Background:Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose.Methods:This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C_0,obs), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C₀, age, body surface area,CYP3A4andCYP3A5genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C_0,pred) were predicted.Results:Of 190 tacrolimus C₀values measured in 59 patients, 121 (63.7%; 95% CI 56.8–70.5) C_0,obswere within the therapeutic range (7.5–12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6–73.0) for C_0,pred(P= 0.89). The median absolute difference between the tacrolimus C₀and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C_0,obsversus 1.6 ng/mL for C_0,pred. In a historical cohort of 114 kidney transplant recipients who received a body weight–based starting dose followed by TDM, 172 of 335 tacrolimus C₀(51.3%) were within the therapeutic range (10.0–15.0 ng/mL).Conclusions:The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small.