Background: Remimazolam is a new benzodiazepine for procedural sedation and general anaesthesia. The aim of this study was to characterise its pharmacokinetic properties and safety in renally and hepatically impaired subjects.
Methods: Two separate trials were conducted in patients with hepatic (n=11) or renal impairment (n=11) compared with matched healthy subjects (n=9 and n=12, respectively). The hepatic impairment trial was an open-label adaptive 'Reduced Design' trial, using a single bolus of remimazolam 0.1 mg kg i.v., whereas the renal impairment trial was an open-label trial of a single bolus dose of remimazolam 1.5 mg i.v. Remimazolam plasma concentrations over time were analysed by population pharmacokinetic modelling.
Results: Remimazolam pharmacokinetic properties were adequately described by a three-compartment, recirculatory model. Exposure in subjects with severe hepatic impairment was 38.1% higher (i.e. clearance was 38.1% lower) compared with healthy volunteers. This increase caused a slightly delayed recovery (8.0 min for healthy, 12.1 min for moderate, and 16.7 min for severe hepatic impairment). With renal impairment, plasma clearance was comparable with that measured in healthy subjects. Simulations of C after a bolus dose of 10 mg showed no relevant impact of hepatic or renal impairment. The overall incidence of adverse events was low, and all adverse events were mild.
Conclusions: As C after a remimazolam bolus i.v. was not affected by hepatic or renal impairment, no dose adjustments are required. No unexpected adverse events related to remimazolam were seen in subjects with renal or hepatic impairment.
Clinical Trial Registration: Hepatic impairment trial: ClinicalTrials.gov, NCT01790607 (https://clinicaltrials.gov/ct2/show/NCT01790607). Renal impairment trial: EudraCT Number: 2014-004575-23.
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http://dx.doi.org/10.1016/j.bja.2021.05.027 | DOI Listing |
Front Pharmacol
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Chronic kidney disease (CKD) prevalence varies widely across different regions of India. We aimed to identify the status of CKD in India, by systematically reviewing the published community-based studies between the period of January 2011 to December 2023. PubMed, Scopus, and EMBASE were searched for peer-reviewed evidence.
View Article and Find Full Text PDFNephrology (Carlton)
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Faculty of Medicine, Dentistry & Health Sciences Melbourne, The University of Melbourne, Melbourne, Victoria, Australia.
Chronic kidney disease is characterised by the progressive loss of kidney function. However, predicting who will progress to kidney failure is difficult. Artificial Intelligence, including Machine Learning, shows promise in this area.
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