Modeling of tumor growth and anticancer effects of combination therapy

Combination therapies are widely used in the treatment of patients with cancer. Selecting synergistic combination strategies is a great challenge during early drug development. Here, we present a pharmacokinetic/pharmacodynamic (PK/PD) model with a smooth nonlinear growth function to characterize and quantify anticancer effect of combination therapies using time-dependent data. To describe the pharmacological effect of combination therapy, an interaction term was introduced into a semi-mechanistic anticancer PK/PD model. This approach enables testing of a pharmacological hypothesis with respect to an anticipated pharmacological synergy of drug combinations, such as an assumed pharmacological synergy of complementary inhibition of a particular signaling pathway. The model was applied to three real data sets derived from preclinical screening experiments using xenograft mice. The suggested model fitted well the observed data from mono- to combination-therapy and allowed physiologically meaningful interpretation of the experiments. The tested drug combinations were assessed for their ability to act as synergistic modulators of tumor growth inhibition by the interaction parameter ψ. The presented approach has practical implications because it can be applied to standard xenograft experiments and may assist in the selection of both optimal drug combinations and administration schedules. The unique feature of the presented approach is the ability to characterize the nature of combined drug interaction as well as to quantify the intensity of such interactions by assessing the time course of combined drug effect.

[1]  S. Loewe,et al.  Über Kombinationswirkungen , 1926, Naunyn-Schmiedebergs Archiv für experimentelle Pathologie und Pharmakologie.

[2]  G. De Nicolao,et al.  A pharmacokinetic-pharmacodynamic model for predicting tumour growth inhibition in mice: a useful tool in oncology drug development. , 2005, Basic & clinical pharmacology & toxicology.

[3]  William J Jusko,et al.  Pharmacodynamic interaction of recombinant human interleukin-10 and prednisolone using in vitro whole blood lymphocyte proliferation. , 2002, Journal of pharmaceutical sciences.

[4]  M. Berenbaum What is synergy? , 1989, Pharmacological reviews.

[5]  W R Greco,et al.  Application of a new approach for the quantitation of drug synergism to the combination of cis-diamminedichloroplatinum and 1-beta-D-arabinofuranosylcytosine. , 1990, Cancer research.

[6]  E. Hairer,et al.  Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems , 1993 .

[7]  M Rocchetti,et al.  Predicting the active doses in humans from animal studies: a novel approach in oncology. , 2007, European journal of cancer.

[8]  Berenbaum Mc What is synergy? , 1989, Pharmacological reviews.

[9]  E. Hairer,et al.  Solving Ordinary Differential Equations I , 1987 .

[10]  W. Greco,et al.  The search for synergy: a critical review from a response surface perspective. , 1995, Pharmacological reviews.

[11]  W J Jusko,et al.  Pharmacodynamics of chemotherapeutic effects: dose-time-response relationships for phase-nonspecific agents. , 1971, Journal of pharmaceutical sciences.

[12]  E J Freireich,et al.  Combination chemotherapy in vitro with adriamycin. Observations of additive, antagonistic, and synergistic effects when used in two-drug combinations on cultured human lymphoma cells. , 1976, Cancer biochemistry biophysics.

[13]  Paolo Magni,et al.  Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents , 2004, Cancer Research.

[14]  M Rocchetti,et al.  A mathematical model to study the effects of drugs administration on tumor growth dynamics. , 2006, Mathematical biosciences.

[15]  Giuseppe De Nicolao,et al.  A Minimal Model of Tumor Growth Inhibition , 2008, IEEE Transactions on Biomedical Engineering.

[16]  E. Hairer,et al.  Solving Ordinary Differential Equations II , 2010 .

[17]  T. Chou,et al.  Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. , 1984, Advances in enzyme regulation.

[18]  William J Jusko,et al.  Diversity of mechanism-based pharmacodynamic models. , 2003, Drug metabolism and disposition: the biological fate of chemicals.

[19]  Evelyn D. Lobo,et al.  Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro , 2008, AAPS PharmSci.

[20]  W J Jusko,et al.  Transit compartments versus gamma distribution function to model signal transduction processes in pharmacodynamics. , 1998, Journal of pharmaceutical sciences.