1
|
Ben Hassine K, Daali Y, Gloor Y, Nava T, Théorêt Y, Krajinovic M, Bittencourt H, Satyanarayana Uppugunduri CR, Ansari M. Simulation-Based Optimization of Sampling Schedules for Model-Informed Precision Dosing of Once-Daily and 4-Times-Daily Busulfan in Pediatric Patients. Ther Drug Monit 2024:00007691-990000000-00240. [PMID: 38885146 DOI: 10.1097/ftd.0000000000001217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/25/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Therapeutic drug monitoring (TDM) is crucial in optimizing the outcomes of hematopoietic stem cell transplantation by guiding busulfan (Bu) dosing. Limited sampling strategies show promise for efficiently adjusting drug doses. However, comprehensive assessments and optimization of sampling schedules for Bu TDM in pediatric patients are limited. We aimed to establish optimal sampling designs for model-informed precision dosing (MIPD) of once-daily (q24h) and 4-times-daily (q6h) Bu administration in pediatric patients. METHODS Simulated data sets were used to evaluate the population pharmacokinetic model-based Bayesian estimation of the area under the concentration-time curve (AUC) for different limited sampling strategy designs. The evaluation was based on the mean prediction error for accuracy and root mean square error for precision. These findings were validated using patient-observed data. In addition, the MIPD protocol was implemented in the Tucuxi software, and its performance was assessed. RESULTS Our Bayesian estimation approach allowed for flexible sampling times while maintaining mean prediction error within ±5% and root mean square error below 10%. Accurate and precise AUC0-24h and cumulative AUC estimations were obtained using 2-sample and single-sample schedules for q6h and q24h dosing, respectively. TDM on 2 separate days was necessary to accurately estimate cumulative exposure, especially in patients receiving q6h Bu. Validation with observed patient data confirmed the precision of the proposed limited sampling scenarios. Implementing the MIPD protocol in Tucuxi software yielded reliable AUC estimations. CONCLUSIONS Our study successfully established precise limited sampling protocols for MIPD of Bu in pediatric patients. Our findings underscore the importance of TDM on at least 2 occasions to accurately achieve desired Bu exposures. The developed MIPD protocol and its implementation in Tucuxi software provide a valuable tool for routine TDM in pediatric hematopoietic stem cell transplantation.
Collapse
Affiliation(s)
- Khalil Ben Hassine
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, University Hospital of Geneva, Geneva, Switzerland
- Faculty of Medicine & Sciences, University of Geneva, Geneva, Switzerland
| | - Yvonne Gloor
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Tiago Nava
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Yves Théorêt
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Maja Krajinovic
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Henrique Bittencourt
- Charles-Bruneau Cancer Center, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Clinical Pharmacology Unit, CHU Sainte-Justine, Montreal, Quebec, Canada; and
| | - Chakradhara Rao Satyanarayana Uppugunduri
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marc Ansari
- CANSEARCH Research Platform for Pediatric Oncology and Hematology, Department of Pediatrics, Gynecology, and Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Pediatric Oncology and Hematology, Department of Women, Child, and Adolescent, University Hospital of Geneva, Geneva, Switzerland
| |
Collapse
|
2
|
Song X, Liu F, Gao H, Yan M, Zhang F, Zhao J, Qin Y, Li Y, Zhang Y. Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation. Pediatr Transplant 2023; 27:e14379. [PMID: 36039686 DOI: 10.1111/petr.14379] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/20/2022] [Accepted: 08/02/2022] [Indexed: 01/24/2023]
Abstract
BACKGROUND This study aims to establish multiple ML models and compare their performance in predicting tacrolimus concentration for infant patients who received LDLT within 3 months after transplantation. METHODS Retrospectively collected basic information and relevant biochemical indicators of included infant patients. CMIA was used to determine tacrolimus C0 . PCR was used to determine the donors' and recipients' CYP3A5 genotypes. Multivariate stepwise regression analysis and stepwise elimination covariates were used for covariates selection. Thirteen machine learning algorithms were applied for the development of prediction models. APE, the ratio of the APE ≤3 ng ml-1 and ideal rate (the proportion of the predicted value with a relative error of 30% or less) were used to evaluate the predictive performance of the model. RESULTS A total of 163 infant patients were included in this study. In the case of the optimal combination of covariates, the Ridge model had the lowest APE, 2.01 (0.85, 3.35 ng ml-1 ). The highest ratio of the APE ≤3 ng ml-1 was the LAR model (71.77%). And the Ridge model showed the highest ideal rate (55.05%). For the Ridge model, GRWR was the most important predictor. CONCLUSIONS Compared with other ML models, the Ridge model had good predictive performance and potential clinical application.
Collapse
Affiliation(s)
- XueWu Song
- First Central Clinical College of Tianjin Medical University, Tianjin, China
| | - FangHao Liu
- College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University, Tianjin, China
| | - HuiEr Gao
- Department of Pharmacy, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - MeiLing Yan
- Department of Pharmacy, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - FeiYu Zhang
- First Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Jia Zhao
- First Central Clinical College of Tianjin Medical University, Tianjin, China
| | - YinPeng Qin
- Department of Pharmacy, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Yue Li
- College of Computer Science, KLMDASR, Key Laboratory for Medical Data Analysis and Statistical Research, Nankai University, Tianjin, China
| | - Yi Zhang
- Department of Pharmacy, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| |
Collapse
|
3
|
Volumetric Absorptive Microsampling to Enhance the Therapeutic Drug Monitoring of Tacrolimus and Mycophenolic Acid: A Systematic Review and Critical Assessment. Ther Drug Monit 2023:00007691-990000000-00082. [PMID: 36728554 DOI: 10.1097/ftd.0000000000001066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/23/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Volumetric absorptive microsampling (VAMS) is an emerging technique that may support multisample collection to enhance therapeutic drug monitoring in solid organ transplantation. This review aimed to assess whether tacrolimus and mycophenolic acid can be reliably assayed using VAMS and to identify knowledge gaps by providing granularity to existing analytical methods and clinical applications. METHODS A systematic literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The PubMed, Embase, and Scopus databases were accessed for records from January 2014 to April 2022 to identify scientific reports on the clinical validation of VAMS for monitoring tacrolimus and mycophenolic acid concentrations. Data on the study population, sample sources, analytical methods, and comparison results were compiled. RESULTS Data from 12 studies were collected, including 9 studies pertaining to tacrolimus and 3 studies on the concurrent analysis of tacrolimus and mycophenolic acid. An additional 14 studies that provided information relevant to the secondary objectives (analytical validation and clinical application) were also included. The results of the clinical validation studies generally met the method agreement requirements described by regulatory agencies, but in many cases, it was essential to apply correction factors. CONCLUSIONSS Current evidence suggests that the existing analytical methods that use VAMS require additional optimization steps for the analysis of tacrolimus and mycophenolic acid. The recommendations put forth in this review can help guide future studies in achieving the goal of improving the care of transplant recipients by simplifying multisample collection for the dose optimization of these drugs.
Collapse
|
4
|
Zwart TC, Guchelaar HJ, van der Boog PJM, Swen JJ, van Gelder T, de Fijter JW, Moes DJAR. Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation. Drug Discov Today 2021; 26:2527-2546. [PMID: 34119665 DOI: 10.1016/j.drudis.2021.06.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/21/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022]
Abstract
Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.
Collapse
Affiliation(s)
- Tom C Zwart
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands; Leiden Network for Personalised Therapeutics, Leiden, the Netherlands
| | - Paul J M van der Boog
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands; LUMC Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands; Leiden Network for Personalised Therapeutics, Leiden, the Netherlands
| | - Teun van Gelder
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands
| | - Johan W de Fijter
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands; LUMC Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands; Leiden Network for Personalised Therapeutics, Leiden, the Netherlands.
| |
Collapse
|
5
|
Analyses of AUC(0–12) and C0 Compliances within Therapeutic Ranges in Kidney Recipients Receiving Cyclosporine or Tacrolimus. J Clin Med 2020; 9:jcm9123903. [PMID: 33271879 PMCID: PMC7760343 DOI: 10.3390/jcm9123903] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/18/2020] [Accepted: 11/28/2020] [Indexed: 02/06/2023] Open
Abstract
The AUC (area under the concentration time curve) is considered the pharmacokinetic exposure parameter best associated with clinical effects. Unfortunately, no prospective studies of clinical outcomes have been conducted in adult transplant recipients to investigate properly the potential benefits of AUC(0–12) monitoring compared to the C0-guided therapy. The aim of the present study was to compare two methods, C0 (through level) and AUC(0–12) (area under the concentration time curve), for assessing cyclosporine and tacrolimus concentrations. The study included 340 kidney recipients. The AUC(0–12) was estimated using a Bayesian estimator and a three-point limited sampling strategy. Therapeutic drug monitoring of tacrolimus performed by using AUC(0–12) and C0 showed that tacrolimus in most cases is overdosed when considering C0, while determination of the AUC(0–12) showed that tacrolimus is effectively dosed for 27.8–40.0% of patients receiving only tacrolimus and for 25.0–31.9% of patients receiving tacrolimus with MMF (mycophenolate mofetil). In the 1–5 years post-transplantation group, 10% higher CsA (cyclosporine) dose was observed, which was proportionate with a 10% higher AUC(0–12) exposure value. This indicates good compatibility of the dosage and the AUC(0–12) method. The Bland–Altman plot demonstrated that C0 and AUC(0–12) might be interchangeable methods, while the ROC (receiver operating characteristic) curve analysis of the C0/AUC(0–12) ratio in the tacrolimus-receiving patient group demonstrated reliable performance to predict IFTA (interstitial fibrosis and tubular atrophy) after kidney transplantation, with an ROC curve of 0.660 (95% confidence interval (CI): 0.576–0.736), p < 0.01. Moreover, AUC(0–12) and C0 of tacrolimus depend on concomitant medication and adjustment of the therapeutic range for AUC(0–12) might influence the results.
Collapse
|
6
|
Xu Q, Xu K. Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17113790. [PMID: 32471060 PMCID: PMC7312879 DOI: 10.3390/ijerph17113790] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/22/2020] [Accepted: 05/25/2020] [Indexed: 12/14/2022]
Abstract
The metallurgical industry is a significant component of the national economy. The main purpose of this study was to establish a composite risk analysis method for fatal accidents in the metallurgical industry. We collected 152 fatal accidents in the Chinese metallurgical industry from 2001 to 2018, including 141 major accidents, 10 severe accidents, and 1 extraordinarily severe accident, together resulting in 731 deaths. Different from traffic or chemical industry accidents, most of the accidents in the metallurgical industry are poisoning and asphyxiation accidents, which account for 40% of the total number of fatal accidents. As the original statistical data of fatal accidents in the metallurgical industry have irregular fluctuations, the traditional prediction methods, such as linear or quadratic regression models, cannot be used to predict their future characteristics. To overcome this issue, the grey interval predicting method and the GM(1,1) model of grey system theory are introduced to predict the future characteristics of fatal accidents in the metallurgical industry. Different from a fault tree analysis or event tree analysis, the bow tie model integrates the basic causes, possible consequences, and corresponding safety measures of an accident in a transparent diagram. In this study, the bow tie model was used to identify the causes and consequences of fatal accidents in the metallurgical industry; then, corresponding safety measures were adopted to reduce the risk.
Collapse
Affiliation(s)
- Qingwei Xu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
- Correspondence:
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China;
| |
Collapse
|