Extemporaneous Formulations of Metformin for Pediatric Endocrinology: Physicochemical Integrity, Cytotoxicity of Sweeteners, and Quantitation of Plasma Levels.

Radames Alemon-Medina,Juan Luis Chavez-Pacheco,Liliana Rivera-Espinosa,Blanca Ramirez-Mendiola,Raquel Garcia-Alvarez, Cynthia Samano-Salazar,Victor Manuel Davila-Borja

Clinical Therapeutics（2015）SCI 4区

Laboratorio de Farmacología | Secretaria Salud Mexico | Univ Autonoma Metropolitana

Cited 5|Views28

Abstract

Purpose: In response to the lack of pediatric formulations of metformin to control type 2 diabetes mellitus, hyperinsulinemic obesity, and dyslipidemias, we developed liquid formulations of metformin by dissolving 3 generic brands of 500-mg metformint(*,dagger,double dagger) tablets in water sweetened with sucralose. The physicochemical stabilities of these drugs were assessed and compared with those of formulations made with the innovative brand of metformin.(parallel to) A method to measure metformin plasma levels was proposed and then tested in 2 healthy subjects. This method may be useful to survey treatment compliance in the future. The biological safety profiles of the metformin solutions were assessed preliminarily in a system of hormone-dependent cancer cells (human breast cancer MCF-7 cells).Methods: Metformin solutions stored at 25 degrees C exposed to light and at 25 degrees C, 4 degrees C, and 40 degrees C protected from light, underwent physicochemical analysis by ultra-performance liquid chromatography with ultraviolet detection, the mobile phase consisting of 0.2 M potassium monobasic phosphate (pH 6.5), 4.6 mM sodium dodecyl sulphate (SDS), and acetonitrile (63:7:30) at a flow rate of 0.8 mL/min in a Symmetry C8 150 x 4.6 mm column (Milford, Massachusetts) at 40 degrees C (236 nm). MCF-7 cells were grown in 96-well ELISA plates (2 x 10(5) cells/well) and were exposed to 10, 20, and 40 mg/mL sucralose($), Stevia rebaudiana (Svetia; Metco, S.A. de C.V., Mexico, D.F., Mexico), and metformin (50 mg/mL) for 48 hours. Cytotoxicity was determined using the WST-1 colorimetric assay (Roche, USA) in an Epoch ELISA reader (BioTek, Winooski, Vermont) at 440 nm.Findings: All brands of metformin were stable at all storage conditions for up to 30 days and retained >90% of the initial amount. Sucralose and Stevia rebaudiana caused zero cytotoxicity (ANOVA, P <= 0.05). The ultra-performance liquid chromatography with ultraviolet detection method was adapted to determine metformin level in very small blood samples (40 mu L), which was linear within the range of 20 to 600 ng/mL metformin (retention time 2 minutes). Metformin was physically and chemically stable within the processed blood for up to 30 days at 4 degrees C. (C) 2015 Elsevier HS Journals, Inc. All rights reserved.

Translated text

Key words

extemporaneous formulation,metformin,pediatrics,Splenda,Stevia,UPLC-UV

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper