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In spite of rigorous dose adjustments by way of therapeutic drug monitoring, a large proportion of kidney transplant recipients are unable to achieve the target tacrolimus trough concentrations. This is attributed to the narrow therapeutic window of the drug (10-15 ng/mL) and large inter-individual variability in pharmacokinetic parameter such as clearance. There is a need for development of clinical dosing models that can help prospectively predict the dose for an individual, especially in the critical period immediately post-transplant. Therefore, we established and quantified the effect of clinical and genetic factors on tacrolimus clearance (CL/F) using a large population of adult kidney transplant recipients. Tacrolimus troughs (n=11823) from 681 transplant recipients over the first 6-months post-transplant were analyzed using non-linear mixed effects modeling approach in NONMEM®. The troughs were characterized by a steady state infusion model. Covariates were analyzed using a forward selection (pTM. One desirable feature in this new software package is a graphical user interface and menu-driven covariate selection options. Therefore, we compared these two software packages in terms of covariates selected and predictive performance using both clinical and simulated data. For the tacrolimus data, NONMEM® predictions had lower bias and imprecision as compared to Phoenix® NLMETM. For the clinical data, NONMEM® predictions had higher bias but were more precise than the Phoenix® NLMETM predictions.>