Alogliptin - CAS 850649-61-5
Not Intended for Therapeutic Use. For research use only.
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Alogliptin is a potent, selective inhibitor of DPP-4 with IC50 of <10 nM, exhibits greater than 10,000-fold selectivity over DPP-8 and DPP-9.
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CAS 850649-61-5 Alogliptin

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Reference Reading

1.[Treatment of patients with type 2 diabetes mellitus: cardiovascular safety of incretin-based therapy supported by the ELIXA and TECOS trials].
Avogaro A1. G Ital Cardiol (Rome). 2016 Apr;17(4):248-252. doi: 10.1714/2214.23894.
The risk of morbidity and mortality from cardiovascular disease in patients with type 2 diabetes is about 2-fold higher compared to their non-diabetic counterparts. In December 2008, the Food and Drug Administration published guidelines for the evaluation of cardiovascular risk in new antidiabetic therapies. A shift of emphasis occurred from short-term glycated hemoglobin-centered trials to trials testing cardiovascular safety. The SAVOR-TIMI 53 and the EXAMINE trials with the dipeptidyl peptidase 4 (DPP-4) inhibitors saxagliptin and alogliptin were cardiovascular neutral. The publication of the results of the TECOS trial with sitagliptin, another DPP-4 inhibitor, the American Diabetes Association 2015 presentation of the ELIXA trial with lixisenatide, the first cardiovascular safety trial with glucagon-like peptide 1 receptor agonists, have also been completed. They both show cardiovascular neutrality in very high-risk diabetic patients.
2.Glucagon-like peptide-1-mediated modulation of inflammatory pathways in the diabetic brain: Relevance to Alzheimer's disease.
Qin L, Chong T, Rodriguez R, Pugazhenthi S1. Curr Alzheimer Res. 2016 Apr 1. [Epub ahead of print]
Neuroinflammation has emerged as an important cause of cognitive decline during aging and in Alzheimer's disease (AD). Chronic low-grade inflammation is observed in obesity and diabetes, which are important risk factors for AD. Therefore, we examined the markers of inflammation in the brain hippocampal samples of Zucker diabetic fatty (ZDF) rats. Pathway-specific gene expression profiling revealed significant increases in the expression of oxidative stress and inflammatory genes. Western blot analysis further showed activation of NF-kB, defective CREB phosphorylation, and decreases in the levels of neuroprotective CREB target proteins, including Bcl-2, BDNF, and BIRC3 in the diabetic rat brain samples, all of which are related to AD pathology. As therapies based on glucagon-like peptide-1 (GLP-1) are effective in controlling blood glucose levels in type 2 diabetic patients, we tested the in vivo actions of GLP-1 in the diabetic brain by a 10-wk treatment of ZDF rats with alogliptin, an inhibitor of dipeptidyl peptidase.
3.Ipragliflozin as an Initial Therapy in Drug Naïve Subjects with Type 2 Diabetes.
Kutoh E1, Wada A2, Murayama T1, Hirate M1. Drug Res (Stuttg). 2016 Apr 7. [Epub ahead of print]
Objectives: The aim of this study is to investigate ipragliflozin as an initial type 2 diabetes (T2DM) drug. Methods: Ipragliflozin 25-50 mg/day monotherapy was performed with drug naïve subjects with T2DM (n=31). As a comparator, 12.5-25 mg/day alogliptin monotherapy was undertaken (n=32). At 3 months, levels of metabolic parameters were compared with those at baseline. Findings: 4 subjects discontinued ipragliflozin due to intolerance or adverse events, while none dropped out with alogliptin. At 3 months, similar decreases of HbA1c levels were observed with these 2 drugs (10.21-8.31%, p<0.00001, with ipragliflozin, and 10.08-8.25%, p<0.00001, with alogliptin), however fasting blood glucose (FBG) levels decreased with significant inter-group differences (- 23.5% with iprgliflozin and - 10.8% with alogliptin). While similar increases of homeostasis model assessment (HOMA)-B levels were observed with these 2 drugs, HOMA-R levels significantly decreased only with ipragliflozin (-19.
4.Methods for meta-analysis of pharmacodynamic dose-response data with application to multi-arm studies of alogliptin.
Langford O1, Aronson JK1, van Valkenhoef G2, Stevens RJ3. Stat Methods Med Res. 2016 Mar 17. pii: 0962280216637093. [Epub ahead of print]
Standard methods for meta-analysis of dose-response data in epidemiology assume a model with a single scalar parameter, such as log-linear relationships between exposure and outcome; such models are implicitly unbounded. In contrast, in pharmacology, multi-parameter models, such as the widely used Emax model, are used to describe relationships that are bounded above and below. We propose methods for estimating the parameters of a dose-response model by meta-analysis of summary data from the results of randomized controlled trials of a drug, in which each trial uses multiple doses of the drug of interest (possibly including dose 0 or placebo). We assume that, for each randomized arm of each trial, the mean and standard error of a continuous response measure and the corresponding allocated dose are available. We consider weighted least squares fitting of the model to the mean and dose pairs from all arms of all studies, and a two-stage procedure in which scalar inverse-variance meta-analysis is performed at each dose, and the dose-response model is fitted to the results by weighted least squares.