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  • 1.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data2019In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 85, no 6, p. 1326-1336Article in journal (Refereed)
    Abstract [en]

    AIMS: This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example.

    METHODS: We assessed five model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error (PE) percentiles.

    RESULTS: When IOV was lower than IIV, the accuracy was good for all approaches (50th percentile of the PE [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios.

    CONCLUSIONS: Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualisation is to include IOV in the generation of the EBEs, but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

  • 2.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala Univ, Dept Pharmaceut Biosci, Uppsala, Sweden..
    Korth-Bradley, J.
    Pfizer Inc, Collegeville, PA USA..
    Harnisch, L.
    Pfizer Ltd, Global Clin Pharmacol, Sandwich, Kent, England..
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Elucidation of Factor VIII Activity Pharmacokinetics: A Pooled Population Analysis in Patients With Hemophilia A Treated With Moroctocog Alfa2017In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 102, no 6, p. 977-988Article in journal (Refereed)
    Abstract [en]

    This study investigated the disposition of coagulation factor VIII activity in 754 patients with moderate to severe hemophilia A following the administration of moroctocog alfa, a B-domain deleted recombinant factor VIII. Data analyzed included patients aged 1 day to 73 years enrolled in 13 studies conducted over a period of 20 years in 25 countries. A two-compartment population pharmacokinetic model with a baseline model described the pooled data well. Body size, age, inhibitors, race, and analytical assay were identified as significant predictors of factor VIII disposition. In addition, simulations of prophylactic dosing schedules in several pediatric cohorts showed large variability and suggest that younger patients would require higher weight-adjusted doses than adolescents to achieve target factor VIII trough activity when receiving every other day or twice weekly dosing.

  • 3.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Solms, Alexander
    Bayer, Berlin, Germany.
    Garmann, Dirk
    Bayer, Wuppertal, Germany.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bayesian Forecasting Utilizing Bleeding Information to Support Dose Individualization of Factor VIII2019In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 8, no 12, p. 894-903Article in journal (Refereed)
    Abstract [en]

    Bayesian forecasting for dose individualization of prophylactic factor VIII replacement therapy using pharmacokinetic samples is challenged by large interindividual variability in the bleeding risk. A pharmacokinetic‐repeated time‐to‐event model‐based forecasting approach was developed to contrast the ability to predict the future occurrence of bleeds based on individual (i) pharmacokinetic, (ii) bleeding, and (iii) pharmacokinetic, bleeding and covariate information using observed data from the Long‐Term Efficacy Open‐Label Program in Severe Hemophilia A Disease (LEOPOLD) clinical trials (172 severe hemophilia A patients taking prophylactic treatment). The predictive performance assessed by the area under receiver operating characteristic (ROC) curves was 0.67 (95% confidence interval (CI), 0.65–0.69), 0.78 (95% CI, 0.76–0.80), and 0.79 (95% CI, 0.77–0.81) for patients ≥ 12 years when using pharmacokinetics, bleeds, and all data, respectively, suggesting that individual bleed information adds value to the optimization of prophylactic dosing regimens in severe hemophilia A. Further steps to optimize the proposed tool for factor VIII dose adaptation in the clinic are required.

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  • 4.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Solms, Alexander
    Garmann, Dirk
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Relationship between factor VIII activity, bleeds and individual characteristics in severe hemophilia A patients2020In: Haematologica, ISSN 0390-6078, E-ISSN 1592-8721, Vol. 105, no 5, p. 1443-1453Article in journal (Refereed)
    Abstract [en]

    Pharmacokinetic-based prophylaxis of replacement factor VIII products has been encouraged in the past years, but the exposure (factor VIII activity)-response (bleeding frequency) relationship remains unclear. The aim of this study was to characterize the relationship between factor VIII dose, plasma factor VIII activity, bleeding patterns and individual characteristics in severe hemophilia A patients. Pooled pharmacokinetic and bleeding data during prophylactic treatment with BAY 81-8973 (octocog alfa) were obtained from the three LEOPOLD trials. The population pharmacokinetics of factor VIII activity and longitudinal bleeding frequency, as well as bleeding severity, were described using nonlinear mixed effects modelling in NONMEM. In total, 183 patients (median age 22 years [range, 1-61]; weight 60 kg [11-124]) contributed with 1535 plasma factor VIII activity observations, 633 bleeds and 11 patient/study characteristics (median observation period 12 months [3.1-13.1]). A parametric repeated time-to-categorical bleed model, guided by plasma factor VIII activity from a 2-compartment population pharmacokinetic model, described the time to the occurrence of bleeds and their severity. Bleeding probability decreased with time of study, and a bleed was not found to affect the time of the next bleed. Several covariate effects were identified, including the bleeding history in the 12-month pre-study period increasing the bleeding hazard. However, unexplained inter-patient variability for the phenotypic bleeding pattern remained large (111%CV). Further studies to translate the model into a tool for dose individualization that considers the individual bleeding risk are required. Research based on a post-hoc analysis of the LEOPOLD studies (ClinicalTrials.gov identifiers NCT01029340, NCT01233258 and NCT01311648).

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  • 5.
    Abrantes, João
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Solms, Alexander
    Bayer, Berlin, Germany.
    Garmann, Dirk
    Bayer, Wuppertal, Germany.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Relationship between factor VIII activity, bleeds and individual characteristics in severe hemophilia A patientsIn: Article in journal (Refereed)
  • 6.
    Bahnasawy, Salma M.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Skorup, Paul
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine.
    Hanslin, Katja
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Lipcsey, Miklós
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care, Hedenstierna laboratory.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Predicting cytokine kinetics during sepsis; a modelling framework from a porcine sepsis model with live Escherichia coli2023In: Cytokine, ISSN 1043-4666, E-ISSN 1096-0023, Vol. 169, article id 156296Article in journal (Refereed)
    Abstract [en]

    Background: Describing the kinetics of cytokines involved as biomarkers of sepsis progression could help to optimise interventions in septic patients. This work aimed to quantitively characterise the cytokine kinetics upon exposure to live E. coli by developing an in silico model, and to explore predicted cytokine kinetics at different bacterial exposure scenarios.

    Methods: Data from published in vivo studies using a porcine sepsis model were analysed. A model describing the time courses of bacterial dynamics, endotoxin (ETX) release, and the kinetics of TNF and IL-6 was developed. The model structure was extended from a published model that quantifies the ETX-cytokines relationship. An external model evaluation was conducted by applying the model to literature data. Model simulations were performed to explore the sensitivity of the host response towards differences in the input rate of bacteria, while keeping the total bacterial burden constant.

    Results: The analysis included 645 observations from 30 animals. The blood bacterial count was well described by a one-compartment model with linear elimination. A scaling factor was estimated to quantify the ETX release by bacteria. The model successfully described the profiles of TNF, and IL-6 without a need to modify the ETXcytokines model structure. The kinetics of TNF, and IL-6 in the external datasets were well predicted. According to the simulations, the ETX tolerance development results in that low initial input rates of bacteria trigger the lowest cytokine release.

    Conclusion: The model quantitively described and predicted the cytokine kinetics triggered by E. coli exposure. The host response was found to be sensitive to the bacterial exposure rate given the same total bacterial burden.

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  • 7.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Berntorp, Erik
    Skane Univ Hosp, Clin Coagulat Res Unit, Malmo, Sweden.
    Jensen, Kirsten
    Skane Univ Hosp, Clin Coagulat Res Unit, Malmo, Sweden.
    Nielsen, Elisabet I
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Population Pharmacokinetics of Plasma-Derived Factor IX: Procedures for Dose Individualization2016In: Journal of Thrombosis and Haemostasis, ISSN 1538-7933, E-ISSN 1538-7836, Vol. 14, no 4, p. 724-732Article in journal (Refereed)
    Abstract [en]

    Background: Population pharmacokinetic (POPPK) models describing factor IX (FIX) activity levels in plasma, in combination with individual FIX measurements, may be used to individualize dosing in the treatment of hemophilia B. Objectives: The aim was to reevaluate a previously developed POPPK model for FIX activity and to explore the number and timing of FIX samples required in pharmacokinetic (PK) dose individualization. Methods: The POPPK model was reevaluated using an extended data set. Several sampling schedules, varying with respect to the timing and number of samples, were evaluated in a simulation study with relative dose errors compared between schedules. The performance of individually calculated doses was compared with commonly prescribed FIX doses with respect to the number of patients with a trough FIX activity > 0.01 U mL(-1). Results and conclusions: A three-compartment PK model best described the FIX activity levels. The number and timing of samples greatly influenced imprecision in dose prediction. Schedules with single samples taken on both day 2 and day 3 were identified as being convenient schedules with an acceptable performance level. Individually calculated doses performed better with respect to patient target attainment than a fixed 40 U kg(-1) dose regardless of how many samples were available to calculate individual doses. The results of this study suggest that PK dose tailoring with limited sampling may be applicable for plasma-derived FIX products.

  • 8. Brem, Jürgen
    et al.
    Panduwawala, Tharindi
    Hansen, Jon Ulf
    Hewitt, Joanne
    Liepins, Edgars
    Donets, Pawel
    Espina, Laura
    Farley, Alistair J M
    Shubin, Kirill
    Campillos, Gonzalo Gomez
    Kiuru, Paula
    Shishodia, Shifali
    Krahn, Daniel
    Leśniak, Robert K
    Schmidt Adrian, Juliane
    Calvopiña, Karina
    Turrientes, María-Carmen
    Kavanagh, Madeline E
    Lubriks, Dmitrijs
    Hinchliffe, Philip
    Langley, Gareth W
    Aboklaish, Ali F
    Eneroth, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Backlund, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Baran, Andrei G
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Speake, Michael
    Kuka, Janis
    Robinson, John
    Grinberga, Solveiga
    Robinson, Lindsay
    McDonough, Michael A
    Rydzik, Anna M
    Leissing, Thomas M
    Jimenez-Castellanos, Juan Carlos
    Avison, Matthew B
    Da Silva Pinto, Solange
    Pannifer, Andrew D
    Martjuga, Marina
    Widlake, Emma
    Priede, Martins
    Hopkins Navratilova, Iva
    Gniadkowski, Marek
    Belfrage, Anna Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Drug Design and Discovery.
    Brandt, Peter
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Drug Design and Discovery.
    Yli-Kauhaluoma, Jari
    Bacque, Eric
    Page, Malcolm G P
    Björkling, Fredrik
    Tyrrell, Jonathan M
    Spencer, James
    Lang, Pauline A
    Baranczewski, Pawel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Cantón, Rafael
    McElroy, Stuart P
    Jones, Philip S
    Baquero, Fernando
    Suna, Edgars
    Morrison, Angus
    Walsh, Timothy R
    Schofield, Christopher J
    Imitation of β-lactam binding enables broad-spectrum metallo-β-lactamase inhibitors2022In: Nature Chemistry, ISSN 1755-4330, E-ISSN 1755-4349, Vol. 14, no 1, p. 15-24Article in journal (Refereed)
    Abstract [en]

    Carbapenems are vital antibiotics, but their efficacy is increasingly compromised by metallo-β-lactamases (MBLs). Here we report the discovery and optimization of potent broad-spectrum MBL inhibitors. A high-throughput screen for NDM-1 inhibitors identified indole-2-carboxylates (InCs) as potential β-lactamase stable β-lactam mimics. Subsequent structure-activity relationship studies revealed InCs as a new class of potent MBL inhibitor, active against all MBL classes of major clinical relevance. Crystallographic studies revealed a binding mode of the InCs to MBLs that, in some regards, mimics that predicted for intact carbapenems, including with respect to maintenance of the Zn(II)-bound hydroxyl, and in other regards mimics binding observed in MBL-carbapenem product complexes. InCs restore carbapenem activity against multiple drug-resistant Gram-negative bacteria and have a low frequency of resistance. InCs also have a good in vivo safety profile, and when combined with meropenem show a strong in vivo efficacy in peritonitis and thigh mouse infection models.

  • 9.
    Brill, Margreke J. E.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kristoffersson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Zhao, Chenyan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations2018In: Clinical Microbiology and Infection, ISSN 1198-743X, E-ISSN 1469-0691, Vol. 24, no 7, p. 697-706Article, review/survey (Refereed)
    Abstract [en]

    Background: Deriving suitable dosing regimens for antibiotic combination therapy poses several challenges as the drug interaction can be highly complex, the traditional pharmacokinetic-pharmacodynamic (PKPD) index methodology cannot be applied straightforwardly, and exploring all possible dose combinations is unfeasible. Therefore, semi-mechanistic PKPD models developed based on in vitro single and combination experiments can be valuable to suggest suitable combination dosing regimens. Aims: To outline how the interaction between two antibiotics has been characterized in semi-mechanistic PKPD models. We also explain how such models can be applied to support dosing regimens and design future studies. Sources: PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations. Content: Thirteen publications were identified where ten had applied subpopulation synergy to characterize the combined effect, i.e. independent killing rates for each drug and bacterial subpopulation. We report the various types of interaction functions that have been used to describe the combined drug effects and that characterized potential deviations from additivity under the PKPD model. Simulations from the models had commonly been performed to compare single versus combined dosing regimens and/or to propose improved dosing regimens.

  • 10.
    Bulman, Zackery P.
    et al.
    Univ Illinois, Dept Pharm Practice, Chicago, IL 60612 USA..
    Wicha, Sebastian G.
    Univ Hamburg, Inst Pharm, Dept Clin Pharm, Hamburg, Germany..
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Lenhard, Justin R.
    Calif Northstate Univ, Dept Clin & Adm Sci, Coll Pharm, Elk Grove, CA USA..
    Nation, Roger L.
    Monash Univ, Monash Inst Pharmaceut Sci, Drug Delivery Disposit & Dynam, Melbourne, Vic, Australia..
    Theuretzbacher, Ursula
    Ctr Antiinfect Agents, Vienna, Austria..
    Derendorf, Hartmut
    Univ Florida, Coll Pharm, Dept Pharmaceut, Gainesville, FL 32610 USA..
    Tängdén, Thomas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine.
    Zeitlinger, Markus
    Med Univ Vienna, Dept Clin Pharmacol, Vienna, Austria..
    Landersdorfer, Cornelia B.
    Monash Univ, Ctr Med Use & Safety, Monash Inst Pharmaceut Sci, Melbourne, Vic, Australia..
    Bulitta, Jürgen B.
    Univ Florida, Coll Pharm, Dept Pharmacotherapy & Translat Res, Orlando, FL USA..
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Li, Jian
    Monash Univ, Monash Biomed Discovery Inst, Infect & Immun Program, Melbourne, Vic, Australia.;Monash Univ, Dept Microbiol, Melbourne, Vic, Australia..
    Tsuji, Brian T.
    Univ Buffalo, Dept Pharm Practice, Buffalo, NY USA..
    Research priorities towards precision antibiotic therapy to improve patient care2022In: LANCET MICROBE, ISSN 2666-5247, Vol. 3, no 10, p. e795-e802Article in journal (Refereed)
    Abstract [en]

    Antibiotic resistance presents an incessant threat to our drug armamentarium that necessitates novel approaches to therapy. Over the past several decades, investigation of pharmacokinetic and pharmacodynamic (PKPD) principles has substantially improved our understanding of the relationships between the antibiotic, pathogen, and infected patient. However, crucial gaps in our understanding of the pharmacology of antibacterials and their optimal use in the care of patients continue to exist; simply attaining antibiotic exposures that are considered adequate based on traditional targets can still result in treatment being unsuccessful and resistance proliferation for some infections. It is this salient paradox that points to key future directions for research in antibiotic therapeutics. This Personal View discusses six priority areas for antibiotic pharmacology research: (1) antibiotic-pathogen interactions, (2) antibiotic targets for combination therapy, (3) mechanistic models that describe the time-course of treatment response, (4) understanding and modelling of host response to infection, (5) personalised medicine through therapeutic drug management, and (6) application of these principles to support development of novel therapies. Innovative approaches that enhance our understanding of antibiotic pharmacology and facilitate more accurate predictions of treatment success, coupled with traditional pharmacology research, can be applied at the population level and to individual patients to improve outcomes.

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  • 11.
    Cam, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Hospital Pharmacy Department, Uppsala University Hospital, Sweden.
    Gillespie, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Hospital Pharmacy Department, Uppsala University Hospital, Sweden.
    Kälvemark Sporrong, Sofia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Kempen, Thomas Gerardus Hendrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Franzon, Kristin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Clinical geriatrics.
    Failure to Involve Older Hospitalised Patients in Medication Decisions: A Change of Approach is Called For2024In: Research in Social and Administrative Pharmacy, ISSN 1551-7411, E-ISSN 1934-8150, Vol. 20, no 2, p. 216-217Article in journal (Refereed)
    Abstract [en]

    Background: Patient involvement in medical-decision making is linked to improved patient outcomes and increased patient satisfaction.

    Objectives: The aim was to explore how hospitalised older patients are and wish to be involved in medication decisions affecting their medication therapy after hospital discharge.

    Methods: Naturalistic observations of consultations between healthcare professionals and hospitalised older patients who were about to be discharged were performed at in total three medical wards at two hospitals in Sweden. Subsequent semi-structured interviews with the patients were conducted within one week after discharge. The data were thematically analysed, guided by systematic text condensation.

    Results: Twenty patients were included (mean age: 81 (SD 8) years, 45 % female). Three themes were identified: 1) Predetermined authoritarian structures; describes that neither patients nor healthcare professionals expected patients to be involved in medication decisions. The medication decisions were frequently already taken by the healthcare professionals prior to the consultations, 2) Difficulties in finding the right time and setting; displays inhibitory factors in patient involvement in medication decisions when the consultations occur in hospital, and 3) Communication focusing on benefits over side-effects; demonstrates that newly prescribed medications were rarely accompanied with information about side-effects. Patients felt they lacked sufficient knowledge to take informed decisions about medications.

    Conclusions: There are structures limiting involvement of older patients in medication decisions prior to hospital discharge. A change in approach to consultations from both the patients and healthcare professionals is needed to provide patients with the knowledge they feel is needed to be sufficiently involved.

  • 12.
    Cam, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala Univ Hosp, Hosp Pharm Dept, Uppsala, Sweden..
    Wennlöf, Björn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Centre for Clinical Research, County of Västmanland. Narvarden Viksang Irsta, Reg Vastmanland, Västerås, Sweden..
    Gillespie, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala Univ Hosp, Hosp Pharm Dept, Uppsala, Sweden..
    Franzon, Kristin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Clinical geriatrics.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Ling, Mia
    Reg Vastmanland, Dept Pharm, Västerås, Sweden..
    Lindner, Karl-Johan
    Reg Vastmanland, Dept Pharm, Västerås, Sweden..
    Kempen, Thomas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Univ Utrecht, Utrecht Inst Pharmaceut Sci, Utrecht, Netherlands..
    Kälvemark Sporrong, Sofia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Univ Copenhagen, Dept Pharm, Copenhagen, Denmark..
    The complexities of communication at hospital discharge of older patients: a qualitative study of healthcare professionals' views2023In: BMC Health Services Research, E-ISSN 1472-6963, Vol. 23, article id 1211Article in journal (Refereed)
    Abstract [en]

    Background: Hospital discharge of older patients is a high-risk situation in terms of patient safety. Due to the fragmentation of the healthcare system, communication and coordination between stakeholders are required at discharge. The aim of this study was to explore communication in general and medication information transfer in particular at hospital discharge of older patients from the perspective of healthcare professionals (HCPs) across different organisations within the healthcare system.

    Methods: We conducted a qualitative study using focus group and individual or group interviews with HCPs (physicians, nurses and pharmacists) across different healthcare organisations in Sweden. Data were collected from September to October 2021. A semi-structured interview guide including questions on current medication communication practices, possible improvements and feedback on suggestions for alternative processes was used. The data were analysed thematically, guided by the systematic text condensation method.

    Results: In total, four focus group and three semi-structured interviews were conducted with 23 HCPs. Three main themes were identified: 1) Support systems that help and hinder describes the use of support systems in the discharge process to compensate for the fragmentation of the healthcare system and the impact of these systems on HCPs' communication; 2) Communication between two separate worlds depicts the difficulties in communication experienced by HCPs in different healthcare organisations and how they cope with them; and 3) The large number of medically complex patients disrupts the communication reveals how the highly pressurised healthcare system impacts on HCPs' communication at hospital discharge.

    Conclusions: Communication at hospital discharge is hindered by the fragmented, highly pressurised healthcare system. HCPs are at risk of moral distress when coping with communication difficulties. Improved communication methods at hospital discharge are needed for the benefit of both patients and HCPs.

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  • 13. De Cock, Roosmarijn F W
    et al.
    Allegaert, Karel
    Sherwin, Catherine M T
    Nielsen, Elisabet I
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    de Hoog, Matthijs
    van den Anker, Johannes N
    Danhof, Meindert
    Knibbe, Catherijne A J
    A Neonatal Amikacin Covariate Model Can Be Used to Predict Ontogeny of Other Drugs Eliminated Through Glomerular Filtration in Neonates2014In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 31, no 3, p. 754-767Article in journal (Refereed)
    Abstract [en]

    PURPOSE

    Recently, a covariate model characterizing developmental changes in clearance of amikacin in neonates has been developed using birth bodyweight and postnatal age. The aim of this study was to evaluate whether this covariate model can be used to predict maturation in clearance of other renally excreted drugs.

    METHODS

    Five different neonatal datasets were available on netilmicin, vancomycin, tobramycin and gentamicin. The extensively validated covariate model for amikacin clearance was used to predict clearance of these drugs. In addition, independent reference models were developed based on a systematic covariate analysis.

    RESULTS

    The descriptive and predictive properties of the models developed using the amikacin covariate model were good, and fairly similar to the independent reference models (goodness-of-fit plots, NPDE). Moreover, similar clearance values were obtained for both approaches. Finally, the same covariates as in the covariate model of amikacin, i.e. birth bodyweight and postnatal age, were identified on clearance in the independent reference models.

    CONCLUSIONS

    This study shows that pediatric covariate models may contain physiological information since information derived from one drug can be used to describe other drugs. This semi-physiological approach may be used to optimize sparse data analysis and to derive individualized dosing algorithms for drugs in children.

  • 14. Di Paolo, Antonello
    et al.
    Tascini, Carlo
    Polillo, Marialuisa
    Gemignani, Giulia
    Nielsen, Elisabet I
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bocci, Guido
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Menichetti, Francesco
    Danesi, Romano
    Population pharmacokinetics of daptomycin in patients affected by severe Gram-positive infections2013In: International Journal of Antimicrobial Agents, ISSN 0924-8579, E-ISSN 1872-7913, Vol. 42, no 3, p. 250-255Article in journal (Refereed)
    Abstract [en]

    A population pharmacokinetic analysis of daptomycin was performed based on therapeutic drug monitoring (TDM) data from 58 patients receiving doses of 4–12 mg/kg for the treatment of severe Gram-positive infections. At a daily dose of 8 mg/kg, daptomycin plasma concentrations (mean ± S.D.) were 76.9 ± 9.8 mg/L at the end of infusion and 52.7 ± 15.4 mg/L and 11.4 ± 5.4 mg/L at 0.5 h and 23 h after drug administration, respectively. The final model was a one-compartmental model with first-order elimination, with estimated clearance (CL) of 0.80 ± 0.14 L/h and a volume of distribution (Vd) of 0.19 ± 0.05 L/kg. Creatinine clearance (CLCr) was identified as having a significant influence on daptomycin CL, and a decrease in CLCr of 30 mL/min from the median value (80 mL/min) was associated with a reduction of daptomycin CL from 0.80 L/h to 0.73 L/h. These results confirm that the presence of severe infection may be associated with an altered disposition of daptomycin, with an increased Vd. MICs were available in 41 patients and results showed that 38 and 31 subjects achieved AUC/MIC values associated with bacteriostatic (>400) and bactericidal effects (>800), respectively. Of note, 31 of these 41 subjects experienced a clinical improvement or were cured. Although daptomycin pharmacokinetics may be influenced by infections, effective AUC/MIC values were achieved in the majority of patients. The present model may be applied in clinical settings for a TDM routine on the basis of a sparse blood sampling protocol.

  • 15.
    Germovsek, Eva
    et al.
    UCL, Inst Child Hlth, Inflammat Infect & Rheumatol Sect, London, England..
    Kent, Alison
    St Georges Univ London, Inst Infect & Immun, Paediat Infect Dis Res Grp, London, England..
    Metsvaht, Tuuli
    Univ Tartu, Dept Microbiol, Tartu, Estonia..
    Lutsar, Irja
    Univ Tartu, Dept Microbiol, Tartu, Estonia..
    Klein, Nigel
    UCL, Inst Child Hlth, Inflammat Infect & Rheumatol Sect, London, England..
    Turner, Mark A.
    Univ Liverpool, Inst Translat Med, Dept Womens & Childrens Hlth, Liverpool, Merseyside, England..
    Sharland, Mike
    St Georges Univ London, Inst Infect & Immun, Paediat Infect Dis Res Grp, London, England..
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Heath, Paul T.
    St Georges Univ London, Inst Infect & Immun, Paediat Infect Dis Res Grp, London, England..
    Standing, Joseph F.
    UCL, Inst Child Hlth, Inflammat Infect & Rheumatol Sect, London, England..
    Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants2016In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 60, no 8, p. 4869-4877Article in journal (Refereed)
    Abstract [en]

    Trough gentamicin therapeutic drug monitoring (TDM) is time-consuming, disruptive to neonatal clinical care, and a patient safety issue. Bayesian models could allow TDM to be performed opportunistically at the time of routine blood tests. This study aimed to develop and prospectively evaluate a new gentamicin model and a novel Bayesian computer tool (neoGent) for TDM use in neonatal intensive care. We also evaluated model performance for predicting peak concentrations and the area under the concentration-time curve from time 0 h to time t h (AUC(0-t)). A pharmacokinetic meta-analysis was performed on pooled data from three studies (1,325 concentrations from 205 patients). A 3-compartment model was used with the following covariates: allometric weight scaling, postmenstrual and postnatal age, and serum creatinine concentration. Final parameter estimates (standard errors) were as follows: clearance, 6.2 (0.3) liters/h/70 kg of body weight; central volume (V), 26.5 (0.6) liters/70 kg; intercompartmental disposition (Q), 2.2 (0.3) liters/h/70 kg; peripheral volume V2, 21.2 (1.5) liters/70 kg; intercompartmental disposition (Q2), 0.3 (0.05) liters/h/70 kg; peripheral volume V3, 148 (52.0) liters/70 kg. The model's ability to predict trough concentrations from an opportunistic sample was evaluated in a prospective observational cohort study that included data from 163 patients and 483 concentrations collected in five hospitals. Unbiased trough predictions were obtained; the median (95% confidence interval [CI]) prediction error was 0.0004 (-1.07, 0.84) mg/liter. Results also showed that peaks and AUC(0-t) values could be predicted (from one randomly selected sample) with little bias but relative imprecision, with median (95% CI) prediction errors being 0.16 (-4.76, 5.01) mg/liter and 10.8 (-24.9, 62.2) mg center dot h/liter, respectively. neoGent was implemented in R/NONMEM and in the freely available TDMx software.

  • 16.
    Gordi, Toufigh
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Yu, Zuoxiang
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry.
    Westerlund, Douglas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry.
    Ashton, Michael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Direct analysis of artemisinin in plasma and saliva using coupled-column high-performance liquid chromatography with a restricted-access material pre-column2000In: Journal of Chromatography B: Biomedical Sciences and Applications, ISSN 1387-2273, E-ISSN 1878-5603, Vol. 742, no 1, p. 155-162Article in journal (Refereed)
    Abstract [en]

    A previously established HPLC system with post-column derivatization for the analysis of artemisinin was coupled to an ADS (alkyl-diol silica) pre-column, allowing direct and repetitive injection of protein-rich fluids such as plasma. The limit of quantitation for 100 μl of plasma was 10 ng/ml (CV=10.5%) while concentrations down to 2 ng/ml could be quantified for 1.00 ml saliva samples (CV=11.1%). The system was linear in the tested range of 10–2000 ng/ml for plasma and 2–240 ng/ml for saliva samples, respectively. This paper introduces coupled column HPLC as a simplified method for the routine analysis of artemisinin in biological fluids.

  • 17. Jensen, Kirsten
    et al.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Berntorp, Erik
    Population pharmacokinetics of plasma-derived factor IX2014In: Haemophilia, ISSN 1351-8216, E-ISSN 1365-2516, Vol. 20, no S3, p. 80-80Article in journal (Other academic)
  • 18.
    Johansson, Anna
    et al.
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Artillerigatan 12, S-58758 Linköping, Sweden..
    Ahlner, Johan
    Linköping Univ, Div Clin Chem & Pharmacol, Dept Biomed & Clin Sci, Linköping, Sweden..
    Lennborn, Ulrica
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sandler, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Forensic Medicine. Natl Board Forens Med, Dept Forens Med, Uppsala, Sweden..
    Rubertsson, Sten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Kronstrand, Robert
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Artillerigatan 12, S-58758 Linköping, Sweden.;Linköping Univ, Div Clin Chem & Pharmacol, Dept Biomed & Clin Sci, Linköping, Sweden..
    Kugelberg, Fredrik C.
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Artillerigatan 12, S-58758 Linköping, Sweden.;Linköping Univ, Div Clin Chem & Pharmacol, Dept Biomed & Clin Sci, Linköping, Sweden..
    Quantitation of seven sedative and analgesic drugs in whole blood from intensive care patients using liquid chromatography mass spectrometry2021In: TOXICOLOGIE ANALYTIQUE ET CLINIQUE, ISSN 2352-0078, Vol. 33, no 4, p. 327-337Article in journal (Refereed)
    Abstract [en]

    We present the development and validation of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantification of clonidine, dexmedetomidine, fentanyl, ketamine, ketobemidone, midazolam and morphine in whole blood. These are drugs predominately used in intensive care units (ICUs) but they are also encountered in forensic investigations. The analytes were recovered from 0.25 g of blood by protein precipitation with a mixture of acetonitrile and ethanol. Separation was performed on a BEH phenyl column. Mobile phases consisted of 0.05% formic acid in 10 mM ammonium formate and 0.05% formic acid in methanol, respectively, and the flow rate was 600 mu L/min. The mass spectrometer was operated in positive electrospray ionization mode with multiple reaction monitoring. Validation included selectivity, qualitative matrix effects, calibration model, limit of detection, lower limit of quantification, within- and between-day accuracy and precision, process efficiency, dilution integrity, carry over and stability. Selectivity was high and no ion suppression or enhancementwas observed in the areas were the analytes eluted. Calibration curves were linear over arange of 0.25-50 ng/g for dexmedetomidine, 0.05-50 ng/g for fentanyl and 5.0-500 ng/g formorphine and quadratic over a range of 0.5-50 ng/g for clonidine, 50-5000 ng/g for ketamine, 5.0-500 ng/g for ketobemidone and midazolam. The method showed acceptable within- and betweenday accuracies and precisions. All analytes were stable in whole blood for three weeksat 4. C. Concentrations in patient samples ranged between 42-760 ng/g for midazolam (n = 15), 0.3-1.5 ng/g for dexmedetomidine (n = 13), 0.6-6.4 ng/g for clonidine (n = 13), 8-62 ng/g for morphine (n = 16), 5-19 ng/g for ketobemidone (n = 5), 0.07-3.1 ng/g for fentanyl (n = 43), and 562000 ng/g for ketamine (n = 10). We conclude that the method was successfully validatedand applied to ante-mortem and post-mortem blood samples from critically ill adult patientsin a general ICU.

  • 19.
    Johansson, Anna
    et al.
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Linkoping, Sweden..
    Lindstedt, Daniel
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Linkoping, Sweden..
    Roman, Markus
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Linkoping, Sweden..
    Thelander, Gunilla
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Linkoping, Sweden..
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lennborn, Ulrica
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Sandler, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Forensic Medicine. Natl Board Forens Med, Dept Forens Med, Uppsala, Sweden.
    Rubertsson, Sten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Ahlner, Johan
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Linkoping, Sweden.;Linkoping Univ, Dept Med & Hlth Sci, Div Drug Res, Linkoping, Sweden..
    Kronstrand, Robert
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Linkoping, Sweden.;Linkoping Univ, Dept Med & Hlth Sci, Div Drug Res, Linkoping, Sweden..
    Kugelberg, Fredrik C.
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, Linkoping, Sweden.;Linkoping Univ, Dept Med & Hlth Sci, Div Drug Res, Linkoping, Sweden..
    A non-fatal intoxication and seven deaths involving the dissociative drug 3-MeO-PCP2017In: Forensic Science International, ISSN 0379-0738, E-ISSN 1872-6283, Vol. 275, p. 76-82Article in journal (Refereed)
    Abstract [en]

    Introduction: 3-methoxyphencyclidine (3-MeO-PCP) appeared on the illicit drug market in 2011 and is an analogue of phencyclidine, which exhibits anesthetic, analgesic and hallucinogenic properties. In this paper, we report data from a non-fatal intoxication and seven deaths involving 3-MeO-PCP in Sweden during the period March 2014 until June 2016. Case descriptions: The non-fatal intoxication case, a 19-year-old male with drug problems and a medical history of depression, was found awake but tachycardic, hypertensive, tachypnoeic and catatonic at home. After being hospitalized, his condition worsened as he developed a fever and lactic acidosis concomitant with psychomotor agitation and hallucinations. After 22 h of intensive care, the patient had made a complete recovery. During his hospitalization, a total of four blood samples were collected at different time points. The seven autopsy cases, six males and one female, were all in their twenties to thirties with psychiatric problems and/or an ongoing drug abuse. Methods: 3-MeO-PCP was identified with liquid chromatography (LC)/time-of-flight technology and quantified using LC-tandem mass spectrometry. Results: In the clinical case, the concentration of 3-MeO-PCP was 0.14 mu g/g at admission, 0.08 mu g/g 2.5 h after admission, 0.06 mu g/g 5 h after admission and 0.04 mu g/g 17 h after admission. The half-life of 3-MeO-PCP was estimated to 11 h. In the autopsy cases, femoral blood concentrations ranged from 0.05 mu g/g to 0.38 mu g/g. 3-MeO-PCP was the sole finding in the case with the highest concentration and the cause of death was established as intoxication with 3-MeO-PCP. In the remaining six autopsy cases, other medications and drugs of abuse were present as well. Conclusion: Despite being scheduled in January 2015, 3-MeO-PCP continues to be abused in Sweden. Exposure to 3-MeO-PCP may cause severe adverse events and even death, especially if the user does not receive life-supporting treatment.

  • 20.
    Kempen, Thomas
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis. Hospital Pharmacy Department, Uppsala University Hospital.
    Bertilsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Uppsala Clinical Research Center (UCR).
    Hadziosmanovic, Nermin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Uppsala Clinical Research Center (UCR).
    Lindner, Karl-Johan
    Pharmacy Department, Region Västmanland, Västerås.
    Melhus, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Sulku, Johanna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Centre for Research and Development, Gävleborg. Pharmacy Department, Region Gävleborg, Gävle.
    Gillespie, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Hospital Pharmacy Department, Uppsala University Hospital, Uppsala.
    Effects of Hospital-Based Comprehensive Medication Reviews Including Postdischarge Follow-up on Older Patients' Use of Health Care: A Cluster Randomized Clinical Trial2021In: JAMA Network Open, E-ISSN 2574-3805, Vol. 4, no 4, p. 67article id e216303Article in journal (Refereed)
    Abstract [en]

    Importance: Suboptimal use of medications is a leading cause of health care–related harm. Medication reviews improve medication use, but evidence of the possible benefit of inpatient medication review for hard clinical outcomes after discharge is scarce.

    Objective: To study the effects of hospital-based comprehensive medication reviews (CMRs), including postdischarge follow-up of older patients’ use of health care resources, compared with only hospital-based reviews and usual care.

    Design, Setting, and Participants: The Medication Reviews Bridging Healthcare trial is a cluster randomized crossover trial that was conducted in 8 wards with multiprofessional teams at 4 hospitals in Sweden from February 6, 2017, to October 19, 2018, with 12 months of follow-up completed December 6, 2019. The study was prespecified in the trial protocol. Outcome assessors were blinded to treatment allocation. In total, 2644 patients aged 65 years or older who had been admitted to 1 of the study wards for at least 1 day were included. Data from the modified intention-to-treat population were analyzed from December 10, 2019, to September 9, 2020.

    Interventions: Each ward participated in the trial for 6 consecutive 8-week periods. The wards were randomized to provide 1 of 3 treatments during each period: CMR, CMR plus postdischarge follow-up, and usual care without a clinical pharmacist.

    Main Outcomes and Measures: The primary outcome measure was the incidence of unplanned hospital visits (admissions plus emergency department visits) within 12 months. Secondary outcomes included medication-related admissions, visits with primary care clinicians, time to first unplanned hospital visit, mortality, and costs of hospital-based care.

    Results: Of the 2644 participants, 7 withdrew after inclusion, leaving 2637 for analysis (1357 female [51.5%]; median age, 81 [interquartile range, 74-87] years; median number of medications, 9 [interquartile range, 5-13]). In the modified intention-to-treat analysis, 922 patients received CMR, 823 received CMR plus postdischarge follow-up, and 892 received usual care. The crude incidence rate of unplanned hospital visits was 1.77 per patient-year in the total study population. The primary outcome did not differ between the intervention groups and usual care (adjusted rate ratio, 1.04 [95% CI, 0.89-1.22] for CMR and 1.15 [95% CI, 0.98-1.34] for CMR plus postdischarge follow-up). However, CMR plus postdischarge follow-up was associated with an increased incidence of emergency department visits within 12 months (adjusted rate ratio, 1.29; 95% CI, 1.05-1.59) compared with usual care. There were no differences between treatment groups regarding other secondary outcomes.

    Conclusions and Relevance: In this study of older hospitalized patients, CMR plus postdischarge follow-up did not decrease the incidence of unplanned hospital visits. The findings do not support the performance of hospital-based CMRs as conducted in this trial. Alternative forms of medication reviews that aim to improve older patients’ health outcomes should be considered and subjected to randomized clinical trials.

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  • 21.
    Kempen, Thomas
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis.
    Bertilsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
    Lindner, Karl-Johan
    Pharmacy Department, Västmanland County Council, Västerås, Sweden.
    Sulku, Johanna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Research and Development, Gävleborg. Reg Gavleborg, Dept Dev, Gavle, Sweden.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Högberg, Angelica
    Reg Gavleborg, Dept Dev, Gavle, Sweden.
    Vikerfors, Tomas
    Vasteras Hosp, Dept Infect Dis, Vasteras, Sweden.
    Melhus, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis.
    Gillespie, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Medication Reviews Bridging Healthcare (MedBridge): Study protocol for a pragmatic cluster-randomised crossover trial2017In: Contemporary Clinical Trials, ISSN 1551-7144, E-ISSN 1559-2030, Vol. 61, p. 126-132, article id S1551-7144(16)30455-4Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Mismanaged prescribing and use of medication among elderly puts major pressure on current healthcare systems. Performing a medication review, a structured critical examination of a patient's medications, during hospital stay with active follow-up into primary care could optimise treatment benefit and minimise harm. However, a lack of high quality evidence inhibits widespread implementation. This manuscript describes the rationale and design of a pragmatic cluster-randomised, crossover trial to fulfil this need for evidence.

    AIM: To study the effects of hospital-initiated comprehensive medication reviews, including active follow-up, on elderly patients' healthcare utilisation compared to 1) usual care and 2) solely hospital based reviews.

    DESIGN: Multicentre, three-treatment, replicated, cluster-randomised, crossover trial.

    SETTING: 8 wards with a multidisciplinary team within 4 hospitals in 3 Swedish counties.

    PARTICIPANTS: Patients aged 65years or older, admitted to one of the study wards.

    EXCLUSION CRITERIA: Palliative stage; residing in other than the hospital's county; medication review within the last 30days; one-day admission.

    INTERVENTIONS: 1, comprehensive medication review during hospital stay; 2, same as 1 with the addition of active follow-up into primary care; 3, usual care.

    PRIMARY OUTCOME MEASURE: Incidence of unplanned hospital visits during a 12-month follow-up period.

    DATA COLLECTION AND ANALYSES: Extraction and collection from the counties' medical record system into a GCP compliant electronic data capture system. Intention-to-treat-analyses using hierarchical models.

    RELEVANCE: This study has a high potential to show a reduction in elderly patients' morbidity, contributing to more sustainable healthcare in the long run.

  • 22.
    Kempen, Thomas
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis.
    Cam, Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Kälvemark, Amanda
    Lindner, Karl-Johan
    Melhus, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Uppsala Clinical Research Center (UCR). Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Sulku, Johanna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Gillespie, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Intervention fidelity and process outcomes of medication reviews including post-discharge follow-up in older hospitalized patients: Process evaluation of the MedBridge trial.2020In: Journal of Clinical Pharmacy and Therapeutics, ISSN 0269-4727, E-ISSN 1365-2710, Vol. 45, no 5, p. 1021-1029Article in journal (Refereed)
    Abstract [en]

    WHAT IS KNOWN AND OBJECTIVE: Drug-related problems (DRPs) are a growing healthcare burden worldwide. In an ongoing cluster-randomized controlled trial in Sweden (MedBridge), comprehensive medication reviews (CMRs) including post-discharge follow-up have been conducted in older hospitalized patients to prevent and solve DRPs. As part of a process evaluation of the MedBridge trial, this study aimed to assess the intervention fidelity and process outcomes of the trial's interventions.

    METHODS: For intervention delivery, the percentage of patients that received intervention components was calculated per study group. Process outcomes, measured in about one-third of all intervention patients, included the following: the number of identified medication discrepancies, DRPs and recommendations to solve DRPs, correction rate of discrepancies, and implementation rate of recommendations.

    RESULTS AND DISCUSSION: The MedBridge trial included 2637 patients (mean age: 81 years). The percentage of intervention patients (n = 1745) that received the intended intervention components was 94%-98% during admission, and 40%-81% upon and after discharge. The percentage of control patients (n = 892) that received at least one unintended intervention component was 15%. On average, 1.1 discrepancies and 2.0 DRPs were identified in 652 intervention patients. The correction and implementation rates were 79% and 73%, respectively. Stop medication was the most frequently implemented recommendation (n = 293) and 77% of the patients had at least one corrected discrepancy or implemented recommendation.

    WHAT IS NEW AND CONCLUSION: The intervention fidelity within the MedBridge trial was high for CMRs during hospital stay and lower for intervention components upon and after discharge. The high prevalence of corrected discrepancies and implemented recommendations may explain potential effects of CMRs in the MedBridge trial.

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  • 23.
    Kempen, Thomas
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Hospital Pharmacy Department Uppsala University Hospital Uppsala Sweden;Primary Care and Health Uppsala County Council Uppsala Sweden.
    Hedman, Anton N.
    Hospital Pharmacy Department Uppsala University Hospital Uppsala Sweden;Linköping University Hospital Linköping Sweden.
    Hadziosmanovic, Nermin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Uppsala Clinical Research Center (UCR).
    Lindner, Karl‐Johan
    Pharmacy Department Region Västmanland Västerås Sweden.
    Melhus, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical pharmacogenomics and osteoporosis.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Sulku, Johanna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Pharmacy Department Region Gävleborg Gävle Sweden;GSK Solna Sweden.
    Gillespie, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Hospital Pharmacy Department Uppsala University Hospital Uppsala Sweden.
    Risk factors for and preventability of drug‐related hospital revisits in older patients: A post‐hoc analysis of a randomized clinical trial2023In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 89, no 5, p. 1575-1587Article in journal (Refereed)
    Abstract [en]

    Aim

    The aims of this study were (1) to identify older patients' risk factors for drug-related readmissions and (2) to assess the preventability of older patients' drug-related revisits.

    Methods

    Post hoc analysis of a randomized clinical trial with patients aged >= 65 years at eight wards within four hospitals in Sweden. (1) The primary outcome was risk factors for drug-related readmission within 12 months post-discharge. A Cox proportional hazards model was made with sociodemographic and clinical baseline characteristics. (2) Four hundred trial participants were randomly selected and their revisits (admissions and emergency department visits) were assessed to identify potentially preventable drug-related revisits, related diseases and causes.

    Results

    (1) Among 2637 patients (median age 81 years), 582 (22%) experienced a drug-related readmission within 12 months. Sixteen risk factors (hazard ratio >1, P < 0.05) related to age, previous hospital visits, medication use, multimorbidity and cardiovascular, liver, lung and peptic ulcer disease were identified. (2) The 400 patients experienced a total of 522 hospital revisits, of which 85 (16%) were potentially preventable drug-related revisits. The two most prevalent related diseases were heart failure (n = 24, 28%) and chronic obstructive pulmonary disease (n = 13, 15%). The two most prevalent causes were inadequate treatment (n = 23, 27%) and insufficient or no follow-up (n = 22, 26%).

    Conclusion

    (1) Risk factors for drug-related readmissions in older hospitalized patients were age, previous hospital visits, medication use and multiple diseases. (2) Potentially preventable drug-related hospital revisits are common and might be prevented through adequate pharmacotherapy and continuity of care in older patients with cardiovascular or lung disease.

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  • 24.
    Khan, David D.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A pharmacokinetic-pharmacodynamic (PKPD) model based on in vitro time-kill data predicts the in vivo PK/PD index of colistin2016In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 71, no 7, p. 1881-1884Article in journal (Refereed)
    Abstract [en]

    Objectives: For antibiotics, extensive animal PKPD studies are often performed to evaluate the PK/PD driver for subsequent use when recommending dosing regimens. The aim of this work was to evaluate a PKPD model, developed based on in vitro time-kill data for colistin, in predicting the relationships between PK/PD indices and the bacterial killing previously observed in mice. Methods: An in silico PKPD model for Pseudomonas aeruginosa exposed to colistin was previously developed based on static in vitro time-kill data. The model was here applied to perform an in silico replication of an in vivo study where the effect of colistin on P. aeruginosa was studied in the thigh infection model. Concentration-time profiles of unbound colistin were predicted and used as input to drive the bacterial killing in the PKPD model. The predicted bacterial count at 24 h was related to each of the PK/PD indices and the results were compared with reported observations in vivo. Results: The model was found to adequately predict in vivo results from mice; both in terms of which PK/PD index best correlates to effect (fAUC/MIC) as well as the magnitude needed for a 2 log kill. The fAUC/MIC needed to achieve a 2 log reduction in bacterial counts after 24 h was here predicted to be 9 compared with 31 previously reported in vivo. Conclusions: This study provides further support that PKPD models based on longitudinal data can be a useful tool to make drug development more efficient within the infectious diseases area.

  • 25.
    Khan, David D.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lagerbäck, Pernilla
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Cao, Sha
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Lustig, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cars, Otto
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Hughes, Diarmaid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Andersson, Dan I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A mechanism-based pharmacokinetic/pharmacodynamic model allows prediction of antibiotic killing from MIC values for WT and mutants2015In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 70, no 11, p. 3051-3060Article in journal (Refereed)
    Abstract [en]

    Objectives: In silico pharmacokinetic/pharmacodynamic (PK/PD) models can be developed based on data from in vitro time-kill experiments and can provide valuable information to guide dosing of antibiotics. The aim was to develop a mechanism-based in silico model that can describe in vitro time-kill experiments of Escherichia coli MG1655 WT and six isogenic mutants exposed to ciprofloxacin and to identify relationships that may be used to simplify future characterizations in a similar setting. Methods: In this study, we developed a mechanism-based PK/PD model describing killing kinetics for E. coli following exposure to ciprofloxacin. WT and six well-characterized mutants, with one to four clinically relevant resistance mutations each, were exposed to a wide range of static ciprofloxacin concentrations. Results: The developed model includes susceptible growing bacteria, less susceptible (pre-existing resistant) growing bacteria, non-susceptible non-growing bacteria and non-colony-forming non-growing bacteria. The non-colony-forming state was likely due to formation of filaments and was needed to describe data close to the MIC. A common model structure with different potency for bacterial killing (EC50) for each strain successfully characterized the time-kill curves for both WT and the six E. coli mutants. Conclusions: The model-derived mutant-specific EC50 estimates were highly correlated (r(2) = 0.99) with the experimentally determined MICs, implying that the in vitro time-kill profile of a mutant strain is reasonably well predictable by the MIC alone based on the model.

  • 26.
    Khan, David
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    PK/PD Index Versus Mechanism-Based PKPD Modeling to Describe Antibacterial Efficacy of Ciprofloxacin and Colistin2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no S1, p. S125-S126Article in journal (Other academic)
  • 27.
    Khan, David
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lagerbäck, Pernilla
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Malmberg, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Kristoffersson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Gullberg, Erik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Cao, Sha
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Cars, Otto
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Andersson, Dan I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Hughes, Diarmaid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Predicting mutant selection in competition experiments with ciprofloxacin-exposed Escherichia coli2018In: International Journal of Antimicrobial Agents, ISSN 0924-8579, E-ISSN 1872-7913, Vol. 51, no 3, p. 399-406, article id S0924-8579(17)30392-8Article in journal (Refereed)
    Abstract [en]

    Predicting competition between antibiotic-susceptible wild-type (WT) and less susceptible mutant (MT) bacteria is valuable for understanding how drug concentrations influence the emergence of resistance. Pharmacokinetic/pharmacodynamic (PK/PD) models predicting the rate and extent of takeover of resistant bacteria during different antibiotic pressures can thus be a valuable tool in improving treatment regimens. The aim of this study was to evaluate a previously developed mechanism-based PK/PD model for its ability to predict in vitro mixed-population experiments with competition between Escherichia coli (E. coli) WT and three well-defined E. coli resistant MTs when exposed to ciprofloxacin. Model predictions for each bacterial strain and ciprofloxacin concentration were made for in vitro static and dynamic time–kill experiments measuring CFU (colony forming units)/mL up to 24 h with concentrations close to or below the minimum inhibitory concentration (MIC), as well as for serial passage experiments with concentrations well below the MIC measuring ratios between the two strains with flow cytometry. The model was found to reasonably well predict the initial bacterial growth and killing of most static and dynamic time–kill competition experiments without need for parameter re-estimation. With parameter re-estimation of growth rates, an adequate fit was also obtained for the 6-day serial passage competition experiments. No bacterial interaction in growth was observed. This study demonstrates the predictive capacity of a PK/PD model and further supports the application of PK/PD modelling for prediction of bacterial kill in different settings, including resistance selection.

  • 28.
    Kristoffersson, Anders
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    David-Pierson, Pascale
    Parrott, Neil J
    Kuhlmann, Olaf
    Lave, Thierry
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Simulation-Based Evaluation of PK/PD Indices for Meropenem Across Patient Groups and Experimental Designs2016In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 33, no 5, p. 1115-1125Article in journal (Refereed)
    Abstract [en]

    PURPOSE: Antibiotic dose predictions based on PK/PD indices rely on that the index type and magnitude is insensitive to the pharmacokinetics (PK), the dosing regimen, and bacterial susceptibility. In this work we perform simulations to challenge these assumptions for meropenem and Pseudomonas aeruginosa.

    METHODS: A published murine dose fractionation study was replicated in silico. The sensitivity of the PK/PD index towards experimental design, drug susceptibility, uncertainty in MIC and different PK profiles was evaluated.

    RESULTS: The previous murine study data were well replicated with fT > MIC selected as the best predictor. However, for increased dosing frequencies fAUC/MIC was found to be more predictive and the magnitude of the index was sensitive to drug susceptibility. With human PK fT > MIC and fAUC/MIC had similar predictive capacities with preference for fT > MIC when short t1/2 and fAUC/MIC when long t1/2.

    CONCLUSIONS: A longitudinal PKPD model based on in vitro data successfully predicted a previous in vivo study of meropenem. The type and magnitude of the PK/PD index were sensitive to the experimental design, the MIC and the PK. Therefore, it may be preferable to perform simulations for dose selection based on an integrated PK-PKPD model rather than using a fixed PK/PD index target.

  • 29.
    Kristoffersson, Anders
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    David-Pierson, Pascale
    F. Hoffmann-La Roche Ltd.
    Parrott, Neil
    F. Hoffmann-La Roche Ltd.
    Kuhlmann, Olaf
    F. Hoffmann-La Roche Ltd.
    Lave, Thierry
    F. Hoffmann-La Roche Ltd.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Simulation-based evaluation of PK/PD indices for meropenem across patient groups and experimental designsIn: Article in journal (Refereed)
  • 30.
    Kristoffersson, Anders N.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bissantz, Caterina
    Okujava, Rusudan
    Haldimann, Andreas
    Walter, Isabelle
    Shi, Tianlai
    Zampaloni, Claudia
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A novel mechanism-based pharmacokinetic-pharmacodynamic (PKPD) model describing ceftazidime/avibactam efficacy against β-lactamase-producing Gram-negative bacteria2020In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 75, no 2, p. 400-408Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Diazabicyclooctanes (DBOs) are an increasingly important group of non β-lactam β-lactamase inhibitors, employed clinically in combinations such as ceftazidime/avibactam. The dose finding of such combinations is complicated using the traditional pharmacokinetic/pharmacodynamic (PK/PD) index approach, especially if the β-lactamase inhibitor has an antibiotic effect of its own.

    OBJECTIVES: To develop a novel mechanism-based pharmacokinetic-pharmacodynamic (PKPD) model for ceftazidime/avibactam against Gram-negative pathogens, with the potential for combination dosage simulation.

    METHODS: Four β-lactamase-producing Enterobacteriaceae, covering Ambler classes A, B and D, were exposed to ceftazidime and avibactam, alone and in combination, in static time-kill experiments. A PKPD model was developed and evaluated using internal and external evaluation, and combined with a population PK model and applied in dosage simulations.

    RESULTS: The developed PKPD model included the effects of ceftazidime alone, avibactam alone and an 'enhancer' effect of avibactam on ceftazidime in addition to the β-lactamase inhibitory effect of avibactam. The model could describe an extensive external Pseudomonas aeruginosa data set with minor modifications to the enhancer effect, and the utility of the model for clinical dosage simulation was demonstrated by investigating the influence of the addition of avibactam.

    CONCLUSIONS: A novel mechanism-based PKPD model for the DBO/β-lactam combination ceftazidime/avibactam was developed that enables future comparison of the effect of avibactam with other DBO/β-lactam inhibitors in simulations, and may be an aid in translating PKPD results from in vitro to animals and humans.

  • 31.
    Kurland, Siri
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Furebring, Mia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine.
    Löwdin, Elisabeth
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Eliasson, E.
    Karolinska Inst, Karolinska Univ Hosp, Dept Lab Med, Clin Pharmacol, Stockholm, Sweden.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sjölin, Jan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Pharmacokinetics of Caspofungin in Critically Ill Patients in Relation to Liver Dysfunction: Differential Impact of Plasma Albumin and Bilirubin Levels2019In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 63, no 6, article id e02466-18Article in journal (Refereed)
    Abstract [en]

    Caspofungin has a liver-dependent metabolism. Reduction of the dose is recommended based on Child-Pugh (C-P) score. In critically ill patients, drug pharmacokinetics (PK) may be altered. The aim of this study was to investigate the prevalence of abnormal liver function tests, increased C-P scores, their effects on caspofungin PK, and whether pharmacokinetic-pharmacodynamic (PK/PD) targets were attained in patients with suspected candidiasis. Intensive care unit patients receiving caspofungin were prospectively included. PK parameters were determined on days 2, 5, and 10, and their correlations to the individual liver function tests and the C-P score were analyzed. Forty-six patients were included with C-P class A (n = 5), B (n = 40), and C (n = 1). On day 5 (steady state), the median and interquartile range for area under the curve from 0 to 24 h (AUC(0-24)), clearance (CL), and central volume of distribution (V-1) were 57.8 (51.6 to 69.8) mg.h/liter, 0.88 (0.78 to 1.04) liters/h, and 11.9 (9.6 to 13.1) liters, respectively. The C-P score did not correlate with AUC(0-24) (r = 0.03; P = 0.84), CL (r = -0.07; P = 0.68), or V-1 (r = 0.19; P = 0.26), but there was a bilirubin-driven negative correlation with the elimination rate constant (r = -0.46; P = 0.004). Hypoalbuminemia correlated with low AUC(0-24) (r = 0.45; P = 0.005) and was associated with higher clearance (r = -0.31; P = 0.062) and somewhat higher V-1 (r = -0.15; P = 0.37), resulting in a negative correlation with the elimination rate constant (r = -0.34; P = 0.042). For Candida strains with minimal inhibitory concentrations of >= 0.064 mu g/ml, PK/PD targets were not attained in all patients. The caspofungin dose should not be reduced in critically ill patients in the absence of cirrhosis, and we advise against the use of the C-P score in patients with trauma- or sepsis-induced liver injury.

  • 32.
    Kurland, Siri
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Furebring, Mia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Löwdin, Elisabeth
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sjölin, Jan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Elimination ability of caspofungin in critically ill patients in relation to liver dysfunction in an ICU setting2017In: Mycoses, ISSN 0933-7407, E-ISSN 1439-0507, Vol. 60, p. 225-225Article in journal (Other academic)
  • 33.
    Maarbjerg, Sabine F.
    et al.
    Department of Pediatrics and Adolescent Medicine Aarhus University Hospital Aarhus Denmark.
    Thorsted, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Wang, Mikala
    Department of Clinical Microbiology Aarhus University Hospital Aarhus Denmark.
    Schrøder, Henrik
    Department of Pediatrics and Adolescent Medicine Aarhus University Hospital Aarhus Denmark.
    Albertsen, Birgitte K.
    Department of Pediatrics and Adolescent Medicine Aarhus University Hospital Aarhus Denmark.
    Continuous infusion of piperacillin‐tazobactam significantly improves target attainment in children with cancer and fever2021In: Cancer Reports, E-ISSN 2573-8348, Vol. 5, no 10, article id e1585Article in journal (Refereed)
    Abstract [en]

    Background

    Children with febrile neutropenia commonly exhibit alterations of pharmacokinetic (PK) parameters, leading to decreased β-lactam concentrations.

    Aims

    This study evaluated piperacillin PK and probability of target attainment (PTA) with continuous infusion of piperacillin-tazobactam, in order to optimize the dosing regimen.

    Methods

    This prospective PK study included children with cancer, aged 1–17 years, who were treated with piperacillin-tazobactam for suspected or verified infection. A piperacillin-tazobactam loading dose (100 mg/kg) was administered followed by continuous infusion (300 mg/kg/day). The unbound fraction of piperacillin was quantified by high-performance liquid chromatography and PK were described using population PK modeling. PK data was used to update and extend a previous PK model built on data following intermittent administration. Monte Carlo simulations were performed to assess PTA for targets of 100% time above the minimum inhibitory concentration (100% fT > MIC) and 50% fT > 4xMIC.

    Results

    We included 68 fever episodes among 38 children with a median (IQR) age of 6.5 years and body weight of 27.4 kg (15.1–54.0). A three-compartment model adequately described the concentration-time data. Median (95% confidence interval) estimates for clearance and piperacillin concentration at steady state were 14.2 L/h/70 kg (13.0; 15.3) and 47.6 mg/L (17.2; 129.5), respectively. Body weight or lean body weight was significantly associated with the PK parameters, and body weight was integrated in the final PK model. Based on piperacillin exposure, continuous infusion was the only dosing regimen to achieve optimal PTA for the P. aeruginosa breakpoint (16 mg/L) with the target of 100% fT > MIC, and a daily dose of 300 mg/kg reached optimal PTA. The strict target of 50% fT > 4xMIC (64 mg/L) was not feasibly attained by any dosing regimen at recommended doses.

    Conclusion

    Unlike conventional piperacillin intermittent administration and extended infusion regimens, continuous infusion allows the target of 100% fT > MIC to be reached for children with febrile neutropenia.

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  • 34.
    Maarbjerg, Sabine F.
    et al.
    Aarhus Univ Hosp, Dept Pediat & Adolescent Med, Palle Juul Jensens Blvd 99, DK-8200 Aarhus N, Denmark.
    Thorsted, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kristoffersson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wang, Mikala
    Aarhus Univ Hosp, Dept Clin Microbiol, Aarhus, Denmark.
    Brock, Birgitte
    Steno Diabet Ctr, Gentofte, Denmark.
    Schroder, Henrik
    Aarhus Univ Hosp, Dept Pediat & Adolescent Med, Palle Juul Jensens Blvd 99, DK-8200 Aarhus N, Denmark.
    Piperacillin pharmacokinetics and target attainment in children with cancer and fever: Can we optimize our dosing strategy?2019In: Pediatric Blood & Cancer, ISSN 1545-5009, E-ISSN 1545-5017, Vol. 66, no 6, article id e27654Article in journal (Refereed)
    Abstract [en]

    Background

    Data on piperacillin-tazobactam pharmacokinetics and optimal dosing in children with cancer and fever are limited. Our objective was to investigate piperacillin pharmacokinetics and the probability of target attainment (PTA) with standard intermittent administration (IA), and to simulate PTA in other dosing regimens.

    Procedure

    This prospective pharmacokinetic study was conducted from April 2016 to January 2018. Children with cancer receiving empiric piperacillin-tazobactam to treat infections were included. Piperacillin-tazobactam 100 mg/kg was infused over 5 min every 8 hours (IA). An optimized sample schedule provided six blood samples per subject for piperacillin concentration determination. The evaluated targets included: (1) 100% time of free piperacillin concentration above the minimum inhibitory concentration (fT > MIC) and (2) 50% fT > 4x MIC. MIC50 and MIC90 were defined based on an intrainstitutional MIC range.

    Results

    A total of 482 piperacillin concentrations were obtained from 43 children (aged 1-18 years) during 89 fever episodes. Standard IA resulted in insufficient target attainment, with significant differences in piperacillin pharmacokinetics for different body weights. Median fT > MIC was 61.2%, 53.5%, and 36.3% for MIC50 (2.0 mg/L), MIC90 (4.0 mg/L), and breakpoint for Pseudomonas aeruginosa (16.0 mg/L), respectively. Correspondingly, the median fT > 4x MIC was 43%, 36.3%, and 20.1%. Simulations showed that only continuous infusion reached a PTA of 95% for MIC = 16.0 mg/L, while extended infusion lasting half of the dosing interval reached a PTA of 95% for MIC <= 8 mg/L.

    Conclusions

    Our data revealed insufficient PTA with standard IA of piperacillin-tazobactam in children with cancer and fever. Alternative dosing strategies, preferably continuous infusion, are required to ensure adequate PTA.

  • 35.
    Mangles, S.
    et al.
    Hampshire Hosp NHS Fdn Trust, Haemophilia Haemostasis & Thrombosis Ctr, Basingstoke, Hants, England.
    Rea, C.
    Hampshire Hosp NHS Fdn Trust, Haemophilia Haemostasis & Thrombosis Ctr, Basingstoke, Hants, England.
    Madan, B.
    St Thomas Hosp, Ctr Haemostasis & Thrombosis, London, England.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Needham, J.
    Hampshire Hosp NHS Fdn Trust, Haemophilia Haemostasis & Thrombosis Ctr, Basingstoke, Hants, England.
    Collins, P. W.
    Univ Hosp Wales, Arthur Bloom Haemophilia Ctr, Cardiff, S Glam, Wales.
    Rangarajanl, S.
    Hampshire Hosp NHS Fdn Trust, Haemophilia Haemostasis & Thrombosis Ctr, Basingstoke, Hants, England.
    Real life experiences of a PK dosing study: Challenges and lessons learned2018In: Haemophilia, ISSN 1351-8216, E-ISSN 1365-2516, Vol. 24, no 3, p. E145-E148Article in journal (Other academic)
  • 36.
    Mohamed, Ami F
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kristoffersson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karvanen, Matti
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Nielsen, Elisabet
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cars, Otto
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Dynamic interaction of colistin and meropenem on a WT and a resistant strain of Pseudomonas aeruginosa as quantified in a PK/PD model2016In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 71, no 5, p. 1279-1290Article in journal (Refereed)
    Abstract [en]

    OBJECTIVES: Combination therapy can be a strategy to ensure effective bacterial killing when treating Pseudomonas aeruginosa, a Gram-negative bacterium with high potential for developing resistance. The aim of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model that describes the in vitro bacterial time-kill curves of colistin and meropenem alone and in combination for one WT and one meropenem-resistant strain of P. aeruginosa.

    METHODS: In vitro time-kill curve experiments were conducted with a P. aeruginosa WT (ATCC 27853) (MICs: meropenem 1 mg/L; colistin 1 mg/L) and a meropenem-resistant type (ARU552) (MICs: meropenem 16 mg/L; colistin 1.5 mg/L). PK/PD models characterizing resistance were fitted to the observed bacterial counts in NONMEM. The final model was applied to predict the bacterial killing of ARU552 for different combination dosages of colistin and meropenem.

    RESULTS: A model with compartments for growing and resting bacteria, where the bacterial killing by colistin reduced with continued exposure and a small fraction (0.15%) of the start inoculum was resistant to meropenem, characterized the bactericidal effect and resistance development of the two antibiotics. For a typical patient, a loading dose of colistin combined with a high dose of meropenem (2000 mg q8h) was predicted to result in a pronounced kill of the meropenem-resistant strain over 24 h.

    CONCLUSIONS: The developed PK/PD model successfully described the time course of bacterial counts following exposures to colistin and meropenem, alone and in combination, for both strains, and identified a dynamic drug interaction. The study illustrates the application of a PK/PD model and supports high-dose combination therapy of colistin and meropenem to overcome meropenem resistance.

  • 37.
    Mohamed, Ami F
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cars, Otto
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pharmacokinetic-pharmacodynamic model for gentamicin and its adaptive resistance with predictions of dosing schedules in newborn infants2012In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 56, no 1, p. 179-188Article in journal (Refereed)
    Abstract [en]

    Gentamicin is commonly used in the management of neonatal infections. Development of adaptive resistance is typical for aminoglycosides and reduces the antibacterial effect. There is, however, a lack of understanding of how this phenomenon influences the effect of different dosing schedules. The aim was to develop a pharmacokinetic-pharmacodynamic (PKPD) model that describes the time course of the bactericidal activity of gentamicin and its adaptive resistance and to investigate different dosing schedules in preterm and term newborn infants based on the developed model. In vitro time-kill curve experiments were conducted on a strain of Escherichia coli (MIC of 2 mg/liter). The gentamicin exposure was either constant (0.125 to 16 mg/liter) or dynamic (simulated concentration-time profiles in a kinetic system with peak concentrations of 2.0, 3.9, 7.8, and 16 mg/liter given as single doses or as repeated doses every 6, 12, or 24 h). Semimechanistic PKPD models were fitted to the bacterial counts in the NONMEM (nonlinear mixed effects modeling) program. A model with compartments for growing and resting bacteria, with a function allowing the maximal bacterial killing of gentamicin to reduce with exposure, characterized both the fast bactericidal effect and the adaptive resistance. Despite a lower peak concentration, preterm neonates were predicted to have a higher bacterial killing effect than term neonates for the same per-kg dose because of gentamicin's longer half-life. The model supported an extended dosing interval of gentamicin in preterm neonates, and for all neonates, dosing intervals of 36 to 48 h were as effective as a 24-h dosing interval for the same total dose.

  • 38.
    Netterberg, Ida
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Quartino, Angelica L
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Genentech Inc, Dept Clin Pharmacol, Genentech, San Francisco, USA.
    Lindman, Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    The risk of febrile neutropenia in breast cancer patients following adjuvant chemotherapy is predicted by the time course of interleukin-6 and C-reactive protein by modelling.2018In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 84, no 3, p. 490-500Article in journal (Refereed)
    Abstract [en]

    AIMS: Early identification of patients with febrile neutropenia (FN) is desirable for initiation of preventive treatment, such as with antibiotics. In this study, the time courses of two inflammation biomarkers, interleukin (IL)-6 and C-reactive protein (CRP), following adjuvant chemotherapy of breast cancer, were characterized. The potential to predict development of FN by IL-6 and CRP, and other model-derived and clinical variables, was explored.

    METHODS: The IL-6 and CRP time courses in cycles 1 and 4 of breast cancer treatment were described by turnover models where the probability for an elevated production following initiation of chemotherapy was estimated. Parametric time-to-event models were developed to describe FN occurrence to assess: (i) predictors available before chemotherapy is initiated; (ii) predictors available before FN occurs; and (iii) predictors available when FN occurs.

    RESULTS: The IL-6 and CRP time courses were successfully characterized with peak IL-6 typically occurring 2 days prior to CRP peak. Of all evaluated variables the CRP time course was most closely associated with the occurrence of FN. Since the CRP peak typically occurred at the time of FN diagnosis it will, however, have limited value for identifying the need for preventive treatment. The time course of IL-6 was the predictor that could best forecast FN events. Of the variables available at baseline, age was the best, although in comparison a relatively weak, predictor.

    CONCLUSIONS: The developed models add quantitative knowledge about IL-6 and CRP and their relationship to the development of FN. The study suggests that IL-6 may have potential as a clinical predictor of FN if monitored during myelosuppressive chemotherapy.

  • 39.
    Netterberg, Ida
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring2017In: Cancer Chemotherapy and Pharmacology, ISSN 0344-5704, E-ISSN 1432-0843, Vol. 80, no 2, p. 343-353Article in journal (Refereed)
    Abstract [en]

    Purpose To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelo-suppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today. Methods Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements. Results The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (>= 90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (+/- 1 day) before the typical value occurred on day 17. Conclusions Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

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  • 40.
    Nielsen, Elisabet I
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pharmacometric Models for Antibacterial Agents to Improve Dosing Strategies2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Antibiotics are among the most commonly prescribed drugs. Although the majority of these drugs were developed several decades ago, optimal dosage (dose, dosing interval and treatment duration) have still not been well defined. This thesis focuses on the development and evaluation of pharmacometric models that can be used as tools in the establishment of improved dosing strategies for novel and already clinically available antibacterial drugs.

    Infectious diseases are common causes of death in preterm and term newborn infants. A population pharmacokinetic (PK) model for gentamicin was developed based on data from a prospective study. Body-weight and age (gestational and post-natal age) were found to be major factors contributing to variability in gentamicin clearance and therefore important patient characteristics to consider for improved dosing regimens.

    A semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) model was also developed, to characterize in vitro bacterial growth and killing kinetics following exposure to six antibacterial drugs, representing a broad selection of mechanisms of action and PK as well as PD characteristics. The model performed well in describing a wide range of static and dynamic drug exposures and was easily applied to other bacterial strains and antibiotics. It is, therefore, likely to find application in early drug development programs.

    Dosing of antibiotics is usually based on summary endpoints such as the PK/PD indices. Predictions based on the PKPD model showed that the commonly used PK/PD indices were well identified for all investigated drugs, supporting that models based on in vitro data can be predictive of antibacterial effects observed in vivo. However, the PK/PD indices were sensitive to the study conditions and were not always consistent between patient populations. The PK/PD indices may therefore extrapolate poorly across sub-populations. A semi-mechanistic modeling approach, utilizing the type of models described here, may thus have higher predictive value in a dose optimization tailored to specific patient populations.

    List of papers
    1. Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments
    Open this publication in new window or tab >>Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments
    Show others...
    2007 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 51, no 1, p. 128-136Article in journal (Refereed) Published
    Abstract [en]

    Dosing of antibacterial agents is generally based on point estimates of the effect, even though bacteria exposed to antibiotics show complex kinetic behaviors. The use of the whole time course of the observed effects would be more advantageous. The aim of the present study was to develop a semimechanistic pharmacokinetic (PK)/pharmacodynamic (PD) model characterizing the events seen in a bacterial system when it is exposed to antibacterial agents with different mechanisms of action. Time-kill curve experiments were performed with a strain of Streptococcus pyogenes exposed to a wide range of concentrations of the following antibiotics: benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, and vancomycin. Bacterial counts were monitored with frequent sampling during the experiment. A simultaneous fit of all data was accomplished. The degradation of the drugs was monitored and corrected for in the model, and a link model was used to account for an effect delay. In the final PK/PD model, the total bacterial population was divided into two subpopulations: one growing drug-susceptible population and one resting insusceptible population. The drug effect was included as an increase of the killing rate of bacteria in the susceptible state, according to a maximum-effect (Emax) model. An internal model validation showed that the model was robust and had good predictability. In conclusion, for all drugs, the final PK/PD model successfully described bacterial growth and killing kinetics when the bacteria were exposed to different antibiotic concentrations. The semimechanistic model that was developed might, after further refinement, serve as a tool for the development of optimal dosing strategies for antibacterial agents.

    National Category
    Pharmaceutical Sciences
    Identifiers
    urn:nbn:se:uu:diva-94169 (URN)10.1128/AAC.00604-06 (DOI)000243214200016 ()17060524 (PubMedID)
    Available from: 2006-03-31 Created: 2006-03-31 Last updated: 2018-01-13Bibliographically approved
    2. Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model
    Open this publication in new window or tab >>Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model
    2011 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 55, no 4, p. 1571-1579Article in journal (Refereed) Published
    Abstract [en]

    We have previously described a general semimechanistic pharmacokinetic-pharmacodynamic (PKPD) model that successfully characterized the time course of antibacterial effects seen in bacterial cultures when exposed to static concentrations of five antibacterial agents of different classes. In this PKPD model, the total bacterial population was divided into two subpopulations, one growing drug-susceptible population and one resting drug-insensitive population. The drug effect was included as an increase in the killing rate of the drug-susceptible bacteria with a maximum-effect (Emax) model. The aim of the present study was to evaluate the ability of this PKPD model to describe and predict data from in vitro experiments with dynamic concentration-time profiles. Dynamic time-kill curve experiments were performed by using an in vitro kinetic system, where cultures of Streptococcus pyogenes were exposed to benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, or vancomycin using different starting concentrations (2 and 16 times the MIC) and elimination conditions (human half-life, reduced half-life, and constant concentrations). The PKPD model was applied, and the observations for the static as well as dynamic experiments were compared to model predictions based on parameter estimation using (i) static data, (ii) dynamic data, and (iii) combined static and dynamic data. Differences in experimental settings between static and dynamic experiments did not affect the growth kinetics of the bacteria significantly. With parameter reestimation, the structure of our previously proposed PKPD model could well characterize the bacterial growth and killing kinetics when exposed to dynamic concentrations with different elimination rates of all five investigated antibiotics. Furthermore, the model with parameter estimates based on data from only the static time-kill curve experiments could predict the majority of the time-kill curves from the dynamic experiments reasonably well. Adding data from dynamic experiments in the estimation improved the model fit for cefuroxime and vancomycin, indicating some differences in sensitivity to experimental conditions among the antibiotics studied.

    National Category
    Pharmaceutical Sciences Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-144787 (URN)10.1128/AAC.01286-10 (DOI)000288594600031 ()21282424 (PubMedID)
    Available from: 2011-02-02 Created: 2011-02-02 Last updated: 2018-01-12Bibliographically approved
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    4. Pharmacokinetic-pharmacodynamic model for gentamicin and its adaptive resistance with predictions of dosing schedules in newborn infants
    Open this publication in new window or tab >>Pharmacokinetic-pharmacodynamic model for gentamicin and its adaptive resistance with predictions of dosing schedules in newborn infants
    2012 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 56, no 1, p. 179-188Article in journal (Refereed) Published
    Abstract [en]

    Gentamicin is commonly used in the management of neonatal infections. Development of adaptive resistance is typical for aminoglycosides and reduces the antibacterial effect. There is, however, a lack of understanding of how this phenomenon influences the effect of different dosing schedules. The aim was to develop a pharmacokinetic-pharmacodynamic (PKPD) model that describes the time course of the bactericidal activity of gentamicin and its adaptive resistance and to investigate different dosing schedules in preterm and term newborn infants based on the developed model. In vitro time-kill curve experiments were conducted on a strain of Escherichia coli (MIC of 2 mg/liter). The gentamicin exposure was either constant (0.125 to 16 mg/liter) or dynamic (simulated concentration-time profiles in a kinetic system with peak concentrations of 2.0, 3.9, 7.8, and 16 mg/liter given as single doses or as repeated doses every 6, 12, or 24 h). Semimechanistic PKPD models were fitted to the bacterial counts in the NONMEM (nonlinear mixed effects modeling) program. A model with compartments for growing and resting bacteria, with a function allowing the maximal bacterial killing of gentamicin to reduce with exposure, characterized both the fast bactericidal effect and the adaptive resistance. Despite a lower peak concentration, preterm neonates were predicted to have a higher bacterial killing effect than term neonates for the same per-kg dose because of gentamicin's longer half-life. The model supported an extended dosing interval of gentamicin in preterm neonates, and for all neonates, dosing intervals of 36 to 48 h were as effective as a 24-h dosing interval for the same total dose.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-144897 (URN)10.1128/AAC.00694-11 (DOI)000298404900024 ()
    Available from: 2011-02-03 Created: 2011-02-03 Last updated: 2017-12-11Bibliographically approved
    5. Pharmacokinetic/Pharmacodynamic (PK/PD) indices of antibiotics predicted by a semi-mechanistic PKPD model: a step toward model-based dose optimization
    Open this publication in new window or tab >>Pharmacokinetic/Pharmacodynamic (PK/PD) indices of antibiotics predicted by a semi-mechanistic PKPD model: a step toward model-based dose optimization
    2011 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 55, no 10, p. 4619-4630Article in journal (Refereed) Published
    Abstract [en]

    A pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course of in vitro time-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, adose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure (in vitro PKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [fCmax]/MIC, the area under the unbound drug concentration-time curve [fAUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [fT>MIC]) that was most predictive of the effect. The in silico predictions based on the in vitro PKPD model identified the previously determined PK/PD indices,with fT>MIC being the best predictor of the effect for β-lactams and fAUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built from in vitro time-kill curves in the development of optimal dosing regimens for antibacterial drugs.

    National Category
    Pharmaceutical Sciences
    Research subject
    Pharmacokinetics and Drug Therapy
    Identifiers
    urn:nbn:se:uu:diva-144792 (URN)10.1128/AAC.00182-11 (DOI)000294952600019 ()
    Available from: 2011-02-03 Created: 2011-02-02 Last updated: 2022-01-28Bibliographically approved
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  • 41.
    Nielsen, Elisabet I.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Al-Saqi, Shahla H.
    Karolinska Inst, Dept Clin Sci Intervent & Technol, Stockholm, Sweden..
    Jonasson, Aino F.
    Karolinska Inst, Dept Clin Sci Intervent & Technol, Stockholm, Sweden..
    Uvnäs-Moberg, Kerstin
    Swedish Univ Agr Sci, Dept Anim Environm & Hlth, Skara, Sweden..
    Population Pharmacokinetic Analysis of Vaginally and Intravenously Administered Oxytocin in Postmenopausal Women2017In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 57, no 12, p. 1573-1581Article in journal (Refereed)
    Abstract [en]

    Oxytocin is a neuropeptide hormone used clinically for more than 50 years due to its ability to induce uterine contractions and milk ejection. Vagitocin is a vaginal oxytocin gel developed as a potential treatment of vaginal atrophy in postmenopausal women. The aim of this study was to characterize the oxytocin pharmacokinetics following vaginal and intravenous administration in postmenopausal women. Data from 33 participants enrolled in 2 clinical studies were used in the analysis, with a total of 651 observed oxytocin plasma concentrations, of which 78 were baseline observations, 178 observations following intravenous administration (10 IU), and 395 observations following vaginal administration (100 or 400 IU). The population pharmacokinetics of oxytocin was described using a 2-compartment disposition model with a flexible parallel absorption model accounting for double-peak profiles following vaginal administration. The clearance, volume of distribution at steady state, distribution half-life, and terminal half-life were estimated to be 27 L/h, 15 L, 5.5 minutes, and 1.2 hours, respectively. The bioavailability following vaginal administration was estimated to be 2.5% for the typical patient, but with considerable variability both between individuals (interindividual variability of 374%) and between occasions (interoccasion variability of 79%). The data and the developed model add new and important information as to the clinical pharmacokinetics of oxytocin.

  • 42.
    Nielsen, Elisabet I
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cars, Otto
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pharmacokinetic/Pharmacodynamic (PK/PD) indices of antibiotics predicted by a semi-mechanistic PKPD model: a step toward model-based dose optimization2011In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 55, no 10, p. 4619-4630Article in journal (Refereed)
    Abstract [en]

    A pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course of in vitro time-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, adose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure (in vitro PKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [fCmax]/MIC, the area under the unbound drug concentration-time curve [fAUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [fT>MIC]) that was most predictive of the effect. The in silico predictions based on the in vitro PKPD model identified the previously determined PK/PD indices,with fT>MIC being the best predictor of the effect for β-lactams and fAUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built from in vitro time-kill curves in the development of optimal dosing regimens for antibacterial drugs.

  • 43.
    Nielsen, Elisabet I.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cars, Otto
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model2011In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 55, no 4, p. 1571-1579Article in journal (Refereed)
    Abstract [en]

    We have previously described a general semimechanistic pharmacokinetic-pharmacodynamic (PKPD) model that successfully characterized the time course of antibacterial effects seen in bacterial cultures when exposed to static concentrations of five antibacterial agents of different classes. In this PKPD model, the total bacterial population was divided into two subpopulations, one growing drug-susceptible population and one resting drug-insensitive population. The drug effect was included as an increase in the killing rate of the drug-susceptible bacteria with a maximum-effect (Emax) model. The aim of the present study was to evaluate the ability of this PKPD model to describe and predict data from in vitro experiments with dynamic concentration-time profiles. Dynamic time-kill curve experiments were performed by using an in vitro kinetic system, where cultures of Streptococcus pyogenes were exposed to benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, or vancomycin using different starting concentrations (2 and 16 times the MIC) and elimination conditions (human half-life, reduced half-life, and constant concentrations). The PKPD model was applied, and the observations for the static as well as dynamic experiments were compared to model predictions based on parameter estimation using (i) static data, (ii) dynamic data, and (iii) combined static and dynamic data. Differences in experimental settings between static and dynamic experiments did not affect the growth kinetics of the bacteria significantly. With parameter reestimation, the structure of our previously proposed PKPD model could well characterize the bacterial growth and killing kinetics when exposed to dynamic concentrations with different elimination rates of all five investigated antibiotics. Furthermore, the model with parameter estimates based on data from only the static time-kill curve experiments could predict the majority of the time-kill curves from the dynamic experiments reasonably well. Adding data from dynamic experiments in the estimation improved the model fit for cefuroxime and vancomycin, indicating some differences in sensitivity to experimental conditions among the antibiotics studied.

  • 44.
    Nielsen, Elisabet I.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pharmacokinetic-Pharmacodynamic Modeling of Antibacterial Drugs2013In: Pharmacological Reviews, ISSN 0031-6997, E-ISSN 1521-0081, Vol. 65, no 3, p. 1053-1090Article, review/survey (Refereed)
    Abstract [en]

    Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

  • 45.
    Nielsen, Elisabet I.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Khan, David D.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cao, Sha
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Lustig, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Hughes, Diarmaid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Andersson, Dan I
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Can a pharmacokinetic/pharmacodynamic (PKPD) model be predictive across bacterial densities and strains?: External evaluation of a PKPD model describing longitudinal in vitro data2017In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 72, no 11, p. 3108-3116Article in journal (Refereed)
    Abstract [en]

    Background: Pharmacokinetic/pharmacodynamic (PKPD) models developed based on data from in vitro time-kill experiments have been suggested to contribute to more efficient drug development programmes and better dosing strategies for antibiotics. However, for satisfactory predictions such models would have to show good extrapolation properties. Objectives: To evaluate if a previously described mechanism-based PKPD model was able also to predict drug efficacy for higher bacterial densities and across bacterial strains. Methods: A PKPD model describing the efficacy of ciprofloxacin on Escherichia coli was evaluated. The predictive performance of the model was evaluated across several experimental conditions with respect to: (i) bacterial start inoculum ranging from the standard of similar to 10(6) cfu/mL up to late stationary-phase cultures; and (ii) efficacy for seven additional strains (three laboratory and four clinical strains), not included during the model development process, based only on information regarding their MIC. Model predictions were performed according to the intended experimental protocol and later compared with observed bacterial counts. Results: The mechanism-based PKPD model structure developed based on data from standard start inoculum experiments was able to accurately describe the inoculum effect. The model successfully predicted the time course of drug efficacy for additional laboratory and clinical strains based on only the MIC values. The model structure was further developed to better describe the stationary phase data. Conclusions: This study supports the use of mechanism-based PKPD models based on preclinical data for predictions of untested scenarios.

  • 46.
    Nielsen, Elisabet I.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sandström, Marie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Honore, Per Hartvig
    Ewald, Uwe
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Pediatrics.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Developmental Pharmacokinetics of Gentamicin in Preterm and Term Neonates: Population Modelling of a Prospective Study2009In: Clinical Pharmacokinetics, ISSN 0312-5963, E-ISSN 1179-1926, Vol. 48, no 4, p. 253-263Article in journal (Refereed)
    Abstract [en]

    Background and objective:

    Preterm and term newborn infants show wide interindividual variability (IIV) in pharmacokinetic parameters of gentamicin. More extensive knowledge and use of predictive covariates could lead to faster attainment of therapeutic concentrations and a reduced need for concentration monitoring. This study was performed to characterize the population pharmacokinetics of gentamicin in preterm and term neonates and to identify and quantify relationships between patient characteristics and IIV. A secondary aim was to evaluate cystatin C as a marker for gentamicin clearance in this patient population.

    Methods:

    Data were collected in a prospective study performed in the Neonatal Intensive Care Unit at the University Children's Hospital, Uppsala, Sweden. Population pharmacokinetic modelling was performed using nonlinear mixed-effects modelling (NONMEM) software. Bodyweight was included as the primary covariate according to an allometric power model. Other evaluated covariates were age (postmenstrual age, gestational age [GA], postnatal age [PNA]), markers for renal function (serum creatinine, serum cystatin Q and concomitant medication with cefuroxime, vancomycin or indometacin. Covariate-parameter relationships were explored using a stepwise covariate model building procedure. The predictive performance of the developed model was evaluated using an independent external dataset for a similar patient population.

    Results:

    Sixty-one newborn infants (GA range 23.3-42.1 weeks, PNA range 0-45 days) were enrolled in the study. In total, 894 serum gentamicin samples were included in the analysis. The concentration-time profile was described using a three-compartment model. Gentamicin clearance increased with the GA and PNA (included in a nonlinear fashion). The GA was also identified as having a significant influence on the central volume of distribution, with a preterm neonate having a larger central volume of distribution per kilogram of bodyweight than a term neonate. Cystatin C and creatinine were not correlated with gentamicin clearance in this study population. The external dataset was well predicted by the developed model.

    Conclusion:

    Bodyweight and age (GA and PNA) were found to be major factors contributing to IIV in gentamicin clearance in neonates. Based on these data, cystatin C and serum creatinine were not correlated with gentamicin clearance and therefore not likely to be predictive markers of renal function in this patient population. Based on predictions from the developed model, preterm neonates do not reach targeted peak and trough gentamicin concentrations after a standard dosage regimen of 4mg/kg given once daily, suggesting a need for higher loading doses and prolonged dosing intervals in this patient population.

  • 47.
    Nielsen, Elisabet I.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Viberg, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Löwdin, Elisabeth
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Cars, Otto
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Sandström, Marie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments2007In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 51, no 1, p. 128-136Article in journal (Refereed)
    Abstract [en]

    Dosing of antibacterial agents is generally based on point estimates of the effect, even though bacteria exposed to antibiotics show complex kinetic behaviors. The use of the whole time course of the observed effects would be more advantageous. The aim of the present study was to develop a semimechanistic pharmacokinetic (PK)/pharmacodynamic (PD) model characterizing the events seen in a bacterial system when it is exposed to antibacterial agents with different mechanisms of action. Time-kill curve experiments were performed with a strain of Streptococcus pyogenes exposed to a wide range of concentrations of the following antibiotics: benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, and vancomycin. Bacterial counts were monitored with frequent sampling during the experiment. A simultaneous fit of all data was accomplished. The degradation of the drugs was monitored and corrected for in the model, and a link model was used to account for an effect delay. In the final PK/PD model, the total bacterial population was divided into two subpopulations: one growing drug-susceptible population and one resting insusceptible population. The drug effect was included as an increase of the killing rate of bacteria in the susceptible state, according to a maximum-effect (Emax) model. An internal model validation showed that the model was robust and had good predictability. In conclusion, for all drugs, the final PK/PD model successfully described bacterial growth and killing kinetics when the bacteria were exposed to different antibiotic concentrations. The semimechanistic model that was developed might, after further refinement, serve as a tool for the development of optimal dosing strategies for antibacterial agents.

  • 48.
    Olsson, Anna
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine.
    Malmberg, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
    Zhao, Chenyan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lagerbäck, Pernilla
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine.
    Tängdén, Thomas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine.
    Synergy of polymyxin B and minocycline against KPC-3- and OXA-48-producing Klebsiella pneumoniae in dynamic time-kill experiments: agreement with in silico predictions.2023In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, article id dkad394Article in journal (Refereed)
    Abstract [en]

    OBJECTIVES: Combination therapy is often used for carbapenem-resistant Gram-negative bacteria. We previously demonstrated synergy of polymyxin B and minocycline against carbapenem-resistant Klebsiella pneumoniae in static time-kill experiments and developed an in silico pharmacokinetic/pharmacodynamic (PK/PD) model. The present study assessed the synergistic potential of this antibiotic combination in dynamic experiments.

    METHODS: Two clinical K. pneumoniae isolates producing KPC-3 and OXA-48 (polymyxin B MICs 0.5 and 8 mg/L, and minocycline MICs 1 and 8 mg/L, respectively) were included. Activities of the single drugs and the combination were assessed in 72 h dynamic time-kill experiments mimicking patient pharmacokinetics. Population analysis was performed every 12 h using plates containing antibiotics at 4× and 8× MIC. WGS was applied to reveal resistance genes and mutations.

    RESULTS: The combination showed synergistic and bactericidal effects against the KPC-3-producing strain from 12 h onwards. Subpopulations with decreased susceptibility to polymyxin B were frequently detected after single-drug exposures but not with the combination. Against the OXA-48-producing strain, synergy was observed between 4 and 8 h and was followed by regrowth. Subpopulations with decreased susceptibility to polymyxin B and minocycline were detected throughout experiments. For both strains, the observed antibacterial activities showed overall agreement with the in silico predictions.

    CONCLUSIONS: Polymyxin B and minocycline in combination showed synergistic effects, mainly against the KPC-3-producing K. pneumoniae. The agreement between the experimental results and in silico predictions supports the use of PK/PD models based on static time-kill data to predict the activity of antibiotic combinations at dynamic drug concentrations.

  • 49.
    Olsson, Anna
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Malmberg, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Zhao, Chenyan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Lagerbäck, Pernilla
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Tängdén, Thomas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Synergy of polymyxin B and minocycline against KPC-3- and OXA-48-producing Klebsiella pneumoniae in dynamic time-kill experiments: high agreement with in silico predictionsManuscript (preprint) (Other academic)
    Abstract [en]

    Objectives. Combination therapy is often used for carbapenem-resistant Gram-negative bacteria. We previously demonstrated synergy of polymyxin B and minocycline against carbapenem-resistant Klebsiella pneumoniae in static time-kill experiments and developed an in silico pharmacokinetic-pharmacodynamic (PKPD) model. The present study assessed the activity of this antibiotic combination in dynamic experiments. 

    Methods. Two clinical K. pneumoniae isolates producing KPC-3 and OXA-48 (polymyxin B MICs 0.5 mg/L and 8 mg/L, and minocycline MICs 1 mg/L and 8 mg/L, respectively) were included. Activities of the single drugs and the combination were assessed in 72-h dynamic time-kill experiments mimicking patient pharmacokinetics. Population analysis was performed every 12 h using plates containing antibiotics at 4 and 8 x MIC. Whole-genome sequencing was applied to reveal resistance genes and mutations.

    Results. The combination showed synergistic and bactericidal effects against the KPC-3-producing strain from 12 h onwards. Subpopulations with decreased susceptibility to polymyxin B were frequently detected after single-drug exposures but not with the combination. Against the OXA-48-producing strain, synergy was observed between 4 and 8 h and was followed by regrowth. Subpopulations with decreased susceptibility to polymyxin B and minocycline were detected throughout experiments. For both strains, the observed antibacterial activities showed high agreement with the in silico predictions. 

    Conclusion. Polymyxin B and minocycline in combination showed synergistic effects mainly against the KPC-3-producing K. pneumoniae. The high agreement between the experimental results and in silico predictions supports the use of PKPD models based on static time-kill data to predict the activity of antibiotic combinations at dynamic drug concentrations.

  • 50.
    Olsson, Anna
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infection medicine.
    Wistrand-Yuen, Pikkei
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sandegren, Linus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Lagerbäck, Pernilla
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Tängdén, Thomas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Efficacy of Antibiotic Combinations against Multidrug-Resistant Pseudomonas aeruginosa in Automated Time-Lapse Microscopy and Static Time-Kill Experiments2020In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 64, no 6, article id e02111-19Article in journal (Refereed)
    Abstract [en]

    Antibiotic combination therapy is used for severe infections caused by multidrug-resistant (MDR) Gram-negative bacteria, yet data regarding which combinations are most effective are lacking. This study aimed to evaluate the in vitro efficacy of polymyxin B in combination with 13 other antibiotics against four clinical strains of MDR Pseudomonas aeruginosa. We evaluated the interactions of polymyxin B in combination with amikacin, aztreonam, cefepime, chloramphenicol, ciprofloxacin, fosfomycin, linezolid, meropenem, minocycline, rifampin, temocillin, thiamphenicol, or trimethoprim by automated time-lapse microscopy using predefined cutoff values indicating inhibition of growth (<= 10(6) CFU/ml) at 24 h. Promising combinations were subsequently evaluated in static time-kill experiments. All strains were intermediate or resistant to polymyxin B, antipseudomonal beta-lactams, ciprofloxacin, and amikacin. Genes encoding beta-lactamases (e.g., bla(PAO) and bla(OXA-50)) and mutations associated with permeability and efflux were detected in all strains. In the time-lapse microscopy experiments, positive interactions were found with 39 of 52 antibiotic combination/bacterial strain setups. Enhanced activity was found against all four strains with polymyxin B used in combination with aztreonam, cefepime, fosfomycin, minocycline, thiamphenicol, and trimethoprim. Time-kill experiments showed additive or synergistic activity with 27 of the 39 tested polymyxin B combinations, most frequently with aztreonam, cefepime, and meropenem. Positive interactions were frequently found with the tested combinations, against strains that harbored several resistance mechanisms to the single drugs, and with antibiotics that are normally not active against P. aeruginosa. Further study is needed to explore the clinical utility of these combinations.

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