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| 2. |
- Martson, A-G, et al.
(författare)
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How to design a study to evaluate therapeutic drug monitoring in infectious diseases?
- 2020
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Ingår i: Clinical Microbiology and Infection. - : Elsevier BV. - 1198-743X .- 1469-0691. ; 26:8, s. 1008-1016
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Forskningsöversikt (refereegranskat)abstract
- Background: Therapeutic drug monitoring (TDM) is a tool to personalize and optimize dosing by measuring the drug concentration and subsequently adjusting the dose to reach a target concentration or exposure. The evidence to support TDM is however often ranked as expert opinion. Limitations in study design and sample size have hampered definitive conclusions of the potential added value of TDM.Objectives: We aim to give expert opinion and discuss the main points and limitations of available data from antibiotic TDM trials and emphasize key elements for consideration in design of future clinical studies to quantify the benefits of TDM.Sources: The sources were peer-reviewed publications, guidelines and expert opinions from the field of TDM.Content: This review focuses on key aspects of antimicrobial TDM study design: describing the rationale for a TDM study, assessing the exposure of a drug, assessing susceptibility of pathogens and selecting appropriate clinical endpoints. Moreover we provide guidance on appropriate study design.
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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. |
- 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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| 5. |
- 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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| 6. |
- 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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| 7. |
- 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. |
- 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. ; 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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| 10. |
- Keutzer, Lina, et al.
(författare)
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Mobile Health Apps for Improvement of Tuberculosis Treatment : Descriptive Review
- 2020
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Ingår i: JMIR mhealth and uhealth. - : JMIR Publications Inc.. - 2291-5222. ; 8:4
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Forskningsöversikt (refereegranskat)abstract
- Background: Mobile health (mHealth) is a rapidly emerging market, which has been implemented in a variety of different disease areas. Tuberculosis remains one of the most common causes of death from an infectious disease worldwide, and mHealth apps offer an important contribution to the improvement of tuberculosis treatment. In particular, apps facilitating dose individualization, adherence monitoring, or provision of information and education about the disease can be powerful tools to prevent the development of drug-resistant tuberculosis or disease relapse. Objective: The aim of this review was to identify, describe, and categorize mobile and Web-based apps related to tuberculosis that are currently available. Methods: PubMed, Google Play Store, Apple Store, Amazon, and Google were searched between February and July 2019 using a combination of 20 keywords. Apps were included in the analysis if they focused on tuberculosis, and were excluded if they were related to other disease areas or if they were games unrelated to tuberculosis. All apps matching the inclusion criteria were classified into the following five categories: adherence monitoring, individualized dosing, eLearning/information, diagnosis, and others. The included apps were then summarized and described based on publicly available information using 12 characteristics. Results: Fifty-five mHealth apps met the inclusion criteria and were included in this analysis. Of the 55 apps, 8 (15%) were intended to monitor patients' adherence, 6 (11%) were designed for dosage adjustment, 29 (53%) were designed for eLearning/information, 3 (6%) were focused on tuberculosis diagnosis, and 9 (16%) were related to other purposes. Conclusions: The number of mHealth apps related to tuberculosis has increased during the past 3 years. Although some of the discovered apps seem promising, many were found to contain errors or provided harmful or wrong information. Moreover, the majority of mHealth apps currently on the market are focused on making information about tuberculosis available (29/55, 53%). Thus, this review highlights a need for new, high-quality mHealth apps supporting tuberculosis treatment, especially those supporting individualized optimized treatment through model-informed precision dosing and video observed treatment.
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