Abstract
In this work, a semimechanistic tumor growth-response model for gemcitabine in pancreatic (administered as single agent) and ovarian (given as single agent and in combination with carboplatin) cancer in mice was developed. Tumor profiles were obtained from nude mice, previously inoculated with KP4, ASPC1, MIA PACA2, PANC1 (pancreas), A2780, or SKOV3×luc (ovarian) cell lines, and then treated with different dosing schedules of gemcitabine and/or carboplatin. Data were fitted using the population approach with Nonlinear Mixed Effect Models 7.2. In addition to cell proliferation, the tumor progression model for both types of cancer incorporates a carrying capacity representing metabolite pool for DNA synthesis required to tumor growth. Analysis of data from the treated groups revealed that gemcitabine exerted its tumor effects by promoting apoptosis as well as decreasing the carrying capacity compartment. Pharmacodynamic parameters were cell-specific and overall had similar range values between cancer types. In pancreas, a linear model was used to describe both gemcitabine effects with parameter values between 3.26 × 10−2 and 4.2 × 10−1 L/(mg × d). In ovarian cancer, the apoptotic effect was driven by an EMAX model with an efficacy/potency ratio of 5.25–8.65 L/(mg × d). The contribution of carboplatin to tumor effects was lower than the response exerted by gemcitabine and was incorporated in the model as an inhibition of the carrying capacity. The model developed was consistent in its structure across different tumor cell lines and two tumor types where gemcitabine is approved. Simulation-based evaluation diagnostics showed that the model performed well in all experimental design scenarios, including dose, schedule, and tumor type.
Footnotes
- Received September 19, 2016.
- Accepted December 22, 2016.
This work is part of the Innovative Medicine Initiative–funded project Drug Disease Model Resources [DDMORE (http://www.ddmore.eu/)] aiming to develop an interoperability framework including model repository to optimize model development. This work was supported by Eli Lilly and Company and by the DDMORE project.
I.F.T. is an employee of the University of Navarra, and M.G.-C. is a PhD student from University of Navarra. C.P. and P.W.I. are employees of Elli Lilly and Company.
↵This article has supplemental material available at jpet.aspetjournals.org.
- Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics
JPET articles become freely available 12 months after publication, and remain freely available for 5 years.Non-open access articles that fall outside this five year window are available only to institutional subscribers and current ASPET members, or through the article purchase feature at the bottom of the page.
|