| 1. |
- Koehler, Niklas, et al.
(författare)
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Pretomanid-resistant tuberculosis
- 2023
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Ingår i: Journal of Infection. - : W B SAUNDERS CO LTD. - 0163-4453 .- 1532-2742. ; 86:5, s. 520-524
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Toepper, Christoph, et al.
(författare)
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Variable Linezolid Exposure in Intensive Care Unit PatientsPossible Role of Drug-Drug Interactions
- 2016
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Ingår i: Therapeutic Drug Monitoring. - 0163-4356 .- 1536-3694. ; 38:5, s. 573-578
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Tidskriftsartikel (refereegranskat)abstract
- Background:Standard doses of linezolid may not be suitable for all patient groups. Intensive care unit (ICU) patients in particular may be at risk of inadequate concentrations. This study investigated variability of drug exposure and its potential sources in this population.Methods:Plasma concentrations of linezolid were determined by high-performance liquid chromatography in a convenience sample of 20 ICU patients treated with intravenous linezolid 600 mg twice daily. Ultrafiltration applying physiological conditions (pH 7.4/37 degrees C) was used to determine the unbound fraction. Individual pharmacokinetic (PK) parameters were estimated by population PK modeling. As measures of exposure to linezolid, area under the concentration-time curve (AUC) and trough concentrations (C-min) were calculated and compared with published therapeutic ranges (AUC 200-400 mg*h/L, C-min 2-10 mg/L). Coadministered inhibitors or inducers of cytochrome P450 and/or P-glycoprotein were noted.Results:Data from 18 patients were included into the PK evaluation. Drug exposure was highly variable (median, range: AUC 185, 48-618 mg*h/L, calculated C-min 2.92, 0.0062-18.9 mg/L), and only a minority of patients had values within the target ranges (6 and 7, respectively). AUC and C-min were linearly correlated (R = 0.98), and classification of patients (underexposed/within therapeutic range/overexposed) according to AUC or C-min was concordant in 15 cases. Coadministration of inhibitors was associated with a trend to higher drug exposure, whereas 3 patients treated with levothyroxine showed exceedingly low drug exposure (AUC approximate to 60 mg*h/L, C-min <0.4 mg/L). The median unbound fraction in all 20 patients was 90.9%.Conclusions:Drug exposure after standard doses of linezolid is highly variable and difficult to predict in ICU patients, and therapeutic drug monitoring seems advisable. PK drug-drug interactions might partly be responsible and should be further investigated; protein binding appears to be stable and irrelevant.
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| 3. |
- Alffenaar, Jan-Willem C., et al.
(författare)
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Pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs : An evaluation of in vitro, in vivo methodologies and human studies
- 2022
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Ingår i: Frontiers in Pharmacology. - : Frontiers Media S.A.. - 1663-9812. ; 13
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Forskningsöversikt (refereegranskat)abstract
- There has been an increased interest in pharmacokinetics and pharmacodynamics (PKPD) of anti-tuberculosis drugs. A better understanding of the relationship between drug exposure, antimicrobial kill and acquired drug resistance is essential not only to optimize current treatment regimens but also to design appropriately dosed regimens with new anti-tuberculosis drugs. Although the interest in PKPD has resulted in an increased number of studies, the actual bench-to-bedside translation is somewhat limited. One of the reasons could be differences in methodologies and outcome assessments that makes it difficult to compare the studies. In this paper we summarize most relevant in vitro, in vivo, in silico and human PKPD studies performed to optimize the drug dose and regimens for treatment of tuberculosis. The in vitro assessment focuses on MIC determination, static time-kill kinetics, and dynamic hollow fibre infection models to investigate acquisition of resistance and killing of Mycobacterium tuberculosis populations in various metabolic states. The in vivo assessment focuses on the various animal models, routes of infection, PK at the site of infection, PD read-outs, biomarkers and differences in treatment outcome evaluation (relapse and death). For human PKPD we focus on early bactericidal activity studies and inclusion of PK and therapeutic drug monitoring in clinical trials. Modelling and simulation approaches that are used to evaluate and link the different data types will be discussed. We also describe the concept of different studies, study design, importance of uniform reporting including microbiological and clinical outcome assessments, and modelling approaches. We aim to encourage researchers to consider methods of assessing and reporting PKPD of anti-tuberculosis drugs when designing studies. This will improve appropriate comparison between studies and accelerate the progress in the field.
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| 4. |
- Bulman, Zackery P., et al.
(författare)
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Research priorities towards precision antibiotic therapy to improve patient care
- 2022
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Ingår i: LANCET MICROBE. - : Elsevier. - 2666-5247. ; 3:10, s. e795-e802
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Tidskriftsartikel (refereegranskat)abstract
- Antibiotic resistance presents an incessant threat to our drug armamentarium that necessitates novel approaches to therapy. Over the past several decades, investigation of pharmacokinetic and pharmacodynamic (PKPD) principles has substantially improved our understanding of the relationships between the antibiotic, pathogen, and infected patient. However, crucial gaps in our understanding of the pharmacology of antibacterials and their optimal use in the care of patients continue to exist; simply attaining antibiotic exposures that are considered adequate based on traditional targets can still result in treatment being unsuccessful and resistance proliferation for some infections. It is this salient paradox that points to key future directions for research in antibiotic therapeutics. This Personal View discusses six priority areas for antibiotic pharmacology research: (1) antibiotic-pathogen interactions, (2) antibiotic targets for combination therapy, (3) mechanistic models that describe the time-course of treatment response, (4) understanding and modelling of host response to infection, (5) personalised medicine through therapeutic drug management, and (6) application of these principles to support development of novel therapies. Innovative approaches that enhance our understanding of antibiotic pharmacology and facilitate more accurate predictions of treatment success, coupled with traditional pharmacology research, can be applied at the population level and to individual patients to improve outcomes.
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| 5. |
- Chen, Chunli, et al.
(författare)
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Assessing Pharmacodynamic Interactions in Mice using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models
- 2017
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Ingår i: CPT. - : John Wiley & Sons. - 2163-8306. ; 6:11, s. 787-797
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast-multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non-multiplying bacteria were identified, which led to an increase of 0.86 log(10) colony-forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non-multiplying bacteria was quantified, which led to a decrease of 2.84 log(10) CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development.
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| 6. |
- Chen, Chunli, et al.
(författare)
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Comparisons of analysis methods for assessment of pharmacodynamic interactions including design recommendations
- 2018
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP–GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP–GPDI model approach. The MTP–GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.
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| 7. |
- Clewe, Oskar, et al.
(författare)
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A model informed pre-clinical approach for identification of exposure-response and pharmacodynamic interactions in early tuberculosis drug development
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Tuberculosis treatment involves the use of multiple drugs and therefore there is a risk of not only pharmacokinetic interactions but also pharmacodynamic interactions. From many perspectives identification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting. Thus, the aim of this work was to develop a model-informed pre-clinical approach for identification of exposure-response and pharmacodynamic interactions of drug combinations in order to inform early anti-tuberculosis drug development. In vitro time-kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The Multistate Tuberculosis Pharmacometric model was used to characterize the natural growth and exposure-response relationships of each drug after mono-exposure. Pharmacodynamic interactions during combination exposure were characterized using the General Pharmacodynamic Interaction model with successful separation of each drug’s effect on the potency (EC50) of the other drugs. The approach outlined in this work constitutes groundwork for model informed input to the development of new and enhancement of existing anti-tuberculosis combination regimens.
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| 8. |
- Clewe, Oskar, et al.
(författare)
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A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations
- 2018
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Ingår i: Journal of Antimicrobial Chemotherapy. - : Oxford University Press (OUP). - 0305-7453 .- 1460-2091. ; 73:2, s. 437-447
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Tidskriftsartikel (refereegranskat)abstract
- Background: Identification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting for many reasons. The aim of this work was to develop a model-informed preclinical approach for prediction of clinical pharmacodynamic drug interactions in order to inform early anti-TB drug development.Methods: In vitro time-kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The multistate TB pharmacometric (MTP) model was used to characterize the natural growth and exposure-response relationships of each drug after mono exposure. Pharmacodynamic interactions during combination exposure were characterized by linking the MTP model to the general pharmacodynamic interaction (GPDI) model with successful separation of the potential effect on each drug's potency (EC50) by the combining drug(s).Results: All combinations showed pharmacodynamic interactions at cfu level, where all combinations, except isoniazid plus ethambutol, showed more effect (synergy) than any of the drugs alone. Using preclinical information, the MTP-GPDI modelling approach was shown to correctly predict clinically observed pharmacodynamic interactions, as deviations from expected additivity.Conclusions: With the ability to predict clinical pharmacodynamic interactions, using preclinical information, the MTP-GPDI model approach outlined in this study constitutes groundwork for model-informed input to the development of new and enhancement of existing anti-TB combination regimens.
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| 9. |
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| 10. |
- Keutzer, Lina, et al.
(författare)
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Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data : Differences, Similarities and Challenges Illustrated with Rifampicin
- 2022
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Ingår i: Pharmaceutics. - : MDPI. - 1999-4923 .- 1999-4923. ; 14:8
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Tidskriftsartikel (refereegranskat)abstract
- Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0-24 h (AUC(0-24h)) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R-2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC(0-24h) prediction, LASSO showed the highest performance (R-2: 0.97, RMSE: 29.1 h center dot mg/L, MAE: 18.8 h center dot mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC(0-24h) prediction using LASSO, the R-2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.
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