J Magn Reson. 2010 Jan;202(1):85-92. doi: 10.1016/j.jmr.2009.10.003. Epub 2009 Oct 13.

Kinetic modeling of hyperpolarized 13C1-pyruvate metabolism in normal rats and TRAMP mice.

Zierhut ML, Yen YF, Chen AP, Bok R, Albers MJ, Zhang V, Tropp J, Park I, Vigneron DB, Kurhanewicz J, Hurd RE, Nelson SJ.

Source

UCSF/UCB Joint Graduate Group in Bioengineering, San Francisco, CA 94143-2532, USA.

Abstract

PURPOSE:

To investigate metabolic exchange between (13)C(1)-pyruvate, (13)C(1)-lactate, and (13)C(1)-alanine in pre-clinical model systems using kinetic modeling of dynamic hyperpolarized (13)C spectroscopic data and to examine the relationship between fitted parameters and dose-response.

MATERIALS AND METHODS:

Dynamic (13)C spectroscopy data were acquired in normal rats, wild type mice, and mice with transgenic prostate tumors (TRAMP) either within a single slice or using a one-dimensional echo-planar spectroscopic imaging (1D-EPSI) encoding technique. Rate constants were estimated by fitting a set of exponential equations to the dynamic data. Variations in fitted parameters were used to determine model robustness in 15 mm slices centered on normal rat kidneys. Parameter values were used to investigate differences in metabolism between and within TRAMP and wild type mice.

RESULTS:

The kinetic model was shown here to be robust when fitting data from a rat given similar doses. In normal rats, Michaelis-Menten kinetics were able to describe the dose-response of the fitted exchange rate constants with a 13.65% and 16.75% scaled fitting error (SFE) for k(pyr-->lac) and k(pyr-->ala), respectively. In TRAMP mice, k(pyr-->lac) increased an average of 94% after up to 23 days of disease progression, whether the mice were untreated or treated with casodex. Parameters estimated from dynamic (13)C 1D-EPSI data were able to differentiate anatomical structures within both wild type and TRAMP mice.

CONCLUSIONS:

The metabolic parameters estimated using this approach may be useful for in vivo monitoring of tumor progression and treatment efficacy, as well as to distinguish between various tissues based on metabolic activity.