Jerusalem artichoke inulin supplementation ameliorates hepatic lipid metabolism in type 2 diabetes mellitus mice by modulating the gut microbiota and fecal metabolome

The main focus of this study was on the protection mechanism of Jerusalem artichoke inulin (DI) against type 2 diabetes mellitus (T2DM) associated with abnormal hepatic lipid metabolism and gut microbiota dysfunction in T2DM mice.

[Consensus ensemble neural network multitarget model of RAGE inhibitory activity of chemical compounds]

RAGE signal transduction via the RAGE-NF-κB signaling pathway is one of the mechanisms of inflammatory reactions that cause severe complications in diabetes mellitus. RAGE inhibitors are promising pharmacological compounds that require the development of new predictive models. Based on the methodology of artificial neural networks, consensus ensemble neural network multitarget model has been constructed. This model describes the dependence of the level of the RAGE inhibitory activity on the affinity of compounds for 34 target proteins of the RAGE-NF-κB signal pathway. For this purpose an expanded database of valid three-dimensional models of target proteins of the RAGE-NF-κB signal chain was created on the basis of a previously created database of three-dimensional models of relevant biotargets. Ensemble molecular docking of known RAGE inhibitors from a verified database into the sites of added models of target proteins was performed, and the minimum docking energies for each compound in relation to each target were determined. An extended training set for neural network modeling was formed. Using seven variants of sampling by the method of artificial multilayer perceptron neural networks, three ensembles of classification decision rules were constructed to predict three level of the RAGE-inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-κB signaling pathway. Using a simple consensus of the second level, the predictive ability of the created model was assessed and its high accuracy and statistical significance were shown. The resultant consensus ensemble neural network multitarget model has been used for virtual screening of new derivatives of different chemical classes. The most promising substances have been synthesized and sent for experimental studies.

Nitrothiadiazolo[3,2-a]pyrimidines as promising antiglycating agents

Managing the advanced glycation end-products (AGEs) concentration is a reliable approach to achieve control over the pathogenesis of diabetic vascular complications. Inhibition of dipeptidyl peptidase-4 (DPP-4) is also an attractive way to tackle type 2 diabetes mellitus (T2DM). We showed previously that azoloazine heterocycles have the potential to prevent the formation of AGEs and in this work, we conducted docking studies with DPP-4 of 5-alkylamino-6-nitro-1,3,4-thiadiazolo[3,2-a]pyrimidines. Consequently, we have developed a synthetic approach to these structures by chlorodeoxygenation and amination reactions. Antidiabetic properties of obtained compounds were studied by evaluating DPP-4 (ex vivo/in vitro) and AGEs formation inhibition (in vitro). It was shown that the nitrothiadiazolopyrimidines exhibit a higher antiglycation activity than reference compound aminoguanidine, but only moderate inhibition of DPP-4. The most active DPP-4 inhibitor 1l had IC₅₀ of 55.87 μM and showed the ability to inhibit serum DPP-4 activity in rats after 10 mg/kg oral administration but with the less and shorter effect than vildagliptin. At the same time, 1l was the most active antiglycating compound in the series (IC₅₀ 134.4 μM). Copper chelation properties of synthesized compounds were also investigated since the formation of AGEs is catalyzed by the transition metal cations. A noticeable correlation between antiglycation activity and metal chelation was revealed. Both activities (antiglycation and copper chelation) correlated with quantum-chemical properties (calculated with ab initio) of the tested compounds. These findings will allow us to predict both activities in the future, without the need to model multiple steps of glycation reaction.