Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development

Abstract

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.

Figure 1

Overview of experimental/clinical study workflow, with blue labels indicating how the application of modelling can support each step. Constrained by biological knowledge, mechanistic modelling has the potential to support all steps in this workflow, including the generation of testable hypotheses or predictions in silico, or the prediction of experimental or clinical study outcomes across a range of possible study designs.

Figure 2

A summary of key mechanistic models of viral kinetics. (a) A model that predicted that the action of IFNα on HCV is to inhibit viral production rather than infection, that the biphasic decline of viral load is due to early viral clearance followed by infected cell death, and that the ‘shoulder’ of constant viral load sometimes observed is due to the temporary balance of infected cell death with division and infection 8, 9, 19; (b) A model that provides a mechanistic basis for HCV rebound due to random mutations, and could predict sustained virologic response in phase II and III clinical trials 12, 13.

Figure 3

A summary of key mechanistic tumour models. Each panel shows a model(s) that, (a) predicted various features of tumour invasion mediated by acid‐producing cells 20 (b) provided an evolutionary basis for the Warburg effect, as neoplastic cells that outcompete other cells in low oxygen concentrations (green shaded region) have a fitness advantage in an environment with variable oxygen concentrations (shown by the black line) and take over the tumour population (background of plot) 22; (c) showed how moderate therapy improves patient survival over intensive therapy, that may select for resistant cells 23; (d) showed that a less intense, more frequent therapy schedule improves survival in a dynamic model of resistance 24; (e) predicted patient survival in a phase III clinical trial of a drug by parameterising tumour and survival models 2.

Figure 4

A summary of key mechanistic models of T‐cell activation and dynamics. Each panel shows a model that, (a) found that chemokines cannot attract T‐cells to antigen‐bearing dendritic cells in an optimum search strategy 31; (b) showed that the dynamics of T‐cell movement can be explained entirely by interactions with their environment (as opposed to e.g. chemokines) 32; (c) investigated how T‐cells can integrate many low‐affinity interactions with dendritic cells to activate 33; (d) considered the minimum number of dendritic cells required for T‐cell response 36.