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Am J Physiol Endocrinol Metab (June 21, 2005). doi:10.1152/ajpendo.00503.2003
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Submitted on November 7, 2003
Accepted on June 15, 2005

Approximate linear confidence and curvature of a kinetic model of dodecanedioic acid in humans

Simona Panunzi1*, Andrea De Gaetano1, and Geltrude Mingrone2

1 Laboratorio di Biomatematica, C.N.R IASI, Rome, Italy
2 Istituto di Medicina Interna, Universita Cattolica, Rome, Italy

* To whom correspondence should be addressed. E-mail: simona.panunzi{at}biomatematica.it.

Dicarboxylic Acids (DA) with an even number of carbon atoms have been proposed as an alternate energy substrate for enteral or parenteral nutrition in the acutely ill patient, due to their water-solubility and to their yielding Krebs' cycle intermediates upon beta-oxidation. In the present work a non-linear compartmental model of the kinetics of dodecanedioic acid (C12) is developed and its parameters are estimated from time-concentration experimental observations, obtained from six healthy volunteers undergoing a per-os administration of 3 g. of the substance. While the model is linear in the transfer of the free substance from plasma to the tissues, the exchange between gut and plasma compartments is represented as a saturable function. Albumin binding is then incorporated in order to obtain the final model in terms of the measured total concentrations. Estimates of the model's structural parameters were computed for each experimental subject, and the usual single-subject approximate confidence regions for the parameters were derived by inversion of the Hessian at the optimum. In order to verify the applicability of this approximation, the non-linearity of the expectation surface at the optimum was measured by computing the normal (intrinsic) component of curvature. Since the model curvature resulted excessive in all subjects, the usual approximation could not be trusted to provide acceptable approximations to the parameter confidence regions: a suitable Monte Carlo simulation yielded empirical joint parameter distributions, from which the approximate parameter variances could finally be obtained.







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