Article Dans Une Revue European Journal of Clinical Pharmacology Année : 2022

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

Résumé

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 ®-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 ®-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations.

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hal-04414330 , version 1 (26-01-2024)

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Laure Ponthier, Pierre Marquet, Dirk Jan A. R. Moes, Lionel Rostaing, Bart van Hoek, et al.. Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation. European Journal of Clinical Pharmacology, 2022, 79 (2), pp.311-319. ⟨10.1007/s00228-022-03445-5⟩. ⟨hal-04414330⟩
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