Pharmacometric and Digital Twin modeling for adaptive scheduling of combination therapy in advanced gastric cancer

Abstract

Background and objective: Combining targeted therapeutics can significantly help address the dynamic changes in cancer biology abnormalities and thus improve the duration of response and outcome. However, the efficacy of such approaches is highly dependent on the combination, interactions, and timing between the administered drugs. Current clinical trials can test only a low number of schedules with fixed designs. Pharmacometric tools can assist in exploring and selecting the most effective drug dosages and schedules by modeling traits of patients with different clinical and biological characteristics.

Methods: This study proposes a pharmacokinetic-pharmacodynamic model describing the networked system of tumor development and angiogenesis under the control of antiangiogenic and cytotoxic, i.e., Ramucirumab and Paclitaxel second-line combination therapy. A two-step scalable algorithm is proposed to calibrate model parameters and match virtual to real population therapy outcomes, followed by fine-tuning directly on the Progression-free Survival (PFS)-2 Kaplan-Meier curve. Two cohorts of advanced gastric cancer patients were considered: a calibration cohort from South Korea, and an external verification cohort from IRCCS "S. De Bellis", an Italian research hospital. These real-world patients had heterogeneous clinical starting conditions. We perform prospective evaluations of new combination regimens that adhere to pharmacological constraints that are paramount for clinical translation, in which the administration time of the cytotoxic agent is triggered by the normalization window opening, monitored by a tumor microenvironment digital biomarker.

Results: The calibration procedure led to the discovery of a new mathematical biomarker describing the influence of intrinsic tumor growth and angiogenesis on treatment outcomes. The predictive value was assessed through the log-rank test between two PFS-2 groups, which exhibited different (p-value <0.0001) therapy response trends. Our results showcase a new regimen that, by using 33% less cytotoxic drug, achieves indistinguishable PFS-2. Additionally, we present another regimen that extends PFS-2 from 49.2% to 60.9% after 121 days of therapy (p-value <0.0001), by using the same dosing as the standard protocol.

Conclusions: This study proposes an in-silico quantitative platform for virtual expansion of real-world patient cohorts. Furthermore, the estimation of the efficacy of adaptive dose schedules of a combined therapy can complement and inform clinical trial design.

Keywords: Adaptive drug regimens; Advanced gastric cancer; Cancer digital twin; Pharmacokinetic–pharmacodynamic modeling; Quantitative systems pharmacology; Therapeutic window.