Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development. (21st September 2018)

• Record Type: Journal Article
• Authors: Brown, L. V.; Gaffney, E. A.; Wagg, J.; Coles, M. C.
• Is Part Of: Clinical and experimental immunology. Volume 193:Number 3(2018:Sep.)
• Page Start: 284
• Page End: 292
• Publication Date: 2018-09-21
• Subjects: Immunopathology -- Periodicals; 616.079
• DOI: 10.1111/cei.13182
• Languages: English
• ISSNs: 0009-9104

Abstract

Summary: 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. Abstract : This perspective presents a non‐technical discussion of recent and historical applications of mathematical, statistical and clinical and experimental immunology. These examples highlight the potential for these approaches to computational modelling to advance our mechanistic understanding of impact on the field and to generate useful and novel insights. We outline the reasons why mechanistic modelling is not currently commonly integrated with experimental and clinical studies, and offer strategies to overcome the associated challenges.