De-risking clinical trial failure through mechanistic simulation

Abstract

Drug development typically comprises a combination of pre-clinical experimentation, clinical trials, and statistical 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 (DC) binding, and DC migration times. These insights were used to hypothesise alternate trial designs predicted to improve effcacy
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.

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Identifiers

ORCID for Andy van Hateren: orcid.org/0000-0002-3915-0239

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