Role of Drug Metabolic Enzymes and Transporters in Drug-Drug Interactions Between Antiretroviral and Antituberculosis Drugs

2018 ◽  
Vol 1 (1) ◽  
pp. 17
Author(s):  
Md Parvez ◽  
Nazia Kaisar ◽  
JaeGook Shin
2016 ◽  
Vol 17 (7) ◽  
pp. 681-691 ◽  
Author(s):  
Ruirui Yang ◽  
Zhiqiang Luo ◽  
Yang Liu ◽  
Mohan Sun ◽  
Ling Zheng ◽  
...  

Author(s):  
Audrey Bellesoeur ◽  
Ithar Gataa ◽  
Anne Jouinot ◽  
Sarah El Mershati ◽  
Anne-Catherine Piketty ◽  
...  

2006 ◽  
Vol 26 (11) ◽  
pp. 1601-1607 ◽  
Author(s):  
Carol W Holtzman ◽  
Barbara S Wiggins ◽  
Sarah A Spinler

1994 ◽  
Vol 40 (4) ◽  
pp. 259-264 ◽  
Author(s):  
Joel M. Wierenga ◽  
Robert M. Hollingworth
Keyword(s):  

2014 ◽  
Author(s):  
Ivana Petrovska ◽  
Elisabeth Nüske ◽  
Matthias C Munder ◽  
Gayathrie Kulasegaran ◽  
Liliana Malinovska ◽  
...  

One of the key questions in biology is how the metabolism of a cell responds to changes in the environment. In budding yeast, starvation causes a drop in intracellular pH, but the functional role of this pH change is not well understood. Here, we show that the enzyme glutamine synthetase (Gln1) forms filaments at low pH and that filament formation leads to enzymatic inactivation. Filament formation by Gln1 is a highly cooperative process, strongly dependent on macromolecular crowding, and involves back-to-back stacking of cylindrical homo-decamers into filaments that associate laterally to form higher order fibrils. Other metabolic enzymes also assemble into filaments at low pH. Hence, we propose that filament formation is a general mechanism to inactivate and store key metabolic enzymes during a state of advanced cellular starvation. These findings have broad implications for understanding the interplay between nutritional stress, the metabolism and the physical organization of a cell.


2021 ◽  
Vol 12 ◽  
Author(s):  
Milo Gatti ◽  
Pier Giorgio Cojutti ◽  
Caterina Campoli ◽  
Fabio Caramelli ◽  
Luigi Tommaso Corvaglia ◽  
...  

Introduction: Antimicrobial treatment is quite common among hospitalized children. The dynamic age-associated physiological variations coupled with the pathophysiological alterations caused by underlying illness and potential drug-drug interactions makes the implementation of appropriate antimicrobial dosing extremely challenging among paediatrics. Therapeutic drug monitoring (TDM) may represent a valuable tool for assisting clinicians in optimizing antimicrobial exposure. Clinical pharmacological advice (CPA) is an approach based on the correct interpretation of the TDM result by the MD Clinical Pharmacologist in relation to specific underlying conditions, namely the antimicrobial susceptibility of the clinical isolate, the site of infection, the pathophysiological characteristics of the patient and/or the drug-drug interactions of cotreatments. The aim of this study was to assess the role of TDM-based CPAs in providing useful recommendations for the real-time personalization of antimicrobial dosing regimens in various paediatric settings.Materials and methods: Paediatric patients who were admitted to different settings of the IRCCS Azienda Ospedaliero-Universitaria of Bologna, Italy (paediatric intensive care unit [ICU], paediatric onco-haematology, neonatology, and emergency paediatric ward), between January 2021 and June 2021 and who received TDM-based CPAs on real-time for personalization of antimicrobial therapy were retrospectively assessed. Demographic and clinical features, CPAs delivered in relation to different settings and antimicrobials, and type of dosing adjustments were extracted. Two indicators of performance were identified. The number of dosing adjustments provided over the total number of delivered CPAs. The turnaround time (TAT) of CPAs according to a predefined scale (optimal, <12 h; quasi-optimal, between 12–24 h; acceptable, between 24–48 h; suboptimal, >48 h).Results: Overall, 247 CPAs were delivered to 53 paediatric patients (mean 4.7 ± 3.7 CPAs/patient). Most were delivered to onco-haematological patients (39.6%) and to ICU patients (35.8%), and concerned mainly isavuconazole (19.0%) and voriconazole (17.8%). Overall, CPAs suggested dosing adjustments in 37.7% of cases (24.3% increases and 13.4% decreases). Median TAT was 7.5 h (IQR 6.1–8.8 h). Overall, CPAs TAT was optimal in 91.5% of cases, and suboptimal in only 0.8% of cases.Discussion: Our study provides a proof of concept of the helpful role that TDM-based real-time CPAs may have in optimizing antimicrobial exposure in different challenging paediatric scenarios.


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