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Sarraf E. The drug titration paradox: a control engineering perspective. Curr Opin Anaesthesiol 2024; 37:362-370. [PMID: 38841991 DOI: 10.1097/aco.0000000000001396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
PURPOSE OF REVIEW The drug titration paradox describes that, from a population standpoint, drug doses appear to have a negative correlation with its clinical effect. This paradox is a relatively modern discovery in anesthetic pharmacology derived from large clinical data sets. This review will interpret the paradox using a control engineering perspective. RECENT FINDINGS Drug titration is a challenging endeavor, and the medication delivery systems used in everyday clinical practice, including infusion pumps and vaporizers, typically do not allow for rapid or robust titration of medication being delivered. In addition, clinicians may be reluctant to deviate from a predetermined plan or may be content to manage patients within fixed goal boundaries. SUMMARY This drug titration paradox describes the constraints of how the average clinician will dose a patient with an unknown clinical response. While our understanding of the paradox is still in its infancy, it remains unclear how alternative dosing schemes, such as through automation, may exceed the boundaries of the paradox and potentially affect its conclusions.
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Affiliation(s)
- Elie Sarraf
- Penn State College of Medicine, Hershey, Pennsylvania, USA
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Ngougni-Pokem P, Vanneste D, Schouwenburg S, Abdulla A, Gijsen M, Dhont E, Vanderlinden D, Spriet I, De Cock P, Koch B, Van Bambeke F, Wijnant GJ. Dose optimization of β-lactam antibiotics in children: from population pharmacokinetics to individualized therapy. Expert Opin Drug Metab Toxicol 2024. [PMID: 39078238 DOI: 10.1080/17425255.2024.2385403] [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: 04/16/2024] [Revised: 06/21/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION β-lactams are the most widely used antibiotics in children. Their optimal dosing is essential to maximize their efficacy, while minimizing the risk for toxicity and the further emergence of antimicrobial resistance. However, most β-lactams were developed and licensed long before regulatory changes mandated pharmacokinetic studies in children. As a result, pediatric dosing practices are poorly harmonized and off-label use remains common today. AREAS COVERED β-lactam pharmacokinetics and dose optimization strategies in pediatrics, including fixed dose regimens, therapeutic drug monitoring, and model-informed precision dosing are reviewed. EXPERT OPINION/COMMENTARY Standard pediatric doses can result in subtherapeutic exposure and non-target attainment for specific patient subpopulations (neonates, critically ill children, e.g.). Such patients could benefit greatly from more individualized approaches to dose optimization, beyond a relatively simple dose adaptation based on weight, age or renal function. In this context, Therapeutic Drug Monitoring (TDM) and Model-Informed Precision Dosing (MIPD) emerge as particularly promising avenues. Obstacles to their implementation include the lack of strong evidence of clinical benefit due to the paucity of randomized clinical trials, of standardized assays for monitoring concentrations, or of adequate markers for renal function. The development of precision medicine tools is urgently needed to individualize therapy in vulnerable pediatric subpopulations.
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Affiliation(s)
- Perrin Ngougni-Pokem
- Pharmacologie Cellulaire et Moléculaire, Louvain Drug Research Institute, Université catholique de Louvain, Brussels, Belgium
- Department of Microbiology, Cliniques Universitaires Saint-Luc - Université catholique de Louvain, Brussels, Belgium
| | - Dorian Vanneste
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU, Leuven, Leuven, Belgium
| | - Stef Schouwenburg
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
- Rotterdam Clinical Pharmacometrics Group, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Alan Abdulla
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
- Rotterdam Clinical Pharmacometrics Group, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Matthias Gijsen
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU, Leuven, Leuven, Belgium
- Pharmacy Department, UZ, Leuven, Belgium
| | - Evelyn Dhont
- Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of Pediatric Intensive Care, Ghent University Hospital, Ghent, Belgium
| | - Dimitri Vanderlinden
- Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium
- Pediatric Infectious Diseases, Service of Specialized Pediatrics, Department of Pediatrics, Cliniques Universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium
| | - Isabel Spriet
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU, Leuven, Leuven, Belgium
- Pharmacy Department, UZ, Leuven, Belgium
| | - Pieter De Cock
- Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
- Department of Pediatric Intensive Care, Ghent University Hospital, Ghent, Belgium
- Department of Pharmacy, Ghent University Hospital, Ghent, Belgium
| | - Birgit Koch
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
- Rotterdam Clinical Pharmacometrics Group, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Françoise Van Bambeke
- Pharmacologie Cellulaire et Moléculaire, Louvain Drug Research Institute, Université catholique de Louvain, Brussels, Belgium
| | - Gert-Jan Wijnant
- Pharmacologie Cellulaire et Moléculaire, Louvain Drug Research Institute, Université catholique de Louvain, Brussels, Belgium
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, KU, Leuven, Leuven, Belgium
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Kimura K, Yoshida A. A prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in inflammatory bowel disease. J Pharm Pharmacol 2024:rgae092. [PMID: 39010700 DOI: 10.1093/jpp/rgae092] [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: 02/12/2024] [Accepted: 06/24/2024] [Indexed: 07/17/2024]
Abstract
OBJECTIVES Adalimumab (ADM) therapy is effective for inflammatory bowel disease (IBD), but a significant number of IBD patients lose response to ADM. Thus, it is crucial to devise methods to enhance ADM's effectiveness. This study introduces a strategy to predict individual serum concentrations and therapeutic effects to optimize ADM therapy for IBD during the induction phase. METHODS We predicted the individual serum concentration and therapeutic effect of ADM during the induction phase based on pharmacokinetic and pharmacodynamic (PK/PD) parameters calculated using the empirical Bayesian method. We then examined whether the predicted therapeutic effect, defined as clinical remission or treatment failure, matched the observed effect. RESULTS Data were obtained from 11 IBD patients. The therapeutic effect during maintenance therapy was successfully predicted at 40 of 47 time points. Moreover, the predicted effects at each patient's final time point matched the observed effects in 9 of the 11 patients. CONCLUSION This is the inaugural report predicting the individual serum concentration and therapeutic effect of ADM using the Bayesian method and PK/PD modelling during the induction phase. This strategy may aid in optimizing ADM therapy for IBD.
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Affiliation(s)
- Koji Kimura
- Department of Clinical Evaluation of Drug Efficacy, School of Pharmacy, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji, Tokyo 192-0392, Japan
| | - Atsushi Yoshida
- Center for Gastroenterology and Inflammatory Bowel Disease, Ofuna Chuo Hospital, 6-2-24 Ofuna, Kamakura, Kanagawa 247-0056, Japan
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Meesters K, Balbas-Martinez V, Allegaert K, Downes KJ, Michelet R. Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians. Paediatr Drugs 2024; 26:365-379. [PMID: 38755515 DOI: 10.1007/s40272-024-00633-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/25/2024] [Indexed: 05/18/2024]
Abstract
The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.
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Affiliation(s)
- Kevin Meesters
- Department of Pediatrics, University of British Columbia, 4480 Oak Street, Vancouver, BC, V6H 3V4, Canada.
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada.
| | | | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Department of Hospital Pharmacy, Erasmus MC, Rotterdam, The Netherlands
| | - Kevin J Downes
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Infectious Diseases, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Robin Michelet
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany
- qPharmetra LLC, Berlin, Germany
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Wang WJ, Li Y, Hu YH, Wang J, Zhang YY, Fan L, Dai HR, Guo HL, Ding XS, Chen F. Population pharmacokinetics of valproic acid in children with epilepsy: Implications for dose tailoring when switching from oral syrup to sustained-release tablets. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 38923247 DOI: 10.1002/psp4.13191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/14/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Significant pharmacokinetic (PK) differences exist between different forms of valproic acid (VPA), such as syrup and sustained-release (SR) tablets. This study aimed to develop a population pharmacokinetic (PopPK) model for VPA in children with epilepsy and offer dose adjustment recommendation for switching dosage forms as needed. The study collected 1411 VPA steady-state trough concentrations (Ctrough) from 617 children with epilepsy. Using NONMEM software, a PopPK model was developed, employing a stepwise approach to identify possible variables such as demographic information and concomitant medications. The final model underwent internal and external evaluation via graphical and statistical methods. Moreover, Monte Carlo simulations were used to generate a dose tailoring strategy for typical patients weighting 20-50 kg. As a result, the PK characteristics of VPA were described using a one-compartment model with first-order absorption. The absorption rate constant (ka) was set at 2.64 and 0.46 h-1 for syrup and SR tablets. Body weight and sex were identified as significant factors affecting VPA's pharmacokinetics. The final PopPK model demonstrated acceptable prediction performance and stability during internal and external evaluation. For children taking syrup, a daily dose of 25 mg/kg resulted in the highest probability of achieving the desired target Ctrough, while a dose of 20 mg/kg/day was appropriate for those taking SR tablets. In conclusion, we established a PopPK model for VPA in children with epilepsy to tailor VPA dosage when switching between syrup and SR tablets, aiming to improve plasma VPA concentrations fluctuations.
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Affiliation(s)
- Wei-Jun Wang
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yue Li
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Ya-Hui Hu
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Jie Wang
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Yuan-Yuan Zhang
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Lin Fan
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Hao-Ran Dai
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hong-Li Guo
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Xuan-Sheng Ding
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Feng Chen
- Department of Pharmacy, Pharmaceutical Sciences Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China
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Oda K, Matsumoto K, Shoji K, Shigemi A, Kawamura H, Takahashi Y, Katanoda T, Hashiguchi Y, Jono H, Saito H, Takesue Y, Kimura T. Validation and development of population pharmacokinetic model of vancomycin using a real-world database from a nationwide free web application. J Infect Chemother 2024:S1341-321X(24)00146-6. [PMID: 38825002 DOI: 10.1016/j.jiac.2024.05.014] [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: 03/28/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/04/2024]
Abstract
INTRODUCTION Vancomycin requires a population pharmacokinetic (popPK) model to estimate the area under the concentration-time curve (AUC), and an AUC-guided dosing strategy is necessary. This study aimed to develop a popPK model for vancomycin using a real-world database pooled from a nationwide web application (PAT). METHODS In this retrospective study, the PAT database between December 14, 2022 and April 6, 2023 was used to develop a popPK model. The model was validated and compared with six existing models based on the predictive performance of datasets from another PAT database and the Kumamoto University Hospital. The developed model determined the dosing strategy for achieving the target AUC. RESULTS The modeling populations consisted of 7146 (13,372 concentrations from the PAT database), 3805 (7540 concentrations from the PAT database), and 783 (1775 concentrations from Kumamoto University Hospital) individuals. A two-compartment popPK model was developed that incorporated creatinine clearance as a covariate for clearance and body weight for central and peripheral volumes of distribution. The validation demonstrated that the popPK model exhibited the smallest mean absolute prediction error of 5.07, outperforming others (ranging from 5.10 to 5.83). The dosing strategies suggested a first dose of 30 mg/kg and maintenance doses adjusted for kidney function and age. CONCLUSIONS This study demonstrated the updating of PAT through the validation and development of a popPK model using a vast amount of data collected from anonymous PAT users.
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Affiliation(s)
- Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto City, Kumamoto, 860-8556, Japan; Department of Infection Control, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto City, Kumamoto, 860-8556, Japan.
| | - Kazuaki Matsumoto
- Division of Pharmacodynamics, Keio University Faculty of Pharmacy, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan
| | - Kensuke Shoji
- Division of Infectious Diseases, Department of Medical Subspecialties, National Center for Child Health and Development, 2-10-1 Okura, Setagaya-ku, Tokyo, 157-8535, Japan
| | - Akari Shigemi
- Department of Pharmacy, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima City, Kagoshima, 890-8520, Japan
| | - Hideki Kawamura
- Department of Infection Control and Prevention, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima City, Kagoshima, 890-8520, Japan
| | - Yoshiko Takahashi
- Department of Pharmacy, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya City, Hyogo, 663-8501, Japan
| | - Tomomi Katanoda
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto City, Kumamoto, 860-8556, Japan
| | - Yumi Hashiguchi
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto City, Kumamoto, 860-8556, Japan
| | - Hirofumi Jono
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto City, Kumamoto, 860-8556, Japan
| | - Hideyuki Saito
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto City, Kumamoto, 860-8556, Japan
| | - Yoshio Takesue
- Department of Infection Control and Prevention, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya City, Hyogo, 663-8501, Japan; Department of Clinical Infectious Diseases, Tokoname City Hospital, 3-3 Hika-dai 3-chome, Tokoname City, Aichi, 479-8510, Japan
| | - Toshimi Kimura
- Department of Pharmacy, Juntendo University Hospital, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8431, Japan
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Rostami-Hodjegan A, Al-Majdoub ZM, von Grabowiecki Y, Yee KL, Sahoo S, Breitwieser W, Galetin A, Gibson C, Achour B. Dealing With Variable Drug Exposure Due to Variable Hepatic Metabolism: A Proof-of-Concept Application of Liquid Biopsy in Renal Impairment. Clin Pharmacol Ther 2024. [PMID: 38738484 DOI: 10.1002/cpt.3291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/20/2024] [Indexed: 05/14/2024]
Abstract
Precision dosing strategies require accounting for between-patient variability in pharmacokinetics (PK), affecting drug exposure, and in pharmacodynamics (PD), affecting response achieved at the same drug concentration at the site of action. Although liquid biopsy for assessing different levels of molecular drug targets has yet to be established, individual characterization of drug elimination pathways using liquid biopsy has recently been demonstrated. The feasibility of applying this approach in conjunction with modeling tools to guide individual dosing remains unexplored. In this study, we aimed to individualize physiologically-based pharmacokinetic (PBPK) models based on liquid biopsy measurements in plasma from 25 donors with different grades of renal function who were previously administered oral midazolam as part of a microdose cocktail. Virtual twin models were constructed based on demographics, renal function, and hepatic expression of relevant pharmacokinetic pathways projected from liquid biopsy output. Simulated exposure (AUC) to midazolam was in agreement with observed data (AFE = 1.38, AAFE = 1.78). Simulated AUC variability with three dosing approaches indicated higher variability with uniform dosing (14-fold) and stratified dosing (13-fold) compared with individualized dosing informed by liquid biopsy (fivefold). Further, exosome screening revealed mRNA expression of 532 targets relevant to drug metabolism and disposition (169 enzymes and 361 transporters). Data related to these targets can be used to further individualize PBPK models for pathways relevant to PK of other drugs. This study provides additional verification of liquid biopsy-informed PBPK modeling approaches, necessary to advance strategies that seek to achieve precise dosing from the start of treatment.
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Affiliation(s)
- Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, UK
- Certara, Princeton, New Jersey, USA
| | - Zubida M Al-Majdoub
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, UK
| | | | - Ka Lai Yee
- Merck & Co., Inc., Rahway, New Jersey, USA
| | - Sudhakar Sahoo
- Cancer Research UK Manchester Institute, University of Manchester, Manchester, UK
| | - Wolfgang Breitwieser
- Cancer Research UK Manchester Institute, University of Manchester, Manchester, UK
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, UK
| | | | - Brahim Achour
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, The University of Rhode Island, Kingston, Rhode Island, USA
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Janssen Daalen JM, Doesburg D, Hunik L, Kessel R, Herngreen T, Knol D, Ruys T, van den Bemt BJF, Schers HJ. Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial. Clin Pharmacol Ther 2024. [PMID: 38711388 DOI: 10.1002/cpt.3293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/22/2024] [Indexed: 05/08/2024]
Abstract
Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dosage and the narrow therapeutic window. Model-informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision-support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty-one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 μg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 μg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.
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Affiliation(s)
- Jules M Janssen Daalen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Liesbeth Hunik
- Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rogier Kessel
- Amsterdam Data Collective, Amsterdam, The Netherlands
| | | | - Dennis Knol
- Amsterdam Data Collective, Amsterdam, The Netherlands
| | - Thony Ruys
- Amsterdam Data Collective, Amsterdam, The Netherlands
| | - Bart J F van den Bemt
- Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Pharmacy, Sint Maartenskliniek, Nijmegen, The Netherlands
- Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Henk J Schers
- Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands
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Kuang Y, Cao DS, Zuo YH, Yuan JH, Lu F, Zou Y, Wang H, Jiang D, Pei Q, Yang GP. CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108137. [PMID: 38520784 DOI: 10.1016/j.cmpb.2024.108137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/15/2024] [Accepted: 03/17/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis. METHODS In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin. RESULTS The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin. CONCLUSIONS In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.
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Affiliation(s)
- Yun Kuang
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; XiangYa School of Pharmaceutical Sciences, Central South University, Changsha, 410083, China
| | - Dong-Sheng Cao
- XiangYa School of Pharmaceutical Sciences, Central South University, Changsha, 410083, China
| | - Yong-Hui Zuo
- Changsha Xutong Technology Co., LTD, Changsha, 410205, China
| | - Jing-Han Yuan
- Changsha Xutong Technology Co., LTD, Changsha, 410205, China
| | - Feng Lu
- Changsha Xutong Technology Co., LTD, Changsha, 410205, China
| | - Yi Zou
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Hong Wang
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Dan Jiang
- Changsha Xutong Technology Co., LTD, Changsha, 410205, China.
| | - Qi Pei
- Department of pharmacy, the Third Xiangya Hospital, Central South University, Changsha, 410013, China; Furong Laboratory, Changsha, 410013, China.
| | - Guo-Ping Yang
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; XiangYa School of Pharmaceutical Sciences, Central South University, Changsha, 410083, China; Furong Laboratory, Changsha, 410013, China.
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10
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Sierra T, Achour B. In Vitro to In Vivo Scalars for Drug Clearance in Nonalcoholic Fatty Liver and Steatohepatitis. Drug Metab Dispos 2024; 52:390-398. [PMID: 38423789 DOI: 10.1124/dmd.123.001629] [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: 12/30/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
In vitro-in vivo extrapolation (IVIVE) allows prediction of clinical outcomes across populations from in vitro data using specific scalars tailored to the biologic characteristics of each population. This study experimentally determined scalars for patients with varying degrees of nonalcoholic fatty liver disease (NAFLD), ranging from fatty liver to nonalcoholic steatohepatitis (NASH) and cirrhosis. Microsomal, S9, and cytosol fractions were extracted from 36 histologically normal and 66 NAFLD livers (27 nonalcoholic fatty liver [NAFL], 13 NASH, and 26 NASH with cirrhosis). Corrected microsomal protein per gram liver (MPPGL) progressively decreased with disease severity (26.8, 27.4, and 24.3 mg/g in NAFL, NASH, and NASH/cirrhosis, respectively, compared with 35.6 mg/g in normal livers; ANOVA, P < 0.001). Homogenate, S9, and cytosolic protein showed a consistent trend of decline in NASH/cirrhosis relative to normal control (post-hoc t test, P < 0.05). No differences across the groups were observed in homogenate, S9, cytosolic, and microsomal protein content in matched kidney samples. MPPGL-based scalars that combine protein content with liver size revealed that the reduction in MPPGL in NAFL and NASH was compensated by the reported increase in liver size (relative scalar ratios of 0.96 and 0.99, respectively), which was not the case with NASH/cirrhosis (ratio of 0.63), compared with healthy control. Physiologically based pharmacokinetics-informed global sensitivity analysis of the relative contribution of IVIVE scalars (hepatic CYP3A4 abundance, MPPGL, and liver size) to variability in exposure (area under the curve) to three CYP3A substrates (alprazolam, midazolam, and ibrutinib) revealed enzyme abundance as the most significant parameter, followed by MPPGL, whereas liver volume was the least impactful factor. SIGNIFICANCE STATEMENT: Nonalcoholic fatty liver disease-specific scalars necessary for extrapolation from in vitro systems to liver tissue are lacking. These are required in clearance prediction and dose selection in nonalcoholic fatty liver and steatohepatitis populations. Previously reported disease-driven changes have focused on cirrhosis, with no data on the initial stages of liver disease. The authors obtained experimental values for microsomal, cytosolic, and S9 fractions and assessed the relative impact of microsomal scalars on predicted exposure to substrate drugs using physiologically based pharmacokinetics.
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Affiliation(s)
- Teresa Sierra
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island
| | - Brahim Achour
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island
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Poweleit EA, Vaughn SE, Desta Z, Dexheimer JW, Strawn JR, Ramsey LB. Machine Learning-Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents. Clin Pharmacol Ther 2024; 115:860-870. [PMID: 38297828 PMCID: PMC11046530 DOI: 10.1002/cpt.3184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024]
Abstract
Selective serotonin reuptake inhibitors (SSRI) are the first-line pharmacologic treatment for anxiety and depressive disorders in children and adolescents. Many patients experience side effects that are difficult to predict, are associated with significant morbidity, and can lead to treatment discontinuation. Variation in SSRI pharmacokinetics could explain differences in treatment outcomes, but this is often overlooked as a contributing factor to SSRI tolerability. This study evaluated data from 288 escitalopram-treated and 255 sertraline-treated patients ≤ 18 years old to develop machine learning models to predict side effects using electronic health record data and Bayesian estimated pharmacokinetic parameters. Trained on a combined cohort of escitalopram- and sertraline-treated patients, a penalized logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval (CI): 0.66-0.88), with 0.69 sensitivity (95% CI: 0.54-0.86), and 0.82 specificity (95% CI: 0.72-0.87). Medication exposure, clearance, and time since the last dose increase were among the top features. Individual escitalopram and sertraline models yielded an AUROC of 0.73 (95% CI: 0.65-0.81) and 0.64 (95% CI: 0.55-0.73), respectively. Post hoc analysis showed sertraline-treated patients with activation side effects had slower clearance (P = 0.01), which attenuated after accounting for age (P = 0.055). These findings raise the possibility that a machine learning approach leveraging pharmacokinetic data can predict escitalopram- and sertraline-related side effects. Clinicians may consider differences in medication pharmacokinetics, especially during dose titration and as opposed to relying on dose, when managing side effects. With further validation, application of this model to predict side effects may enhance SSRI precision dosing strategies in youth.
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Affiliation(s)
- Ethan A. Poweleit
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Biomedical Informatics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
- Division of Research in Patient Services, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Samuel E. Vaughn
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH
- Division of Child and Adolescent Psychiatry, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - Zeruesenay Desta
- Division of Clinical Pharmacology, Indiana University, School of Medicine, Indianapolis, IN
| | - Judith W. Dexheimer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH
- Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Jeffrey R. Strawn
- Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH
- Division of Child and Adolescent Psychiatry, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - Laura B. Ramsey
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children’s Mercy Kansas City, Kansas City, MO, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
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Ju G, Liu X, Yang W, Xu N, Chen L, Zhang C, He Q, Zhu X, Ouyang D. Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository. Drug Des Devel Ther 2024; 18:801-818. [PMID: 38500691 PMCID: PMC10946406 DOI: 10.2147/dddt.s434919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 03/07/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use. Methods A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca. Results Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00-200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults). Discussion Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.
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Affiliation(s)
- Gehang Ju
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China
| | - Xin Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China
| | - Wenyu Yang
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Nuo Xu
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Lulu Chen
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China
- Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China
| | - Chenchen Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Qingfeng He
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Xiao Zhu
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Dongsheng Ouyang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China
- Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China
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Mostafa S, Rafizadeh R, Polasek TM, Bousman CA, Rostami‐Hodjegan A, Stowe R, Carrion P, Sheffield LJ, Kirkpatrick CMJ. Virtual twins for model-informed precision dosing of clozapine in patients with treatment-resistant schizophrenia. CPT Pharmacometrics Syst Pharmacol 2024; 13:424-436. [PMID: 38243630 PMCID: PMC10941576 DOI: 10.1002/psp4.13093] [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: 07/18/2023] [Revised: 10/14/2023] [Accepted: 11/02/2023] [Indexed: 01/21/2024] Open
Abstract
Model-informed precision dosing using virtual twins (MIPD-VTs) is an emerging strategy to predict target drug concentrations in clinical practice. Using a high virtualization MIPD-VT approach (Simcyp version 21), we predicted the steady-state clozapine concentration and clozapine dosage range to achieve a target concentration of 350 to 600 ng/mL in hospitalized patients with treatment-resistant schizophrenia (N = 11). We confirmed that high virtualization MIPD-VT can reasonably predict clozapine concentrations in individual patients with a coefficient of determination (R2 ) ranging between 0.29 and 0.60. Importantly, our approach predicted the final dosage range to achieve the desired target clozapine concentrations in 73% of patients. In two thirds of patients treated with fluvoxamine augmentation, steady-state clozapine concentrations were overpredicted two to four-fold. This work supports the application of a high virtualization MIPD-VT approach to inform the titration of clozapine doses in clinical practice. However, refinement is required to improve the prediction of pharmacokinetic drug-drug interactions, particularly with fluvoxamine augmentation.
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Affiliation(s)
- Sam Mostafa
- Centre for Medicine Use and SafetyMonash UniversityParkvilleVictoriaAustralia
- MyDNA Life Australia LimitedVictoriaAustralia
| | - Reza Rafizadeh
- BC Mental Health and Substance Use Services, BC Psychosis ProgramLower Mainland Pharmacy ServicesVancouverBritish ColumbiaCanada
| | - Thomas M. Polasek
- Centre for Medicine Use and SafetyMonash UniversityParkvilleVictoriaAustralia
- CertaraPrincetonNew JerseyUSA
- Department of Clinical PharmacologyRoyal Adelaide HospitalAdelaideSouth AustraliaAustralia
| | - Chad A. Bousman
- Department of Psychiatry, Melbourne Neuropsychiatry CentreUniversity of Melbourne and Melbourne HealthMelbourneVictoriaAustralia
- Alberta Children's Hospital Research Institute, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain Institute, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Departments of Medical Genetics, Psychiatry, Physiology and Pharmacology, and Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
| | - Amin Rostami‐Hodjegan
- Centre for Applied Pharmacokinetic Research (CAPKR), School of Health SciencesUniversity of ManchesterManchesterUK
- Simcyp DivisionCertara UK LimitedSheffieldUK
| | - Robert Stowe
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Djavid Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Neurology (Medicine)University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Prescilla Carrion
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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Polasek TM. Pharmacogenomics - a minor rather than major force in clinical medicine. Expert Rev Clin Pharmacol 2024; 17:203-212. [PMID: 38307498 DOI: 10.1080/17512433.2024.2314726] [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: 11/01/2023] [Accepted: 02/01/2024] [Indexed: 02/04/2024]
Abstract
INTRODUCTION Pharmacogenomics (PGx) is touted as essential for the future of precision medicine. But the opportunity cost of PGx from the prescribers' perspective is rarely considered. The aim of this article is to critique PGx-guided prescribing using clinical pharmacology principles so that important cases for PGx testing are not missed by doctors responsible for therapeutic decision making. AREAS COVERED Three categories of PGx and their limitations are outlined - exposure PGx, response PGx, and immune-mediated safety PGx. Clinical pharmacology reasons are given for the narrow scope of PGx-guided prescribing apart from a few medical specialties. Clinical problems for doctors that may arise from PGx are then explained, including mismatch between patients' expectations of PGx testing and the benefits or answers it provides. EXPERT OPINION Contrary to popular opinion, PGx is unlikely to become the cornerstone of precision medicine. Sound clinical pharmacology reasons explain why PGx-guided prescribing is unnecessary for most drugs. Pharmacogenomics is important for niche areas of prescribing but has limited clinical utility more broadly. The opportunity cost of PGx-guided prescribing is currently too great for most doctors.
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Affiliation(s)
- Thomas M Polasek
- Centre for Medicine Use and Safety, Monash University, Melbourne, Australia
- CMAX Clinical Research, Adelaide, Australia
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15
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Chen Y, Han Y, Guo F, Yu Z. Model-Informed Precision Dosing of Imipenem in an Obese Adolescent Patient with Augmented Renal Clearance and History of Schizophrenia. Infect Drug Resist 2024; 17:761-767. [PMID: 38433781 PMCID: PMC10908274 DOI: 10.2147/idr.s450294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
Imipenem is a broad-spectrum antibiotic that has been used in treating severe infections and exhibits a time-dependent PK/PD profile. Its dose should be adjusted based on renal function. However, there is little experience with imipenem dosing in obese adolescent patients with augmented renal clearance (ARC) and history of schizophrenia. This case reported successful dosing of imipenem in an obese adolescent patient with ARC based on therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD). A 15-year-old male adolescent patient with history of schizophrenia was diagnosed with ventilator-associated pneumonia due to carbapenem-susceptible Klebsiella pneumoniae and received imipenem treatment (0.5 g every 8 hours with a 1-hour infusion). However, the exposure of imipenem was suboptimal due to ARC, and there is no available model for MIPD in this patient. Thus, we utilized prediction error to find a population pharmacokinetic model that fit this patient and ran Maximum a posteriori Bayesian estimation and Monte Carlo simulation based on screened models to predict changes in drug concentrations. The dose of imipenem was adjusted to 0.5 g every 6 hours with a 2-hour infusion, and subsequent TDM revealed that dosing adjustment was accurate and successful. Finally, the patient's status of infection improved. This study will be beneficial to imipenem dosing in similar cases in the future, thereby improving the safety and effectiveness of imipenem or other antibiotics.
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Affiliation(s)
- Yueliang Chen
- Intensive Care Unit, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Yun Han
- Department of Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
- Research Center for Clinical Pharmacy, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Feng Guo
- Intensive Care Unit, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Zhenwei Yu
- Department of Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
- Research Center for Clinical Pharmacy, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
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Polasek TM, Peck RW. Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses. Clin Pharmacol Ther 2024. [PMID: 38328977 DOI: 10.1002/cpt.3197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024]
Abstract
The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.
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Affiliation(s)
- Thomas M Polasek
- Centre for Medicine Use and Safety, Monash University, Melbourne, Victoria, Australia
- CMAX Clinical Research, Adelaide, South Australia, Australia
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research & Development (pRED), Roche Innovation Center Basel, Basel, Switzerland
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Nguyen TVA, Le BH, Nguyen MT, Le VT, Tran VT, Le DT, Vu DAM, Truong QK, Le TH, Nguyen HTL. Pharmacogenomic Analysis of CYP3A5*3 and Tacrolimus Trough Concentrations in Vietnamese Renal Transplant Outcomes. Pharmgenomics Pers Med 2024; 17:53-64. [PMID: 38332855 PMCID: PMC10850765 DOI: 10.2147/pgpm.s439400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024] Open
Abstract
Purpose CYP3A5 polymorphisms have been associated with variations in the pharmacokinetics of tacrolimus (Tac) in kidney transplant patients. Our study aims to quantify how the CYP3A5 genotype influences tacrolimus trough concentrations (C0) in a Vietnamese outpatient population by selecting an appropriate population pharmacokinetic model of Tac for our patients. Patients and Methods The external dataset was obtained prospectively from 54 data of adult kidney transplant recipients treated at the 103 Military Hospital. All published Tac population pharmacokinetic models were systematically screened from PubMed and Scopus databases and were selected based on our patient's available characteristics. Mean absolute prediction error (MAPE), mean prediction error, and goodness-of-fit plots were used to identify the appropriate model for finding the formula that identifies the influence of CYP3A5 genotype on the pharmacokinetic data of Vietnamese patients. Results The model of Zhu et al had a good predictive ability with MAPE of 19.29%. The influence of CYP3A5 genotype on tacrolimus clearance was expressed by the following formulas: CL/F=27 , 2 × [ ( WT/70 ) 0 , 75 ] × [ ( HCT/0 , 35 ) -0 , 501 ] × [ ( POD/180 ) 0 , 0306 ] × CYP3A5 ( L/h ) . The simulation result showed that Tac C0 was significantly higher in patients not expressing CYP3A5 (p< 0.001). Conclusion The incorporation of the CYP3A5 phenotype into Zhu's structural model has significantly enhanced our ability to predict Tacrolimus trough levels in the Vietnamese population. This study's results underscore the valuable role of CYP3A5 phenotype in optimizing the forecast of Tac concentrations, offering a promising avenue to assist health-care practitioners in their clinical decision-making and ultimately advance patient care outcomes.
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Affiliation(s)
| | - Ba Hai Le
- Department of Clinical Pharmacy, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - Minh Thanh Nguyen
- Department of Clinical Pharmacy, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - Viet Thang Le
- Department of Nephrology and Dialysis, 103 Military Hospital, Hanoi, Vietnam
| | - Viet Tien Tran
- Department of Infectious Diseases, 103 Military Hospital, Hanoi, Vietnam
| | - Dinh Tuan Le
- Department of Rheumatology and Endocrinology, 103 Military Hospital, Hanoi, Vietnam
| | - Duong Anh Minh Vu
- Department of Clinical Pharmacy, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - Quy Kien Truong
- Department of Nephrology and Dialysis, 103 Military Hospital, Hanoi, Vietnam
| | - Trong Hieu Le
- Department of Clinical Pharmacy, Hanoi University of Pharmacy, Hanoi, Vietnam
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Sobsey CA, Mady N, Richard VR, LeBlanc A, Zakharov T, Borchers CH, Jagoe RT. Measurement of CYP1A2 and CYP3A4 activity by a simplified Geneva cocktail approach in a cohort of free-living individuals: a pilot study. Front Pharmacol 2024; 15:1232595. [PMID: 38370474 PMCID: PMC10869543 DOI: 10.3389/fphar.2024.1232595] [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: 06/01/2023] [Accepted: 01/18/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction: The cytochrome P450 enzyme subfamilies, including CYP3A4 and CYP1A2, have a major role in metabolism of a range of drugs including several anti-cancer treatments. Many factors including environmental exposures, diet, diseaserelated systemic inflammation and certain genetic polymorphisms can impact the activity level of these enzymes. As a result, the net activity of each enzyme subfamily can vary widely between individuals and in the same individual over time. This variability has potential major implications for treatment efficacy and risk of drug toxicity, but currently no assays are available for routine use to guide clinical decision-making. Methods: To address this, a mass spectrometry-based method to measure activities of CYP3A4, CYP1A2 was adapted and tested in free-living participants. The assay results were compared with the predicted activity of each enzyme, based on a self-report tool capturing diet, medication, chronic disease state, and tobacco usage. In addition, a feasibility test was performed using a low-volume dried blood spots (DBS) on two different filter-paper supports, to determine if the same assay could be deployed without the need for repeated standard blood tests. Results: The results confirmed the methodology is safe and feasible to perform in free-living participants using midazolam and caffeine as test substrates for CYP3A4 and CYP1A2 respectively. Furthermore, though similar methods were previously shown to be compatible with the DBS format, the assay can also be performed successfully while incorporating glucuronidase treatment into the DBS approach. The measured CYP3A4 activity score varied 2.6-fold across participants and correlated with predicted activity score obtained with the self-report tool. The measured CYP1A2 activity varied 3.5-fold between participants but no correlation with predicted activity from the self-report tool was found. Discussion: The results confirm the wide variation in CYP activity between individuals and the important role of diet and other exposures in determining CYP3A4 activity. This methodology shows great potential and future cross-sectional and longitudinal studies using DBS are warranted to determine how best to use the assay results to guide drug treatments.
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Affiliation(s)
- Constance A. Sobsey
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Noor Mady
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Peter Brojde Lung Cancer Centre, Jewish General Hospital, Montreal, QC, Canada
| | - Vincent R. Richard
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Andre LeBlanc
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Thomas Zakharov
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Peter Brojde Lung Cancer Centre, Jewish General Hospital, Montreal, QC, Canada
| | - Christoph H. Borchers
- Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - R. Thomas Jagoe
- Peter Brojde Lung Cancer Centre, Jewish General Hospital, Montreal, QC, Canada
- Department of Medicine, Jewish General Hospital, Montreal, QC, Canada
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Centanni M, van de Velde ME, Uittenboogaard A, Kaspers GJL, Karlsson MO, Friberg LE. Model-Informed Precision Dosing to Reduce Vincristine-Induced Peripheral Neuropathy in Pediatric Patients: A Pharmacokinetic and Pharmacodynamic Modeling and Simulation Analysis. Clin Pharmacokinet 2024; 63:197-209. [PMID: 38141094 PMCID: PMC10847206 DOI: 10.1007/s40262-023-01336-1] [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] [Accepted: 11/29/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Vincristine-induced peripheral neuropathy (VIPN) is a common adverse effect of vincristine, a drug often used in pediatric oncology. Previous studies demonstrated large inter- and intrapatient variability in vincristine pharmacokinetics (PK). Model-informed precision dosing (MIPD) can be applied to calculate patient exposure and individualize dosing using therapeutic drug monitoring (TDM) measurements. This study set out to investigate the PK/pharmacodynamic (PKPD) relationship of VIPN and determine the utility of MIPD to support clinical decisions regarding dose selection and individualization. METHODS Data from 35 pediatric patients were utilized to quantify the relationship between vincristine dose, exposure and the development of VIPN. Measurements of vincristine exposure and VIPN (Common Terminology Criteria for Adverse Events [CTCAE]) were available at baseline and for each subsequent dosing occasions (1-5). A PK and PKPD analysis was performed to assess the inter- and intraindividual variability in vincristine exposure and VIPN over time. In silico trials were performed to portray the utility of vincristine MIPD in pediatric subpopulations with a certain age, weight and cytochrome P450 (CYP) 3A5 genotype distribution. RESULTS A two-compartmental model with linear PK provided a good description of the vincristine exposure data. Clearance and distribution parameters were related to bodyweight through allometric scaling. A proportional odds model with Markovian elements described the incidence of Grades 0, 1 and ≥ 2 VIPN overdosing occasions. Vincristine area under the curve (AUC) was the most significant exposure metric related to the development of VIPN, where an AUC of 50 ng⋅h/mL was estimated to be related to an average VIPN probability of 40% over five dosing occasions. The incidence of Grade ≥ 2 VIPN reduced from 62.1 to 53.9% for MIPD-based dosing compared with body surface area (BSA)-based dosing in patients. Dose decreases occurred in 81.4% of patients with MIPD (vs. 86.4% for standard dosing) and dose increments were performed in 33.4% of patients (no dose increments allowed for standard dosing). CONCLUSIONS The PK and PKPD analysis supports the use of MIPD to guide clinical dose decisions and reduce the incidence of VIPN. The current work can be used to support decisions with respect to dose selection and dose individualization in children receiving vincristine.
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Affiliation(s)
- Maddalena Centanni
- Department of Pharmacy, Uppsala University, Box 580, 751 23, Uppsala, Sweden
| | - Mirjam E van de Velde
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Pediatric Oncology, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Aniek Uittenboogaard
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Pediatric Oncology, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gertjan J L Kaspers
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Pediatric Oncology, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mats O Karlsson
- Department of Pharmacy, Uppsala University, Box 580, 751 23, Uppsala, Sweden
| | - Lena E Friberg
- Department of Pharmacy, Uppsala University, Box 580, 751 23, Uppsala, Sweden.
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20
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Tremmel R, Hofmann U, Haag M, Schaeffeler E, Schwab M. Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes. Annu Rev Pharmacol Toxicol 2024; 64:65-87. [PMID: 37585662 DOI: 10.1146/annurev-pharmtox-032023-121106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.
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Affiliation(s)
- Roman Tremmel
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
- Departments of Clinical Pharmacology, and Pharmacy and Biochemistry, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg (DKFZ), Partner Site, Tübingen, Germany
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21
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Meyer UA, Amara SG, Blaschke TF, Insel PA. Introduction to the Theme "Pharmacological Individuality: New Insights and Strategies for Personalized and Precise Drug Treatment". Annu Rev Pharmacol Toxicol 2024; 64:27-31. [PMID: 37816308 DOI: 10.1146/annurev-pharmtox-090123-010552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
The reviews in Volume 64 of the Annual Review of Pharmacology and Toxicology cover diverse topics. A common theme in many of the reviews is the interindividual variability in the clinical response to drugs. Highlighted areas include emerging developments in pharmacogenomics that can predict the personal risk for drug inefficacy and/or adverse drug reactions. Other reviews focus on the use of circulating biomarkers to define drug metabolism phenotypes and the effect of circadian regulation on drug response. Another emerging technology, digital twins that model individual patients, is used to generate computational simulations of drug effects and identify optimal personalized treatments. Another variable that may affect clinical outcomes, the nocebo response (an adverse reaction to a placebo), complicates clinical trials. These reviews further document that pharmacological individuality is an essential component of the concepts of personalized medicine and precision medicine and will likely have an important impact on patient care.
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Affiliation(s)
- Urs A Meyer
- Biozentrum, University of Basel, Basel, Switzerland;
| | - Susan G Amara
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Paul A Insel
- Departments of Pharmacology and Medicine, University of California, San Diego, La Jolla, California, USA
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22
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Kamp J, Zwart TC, Meziyerh S, van der Boog PJM, Nijgh EE, van Duin K, de Vries APJ, Moes DJAR. Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients. Pharmaceutics 2023; 16:17. [PMID: 38276495 PMCID: PMC10819724 DOI: 10.3390/pharmaceutics16010017] [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: 11/15/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Meltdose tacrolimus (Envarsus®) has been marketed as a formulation achieving a more consistent tacrolimus exposure. Due to the narrow therapeutic window of tacrolimus, dose individualization is essential. Relaxation of the upper age limits for kidney transplantations has resulted in larger numbers of elderly patients receiving tacrolimus. However, due to the physiological changes caused by aging, the tacrolimus pharmacokinetics (PK) might be altered. The primary aim was to develop a population PK model in elderly kidney transplant recipients. Secondary aims were the development and evaluation of a limited sampling strategy (LSS) for AUC estimation. METHODS A total of 34 kidney transplant recipients aged ≥65 years, starting on meltdose tacrolimus directly after transplantation, were included. An eight-point whole blood AUC0-24h and an abbreviated dried blood spot (DBS) AUC0-24h were obtained. The PK data were analyzed using nonlinear mixed effect modeling methods. RESULTS The PK data were best described using a two-compartment model, including three transit compartments and a mixture model for oral absorption. The best three-sample LSS was T = 0, 2, 6 h. The best four-sample LSSs were T = 0, 2, 6, 8 h and T = 0, 1, 6, 8 h. CONCLUSIONS The developed population PK model adequately described the tacrolimus PK data in a population of elderly kidney transplant recipients. In addition, the developed population PK model and LSS showed an adequate estimation of tacrolimus exposure, and may therefore be used to aid in tacrolimus dose individualization.
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Affiliation(s)
- Jasper Kamp
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (J.K.); (T.C.Z.)
| | - Tom C. Zwart
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (J.K.); (T.C.Z.)
| | - Soufian Meziyerh
- Transplant Center, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (S.M.); (A.P.J.d.V.)
- Division of Nephrology, Department of Internal Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Paul J. M. van der Boog
- Transplant Center, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (S.M.); (A.P.J.d.V.)
- Division of Nephrology, Department of Internal Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Esther E. Nijgh
- Transplant Center, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (S.M.); (A.P.J.d.V.)
- Division of Nephrology, Department of Internal Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Koen van Duin
- Transplant Center, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (S.M.); (A.P.J.d.V.)
- Division of Nephrology, Department of Internal Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Aiko P. J. de Vries
- Transplant Center, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (S.M.); (A.P.J.d.V.)
- Division of Nephrology, Department of Internal Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Dirk Jan A. R. Moes
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (J.K.); (T.C.Z.)
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Terrier J, Gaspar F, Gosselin P, Raboud O, Lenoir C, Rollason V, Csajka C, Samer C, Fontana P, Daali Y, Reny J. Apixaban and rivaroxaban's physiologically-based pharmacokinetic model validation in hospitalized patients: A first step for larger use of a priori modeling approach at bed side. CPT Pharmacometrics Syst Pharmacol 2023; 12:1872-1883. [PMID: 37794718 PMCID: PMC10725260 DOI: 10.1002/psp4.13036] [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: 04/16/2023] [Revised: 06/21/2023] [Accepted: 08/14/2023] [Indexed: 10/06/2023] Open
Abstract
When used in real-world conditions, substantial interindividual variations in direct oral anticoagulant (DOAC) plasma concentrations are observed for a given dose, leading to a risk of over- or under-exposure and clinically significant adverse events. Physiologically-based pharmacokinetic (PBPK) models could help physicians to tailor DOAC prescriptions in vulnerable patient populations, such as those in the hospital setting. The present study aims to validate prospectively PBPK models for rivaroxaban and apixaban in a large cohort of elderly, polymorbid, and hospitalized patients. In using a model of geriatric population integrating appropriate physiological parameters into models first optimized with healthy volunteer data, observed plasma concentration collected in hospitalized patients on apixaban (n = 100) and rivaroxaban (n = 100) were adequately predicted (ratio predicted/observed area under the concentration curve for a dosing interval [AUCtau ] = 0.97 [0.96-0.99] geometric mean, 90% confidence interval, ratio predicted/observed AUCtau = 1.03 [1.02-1.05]) for apixaban and rivaroxaban, respectively. Validation of the present PBPK models for rivaroxaban and apixaban in in-patients represent an additional step toward the feasibility of bedside use.
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Affiliation(s)
- Jean Terrier
- Division of General Internal MedicineGeneva University HospitalsGenevaSwitzerland
- Geneva Platelet Group, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
- Clinical Pharmacology and Toxicology Service, Anesthesiology, Pharmacology and Intensive Care DepartmentGeneva University HospitalsGenevaSwitzerland
| | - Frédéric Gaspar
- Center for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
- School of Pharmaceutical SciencesUniversity of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western SwitzerlandUniversity of Geneva, University of LausanneGeneva, LausanneSwitzerland
- Service of Clinical PharmacologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Pauline Gosselin
- Division of General Internal MedicineGeneva University HospitalsGenevaSwitzerland
| | - Olivier Raboud
- Center for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
- School of Pharmaceutical SciencesUniversity of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western SwitzerlandUniversity of Geneva, University of LausanneGeneva, LausanneSwitzerland
- Service of Clinical PharmacologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Camille Lenoir
- Clinical Pharmacology and Toxicology Service, Anesthesiology, Pharmacology and Intensive Care DepartmentGeneva University HospitalsGenevaSwitzerland
| | - Victoria Rollason
- Clinical Pharmacology and Toxicology Service, Anesthesiology, Pharmacology and Intensive Care DepartmentGeneva University HospitalsGenevaSwitzerland
| | - Chantal Csajka
- School of Pharmaceutical SciencesUniversity of GenevaGenevaSwitzerland
- Institute of Pharmaceutical Sciences of Western SwitzerlandUniversity of Geneva, University of LausanneGeneva, LausanneSwitzerland
- Service of Clinical PharmacologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Caroline Samer
- Clinical Pharmacology and Toxicology Service, Anesthesiology, Pharmacology and Intensive Care DepartmentGeneva University HospitalsGenevaSwitzerland
- School of Pharmaceutical SciencesUniversity of GenevaGenevaSwitzerland
| | - Pierre Fontana
- Geneva Platelet Group, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
- Division of Angiology and HaemostasisGeneva University HospitalsGenevaSwitzerland
| | - Youssef Daali
- Geneva Platelet Group, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
- Clinical Pharmacology and Toxicology Service, Anesthesiology, Pharmacology and Intensive Care DepartmentGeneva University HospitalsGenevaSwitzerland
| | - Jean‐Luc Reny
- Division of General Internal MedicineGeneva University HospitalsGenevaSwitzerland
- Geneva Platelet Group, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
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Maruyama T, Kimura T, Ebihara F, Kasai H, Matsunaga N, Hamada Y. Comparison of the predictive accuracy of the physiologically based pharmacokinetic (PBPK) model and population pharmacokinetic (PPK) model of vancomycin in Japanese patients with MRSA infection. J Infect Chemother 2023; 29:1152-1159. [PMID: 37673298 DOI: 10.1016/j.jiac.2023.08.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/26/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023]
Abstract
INTRODUCTION The latest therapeutic drug monitoring guidelines for vancomycin (VCM) recommend that area under the concentration-time curve is estimated based on model-informed precision dosing and used to evaluate efficacy and safety. Therefore, we predicted VCM concentrations in individual methicillin-resistant Staphylococcus aureus-infected patients using existing a physiologically based pharmacokinetic (PBPK) model and 1- and 2-compartment population pharmacokinetic (PPK) models and confirmed and verified the accuracy of the PBPK model in estimating VCM concentrations with the PPK model. METHODS The subjects of the study are 20 patients, and the predicted concentrations were evaluated by comparing the observed and predicted trough and peak values of VCM concentrations for individual patients. RESULTS The results showed good correlation between the observed and predicted trough and peak concentrations of VCM was observed generally in the PBPK model, R2 values of 0.72, 0.62, and 0.40 with trough values of 0.49, 0.40, and 0.34 with peak values for PBPK model, 1-compartment, and 2-compartment model, respectively. CONCLUSIONS Although the performance of the PBPK model is not as predictive as the PPK model, generally similar predictive trends were obtained, suggesting that it may be a valuable tool for rapid and accurate prediction of AUC for VCM.
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Affiliation(s)
- Takumi Maruyama
- Department of Pharmacy, Tokyo Women's Medical University Hospital, 8-1, Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Toshimi Kimura
- Department of Pharmacy, Juntendo University Hospital, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8431, Japan
| | - Fumiya Ebihara
- Department of Pharmacy, Tokyo Women's Medical University Hospital, 8-1, Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Hidefumi Kasai
- Laboratory of Pharmacometrics and Systems Pharmacology Keio Frontier Research and Education Collaboration Square (K-FRECS) at Tonomachi, Keio University Kawasaki, Kanagawa, 210-0821, Japan
| | - Nobuaki Matsunaga
- AMR Clinical Reference Center, National Center for Global Health and Medicine Hospital, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| | - Yukihiro Hamada
- Department of Pharmacy, Tokyo Women's Medical University Hospital, 8-1, Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan.
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Taylor ZL, Poweleit EA, Paice K, Somers KM, Pavia K, Vinks AA, Punt N, Mizuno T, Girdwood ST. Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing. CPT Pharmacometrics Syst Pharmacol 2023; 12:1827-1845. [PMID: 37771190 PMCID: PMC10725261 DOI: 10.1002/psp4.13056] [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: 03/12/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 09/30/2023] Open
Abstract
There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.
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Affiliation(s)
- Zachary L. Taylor
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Ethan A. Poweleit
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of Biomedical InformaticsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
- Division of Biomedical InformaticsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Research in Patient ServicesCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Kelli Paice
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Critical Care Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Katherine M. Somers
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Critical Care Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Hematology and Oncology, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Kathryn Pavia
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Critical Care Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Alexander A. Vinks
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
- Division of Research in Patient ServicesCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Nieko Punt
- Department of Clinical Pharmacy and Pharmacology, University of GroningenUniversity Medical Center GroningenGroningenThe Netherlands
- MedimaticsMaastrichtThe Netherlands
| | - Tomoyuki Mizuno
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Sonya Tang Girdwood
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
- Division of Hospital Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
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Barreto EF, Chang J, Rule AD, Mara KC, Meade LA, Paul J, Jannetto PJ, Athreya AP, Scheetz MH. Population pharmacokinetic model of cefepime for critically ill adults: a comparative assessment of eGFR equations. Antimicrob Agents Chemother 2023; 67:e0081023. [PMID: 37882514 PMCID: PMC10648925 DOI: 10.1128/aac.00810-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/15/2023] [Indexed: 10/27/2023] Open
Abstract
Cefepime exhibits highly variable pharmacokinetics in critically ill patients. The purpose of this study was to develop and qualify a population pharmacokinetic model for use in the critically ill and investigate the impact of various estimated glomerular filtration rate (eGFR) equations using creatinine, cystatin C, or both on model parameters. This was a prospective study of critically ill adults hospitalized at an academic medical center treated with intravenous cefepime. Individuals with acute kidney injury or on kidney replacement therapy or extracorporeal membrane oxygenation were excluded. A nonlinear mixed-effects population pharmacokinetic model was developed using data collected from 2018 to 2022. The 120 included individuals contributed 379 serum samples for analysis. A two-compartment pharmacokinetic model with first-order elimination best described the data. The population mean parameters (standard error) in the final model were 7.84 (0.24) L/h for CL1 and 15.6 (1.45) L for V1. Q was fixed at 7.09 L/h and V2 was fixed at 10.6 L, due to low observed interindividual variation in these parameters. The final model included weight as a covariate for volume of distribution and the eGFRcr-cysC (mL/min) as a predictor of drug clearance. In summary, a population pharmacokinetic model for cefepime was created for critically ill adults. The study demonstrated the importance of cystatin C to prediction of cefepime clearance. Cefepime dosing models which use an eGFR equation inclusive of cystatin C are likely to exhibit improved accuracy and precision compared to dosing models which incorporate an eGFR equation with only creatinine.
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Affiliation(s)
- Erin F. Barreto
- Department of Pharmacy, Mayo Clinic, Rochester, Minnesota, USA
| | - Jack Chang
- Department of Pharmacy Practice, Chicago College of Pharmacy, Pharmacometrics Center of Excellence, Midwestern University, Downers Grove, Illinois, USA
- Department of Pharmacy, Northwestern Medicine, Chicago, Illinois, USA
| | - Andrew D. Rule
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kristin C. Mara
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
| | - Laurie A. Meade
- Anesthesia Clinical Research Unit, Mayo Clinic, Rochester, Minnesota, USA
| | - Johar Paul
- Anesthesia Clinical Research Unit, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul J. Jannetto
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Marc H. Scheetz
- Department of Pharmacy Practice, Chicago College of Pharmacy, Pharmacometrics Center of Excellence, Midwestern University, Downers Grove, Illinois, USA
- Department of Pharmacy, Northwestern Medicine, Chicago, Illinois, USA
| | - for the BLOOM Study Group
- Department of Pharmacy, Mayo Clinic, Rochester, Minnesota, USA
- Department of Pharmacy Practice, Chicago College of Pharmacy, Pharmacometrics Center of Excellence, Midwestern University, Downers Grove, Illinois, USA
- Department of Pharmacy, Northwestern Medicine, Chicago, Illinois, USA
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
- Anesthesia Clinical Research Unit, Mayo Clinic, Rochester, Minnesota, USA
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
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Nikanjam M, Kato S, Sicklick JK, Kurzrock R. At the right dose: personalised (N-of-1) dosing for precision oncology. Eur J Cancer 2023; 194:113359. [PMID: 37832506 DOI: 10.1016/j.ejca.2023.113359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
The objective of oncology therapeutics, especially in the age of precision medicine, is to give the right drug(s) to the right patient at the right time. Yet, a major challenge is finding the right dose for each patient. Determining safe and efficacious doses of oncology treatments, especially for novel combination therapies, can be challenging. Moreover, traditionally, dosing cancer drugs is based on giving each patient the same dose (a flat dose) or a dose based on surface area/weight. But patients' ability to tolerate drugs is influenced by additional factors including, but not limited to age, gender, race, comorbidities, organ function, and metabolism. Herein, we present evidence that, in the era of targeted drugs, individualised drug dosing determined by starting at reduced doses and using intrapatient dose escalation can yield safe and effective personalised dosing of novel combinations of approved drugs that have not previously undergone formal phase I trials and can also optimise dosing of tested drug regimens.
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Affiliation(s)
- Mina Nikanjam
- Division of Hematology-Oncology, University of California San Diego, La Jolla, CA, USA.
| | - Shumei Kato
- Division of Hematology-Oncology, University of California San Diego, La Jolla, CA, USA
| | - Jason K Sicklick
- Department of Surgery, Division of Surgical Oncology, University of California San Diego, 3855 Health Sciences Drive, La Jolla, CA, USA; Department of Pharmacology, University of California San Diego, 3855 Health Sciences Drive, La Jolla, CA, USA
| | - Razelle Kurzrock
- Division of Hematology and Oncology, Medical College of Wisconsin Cancer Center, Milwaukee, WI, USA; WIN Consortium, Paris, France
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Augustin D, Lambert B, Robinson M, Wang K, Gavaghan D. Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case. Front Pharmacol 2023; 14:1270443. [PMID: 37927586 PMCID: PMC10621790 DOI: 10.3389/fphar.2023.1270443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.
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Affiliation(s)
- David Augustin
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ben Lambert
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ken Wang
- Research and Early Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Jackson KD, Achour B, Lee J, Geffert RM, Beers JL, Latham BD. Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine. Drug Metab Dispos 2023; 51:1238-1253. [PMID: 37419681 PMCID: PMC10506699 DOI: 10.1124/dmd.122.001066] [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: 08/14/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023] Open
Abstract
Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs-particularly the cytochrome P450 enzymes-in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT: This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.
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Affiliation(s)
- Klarissa D Jackson
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Brahim Achour
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Jonghwa Lee
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Raeanne M Geffert
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Jessica L Beers
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
| | - Bethany D Latham
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (K.D.J., J.L., R.M.G., J.L.B., B.D.L.); and Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, Rhode Island (B.A.)
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Wyszogrodzka-Gaweł G, Shuklinova O, Lisowski B, Wiśniowska B, Polak S. 3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet? Drug Discov Today 2023; 28:103731. [PMID: 37541422 DOI: 10.1016/j.drudis.2023.103731] [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: 06/30/2023] [Revised: 07/25/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023]
Abstract
Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.
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Affiliation(s)
- Gabriela Wyszogrodzka-Gaweł
- Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
| | - Olha Shuklinova
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland
| | - Bartek Lisowski
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
| | - Barbara Wiśniowska
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
| | - Sebastian Polak
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
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Polasek TM. Virtual twin for healthcare management. Front Digit Health 2023; 5:1246659. [PMID: 37781454 PMCID: PMC10540783 DOI: 10.3389/fdgth.2023.1246659] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023] Open
Abstract
Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.
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Affiliation(s)
- Thomas M. Polasek
- Certara, Princeton, NJ, United States
- Centre for Medicines Use and Safety, Monash University, Melbourne, VIC, Australia
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Oda K, Jono H, Saito H. Model-Informed Precision Dosing of Vancomycin in Adult Patients Undergoing Hemodialysis. Antimicrob Agents Chemother 2023; 67:e0008923. [PMID: 37195225 PMCID: PMC10286780 DOI: 10.1128/aac.00089-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/19/2023] [Indexed: 05/18/2023] Open
Abstract
Model-informed precision dosing (MIPD) maximizes the probability of successful dosing in patients undergoing hemodialysis. In these patients, area under the concentration-time curve (AUC)-guided dosing is recommended for vancomycin. However, this model is yet to be developed. The purpose of this study was to address this issue. The overall mass transfer-area coefficient (KoA) was used for the estimation of vancomycin hemodialysis clearance. A population pharmacokinetic (popPK) model was developed, resulting in a fixed-effect parameter for nonhemodialysis clearance of 0.316 liters/h. This popPK model was externally evaluated, with a resulting mean absolute error of 13.4% and mean prediction error of -0.17%. KoA-predicted hemodialysis clearance was prospectively evaluated for vancomycin (n = 10) and meropenem (n = 10), with a correlation equation being obtained (slope of 1.099, intercept of 1.642; r = 0.927, P < 0.001). An experimental evaluation using an in vitro hemodialysis circuit validated the developed model of KoA-predicted hemodialysis clearance using vancomycin, meropenem, vitamin B6, and inulin in 12 hemodialysis settings. This popPK model indicated a maximum a priori dosing for vancomycin-a loading dose of 30 mg/kg, which achieves the target AUC for 24 h after first dose with a probability of 93.0%, ensured by a predialysis concentration of >15 μg/mL. Maintenance doses of 12 mg/kg after every hemodialysis session could achieve the required exposure, with a probability of 80.6%. In conclusion, this study demonstrated that KoA-predicted hemodialysis clearance may lead to an upgrade from conventional dosing to MIPD for vancomycin in patients undergoing hemodialysis.
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Affiliation(s)
- Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Hirofumi Jono
- Department of Pharmacy, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
- Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University, Chuo-ku, Kumamoto, Japan
| | - Hideyuki Saito
- Department of Pharmacy, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
- Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University, Chuo-ku, Kumamoto, Japan
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Raina M, Ashraf A, Soundararajan A, Mandal AK, Sethi SK. Pharmacokinetics in Critically Ill Children with Acute Kidney Injury. Paediatr Drugs 2023:10.1007/s40272-023-00572-z. [PMID: 37266815 DOI: 10.1007/s40272-023-00572-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/03/2023]
Abstract
Acute kidney injury (AKI) is a commonly encountered comorbidity in critically ill children. The coexistence of AKI disturbs drug pharmacokinetics and pharmacodynamics, leading to clinically significant consequences. This can complicate an already critical clinical scenario by causing potential underdosing or overdosing giving way to possible therapeutic failures and adverse reactions. Current available studies offer little guidance to help maneuver such complex dosing regimens and decision-making in pediatric patients as most of them are done on heterogeneous groups of adult populations. Though there are some studies on drug dosing during continuous renal replacement therapy (CRRT), their utility is in question because of the recent advances in CRRT technology. Our review aims to discuss the principles of pharmacokinetics pertinent for honing the existing practices of drug dosing in critically ill children with AKI, and the various complexities and intricate challenges involved. This in turn will provide a framework to help enable caretakers to tailor dosing regimens in complex clinical setups with further ease and precision.
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Affiliation(s)
| | - Amani Ashraf
- Northeast Ohio Medical University, Rootstown, OH, USA
| | - Anvitha Soundararajan
- Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA
| | | | - Sidharth Kumar Sethi
- Pediatric Nephrology, Kidney Institute, Medanta, The Medicity Hospital, Gurgaon, Haryana, 122001, India.
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Telles JP, Diegues MS, Migotto KC, de Souza Borges O, Reghini R, Gavazza BV, Pinto L, Caruso P, E Silva ILF, Schmidt S, de Lima Moreira F. Failure to predict amikacin elimination in critically ill patients with cancer based on the estimated glomerular filtration rate: applying PBPK approach in a therapeutic drug monitoring study. Eur J Clin Pharmacol 2023:10.1007/s00228-023-03516-1. [PMID: 37256410 DOI: 10.1007/s00228-023-03516-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/22/2023] [Indexed: 06/01/2023]
Abstract
PURPOSE The aim of this work was to integrate the Therapeutic Drug Monitoring (TDM) with the model-informed precision dosing (MIPD) approach, using Physiologically-based Pharmacokinetic/Pharmacodynamic (PBPK/PD) modelling and simulation, to explore the relationship between amikacin exposure and estimated glomerular filtration rate (GFR) in critically ill patients with cancer. METHODS In the TDM study, samples from 51 critically-ill patients with cancer treated with amikacin were analysed. Patients were stratified according to renal function based on GFR status. A full-body PBPK model with 12 organs model was developed using Simcyp V. 21, including steady-state volume of distribution of 0.21 L/kg and renal clearance of 6.9 L/h in healthy adults. PK parameters evaluated were within the 2-fold error range. RESULTS During the validation step, predicted vs observed amikacin clearance values after single infusion dose in patients with normal renal function, mild and moderate renal impairment were 7.6 vs 8.1 L/h (7.5 mg/kg dose); 3.8 vs 4.5 L/h (1500 mg dose) and 2.2 vs 3.1 L/h (25 mg/kg dose), respectively. However, predicted vs observed amikacin clearance after a single dose infusion of 1400 mg in critically-ill patients with cancer were 1.46 vs 1.63 (P = 0.6406) L/h (severe), 2.83 vs 1.08 (P < 0.05) L/h (moderate), 4.23 vs 2.49 (P = 0.0625) L/h (mild) and 7.41 vs 3.36 (P < 0.05) L/h (normal renal function). CONCLUSION This study demonstrated that estimated GFR did not predict amikacin elimination in critically-ill patients with cancer. Further studies are necessary to find amikacin PK covariates to optimize the pharmacotherapy in this population. Therefore, TDM of amikacin is imperative in cancer patients.
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Affiliation(s)
- João Paulo Telles
- Department of Infectious Diseases, AC Camargo Cancer Center, Professor Antonio Prudente Street, N. 211, São Paulo-SP, 01509-001, Brazil.
| | | | | | | | - Rodrigo Reghini
- Department of Infectious Diseases, AC Camargo Cancer Center, Professor Antonio Prudente Street, N. 211, São Paulo-SP, 01509-001, Brazil
| | - Brenda Vianna Gavazza
- Faculty of Pharmacy, Federal University of Rio de Janeiro, Rio de Janeiro-RJ, Brazil
| | - Leonardo Pinto
- Laboratory of Immunopathology, Nucleus of Biological Sciences Research, Federal University of Ouro Preto, Ouro Preto-MG, Brazil
| | - Pedro Caruso
- Department of Intensive Care Unit, AC Camargo Cancer Center, São Paulo-SP, Brazil
| | - Ivan Leonardo França E Silva
- Department of Infectious Diseases, AC Camargo Cancer Center, Professor Antonio Prudente Street, N. 211, São Paulo-SP, 01509-001, Brazil
| | - Stephan Schmidt
- Department of Pharmaceutics Lake Nona, University of Florida, Orlando-FL, USA
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Oda K, Saito H, Jono H. Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring. Pharmacol Ther 2023; 246:108433. [PMID: 37149156 DOI: 10.1016/j.pharmthera.2023.108433] [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: 12/26/2022] [Revised: 04/19/2023] [Accepted: 05/02/2023] [Indexed: 05/08/2023]
Abstract
As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.
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Affiliation(s)
- Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan
| | - Hideyuki Saito
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan; Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University; 1-1-1, Honjo, Chuo-ku, Kumamoto, Japan
| | - Hirofumi Jono
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan; Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University; 1-1-1, Honjo, Chuo-ku, Kumamoto, Japan.
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Hashiguchi Y, Matsumoto N, Oda K, Jono H, Saito H. Population Pharmacokinetics and AUC-Guided Dosing of Tobramycin in the Treatment of Infections Caused by Glucose-Nonfermenting Gram-Negative Bacteria. Clin Ther 2023:S0149-2918(23)00128-5. [PMID: 37120413 DOI: 10.1016/j.clinthera.2023.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/06/2023] [Accepted: 03/27/2023] [Indexed: 05/01/2023]
Abstract
PURPOSE Tobramycin (TOB) exhibits variable pharmacokinetic properties due to the clinical condition of patients. This study aimed to investigate the AUC-guided dosing of TOB based on population pharmacokinetic analysis in the treatment of infections caused by Pseudomonas aeruginosa, Acinetobacter baumannii, and Stenotrophomonas maltophilia. METHODS This retrospective study was conducted between January 2010 and December 2020 after obtaining approval from our institutional review board. For 53 patients who received therapeutic drug monitoring of TOB, a population pharmacokinetic model was developed with covariates of estimated glomerular filtration rate using serum creatinine (eGFRcre) on clearance (CL) and weight on both CL and Vd in exponential error modeling (CL = 2.84 × [weight/70] × eGFRcre0.568, interindividual variability [IIV] = 31.1%; Vd = 26.3 × [weight/70], IIV = 20.2%; residual variability = 28.8%). FINDINGS The final regression model for predicting 30-day mortality was developed with risk factors of AUC during a 24-hour period after the first dose to MIC ratio (odds ratio [OR] = 0.996; 95% CI, 0.968-1.003) and serum albumin (OR = 0.137; 95% CI, 0.022-0.632). The final regression model for predicting acute kidney injury was developed with the risk factors of C-reactive protein (OR = 1.136; 95% CI, 1.040-1.266) and AUC during a 72-hour period after the first dose (OR = 1.004; 95% CI, 1.000-1.001). A dose of 8 or 15 mg/kg was beneficial for achievement of AUC during a 24-hour period after the first dose/MIC >80 and trough concentration <1 µg/mL in patients with preserved kidney function and TOB CL >4.47 L/h/70 kg in the events of MIC of 1 or 2 µg/mL, respectively. We propose that the first dose of 15, 11, 10, 8, and 7 mg/kg for eGFRcre >90, 60 to 89, 45 to 59, 30 to 44, and 15 to 29 mL/min/1.73 m2 be followed by therapeutic drug monitoring at peak and 24 hours after the first dose. IMPLICATIONS This study suggests that TOB use encourages the replacement of trough- and peak-targeted dosing with AUC-guided dosing.
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Affiliation(s)
- Yumi Hashiguchi
- Department of Pharmacy, Kumamoto University Hospital, Kumamoto, Japan
| | - Naoya Matsumoto
- Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan
| | - Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital, Kumamoto, Japan; Department of Infection Control, Kumamoto University Hospital, Kumamoto, Japan.
| | - Hirofumi Jono
- Department of Pharmacy, Kumamoto University Hospital, Kumamoto, Japan; Department of Infection Control, Kumamoto University Hospital, Kumamoto, Japan
| | - Hideyuki Saito
- Department of Pharmacy, Kumamoto University Hospital, Kumamoto, Japan; Department of Infection Control, Kumamoto University Hospital, Kumamoto, Japan
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Macente J, Nauwelaerts N, Russo FM, Deprest J, Allegaert K, Lammens B, Hernandes Bonan R, Turner JM, Kumar S, Diniz A, Martins FS, Annaert P. PBPK-based dose finding for sildenafil in pregnant women for antenatal treatment of congenital diaphragmatic hernia. Front Pharmacol 2023; 14:1068153. [PMID: 36998614 PMCID: PMC10043195 DOI: 10.3389/fphar.2023.1068153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/20/2023] [Indexed: 03/17/2023] Open
Abstract
Sildenafil is a potent vasodilator and phosphodiesterase type five inhibitor, commercially known as Revatio® and approved for the treatment of pulmonary arterial hypertension. Maternal administration of sildenafil during pregnancy is being evaluated for antenatal treatment of several conditions, including the prevention of pulmonary hypertension in fetuses with congenital diaphragmatic hernia. However, determination of a safe and effective maternal dose to achieve adequate fetal exposure to sildenafil remains challenging, as pregnancy almost always is an exclusion criterion in clinical studies. Physiologically-based pharmacokinetic (PBPK) modelling offers an attractive approach for dose finding in this specific population. The aim of this study is to exploit physiologically-based pharmacokinetic modelling to predict the required maternal dose to achieve therapeutic fetal exposure for the treatment congenital diaphragmatic hernia. A full-PBPK model was developed for sildenafil and N-desmethyl-sildenafil using the Simcyp simulator V21 platform, and verified in adult reference individuals, as well as in pregnant women, taking into account maternal and fetal physiology, along with factors known to determine hepatic disposition of sildenafil. Clinical pharmacokinetic data in mother and fetus were previously obtained in the RIDSTRESS study and were used for model verification purposes. Subsequent simulations were performed relying either on measured values for fetal fraction unbound (fu = 0.108) or on values predicted by the simulator (fu = 0.044). Adequate doses were predicted according to the efficacy target of 15 ng/mL (or 38 ng/mL) and safety target of 166 ng/mL (or 409 ng/mL), assuming measured (or predicted) fu values, respectively. Considering simulated median profiles for average steady state sildenafil concentrations, dosing regimens of 130 mg/day or 150 mg/day (administered as t.i.d.), were within the therapeutic window, assuming either measured or predicted fu values, respectively. For safety reasons, dosing should be initiated at 130 mg/day, under therapeutic drug monitoring. Additional experimental measurements should be performed to confirm accurate fetal (and maternal) values for fu. Additional characterization of pharmacodynamics in this specific population is required and may lead to further optimization of the dosing regimen.
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Affiliation(s)
- Julia Macente
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Nina Nauwelaerts
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | | | - Jan Deprest
- Gynecology and Obstetrics, UZ Leuven, Leuven, Belgium
| | - Karel Allegaert
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Clinical Pharmacy, Erasmus MC, Rotterdam, Netherlands
| | | | | | - Jessica M. Turner
- Mater Research Institute, University of Queensland, Brisbane, QLD, Australia
| | - Sailesh Kumar
- Mater Research Institute, University of Queensland, Brisbane, QLD, Australia
| | - Andrea Diniz
- Pharmacokinetics and Biopharmaceutical Laboratory (PKBio), Department of Pharmacy, State University of Maringa, Maringa, Brazil
| | - Frederico S. Martins
- Pharmacokinetics and Biopharmaceutical Laboratory (PKBio), Department of Pharmacy, State University of Maringa, Maringa, Brazil
| | - Pieter Annaert
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- BioNotus GCV, Niel, Belgium
- *Correspondence: Pieter Annaert,
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Barber J, Al-Majdoub ZM, Couto N, Howard M, Elmorsi Y, Scotcher D, Alizai N, de Wildt S, Stader F, Sepp A, Rostami-Hodjegan A, Achour B. Toward systems-informed models for biologics disposition: covariates of the abundance of the neonatal Fc Receptor (FcRn) in human tissues and implications for pharmacokinetic modelling. Eur J Pharm Sci 2023; 182:106375. [PMID: 36626943 DOI: 10.1016/j.ejps.2023.106375] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/21/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
Biologics are a fast-growing therapeutic class, with intertwined pharmacokinetics and pharmacodynamics, affected by the abundance and function of the FcRn receptor. While many investigators assume adequacy of classical models, such as allometry, for pharmacokinetic characterization of biologics, advocates of physiologically-based pharmacokinetics (PBPK) propose consideration of known systems parameters that affect the fate of biologics to enable a priori predictions, which go beyond allometry. The aim of this study was to deploy a systems-informed modelling approach to predict the disposition of Fc-containing biologics. We used global proteomics to quantify the FcRn receptor [p51 and β2-microglobulin (B2M) subunits] in 167 samples of human tissue (liver, intestine, kidney and skin) and assessed covariates of its expression. FcRn p51 subunit was highest in liver relative to other tissues, and B2M was 1-2 orders of magnitude more abundant than FcRn p51 across all sets. There were no sex-related differences, while higher expression was confirmed in neonate liver compared with adult liver. Trends of expression in liver and kidney indicated a moderate effect of body mass index, which should be confirmed in a larger sample size. Expression of FcRn p51 subunit was approximately 2-fold lower in histologically normal liver tissue adjacent to cancer compared with healthy liver. FcRn mRNA in plasma-derived exosomes correlated moderately with protein abundance in matching liver tissue, opening the possibility of use as a potential clinical tool. Predicted effects of trends in FcRn abundance in healthy and disease (cancer and psoriasis) populations using trastuzumab and efalizumab PBPK models were in line with clinical observations, and global sensitivity analysis revealed endogenous IgG plasma concentration and tissue FcRn abundance as key systems parameters influencing exposure to Fc-conjugated biologics.
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Affiliation(s)
- Jill Barber
- Centre for Applied Pharmacokinetic Research, the University of Manchester, Manchester, United Kingdom
| | - Zubida M Al-Majdoub
- Centre for Applied Pharmacokinetic Research, the University of Manchester, Manchester, United Kingdom
| | - Narciso Couto
- Centre for Applied Pharmacokinetic Research, the University of Manchester, Manchester, United Kingdom
| | - Martyn Howard
- Centre for Applied Pharmacokinetic Research, the University of Manchester, Manchester, United Kingdom
| | - Yasmine Elmorsi
- Centre for Applied Pharmacokinetic Research, the University of Manchester, Manchester, United Kingdom
| | - Daniel Scotcher
- Centre for Applied Pharmacokinetic Research, the University of Manchester, Manchester, United Kingdom
| | | | - Saskia de Wildt
- Radboud University Medical Center, Radboud University, Nijmegen, the Netherlands
| | - Felix Stader
- Certara UK Ltd. (Simcyp Division), Sheffield, United Kingdom
| | - Armin Sepp
- Certara UK Ltd. (Simcyp Division), Sheffield, United Kingdom
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, the University of Manchester, Manchester, United Kingdom; Certara UK Ltd. (Simcyp Division), Sheffield, United Kingdom
| | - Brahim Achour
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, the University of Rhode Island, 495A Avedisian Hall, 7 Greenhouse Road, Kingston, RI 02881, United States.
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Magnan A, Nicolas JF, Caimmi D, Vocanson M, Haddad T, Colas L, Scurati S, Mascarell L, Shamji MH. Deciphering Differential Behavior of Immune Responses as the Foundation for Precision Dosing in Allergen Immunotherapy. J Pers Med 2023; 13:jpm13020324. [PMID: 36836557 PMCID: PMC9964800 DOI: 10.3390/jpm13020324] [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: 12/06/2022] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
Like in many fields of medicine, the concept of precision dosing has re-emerged in routine practice in allergology. Only one retrospective study on French physicians' practice has addressed this topic so far and generated preliminary data supporting dose adaptation, mainly based on experience, patient profile understanding and response to treatment. Both intrinsic and extrinsic factors shape the individual immune system response to allergen immunotherapy (AIT). Herein, we focus on key immune cells (i.e., dendritic cells, innate lymphoid cells, B and T cells, basophils and mast cells) involved in allergic disease and its resolution to further understand the effect of AIT on the phenotype, frequency or polarization of these cells. We strive to discriminate differences in immune responses between responders and non-responders to AIT, and discuss the eligibility of a non/low-responder subset for dose adaptation. A differential behavior in immune cells is clearly observed in responders, highlighting the importance of conducting clinical trials with large cohorts of well-characterized subjects to decipher the immune mechanism of AIT. We conclude that there is a need for designing new clinical and mechanistic studies to support the scientific rationale of dose adaptation in the interest of patients who do not properly respond to AIT.
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Affiliation(s)
- Antoine Magnan
- INRAe UMR 0892, Hôpital Foch, Université de Versailles Saint Quentin, Paris-Saclay, 92150 Suresnes, France
| | - Jean-François Nicolas
- CIRI-International Center for Infectiology Research, INSERM U1111, Lyon1 University, Ecole Normale Supérieure de Lyon, CNRS, UMR 5308, 69007 Lyon, France
| | - Davide Caimmi
- Allergy Unit, Department Respiratory Medicine and Allergy, Hôpital Arnaud de Villeneuve, University Hospital of Montpellier, 34090 Montpellier, France
| | - Marc Vocanson
- CIRI-International Center for Infectiology Research, INSERM U1111, Lyon1 University, Ecole Normale Supérieure de Lyon, CNRS, UMR 5308, 69007 Lyon, France
| | - Thierry Haddad
- Dermatology, Allergology and Vascular Medicine, Tenon Hospital, 75020 Paris, France
| | - Luc Colas
- Plateforme Transversale d’Allergologie, Clinique Dermatologique, CHU de Nantes, 44093 Nantes, France
- UMR 1064, Center for Research in Transplantation and Translational Immunology, INSERM, Nantes Université, 44093 Nantes, France
| | - Silvia Scurati
- Stallergenes Greer, 92160 Antony, France
- Correspondence: ; Tel.: +33-(0)-6-12-88-40-93
| | | | - Mohamed H. Shamji
- National Heart & Lung Institute, Imperial College London, London SW7 2AZ, UK
- NIHR Imperial Biomedical Research Centre, London W2 1NY, UK
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External Evaluation of Population Pharmacokinetic Models of Methotrexate for Model-Informed Precision Dosing in Pediatric Patients with Acute Lymphoid Leukemia. Pharmaceutics 2023; 15:pharmaceutics15020569. [PMID: 36839891 PMCID: PMC9962320 DOI: 10.3390/pharmaceutics15020569] [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: 12/30/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Methotrexate (MTX) is a key immunosuppressant for children with acute lymphoid leukemia (ALL), and it has a narrow therapeutic window and relatively high pharmacokinetic variability. Several population pharmacokinetic (PopPK) models of MTX in ALL children have been reported, but the validity of these models for model-informed precision dosing in clinical practice is unclear. This study set out to evaluate the predictive performance of published pediatric PopPK models of MTX using an independent patient cohort. METHODS A PubMed literature search was performed to identify suitable models for evaluation. Demographics and measurements of the validation dataset were retrospectively collected from the medical records of ALL children who had received intravenous MTX. Predictive performance for each model was assessed by visual comparison of predictions to observations, median and mean predicted error (PE), and relative root mean squared error (RMSE). RESULTS Six models were identified for external evaluation, carried out on a dataset containing 354 concentrations from 51 pediatrics. Model performance varied considerably from one model to another. Different models had the median PE for population and individual predictions at -33.23% to 442.04% and -25.20% to 6.52%, mean PE for population and individual predictions at -25.51% to 780.87% and 1.33% to 64.44%, and RMSE for population and individual predictions at 62.88% to 1182.24% and 63.39% to 152.25%. All models showed relatively high RMSE. CONCLUSIONS Some of the published models showed reasonably low levels of bias but had some problems with imprecision, and extensive evaluation is needed before model application in clinical practice.
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Li Z, Zhang Q, He H, Sun N, Zhang R, Yang CQ, Zhao LB. Population pharmacokinetics of ruxolitinib in children with hemophagocytic lymphohistiocytosis: focus on the drug-drug interactions. Cancer Chemother Pharmacol 2023; 91:121-132. [PMID: 36510033 DOI: 10.1007/s00280-022-04494-4] [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: 08/17/2022] [Accepted: 11/15/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE The optimal dose regimen of ruxolitinib (RUX) in children with hemophagocytic lymphohistiocytosis (HLH) remains to be determined. The aim was to develop and verify a population pharmacokinetic (PPK) model, and then provide references for the optimization of dose regimen of RUX in children with HLH. METHODS A total of 189 RUX concentrations from 32 children were included. The PPK model was established using the nonlinear mixed-effects model approach. Predictive performance and stability of the final PPK model were evaluated. The exposure of RUX in different clinical scenarios was simulated through Monte Carlo simulations. RESULTS A one-compartment model with first-order absorption and linear elimination was identified to describe the disposition of RUX. The absorption rate constant (Ka) in the final PPK model was 1.05 h-1, and the apparent clearance (CL/F) and volume of distribution (V/F) were 9.80 L/h and 30.6 L, respectively. Coadministration with triazoles (TZS) and azithromycin (AZM) resulted in approximately 31.0% and 32.4% reductions in the CL/F of RUX, respectively. Multiple evaluation procedures showed satisfactory predictive performance and stability of the final model. Monte Carlo simulations showed that the exposure of RUX was significantly affected by the coadministration with TZS and/or AZM under different clinical scenarios. CONCLUSION For the first time, a PPK model of RUX in children with HLH was developed and evaluated. The coadministration with TZS and/or AZM were found to reduce the clearance of RUX in children. These findings could provide new insights for the precise treatment of RUX in children with HLH.
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Affiliation(s)
- Zhuo Li
- Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.,School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Qing Zhang
- Hematology Center, Beijing Key Laboratory of Pediatric Hematology Oncology, National Key Discipline of Pediatrics (Capital Medical University), Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Huan He
- Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Ning Sun
- Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Rui Zhang
- Hematology Center, Beijing Key Laboratory of Pediatric Hematology Oncology, National Key Discipline of Pediatrics (Capital Medical University), Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Chang-Qing Yang
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
| | - Li-Bo Zhao
- Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
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Zhao J, Zhu X, Tan S, Chen C, Kaddoumi A, Guo XL, Lin YW, Cheung SYA. Editorial: Model-informed drug development and evidence-based translational pharmacology. Front Pharmacol 2022; 13:1086551. [PMID: 36578539 PMCID: PMC9791580 DOI: 10.3389/fphar.2022.1086551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
- Jinxin Zhao
- Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University, Melbourne, VIC, Australia
| | - Xiao Zhu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Songwen Tan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Chuanpin Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Amal Kaddoumi
- Department of Drug Discovery and Development, Harrison College of Pharmacy, Auburn University, Auburn, AL, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Xiu-Li Guo
- Department of Pharmacology, School of Pharmaceutical Science, Shandong University, Jinan, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Yu-Wei Lin
- Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University, Melbourne, VIC, Australia,Malaya Translational and Clinical Pharmacometrics Group, Faculty of Pharmacy, University of Malaya, Kuala Lumpur, Malaysia,Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, University of Malaya, Kuala Lumpur, Malaysia,Integrated Drug Development, Certara, NJ, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - S. Y. Amy Cheung
- Integrated Drug Development, Certara, NJ, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
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43
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Achour B, Rostami-Hodjegan A. Is Liquid Biopsy Only Restricted to Diagnostics or Can it Go Beyond the Confines of Genotyping and Phenotyping for Quantitative Pharmacology? Clin Pharmacol Ther 2022; 112:1152-1153. [PMID: 36130176 DOI: 10.1002/cpt.2732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/06/2022] [Indexed: 01/31/2023]
Affiliation(s)
- Brahim Achour
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, the University of Rhode Island, Kingston, Rhode Island, USA
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester, UK.,Certara, Princeton, New Jersey, USA
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44
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Shen X, Chen X, Lu J, Chen Q, Li W, Zhu J, He Y, Guo H, Xu C, Fan X. Pharmacogenetics-based population pharmacokinetic analysis and dose optimization of valproic acid in Chinese southern children with epilepsy: Effect of ABCB1 gene polymorphism. Front Pharmacol 2022; 13:1037239. [PMID: 36506519 PMCID: PMC9733833 DOI: 10.3389/fphar.2022.1037239] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/17/2022] [Indexed: 11/26/2022] Open
Abstract
Objective: The aim of this study was to establish a population pharmacokinetic (PPK) model of valproic acid (VPA) in pediatric patients with epilepsy in southern China, and provide guidance for individualized medication of VPA therapy. Methods: A total of 376 VPA steady-state trough concentrations were collected from 103 epileptic pediatric patients. The PPK parameter values for VPA were calculated by using the nonlinear mixed-effects modeling (NONMEM) method, and a one-compartment model with first-order absorption and elimination processes was applied. Covariates included demographic information, concomitant medications and selected gene polymorphisms. Goodness-of-fit (GOF), bootstrap analysis, and visual predictive check (VPC) were used for model evaluation. In addition, we used Monte Carlo simulations to propose dose recommendations for different subgroup patients. Results: A significant effect of the patient age and ABCB1 genotypes was observed on the VPA oral clearance (CL/F) in the final PPK model. Compared with patients with the ABCB1 rs3789243 AA genotype, CL/F in patients with GG and AG genotypes was increased by 8% and reduced by 4.7%, respectively. The GOF plots indicated the satisfactory predictive performance of the final model, and the evaluation by bootstrap and VPC showed that a stable model had been developed. A table of individualized dosing regimens involving age and ABCB1 genotype was constructed based on the final PPK model. Conclusion: This study quantitatively investigated the effects of patient age and ABCB1 rs3789243 variants on the pharmacokinetic variability of VPA. The PPK models could be beneficial to individual dose optimization in epileptic children on VPA therapy.
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Affiliation(s)
- Xianhuan Shen
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China,College of Pharmacy, Jinan University, Guangzhou, China
| | - Xinyi Chen
- School of Pharmaceutical Sciences, Health Science Center, Shenzhen University, Shenzhen, China
| | - Jieluan Lu
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China,College of Pharmacy, Jinan University, Guangzhou, China
| | - Qing Chen
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China
| | - Wenzhou Li
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China
| | - Jiahao Zhu
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China,College of Pharmacy, Jinan University, Guangzhou, China
| | - Yaodong He
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China,College of Pharmacy, Jinan University, Guangzhou, China
| | - Huijuan Guo
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China
| | - Chenshu Xu
- School of Pharmaceutical Sciences, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xiaomei Fan
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China,*Correspondence: Xiaomei Fan,
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Mostafa S, Polasek TM, Bousman C, Rostami‐Hodjegan A, Sheffield LJ, Everall I, Pantelis C, Kirkpatrick CMJ. Delineating gene-environment effects using virtual twins of patients treated with clozapine. CPT Pharmacometrics Syst Pharmacol 2022; 12:168-179. [PMID: 36424701 PMCID: PMC9931435 DOI: 10.1002/psp4.12886] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/27/2022] Open
Abstract
Studies that focus on individual covariates, while ignoring their interactions, may not be adequate for model-informed precision dosing (MIPD) in any given patient. Genetic variations that influence protein synthesis should be studied in conjunction with environmental covariates, such as cigarette smoking. The aim of this study was to build virtual twins (VTs) of real patients receiving clozapine with interacting covariates related to genetics and environment and to delineate the impact of interacting covariates on predicted clozapine plasma concentrations. Clozapine-treated patients with schizophrenia (N = 42) with observed clozapine plasma concentrations, demographic, environmental, and genotype data were used to construct VTs in Simcyp. The effect of increased covariate virtualization was assessed by performing simulations under three conditions: "low" (demographic), "medium" (demographic and environmental interaction), and "high" (demographic and environmental/genotype interaction) covariate virtualization. Increasing covariate virtualization with interaction improved the coefficient of variation (R2 ) from 0.07 in the low model to 0.391 and 0.368 in the medium and high models, respectively. Whereas R2 was similar between the medium and high models, the high covariate virtualization model had improved accuracy, with systematic bias of predicted clozapine plasma concentration improving from -138.48 ng/ml to -74.65 ng/ml. A high level of covariate virtualization (demographic, environmental, and genotype) may be required for MIPD using VTs.
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Affiliation(s)
- Sam Mostafa
- Centre for Medicine Use and SafetyMonash UniversityVictoriaParkvilleAustralia,MyDNA LifeAustralia LimitedVictoriaSouth YarraAustralia
| | - Thomas M. Polasek
- Centre for Medicine Use and SafetyMonash UniversityVictoriaParkvilleAustralia,CertaraNew JerseyPrincetonUSA,Department of Clinical PharmacologyRoyal Adelaide HospitalSouth AustraliaAdelaideAustralia
| | - Chad Bousman
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Alberta Children's Hospital Research Institute, Cumming School of MedicineUniversity of CalgaryAlbertaCalgaryCanada,Hotchkiss Brain Institute, Cumming School of MedicineUniversity of CalgaryAlbertaCalgaryCanada,Departments of Medical Genetics, Psychiatry, and Physiology and PharmacologyUniversity of CalgaryAlbertaCalgaryCanada
| | - Amin Rostami‐Hodjegan
- Centre for Applied Pharmacokinetic Research (CAPKR), School of Health SciencesUniversity of ManchesterManchesterUK,Simcyp DivisionCertara UK LimitedSheffieldUK
| | | | - Ian Everall
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Western Australian Health Translation NetworkNedlandsWestern AustraliaAustralia,Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneVictoriaMelbourneAustralia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneVictoriaMelbourneAustralia
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Oda K. Development of Novel Dosing Strategy According to the Area under the Concentration-Time Curve for Vancomycin. YAKUGAKU ZASSHI 2022; 142:1185-1190. [DOI: 10.1248/yakushi.22-00131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital
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47
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Takesue Y, Hanai Y, Oda K, Hamada Y, Ueda T, Mayumi T, Matsumoto K, Fujii S, Takahashi Y, Miyazaki Y, Kimura T. Clinical Practice Guideline for the Therapeutic Drug Monitoring of Voriconazole in Non-Asian and Asian Adult Patients: Consensus Review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring. Clin Ther 2022; 44:1604-1623. [DOI: 10.1016/j.clinthera.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/18/2022] [Accepted: 10/28/2022] [Indexed: 11/23/2022]
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[Progress and Application of Bayesian Approach in the Early Research and Development of New Anticancer Drugs]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:730-734. [PMID: 36285392 PMCID: PMC9619348 DOI: 10.3779/j.issn.1009-3419.2022.102.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Bayesian statistics is an approach for learning from evidences as it accumulates, combining prior distribution with current information on a quantity of interest, in which posterior distribution and inferences are being updated each time new data become available using Bayes' Theorem. Though frequentist approach has dominated medical studies, Bayesian approach has been more and more widely recognized by its flexibility and efficiency. Research and development (R&D) on anti-cancer new drugs have been so hot globally in recent years in spite of relatively high failure rate. It is the common demand of pharmaceutical enterprises and researchers to identify the optimal dose, regime and right population in the early-phase R&D stage more accurately and efficiently, especially when the following three major changes have been observed. The R&D on anticancer drugs have transformed from chemical drugs to biological products, from monotherapy to combination therapy, and the study design has also gradually changed from traditional way to innovative and adaptive mode. This also raises a number of subsequent challenges on decision-making of early R&D, such as inability to determine MTD, flexibility to deal with delayed toxicity, delayed response and dose-response changing relationships. It is because of the above emerging changes and challenges that the Bayesian approach is getting more and more attention from the industry. At least, Bayesian approach has more information for decision-making, which could potentially help enterprises achieve higher efficiency, shorter period and lower investment. This study also expounds the application of Bayesian statistics in the early R&D on anticancer new drugs, and compares and analyzes its idea and application scenarios with frequentist statistics, aiming to provide macroscopic and systematic reference for all related stakeholders.
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Zhu X, Zhang M, Wen Y, Shang D. Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example. Front Pharmacol 2022; 13:994665. [PMID: 36324679 PMCID: PMC9621318 DOI: 10.3389/fphar.2022.994665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/03/2022] [Indexed: 11/24/2022] Open
Abstract
Background and Aim: Many studies associated with the combination of machine learning (ML) and pharmacometrics have appeared in recent years. ML can be used as an initial step for fast screening of covariates in population pharmacokinetic (popPK) models. The present study aimed to integrate covariates derived from different popPK models using ML. Methods: Two published popPK models of valproic acid (VPA) in Chinese epileptic patients were used, where the population parameters were influenced by some covariates. Based on the covariates and a one-compartment model that describes the pharmacokinetics of VPA, a dataset was constructed using Monte Carlo simulation, to develop an XGBoost model to estimate the steady-state concentrations (Css) of VPA. We utilized SHapley Additive exPlanation (SHAP) values to interpret the prediction model, and calculated estimates of VPA exposure in four assumed scenarios involving different combinations of CYP2C19 genotypes and co-administered antiepileptic drugs. To develop an easy-to-use model in the clinic, we built a simplified model by using CYP2C19 genotypes and some noninvasive clinical parameters, and omitting several features that were infrequently measured or whose clinically available values were inaccurate, and verified it on our independent external dataset. Results: After data preprocessing, the finally generated combined dataset was divided into a derivation cohort and a validation cohort (8:2). The XGBoost model was developed in the derivation cohort and yielded excellent performance in the validation cohort with a mean absolute error of 2.4 mg/L, root-mean-squared error of 3.3 mg/L, mean relative error of 0%, and percentages within ±20% of actual values of 98.85%. The SHAP analysis revealed that daily dose, time, CYP2C19*2 and/or *3 variants, albumin, body weight, single dose, and CYP2C19*1*1 genotype were the top seven confounding factors influencing the Css of VPA. Under the simulated dosage regimen of 500 mg/bid, the VPA exposure in patients who had CYP2C19*2 and/or *3 variants and no carbamazepine, phenytoin, or phenobarbital treatment, was approximately 1.74-fold compared to those with CYP2C19*1/*1 genotype and co-administered carbamazepine + phenytoin + phenobarbital. The feasibility of the simplified model was fully illustrated by its performance in our external dataset. Conclusion: This study highlighted the bridging role of ML in big data and pharmacometrics, by integrating covariates derived from different popPK models.
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Affiliation(s)
- Xiuqing Zhu
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Ming Zhang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yuguan Wen
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- *Correspondence: Yuguan Wen, ; Dewei Shang,
| | - Dewei Shang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- *Correspondence: Yuguan Wen, ; Dewei Shang,
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Mostafa S, Polasek TM, Bousman CA, Müeller DJ, Sheffield LJ, Rembach J, Kirkpatrick CM. Pharmacogenomics in psychiatry - the challenge of cytochrome P450 enzyme phenoconversion and solutions to assist precision dosing. Pharmacogenomics 2022; 23:857-867. [PMID: 36169629 DOI: 10.2217/pgs-2022-0104] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Pharmacogenomic (PGx) testing of cytochrome P450 (CYP) enzymes may improve the efficacy and/or safety of some medications. This is facilitated by increased availability and affordability of genotyping, the development of clinical practice PGx guidelines and regulatory support. However, the common occurrence of CYP phenoconversion, a mismatch between genotype-predicted CYP phenotype and the actual CYP phenotype, currently limits the application of PGx testing for precision dosing in psychiatry. This review proposes a stepwise approach to assist precision dosing in psychiatry via the introduction of PGx stewardship programs and innovative PGx education strategies. A future perspective on delivering precision dosing for psychiatrists is discussed that involves innovative clinical decision support systems powered by model-informed precision dosing.
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Affiliation(s)
- Sam Mostafa
- Centre for Medicine Use & Safety, Monash University, Parkville, Victoria, 3052, Australia.,MyDNA Life, Australia Limited, South Yarra, Victoria, Australia
| | - Thomas M Polasek
- Centre for Medicine Use & Safety, Monash University, Parkville, Victoria, 3052, Australia.,Certara, Princeton, NJ 08540, USA.,Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, 5000, Australia
| | - Chad A Bousman
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, Victoria, 3010, Australia.,The Cooperative Research Centre (CRC) for Mental Health, Carlton, Victoria, 3053, Australia.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.,Departments of Medical Genetics, Psychiatry, & Physiology & Pharmacology, University of Calgary, Calgary, Alberta, T2N 1N4, Canada
| | - Daniel J Müeller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, M5T 1R8, Canada
| | | | - Joel Rembach
- MyDNA Life, Australia Limited, South Yarra, Victoria, Australia
| | - Carl Mj Kirkpatrick
- Centre for Medicine Use & Safety, Monash University, Parkville, Victoria, 3052, Australia
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