Comparison of neural network, Bayesian, and multiple stepwise regression-based limited sampling models to estimate area under the curve

This study compared limited sampling methods (LSM) of estimating area under the plasma concentration versus time curve (AUC) based on a Bayesian regularized neural network, the Bayesian approach, and multiple forward stepwise regression models from selected concentration-time points. Plasma concentration versus time data sets with a linear two-compartmental pharmacokinetic model were simulated. A limited sampling method based on the forward stepwise regression model was developed and validated. Plasma concentration-time points selected by the stepwise regression model were used for neural network and Bayesian evaluation. In addition, 55 plasma concentration-time profiles from two clinical studies were used to develop and compare the predicted AUC(last) for the three approaches. From simulated data sets, mean prediction errors for AUC(last) estimation were 0.00, -5.32, and -6.06 for the neural network, Bayesian approach, and forward stepwise regression LSM, respectively. Mean square errors were 581, 588, and 618, respectively. For clinical data set, model mean prediction errors were 0.00, 3.51, and 3.87, respectively. Model mean square errors were 30.6, 109, and 76, respectively. For both simulated and clinical data sets, the neural network approach to estimate AUC(last) from selected time points was numerically more precise and significantly less biased than the other two methods.