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Jiang X, Yu KS, Nam DH, Oh J. A Population Pharmacokinetic Study to Compare a Novel Empagliflozin L-Proline Formulation with Its Conventional Formulation in Healthy Subjects. Pharmaceuticals (Basel) 2024; 17:522. [PMID: 38675482 PMCID: PMC11054906 DOI: 10.3390/ph17040522] [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: 03/11/2024] [Revised: 04/06/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
Empagliflozin is a sodium-glucose cotransporter 2 (SGLT2) inhibitor that is commonly used for the treatment of type 2 diabetes mellitus (T2DM). CKD-370 was newly developed as a cocrystal formulation of empagliflozin with co-former L-proline, which has been confirmed to be bioequivalent in South Korea. This study aimed to quantify the differences in the absorption phase and pharmacokinetic (PK) parameters of two empagliflozin formulations in healthy subjects by using population PK analysis. The plasma concentration data of empagliflozin were obtained from two randomized, open-label, crossover, phase 1 clinical studies in healthy Korean subjects after a single-dose administration. A population PK model was constructed by using a nonlinear mixed-effects (NLME) approach (Monolix Suite 2021R1). Interindividual variability (IIV) and interoccasion variability (IOV) were investigated. The final model was evaluated by goodness-of-fit (GOF) diagnostic plots, visual predictive checks (VPCs), prediction errors, and bootstrapping. The PK of empagliflozin was adequately described with a two-compartment combined transit compartment model with first-order absorption and elimination. Log-transformed body weight significantly influenced systemic clearance (CL) and the volume of distribution in the peripheral compartment (V2) of empagliflozin. GOF plots, VPCs, prediction errors, and the bootstrapping of the final model suggested that the proposed model was adequate and robust, with good precision at different dose strengths. The cocrystal form did not affect the absorption phase of the drug, and the PK parameters were not affected by the different treatments.
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Affiliation(s)
- Xu Jiang
- Department of Pharmacology, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea;
| | - Kyung-Sang Yu
- Department of Clinical Pharmacology and Therapeutics, College of Medicine, Seoul National University and Hospital, Seoul 03080, Republic of Korea;
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
| | - Dong Hyuk Nam
- Department of Chemical Research Laboratory, Chong Kun Dang Research Institute, Chong Kun Dang Pharmaceutical Corporation, Yongin 16995, Republic of Korea;
| | - Jaeseong Oh
- Department of Pharmacology, College of Medicine, Jeju National University, Jeju 63243, Republic of Korea
- Clinical Research Institute, Jeju National University Hospital, Jeju 63243, Republic of Korea
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Corral Alaejos Á, Zarzuelo Castañeda A, Jiménez Cabrera S, Sánchez-Guijo F, Otero MJ, Pérez-Blanco JS. External evaluation of population pharmacokinetic models of imatinib in adults diagnosed with chronic myeloid leukaemia. Br J Clin Pharmacol 2021; 88:1913-1924. [PMID: 34705297 DOI: 10.1111/bcp.15122] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/29/2021] [Accepted: 10/21/2021] [Indexed: 12/30/2022] Open
Abstract
AIMS Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions. METHODS A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis. RESULTS Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions. CONCLUSIONS Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.
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Affiliation(s)
| | | | | | - Fermín Sánchez-Guijo
- Institute for Biomedical Research of Salamanca, Salamanca, Spain.,Haematology Department, University Hospital of Salamanca, Salamanca, Spain.,Department of Medicine, University of Salamanca, Salamanca, Spain
| | - María José Otero
- Pharmacy Service, University Hospital of Salamanca, Salamanca, Spain.,Institute for Biomedical Research of Salamanca, Salamanca, Spain
| | - Jonás Samuel Pérez-Blanco
- Department of Pharmaceutical Sciences, Pharmacy Faculty, University of Salamanca, Salamanca, Spain.,Institute for Biomedical Research of Salamanca, Salamanca, Spain
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Kristoffersson AN, Rognås V, Brill MJE, Dishon-Benattar Y, Durante-Mangoni E, Daitch V, Skiada A, Lellouche J, Nutman A, Kotsaki A, Andini R, Eliakim-Raz N, Bitterman R, Antoniadou A, Karlsson MO, Theuretzbacher U, Leibovici L, Daikos GL, Mouton JW, Carmeli Y, Paul M, Friberg LE. Population pharmacokinetics of colistin and the relation to survival in critically ill patients infected with colistin susceptible and carbapenem-resistant bacteria. Clin Microbiol Infect 2020; 26:1644-1650. [PMID: 32213316 DOI: 10.1016/j.cmi.2020.03.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 02/26/2020] [Accepted: 03/15/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVES The aim was to analyse the population pharmacokinetics of colistin and to explore the relationship between colistin exposure and time to death. METHODS Patients included in the AIDA randomized controlled trial were treated with colistin for severe infections caused by carbapenem-resistant Gram-negative bacteria. All subjects received a 9 million units (MU) loading dose, followed by a 4.5 MU twice daily maintenance dose, with dose reduction if creatinine clearance (CrCL) < 50 mL/min. Individual colistin exposures were estimated from the developed population pharmacokinetic model and an optimized two-sample per patient sampling design. Time to death was evaluated in a parametric survival analysis. RESULTS Out of 406 randomized patients, 349 contributed pharmacokinetic data. The median (90% range) colistin plasma concentration was 0.44 (0.14-1.59) mg/L at 15 minutes after the end of first infusion. In samples drawn 10 hr after a maintenance dose, concentrations were >2 mg/L in 94% (195/208) and 44% (38/87) of patients with CrCL ≤120 mL/min, and >120 mL/min, respectively. Colistin methanesulfonate sodium (CMS) and colistin clearances were strongly dependent on CrCL. High colistin exposure to MIC ratio was associated with increased hazard of death in the multivariate analysis (adjusted hazard ratio (95% CI): 1.07 (1.03-1.12)). Other significant predictors included SOFA score at baseline (HR 1.24 (1.19-1.30) per score increase), age and Acinetobacter or Pseudomonas as index pathogen. DISCUSSION The population pharmacokinetic model predicted that >90% of the patients had colistin concentrations >2 mg/L at steady state, but only 66% at 4 hr after start of treatment. High colistin exposure was associated with poor kidney function, and was not related to a prolonged survival.
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Affiliation(s)
- A N Kristoffersson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - V Rognås
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - M J E Brill
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Y Dishon-Benattar
- Institute of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel; The Cheryl Spencer Institute for Nursing Research, University of Haifa, Israel
| | - E Durante-Mangoni
- Department of Precision Medicine, University of Campania 'L Vanvitelli' and AORN dei Colli-Monaldi Hospital, Napoli, Italy
| | - V Daitch
- Infectious Diseases University Research Centre, Rabin Medical Centre, Beilinson Hospital, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, and Department of Medicine E, Rabin Medical Centre, Beilinson Hospital, Petah Tikva, Israel
| | - A Skiada
- First Department of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - J Lellouche
- National Centre for Infection Control and Antibiotic Resistance, Tel Aviv Medical Centre, Tel Aviv, Israel; National Laboratory for Antibiotic Resistance and Investigation of Outbreaks in Medical Institutions, Tel Aviv Medical Centre, Tel Aviv, Israel
| | - A Nutman
- National Centre for Infection Control and Antibiotic Resistance, Tel Aviv Medical Centre, Tel Aviv, Israel
| | - A Kotsaki
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, School of Medicine, University General Hospital Attikon, Athens, Greece
| | - R Andini
- Department of Precision Medicine, University of Campania 'L Vanvitelli' and AORN dei Colli-Monaldi Hospital, Napoli, Italy
| | - N Eliakim-Raz
- Infectious Diseases University Research Centre, Rabin Medical Centre, Beilinson Hospital, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, and Department of Medicine E, Rabin Medical Centre, Beilinson Hospital, Petah Tikva, Israel
| | - R Bitterman
- Institute of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel; The Ruth and Bruce Rappaport Faculty of Medicine, Techion - Israel Institute of Technology, Haifa, Israel
| | - A Antoniadou
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, School of Medicine, University General Hospital Attikon, Athens, Greece
| | - M O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - L Leibovici
- Sackler Faculty of Medicine, Tel-Aviv University, and Department of Medicine E, Rabin Medical Centre, Beilinson Hospital, Petah Tikva, Israel; Department of Medicine E, Rabin Medical Centre, Beilinson Hospital, Petah Tikva, Israel
| | - G L Daikos
- First Department of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - J W Mouton
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, the Netherlands
| | - Y Carmeli
- National Centre for Infection Control and Antibiotic Resistance, Tel Aviv Medical Centre, Tel Aviv, Israel; National Laboratory for Antibiotic Resistance and Investigation of Outbreaks in Medical Institutions, Tel Aviv Medical Centre, Tel Aviv, Israel
| | - M Paul
- Institute of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel; The Ruth and Bruce Rappaport Faculty of Medicine, Techion - Israel Institute of Technology, Haifa, Israel
| | - L E Friberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
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Beechinor RJ, Thompson PA, Hwang MF, Vargo RC, Bomgaars LR, Gerhart JG, Dreyer ZE, Gonzalez D. The Population Pharmacokinetics of High-Dose Methotrexate in Infants with Acute Lymphoblastic Leukemia Highlight the Need for Bedside Individualized Dose Adjustment: A Report from the Children's Oncology Group. Clin Pharmacokinet 2019; 58:899-910. [PMID: 30810947 PMCID: PMC6658326 DOI: 10.1007/s40262-018-00734-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
BACKGROUND Infants with acute lymphoblastic leukemia (ALL) treated with high-dose methotrexate may have reduced methotrexate clearance (CL) due to renal immaturity, which may predispose them to toxicity. OBJECTIVE The aim of this study was to develop a population pharmacokinetic (PK) model of methotrexate in infants with ALL. METHODS A total of 672 methotrexate plasma concentrations were obtained from 71 infants enrolled in the Children's Oncology Group (COG) Clinical Trial P9407. Infants received methotrexate 4 g/m2 intravenously for four cycles during weeks 4-12 of intensification. A population PK analysis was performed using NONMEM® version 7.4. The final model was evaluated using a non-parametric bootstrap and a visual predictive check. Simulations were performed to evaluate methotrexate dose and the utility of a bedside algorithm for dose individualization. RESULTS Methotrexate was best characterized by a two-compartment model with allometric scaling. Weight was the only covariate included in the final model. The coefficient of variation for interoccasion variability (IOV) on CL was relatively high at 25.4%, compared with the interindividual variability for CL and central volume of distribution (10.7% and 13.2%, respectively). Simulations identified that 21.1% of simulated infants benefitted from bedside dose adjustment, and adjustment of methotrexate doses during infusions can avoid supratherapeutic concentrations. CONCLUSION Infants treated with high-dose methotrexate demonstrated a relatively high degree of IOV in methotrexate CL. The magnitude of IOV in the CL of methotrexate suggests that use of a bedside algorithm may avoid supratherapeutic methotrexate concentrations resulting from high IOV in methotrexate CL.
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Affiliation(s)
- Ryan J Beechinor
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, CB #7569, Chapel Hill, NC, 27599-7569, USA
| | - Patrick A Thompson
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
| | - Michael F Hwang
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, CB #7569, Chapel Hill, NC, 27599-7569, USA
| | - Ryan C Vargo
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Lisa R Bomgaars
- Texas Children's Cancer and Hematology Center, Baylor College of Medicine, Houston, TX, USA
| | - Jacqueline G Gerhart
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, CB #7569, Chapel Hill, NC, 27599-7569, USA
| | - ZoAnn E Dreyer
- Texas Children's Cancer and Hematology Center, Baylor College of Medicine, Houston, TX, USA
| | - Daniel Gonzalez
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, CB #7569, Chapel Hill, NC, 27599-7569, USA.
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Bon C, Toutain PL, Concordet D, Gehring R, Martin-Jimenez T, Smith J, Pelligand L, Martinez M, Whittem T, Riviere JE, Mochel JP. Mathematical modeling and simulation in animal health. Part III: Using nonlinear mixed-effects to characterize and quantify variability in drug pharmacokinetics. J Vet Pharmacol Ther 2018; 41:171-183. [PMID: 29226975 DOI: 10.1111/jvp.12473] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 11/16/2017] [Indexed: 01/12/2023]
Abstract
A common feature of human and veterinary pharmacokinetics is the importance of identifying and quantifying the key determinants of between-patient variability in drug disposition and effects. Some of these attributes are already well known to the field of human pharmacology such as bodyweight, age, or sex, while others are more specific to veterinary medicine, such as species, breed, and social behavior. Identification of these attributes has the potential to allow a better and more tailored use of therapeutic drugs both in companion and food-producing animals. Nonlinear mixed effects (NLME) have been purposely designed to characterize the sources of variability in drug disposition and response. The NLME approach can be used to explore the impact of population-associated variables on the relationship between drug administration, systemic exposure, and the levels of drug residues in tissues. The latter, while different from the method used by the US Food and Drug Administration for setting official withdrawal times (WT) can also be beneficial for estimating WT of approved animal drug products when used in an extralabel manner. Finally, NLME can also prove useful to optimize dosing schedules, or to analyze sparse data collected in situations where intensive blood collection is technically challenging, as in small animal species presenting limited blood volume such as poultry and fish.
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Affiliation(s)
- C Bon
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - P L Toutain
- Department of Veterinary Basic Sciences, Royal Veterinary College, Hatfield, UK
| | - D Concordet
- Toxalim, Research Centre in Food Toxicology, Toulouse, France
- Université de Toulouse, ENVT, INP, Toxalim, Toulouse, France
- Laboratoire de Physiologie et Thérapeutique, École Nationale Vétérinaire de Toulouse INRA, UMR 1331, Toulouse, France
| | - R Gehring
- Department of Anatomy and Physiology, College of Veterinary Medicine, Institute of Computational Comparative Medicine (ICCM), Kansas State University, Manhattan, KS, USA
| | - T Martin-Jimenez
- Department of Comparative Medicine, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA
| | - J Smith
- Veterinary Diagnostic and Production Animal Medicine, Iowa State University College of Veterinary Medicine, Ames, IA, USA
| | - L Pelligand
- Department of Veterinary Basic Sciences, Royal Veterinary College, Hatfield, UK
| | - M Martinez
- Center for Veterinary Medicine, US Food and Drug Administration, Rockville, MD, USA
| | - T Whittem
- Translational Research and Animal Clinical Trials (TRACTs) Group, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Werribee, Vic., Australia
| | - J E Riviere
- Department of Anatomy and Physiology, College of Veterinary Medicine, Institute of Computational Comparative Medicine (ICCM), Kansas State University, Manhattan, KS, USA
| | - J P Mochel
- Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA, USA
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Wiczling P, Daghir-Wojtkowiak E, Yumba Mpanga A, Szczesny D, Kaliszan R, Markuszewski MJ. How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective. J Sep Sci 2017; 40:4667-4676. [DOI: 10.1002/jssc.201700918] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 09/25/2017] [Accepted: 10/06/2017] [Indexed: 12/21/2022]
Affiliation(s)
- Paweł Wiczling
- Department of Biopharmaceutics and Pharmacodynamics; Medical University of Gdańsk; Gdańsk Poland
| | - Emilia Daghir-Wojtkowiak
- Department of Biopharmaceutics and Pharmacodynamics; Medical University of Gdańsk; Gdańsk Poland
| | - Arlette Yumba Mpanga
- Department of Biopharmaceutics and Pharmacodynamics; Medical University of Gdańsk; Gdańsk Poland
| | - Damian Szczesny
- Department of Biopharmaceutics and Pharmacodynamics; Medical University of Gdańsk; Gdańsk Poland
| | - Roman Kaliszan
- Department of Biopharmaceutics and Pharmacodynamics; Medical University of Gdańsk; Gdańsk Poland
| | - Michał Jan Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics; Medical University of Gdańsk; Gdańsk Poland
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Dickstein Y, Leibovici L, Yahav D, Eliakim-Raz N, Daikos GL, Skiada A, Antoniadou A, Carmeli Y, Nutman A, Levi I, Adler A, Durante-Mangoni E, Andini R, Cavezza G, Mouton JW, Wijma RA, Theuretzbacher U, Friberg LE, Kristoffersson AN, Zusman O, Koppel F, Dishon Benattar Y, Altunin S, Paul M. Multicentre open-label randomised controlled trial to compare colistin alone with colistin plus meropenem for the treatment of severe infections caused by carbapenem-resistant Gram-negative infections (AIDA): a study protocol. BMJ Open 2016; 6:e009956. [PMID: 27098822 PMCID: PMC4838684 DOI: 10.1136/bmjopen-2015-009956] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The emergence of antibiotic-resistant bacteria has driven renewed interest in older antibacterials, including colistin. Previous studies have shown that colistin is less effective and more toxic than modern antibiotics. In vitro synergy studies and clinical observational studies suggest a benefit of combining colistin with a carbapenem. A randomised controlled study is necessary for clarification. METHODS AND ANALYSIS This is a multicentre, investigator-initiated, open-label, randomised controlled superiority 1:1 study comparing colistin monotherapy with colistin-meropenem combination therapy for infections caused by carbapenem-resistant Gram-negative bacteria. The study is being conducted in 6 centres in 3 countries (Italy, Greece and Israel). We include patients with hospital-associated and ventilator-associated pneumonia, bloodstream infections and urosepsis. The primary outcome is treatment success at day 14, defined as survival, haemodynamic stability, stable or improved respiratory status for patients with pneumonia, microbiological cure for patients with bacteraemia and stability or improvement of the Sequential Organ Failure Assessment (SOFA) score. Secondary outcomes include 14-day and 28-day mortality as well as other clinical end points and safety outcomes. A sample size of 360 patients was calculated on the basis of an absolute improvement in clinical success of 15% with combination therapy. Outcomes will be assessed by intention to treat. Serum colistin samples are obtained from all patients to obtain population pharmacokinetic models. Microbiological sampling includes weekly surveillance samples with analysis of resistance mechanisms and synergy. An observational trial is evaluating patients who met eligibility requirements but were not randomised in order to assess generalisability of findings. ETHICS AND DISSEMINATION The study was approved by ethics committees at each centre and informed consent will be obtained for all patients. The trial is being performed under the auspices of an independent data and safety monitoring committee and is included in a broad dissemination strategy regarding revival of old antibiotics. TRIAL REGISTRATION NUMBER NCT01732250 and 2012-004819-31; Pre-results.
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Affiliation(s)
- Yaakov Dickstein
- Division of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel
| | - Leonard Leibovici
- Department of Medicine E, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Israel
| | - Dafna Yahav
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Israel
- Unit of Infectious Diseases, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel
| | - Noa Eliakim-Raz
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Israel
- Unit of Infectious Diseases, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel
| | - George L Daikos
- First Department of Medicine, University of Athens, Athens, Greece
| | - Anna Skiada
- First Department of Medicine, University of Athens, Athens, Greece
| | | | - Yehuda Carmeli
- Division of Epidemiology and Preventive Medicine, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Amir Nutman
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Israel
- Division of Epidemiology and Preventive Medicine, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Inbar Levi
- Division of Epidemiology and Preventive Medicine, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Amos Adler
- Microbiology Laboratory, Tel Aviv Sourasky Medical Centre, Tel-Aviv, Israel
| | | | - Roberto Andini
- Internal Medicine, Second University of Naples, Monaldi Hospital-AORN dei Colli, Napoli, Italy
| | - Giusi Cavezza
- Internal Medicine, Second University of Naples, Monaldi Hospital-AORN dei Colli, Napoli, Italy
| | - Johan W Mouton
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, The Netherlands
- Department of Medical Microbiology, Radboudumc, Nijmegen, The Netherlands
| | - Rixt A Wijma
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, The Netherlands
| | | | - Lena E Friberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Oren Zusman
- Department of Medicine E, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Israel
| | - Fidi Koppel
- Division of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel
| | | | - Sergey Altunin
- Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel
| | - Mical Paul
- Division of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel
- Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel
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