Page 2018 3
Modeling Primary Tumor Growth in Xenograft Mouse Model of Non-Small Cell Lung Cancer Treated With Pemetrexed-Cisplatin and Bevacizumab
Benjamin K Schneider (1), Arnaud Boyer (2), Joseph Ciccolini (2,3), Kenneth Wang (4), Martin Fernandez-Zapico (4), Sebastien Benzekry* (5)* and Jonathan P Mochel* (1)
(1) Iowa State University College of Veterinary Medicine, Ames, IA, U.S.A, (2) SMARTc Unit, Inserm S911 CRO2, Aix-Marseille University, Marseille, France, (3) Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hopitaux de Marseille, Marseille, France, (4) Mayo Clinic, Rochester, MS, U.S.A, (5) Team MONC, Inria Bordeaux Sud-Ouest, France. *: co-last authors.
Bevacizumab, an anti-angiogenic drug, is commonly administered along with chemotherapeutic drugs for advanced non-squamous non-small cell lung cancer (NSCLC) . Bevacizumab administration transiently enhances chemotherapeutic drug delivery, resulting in increased efficacy of chemotherapeutic drugs. The objective of this analysis was to characterize the respective efficacy of concurrent vs. sequential administration of pemetrexed-cisplatin and bevacizumab, as compared with pemetrexed cisplatin alone in NSCLC tumor carrying mice.
77 xenografted mice were randomized into 5 treatment groups, as follows: i) control (saline, N=15), ii) bevacizumab + pemetrexed-cisplatan 3 days apart (N=16), iii) bevacizumab + pemetrexed-cisplatan 8 days apart (N=15), iv) concurrent bevacizumab + pemetrexed-cisplatan (N=16), and pemetrexed cisplatin alone (N=15). Treatments were administered as single I.P bolus at a dose of 20 mg/kg, 100 mg/kg, and 3 mg/kg for bevacizumab, pemetrexed, and cisplatin respectively. Tumor size was evaluated by fluorescence (excitation: 554 nm, emission: 581 nm), and the resulting data were analyzed using the stochastic approximation expectation maximization algorithm implemented in Monolix 2016 R1. Standard goodness-of-fit (GOF) diagnostics, including population and individual predictions vs. observations, and the distributions of weighted residuals were used to evaluate the performances of the final model. Model selection was based on statistical significance between competing models using the objective function value and the Bayesian information criteria, together with the evaluation of GOFs. Residual error estimates from the mathematical models were used as supportive information for evaluation of lack of fit. Normality and independence of residuals were evaluated using histograms and quantile-quantile plots.
Tumor size kinetics in the control group was best described using a revisited Gompertz model governed by parameters α (cell proliferation rate) and β (rate of exponential decrease of the tumor). A proportional error model was used to account for the residual noise in the measurement of tumor size. Bevacizumab increased cisplatin drug delivery by improving the vasculature quality (Q). The dynamics of this improvement was assumed to follow bevacizumab concentrations, delayed by a time τ shift for the normalization to occur. The magnitude of the improvement was controlled by parameter δ. Structural identifiability of the model parameters was further confirmed using sensitivity analyses, the estimated correlation of the random effects (<0.10 for most parameters) and the accurate precision of the final model parameters (RSE<20%). The validity of final model parameter estimates was further confirmed through visual predictive checks using 500 Monte-Carlo simulations.through visual predictive checks using 500 Monte-Carlo simulations.
Our model-based analysis showed that a revisited Gompertzian growth function was predictive for modeling the effect of various scheduling of pemetrexed-cisplatin and bevacizumab in NSCLC tumor carrying mice. The model can be used to anticipate the optimal delay between anti-angiogenesis therapy and chemotherapy, and its dependence on the therapeutic dosing schedule.
 Zhao S, Gao F, Zhang Y, Zhang Z, Zhang L. Bevacizumab in combination with different platinum-based doublets in the first-line treatment for advanced nonsquamous non-small-cell lung cancer: A network meta-analysis. Int J Cancer. 2018 Apr 15;142(8):1676-1688.