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The application of in-silico modelling is beginning to emerge as a key methodology to advance our understanding of mechanisms of disease pathophysiology and related drug action, and in the design of experimental medicine and clinical studies. From this perspective, we will present a non-technical discussion of a small number of recent and historical applications of mathematical, statistical and computational modelling to clinical and experimental immunology. We focus specifically upon mechanistic questions relating to human viral infection, tumour growth and metastasis and T cell activation. These exemplar applications highlight the potential of this approach to impact upon human immunology informed by ever-expanding experimental, clinical and 'omics' data. Despite the capacity of mechanistic modelling to accelerate therapeutic discovery and development and to de-risk clinical trial design, it is not utilized widely across the field. We outline ongoing challenges facing the integration of mechanistic modelling with experimental and clinical immunology, and suggest how these may be overcome. Advances in key technologies, including multi-scale modelling, machine learning and the wealth of 'omics' data sets, coupled with advancements in computational capacity, are providing the basis for mechanistic modelling to impact on immunotherapeutic discovery and development during the next decade.

Original publication

DOI

10.1111/cei.13182

Type

Journal article

Journal

Clin Exp Immunol

Publication Date

09/2018

Volume

193

Pages

284 - 292

Keywords

Animals, Biomedical Research, Datasets as Topic, Drug Development, Drug Discovery, Humans, Immunotherapy, Infections, Lymphocyte Activation, Machine Learning, Models, Immunological, Neoplasm Metastasis, Neoplasms, T-Lymphocytes