De-risking clinical trial failure through mechanistic simulation

Abstract

Drug development typically comprises a combination of pre-clinical experimentation, clinical trials and sta- tistical data driven analyses. Therapeutic failure in late-stage clinical development costs the pharmaceutical industry billions of USD per year. Clinical trial simulation represents a key derisking strategy and combining them with mechanistic models allows one to test hypotheses for mechanisms of failure and to improve trial designs. This is illustrated with a T-cell activation model, used to simulate the clinical trials of IMA901, a short-peptide cancer vaccine. Simulation results were consistent with observed outcomes and predicted that responses are limited by peptide off-rates, peptide competition for dendritic cell binding and dendritic cell mi- gration times. These insights were used to hypothesise alternate trial designs predicted to improve efficacy outcomes. This framework illustrates how mechanistic models can complement clinical, experimental and data driven studies to understand, test and improve trial designs, and how results may differ between humans and mice.

A Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity

“…In particular both optimisation techniques and the Bayesian techniques are iterative, so that the use of a rational but rapid evaluation of an approximation to an optimum in optimisation studies or a posterior for Bayesian techniques can in turn be used to restart the procedure with the full numerical model to further refine the results (Brown et al. 2022 ). As well as investigating the prospective role of such analytical solutions in parameter estimation, future work might also contemplate the analytical structure and solution for problems in higher spatial dimension, though it is unclear at this stage how tractable such a study would be.…”

Advancing treatment of retinal disease through in silico trials

“…One simple method of generating VPs-demographic and model parameters-is to assume a Gaussian distribution for each model parameter used to represent virtual populations. For each patient, a parameter value will be drawn from these Gaussian distributions to represent that patient [43,144]. The mean and standard deviation of the parameters can be adjusted to match empirically observed data either manually or through optimisation [7].…”

“…Running ISCTs requires a close collaboration between experimentalist, modellers and clinicians (see figures 2 and 9). Other medical disciplines such as oncology and cardiology have already benefited from in silico trials [43,238]. Ophthalmology can also benefit from such trials which require large amounts of data.…”