Patient-Centered Application of Α-Amylase-Based Enzymatic Time-Temperature Indicator for Real-Time Insulin Monitoring in Koronadal City

Authors

Keren Luisse V. Suat

Pharmacy Department, St. Alexius College, Inc (Philippines)

Almaira Z. Simpal

Pharmacy Department, St. Alexius College, Inc (Philippines)

Erika M. Resaba

Pharmacy Department, St. Alexius College, Inc (Philippines)

Janelle C. Sotto

Pharmacy Department, St. Alexius College, Inc (Philippines)

Article Information

DOI: 10.51244/IJRSI.2026.1307000269

Subject Category: Pharmacy

Volume/Issue: 13/7 | Page No: 3691-3708

Publication Timeline

Submitted: 2026-07-18

Accepted: 2026-07-23

Published: 2026-08-13

Abstract

Insulin stability remains a major concern in tropical regions where elevated ambient temperatures may compromise its therapeutic efficacy. This study evaluated the potential application of an α-amylase-based enzymatic time–temperature indicator (TTI) for real-time insulin monitoring in Koronadal City, Philippines. The study investigated the physicochemical properties of α-amylase relevant to TTI development, examined the relationship between TTI color change and insulin degradation above 25°C, assessed patient usability, and compared the prototype with a commercially available vaccine vial monitor (VVM). A mixed-methods approach combining laboratory experimentation and descriptive survey design was employed. Enzymatic activity was evaluated under varying temperature and pH conditions, while insulin and TTI samples were monitored for 28 days using UV-Visible spectrophotometry and Turbidimetric analysis. Patient-centered usability was assessed through structured questionnaires among diabetic participants. Results showed that α-amylase exhibited optimal activity at 45°C and pH 7, with progressive decline in activity at higher temperatures. The enzymatic TTI demonstrated a strong negative correlation with insulin degradation (r = −0.84, p < 0.001), indicating that visible color changes reliably reflected cumulative thermal exposure. Participants also reported high usability, clarity of interpretation, and increased confidence in insulin safety monitoring. Comparative analysis revealed that the α-amylase-based TTI performed comparably with commercial VVMs under controlled conditions. These findings suggest that the proposed enzymatic TTI is a practical tool for monitoring insulin stability in hot-climate settings and may support safer insulin storage practices in resource-limited communities.

Keywords

α-amylase, time-temperature indicator, insulin stability, patient-centered monitoring, Koronadal City

Downloads

References

1. Abd-Elhalim, B. T., Gamal, R. F., El-Sayed, S. M., & Abu-Hussien, S. H. (2023). Optimizing alphaamylase from Bacillus amyloliquefaciens on bread waste for effective industrial wastewater treatment and textile desizing through response surface methodology. Scientific Reports, 13(1), 19216. https://doi.org/10.1038/s41598-023-46384-6 [Google Scholar] [Crossref]

2. Agustien, A., Bendrianis, D., Alamsyah, F., Muqarramah, M., & Jannah, M. (2024). Enhanced amylase activity by modulating abiotic factors and enzyme stability in the thermophilic bacterial isolates. OnLine Journal of Biological Sciences, 24(2), 295–301. https://doi.org/10.3844/ojbsci.2024.295.301 [Google Scholar] [Crossref]

3. Akbarian, M., & Chen, S. H. (2022). Instability challenges and stabilization strategies of pharmaceutical proteins. Pharmaceutics, 14(11), 2533. https://doi.org/10.3390/pharmaceutics14112533 [Google Scholar] [Crossref]

4. American Diabetes Association Professional Practice Committee. (2025). 9. Pharmacologic approaches to glycemic treatment: Standards of care in diabetes—2025. Diabetes Care, 48(Supplement_1), S181– S206. https://doi.org/10.2337/dc25-S009 [Google Scholar] [Crossref]

5. Balakrishnan, M., Jeevarathinam, G., Kumar, S. K. S., Muniraj, I., & Uthandi, S. (2021). Optimization and scale-up of α-amylase production by Aspergillus oryzae using solid-state fermentation of edible oil cakes. BMC Biotechnology, 21(1), 33. https://doi.org/10.1186/s12896-021-00686-7 [Google Scholar] [Crossref]

6. Beattie, M., Murphy, D. J., Atherton, I., & Lauder, W. (2015). Instruments to measure patient experience of healthcare quality in hospitals: A systematic review. Systematic Reviews, 4, 97. https://doi.org/10.1186/s13643-015-0089-0 [Google Scholar] [Crossref]

7. Brizio, A. P. D. R., & Prentice, C. (2015). Development of an intelligent enzyme indicator for dynamic monitoring of the shelf-life of food products. Innovative Food Science & Emerging Technologies, 30, 208–217. https://doi.org/10.1016/j.ifset.2015.04.001 [Google Scholar] [Crossref]

8. Casanova, C. F., De Souza, M. A., Fischer, B., Colet, R., Marchesi, C., Zeni, J., Dallago, R., Paroul, N., Cansian, R., Backes, G. T., Man, C., Rahman, U., Sahar, A., Ishaq, A., Aadil, R., Zahoor, T., Ahmad, M., Wu, J., Hsiao, H., . . . Taoukis, P. (2020). Development of enzymatic-colorimetric time-temperature integrator for smart packaging. Biointerface Research in Applied Chemistry, 11(2), 9335–9345. https://doi.org/10.33263/briac112.93359345 [Google Scholar] [Crossref]

9. Chen, Y., Armstrong, Z., Artola, M., Florea, B. I., Kuo, C., De Boer, C., Rasmussen, M. S., Hachem, M. A., Van Der Marel, G. A., Codée, J. D. C., Aerts, J. M. F. G., Davies, G. J., & Overkleeft, H. S. (2021). Activity-based protein profiling of retaining α-amylases in complex biological samples. Journal of the American Chemical Society, 143(5), 2423–2432. https://doi.org/10.1021/jacs.0c13059 [Google Scholar] [Crossref]

10. Cho, H. W., Shin, D. U., Kim, S. W., Kim, E. S., Park, B. J., Kim, D. H., Jung, Y. W., & Lee, S. J. (2023). Enzymatic time-temperature indicator with cysteine-loaded chitosan microspheres/silver nanoparticles. Food Science and Biotechnology, 32(13), 1873–1881. https://doi.org/10.1007/s10068-023-01369-z [Google Scholar] [Crossref]

11. Chouchane, K., Frachon, T., Marichal, L., Nault, L., Vendrely, C., Maze, A., Bruckert, F., & Weidenhaupt, M. (2022). Insulin aggregation starts at dynamic triple interfaces, originating from solution agitation. Colloids and Surfaces B: Biointerfaces, 214, 112451. https://doi.org/10.1016/j.colsurfb.2022.112451 [Google Scholar] [Crossref]

12. Christopher, M. W., Veigle, O., Lloyd, S., McGrail, S., Lee, J. H., Bazargani, S. F., Atkinson, P. M., Haller, M. J., Atkinson, M. A., & Garrett, T. J. (2024). One-year thermostability of commercial glargine and human insulin. Diabetes Care, 47(12), e104–e105. https://doi.org/10.2337/dc24-1749 [Google Scholar] [Crossref]

13. Dadi, M., & Yasir, M. (2022). Spectroscopy and spectrophotometry: Principles and applications for colorimetric and related other analysis. In IntechOpen eBooks. https://doi.org/10.5772/intechopen.101106 [Google Scholar] [Crossref]

14. Dai, Y., Chen, Y., Lin, X., & Zhang, S. (2024). Recent applications and prospects of enzymes in quality and safety control of fermented foods. Foods, 13(23), 3804. https://doi.org/10.3390/foods13233804 [Google Scholar] [Crossref]

15. Das, A., Shah, M., & Saraogi, I. (2022). Molecular aspects of insulin aggregation and various therapeutic interventions. ACS Bio & Med Chem Au, 2(3), 205–221. https://doi.org/10.1021/acsbiomedchemau.1c00054 [Google Scholar] [Crossref]

16. Dehghani, Z., Mohammadnejad, J., & Hosseini, M. (2019). A new colorimetric assay for amylase based on starch-supported Cu/Au nanocluster peroxidase-like activity. Analytical and Bioanalytical Chemistry, 411(16), 3621–3629. https://doi.org/10.1007/s00216-019-01844-9 [Google Scholar] [Crossref]

17. Dutta, C., Yang, M., Long, F., Shahbazian-Yassar, R., & Tiwari, A. (2015). Preformed seeds modulate native insulin aggregation kinetics. The Journal of Physical Chemistry B, 119(49), 15089–15099. https://doi.org/10.1021/acs.jpcb.5b07221 [Google Scholar] [Crossref]

18. Eli Lilly and Company. (2015). Humulin R U-100 insulin human injection prescribing information. U.S. Food and Drug Administration. https://www.accessdata.fda.gov/drugsatfda_docs/label/2015/018780s150lbl.pdf [Google Scholar] [Crossref]

19. ElSayed, N. A., Aleppo, G., Aroda, V. R., Bannuru, R. R., Brown, F. M., Bruemmer, D., Collins, B. S., Hilliard, M. E., Isaacs, D., Johnson, E. L., Kahan, S., Khunti, K., Leon, J., Lyons, S. K., Perry, M. L., Prahalad, P., Pratley, R. E., Seley, J. J., Stanton, R. C., . . . Gabbay, R. A. (2023). 2. Classification and diagnosis of diabetes: Standards of care in diabetes—2023. Diabetes Care, 46(Suppl. 1), S19–S40. https://doi.org/10.2337/dc23-S002 [Google Scholar] [Crossref]

20. Fagan, A., Bateman, L. M., O’Shea, J. P., & Crean, A. M. (2024). Elucidating the degradation pathways of human insulin in the solid state. Journal of Analysis and Testing, 8(3), 288–299. https://doi.org/10.1007/s41664-024-00302-5 [Google Scholar] [Crossref]

21. Fagihi, M. H. A., & Bhattacharjee, S. (2022). Amyloid fibrillation of insulin: Amelioration strategies and implications for translation. ACS Pharmacology & Translational Science, 5(11), 1050–1061. https://doi.org/10.1021/acsptsci.2c00174 [Google Scholar] [Crossref]

22. Farooq, M. A., Ali, S., Hassan, A., Tahir, H. M., Mumtaz, S., & Mumtaz, S. (2021). Biosynthesis and industrial applications of α-amylase: A review. Archives of Microbiology, 203(4), 1281–1292. https://doi.org/10.1007/s00203-020-02128-y [Google Scholar] [Crossref]

23. Freitas, D., Gwala, S., Henry, G., Lazaridou, A., Boesch, C., Duijsens, D., Wheller, F., Lopez-Rodulfo, I. M., Kotsiou, K., Corbin, K. R., Alongi, M., Martinez, M. M., Hafiz, M. S., Tomassen, M. M. M., PerezMoral, N., Vidal, N. P., Ariëns, R. M. C., Simsek, S., El, S. N., . . . Grassby, T. (2025). Interlaboratory validation of an optimized protocol for measuring α-amylase activity by the INFOGEST international research network. Scientific Reports, 15(1), 30985. https://doi.org/10.1038/s41598-025-12561-y [Google Scholar] [Crossref]

24. Gangadharan, D., Jose, A., & Nampoothiri, K. M. (2020). Recapitulation of the stability and diversity of microbial α-amylases. Amylase, 4(1), 11–23. https://doi.org/10.1515/amylase-2020-0002 [Google Scholar] [Crossref]

25. Gao, T., Tian, Y., Zhu, Z., & Sun, D. (2020). Modelling, responses and applications of time-temperature indicators in monitoring fresh food quality. Trends in Food Science & Technology, 99, 311–322. https://doi.org/10.1016/j.tifs.2020.02.019 [Google Scholar] [Crossref]

26. Ghevondyan, D., Soghomonyan, T., Hovhannisyan, P., et al. (2024). Detergent-resistant α-amylase derived from Anoxybacillus karvacharensis K1 and its production based on whey. Scientific Reports, 14, 12682. https://doi.org/10.1038/s41598-024-63606-7 [Google Scholar] [Crossref]

27. Ghosh, A., & Shukla, P. (2021). Kinetics of starch–iodine complex formation in presence of amylolytic enzymes. Journal of Enzyme Inhibition and Medicinal Chemistry, 36(1), 110. https://doi.org/10.1080/14756366.2021.1877890 [Google Scholar] [Crossref]

28. Gómez-Villegas, P., Vigara, J., Romero, L., Gotor, C., Raposo, S., Gonçalves, B., & León, R. (2021). Biochemical characterization of the amylase activity from the new haloarchaeal strain Haloarcula sp. HS isolated in the Odiel Marshlands. Biology, 10(4), 337. https://doi.org/10.3390/biology10040337 [Google Scholar] [Crossref]

29. Heinemann, L., Braune, K., Carter, A., Zayani, A., & Krämer, L. A. (2021). Insulin storage: A critical reappraisal. Journal of Diabetes Science and Technology, 15(1), 147–159. https://doi.org/10.1177/1932296819900258 [Google Scholar] [Crossref]

30. Jaiswal, N., & Jaiswal, P. (2024). Thermostable α-amylases and laccases: Paving the way for sustainable industrial applications. Processes, 12(7), 1341. https://doi.org/10.3390/pr12071341 [Google Scholar] [Crossref]

31. Jaiswal, P., Verma, A., & Yadav, S. (2019). Development of an enzymatic time-temperature indicator based on α-amylase–starch kinetics. Journal of Food Processing and Preservation, 43(3), e13878. https://doi.org/10.1111/jfpp.13878 [Google Scholar] [Crossref]

32. Jaiswal, R. K., Mendiratta, S. K., Talukder, S., & Chand, S. (2019). Amylase-based enzymatic time temperature indicator as a thermal abuse marker for frozen chicken meat. Indian Journal of Poultry Science, 54(1), 59. https://doi.org/10.5958/0974-8180.2019.00010.2 [Google Scholar] [Crossref]

33. Jaiswal, R. K., Mendiratta, S. K., Talukder, S., Soni, A., & Saini, B. L. (2018). Enzymatic time temperature indicators: A review. The Pharma Innovation Journal, 7(10), 643–647. https://www.researchgate.net/publication/349336900_Enzymatic_time_temperature_indicators_A_revi ew [Google Scholar] [Crossref]

34. Kaufmann, B., Boulle, P., Berthou, F., Fournier, M., Beran, D., Ciglenecki, I., Townsend, M., Schmidt, G., Shah, M., Cristofani, S., Cavailler, P., Foti, M., & Scapozza, L. (2021). Heat-stability study of various insulin types in tropical temperature conditions: New insights towards improving diabetes care. PLOS ONE, 16(2), e0245372. https://doi.org/10.1371/journal.pone.0245372 [Google Scholar] [Crossref]

35. Khurana, G., & Gupta, V. (2019). Effect on insulin upon storage in extreme climatic conditions temperature and pressure and their preventive measures. Journal of Social Health and Diabetes, 7(1), 6–10. https://doi.org/10.1055/s-0039-1692371 [Google Scholar] [Crossref]

36. Kimaro, E., John, J., Damiano, P., Konje, E. T., Mori, A. T., Kidenya, B. R., Mshana, S. E., & Kaale, E. (2025). Impact of insulin storage and syringe reuse on insulin sterility in diabetes mellitus patients in Mwanza Tanzania. Scientific Reports, 15(1), 6232. https://doi.org/10.1038/s41598-025-91029-5 [Google Scholar] [Crossref]

37. Kongmalai, T., Orarachin, P., Dechates, B., Chanphibun, P., Junnu, S., Srisawat, C., & Sriwijitkamol, A. (2022). The effect of high temperature on the stability of basal insulin in a pen: A randomized controlled, crossover, equivalence trial. BMJ Open Diabetes Research & Care, 10(6), e003105. https://doi.org/10.1136/bmjdrc-2022-003105 [Google Scholar] [Crossref]

38. Koo, M., & Yang, S.-W. (2025). Questionnaire use and development in health research. Encyclopedia, 5(2), 65. https://doi.org/10.3390/encyclopedia5020065 [Google Scholar] [Crossref]

39. Labuza, T. P., & Fu, B. (1993). Use of kinetics in time-temperature indicators for food quality. Food Technology, 47(10), 36–41. Ling, B., Tang, J., Kong, F., Mitcham, E. J., & Wang, S. (2014). Kinetics of food quality changes during thermal processing: A review. Food and Bioprocess Technology, 8(2), 343– 358. https://doi.org/10.1007/s11947-014-1398-3 [Google Scholar] [Crossref]

40. Nyarko, Christian & Mills, Joseph & Afortude, John & Kizzie, Nazir & Agbale, Caleb & Badu Nyarko, Samuel. (2019). EFFECT OF pH STABILITY ON ALPHA AMYLASE EXTRACTED FROM Aspergillus niger ON STARCH FROM LOCAL RICE IN GHANA. [Google Scholar] [Crossref]

41. Ogle, G. D., Abdullah, M., Mason, D., Januszewski, A. S., & Besançon, S. (2016). Insulin storage in hot climates without refrigeration: Temperature reduction efficacy of clay pots and other techniques. Diabetic Medicine, 33(11), 1544–1553. https://doi.org/10.1111/dme.13194 [Google Scholar] [Crossref]

42. Oliveira, H. M., Pinheiro, A. Q., Fonseca, A. J., Cabrita, A. R., & Maia, M. R. (2019). Flexible and expeditious assay for quantitative monitoring of alpha-amylase and amyloglucosidase activities. MethodsX, 6, 246–258. https://doi.org/10.1016/j.mex.2019.01.007 [Google Scholar] [Crossref]

43. Omotoyinbo, O. V. (2023). Exploring the kinetic and thermodynamic profiles of amylase thermal inactivation derived from Bacillus sp. Science Frontiers. https://doi.org/10.11648/j.sf.20230404.11 [Google Scholar] [Crossref]

44. Özdemir, S., Matpan, F., Güven, K., & Baysal, Z. (2011). Production and characterization of partially purified extracellular thermostable α-amylase by Bacillus subtilis in submerged fermentation. Preparative Biochemistry & Biotechnology, 41(4), 365–381. https://doi.org/10.1080/10826068.2011.552142 [Google Scholar] [Crossref]

45. Pandian, A. T., Chaturvedi, S., & Chakraborty, S. (2020). Applications of enzymatic time-temperature indicator devices in quality monitoring and shelf-life estimation of food products during storage. Journal of Food Measurement and Characterization, 15(2), 1523–1540. https://doi.org/10.1007/s11694-02000730-8 [Google Scholar] [Crossref]

46. Patel, P., Dasgupta, D., Ray, A., & Suman, S. (2023). Challenges and prospects of microbial α-amylases for industrial application: A review. World Journal of Microbiology and Biotechnology, 40, Article 10. https://doi.org/10.1007/s11274-023-03821-y [Google Scholar] [Crossref]

47. Rasmussen, M., Hach, M., Iwersen, J., Anil, G., Rosborg, H., Larsen, J., Hoffmann, L. C., & Kurtzhals, P. (2024). The Human Insulin Thermal Solution project: A private sector initiative to address the thermostability of insulin. The Lancet Diabetes & Endocrinology, 12(5), 292–294. https://doi.org/10.1016/S2213-8587(24)00094-9 [Google Scholar] [Crossref]

48. Raviyan, P., Tang, J., & Rasco, B. A. (2003). Thermal stability of alpha-amylase from Aspergillus oryzae entrapped in polyacrylamide gel. Journal of Agricultural and Food Chemistry, 51(18), 5462–5466. https://doi.org/10.1021/jf020906j [Google Scholar] [Crossref]

49. Richter, B., Bongaerts, B., & Metzendorf, M. I. (2023). Thermal stability and storage of human insulin. [Google Scholar] [Crossref]

50. Cochrane Database of Systematic Reviews, 2023(11), CD015385. https://doi.org/10.1002/14651858.CD015385.pub2 [Google Scholar] [Crossref]

51. Sanofi-Aventis Deutschland GmbH. (2023). Insulin glargine 100 IU/mL solution for injection. World [Google Scholar] [Crossref]

52. Health Organization Prequalification Programme. https://extranet.who.int/prequal/sites/default/files/whopar_files/bt-dm005-part6bv1.03_jul2023.pdf [Google Scholar] [Crossref]

53. Santos, J. (2025, August 11). DOH: Over 656,000 Filipinos have diabetes, many still undiagnosed. Manila Bulletin. https://mb.com.ph/2025/08/08/doh-over-656000-filipinos-have-diabetes-many-stillundiagnosed [Google Scholar] [Crossref]

54. Shelley, S., Teixeira, R., Frick, A., Orth, L., Ellingson, A., Azimi, S., Mackay, H., & Lin, J. (2023). ASHP executive forums on cold chain management of pharmaceuticals in health systems. American Journal of Health-System Pharmacy, 80(22), 1677–1684. https://doi.org/10.1093/ajhp/zxad185 [Google Scholar] [Crossref]

55. Taerahkun, M., & Sriphrapradang, C. (2022). Evaluation of household cooling strategies for insulin storage in hot climates. Scientific Reports, 12(1), 18723. https://doi.org/10.1038/s41598-022-22721-6 [Google Scholar] [Crossref]

56. Tong, Z., & Ramaswamy, H. S. (2023). Continuous-flow microwave heating inactivation kinetics of αamylase from Bacillus subtilis and a comparison with conventional heating conditions. Applied Sciences, 13(16), 9220. https://doi.org/10.3390/app13169220 [Google Scholar] [Crossref]

57. Wal, P., Wadhwa, S., & Wal, A. (2019). Current practices in insulin and vaccine storage. Pharmacophore, 10(3), 70–81. http://www.pharmacophorejournal.com [Google Scholar] [Crossref]

58. World Health Organization. (2018). Guidelines on the stability evaluation of biotherapeutic products. WHO Press. https://www.who.int/publications/i/item/9789241210195 [Google Scholar] [Crossref]

59. World Health Organization. (2024). Thermostability of human insulin. World Health Organization. https://www.who.int/publications/i/item/9789240089044 Xiao, Z., Storms, R., & Tsang, A. (2006). A quantitative starch-iodine method for measuring alpha-amylase and glucoamylase activities. Analytical Biochemistry, 351(1), 146–148. https://doi.org/10.1016/j.ab.2006.01.036 [Google Scholar] [Crossref]

60. Yandri, Y., Tiarsa, E., Suhartati, T., Irawan, B., & Hadi, S. (2022). Immobilization and stabilization of Aspergillus fumigatus α-amylase by adsorption on chitin. Emerging Science Journal, 7(1), 77–89. https://doi.org/10.28991/ESJ-2023-07-01-06 [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles