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Lee YW, Kim JH, Park JJ, Park H, Seo H, Kim YK. Development and external validation of a machine learning model to predict the initial dose of vancomycin for targeting an area under the concentration-time curve of 400-600 mg∙h/L. Int J Med Inform 2025; 196:105817. [PMID: 39893767 DOI: 10.1016/j.ijmedinf.2025.105817] [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: 08/27/2024] [Revised: 12/30/2024] [Accepted: 01/28/2025] [Indexed: 02/04/2025]
Abstract
PURPOSE To develop and validate a novel artificial intelligence model for predicting the initial empiric dose of vancomycin, with the aim of achieving an area under the concentration-time curve (AUC) of 400-600 mg∙h/L, using individual clinical data. METHODS Machine learning models were developed and validated internally and externally using data from adult patients who received intravenous vancomycin treatment between November 2020 and June 2023, using records from July to September 2023, sourced from two hospitals. This study included 205, 44, and 35 patients in the training, internal validation, and external validation sets, respectively. The Random Forest, XGBoost, and Ensemble models were evaluated based on the mean squared error, root mean squared error, R-squared value, and accuracy (±20 % of the actual dose). FINDINGS In internal validation, the Random Forest model achieved the highest 20% Accuracy at 56.8%, while the XGBoost model achieved 56.8% and Voting Ensemble models attained 54.5% accuracy. In external validation, the XGBoost model achieved the highest 20% Accuracy at 74.3%, followed by Random Forest and Voting Ensemble, both also achieving 74.3% accuracy. The estimated glomerular filtration rate was the most significant predictor in all models, with body weight, serum creatinine, height, and age also prominently influencing the XGBoost model's predictions. IMPLICATIONS The proposed model is capable of accurately predicting the optimal dose of vancomycin, which could aid physicians in making more informed treatment decisions regarding early therapy, particularly when AUC estimation systems are not accessible or readily available in routine clinical practice.
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Affiliation(s)
- Yun Woo Lee
- Division of Infectious Diseases, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Ji-Hun Kim
- Department of Emergency Medicine, The Catholic University of Korea, Uijeongbu St. Mary's Hospital, Uijeongbu, Gyeonggi-do, Republic of Korea
| | - Jin Ju Park
- Division of Infectious Diseases, Department of Internal Medicine, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Hyejin Park
- Division of Infectious Diseases, Department of Internal Medicine, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Hyeonji Seo
- Division of Infectious Diseases, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Yong Kyun Kim
- Division of Infectious Diseases, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea.
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Yao N, Zhao Q, Cao Y, Gu D, Zhang N. Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques. Pharm Res 2025; 42:79-91. [PMID: 39843764 DOI: 10.1007/s11095-025-03817-3] [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: 10/08/2024] [Accepted: 01/02/2025] [Indexed: 01/24/2025]
Abstract
OBJECTIVE This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chinese adult epilepsy patients. METHODS A single-center retrospective study was carried out at the Jinshan Hospital affiliated with Fudan University from January 2022 to December 2023. A total of 102 VPA trough concentrations were split into a derivation cohort and a validation cohort at a ratio of 8:2. Thirteen ML algorithms were developed using 27 variables in the derivation cohort and were filtered by the lowest mean absolute error (MAE) value. In addition, feature selection was applied to optimize the model. RESULTS Ultimately, the extra tree algorithm was chosen to establish the personalized VPA trough concentration prediction model due to its best performance (MAE = 13.08). The SHapley Additive exPlanations (SHAP) plots were used to visualize and rank the importance of features, providing insights into how each feature influences the model's predictions. After feature selection, we found that the model with the top 9 variables [including daily dose, last dose, uric acid (UA), platelet (PLT), combination, gender, weight, albumin (ALB), aspartate aminotransferase (AST)] outperformed the model with 27 variables, with MAE of 6.82, RMSE of 9.62, R2 value of 0.720, relative accuracy (±20%) of 61.90%, and absolute accuracy (±20 mg/L) of 90.48%. CONCLUSION In conclusion, the trough concentration prediction model for VPA in Chinese adult epileptic patients based on the extra tree algorithm demonstrated strong predictive ability which is valuable for clinicians in medication guidance.
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Affiliation(s)
- Nannan Yao
- Department of Pharmacy, Jinshan Hospital Affiliated to Fudan University, Shanghai, China
| | - Qiongyue Zhao
- Department of Pharmacy, Jinshan Hospital Affiliated to Fudan University, Shanghai, China
| | - Ying Cao
- Department of Pharmacy, Jinshan Hospital Affiliated to Fudan University, Shanghai, China
| | - Dongshi Gu
- Department of Pharmacy, Jinshan Hospital Affiliated to Fudan University, Shanghai, China.
| | - Ning Zhang
- Department of Pharmacy, Jinshan Hospital Affiliated to Fudan University, Shanghai, 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; 46:1134-1142. [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] [MESH Headings] [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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Ma H, Huang S, Li F, Pang Z, Luo J, Sun D, Liu J, Chen Z, Qu J, Qu Q. Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy. Arch Toxicol 2024; 98:3049-3061. [PMID: 38879852 DOI: 10.1007/s00204-024-03803-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 06/05/2024] [Indexed: 08/15/2024]
Abstract
Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase of transaminase levels while undergoing VPA therapy for 1995 epilepsy patients. The study employed the two-tailed T test, Chi-square test, and binary logistic regression analysis, selecting six clinical parameters, including age, stature, leukocyte count, Total Bilirubin, oral dosage of VPA, and VPA concentration. These variables were used to build a risk prediction model using "H2O" autoML platform, achieving the best performance (AUC training = 0.855, AUC test = 0.789) in the training and testing data set. The model also exhibited robust accuracy (AUC valid = 0.742) in an external validation set, underscoring its credibility in anticipating VPA-induced transaminase abnormalities. The significance of the six variables was elucidated through importance ranking, partial dependence, and the TreeSHAP algorithm. This novel model offers enhanced versatility and explicability, rendering it suitable for clinicians seeking to refine parameter adjustments and address imbalanced data sets, thereby bolstering classification precision. To summarize, the personalized prediction model for VPA-treated epilepsy, established with an autoML model, displayed commendable predictive capability, furnishing clinicians with valuable insights for fostering pharmacovigilance.
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Affiliation(s)
- Hongying Ma
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Sihui Huang
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- College of Plant Protection, Hunan Agricultural University, Changsha, 410125, China
| | - Fengxin Li
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- College of Biology, Hunan University, Changsha, 410082, China
| | - Zicheng Pang
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Jian Luo
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Danfeng Sun
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410125, China
| | - Junsong Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410125, China
| | - Zhuoming Chen
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410125, China
| | - Jian Qu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, 410013, China
- Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, 410219, China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, China.
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, 410219, China.
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Tootooni MS, Barreto EF, Wutthisirisart P, Kashani KB, Pasupathy KS. Determining steady-state trough range in vancomycin drug dosing using machine learning. J Crit Care 2024; 82:154784. [PMID: 38503008 PMCID: PMC11139571 DOI: 10.1016/j.jcrc.2024.154784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/05/2024] [Accepted: 03/10/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate dosing for each ICU patient. METHODS Observed vancomycin trough levels were categorized into sub-therapeutic, therapeutic, and supra-therapeutic levels to train and compare different classification models. We included adult ICU patients (≥ 18 years) with at least one vancomycin concentration measurement during hospitalization at Mayo Clinic, Rochester, MN, from January 2007 to December 2017. RESULT The final cohort consisted of 5337 vancomycin courses. The XGBoost models outperformed other machine learning models with the AUC-ROC of 0.85 and 0.83, specificity of 53% and 47%, and sensitivity of 94% and 94% for sub- and supra-therapeutic categories, respectively. Kinetic estimated glomerular filtration rate and other creatinine-based measurements, vancomycin regimen (dose and interval), comorbidities, body mass index, age, sex, and blood pressure were among the most important variables in the models. CONCLUSION We developed models to assess the risk of sub- and supra-therapeutic vancomycin trough levels to improve the accuracy of drug dosing in critically ill patients.
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Affiliation(s)
- M Samie Tootooni
- Department of Health Informatics and Data Science, Loyola University Chicago, Maywood, IL, United States of America.
| | - Erin F Barreto
- Department of Pharmacy, Mayo Clinic, Rochester, MN, United States of America
| | - Phichet Wutthisirisart
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Kalyan S Pasupathy
- Department of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, United States of America.
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González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. [Translated article] Introducing artificial intelligence to hospital pharmacy departments. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:TS35-TS44. [PMID: 39097375 DOI: 10.1016/j.farma.2024.04.001] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.
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Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, Spain.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, Spain
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González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. Approaching artificial intelligence to Hospital Pharmacy. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:S35-S44. [PMID: 39097366 DOI: 10.1016/j.farma.2024.02.007] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.
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Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, España.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, España
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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: 11] [Impact Index Per Article: 11.0] [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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Fu R, Hao X, Yu J, Wang D, Zhang J, Yu Z, Gao F, Zhou C. Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring. Front Pharmacol 2024; 15:1289673. [PMID: 38510645 PMCID: PMC10953499 DOI: 10.3389/fphar.2024.1289673] [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: 09/06/2023] [Accepted: 02/21/2024] [Indexed: 03/22/2024] Open
Abstract
Background: Sertraline is a commonly employed antidepressant in clinical practice. In order to control the plasma concentration of sertraline within the therapeutic window to achieve the best effect and avoid adverse reactions, a personalized model to predict sertraline concentration is necessary. Aims: This study aimed to establish a personalized medication model for patients with depression receiving sertraline based on machine learning to provide a reference for clinicians to formulate drug regimens. Methods: A total of 415 patients with 496 samples of sertraline concentration from December 2019 to July 2022 at the First Hospital of Hebei Medical University were collected as the dataset. Nine different algorithms, namely, XGBoost, LightGBM, CatBoost, random forest, GBDT, SVM, lasso regression, ANN, and TabNet, were used for modeling to compare the model abilities to predict sertraline concentration. Results: XGBoost was chosen to establish the personalized medication model with the best performance (R 2 = 0.63). Five important variables, namely, sertraline dose, alanine transaminase, aspartate transaminase, uric acid, and sex, were shown to be correlated with sertraline concentration. The model prediction accuracy of sertraline concentration in the therapeutic window was 62.5%. Conclusion: In conclusion, the personalized medication model of sertraline for patients with depression based on XGBoost had good predictive ability, which provides guidance for clinicians in proposing an optimal medication regimen.
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Affiliation(s)
- Ran Fu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd, Dalian, China
| | - Jing Yu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Donghan Wang
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Ze Yu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Chunhua Zhou
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
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10
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Ran P, Tan T, Li J, Yang H, Li J, Zhang J. Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning. BMC Cancer 2024; 24:264. [PMID: 38402382 PMCID: PMC10894477 DOI: 10.1186/s12885-024-11930-6] [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: 09/26/2023] [Accepted: 01/29/2024] [Indexed: 02/26/2024] Open
Abstract
AIM Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. METHODS Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. RESULTS Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). CONCLUSION For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.
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Affiliation(s)
- Pan Ran
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Tao Tan
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jinjin Li
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Hao Yang
- Department of Internal Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Juan Li
- Department of Pharmacy, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Jun Zhang
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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11
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Matsuzaki T, Mizoguchi H, Yamada K. The Use of Text Mining to Obtain a Historical Overview of Research on Therapeutic Drug Monitoring. Biol Pharm Bull 2024; 47:1883-1892. [PMID: 39551526 DOI: 10.1248/bpb.b24-00319] [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] [Indexed: 11/19/2024]
Abstract
Therapeutic drug monitoring (TDM) is a routine clinical practice used to individualize drug dosing to maintain drug efficacy and minimize the consequences of overexposure. TDM is applied to many drug classes, including immunosuppressants, antineoplastic agents, and antibiotics. Considerable effort has been made to establish routine TDM practices for each drug. However, because TDM has been developed within the context of specific drugs, there is insufficient understanding of historical trends within the field of TDM research as a whole. In this study, we employed text-mining approaches to explore trends in the TDM research field. We first performed a PubMed search to determine which drugs and drug classes have been extensively studied in the context of TDM. This investigation revealed that the most commonly studied drugs are tacrolimus, followed by cyclosporine and vancomycin. With regard to drug classes, most studies focused on immunosuppressants, antibiotics, and antineoplastic agents. We also subjected PubMed records of TDM-related studies to a series of text-mining pipelines. Our analyses revealed how TDM research has evolved over the years, thereby serving as a cornerstone for forecasting future research trends.
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Affiliation(s)
- Tetsuo Matsuzaki
- Department of Neuropsychopharmacology and Hospital Pharmacy, Nagoya University Graduate School of Medicine
| | - Hiroyuki Mizoguchi
- Department of Neuropsychopharmacology and Hospital Pharmacy, Nagoya University Graduate School of Medicine
| | - Kiyofumi Yamada
- Department of Neuropsychopharmacology and Hospital Pharmacy, Nagoya University Graduate School of Medicine
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12
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Yang L, Zhang J, Yu J, Yu Z, Hao X, Gao F, Zhou C. Predicting plasma concentration of quetiapine in patients with depression using machine learning techniques based on real-world evidence. Expert Rev Clin Pharmacol 2023; 16:741-750. [PMID: 37466101 DOI: 10.1080/17512433.2023.2238604] [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: 03/31/2023] [Revised: 06/19/2023] [Accepted: 07/13/2023] [Indexed: 07/20/2023]
Abstract
OBJECTIVES We develop a model for predicting quetiapine levels in patients with depression, using machine learning to support decisions on clinical regimens. METHODS Inpatients diagnosed with depression at the First Hospital of Hebei Medical University from 1 November 2019, to 31 August were enrolled. The ratio of training cohort to testing cohort was fixed at 80%:20% for the whole dataset. Univariate analysis was executed on all information to screen the important variables influencing quetiapine TDM. The prediction abilities of nine machine learning and deep learning algorithms were compared. The prediction model was created using an algorithm with better model performance, and the model's interpretation was done using the SHapley Additive exPlanation. RESULTS There were 333 individuals and 412 cases of quetiapine TDM included in the study. Six significant variables were selected to establish the individualized medication model. A quetiapine concentration prediction model was created through CatBoost. In the testing cohort, the projected TDM's accuracy was 61.45%. The prediction accuracy of quetiapine concentration within the effective range (200-750 ng/mL) was 75.47%. CONCLUSIONS This study predicts the plasma concentration of quetiapine in depression patients by machine learning, which is meaningful for the clinical medication guidance.
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Affiliation(s)
- Lin Yang
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co, Ltd, Beijing, China
| | - Jing Yu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ze Yu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Hao
- Dalian Medicinovo Technology Co, Ltd, Dalian, China
| | - Fei Gao
- Beijing Medicinovo Technology Co, Ltd, Beijing, China
| | - Chunhua Zhou
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
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13
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Ruiz-Ramos J, Gras-Martín L, Ramírez P. Antimicrobial Pharmacokinetics and Pharmacodynamics in Critical Care: Adjusting the Dose in Extracorporeal Circulation and to Prevent the Genesis of Multiresistant Bacteria. Antibiotics (Basel) 2023; 12:antibiotics12030475. [PMID: 36978342 PMCID: PMC10044431 DOI: 10.3390/antibiotics12030475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 03/02/2023] Open
Abstract
Critically ill patients suffering from severe infections are prone to pathophysiological pharmacokinetic changes that are frequently associated with inadequate antibiotic serum concentrations. Minimum inhibitory concentrations (MICs) of the causative pathogens tend to be higher in intensive care units. Both pharmacokinetic changes and high antibiotic resistance likely jeopardize the efficacy of treatment. The use of extracorporeal circulation devices to support hemodynamic, respiratory, or renal failure enables pharmacokinetic changes and makes it even more difficult to achieve an adequate antibiotic dose. Besides a clinical response, antibiotic pharmacokinetic optimization is important to reduce the selection of strains resistant to common antibiotics. In this review, we summarize the present knowledge regarding pharmacokinetic changes in critically ill patients and we discuss the effects of extra-corporeal devices on antibiotic treatment together with potential solutions.
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Affiliation(s)
- Jesus Ruiz-Ramos
- Pharmacy Department, Hospital Santa Creu i Sant Pau, 08025 Barcelona, Spain
| | - Laura Gras-Martín
- Pharmacy Department, Hospital Santa Creu i Sant Pau, 08025 Barcelona, Spain
| | - Paula Ramírez
- Intensive Care Unit, Hospital Universitario y Politécnico La Fe, 46026 Valencia, Spain
- Correspondence:
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14
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Miyai T, Imai S, Yoshimura E, Kashiwagi H, Sato Y, Ueno H, Takekuma Y, Sugawara M. Machine Learning-Based Model for Estimating Vancomycin Maintenance Dose to Target the Area under the Concentration Curve of 400–600 mg·h/L in Japanese Patients. Biol Pharm Bull 2022; 45:1332-1339. [DOI: 10.1248/bpb.b22-00305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
| | | | - Eri Yoshimura
- Department of Pharmacy, Sunagawa City Medical Center
| | | | - Yuki Sato
- Faculty of Pharmaceutical Sciences, Hokkaido University
| | - Hidefumi Ueno
- Department of Pharmacy, Sunagawa City Medical Center
| | - Yoh Takekuma
- Department of Pharmacy, Hokkaido University Hospital
| | - Mitsuru Sugawara
- Global Station for Biosurfaces and Drug Discovery, Hokkaido University
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