Mycophenolic Acid Exposure Prediction Using Machine Learning.
Journal
Clinical pharmacology and therapeutics
ISSN: 1532-6535
Titre abrégé: Clin Pharmacol Ther
Pays: United States
ID NLM: 0372741
Informations de publication
Date de publication:
08 2021
08 2021
Historique:
received:
18
12
2020
accepted:
01
02
2021
pubmed:
25
2
2021
medline:
26
8
2021
entrez:
24
2
2021
Statut:
ppublish
Résumé
Therapeutic drug monitoring of mycophenolic acid (MPA) based on area under the curve (AUC) is well-established and machine learning (ML) approaches could help to estimate AUC. The aim of this work is to estimate the AUC of MPA in organ transplant patients using extreme gradient boosting (Xgboost R package) ML models. A total of 12,877 MPA AUC from 0 to 12 hours (AUC
Substances chimiques
Immunosuppressive Agents
0
Mycophenolic Acid
HU9DX48N0T
Types de publication
Clinical Trial
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
370-379Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021 The Authors. Clinical Pharmacology & Therapeutics © 2021 American Society for Clinical Pharmacology and Therapeutics.
Références
Allison, A.C. & Eugui, E.M. Mycophenolate mofetil and its mechanisms of action. Immunopharmacology 47, 85-118 (2000).
Bergan, S. et al. Mycophenolate personalized therapy: consensus report by the international association of therapeutic drug monitoring and clinical toxicology. Ther. Drug Monit. 43, 150-200 (2021). https://doi.org/10.1097/FTD.0000000000000871
ISBA Website <https://abis.chu-limoges.fr/>. Accessed October 14, 2020.
Debord, J. et al. Application of a gamma model of absorption to oral cyclosporin. Clin. Pharmacokinet. 40, 375-382 (2001).
Prémaud, A. et al. A double absorption-phase model adequately describes mycophenolic acid plasma profiles in de novo renal transplant recipients given oral mycophenolate mofetil. Clin. Pharmacokinet. 44, 837-847 (2005).
Saint-Marcoux, F. et al. Pharmacokinetic modelling and development of Bayesian estimators for therapeutic drug monitoring of mycophenolate mofetil in reduced-intensity haematopoietic stem cell transplantation. Clin. Pharmacokinet. 48, 667-675 (2009).
Zahr, N. et al. Pharmacokinetic study of mycophenolate mofetil in patients with systemic lupus erythematosus and design of Bayesian estimator using limited sampling strategies. Clin. Pharmacokinet. 47, 277-284 (2008).
Saint-Marcoux, F. et al. Development of a Bayesian estimator for the therapeutic drug monitoring of mycophenolate mofetil in children with idiopathic nephrotic syndrome. Pharmacol. Res. 63, 423-431 (2011).
Woillard, J.-B. et al. Pharmacokinetics of mycophenolate mofetil in children with lupus and clinical findings in favour of therapeutic drug monitoring. Br. J. Clin. Pharmacol. 78, 867-876 (2014).
Woillard, J.-B. et al. Mycophenolic mofetil optimized pharmacokinetic modelling, and exposure-effect associations in adult heart transplant recipients. Pharmacol. Res. 99, 308-315 (2015).
Labriffe, M. et al. Population pharmacokinetics and Bayesian estimators for intravenous mycophenolate mofetil in haematopoietic stem cell transplant patients. Br. J. Clin. Pharmacol. 86, 1550-1559 (2020).
Lima, A.N. et al. Use of machine learning approaches for novel drug discovery. Expert Opin. Drug Discov. 11, 225-239 (2016).
Hammann, F., Schöning, V. & Drewe, J. Prediction of clinically relevant drug-induced liver injury from structure using machine learning. J. Appl. Toxicol. 39, 412-419 (2019).
Stokes, J.M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688-702.e13 (2020).
Hirayu, K., Kawano, H., Orii, H. & Tsuji, Y. Estimation of blood drug concentration by LSTM network. In Proceedings of The 6th IIAE International Conference on Intelligent Systems and Image Processing 2018. 222-227 (The Institute of Industrial Application Engineers, 2018).
Tang, J. et al. Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients. Sci. Rep. 7, 42192 (2017).
Imai, S., Takekuma, Y., Miyai, T. & Sugawara, M. A new algorithm optimized for initial dose settings of vancomycin using machine learning. Biol. Pharm. Bull. 43, 188-193 (2020).
Woillard, J.-B. et al. A machine learning approach to estimate the glomerular filtration rate in intensive care unit patients based on plasma iohexol concentrations and covariates. Clin. Pharmacokinet. 60, 223-233 (2020).
Woillard, J.-B., Labriffe, M., Debord, J. & Marquet, P. Tacrolimus exposure prediction using machine learning. Clin. Pharmacol. Ther. https://doi.org/10.1002/cpt.2123. [e-pub ahead of print].
Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16. 785-794 (2016).
Badillo, S. et al. An introduction to machine learning. Clin. Pharmacol. Ther. 107, 871-885 (2020).
Ye, A.XGBoost, LightGBM, and Other Kaggle Competition Favorites <https://towardsdatascience.com/xgboost-lightgbm-and-other-kaggle-competition-favorites-6212e8b0e835> (2020).
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
Benkali, K. et al. Tacrolimus population pharmacokinetic-pharmacogenetic analysis and Bayesian estimation in renal transplant recipients. Clin. Pharmacokinet. 48, 805-816 (2009).
Le Guellec, C. et al. Simultaneous estimation of cyclosporin and mycophenolic acid areas under the curve in stable renal transplant patients using a limited sampling strategy. Eur. J. Clin. Pharmacol. 57, 805-811 (2002).
Djebli, N., Picard, N., Rérolle, J.-P., Le Meur, Y. & Marquet, P. Influence of the UGT2B7 promoter region and exon 2 polymorphisms and comedications on Acyl-MPAG production in vitro and in adult renal transplant patients. Pharmacogenet. Genomics 17, 321-330 (2007).
Schloerke, B. et al. GGally: Extension to ‘ggplot2’ <https://CRAN.R-project.org/package=GGally> (2020).
Sherwin, C.M.T., Fukuda, T., Brunner, H.I., Goebel, J. & Vinks, A.A. The evolution of population pharmacokinetic models to describe the enterohepatic recycling of mycophenolic acid in solid organ transplantation and autoimmune disease. Clin. Pharmacokinet. 50, 1-24 (2011).
Kuhn, M. & Wickham, H. tidymodels: Easily Install and load the ‘tidymodels’ packages version 0.1.0 from CRAN <https://rdrr.io/cran/tidymodels/>.
Brunet, M. et al. Therapeutic drug monitoring of tacrolimus-personalized therapy: second consensus report. Ther. Drug Monit. 41, 261-307 (2019).
de Winter, B.C.M. et al. Bayesian estimation of mycophenolate mofetil in lung transplantation, using a population pharmacokinetic model developed in kidney and lung transplant recipients. Clin. Pharmacokinet. 51, 29-39 (2012).
Musuamba, F.T. et al. Limited sampling models and Bayesian estimation for mycophenolic acid area under the curve prediction in stable renal transplant patients co-medicated with ciclosporin or sirolimus. Clin. Pharmacokinet. 48, 745-758 (2009).
Le Guellec, C. et al. Population pharmacokinetics and Bayesian estimation of mycophenolic acid concentrations in stable renal transplant patients. Clin. Pharmacokinet. 43, 253-266 (2004).
Chen, B. et al. Population pharmacokinetics and Bayesian estimation of mycophenolic acid exposure in Chinese renal allograft recipients after administration of EC-MPS. J. Clin. Pharmacol. 59, 578-589 (2019).
Langers, P. et al. Limited sampling model for advanced mycophenolic acid therapeutic drug monitoring after liver transplantation. Ther. Drug. Monit. 36, 141-147 (2014).
Zhao, W. et al. Population pharmacokinetics and Bayesian estimator of mycophenolic acid in children with idiopathic nephrotic syndrome. Br. J. Clin. Pharmacol. 69, 358-366 (2010).
Le Meur, Y. et al. Individualized mycophenolate mofetil dosing based on drug exposure significantly improves patient outcomes after renal transplantation. Am. J. Transplant. 7, 2496-2503 (2007).
Metz, D.K. et al. Optimizing mycophenolic acid exposure in kidney transplant recipients: time for target concentration intervention. Transplantation 103, 2012-2030 (2019).