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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.
    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.

  • 2.
    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.

  • 3.
    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.

  • 4. 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.

  • 5. 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.

  • 6.
    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.

  • 7. 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)
  • 8.
    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.

  • 9.
    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.

  • 10.
    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.

  • 11.
    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)
  • 12.
    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.

  • 13.
    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.

  • 14.
    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 (Berlin), ISSN 0933-7407, E-ISSN 1439-0507, Vol. 60, p. 225-225Article in journal (Other academic)
  • 15.
    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)
  • 16.
    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.

  • 17.
    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.

  • 18.
    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.

  • 19.
    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.

  • 20.
    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
    3.
    The record could not be found. The reason may be that the record is no longer available or you may have typed in a wrong id in the address field.
    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: 2018-01-12Bibliographically approved
  • 21.
    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.

  • 22.
    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.

  • 23.
    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.

  • 24.
    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.

  • 25.
    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.

  • 26.
    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.

  • 27.
    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.

  • 28.
    Sadiq, Muhammad W
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Astrazeneca, DMPK, CVMD iMED, Molndal, Sweden.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Khachman, Dalia
    INRA, Toxalim, Toulouse, France.; Univ Toulouse, Toulouse, France.
    Conil, Jean-Marie
    Hosp Purpan, Inst Federatif Biol, Lab Pharmacocinet & Toxicol Clin, Toulouse, France.; Hop Rangueil, Pole Anesthesie Reanimat, Toulouse, France.
    Georges, Bernard
    Hosp Purpan, Inst Federatif Biol, Lab Pharmacocinet & Toxicol Clin, Toulouse, France.; Hop Rangueil, Pole Anesthesie Reanimat, Toulouse, France.
    Houin, Georges
    Hosp Purpan, Inst Federatif Biol, Lab Pharmacocinet & Toxicol Clin, Toulouse, France.
    Laffont, Celine M
    INRA, Toxalim, Toulouse, France.; Univ Toulouse, Toulouse, France.
    Karlsson, Mats O.
    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.
    A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection.2017In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 2, p. 69-79Article in journal (Refereed)
    Abstract [en]

    The purpose of this study was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model for ciprofloxacin for ICU patients, based on only plasma concentration data. In a next step, tissue and organ concentration time profiles in patients were predicted using the developed model. The WB-PBPK model was built using a non-linear mixed effects approach based on data from 102 adult intensive care unit patients. Tissue to plasma distribution coefficients (Kp) were available from the literature and used as informative priors. The developed WB-PBPK model successfully characterized both the typical trends and variability of the available ciprofloxacin plasma concentration data. The WB-PBPK model was thereafter combined with a pharmacokinetic-pharmacodynamic (PKPD) model, developed based on in vitro time-kill data of ciprofloxacin and Escherichia coli to illustrate the potential of this type of approach to predict the time-course of bacterial killing at different sites of infection. The predicted unbound concentration-time profile in extracellular tissue was driving the bacterial killing in the PKPD model and the rate and extent of take-over of mutant bacteria in different tissues were explored. The bacterial killing was predicted to be most efficient in lung and kidney, which correspond well to ciprofloxacin's indications pneumonia and urinary tract infections. Furthermore, a function based on available information on bacterial killing by the immune system in vivo was incorporated. This work demonstrates the development and application of a WB-PBPK-PD model to compare killing of bacteria with different antibiotic susceptibility, of value for drug development and the optimal use of antibiotics.

  • 29.
    Senek, Marina
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyholm, Dag
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Levodopa-entacapone-carbidopa intestinal gel in Parkinson's disease: A randomized crossover study2017In: Movement Disorders, ISSN 0885-3185, E-ISSN 1531-8257, Vol. 32, no 2, p. 283-286Article in journal (Refereed)
    Abstract [en]

    BackgroundThe addition of oral entacapone to levodopa-carbidopa intestinal gel treatment leads to less conversion of levodopa to 3-O-methyldopa, thereby increasing levodopa plasma concentration. The objective of this study was to compare systemic levodopa exposure of the newly developed levodopa-entacapone-carbidopa intestinal gel after a 20% dose reduction with levodopa exposure after the usual levodopa-carbidopa intestinal gel dose in a randomized crossover trial in advanced Parkinson's disease patients. MethodsIn this 48-hour study, 11 patients treated with levodopa-carbidopa intestinal gel were randomized to a treatment sequence. Blood samples were drawn at prespecified times, and patient motor function was assessed according to the treatment response scale. ResultsSystemic exposure of levodopa did not differ significantly between treatments (ratio, 1.10 [95% confidence interval, 0.951-1.17]). Treatment response scale scores did not significantly differ between treatments (P=0.84). ConclusionsLevodopa-entacapone-carbidopa intestinal gel allowed a lower amount of levodopa administration and was well tolerated. Long-term studies are needed to confirm the results.

  • 30.
    Senek, Marina
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyholm, Dag
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Population pharmacokinetics of levodopa/carbidopa microtablets in healthy subjects and Parkinson’s disease patients2018In: European Journal of Clinical Pharmacology, ISSN 0031-6970, E-ISSN 1432-1041, Vol. 74, no 10, p. 1299-1307Article in journal (Refereed)
    Abstract [en]

    Objectives: Low dose, dispersible, levodopa/carbidopa microtablets with an automatic dose dispenser have been developed to facilitate individualized levodopa treatment. The aim of this study was to characterize the pharmacokinetics (PK) of levodopa and carbidopa after microtablet administration, and evaluate the impact of potential covariates.

    Methods: The population PK analysis involved data from 18 healthy subjects and 18 Parkinson's disease patients included in two single-dose, open-label levodopa/carbidopa microtablet studies. The analysis was carried out using non-linear mixed effects modeling. Bodyweight was included on all disposition parameters according to allometric scaling. Potential influence of additional covariates was investigated using graphical evaluation and adjusted adaptive least absolute shrinkage and selection operator.

    Results: Dispositions of levodopa and carbidopa were best described by a two- and one-compartment model respectively. Double-peak profiles were described using two parallel absorption compartments. Levodopa apparent clearance was found to decrease with increasing carbidopa dose (15% lower with 75 compared to 50mg of carbidopa) and disease stage (by 18% for Hoehn and Yahr 1 to 4). Carbidopa apparent clearance was found to decrease with age (28% between the age of 60 and 80years). An external evaluation showed the model to be able to reasonably well predict levodopa concentrations following multiple-dose microtablet administration in healthy subjects.

    Conclusions: The presented models adequately described the PK of levodopa and carbidopa, following microtablet administration. The developed model may in the future be combined with a pharmacokinetic-pharmacodynamic target and used for individualized dose selection, utilizing the flexibility offered by the microtablets.

  • 31.
    Torres, Bruna G. S.
    et al.
    Univ Fed Rio Grande do Sul, Coll Pharm, Pharmaceut Sci Grad Program, Porto Alegre, RS, Brazil..
    Helfer, Victoria E.
    Univ Fed Rio Grande do Sul, Coll Pharm, Pharmaceut Sci Grad Program, Porto Alegre, RS, Brazil..
    Bernardes, Priscila M.
    Univ Fed Rio Grande do Sul, Coll Pharm, Pharmaceut Sci Grad Program, Porto Alegre, RS, Brazil..
    Macedo, Alexandre Jose
    Univ Fed Rio Grande do Sul, Coll Pharm, Pharmaceut Sci Grad Program, Porto Alegre, RS, Brazil.;Univ Fed Rio Grande do Sul, Ctr Biotecnol, Porto Alegre, RS, Brazil..
    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.
    Dalla Costa, Teresa
    Univ Fed Rio Grande do Sul, Coll Pharm, Pharmaceut Sci Grad Program, Porto Alegre, RS, Brazil..
    Population Pharmacokinetic Modeling as a Tool To Characterize the Decrease in Ciprofloxacin Free Interstitial Levels Caused by Pseudomonas aeruginosa Biofilm Lung Infection in Wistar Rats2017In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 61, no 7, article id e02553-16Article in journal (Refereed)
    Abstract [en]

    Biofilm formation plays an important role in the persistence of pulmonary infections, for example, in cystic fibrosis patients. So far, little is known about the antimicrobial lung disposition in biofilm-associated pneumonia. This study aimed to evaluate, by microdialysis, ciprofloxacin (CIP) penetration into the lungs of healthy and Pseudomonas aeruginosa biofilm-infected rats and to develop a comprehensive model to describe the CIP disposition under both conditions. P. aeruginosa was immobilized into alginate beads and intratracheally inoculated 14 days before CIP administration (20 mg/kg of body weight). Plasma and microdialysate were sampled from different animal groups, and the observations were evaluated by noncompartmental analysis (NCA) and population pharmacokinetic (popPK) analysis. The final model that successfully described all data consisted of an arterial and a venous central compartment and two peripheral distribution compartments, and the disposition in the lung was modeled as a two-compartment model structure linked to the venous compartment. Plasma clearance was approximately 32% lower in infected animals, leading to a significantly higher level of plasma CIP exposure (area under the concentration-time curve from time zero to infinity, 27.3 +/- 12.1 mu g . h/ml and 13.3 +/- 3.5 mu g . h/ml in infected and healthy rats, respectively). Despite the plasma exposure, infected animals showed a four times lower tissue concentration/plasma concentration ratio (lung penetration factor = 0.44 and 1.69 in infected and healthy rats, respectively), and lung clearance (CLlung) was added to the model for these animals (CLlung = 0.643 liters/h/kg) to explain the lower tissue concentrations. Our results indicate that P. aeruginosa biofilm infection reduces the CIP free interstitial lung concentrations and increases plasma exposure, suggesting that plasma concentrations alone are not a good surrogate of lung concentrations.

  • 32.
    Tängdén, Thomas
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Martin, V. Ramos
    Univ Liverpool, Dept Mol & Clin Pharmacol, Liverpool, Merseyside, England..
    Felton, T. W.
    Univ South Manchester Hosp, Intens Care Unit, Manchester, Lancs, England..
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Marchand, S.
    INSERM, Pole Biol St, U1070, Poitiers, France.;Univ Poitiers, UFR Med Pharm, Poitiers, France..
    Brueggemann, R. J.
    Radboud Univ Nijmegen, Med Ctr, Dept Pharm, Nijmegen, Netherlands..
    Bulitta, J. B.
    Univ Florida, Coll Pharm, Ctr Pharmacometr & Syst Pharmacol, Orlando, FL USA..
    Bassetti, M.
    Santa Maria Misericordia Univ Hosp, Div Infect Dis, Udine, Italy.;Univ Udine, Udine, Italy..
    Theuretzbacher, U.
    Ctr Anti Infect Agents, Vienna, Austria..
    Tsuji, B. T.
    SUNY Buffalo, Sch Pharm & Pharmaceut Sci, Buffalo, NY USA..
    Wareham, D. W.
    Queen Mary Univ London, Barts & London Sch Med & Dent, Antimicrobial Res Grp, London, England..
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    De Waele, J. J.
    Ghent Univ Hosp, Dept Crit Care Med, Ghent, Belgium..
    Tam, V. H.
    Univ Houston, Coll Pharm, Dept Pharm Practice & Translat Res, Houston, TX USA..
    Roberts, Jason A.
    Univ Queensland, Trauma & Crit Care Res Ctr, Burns, Brisbane, Qld, Australia.;Univ Queensland, Ctr Translat Antiinfect Pharmacodynam, Brisbane, Qld, Australia.;Royal Brisbane & Womens Hosp, Dept Intens Care Med & Pharm, Level 3,Ned Hanlon Bldg, Brisbane, Qld 4029, Australia..
    The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections2017In: Intensive Care Medicine, ISSN 0342-4642, E-ISSN 1432-1238, Vol. 43, no 7, p. 1021-1032Article, review/survey (Refereed)
    Abstract [en]

    Critically ill patients with severe infections are at high risk of suboptimal antimicrobial dosing. The pharmacokinetics (PK) and pharmacodynamics (PD) of antimicrobials in these patients differ significantly from the patient groups from whose data the conventional dosing regimens were developed. Use of such regimens often results in inadequate antimicrobial concentrations at the site of infection and is associated with poor patient outcomes. In this article, we describe the potential of in vitro and in vivo infection models, clinical pharmacokinetic data and pharmacokinetic/ pharmacodynamic models to guide the design of more effective antimicrobial dosing regimens. Individualised dosing, based on population PK models and patient factors (e.g. renal function and weight) known to influence antimicrobial PK, increases the probability of achieving therapeutic drug exposures while at the same time avoiding toxic concentrations. When therapeutic drug monitoring (TDM) is applied, early dose adaptation to the needs of the individual patient is possible. TDM is likely to be of particular importance for infected critically ill patients, where profound PK changes are present and prompt appropriate antibiotic therapy is crucial. In the light of the continued high mortality rates in critically ill patients with severe infections, a paradigm shift to refined dosing strategies for antimicrobials is warranted to enhance the probability of achieving drug concentrations that increase the likelihood of clinical success.

  • 33.
    Ungphakorn, Wanchana
    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.
    Tängdén, Thomas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Automated time-lapse microscopy a novel method for screening of antibiotic combination effects against multidrug-resistant Gram-negative bacteria2018In: Clinical Microbiology and Infection, ISSN 1198-743X, E-ISSN 1469-0691, Vol. 24, no 7, article id 778.e7Article in journal (Refereed)
    Abstract [en]

    Objectives

    Antibiotic combinations are often used for carbapenemase-producing Enterobacteriaceae (CPE) but more data are needed on the optimal selection of drugs. This study aimed to evaluate the feasibility of a novel automated method based on time-lapse microscopy (the oCelloScope, Philips BioCell A/S, Allerød, Denmark) to determine in vitro combination effects against CPE and to discuss advantages and limitations of the oCelloScope in relation to standard methods.

    Methods

    Four Klebsiella pneumoniae and two Escherichia coli were exposed to colistin, meropenem, rifampin and tigecycline, alone and in combination. In the oCelloScope experiments, a background corrected absorption (BCA) value of ≤8 at 24 h was used as a primary cut-off indicating inhibition of bacterial growth. A new approach was used to determine synergy, indifference and antagonism based on the number of objects (bacteria) in the images. Static time–kill experiments were performed for comparison.

    Results

    The time–kill experiments showed synergy with 12 of 36 regimens, most frequently with colistin plus rifampin. BCA values ≤8 consistently correlated with 24-h bacterial concentrations ≤6 log10 CFU/mL. The classification of combination effects agreed with the time–kill results for 33 of 36 regimens. In three cases, the interactions could not be classified with the microscopy method because of low object counts.

    Conclusions

    Automated time-lapse microscopy can accurately determine the effects of antibiotic combinations. The novel method is highly efficient compared with time–kill experiments, more informative than checkerboards and can be useful to accelerate the screening for combinations active against multidrug-resistant Gram-negative bacteria.

  • 34.
    Ungphakorn, Wanchana
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Malmberg, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Lagerbäck, Pernilla
    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.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Tängdén, Thomas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
    Evaluation of automated time-lapse microscopy for assessment of in vitro activity of antibiotics2017In: Journal of Microbiological Methods, ISSN 0167-7012, E-ISSN 1872-8359, Vol. 132, p. 69-75Article in journal (Refereed)
    Abstract [en]

    This study aimed to evaluate the potential of a new time-lapse microscopy based method (oCelloScope) to efficiently assess the in vitro antibacterial effects of antibiotics. Two E. con and one P. aeruginosa strain were exposed to ciprofloxacin, colistin, ertapenem and meropenem in 24-h experiments. Background corrected absorption (BCA) derived from the oCelloScope was used to detect bacterial growth. The data obtained with the oCelloScope were compared with those of the automated Bioscreen C method and standard time-kill experiments and a good agreement in results was observed during 6-24 h of experiments. Viable counts obtained at 1, 4, 6 and 24 h during oCelloScope and Bioscreen C experiments were well correlated with the corresponding BCA and optical density (OD) data. Initial antibacterial effects during the first 6 h of experiments were difficult to detect with the automated methods due to their high detection limits (approximately 105 CFU/mL for oCelloScope and 107 CFU/mL for Bioscreen C), the inability to distinguish between live and dead bacteria and early morphological changes of bacteria during exposure to ciprofloxacin, ertapenem and meropenem. Regrowth was more frequently detected in time-kill experiments, possibly related to the larger working volume with an increased risk of preexisting or emerging resistance. In comparison with Bioscreen C, the oCelloScope provided additional information on bacterial growth dynamics in the range of 105 to 107 CFU/mL and morphological features. In conclusion, the oCelloScope would be suitable for detection of in vitro effects of antibiotics, especially when a large number of regimens need to be tested.

1 - 34 of 34
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