The goal of the first project was to evaluate strategies for determining the in vitro intrinsic clearance (CLint) of dextrorphan (DR) as metabolized by the UGT2B7 enzyme to obtain dextrorphan glucuronide (DR-G). A direct injection liquid chromatography-mass spectrometry (LC-MS) method was used to monitor products using the pseudo-first-order (PFO) model. Standard enzymatic incubations were also quantified using LC-MS. These data were fit utilizing both PFO and Michaelis-Menten (MM) models to determine estimates of kinetic parameters. The CLint was determined to be 0.28 (± 0.08) µL/min/mg protein for a baculovirus insect cell-expressed UGT2B7 enzyme. This is the first confirmation that dextrorphan is specifically metabolized by UGT2B7 and the first report of these kinetic parameters. Simulated chromatographic data were used to determine the precision and accuracy in the estimation of peak volumes in comprehensive two-dimensional liquid chromatography (2D-LC). Volumes were determined both by summing the areas in the second dimension chromatograms via the moments method and by fitting the second dimension areas to a Gaussian peak. When only two second dimension signals are substantially above baseline, the accuracy and precision are poor because the solution to the Gaussian fitting algorithm is indeterminate. The fit of a Gaussian peak to the areas of the second dimension peaks is better at predicting the peak volume when there are at least three second dimension injections above the limit of detection. Based on simulations where the sampling interval and sampling phase were varied, we conclude for well-resolved peaks that the optimum precision in peak volumes in 2D separations will be obtained when the sampling ratio is approximately two. This provides an RSD of approximately 2 % for the signal-to-noise (S/N) used in this work. The precision of peak volume estimation for experimental data was also assessed, and RSD values were in the 4-5 % range. We conclude that the poorer precision found in the 2D-LC experimental data as compared to 1D-LC is due to a combination of factors, including variations in the first dimension peak shape related to undersampling and loss in S/N due to the injection of multiple smaller peaks onto the second dimension column.