Glucokinase (GK) plays a critical role for maintaining glucose homeostasis with regulating glucose uptake in liver and insulin secretion in pancreas. GK activators have been reported to decrease blood glucose levels in patients with type 2 diabetes mellitus. However, clinical development of GK activators has failed due to the loss of glucose-lowering effects and increased plasma triglyceride levels after chronic treatment. Here, we generated a novel GK activator, TMG-123, examined its in vitro and in vivo pharmacological characteristics, and evaluated its risks of aforementioned clinical issues. TMG-123 selectively activated GK enzyme activity without increasing Vmax. TMG-123 improved glucose tolerance without increasing plasma insulin levels in both insulin-deficient (Goto-Kakizaki rats) and insulinresistant (db/db mice) models. The beneficial effect on glucose tolerance was greater than results observed with clinically available antidiabetic drugs such as metformin and glibenclamide in Zucker Diabetic Fatty rats. TMG-123 also improved glucose tolerance in combination with metformin. After 4 weeks of administration, TMG-123 reduced the Hemoglobin A1c levels without affecting liver and plasma triglyceride levels in Goto-Kakizaki rats and Diet- Induced Obesity mice. Moreover, TMG-123 sustained its effect on Hemoglobin A1c levels even after 24 weeks of administration without affecting triglycerides. Taken together, these data indicate that TMG-123 exerts glucose-lowering effects in both insulin-deficient and -resistant diabetes, and sustains reduced Hemoglobin A1c levels without affecting hepatic and plasma triglycerides even after chronic treatment. Therefore, TMG-123 is expected to be an antidiabetic drug that overcomes the concerns previously reported with other GK activators.
CITATION STYLE
Tsumura, Y., Tsushima, Y., Tamura, A., Hasebe, M., Kanou, M., Kato, H., & Kobayashi, T. (2017). TMG-123, a novel glucokinase activator, exerts durable effects on hyperglycemia without increasing triglyceride in diabetic animal models. PLoS ONE, 12(2). https://doi.org/10.1371/journal.pone.0172252
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