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Glibenclamide population pharmacokinetic/pharmacodynamic modeling in South African type 2 diabetic subjects

Authors Rambiritch V, Naidoo P, Pillai G

Received 17 December 2015

Accepted for publication 31 May 2016

Published 26 September 2016 Volume 2016:8 Pages 141—153


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Arthur Frankel

Virendra Rambiritch,1 Poobalan Naidoo,2 Goonaseelan Pillai3

1Pharmacology Department, University of KwaZulu-Natal, Durban, 2Department of Internal Medicine, RK Khan Regional Hospital, Chatsworth, South Africa; 3Scientific Capability Development, Novartis Pharma AG, Basel, Switzerland

To determine the effective dose of glibenclamide by quantifying the dose–response relationship in South African type 2 diabetic patients.
Patients and methods: A total of 24 type 2 diabetic patients participated in a glibenclamide dose-escalation study during which glibenclamide, glucose, and insulin concentrations were quantified, while the dose of glibenclamide was progressively increased. All except four subjects contributed data on all dose-escalation steps; however, data from all 24 patients were included in the model-based analysis. Pharmacokinetic/pharmacodynamic (PKPD) relationships were modeled using the software Nonmem®. Six models were utilized to explore the effect of alternative glibenclamide dose and plasma concentration inputs on various metrics of glucose response.
Results: Six models adequately described the experimental data. The effective dose for a glucose-lowering effect suggested by PKPD modeling is less than 5 mg/day. Doses beyond 5 mg/day do not meaningfully add to glibenclamide effects on blood-glucose response.
Conclusion: The effective dose of glibenclamide, suggested by PKPD modeling, is less than 5 mg/day. Higher doses of glibenclamide, eg, 15 mg/day as originally recommended by the manufacturer, do not produce further decrease in the blood glucose level but may predispose the patients to adverse effects.

Keywords: type 2 diabetes, glibenclamide, pharmacokinetic/pharmacodynamic modeling, dose–response relationships, Nonmem

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