exposure and molecular level events to explore heterogeneity in treatment outcomes, alternate dosing strategies, and combination drug regimens in oncology. Recently, Dr Mager (our collaborator on this project) has transitioned his QSP and network-based modelling approaches to neuroscience and developed a network model to identify drug targets and a re-purposed drug to prevent/treat chemotherapy-induced peripheral neuropathy. Here, we will continue this approach of integrating multilevel data (including transcriptomics and protein levels) for factors governing the complex interplay between the immune and nervous systems in response to neurodegenerative disease and drug therapies.The student will be charged with developing a QSP/ML platform to study the translational potential of animal models of chronic neuroinflammation. Experimental, literature, and online data will be used to calibrate a large dynamic model of major cell signaling pathways implicated in development of chronic neuroinflammation. This model will in turn offer a richer picture of disease biology and inform the development of medicines that deliver greater therapeutic benefit with fewer safety concerns in Alzheimer's disease, multiple sclerosis, Parkinson's disease, and ALS, and the validation of current targets for therapy. We expect the model to be used in the neuroscience department for identifying biomarkers of disease progression, patient selection in clinical trials and response to treatment for the different project in AZ portfolio. Scientists in the neuroscience department at AZ will provide biological knowledge in neuroinflammation, modelling expertise, and access to internal databases.