A predictive pharmacokinetic–pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination

Abstract

PurposeIn clinical oncology, combination treatments are widely used and increasingly preferred over single drug administrations. A better characterization of the interaction between drug effects and the selection of synergistic combinations represent an open challenge in drug development process. To this aim, preclinical studies are routinely performed, even if they are only qualitatively analyzed due to the lack of generally applicable mathematical models.MethodsThis paper presents a new pharmacokinetic–pharmacodynamic model that, starting from the well-known single agent Simeoni TGI model, is able to describe tumor growth in xenograft mice after the co-administration of two anticancer agents. Due to the drug action, tumor cells are divided in two groups: damaged and not damaged ones. The damaging rate has two terms proportional to drug concentrations (as in the single drug administration model) and one interaction term proportional to their product. Six of the eight pharmacodynamic parameters assume the same value as in the corresponding single drug models. Only one parameter summarizes the interaction, and it can be used to compute two important indexes that are a clear way to score the synergistic/antagonistic interaction among drug effects.ResultsThe model was successfully applied to four new compounds co-administered with four drugs already available on the market for the treatment of three different tumor cell lines. It also provided reliable predictions of different combination regimens in which the same drugs were administered at different doses/schedules.ConclusionsA good and quantitative measurement of the intensity and nature of interaction between drug effects, as well as the capability to correctly predict new combination arms, suggest the use of this generally applicable model for supporting the experiment optimal design and the prioritization of different therapies.Electronic supplementary materialThe online version of this article (doi:10.1007/s00280-013-2208-8) contains supplementary material, which is available to authorized users.

Tumor Static Concentration Curves in Combination Therapy

“…The TGI model can be generalized to combination therapy with two or more cytostatic and/or cytotoxic compounds. There have been several reported successes of this (16)(17)(18)(19)(20).…”

Pharmacodynamic modeling of combined chemotherapeutic effects predicts synergistic activity of gemcitabine and trabectedin in pancreatic cancer cells

“…However, the dimension of time is not considered. Furthermore fitted parameters may vary with drug exposure time, concentration, and schedule [16]. More advanced approaches can include cell or tumor dynamics in order to characterize time-dependent data [16–18].…”

“…Furthermore fitted parameters may vary with drug exposure time, concentration, and schedule [16]. More advanced approaches can include cell or tumor dynamics in order to characterize time-dependent data [16–18]. Here we advance PD modeling of drug interactions by extending the single-drug chemotherapy model of Lobo et al [19] to characterize simultaneously both single- and combined drug effects, and add an interaction parameter ψ [20–21] to quantify cytotoxic effects of the combinations.…”

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

“…Added benefits have been gained from mathematical models of tumor size (TS) dynamics4, 5, 6 and tumor growth inhibition,7, 8, 9 which can characterize anticancer drug effects over time and provide improved predictors of long‐term clinical outcomes 10, 11…”