ArticleCancer chemotherapy and pharmacology2026
Bridging tumor growth dynamics and survival in preclinical oncology drug development - a Tumor Growth Inhibition and Time-to-Event (TGI-TTE) modeling framework.
Article in Cancer chemotherapy and pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Bridging tumor growth dynamics and survival in preclinical oncology drug development - a Tumor Growth Inhibition and Time-to-Event (TGI-TTE) modeling framework.Cancer chemotherapy and pharmacology · 2026Article
- Bridging Tumor Growth Dynamics and Survival in Preclinical Oncology Drug Development - A Translational Tumor Growth Inhibition and Time to Event (TGI-TTE) Modeling Framework.Research square · 2026Article
- Physiologically Based Pharmacokinetic Modeling to Predict Human Pharmacokinetics of a Novel Mithramycin Analog for Ewing Sarcoma.Research square · 2026Article
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9 authors.
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Abstract
purposeTo develop a 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.
methodsTumor 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 associated with the model-derived event-time distribution. Models were evaluated using the precision of parameter estimates, goodness-of-fit plots, and bootstraps.
resultsThe 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%). As expected from the endpoint definition, higher regimen-specific average concentration corresponded to longer model-derived survival (coefficient = 0.63), whereas higher exponential and linear tumor growth rates corresponded to shorter model-derived survival (coefficients = - 0.75 and - 0.46, respectively).
conclusionThis 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.
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