Pharmacometrics Modelling-Based Dose-Finding and Synthetic Control Arms Established Using Longitudinal Analyses - Numerical analysis - Mathematical sc

Advancements in the field of oncology are rapidly changing the life expectancy of patients across many cancers. Therapeutic interventions are gradually shifting, from short intermittent cycles of cytotoxic drugs administered to large populations until progression or death, to the life-long chronic treatment of combinations of targeted and/or immunomodulating molecules in smaller populations. These advances raise several challenges in oncology drug development that will be tackled in this project: 1. Do pharmacometrics based dose findings beyond the Maximum Tolerated Dose (MTD), balancing longer-term efficacy and safety, provide a feasible and cost-effective approach to clinical drug development in oncology? Using a systems modelling estimation and optimisation based approach several dose-finding strategies in oncology drug development will be explored. This will be based on virtual development programmes, but the settings will reflect what is currently seen across oncology. The traditional MTD-paradigm will be compared with more extensive dose-ranging, longer-term efficacy and safety evaluations, with decision-making based on the longitudinal analysis. This will be tied in with an analysis of costs to evaluate the feasibility and added value. 2. Can longitudinal analyses of historical clinical trial data and real-world datasets be leveraged to establish synthetic control arms for phase I/II trials to increase efficiency and minimise costs in oncology clinical drug development? Several internal AZ datasets and external databases will be identified and used for longitudinal modelling, linking drug exposure, biomarkers, genetics and other information to relevant clinical trial outcomes. These efforts will be aligned with ongoing development projects and the focus will be on the application of existing modelling and statistical approaches, facilitation decision-making, and preventing negative pivotal trials.