Bridging Tumor Growth Dynamics and Survival in Preclinical Oncology Drug Development – A Translational Tumor Growth Inhibition and Time to Event (TGI-TTE) Modeling Framework

Abstract Purpose To develop a translational tumor growth inhibition and time-to-event (TGI-TTE) modeling framework linking drug exposure, tumor dynamics, and survival. Modeling used preclinical efficacy studies with MTMSA-Trp, a preclinical-stage anti-tumor agent for Ewing sarcoma. Methods Tumor volume and survival data from Ewing sarcoma mouse xenografts treated with MTMSA-Trp (0.3–2.85 mg/kg) were analyzed. A Simeoni TGI model was used to estimate individual exponential and linear tumor growth rates. A parametric log-logistic TTE model was used to describe mouse survival across treatment groups by incorporating post hoc TGI metrics and regimen-specific average concentrations derived from the PK model as covariates predictive of survival. Models were evaluated using the precision of parameter estimates, goodness-of-fit plots, and bootstraps. Results The TGI model accurately described individual tumor growth dynamics, yielding precise parameter estimates (RSEs < 15%). The final TTE model successfully captured the observed Kaplan-Meier curves across treatment groups (RSEs < 20%). Higher regimen-specific average concentration was significantly associated with longer survival (coefficient = 0.63). Conversely, higher exponential and linear tumor growth rates were significantly associated with shorter survival (coefficients = -0.75 and − 0.46, respectively). Conclusion This framework quantitatively links tumor dynamics, drug exposure, and survival and may support the design, analysis, and simulation-based evaluation of dose regimens in preclinical oncology studies.