In silico evaluation of pharmacokinetic optimization for antimitogram-based clinical trials

Antimitograms are prototype in vitro tests for evaluating chemotherapeutic efficacy using patient-derived primary cancer cells. These tests might help optimize treatment from a pharmacodynamic (PD) standpoint by guiding treatment selection. However, they are technically challenging and require refinements and trials to demonstrate benefit in order to be widely used. In this study, we performed simulations aimed at exploring how to validate antimitograms and how to complement them by pharmacokinetic (PK) optimization. A generic model of advanced cancer including PK-PD monitoring was used to link dosing schedules with progression-free survival (PFS), as built from previously validated modules. This model was used to explore different possible situations in terms of PK variability, PD variability, and antimitogram performance. The model recapitulated tumor dynamics and standalone therapeutic drug monitoring efficacy consistent with published clinical results. Simulations showed that combining PK and PD optimization should increase PFS in a synergistic fashion. Simulated data were then used to compute required clinical trial sizes, which were 30 to 90% smaller when PK optimization was added to PD optimization. This improvement was observed even when PK optimization alone exhibited only modest benefit. Overall, our work illustrates the synergy derived from combining antimitograms with therapeutic drug monitoring, permitting a disproportionate reduction of the trial size required to prove a benefit on PFS. Accordingly, we suggest that strategies with benefits too small for standalone clinical trials could be validated in combination in a similar manner.

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