Author(s):
Pratiksha Rai, Nikita Yadav, Dr. Chandaka Madhu, Dr. Shilpi Sharma, Manu Bharti
Email(s):
pratiksharai9090@gmail.com , NikitaYadav672000@gmail.com , pharmamadhuphd@gmail.com , hilpisharma0056@gmail.com , manubharti.pharm@gmail.com
Address:
Assistant professor
Mahayogi Gorakhnath University, Gorakhpur,
Pin - 273007
Assistant Professor
College of Pharmacy
SR Group of Institutions, Ambabai
(JHANSI),284001
Principal & Professor
Department: - Pharmacology
Gokul College of Pharmacy, Piridi, Bobbili,
Vizianagaram-Dist, A.P Pin - 535558.
Associate Professor
School of Pharmacy
Venkateshwara University, Gajraula, Uttar
Pradesh.
Pin -244236, Assistant Professor
Institute address- Shri Venkateshwara
University, Gajraula
Pin - 244235
Published In:
Book, DIABETIC COMPLICATIONS AND CARE MULTIDISCIPLINARY STRATEGIES FOR CONTROL
Year of Publication:
December, 2025
Online since:
January 24, 2026
DOI:
Not Available
ABSTRACT:
The pharmacological management of diabetes mellitus has transitioned from a glucose-centric approach to a comprehensive strategy emphasizing cardiovascular and renal protection alongside weight regulation. This abstract reviews the current therapeutic landscape, dominated by the established efficacy of SGLT2 inhibitors and GLP-1 receptor agonists, and introduces the next generation of "incretin-based" multi-agonists.
A significant focus is placed on tirzepatide (a dual GIP/GLP-1 receptor agonist) and the emerging triple-hormone agonists (targeting GLP-1, GIP, and glucagon receptors), which have demonstrated unprecedented levels of glycemic control and weight reduction in clinical trials. Furthermore, the 2026 therapeutic pipeline highlights the move toward once-weekly basal insulins and the FDA-approved use of teplizumab for the delay of Type 1 Diabetes progression.
Additionally, the role of non-steroidal mineralocorticoid receptor antagonists (MRAs) like finerenone is examined for their specific benefits in mitigating diabetic kidney disease. This review underscores the importance of precision medicine—using AI-driven tools to predict individual drug responses—to optimize therapy, minimize adverse effects such as hypoglycemia, and reduce the global burden of diabetes-related complications.
Cite this article:
.
References not available.