Prediction of permeability-surface area product data by continuous-distribution pore models.

ABSTRACT

OBJECTIVE: To investigate the ability of continuous-distribution pore models to accurately predict permeability-surface area product (PS) experimental data in skeletal muscle. METHODS: Models having a water-only (WO) pathway and continuous distributions of microvascular transport-pathway sizes were fit to solute reflection-coefficient (sigma) experimental data (approximately 0.5-16 nm Stokes radii) obtained from skeletal muscle to determine optimal parameter values. Without further modification, these models were used to predict experimental PS values obtained from the literature for small solutes ranging in size from NaCl to inulin and for three proteins, alpha-lactalbumin, ovalbumin, and albumin (approximately 0.23-3.7 nm radii). The protein PSs were determined from fluorescent tracer-diffusion curves and a nonlinear model of tracer diffusion in the cat hindlimb preparation. The model's PS predictions were compared to those of a discrete-pore model previously developed and a fiber-matrix (FM) model. RESULTS: A log-normal (LN) continuous pore-size distribution plus WO-pathway model (three free parameters) fit the sigma data to within the 95% confidence intervals of each of eight solutes spanning a 32-fold size range and was nearly as close to the data as was the two discrete-pore plus WO-pathway model (four free parameters). Both models closely described the PS data for nine solutes spanning a 14-fold size range. The fit of a fiber-matrix plus WO-pathway model (three free parameters) to the sigma data was much poorer than for the other models. CONCLUSIONS: The LN and two-discrete-pore models accurately describe sigma and PS experimental data in cat and human skeletal muscle. Therefore, experimental data resulting from complex microvascular transport processes are well characterized by simple pore models.