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Li X, Cheng Y, Zhang B, Chen B, Chen Y, Huang Y, Lin H, Zhou L, Zhang H, Liu M, Que W, Qiu H. A systematic evaluation of population pharmacokinetic models for polymyxin B in patients with liver and/or kidney dysfunction. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09916-9. [PMID: 38625507 DOI: 10.1007/s10928-024-09916-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/21/2024] [Indexed: 04/17/2024]
Abstract
Polymyxin B (PMB) is considered a last-line treatment for multidrug-resistant (MDR) gram-negative bacterial infections. Model-informed precision dosing with population pharmacokinetics (PopPK) models could help to individualize PMB dosing regimens and improve therapy. However, the external prediction ability of the established PopPK models has not been fully elaborated. This study aimed to systemically evaluate eleven PMB PopPK models from ten published literature based on a new independent population, which was divided into four different populations, patients with liver dysfunction, kidney dysfunction, liver and kidney dysfunction, and normal liver and kidney function. The whole data set consisted of 146 patients with 391 PMB concentrations. The prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. In the overall evaluation process, none of the models exhibited satisfactory predictive ability in both prediction- and simulation-based diagnostic simultaneously. However, the evaluation of the models in the subgroup of patients with normal liver and kidney function revealed improved predictive performance compared to those with liver and/or kidney dysfunction. Bayesian forecasting demonstrated enhanced predictability with the incorporation of two to three prior observations. The external evaluation highlighted a lack of consistency between the prediction results of published models and the external validation dataset. Nonetheless, Bayesian forecasting holds promise in improving the predictive performance of the models, and feedback from therapeutic drug monitoring is crucial in optimizing individual dosing regimens.
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Affiliation(s)
- Xueyong Li
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Rd, Fuzhou, 350001, Fujian, People's Republic of China
- College of Pharmacy, Fujian Medical University, Fuzhou, 350004, People's Republic of China
| | - Yu Cheng
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Rd, Fuzhou, 350001, Fujian, People's Republic of China
| | - Bingqing Zhang
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Rd, Fuzhou, 350001, Fujian, People's Republic of China
- College of Pharmacy, Fujian Medical University, Fuzhou, 350004, People's Republic of China
| | - Bo Chen
- College of Pharmacy, Fujian Medical University, Fuzhou, 350004, People's Republic of China
| | - Yiying Chen
- College of Pharmacy, Fujian Medical University, Fuzhou, 350004, People's Republic of China
| | - Yingbing Huang
- College of Pharmacy, Fujian Medical University, Fuzhou, 350004, People's Republic of China
| | - Hailing Lin
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Rd, Fuzhou, 350001, Fujian, People's Republic of China
| | - Lili Zhou
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou, 350001, People's Republic of China
| | - Hui Zhang
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou, 350001, People's Republic of China
| | - Maobai Liu
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Rd, Fuzhou, 350001, Fujian, People's Republic of China
| | - Wancai Que
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Rd, Fuzhou, 350001, Fujian, People's Republic of China.
| | - Hongqiang Qiu
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Rd, Fuzhou, 350001, Fujian, People's Republic of China.
- College of Pharmacy, Fujian Medical University, Fuzhou, 350004, People's Republic of China.
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De Carlo A, Tosca EM, Fantozzi M, Magni P. Reinforcement Learning and PK-PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma. Clin Pharmacol Ther 2024; 115:825-838. [PMID: 38339803 DOI: 10.1002/cpt.3176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/15/2023] [Indexed: 02/12/2024]
Abstract
The integration of pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulations with artificial intelligence/machine learning algorithms is one of the most attractive areas of the pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, this paper presents and evaluates a new framework embedding PK-PD models into a reinforcement learning (RL) algorithm, Q-learning (QL), to personalize pharmacological treatment. Each patient is represented with a set of PK-PD parameters and has a personal QL agent which optimizes the individual treatment. In the training phase, leveraging PK-PD simulations, the QL agent assesses different actions, defined consistently with the clinical knowledge to consider only plausible dose-adjustments, in order to find the optimal rules. The proposed framework was evaluated to optimize the erdafitinib treatment in patients with metastatic urothelial carcinoma. This drug was approved by the US Food and Drug Administration (FDA) with a dose-adaptive protocol based on monitoring the levels of serum phosphate, which represent a biomarker of both treatment efficacy and toxicity. To evaluate the flexibility of the methodology, a heterogeneous virtual population of 141 patients was generated using an erdafitinib population PK (PopPK)-PD literature model. For each patient, treatment response was simulated by using both QL-optimized protocol and the clinical one. QL agents outperform the approved dose-adaptive rules, increasing more than 10% the efficacy and the safety of treatment at each end point. Results confirm the great potentialities of the integration of PopPK-PD models and RL algorithms to optimize precision dosing tasks.
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Affiliation(s)
- Alessandro De Carlo
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Elena Maria Tosca
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Martina Fantozzi
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Paolo Magni
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
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Liu Y, He Z, Liang H, Han M, Wang J, Liu Q, Guan Y. A high-throughput UPLC-MS/MS method for the determination of eight anti-tumor drugs in plasma. Anal Biochem 2023:115230. [PMID: 37429484 DOI: 10.1016/j.ab.2023.115230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/21/2023] [Accepted: 07/03/2023] [Indexed: 07/12/2023]
Abstract
Rapidly developing UPLC-MS/MS bioassays with high throughput and quality are challenging yet desired in routine clinics. METHODS & RESULTS: A high-throughput UPLC-MS/MS bioassay has been built for simultaneously quantifying gefitinib, ruxolitinib, dasatinib, imatinib, ibrutinib, methotrexate, cyclophosphamide and paclitaxel. After the protein precipitation with methanol, samples were separated on an Acquity BEH C18 column following a gradient elution system with methanol and 2 mM ammonium acetate in water at 40 °C with a run time of 3 min (flow rate 0.4 mL/min). Mass quantification in the positive ion SRM mode was then performed with electrospray ionization. The method of specificity, linearity, accuracy, precision, matrix effects, recovery, stability, dilution integrity and carryover were all validated as per the guideline of the China Food and Drug Administration whose values met the admissible limits. Application of the bioassay to therapeutic drug monitoring revealed important variability in the studied anti-tumour drugs. CONCLUSION: This validated approach was shown to be reliable and effective in clinical management, being a valuable support in therapeutic drug monitoring and subsequent individualized dosing optimization.
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Affiliation(s)
- Yao Liu
- Department of Pharmacy, The Fifth Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Guangzhou 519000, China; Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China.
| | - Zhichao He
- Department of Pharmacy, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Heng Liang
- School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Minzhen Han
- The Second Affiliated Hospital of Guizhou Medical University, Kaili, Guizhou, 556000, China
| | - Jinxingyi Wang
- The Second Affiliated Hospital of Guizhou Medical University, Kaili, Guizhou, 556000, China
| | - Qian Liu
- The Second Affiliated Hospital of Guizhou Medical University, Kaili, Guizhou, 556000, China; Guangdong RangerBio Technologies Co., Ltd., Dongguan 523000, China.
| | - Yanping Guan
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China.
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Interest of high-resolution mass spectrometry in analytical toxicology: Focus on pharmaceuticals. TOXICOLOGIE ANALYTIQUE ET CLINIQUE 2022. [DOI: 10.1016/j.toxac.2021.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Schräpel C, Kovar L, Selzer D, Hofmann U, Tran F, Reinisch W, Schwab M, Lehr T. External Model Performance Evaluation of Twelve Infliximab Population Pharmacokinetic Models in Patients with Inflammatory Bowel Disease. Pharmaceutics 2021; 13:pharmaceutics13091368. [PMID: 34575443 PMCID: PMC8468301 DOI: 10.3390/pharmaceutics13091368] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 01/12/2023] Open
Abstract
Infliximab is approved for treatment of various chronic inflammatory diseases including inflammatory bowel disease (IBD). However, high variability in infliximab trough levels has been associated with diverse response rates. Model-informed precision dosing (MIPD) with population pharmacokinetic models could help to individualize infliximab dosing regimens and improve therapy. The aim of this study was to evaluate the predictive performance of published infliximab population pharmacokinetic models for IBD patients with an external data set. The data set consisted of 105 IBD patients with 336 infliximab concentrations. Literature review identified 12 published models eligible for external evaluation. Model performance was evaluated with goodness-of-fit plots, prediction- and variability-corrected visual predictive checks (pvcVPCs) and quantitative measures. For anti-drug antibody (ADA)-negative patients, model accuracy decreased for predictions > 6 months, while bias did not increase. In general, predictions for patients developing ADA were less accurate for all models investigated. Two models with the highest classification accuracy identified necessary dose escalations (for trough concentrations < 5 µg/mL) in 88% of cases. In summary, population pharmacokinetic modeling can be used to individualize infliximab dosing and thereby help to prevent infliximab trough concentrations dropping below the target trough concentration. However, predictions of infliximab concentrations for patients developing ADA remain challenging.
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Affiliation(s)
- Christina Schräpel
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (U.H.); (M.S.)
| | - Lukas Kovar
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
| | - Dominik Selzer
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (U.H.); (M.S.)
| | - Florian Tran
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, 24105 Kiel, Germany;
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, 24105 Kiel, Germany
| | - Walter Reinisch
- Department of Internal Medicine III, Medical University of Vienna, 1090 Vienna, Austria;
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (U.H.); (M.S.)
- Departments of Clinical Pharmacology, Pharmacy and Biochemistry, University of Tübingen, 72076 Tübingen, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
- Correspondence: ; Tel.: +49-681-302-70255
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Therapeutic Drug Monitoring of Targeted Anticancer Protein Kinase Inhibitors in Routine Clinical Use: A Critical Review. Ther Drug Monit 2021; 42:33-44. [PMID: 31479043 DOI: 10.1097/ftd.0000000000000699] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Therapeutic response to oral targeted anticancer protein kinase inhibitors (PKIs) varies widely between patients, with insufficient efficacy of some of them and unacceptable adverse reactions of others. There are several possible causes for this heterogeneity, such as pharmacokinetic (PK) variability affecting blood concentrations, fluctuating medication adherence, and constitutional or acquired drug resistance of cancer cells. The appropriate management of oncology patients with PKI treatments thus requires concerted efforts to optimize the utilization of these drug agents, which have probably not yet revealed their full potential. METHODS An extensive literature review was performed on MEDLINE on the PK, pharmacodynamics, and therapeutic drug monitoring (TDM) of PKIs (up to April 2019). RESULTS This review provides the criteria for determining PKIs suitable candidates for TDM (eg, availability of analytical methods, observational PK studies, PK-pharmacodynamics relationship analysis, and randomized controlled studies). It reviews the major characteristics and limitations of PKIs, the expected benefits of TDM for cancer patients receiving them, and the prerequisites for the appropriate utilization of TDM. Finally, it discusses various important practical aspects and pitfalls of TDM for supporting better implementation in the field of cancer treatment. CONCLUSIONS Adaptation of PKIs dosage regimens at the individual patient level, through a rational TDM approach, could prevent oncology patients from being exposed to ineffective or unnecessarily toxic drug concentrations in the era of personalized medicine.
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Guidi M, Csajka C, Buclin T. Parametric Approaches in Population Pharmacokinetics. J Clin Pharmacol 2020; 62:125-141. [DOI: 10.1002/jcph.1633] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/09/2020] [Indexed: 12/17/2022]
Affiliation(s)
- Monia Guidi
- Center for Research and Innovation in Clinical Pharmaceutical Sciences Lausanne University Hospital and University of Lausanne Lausanne Switzerland
- Service of Clinical Pharmacology Lausanne University Hospital and University of Lausanne Lausanne Switzerland
| | - Chantal Csajka
- Center for Research and Innovation in Clinical Pharmaceutical Sciences Lausanne University Hospital and University of Lausanne Lausanne Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland University of Geneva University of Lausanne Geneva Lausanne Switzerland
| | - Thierry Buclin
- Service of Clinical Pharmacology Lausanne University Hospital and University of Lausanne Lausanne Switzerland
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8
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Therapeutic Drug Monitoring of Asparaginase: Intra-individual Variability and Predictivity in Children With Acute Lymphoblastic Leukemia Treated With PEG-Asparaginase in the AIEOP-BFM Acute Lymphoblastic Leukemia 2009 Study. Ther Drug Monit 2020; 42:435-444. [DOI: 10.1097/ftd.0000000000000727] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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9
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Abdel-Rahman SM, Gill H, Carpenter SL, Gueye P, Wicklund B, Breitkreutz M, Ghosh A, Kollu A. Design and Usability of an Electronic Health Record-Integrated, Point-of-Care, Clinical Decision Support Tool for Modeling and Simulation of Antihemophilic Factors. Appl Clin Inform 2020; 11:253-264. [PMID: 32268389 DOI: 10.1055/s-0040-1708050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
BACKGROUND With the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers. OBJECTIVES The aim of this study is to develop and examine the usability of a CDS tool for antihemophilic factor dose individualization. METHODS Our development strategy was based on the features associated with successful CDS tools and driven by a formal requirements analysis. The back-end code was based on algorithms developed for manual individualization and unit tested with 23,000 simulated patient profiles created from the range of patient-derived pharmacokinetic parameter estimates defined in children and adults. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users were recruited to participate in traditional usability testing under an institutional review board approved protocol. RESULTS CDS software was developed to systematically walk the point-of-care clinician through dose individualization after seamlessly importing the requisite patient data from the EHR. Classical and population pharmacokinetic approaches were incorporated with clearly displayed estimates of reliability and uncertainty. Users can perform simulations for prophylaxis and acute bleeds by providing two of four therapeutic targets. Testers were highly satisfied with our CDS and quickly became proficient with the tool. CONCLUSION With early and broad stakeholder engagement, we developed a CDS tool for hematology provider that affords seamless transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent dose selection.
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Affiliation(s)
- Susan M Abdel-Rahman
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy, Kansas City, Missouri, United States.,Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, United States
| | - Harpreet Gill
- Department of Research Informatics, Children's Research Institute, Children's Mercy, Kansas City, Missouri, United States
| | - Shannon L Carpenter
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, United States.,Division of Hematology/Oncology, Children's Mercy, Kansas City, Missouri, United States
| | - Pathe Gueye
- Department of Research Informatics, Children's Research Institute, Children's Mercy, Kansas City, Missouri, United States
| | - Brian Wicklund
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, United States.,Division of Hematology/Oncology, Children's Mercy, Kansas City, Missouri, United States
| | - Matt Breitkreutz
- Department of Research Informatics, Children's Research Institute, Children's Mercy, Kansas City, Missouri, United States
| | - Arindam Ghosh
- Department of Research Informatics, Children's Research Institute, Children's Mercy, Kansas City, Missouri, United States
| | - Avinash Kollu
- Department of Research Informatics, Children's Research Institute, Children's Mercy, Kansas City, Missouri, United States
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Buclin T, Thoma Y, Widmer N, André P, Guidi M, Csajka C, Decosterd LA. The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib. Front Pharmacol 2020; 11:177. [PMID: 32194413 PMCID: PMC7062864 DOI: 10.3389/fphar.2020.00177] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 02/07/2020] [Indexed: 01/07/2023] Open
Abstract
Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.
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Affiliation(s)
- Thierry Buclin
- Service of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Yann Thoma
- School of Management and Engineering Vaud (HEIG-VD), University of Applied Science Western Switzerland (HES-SO), Yverdon-les-Bains, Switzerland
| | - Nicolas Widmer
- Service of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Pharmacy of Eastern Vaud Hospitals, Rennaz, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Pascal André
- Service of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Monia Guidi
- Service of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Chantal Csajka
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.,Center for Research and Innovation in Clinical Pharmaceutical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Laurent A Decosterd
- Service of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Skalová Š, Langmaier J, Barek J, Vyskočil V, Navrátil T. Doxorubicin determination using two novel voltammetric approaches: A comparative study. Electrochim Acta 2020. [DOI: 10.1016/j.electacta.2019.135180] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Abrantes JA, Jönsson S, Karlsson MO, Nielsen EI. Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data. Br J Clin Pharmacol 2019; 85:1326-1336. [PMID: 30767254 DOI: 10.1111/bcp.13901] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 01/15/2019] [Accepted: 02/04/2019] [Indexed: 01/19/2023] Open
Abstract
AIMS This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example. METHODS We assessed 5 model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error percentiles. RESULTS When IOV was lower than IIV, the accuracy was good for all approaches (50th percentile of the prediction error [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios. CONCLUSIONS Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualization is to include IOV in the generation of the EBEs but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.
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Affiliation(s)
- João A Abrantes
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Siv Jönsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Elisabet I Nielsen
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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13
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Miniaturized voltammetric cell for cathodic voltammetry making use of an agar membrane. J Electroanal Chem (Lausanne) 2018. [DOI: 10.1016/j.jelechem.2017.12.073] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Abstract
A brief account is given of various approaches to the individualization of drug dosage, including the use of pharmacodynamic markers, therapeutic monitoring of plasma drug concentrations, genotyping, computer-guided dosage using ‘dashboards’, and automatic closed-loop control of pharmacological action. The potential for linking the real patient to his or her ‘virtual twin’ through the application of physiologically-based pharmacokinetic modeling is also discussed.
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Nekka F, Csajka C, Wilbaux M, Sanduja S, Li J, Pfister M. Pharmacometrics-based decision tools facilitate mHealth implementation. Expert Rev Clin Pharmacol 2016; 10:39-46. [PMID: 27813436 DOI: 10.1080/17512433.2017.1251837] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
INTRODUCTION The healthcare system is experiencing a paradigm shift in delivering its services, evolving from a reactive 'one size-fits-all' structure to a patient-centric model focusing on individualized medicine. This change is driven by scientific progress, including quantitative evaluation and optimization of treatment strategies through pharmacometric approaches, harnessing the power of the digital revolution. Areas covered: This review describes four main steps to apply pharmacometrics-based decision support tools, consisting of validated scientific components, available technical options, consideration of regulatory aspects, and achievement of efficient commercialization. Examples of pharmacometrics-based decision tools that support monitoring of patients and individualization of treatment strategies in neonates, children and adults are presented. Expert commentary: We envision that user-friendly decision support tools will facilitate implementation of mobile health approaches (mHealth) realizing benefits to paediatric and adult patients and their caregivers.
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Affiliation(s)
- Fahima Nekka
- a NSERC-Industrial Chair in Pharmacometrics, Full Professor, Faculty of Pharmacy , Université de Montréal , Montreal , Qc , Canada
| | - Chantal Csajka
- b Division of Pharmacology and Toxicology , Lausanne University Hospital, Head Research Unit , Lausanne , Switzerland
| | - Mélanie Wilbaux
- c Pharmacometrician , University Children's Hospital Basel (UKBB), Paediatric Pharmacology and Pharmacometrics , Basel , Switzerland
| | | | - Jun Li
- e Faculty of Pharmacy , Université de Montréal , Montreal , Qc , Canada
| | - Marc Pfister
- f Vice-Chair Paediatric Pharmacology and Pharmacometrics , University Children's Hospital Basel (UKBB) , Basel , Switzerland
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Decosterd L, Widmer N, André P, Aouri M, Buclin T. The emerging role of multiplex tandem mass spectrometry analysis for therapeutic drug monitoring and personalized medicine. Trends Analyt Chem 2016. [DOI: 10.1016/j.trac.2016.03.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Abdel-Rahman SM, Breitkreutz ML, Bi C, Matzuka BJ, Dalal J, Casey KL, Garg U, Winkle S, Leeder JS, Breedlove J, Rivera B. Design and Testing of an EHR-Integrated, Busulfan Pharmacokinetic Decision Support Tool for the Point-of-Care Clinician. Front Pharmacol 2016; 7:65. [PMID: 27065859 PMCID: PMC4811899 DOI: 10.3389/fphar.2016.00065] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Accepted: 03/07/2016] [Indexed: 12/12/2022] Open
Abstract
Background: Busulfan demonstrates a narrow therapeutic index for which clinicians routinely employ therapeutic drug monitoring (TDM). However, operationalizing TDM can be fraught with inefficiency. We developed and tested software encoding a clinical decision support tool (DST) that is embedded into our electronic health record (EHR) and designed to streamline the TDM process for our oncology partners. Methods: Our development strategy was modeled based on the features associated with successful DSTs. An initial Requirements Analysis was performed to characterize tasks, information flow, user needs, and system requirements to enable push/pull from the EHR. Back-end development was coded based on the algorithm used when manually performing busulfan TDM. The code was independently validated in MATLAB using 10,000 simulated patient profiles. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users (n = 28) were recruited to participate in traditional usability testing under an IRB approved protocol. Results: Decision support software was developed to systematically walk the point-of-care clinician through the TDM process. The system is accessed through the EHR which transparently imports all of the requisite patient data. Data are visually inspected and then curve fit using a model-dependent approach. Quantitative goodness-of-fit are converted to single tachometer where “green” alerts the user that the model is strong, “yellow” signals caution and “red” indicates that there may be a problem with the fitting. Override features are embedded to permit application of a model-independent approach where appropriate. Simulations are performed to target a desired exposure or dose as entered by the clinician and the DST pushes the user approved recommendation back into the EHR. Usability testers were highly satisfied with our DST and quickly became proficient with the software. Conclusions: With early and broad stake-holder engagement we developed a clinical DST for the non-pharmacologist. This tools affords our clinicians the ability to seamlessly transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent prescription order entry.
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Affiliation(s)
- Susan M Abdel-Rahman
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy HospitalKansas City, MO, USA; Department of Pediatrics, University of Missouri-Kansas City School of MedicineKansas City, MO, USA
| | | | - Charlie Bi
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy Hospital Kansas City, MO, USA
| | - Brett J Matzuka
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy Hospital Kansas City, MO, USA
| | - Jignesh Dalal
- Division of Hematology/Oncology, Rainbow Babies and Children's Hospital, Case Western Reserve University Cleveland, OH, USA
| | - K Leigh Casey
- Department of Pharmacy, Children's Mercy Hospital Kansas City, MO, USA
| | - Uttam Garg
- Department of Pediatrics, University of Missouri-Kansas City School of MedicineKansas City, MO, USA; Department of Laboratory Medicine, Children's Mercy HospitalKansas City, MO, USA
| | - Sara Winkle
- Department of Information Systems, Children's Mercy Hospital Kansas City, MO, USA
| | - J Steven Leeder
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy HospitalKansas City, MO, USA; Department of Pediatrics, University of Missouri-Kansas City School of MedicineKansas City, MO, USA
| | - JeanAnn Breedlove
- Department of Information Systems, Children's Mercy Hospital Kansas City, MO, USA
| | - Brian Rivera
- Department of Information Systems, Children's Mercy Hospital Kansas City, MO, USA
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Decosterd LA, Widmer N, Zaman K, Cardoso E, Buclin T, Csajka C. Therapeutic drug monitoring of targeted anticancer therapy. Biomark Med 2015; 9:887-93. [PMID: 26333311 DOI: 10.2217/bmm.15.78] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
New oral targeted anticancer therapies are revolutionizing cancer treatment by transforming previously deadly malignancies into chronically manageable conditions. Nevertheless, drug resistance, persistence of cancer stem cells, and adverse drug effects still limit their ability to stabilize or cure malignant diseases in the long term. Response to targeted anticancer therapy is influenced by tumor genetics and by variability in drug concentrations. However, despite a significant inter-patient pharmacokinetic variability, targeted anticancer drugs are essentially licensed at fixed doses. Their therapeutic use could however be optimized by individualization of their dosage, based on blood concentration measurements via the therapeutic drug monitoring (TDM). TDM can increase the probability of therapeutic responses to targeted anticancer therapies, and would help minimize the risk of major adverse reactions.
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Affiliation(s)
- Laurent A Decosterd
- Laboratory of Clinical Pharmacology, Service of Biomedicine, Lausanne University Hospital & University of Lausanne, Switzerland
| | - Nicolas Widmer
- Division of Clinical Pharmacology, Service of Biomedicine, Lausanne University Hospital & University of Lausanne, Switzerland.,Pharmacy of Eastern Vaud Hospitals, Vevey, Switzerland
| | - Khalil Zaman
- Service of Medical Oncology, Department of Oncology, Lausanne University Hospital & University of Lausanne, Switzerland
| | - Evelina Cardoso
- Division of Clinical Pharmacology, Service of Biomedicine, Lausanne University Hospital & University of Lausanne, Switzerland
| | - Thierry Buclin
- Division of Clinical Pharmacology, Service of Biomedicine, Lausanne University Hospital & University of Lausanne, Switzerland
| | - Chantal Csajka
- Division of Clinical Pharmacology, Service of Biomedicine, Lausanne University Hospital & University of Lausanne, Switzerland
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Sanavio B, Krol S. On the Slow Diffusion of Point-of-Care Systems in Therapeutic Drug Monitoring. Front Bioeng Biotechnol 2015; 3:20. [PMID: 25767794 PMCID: PMC4341557 DOI: 10.3389/fbioe.2015.00020] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 02/09/2015] [Indexed: 12/22/2022] Open
Abstract
Recent advancements in point-of-care (PoC) technologies show great transformative promises for personalized preventative and predictive medicine. However, fields like therapeutic drug monitoring (TDM), that first allowed for personalized treatment of patients' disease, still lag behind in the widespread application of PoC devices for monitoring of patients. Surprisingly, very few applications in commonly monitored drugs, such as anti-epileptics, are paving the way for a PoC approach to patient therapy monitoring compared to other fields like intensive care cardiac markers monitoring, glycemic controls in diabetes, or bench-top hematological parameters analysis at the local drug store. Such delay in the development of portable fast clinically effective drug monitoring devices is in our opinion due more to an inertial drag on the pervasiveness of these new devices into the clinical field than a lack of technical capability. At the same time, some very promising technologies failed in the clinical practice for inadequate understanding of the outcome parameters necessary for a relevant technological breakthrough that has superior clinical performance. We hope, by over-viewing both TDM practice and its yet unmet needs and latest advancement in micro- and nanotechnology applications to PoC clinical devices, to help bridging the two communities, the one exploiting analytical technologies and the one mastering the most advanced techniques, into translating existing and forthcoming technologies in effective devices.
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Affiliation(s)
- Barbara Sanavio
- IRCCS Fondazione Istituto Neurologico Carlo Besta, Milan, Italy
| | - Silke Krol
- IRCCS Fondazione Istituto Neurologico Carlo Besta, Milan, Italy
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21
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Personalized Drug Administrations Using Support Vector Machine. BIONANOSCIENCE 2013. [DOI: 10.1007/s12668-013-0103-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Kshirsagar NA, Bachhav SS, Kulkarni LA, Vijaykumar. Clinical pharmacology training in India: Status and need. Indian J Pharmacol 2013; 45:429-33. [PMID: 24130374 PMCID: PMC3793510 DOI: 10.4103/0253-7613.117718] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Revised: 05/16/2013] [Accepted: 07/06/2013] [Indexed: 11/15/2022] Open
Abstract
Clinical pharmacologists undertake many tasks, and this makes defining a curriculum challenging. This is especially so under the changing circumstances in developing countries, where clinical pharmacology has an expanding role. The clinical pharmacologist may be responsible for conducting ethical clinical trials, supporting the needs of the generic drug industry, providing access to safe, effective and affordable medicines, guiding their rational use, achieving millennium development goals, and supervising medicines management standards for hospital accreditation. Clinical pharmacologists, including those in developing countries, have a great opportunity to contribute to public health and the growth of pharmaceutical industry, but at present, less clinical research is undertaken and fewer clinical trials are done than might be expected. Here we review clinical pharmacology training in India, consider the needs of different professionals contributing to clinical research and medicines utilization, and suggest ways in which current programs can be modified and new programs started. The conclusions are relevant to clinical pharmacology in both the developing and the developed world.
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Affiliation(s)
- Nilima A. Kshirsagar
- National Chair in Clinical Pharmacology, Indian Council of Medical Research (ICMR), Govt. of India; Dean, ESI-PGIMSR, MGM Hospital, Govt. of India, Parel, Mumbai, India
| | - Sagar S. Bachhav
- Senior Research Fellow (SRS) under National Chair in Clinical Pharmacology, ICMR, Parel, Mumbai, India
| | - Laxmikant A. Kulkarni
- Senior Research Fellow (SRS) under National Chair in Clinical Pharmacology, ICMR, Parel, Mumbai, India
| | - Vijaykumar
- Head, Division of Basic Medical Sciences (BMS), Indian Council of Medical Research (ICMR), Government of India, New Delhi, India
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Abstract
Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.
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You W, Simalatsar A, De Micheli G. Parameterized SVM for personalized drug concentration prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:5789-5792. [PMID: 24111054 DOI: 10.1109/embc.2013.6610867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper proposes a parameterized Support Vector Machine (ParaSVM) approach for modeling the Drug Concentration to Time (DCT) curves. It combines the merits of Support Vector Machine (SVM) algorithm that considers various patient features and an analytical model that approximates the predicted DCT points and enables curve calibrations using occasional real Therapeutic Drug Monitoring (TDM) measurements. The RANSAC algorithm is applied to construct the parameter library for the relevant basis functions. We show an example of using ParaSVM to build DCT curves and then calibrate them by TDM measurements on imatinib case study.
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