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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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Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol 2024; 14:1260276. [PMID: 38264526 PMCID: PMC10803549 DOI: 10.3389/fphar.2023.1260276] [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: 07/17/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.
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Affiliation(s)
| | | | | | - Maurizio Polano
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy
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Fu R, Yu Z, Zhou C, Zhang J, Gao F, Wang D, Hao X, Pang X, Yu J. Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study. Expert Rev Clin Pharmacol 2024; 17:177-187. [PMID: 38197873 DOI: 10.1080/17512433.2024.2304009] [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: 07/13/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques. METHODS Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed. RESULTS CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively). CONCLUSION The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the 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
| | - Ze Yu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- 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
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd, Beijing, 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
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd, Dalian, China
| | - Xiaolu Pang
- Department of Physical Diagnostics, Hebei Medical University, Shijiazhuang, 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
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Kita K, Fujimori T, Suzuki Y, Kanie Y, Takenaka S, Kaito T, Taki T, Ukon Y, Furuya M, Saiwai H, Nakajima N, Sugiura T, Ishiguro H, Kamatani T, Tsukazaki H, Sakai Y, Takami H, Tateiwa D, Hashimoto K, Wataya T, Nishigaki D, Sato J, Hoshiyama M, Tomiyama N, Okada S, Kido S. Bimodal artificial intelligence using TabNet for differentiating spinal cord tumors-Integration of patient background information and images. iScience 2023; 26:107900. [PMID: 37766987 PMCID: PMC10520519 DOI: 10.1016/j.isci.2023.107900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/18/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
We proposed a bimodal artificial intelligence that integrates patient information with images to diagnose spinal cord tumors. Our model combines TabNet, a state-of-the-art deep learning model for tabular data for patient information, and a convolutional neural network for images. As training data, we collected 259 spinal tumor patients (158 for schwannoma and 101 for meningioma). We compared the performance of the image-only unimodal model, table-only unimodal model, bimodal model using a gradient-boosting decision tree, and bimodal model using TabNet. Our proposed bimodal model using TabNet performed best (area under the receiver-operating characteristic curve [AUROC]: 0.91) in the training data and significantly outperformed the physicians' performance. In the external validation using 62 cases from the other two facilities, our bimodal model showed an AUROC of 0.92, proving the robustness of the model. The bimodal analysis using TabNet was effective for differentiating spinal tumors.
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Affiliation(s)
- Kosuke Kita
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Takahito Fujimori
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Yuki Suzuki
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Yuya Kanie
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Shota Takenaka
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Takashi Kaito
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Takuyu Taki
- Department of Neurosurgery, Iseikai Hospital, Osaka, Osaka, Japan
| | - Yuichiro Ukon
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | | | - Hirokazu Saiwai
- Department of Orthopedic Surgery, Graduate School of Medical Sciences, Kyusyu University, Higashi, Fukuoka, Japan
| | - Nozomu Nakajima
- Japanese Red Cross Society Himeji Hospital, Himeji, Hyogo, Japan
| | - Tsuyoshi Sugiura
- General Incorporated Foundation Sumitomo Hospital, Osaka, Osaka, Japan
| | - Hiroyuki Ishiguro
- National Hospital Organization Osaka National Hospital, Osaka, Osaka, Japan
| | | | | | | | - Haruna Takami
- Osaka International Cancer Institute, Osaka, Osaka, Japan
| | | | | | - Tomohiro Wataya
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Daiki Nishigaki
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Junya Sato
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | | | - Noriyuki Tomiyama
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Seiji Okada
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Shoji Kido
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
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Zhu J, Xu G, Yang D, Song Y, Tong Y, Kong S, Ding H, Fang L. Dose-sparing effect of lapatinib co-administered with a high-fat enteral nutrition emulsion: preclinical pharmacokinetic study. PeerJ 2023; 11:e16207. [PMID: 37842056 PMCID: PMC10569162 DOI: 10.7717/peerj.16207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023] Open
Abstract
Background Lapatinib is an oral small-molecule tyrosine kinase inhibitor indicated for advanced or metastatic HER2-positive breast cancer. In order to reduce the treatment cost, a high-fat enteral nutrition emulsion TPF-T was selected as a dose-sparing agent for lapatinib-based therapies. This study aimed to investigate the effect of TPF-T on lapatinib pharmacokinetics. Methods First, a simple and rapid liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed to quantitatively evaluate lapatinib in rabbit plasma. The method was fully validated according to the China Pharmacopoeia 2020 guidance. Rabbits and rats were chosen as the animal models due to their low and high bile flows, respectively. The proposed LC-MS/MS method was applied to pharmacokinetic studies of lapatinib, with or without TPF-T, in rabbit and rat plasma. Results The LC-MS/MS method revealed high sensitivity and excellent efficiency. In the rabbit model, co-administration with TPF-T resulted in a 32.2% increase in lapatinib exposure. In the rat model, TPF-T had minimal influence on the lapatinib exposure. In both models, TPF-T was observed to significantly elevate lapatinib concentration in the absorption phase. Conclusion Co-administration with TPF-T had a moderate effect on increasing exposure to lapatinib. Dose sparing using a high-fat liquid diet is potentially feasible for lapatinib-based therapies.
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Affiliation(s)
- Junfeng Zhu
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Gaoqi Xu
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Dihong Yang
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Yu Song
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Yinghui Tong
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Sisi Kong
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Haiying Ding
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Luo Fang
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
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Deng Y, Qiu J, Xiao Z, Tang B, Liu D, Chen S, Shi Z, Tang X, Chen H. Conv-TabNet: an efficient adaptive color correction network for smartphone-based urine component analysis. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2023; 40:1724-1732. [PMID: 37707009 DOI: 10.1364/josaa.491776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/30/2023] [Indexed: 09/15/2023]
Abstract
The camera function of a smartphone can be used to quantitatively detect urine parameters anytime, anywhere. However, the color captured by different cameras in different environments is different. A method for color correction is proposed for a urine test strip image collected using a smartphone. In this method, the color correction model is based on the color information of the urine test strip, as well as the ambient light and camera parameters. Conv-TabNet, which can focus on each feature parameter, was designed to correct the color of the color blocks of the urine test strip. The color correction experiment was carried out in eight light sources on four mobile phones. The experimental results show that the mean absolute error of the new method is as low as 2.8±1.8, and the CIEDE2000 color difference is 1.5±1.5. The corrected color is almost consistent with the standard color by visual evaluation. This method can provide a technology for the quantitative detection of urine test strips anytime and anywhere.
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Hao Y, Zhang J, Yang L, Zhou C, Yu Z, Gao F, Hao X, Pang X, Yu J. A machine learning model for predicting blood concentration of quetiapine in patients with schizophrenia and depression based on real-world data. Br J Clin Pharmacol 2023; 89:2714-2725. [PMID: 37005382 DOI: 10.1111/bcp.15734] [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: 12/08/2022] [Revised: 02/26/2023] [Accepted: 03/25/2023] [Indexed: 04/04/2023] Open
Abstract
AIMS This study aimed to establish a prediction model of quetiapine concentration in patients with schizophrenia and depression, based on real-world data via machine learning techniques to assist clinical regimen decisions. METHODS A total of 650 cases of quetiapine therapeutic drug monitoring (TDM) data from 483 patients at the First Hospital of Hebei Medical University from 1 November 2019 to 31 August 2022 were included in the study. Univariate analysis and sequential forward selection (SFS) were implemented to screen the important variables influencing quetiapine TDM. After 10-fold cross validation, the algorithm with the optimal model performance was selected for predicting quetiapine TDM among nine models. SHapley Additive exPlanation was applied for model interpretation. RESULTS Four variables (daily dose of quetiapine, type of mental illness, sex and CYP2D6 competitive substrates) were selected through univariate analysis (P < .05) and SFS to establish the models. The CatBoost algorithm with the best predictive ability (mean [SD] R2 = 0.63 ± 0.02, RMSE = 137.39 ± 10.56, MAE = 103.24 ± 7.23) was chosen for predicting quetiapine TDM among nine models. The mean (SD) accuracy of the predicted TDM within ±30% of the actual TDM was 49.46 ± 3.00%, and that of the recommended therapeutic range (200-750 ng mL-1 ) was 73.54 ± 8.3%. Compared with the PBPK model in a previous study, the CatBoost model shows slightly higher accuracy within ±100% of the actual value. CONCLUSIONS This work is the first real-world study to predict the blood concentration of quetiapine in patients with schizophrenia and depression using artificial intelligent techniques, which is of significance and value for clinical medication guidance.
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Affiliation(s)
- Yupei Hao
- 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
| | - 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
| | - 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
| | - 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
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd., Dalian, China
| | - Xiaolu Pang
- Department of Physical Diagnostics, Hebei Medical University, Shijiazhuang, 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
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Zheng P, Mo L, Zhao B, Li L, Cen B, Xu Z, Li Y. Pharmaceutical care model in precision medicine in China. FARMACIA HOSPITALARIA 2023; 47:T218-T223. [PMID: 37598018 DOI: 10.1016/j.farma.2023.07.004] [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: 11/27/2022] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 08/21/2023] Open
Abstract
Pharmacy service is to provide individualized pharmaceutical care for patients, which should follow the current evidence-based pharmacy, and constantly verify the evidence and then produce new evidence. In pharmaceutical care, differences are often found in the efficacy and adverse reactions of drugs among individuals, even within individuals, which are closely related to patients' genetics, liver and kidney functions, disease states, and drug interactions. Back in the 1980s, therapeutic drug monitoring (TDM) has been applied to routinely monitor the blood drug concentration of patients taking antiepileptic drugs or immunosuppressants after transplantation to provide individualized dosage recommendations and accumulate a large amount of pharmacokinetic (PK)/pharmacodynamic (PD) data. As individualized pharmaceutical care proceeds, the concept of precision medicine was introduced into pharmacy services in combination with evidence-based pharmacy, PK/PD theories, and big data to further promote the TDM technology and drugs, and carry out pharmacogenomics analysis. The TDM and pharmacogenomics have been applied gradually to the fields of antimicrobial, antitumor, and antipsychotic drugs and immunosuppressants. Based on the concept of precision pharmacy, we adopted approaches including PK/PD, quantitative pharmacology, population pharmacokinetics, and big data machine learning to provide more personalized pharmacy services, which is mainly for special patients, such as critical patients, patients with interaction risk of multiple drugs, patients with liver and renal insufficiency, pregnant women, children, and elderly patients. As the service pattern of precision pharmacy has been constructed and constantly improved, better evidence in clinical practice will be produced to provide patients with better precision pharmacy service.
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Affiliation(s)
- Ping Zheng
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Liqian Mo
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Boxin Zhao
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Liren Li
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Baihong Cen
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
| | - Zhongyuan Xu
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China.
| | - Yilei Li
- Unidad de Farmacia Clínica, Hospital Nanfang, Universidad Médica del Sur, Guangzhou, China
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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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