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Tang BH, Li QY, Liu HX, Zheng Y, Wu YE, van den Anker J, Hao GX, Zhao W. Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making. Paediatr Drugs 2024; 26:355-363. [PMID: 38880837 DOI: 10.1007/s40272-024-00638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 06/18/2024]
Abstract
Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.
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Affiliation(s)
- Bo-Hao Tang
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Department of Pediatrics, Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Departments of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Wei Zhao
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Yin M, Jiang Y, Yuan Y, Li C, Gao Q, Lu H, Li Z. Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age. Int J Clin Pharm 2024:10.1007/s11096-024-01745-7. [PMID: 38861047 DOI: 10.1007/s11096-024-01745-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/25/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation. AIM This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms. METHOD A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked. RESULTS The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration. CONCLUSION An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.
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Affiliation(s)
- Minghui Yin
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yuelian Jiang
- Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yawen Yuan
- Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Chensuizi Li
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Qian Gao
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Hui Lu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Zhiling Li
- Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
- NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, 200040, China.
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Wang YP, Lu XL, Shao K, Shi HQ, Zhou PJ, Chen B. Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients. Front Pharmacol 2024; 15:1389271. [PMID: 38783953 PMCID: PMC11111944 DOI: 10.3389/fphar.2024.1389271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/15/2024] [Indexed: 05/25/2024] Open
Abstract
Aims The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients. Methods Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group. Results The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance. Conclusion The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.
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Affiliation(s)
- Yu-Ping Wang
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Xiao-Ling Lu
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Kun Shao
- Center for Organ Transplantation, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Hao-Qiang Shi
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Pei-Jun Zhou
- Center for Organ Transplantation, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Bing Chen
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
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Codde C, Rivals F, Destere A, Fromage Y, Labriffe M, Marquet P, Benoist C, Ponthier L, Faucher JF, Woillard JB. A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations. Antimicrob Agents Chemother 2024; 68:e0141523. [PMID: 38501807 PMCID: PMC11064575 DOI: 10.1128/aac.01415-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.
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Affiliation(s)
- Cyrielle Codde
- Service de Maladies Infectieuses et Tropicales, CHU Dupuytren, Limoges, France
| | - Florence Rivals
- Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Dupuytren, Limoges, France
| | | | - Yeleen Fromage
- Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Dupuytren, Limoges, France
| | - Marc Labriffe
- Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Dupuytren, Limoges, France
- Inserm, Univ. Limoges, CHU Limoges, Pharmacology & Toxicology, Limoges, France
| | - Pierre Marquet
- Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Dupuytren, Limoges, France
- Inserm, Univ. Limoges, CHU Limoges, Pharmacology & Toxicology, Limoges, France
| | - Clément Benoist
- Inserm, Univ. Limoges, CHU Limoges, Pharmacology & Toxicology, Limoges, France
| | - Laure Ponthier
- Inserm, Univ. Limoges, CHU Limoges, Pharmacology & Toxicology, Limoges, France
| | | | - Jean-Baptiste Woillard
- Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Dupuytren, Limoges, France
- Inserm, Univ. Limoges, CHU Limoges, Pharmacology & Toxicology, Limoges, France
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Li QY, Tang BH, Wu YE, Yao BF, Zhang W, Zheng Y, Zhou Y, van den Anker J, Hao GX, Zhao W. Machine Learning: A New Approach for Dose Individualization. Clin Pharmacol Ther 2024; 115:727-744. [PMID: 37713106 DOI: 10.1002/cpt.3049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/03/2023] [Indexed: 09/16/2023]
Abstract
The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.
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Affiliation(s)
- Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bo-Hao Tang
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhang
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue Zhou
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Departments of Pediatrics, Pharmacology & Physiology, Genomics & Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
- NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Shandong University, Jinan, China
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Ponthier L, Autmizguine J, Franck B, Åsberg A, Ovetchkine P, Destere A, Marquet P, Labriffe M, Woillard JB. Optimization of Ganciclovir and Valganciclovir Starting Dose in Children by Machine Learning. Clin Pharmacokinet 2024:10.1007/s40262-024-01362-7. [PMID: 38492206 DOI: 10.1007/s40262-024-01362-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND AND OBJECTIVES Ganciclovir (GCV) and valganciclovir (VGCV) show large interindividual pharmacokinetic variability, particularly in children. The objectives of this study were (1) to develop machine learning (ML) algorithms trained on simulated pharmacokinetics profiles obtained by Monte Carlo simulations to estimate the best ganciclovir or valganciclovir starting dose in children and (2) to compare its performances on real-world profiles to previously published equation derived from literature population pharmacokinetic (POPPK) models achieving about 20% of profiles within the target. MATERIALS AND METHODS The pharmacokinetic parameters of four literature POPPK models in addition to the World Health Organization (WHO) growth curve for children were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles. ML algorithms were developed and benchmarked to predict the probability to reach the steady-state, area-under-the-curve target (AUC0-24 within 40-60 mg × h/L) based on demographic characteristics only. The best ML algorithm was then used to calculate the starting dose maximizing the target attainment. Performances were evaluated for ML and literature formula in a test set and in an external set of 32 and 31 actual patients (GCV and VGCV, respectively). RESULTS A combination of Xgboost, neural network, and random forest algorithms yielded the best performances and highest target attainment in the test set (36.8% for GCV and 35.3% for the VGCV). In actual patients, the best GCV ML starting dose yielded the highest target attainment rate (25.8%) and performed equally for VGCV with the Franck model formula (35.3% for both). CONCLUSION The ML algorithms exhibit good performances in comparison with previously validated models and should be evaluated prospectively.
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Affiliation(s)
- Laure Ponthier
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France
- Department of Pediatrics, University Hospital of Limoges, Limoges, France
| | - Julie Autmizguine
- Research Center, Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Department of Pediatrics, Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, QC, Canada
| | - Benedicte Franck
- Department of Clinical and Biological Pharmacology and Pharmacovigilance, Clinical Investigation Center, CIC-P 1414, Rennes, France
- University of Rennes, Centre Hospitalier Universitaire Rennes, École des Hautes Études en Santé Publique, IRSET (Institut de Recherche en Santé, Environnement et Travail), UMR S 1085, Rennes, France
| | - Anders Åsberg
- Department of Transplantation Medicine, Oslo University Hospital-Rikshospitalet, Oslo, Norway
- Section of Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Philippe Ovetchkine
- Department of Pediatrics, Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Alexandre Destere
- Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Nice, Nice, France
| | - Pierre Marquet
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France
| | - Marc Labriffe
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France
| | - Jean-Baptiste Woillard
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France.
- Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France.
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Destere A, Teisseyre M, Merino D, Cremoni M, Gérard AO, Crepin T, Jourde-Chiche N, Graça D, Zorzi K, Fernandez C, Brglez V, Benzaken S, Esnault VL, Benito S, Drici MD, Seitz-Polski B. Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence. Kidney Int Rep 2024; 9:134-144. [PMID: 38312797 PMCID: PMC10831377 DOI: 10.1016/j.ekir.2023.10.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/12/2023] [Accepted: 10/23/2023] [Indexed: 02/06/2024] Open
Abstract
Introduction Rituximab is a first-line treatment for membranous nephropathy. Nephrotic syndrome limits rituximab exposure due to urinary drug loss. Rituximab underdosing (serum level <2 μg/ml at month-3) is a risk factor for treatment failure. We developed a machine learning algorithm to predict the risk of underdosing based on patients' characteristics at rituximab infusion. We investigated the relationship between the predicted risk of underdosing and the cumulative dose of rituximab required to achieve remission. Methods Rituximab concentrations were measured at month-3 in 92 sera from adult patients with primary membranous nephropathy, split into a training (75%) and a testing set (25%). A forward-backward machine-learning procedure determined the best combination of variables to predict rituximab underdosing in the training data set, which was tested in the test set. The performances were evaluated for accuracy, sensitivity, and specificity in 10-fold cross-validation training and test sets. Results The best variables combination to predict rituximab underdosing included age, gender, body surface area (BSA), anti-phospholipase A2 receptor type 1 (anti-PLA2R1) antibody titer on day-0, serum albumin on day-0 and day-15, and serum creatinine on day-0 and day-15. The accuracy, sensitivity, and specificity were respectively 79.4%, 78.7%, and 81.0% (training data set), and 79.2%, 84.6% and 72.7% (testing data set). In both sets, the algorithm performed significantly better than chance (P < 0.05). Patients with an initial high probability of underdosing experienced a longer time to remission with higher rituximab cumulative doses required to achieved remission. Conclusion This algorithm could allow for early intensification of rituximab regimen in patients at high estimated risk of underdosing to increase the likelihood of remission.
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Affiliation(s)
- Alexandre Destere
- Département de Pharmacologie et de Pharmacovigilance, CHU de Nice, Université Côte d’Azur, France
- Université Côte d’Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France
| | - Maxime Teisseyre
- Centre de Référence Maladies Rares Syndrome Néphrotique Idiopathique, CHU de Nice, Université Côte d’Azur, France
- Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, CHU de Nice, France
- Département de Néphrologie, Dialyse et Transplantation, CHU de Nice, Université Côte d’Azur, France
| | - Diane Merino
- Département de Pharmacologie et de Pharmacovigilance, CHU de Nice, Université Côte d’Azur, France
| | - Marion Cremoni
- Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, CHU de Nice, France
- Laboratoire d’Immunologie, CHU de Nice, Université Côte d’Azur, France
| | - Alexandre O Gérard
- Département de Pharmacologie et de Pharmacovigilance, CHU de Nice, Université Côte d’Azur, France
- Département de Néphrologie, Dialyse et Transplantation, CHU de Nice, Université Côte d’Azur, France
| | - Thomas Crepin
- Département de Néphrologie, Dialyse et Transplantation, CHU de Besançon, Besançon, France
| | - Noémie Jourde-Chiche
- Département de Néphrologie, Dialyse et Transplantation, Hôpital de la Conception, Assistance Publique-Hôpitaux de Marseille, Marseille, France
| | - Daisy Graça
- Centre de Référence Maladies Rares Syndrome Néphrotique Idiopathique, CHU de Nice, Université Côte d’Azur, France
- Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, CHU de Nice, France
- Laboratoire d’Immunologie, CHU de Nice, Université Côte d’Azur, France
| | - Kévin Zorzi
- Centre de Référence Maladies Rares Syndrome Néphrotique Idiopathique, CHU de Nice, Université Côte d’Azur, France
- Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, CHU de Nice, France
| | - Céline Fernandez
- Centre de Référence Maladies Rares Syndrome Néphrotique Idiopathique, CHU de Nice, Université Côte d’Azur, France
- Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, CHU de Nice, France
| | - Vesna Brglez
- Centre de Référence Maladies Rares Syndrome Néphrotique Idiopathique, CHU de Nice, Université Côte d’Azur, France
- Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, CHU de Nice, France
- Laboratoire d’Immunologie, CHU de Nice, Université Côte d’Azur, France
| | - Sylvia Benzaken
- Laboratoire d’Immunologie, CHU de Nice, Université Côte d’Azur, France
| | - Vincent L.M. Esnault
- Centre de Référence Maladies Rares Syndrome Néphrotique Idiopathique, CHU de Nice, Université Côte d’Azur, France
- Département de Néphrologie, Dialyse et Transplantation, CHU de Nice, Université Côte d’Azur, France
| | | | - Milou-Daniel Drici
- Département de Pharmacologie et de Pharmacovigilance, CHU de Nice, Université Côte d’Azur, France
| | - Barbara Seitz-Polski
- Centre de Référence Maladies Rares Syndrome Néphrotique Idiopathique, CHU de Nice, Université Côte d’Azur, France
- Unité de Recherche Clinique Côte d’Azur (UR2CA), Université Côte d’Azur, CHU de Nice, France
- Département de Néphrologie, Dialyse et Transplantation, CHU de Nice, Université Côte d’Azur, France
- Laboratoire d’Immunologie, CHU de Nice, Université Côte d’Azur, France
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Stankevičiūtė K, Woillard JB, Peck RW, Marquet P, van der Schaar M. Bridging the Worlds of Pharmacometrics and Machine Learning. Clin Pharmacokinet 2023; 62:1551-1565. [PMID: 37803104 DOI: 10.1007/s40262-023-01310-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.
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Affiliation(s)
- Kamilė Stankevičiūtė
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK
| | - Jean-Baptiste Woillard
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France.
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research and Development, Roche Innovation Center, Basel, Switzerland
| | - Pierre Marquet
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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Tang BH, Zhang JY, Allegaert K, Hao GX, Yao BF, Leroux S, Thomson AH, Yu Z, Gao F, Zheng Y, Zhou Y, Capparelli EV, Biran V, Simon N, Meibohm B, Lo YL, Marques R, Peris JE, Lutsar I, Saito J, Jacqz-Aigrain E, van den Anker J, Wu YE, Zhao W. Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates. Clin Pharmacokinet 2023; 62:1105-1116. [PMID: 37300630 DOI: 10.1007/s40262-023-01265-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0-24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions. METHODS C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation. RESULTS Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%. CONCLUSION C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.
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Affiliation(s)
- Bo-Hao Tang
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | | | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Department of Hospital Pharmacy, Erasmus MC, Rotterdam, the Netherlands
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | | | - Alison H Thomson
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Ze Yu
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
| | - Fei Gao
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue Zhou
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Edmund V Capparelli
- Pediatric Pharmacology and Drug Discovery, University of California, San Diego, CA, USA
| | - Valerie Biran
- Neonatal Intensive Care Unit, Hospital Robert Debre, Paris, France
| | - Nicolas Simon
- Service de Pharmacologie Clinique, CAP-TV, Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Sainte Marguerite, Marseille, France
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yoke-Lin Lo
- Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia
| | - Remedios Marques
- Department of Pharmacy Services, La Fe Hospital, Valencia, Spain
| | - Jose-Esteban Peris
- Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Valencia, Spain
| | - Irja Lutsar
- Institute of Medical Microbiology, University of Tartu, Tartu, Estonia
| | - Jumpei Saito
- Department of Pharmacy, National Children's Hospital National Center for Child Health and Development, Tokyo, Japan
| | - Evelyne Jacqz-Aigrain
- Department of Pediatric Pharmacology and Pharmacogenetics, Hospital Robert Debre, APHP, Paris, France
- Clinical Investigation Center CIC1426, Hôpital Robert Debré, Paris, France
- University Paris Diderot, Sorbonne Paris Cite, Paris, France
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Department of Pediatrics, Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Yue-E Wu
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
- NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Shandong University, Jinan, China.
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Li D, Zhao J, Xu B, Zheng Y, Liu M, Huang H, Han S, Wu X. Predicting busulfan exposure in patients undergoing hematopoietic stem cell transplantation using machine learning techniques. Expert Rev Clin Pharmacol 2023; 16:751-761. [PMID: 37326641 DOI: 10.1080/17512433.2023.2226866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/13/2023] [Indexed: 06/17/2023]
Abstract
PURPOSE This study aimed to establish an optimal model to predict the busulfan (BU) area under the curve at steady state (AUCss) by using machine learning (ML). PATIENTS AND METHODS Seventy-nine adult patients (age ≥18 years) who received BU intravenously and underwent therapeutic drug monitoring from 2013 to 2021 at Fujian Medical University Union Hospital were enrolled in this retrospective study. The whole dataset was divided into a training group and test group at the ratio of 8:2. BU AUCss were considered as the target variable. Nine different ML algorithms and one population pharmacokinetic (pop PK) model were developed and validated, and their predictive performance was compared. RESULTS All ML models were superior to the pop PK model (R2 = 0.751, MSE = 0.722, 14 and RMSE = 0.830) in model fitting and had better predictive accuracy. The ML model of BU AUCss established through support vector regression (SVR) and gradient boosted regression trees (GBRT) had the best predictive ability (R2 = 0.953 and 0.953, MSE = 0.323 and 0.326, and RMSE = 0.423 and 0.425). CONCLUSION All the ML models can potentially be used to estimate BU AUCss with the aim of facilitating rational use of BU on the individualized level, especially models built by SVR and GBRT algorithms.
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Affiliation(s)
- Dandan Li
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Jingtong Zhao
- School of Economics, Renmin University of China, Beijing, China
| | - Baohua Xu
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - You Zheng
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Maobai Liu
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
| | - Huiping Huang
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Song Han
- School of Economics, Renmin University of China, Beijing, China
| | - Xuemei Wu
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
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Destere A, Marquet P, Labriffe M, Drici MD, Woillard JB. A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction. Pharm Res 2023; 40:951-959. [PMID: 36991227 DOI: 10.1007/s11095-023-03507-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/21/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVES Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic (POPPK) model is used to estimate individual pharmacokinetic parameters. Recently, we proposed a methodology that combined population pharmacokinetic and machine learning (ML) to decrease the bias and imprecision in individual iohexol clearance prediction. The aim of this study was to confirm the previous results by developing a hybrid algorithm combining POPPK, MAP-BE and ML that accurately predicts isavuconazole clearance. METHODS A total of 1727 isavuconazole rich PK profiles were simulated using a POPPK model from the literature, and MAP-BE was used to estimate the clearance based on: (i) the full PK profiles (refCL); and (ii) C24h only (C24h-CL). Xgboost was trained to correct the error between refCL and C24h-CL in the training dataset (75%). C24h-CL as well as ML-corrected C24h-CL were evaluated in a testing dataset (25%) and then in a set of PK profiles simulated using another published POPPK model. RESULTS A strong decrease in mean predictive error (MPE%), imprecision (RMSE%) and the number of profiles outside ± 20% MPE% (n-out20%) was observed with the hybrid algorithm (decreased in MPE% by 95.8% and 85.6%; RMSE% by 69.5% and 69.0%; n-out20% by 97.4% and 100% in the training and testing sets, respectively. In the external validation set, the hybrid algorithm decreased MPE% by 96%, RMSE% by 68% and n-out20% by 100%. CONCLUSION The hybrid model proposed significantly improved isavuconazole AUC estimation over MAP-BE based on the sole C24h and may improve dose adjustment.
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Affiliation(s)
- Alexandre Destere
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Pierre Marquet
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France
| | - Marc Labriffe
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France
| | - Milou-Daniel Drici
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Jean-Baptiste Woillard
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France.
- Department of Pharmacology Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France.
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Destere A, Merino D, Bonesso L, Lavrut T, Bernasconni A, Garraffo R, Gérard AO, Drici MD. A HPLC-DAD method to facilitate large-scale therapeutic drug monitoring of dalbavancin. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1222:123694. [PMID: 37060815 DOI: 10.1016/j.jchromb.2023.123694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/08/2023] [Accepted: 03/26/2023] [Indexed: 04/03/2023]
Abstract
Dalbavancin, a long-acting lipoglycopeptide antibiotic targeting susceptible Gram-positive bacteria, is WHO critically important antibiotic, increasingly used in critical situations such as osteoarticular infections. To ensure its effectiveness and its safety, the therapeutic drug monitoring (TDM) of dalbavancin is strongly recommended. In the absence of an available minimum inhibitory concentration (MIC), the European Committee on Antimicrobial Susceptibility Testing (EUCAST) recommends a breakpoint of 0.125 mg/L for Staphylococcus aureus, corresponding to a trough target concentration of 25 mg/L. Nowadays, the TDM is usually performed using a high-performance liquid chromatography (HPLC) method coupled with a tandem mass spectrometry. However, this expensive and specialized equipment and reagents may be difficult to acquire for non-specialized laboratories. The use of HPLC coupled with diode array detector (DAD) facilitates TDM with a lower cost, while preserving the reliability of the results. Our aim was to provide a sensitive and specific method, relying on HPLC-DAD for extending the TDM of dalbavancin beyond non-specialized labs, therefore maximizing its efficiency and Benefit/risk ratio. Our method complied with the European Medicines Agency guidelines of bioanalytical validation. Irrespective of the concentrations of dalbavancin, the coefficient of variation < 10% confirmed the reliability of this analytical method, with a calibration curve ranging from 5 to 100 mg/L. No interferences nor carryover was observed. Our HPLC-DAD method, combined with a simple extraction, provides a widely usable, inexpensive and easy-to-implement new asset for the TDM of Dalbavancin.
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Affiliation(s)
- Alexandre Destere
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Diane Merino
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Laurent Bonesso
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Thibaud Lavrut
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Anaïs Bernasconni
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Rodolphe Garraffo
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Alexandre O Gérard
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France
| | - Milou-Daniel Drici
- Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France.
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