A new mathematical pharmacodynamic model of clonogenic cancer cell death by doxorubicin
Tóm tắt
Previous models for predicting tumor cell growth are mostly based on measurements of total cell numbers. The purpose of this paper is to provide a new simple mathematical model for calculating tumor cell growth focusing on the fraction of cells that is clonogenic. The non-clonogenic cells are considered to be relatively harmless. We performed a number of different types of experiments: a long-term drug “treatment”, several concentrations/fixed time experiments and time-series experiments, in which human MCF-7 breast cancer cells were exposed to doxorubicin and the total number of cells were counted. In the latter two types, at every measurement point a plating efficiency experiment was started. The final number of colonies formed is equal to the number of clonogenic cells at the onset of the experiment. Based on the intracellular drug concentration, our model predicts cell culture effects taking clonogenic ability and growth inhibition by neighboring cells into account. The model fitted well to the experimental data. The estimated damage parameter which represents the chance of an MCF-7 cell to become non-clonogenic per unit time and per unit intracellular doxorubicin concentration was found to be 0.0025 ± 0.0008 (mean ± SD) nM−1 h−1. The model could be used to calculate the effect of every doxorubicin concentration versus time (C–t) profile. Although in vivo parameters may well be different from those found in vitro, the model can be used to predict trends, e.g. by comparing effects of different in vivo C–t profiles.
Từ khóa
Tài liệu tham khảo
Kalns JE, Millenbaugh NJ, Wientjes MG, Au JL (1995) Design and analysis of in vitro antitumor pharmacodynamic studies. Cancer Res 55(22):5315–5322
Lankelma J, Fernández Luque R, Dekker H, Pinedo HM (2003) Simulation model of doxorubicin activity in islets of human breast cancer cells. Biochim Biophys Acta 1622(3):169–178
Eliaz RE, Nir S, Marty C, Szoka FC Jr (2004) Determination and modeling of kinetics of cancer cell killing by doxorubicin and doxorubicin encapsulated in targeted liposomes. Cancer Res 64(2):711–718
Nguyen-Ngoc T, Vrignaud P, Robert J (1984) Cellular pharmacokinetics of doxorubicin in cultured mouse sarcoma cells originating from autochthonous tumors. Oncology 41(1):55–60
Vrignaud P, Londos-Gagliardi D, Robert J (1986) Cellular pharmacology of doxorubicin in sensitive and resistant rat glioblastoma cells in culture. Oncology 43(1):60–66
Walker MC, Masters JR, Parris CN, Hepburn PJ, English PJ (1986) Intravesical chemotherapy: in vitro studies on the relationship between dose and cytotoxicity. Urol Res 14(3):137–140
Levasseur LM, Slocum HK, Rustum YM, Greco WR (1998) Modeling of the time-dependency of in vitro drug cytotoxicity and resistance. Cancer Res 58(24):5749–5761
Link KH, Leder G, Pillasch J, Butzer U, Staib L, Kornmann M, Bruckner U, Beger HG (1998) In vitro concentration response studies and in vitro phase II tests as the experimental basis for regional chemotherapeutic protocols. Semin Surg Oncol 14(3):189–201
El-Kareh AW, Secomb TW (2005) Two-mechanism peak concentration model for cellular pharmacodynamics of doxorubicin. Neoplasia 7(7):705–713
Mosmann T (1983) Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65(1–2):55–63
Campling BG, Pym J, Baker HM, Cole SP, Lam YM (1991) Chemosensitivity testing of small cell lung cancer using the MTT assay. Br J Cancer 63(1):75–83
Shay JW, Roninson IB (2004) Hallmarks of senescence in carcinogenesis and cancer therapy. Oncogene 23(16):2919–2933
Hamburger AW, Salmon SE (1977) Primary bioassay of human tumor stem cells. Science 197(4302):461–463
Alberts DS, Samon SE, Chen HS, Surwit EA, Soehnlen B, Young L, Moon TE (1980) In-vitro clonogenic assay for predicting response of ovarian cancer to chemotherapy. Lancet 2(8190):340–342
Jusko WJ (1971) Pharmacodynamics of chemotherapeutic effects: dose–time–response relationships for phase-nonspecific agents. J Pharm Sci 60(6):892–895
Simeoni M, Magni P, Cammia C, De NG, Croci V, Pesenti E, Germani M, Poggesi I, Rocchetti M (2004) Predictive pharmacokinetic–pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res 64(3):1094–1101
Sun YN, Jusko WJ (1998) Transit compartments versus gamma distribution function to model signal transduction processes in pharmacodynamics. J Pharm Sci 87(6):732–737
Citron ML, Berry DA, Cirrincione C, Hudis C, Winer EP, Gradishar WJ, Davidson NE, Martino S, Livingston R, Ingle JN, Perez EA, Carpenter J, Hurd D, Holland JF, Smith BL, Sartor CI, Leung EH, Abrams J, Schilsky RL, Muss HB, Norton L ((2003)) Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J Clin Oncol 21(8):1431–1439
Lankelma J, Dekker H, Luque FR, Luykx S, Hoekman K, van der Valk, van Diest PJ, Pinedo HM (1999) Doxorubicin gradients in human breast cancer. Clin Cancer Res 5(7):1703–1707
Lankelma J (2002) Tissue transport of anti-cancer drugs. Curr Pharm Des 8(22):1987–1993
Giordano SH, Lin YL, Kuo YF, Hortobagyi GN, Goodwin JS (2012) Decline in the use of anthracyclines for breast cancer. J Clin Oncol 30(18):2232–2239
Kuh HJ, Jang SH, Wientjes MG, Au JL (2000) Computational model of intracellular pharmacokinetics of paclitaxel. J Pharmacol Exp Ther 293(3):761–770
Stein WD, Wilkerson J, Kim ST, Huang X, Motzer RJ, Fojo AT, Bates SE (2012) Analyzing the pivotal trial that compared sunitinib and IFN-alpha in renal cell carcinoma, using a method that assesses tumor regression and growth. Clin Cancer Res 18(8):2374–2381
Rossi C, Gasparini G, Canobbio L, Galligioni E, Volpe R, Candiani E, Toffoli G, D’Incalci M (1987) Doxorubicin distribution in human breast cancer. Cancer Treat Rep 71(12):1221–1226
Lee C, Raffaghello L, Brandhorst S, Safdie FM, Bianchi G, Martin-Montalvo A, Pistoia V, Wei M, Hwang S, Merlino A, Emionite L, de Cabo R, Longo VD (2012) Fasting cycles retard growth of tumors and sensitize a range of cancer cell types to chemotherapy. Sci Transl Med 4(124):124ra27
Yang XH, Sladek TL, Liu X, Butler BR, Froelich CJ, Thor AD (2001) Reconstitution of caspase 3 sensitizes MCF-7 breast cancer cells to doxorubicin- and etoposide-induced apoptosis. Cancer Res 61(1):348–354
Jang SH, Wientjes MG, Lu D, Au JL (2003) Drug delivery and transport to solid tumors. Pharm Res 20(9):1337–1350
van Rossum JM, de Bie JE (1989) Systems dynamics in clinical pharmacokinetics. An introduction. Clin Pharmacokinet 17(1):27–44
Burgess DJ, Doles J, Zender L, Xue W, Ma B, McCombie WR, Hannon GJ, Lowe SW, Hemann MT (2008) Topoisomerase levels determine chemotherapy response in vitro and in vivo. Proc Natl Acad Sci USA 105(26):9053–9058
Lankelma J, Fernández Luque R, Dekker H, Schinkel W, Pinedo HM (2000) A mathematical model of drug transport in human breast cancer. Microvasc Res 59(1):149–161
Norton L (1988) A Gompertzian model of human breast cancer growth. Cancer Res 48(24 Pt 1):7067–7071