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Sirolimus (SRL) is a novel immunosuppressive agent characterized by a narrow therapeutic index. Monitoring of SRL concentrations is mandatory to optimize drug dosing. The area under the concentration-time curve (AUC) is generally accepted as the best pharmacokinetic marker of daily drug exposure. Assessment of full SRL AUC, however, requires the collection of multiple blood samples, imposing both time and cost constraints. Limited sampling strategies (LSS) may be of clinical relevance to improve the therapeutic drug monitoring of SRL. In this study, stepwise multiple regression analysis is conducted on 30 SRL pharmacokinetic profiles obtained from kidney transplant recipients on a cyclosporine-free regimen, and different LSS equations are identified based on 2 or 3 sampling points collected within the first 6 hours after drug intake. The performance of these equations is tested in a separate validation set (n = 30). Most of the proposed algorithms are associated with good correlations with the measured AUC and acceptable bias and imprecision. Two equations—based on 2 time points collected within the first 4 hours after dosing—are identified that reliably predict SRL exposure in routine clinical practice compared with the traditional Co-based approach. These tools provide additional information to optimize SRL therapeutic drug monitoring in renal transplant recipients.