Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation

Purpose: Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (="true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose (R)-tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE. Methods: Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients. Results: Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = -1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = -1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = -3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = -3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points. Conclusion: The MARS ML models developed using "true" MeltDose (R)-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations.