ALR Technologies Announces Patent Grant for Predictive A1C and Successful Results from Singapore General Hospital-led Study


(September 28, 2023 -- Singapore) ALR Technologies SG Ltd. (“ALRT” or the “Company”) (OTCQB:ALRTF), a diabetes management company, announces the patent application titled Method and System of Monitoring a Diabetes Treatment Plan (“Predictive A1c”) has been granted in Singapore and has received notice of allowance in the United States.

Predictive A1c is the Company’s cornerstone, the first of its kind U.S Food & Drug Administration (“FDA”) cleared Diabetes Management Solution that shifts patient self-management to active patient management while providing best practice guidelines and ensuring adherence to care plans. Predictive A1c is owned by Sidney Chan, Chairman and CEO of ALRT. The Company has the right to utilize Predictive A1c pursuant to an informal license with Mr. Chan and expects to acquire the intellectual property in the next 12 months. Final steps have been taken in the United States Patent and Trademark Office for issuance of the US patent for Predictive A1c, and the US patent is expected to grant in the coming weeks. The Company expects Predictive A1c patent applications submitted in other jurisdictions to advance to grant in 2024.

Additionally, ALRT announces the successful completion of its 24-week study led by Singapore General Hospital (“SGH”). A total of 25 insulin-treated patients with type 2 diabetes mellitus were enrolled into the ALRT Diabetes Solution platform. The patients performed twice daily capillary blood glucose checks and weekly glucometer uploads. The platform then provided insulin dose suggestions and alerts to the physicians. All patients experienced a mean drop of 1.2 percent in glycated hemoglobin (HbA1c). The findings from the study validated the significant value that ALRT Diabetes Solution may potentially bring to patients and health care providers. The results were presented at the recent International Diabetes Federation Western Pacific Congress 2023 in Kyoto, Japan, and are expected to be published in the future.

“We are pleased to see the continued success of the ALRT Diabetes Solution from the outcome of this SGH study that replicated the results of our previous clinical trials in Canada and Kansas City, all of which have resulted in a 1.2 percent drop in mean A1c,” commented Sidney Chan, Chairman and CEO of ALRT. “The successful conclusion of this study gives ALRT the clinical evidence needed to continue forwarding our efforts in deploying ALRT Diabetes Solution in ASEAN territories. On top of that, receiving the patent grant for Predictive A1c protects our clinically proven effective diabetes management system against would be competitors while securing our competitive advantage over other software platforms.”




About ALR Technologies SG Ltd.

ALRT is a data management company that developed the ALRT Diabetes Solution, a comprehensive approach to diabetes care that includes an FDA-cleared and HIPAA compliant diabetes management system that collects data directly from blood glucose meters and continuous glucose monitoring devices, and Predictive A1C algorithm to track treatment success between lab reports and an FDA-cleared Insulin Dosing Adjustment program. The overall goal is to optimize diabetes drug therapies to drive improved patient outcomes.

In addition, the animal health division of ALRT has developed the GluCurve Pet CGM; a solution to assist veterinarians better determine the efficacy of insulin treatments and to help to identify the appropriate dose and frequency of administration for companion animals, thereby delivering the same optimization of diabetic drug therapies to pets as to humans.

More information about ALRT and the GluCurve Pet CGM can be found at https://www.alrt.com and https://www.glucurve.com/Index.


Investor Contact

Investor Relations: ir@alrt.com

Animal Health Inquiries: animalhealth@alrt.com

Media: media@alrt.com

US: +1 (804) 554-3500

Singapore: +65 3129 2924


ALR Technologies SG Ltd. Forward-Looking Statement

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