A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.
Journal
Clinical pharmacokinetics
ISSN: 1179-1926
Titre abrégé: Clin Pharmacokinet
Pays: Switzerland
ID NLM: 7606849
Informations de publication
Date de publication:
31 Jul 2024
31 Jul 2024
Historique:
accepted:
16
07
2024
medline:
1
8
2024
pubmed:
1
8
2024
entrez:
31
7
2024
Statut:
aheadofprint
Résumé
The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.3 mg/L). We previously developed machine learning (ML) algorithms to predict starting doses based on Monte Carlo simulations. We propose a new way to perform probability of target attainment based on an ML algorithm to predict the daptomycin starting dose. The Dvorchik model of daptomycin was implemented in the mrgsolve R package and 4950 pharmacokinetic profiles were simulated with doses ranging from 4 to 12 mg/kg. We trained and benchmarked four machine learning algorithms and selected the best to iteratively search for the optimal dose of daptomycin maximizing the event (AUC/CMI > 666 and C0 < 24.3 mg/L). The ML algorithm was evaluated in simulations and an external database of real patients in comparison with population pharmacokinetics. The performance of the Xgboost algorithms developed to predict the event (ROC AUC) in the training and test set were 0.762 and 0.761, respectively. The most important prediction variables were dose, creatinine clearance, body weight and sex. In the external database of real patients, the starting dose administered based on the ML algorithm significantly improved the target attainment by 7.9% (p-value = 0.02929) in comparison with the dose administered based on body weight. The developed algorithm improved the target attainment for daptomycin in comparison with weight-based dosing. We built a Shiny app to calculate the optimal starting dose.
Sections du résumé
BACKGROUND AND OBJECTIVE
OBJECTIVE
The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.3 mg/L). We previously developed machine learning (ML) algorithms to predict starting doses based on Monte Carlo simulations. We propose a new way to perform probability of target attainment based on an ML algorithm to predict the daptomycin starting dose.
METHODS
METHODS
The Dvorchik model of daptomycin was implemented in the mrgsolve R package and 4950 pharmacokinetic profiles were simulated with doses ranging from 4 to 12 mg/kg. We trained and benchmarked four machine learning algorithms and selected the best to iteratively search for the optimal dose of daptomycin maximizing the event (AUC/CMI > 666 and C0 < 24.3 mg/L). The ML algorithm was evaluated in simulations and an external database of real patients in comparison with population pharmacokinetics.
RESULTS
RESULTS
The performance of the Xgboost algorithms developed to predict the event (ROC AUC) in the training and test set were 0.762 and 0.761, respectively. The most important prediction variables were dose, creatinine clearance, body weight and sex. In the external database of real patients, the starting dose administered based on the ML algorithm significantly improved the target attainment by 7.9% (p-value = 0.02929) in comparison with the dose administered based on body weight.
CONCLUSION
CONCLUSIONS
The developed algorithm improved the target attainment for daptomycin in comparison with weight-based dosing. We built a Shiny app to calculate the optimal starting dose.
Identifiants
pubmed: 39085523
doi: 10.1007/s40262-024-01405-z
pii: 10.1007/s40262-024-01405-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-22-PESN-0017
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
Résumé des caractéristiques du produit - DAPTOMYCINE ACCORD 350 mg, poudre pour solution injectable/pour perfusion - Base de données publique des médicaments [Internet]. [cited 2023 Jan 24]. Available from: https://base-donnees-publique.medicaments.gouv.fr/affichageDoc.php?specid=69082149&typedoc=R
Benvenuto M, Benziger DP, Yankelev S, Vigliani G. Pharmacokinetics and tolerability of daptomycin at doses up to 12 milligrams per kilogram of body weight once daily in healthy volunteers. Antimicrob Agents Chemother. 2006;50:3245–9.
doi: 10.1128/AAC.00247-06
pubmed: 17005801
pmcid: 1610083
Jones TW, Jun AH, Michal JL, Olney WJ. High-dose daptomycin and clinical applications. Ann Pharmacother. 2021;55:1363–78.
doi: 10.1177/1060028021991943
pubmed: 33535792
pmcid: 8573721
Safdar N, Andes D, Craig WA. In vivo pharmacodynamic activity of daptomycin. Antimicrob Agents Chemother. 2004;48:63–8.
doi: 10.1128/AAC.48.1.63-68.2004
pubmed: 14693519
pmcid: 310158
Falcone M, Russo A, Cassetta MI, Lappa A, Tritapepe L, d’Ettorre G, et al. Variability of pharmacokinetic parameters in patients receiving different dosages of daptomycin: is therapeutic drug monitoring necessary? J Infect Chemother. 2013;19:732–9.
doi: 10.1007/s10156-013-0559-z
pubmed: 23361566
Bhavnani SM, Rubino CM, Ambrose PG, Drusano GL. Daptomycin exposure and the probability of elevations in the creatine phosphokinase level: data from a randomized trial of patients with bacteremia and endocarditis. Clin Infect Dis Off Publ Infect Dis Soc Am. 2010;50:1568–74.
doi: 10.1086/652767
Dvorchik BH, Damphousse D. The pharmacokinetics of daptomycin in moderately obese, morbidly obese, and matched nonobese subjects. J Clin Pharmacol. 2005;45:48–56.
doi: 10.1177/0091270004269562
pubmed: 15601805
Pai MP, Norenberg JP, Anderson T, Goade DW, Rodvold KA, Telepak RA, et al. Influence of morbid obesity on the single-dose pharmacokinetics of daptomycin. Antimicrob Agents Chemother. 2007;51:2741–7.
doi: 10.1128/AAC.00059-07
pubmed: 17548489
pmcid: 1932544
Chagnac A, Weinstein T, Korzets A, Ramadan E, Hirsch J, Gafter U. Glomerular hemodynamics in severe obesity. Am J Physiol-Ren Physiol. 2000;278:F817–22.
doi: 10.1152/ajprenal.2000.278.5.F817
Butterfield-Cowper JM, Lodise TP, Pai MP. A fixed versus weight-based dosing strategy of daptomycin may improve safety in obese adults. Pharmacotherapy. 2018;38:981–5.
doi: 10.1002/phar.2157
pubmed: 29906315
Dvorchik B, Arbeit RD, Chung J, Liu S, Knebel W, Kastrissios H. Population pharmacokinetics of daptomycin. Antimicrob Agents Chemother. 2004;48:2799–807.
doi: 10.1128/AAC.48.8.2799-2807.2004
pubmed: 15273084
pmcid: 478529
Garreau R, Bricca R, Gagnieu M-C, Roux S, Conrad A, Bourguignon L, et al. Population pharmacokinetics of daptomycin in patients with bone and joint infection: minimal effect of rifampicin co-administration and confirmation of a sex difference. J Antimicrob Chemother. 2021;76:1250–7.
doi: 10.1093/jac/dkab006
pubmed: 33550409
Woillard J-B, Labriffe M, Prémaud A, Marquet P. Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: the example of tacrolimus. Pharmacol Res. 2021;167: 105578.
doi: 10.1016/j.phrs.2021.105578
pubmed: 33775863
Ponthier L, Ensuque P, Destere A, Marquet P, Labriffe M, Jacqz-Aigrain E, et al. Optimization of vancomycin initial dose in term and preterm neonates by machine learning. Pharm Res. 2022;39:2497–506.
doi: 10.1007/s11095-022-03351-6
pubmed: 35918452
Elmokadem A, Riggs MM, Baron KT. Quantitative systems pharmacology and physiologically-based pharmacokinetic modeling with mrgsolve: a hands-on tutorial. CPT Pharmacomet Syst Pharmacol. 2019;8:883–93.
doi: 10.1002/psp4.12467
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4:1686.
doi: 10.21105/joss.01686
Kuhn M, Wickham H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. [Internet]. 2020. Available from: https://www.tidymodels.org
Tuloup V, Millet A, Taricco A, Parant F, Ferry T, Goutelle S. Evaluation of limited sampling strategies for bayesian estimation of daptomycin area under the concentration-time curve: a short communication. Ther Drug Monit. 2023;45:562–5.
doi: 10.1097/FTD.0000000000001070
pubmed: 36728573
Chaves RL, Chakraborty A, Benziger D, Tannenbaum S. Clinical and pharmacokinetic considerations for the use of daptomycin in patients with Staphylococcus aureus bacteraemia and severe renal impairment. J Antimicrob Chemother. 2014;69:200–10.
doi: 10.1093/jac/dkt342
pubmed: 24030545
Labriffe M, Woillard J, Debord J, Marquet P. Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles. CPT Pharmacomet Syst Pharmacol. 2022;11:1018–28.
doi: 10.1002/psp4.12810