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. 2023 Jul;17(4):1038-1048.
doi: 10.1177/19322968221074406. Epub 2022 Feb 4.

Clinical Evaluation of a Novel Insulin Immunosensor

Eleonora M Aiello  1   2 , Jordan E Pinsker  2 , Eva Vargas  3 , Hazhir Teymourian  3 , Farshad Tehrani  3 , Mei Mei Church  2 , Lori M Laffel  4 , Francis J Doyle 3rd  1   2 , Mary-Elizabeth Patti  4 , Joseph Wang  3 , Eyal Dassau  1   2
Affiliations

Clinical Evaluation of a Novel Insulin Immunosensor

Eleonora M Aiello et al. J Diabetes Sci Technol. 2023 Jul.

Abstract

Background: The estimation of available active insulin remains a limitation of automated insulin delivery systems. Currently, insulin pumps calculate active insulin using mathematical decay curves, while quantitative measurements of insulin would explicitly provide person-specific PK insulin dynamics to assess remaining active insulin more accurately, permitting more effective glucose control.

Methods: We performed the first clinical evaluation of an insulin immunosensor chip, providing near real-time measurements of insulin levels. In this study, we sought to determine the accuracy of the novel insulin sensor and assess its therapeutic risk and benefit by presenting a new tool developed to indicate the potential therapeutic consequences arising from inaccurate insulin measurements.

Results: Nine adult participants with type-1 diabetes completed the study. The change from baseline in immunosensor-measured insulin levels was compared with values obtained by standard enzyme-linked immunosorbant assay (ELISA) after preprandial injection of insulin. The point-of-care quantification of insulin levels revealed similar temporal trends as those from the laboratory insulin ELISA. The results showed that 70% of the paired immunosensor-reference values were concordant, which suggests that the patient could take action safely based on insulin concentration obtained by the novel sensor.

Conclusions: This proposed technology and preliminary feasibility evaluation show encouraging results for near real-time evaluation of insulin levels, with the potential to improve diabetes management. Real-time measurements of insulin provide person-specific insulin dynamics that could be used to make more informed decisions regarding insulin dosing, thus helping to prevent hypoglycemia and improve diabetes outcomes.

Keywords: immunosensor; insulin measurement; point-of-care; type-1 diabetes.

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Conflict of interest statement

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.E.P. is currently an employee and shareholder of Tandem Diabetes Care, Inc. The work presented in the manuscript was performed as part of his academic appointment at Sansum Diabetes Research Institute and is independent of his employment with Tandem Diabetes Care. E.D. reports receiving grants from JDRF, NIH, and Helmsley Charitable Trust, personal fees from Roche and Eli Lilly, patents on artificial pancreas technology, and product support from Dexcom, Insulet, Tandem, and Roche. E.D. is currently an employee and shareholder of Eli Lilly and Company. The work presented in this manuscript was performed as part of his academic appointment and is independent of his employment with Eli Lilly and Company. F.J.D. reports equity, licensed IP and is a member of the Scientific Advisory Board of Mode AGC. L.M.L. reports grant support to her institution from NIH, JDRF, Helmsley Charitable Trust, Eli Lilly and Company, Insulet, Dexcom, and Boehringer Ingelheim; she receives consulting fees unrelated to the current report from Johnson & Johnson, Sanofi, NovoNordisk, Roche, Dexcom, Insulet, Boehringer Ingelheim, ConvaTec, Medtronic, Lifescan, Laxmi, and Insulogic. M.E.P. reports receiving grant support, provided to her institution, from NIH, Helmsley Charitable Trust, Chan Zuckerberg Foundation, and Dexcom, patents related to hypoglycemia and pump therapy for hypoglycemia, and advisory board fees from Fractyl (unrelated to the current report). F.T. and H.T. are currently employees of ActioX LLC. The work presented in the manuscript was performed as part of their academic appointment at UCSD. All other authors report no conflict of interest.

Figures

Figure 1.
Figure 1.
Amperometric results obtained with the immunosensor in the decentralized monitoring of insulin levels variation over time using serum samples from nine subjects with T1D. Chronoamperograms (a) and mean amperometric signals (b) corresponding to serum samples without standard spike, and standard-addition calibration plots considering spiking with insulin standard solutions of 200 and 400 pM concentrations (c) recorded for serum samples collected before (0 h) and at certain times (1, 2, 4 h) after insulin injection from nine different subjects. All subjects evaluated using the optimized serum assay, except for subject HS1-07 for whom incubations of 2.5 µL sample on 15.75 mm2 working electrode (WE) immunostrips were performed.
Figure 2.
Figure 2.
Delta change from baseline of insulin concentrations obtained by the decentralized immunosensor and a centralized ELISA method in serum samples collected from nine individuals with T1D. Comparison of the results obtained on-the-spot with the developed insulin sensor (blue circles) and in a centralized external laboratory with an ELISA kit (orange circles) in the serum insulin quantization in the blood samples collected from the participants at each time point when insulin measurements were available, that is, at 1, 2, and 4 hours after the mealtime measurement. Error bar of immunosensor measurements is also included at each delta. All subjects evaluated using the optimized serum assay, except for subject HS1-07 for whom incubations of 2.5 µL sample on 15.7mm2 working electrode immunostrips were performed. Participants were using insulin Lispro, but HS1-06 and HS1-09 were using insulin Aspart.
Figure 3.
Figure 3.
Insulin measurement error (IME) therapeutic risk assessment grid on the serum samples collected from nine individuals with T1D. The IMEs (e%) are reported in relation to the glucose levels. The error values were computed by normalizing the relative IMEs using the individual CF. Zone A (light green) includes samples with IME less than 20%. Zone B (dark green) includes samples having an IME larger than 20% provided that the error does not compromise the adjustment to the therapy. Zone C (yellow) represents the overcorrection condition associated with an IME larger than 20%. Zone D (orange) represents the suggestion of no action when one was needed having an IME larger than 20 %. Zone E represents a correction that causes hypoglycemia from hyperglycemia and vice versa associated with an IME larger than 70%. The dashed horizontal lines separate the three glycemic levels: hypoglycemia (glucose <70 mg/dL), euglycemia (70-180 mg/dL), and hyperglycemia (>180 mg/dL). The error thresholds for a 20% and 70% measurement error are displayed by the dashed vertical lines. A zoomed detail of the right-side part has been included in the central area.
See this image and copyright information in PMC

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