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Microfluidic detection with acoustic spectroscopy (MIDAS) for analysis of insulin formulation stability

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Abstract

Current techniques to analyze insulin, although efficient and accurate, usually require advanced scientific knowledge, chemical labeling, expensive equipment and/or are destructive. Ultrasonic spectroscopy is a viable alternative since it is simple to use, does not involve any sample preparation, and is non-destructive. Microfluidic ultrasonic spectroscopy detects particles through measuring the amount of acoustic energy transmitted at different frequencies. We have successfully performed acoustic spectroscopy to analyze insulin on the micro-scale, where the main advantage is that only 50 μL (vs. 1 mL) is required for sample analysis. To prove the efficacy of our device, we first measured fresh Sanofi U400 Insuman insulin over a range of frequencies. We then artificially aged insulin for six hours to compare the acoustic signature before and after protein aging. Next, to probe the dynamic range and sensitivity of our new method, we chose three frequencies and measured the progressive deterioration of insulin as it aged over time. Finally, we validated the performance of MIDAS against three benchmark tests: HPLC, ThT fluorescence assay and DLS. Specifically, we show that certain frequencies produce different spectra within 1 hour of accelerated aging. MIDAS compares well with benchmark tests of HPLC, ThT fluorescence assay and DLS. Such a device can be a complementary tool to help determine the stability of insulin in a cost-effective manner.

Graphical abstract: Microfluidic detection with acoustic spectroscopy (MIDAS) for analysis of insulin formulation stability

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Publication details

The article was received on 29 Jul 2017, accepted on 04 Oct 2017 and first published on 05 Oct 2017


Article type: Paper
DOI: 10.1039/C7AY01846K
Citation: Anal. Methods, 2017, Advance Article
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    Microfluidic detection with acoustic spectroscopy (MIDAS) for analysis of insulin formulation stability

    C. McIntosh, K. Scida and S. Pennathur, Anal. Methods, 2017, Advance Article , DOI: 10.1039/C7AY01846K

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