Johan Wallin, Amanda Long, Emmanuel Chigutsa
PKPD&Pharmacometrics, Eli Lilly
Objectives: This work aimed at defining the exposure-response of Necitumumab in the SQUIRE study, a 2nd generation human IgG1 monoclonal antibody that specifically blocks the ligand binding site of epidermal growth factor receptor (EGFR). Based on preclinical data, necitumumab potentially acts by inhibiting tumor growth, angiogenesis, and anti-apoptotic mechanisms.
Methods: To increase predictability and facilitate extrapolation, an integrated model for tumor size dynamics and OS was developed. Data from both necitumumab and control patients were utilized to create the model. Change in tumor size was determined from a summation of tumor growth and shrinkage. Various growth models were tested including linear, exponential and Gompertz growth; a first order process was used to describe tumor shrinkage. Development of resistance to therapy was tested by means of a time-dependent reduction in the first order process of tumor shrinkage. Tumor size at any time during treatment was then tested as a predictor of the hazard of death at the corresponding time in a model simultaneously describing OS and CTS. OS was described using a time to event modeling approach with the Stochastic Approximation Expectation-Maximization (SAEM) estimation algorithm. Necitumumab drug effect was evaluated both as a direct inhibitory function on tumor growth, as well as directly on OS [1]. Various hazard models were tested including exponential, Weibull, Gompertz, combined Weibull and Gompertz, and log-logistic distributions of event times.
Results: The model that best described change in tumor size was comprised of linear growth and first order shrinkage [2]. The time to event model that best described the OS was a combination of Weibull and Gompertz functions for the hazard; tumor size was the most significant predictor. ECOG status at baseline was found influential. The drug effect was estimated as a fractional decrease in the baseline hazard for OS and as a fractional increase in the first order shrink rate of the tumor (separate Emax and EC50 ). Individuals with higher concentrations of necitumumab had improved efficacy; however, 99.6% patients had exposures above the EC50; population median exposure was close to Emax.
Conclusions: The model sufficiently describes the tumor growth dynamics and time of death in the population studied. Model estimates indicate patients treated with recommended dose of necitumumab obtain benefit.
References:
[1] Hansson E, Amantea M, Westwood P, Milligan P, Houk B, French J, Karlsson M and Friberg L. PKPD modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST. CPT: Pharmacometrics Syst Pharmacol. (2013) 2:e84.
[2] Wang Y, Sung C, Dartois C, Ramchandani R, Booth BP, Rock E, Gobburu J. Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin Pharm Ther (2009) 86(2):167-74.
Reference: PAGE 24 (2015) Abstr 3357 [www.page-meeting.org/?abstract=3357]
Poster: Drug/Disease modeling - Oncology