Intracellular Pharmacodynamic Modeling Is Predictive of the Clinical Activity of Fluoroquinolones against Tuberculosis.
Antitubercular Agents
/ pharmacokinetics
Cell Line
Computer Simulation
Decision Support Techniques
Drug Development
Fluoroquinolones
/ pharmacokinetics
Humans
Macrophages
/ drug effects
Models, Biological
Monte Carlo Method
Moxifloxacin
/ pharmacokinetics
Mycobacterium tuberculosis
/ drug effects
THP-1 Cells
Tuberculosis, Multidrug-Resistant
/ drug therapy
Tuberculosis, Pulmonary
/ drug therapy
PDi
infectious disease
pharmacodynamics
pharmacokinetics
preclinical drug studies
tuberculosis
Journal
Antimicrobial agents and chemotherapy
ISSN: 1098-6596
Titre abrégé: Antimicrob Agents Chemother
Pays: United States
ID NLM: 0315061
Informations de publication
Date de publication:
20 12 2019
20 12 2019
Historique:
received:
22
05
2019
accepted:
18
09
2019
pubmed:
16
10
2019
medline:
1
9
2020
entrez:
16
10
2019
Statut:
epublish
Résumé
Clinical studies of new antitubercular drugs are costly and time-consuming. Owing to the extensive tuberculosis (TB) treatment periods, the ability to identify drug candidates based on their predicted clinical efficacy is vital to accelerate the pipeline of new therapies. Recent failures of preclinical models in predicting the activity of fluoroquinolones underline the importance of developing new and more robust predictive tools that will optimize the design of future trials. Here, we used high-content imaging screening and pharmacodynamic intracellular (PDi) modeling to identify and prioritize fluoroquinolones for TB treatment. In a set of studies designed to validate this approach, we show moxifloxacin to be the most effective fluoroquinolone, and PDi modeling-based Monte Carlo simulations accurately predict negative culture conversion (sputum sterilization) rates compared to eight independent clinical trials. In addition, PDi-based simulations were used to predict the risk of relapse. Our analyses show that the duration of treatment following culture conversion can be used to predict the relapse rate. These data further support that PDi-based modeling offers a much-needed decision-making tool for the TB drug development pipeline.
Identifiants
pubmed: 31611354
pii: AAC.00989-19
doi: 10.1128/AAC.00989-19
pmc: PMC7187570
pii:
doi:
Substances chimiques
Antitubercular Agents
0
Fluoroquinolones
0
Moxifloxacin
U188XYD42P
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Wellcome Trust
ID : 105620/Z/14/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N028376/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_14111
Pays : United Kingdom
Organisme : Medical Research Council
ID : G1002586
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L000644/1
Pays : United Kingdom
Informations de copyright
Copyright © 2019 Donnellan et al.
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