Thiazolidine: A Potent Candidate for Central Nervous Systems Diseases.

Thiazolidines are multifaceted molecules and exhibit varied types of biological activities, and also showed anticonvulsants and antidepressants activity. It is the diversified class of heterocyclic compounds. Thiazolidinediones (TZD) has been shown beneficial action in various CNS diseases. The significant mechanism of TZD-induced neuroprotection useful in prevention of microglial activation and cytokine that is responsible for inflammatory condition and chemokine expression. At the molecular level TZDs were also responsible to prevent the activation of pro-inflammatory transcription factors as well as promoting the anti-oxidant mechanisms in the injured CNS. Important SAR, molecular mechanism and potent biological activities with special references to central nervous system are discussed in this article. Various investigations suggest that this moiety pave the way for design and discovery of new drug candidates.

3D QSAR and docking studies of various amido and benzyl-substituted 3-amino-4-(2-cyanopyrrolidide)pyrrolidinyl analogs as DPP-IV inhibitors.

The article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 3-amino-4-(2-cyanopyrrolidide)pyrrolidinyl analogs reported for DPP-IV inhibition using PHASE module of Schrodinger software. The present works also encompass molecular interaction study of 3-amino-4-(2- cyanopyrrolidide)pyrrolidinyl analogs on maestro 8.5 workstation. The Phase study module comprises the five points pharmacophore model (AAHPR.617), consisting two hydrogen bond acceptor (A), one Hydrophobic (H), one Positive(P) and one aromatic ring (R) and with discrete geometries as pharmacophoric feature. The developed pharmacophore model was used to derive a predictive atom-based 3D QSAR model. The obtained 3D QSAR model has an excellent correlation coefficient value (r2=0.9926) along with good statistical significance as shown by high Fisher ratio (F=671.7). The model also exhibits good predictive power, which is confirmed by high value of cross validated correlation coefficient (q2 = 0.7311). The QSAR model suggests that hydrophobic and aromatic characters are crucial for the DPP-IV inhibitory activity. The QSAR model also suggests that the inclusion of hydrophobic substituents would enhance the DPP-IV inhibition. In addition to the hydrogen bond acceptor, hydrophobic character, electro withdrawing character positively contributes to the DPP-IV inhibition. This study provides a set of guidelines for designing compounds with better DPP-IV inhibitory potency.

3D QSAR and docking study of gliptin derivatives as DPP-IV inhibitors.

The article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 46 xanthine derivatives reported for DPP-IV inhibition using PHASE module of Schrodinger software. The present works also encompasses molecular interaction of 46 xanthine ligand through maestro 8.5 software. The QSAR study comprises AHHR.7 pharmacophore hypothesis, which elaborates the three points, e.g. one hydrogen bond acceptor (A), two hydrophobic rings (H) and one aromatic ring (R). The discrete geometries as pharmacophoric feature were developed and the generated pharmacophore model was used to derive a predictive atom-based 3D QSAR model for the studied data set. The obtained 3D QSAR model has an excellent correlation coefficient value (r(2)= 0.9995) along with good statistical significance which is indicated by high Fisher ratio (F= 8537.4). The model also exhibits good predictive power confirmed by the high value of cross validated correlation coefficient (q(2) = 0.6919). The QSAR model suggests that hydrophobic character is crucial for the DPP-IV inhibitory activity exhibited by these compounds and inclusion of hydrophobic substituents will enhance the DPP-IV inhibition. In addition to the hydrophobic character, electron withdrawing groups positively contribute to the DPP-IV inhibition potency. The findings of the QSAR study provide a set of guidelines for designing compounds with better DPP-IV inhibitory potency.

Ligand-based pharmacophore detection, screening of potential gliptins and docking studies to get effective antidiabetic agents.

Three-dimensional pharmacophore hypothesis was established based on a set of known DPP-IV inhibitor using PharmaGist software program understanding the essential structural features for DPP-IV inhibitor. The various marketed or under developmental status, potential gliptins have been opted to build a pharmacophore model, e.g. Sitagliptin (MK- 0431), Saxagliptin, Melogliptin, Linagliptin (BI-1356), Dutogliptin, Carmegliptin, Alogliptin and Vildagliptin (LAF237). PharmaGist web based program is employed for pharmacophore development. Four points pharmacophore with the hydrogen bond acceptor (A), hydrophobic group (H), Spatial Features and aromatic rings (R) have been considered to develop pharmacophoric features by PharmaGist program. The best pharmacophore model bearing the Score 16.971, has been opted to screen on ZincPharmer database to derive the novel potential anti-diabetic ligands. The best pharmacophore bear various Pharmacophore features, including General Features 3, Spatial Features 1, Aromatic 1 and Acceptors 2. The PharmaGist employed algorithm to identify the best pharmacophores by computing multiple flexible alignments between the input ligands. The multiple alignments are generated by combining alignments pair-wise between one of the gliptin input ligands, which acts as pivot and the other gliptin as ligand. The resulting multiple alignments reveal spatial arrangements of consensus features shared by different subsets of input ligands. The best pharmacophore model has been derived using both pair-wise and multiple alignment methods, which have been weighted in Pharmacophore Generation process. The highest-scoring pharmacophore model has been selected as potential pharmacophore model. In conclusion, 3D structure search has been performed on the "ZincPharmer Database" to identify potential compounds that have been matched with the proposed pharmacophoric features. The 3D ZincPharmer Database has been matched with various thousands of Ligands hits. Those matches were screened through the RMSD and max hits per molecule. The physicochemical properties of various "ZincPharmer Database" screened ligands have been calculated by PaDELDescriptor software. The all "ZincPharmer Database" screened ligands have been filtered based on the Lipinski's rule-of-five criteria (i.e. Molecular Weight < 500, H-bond acceptor ≤ 10, H-bond donor ≤ 5, Log P ≤ 5) and were subjected to molecular docking studies to get the potential antidiabetic ligands. We have found various substituted as potential antidiabetic ligands, which can be used for further development of antidiabetic agents. In the present research work, we have covered rational of DPP-IV inhibitor based on Ligand-Based Pharmacophore detection, which is validated via the Docking interaction studies as well as Maximal Common Substructure (MCS).

Novel serine protease dipeptidyl peptidase IV inhibitor: alogliptin.

Alogliptin (codenamed SYR-322) is a recently approved anti-diabetic drug in Japan, which has been under clinical development phase III in USA and Europe. Alogliptin has been developed by Takeda under the brand name "Nesina". Alogliptin is a highly selective ( > 10,000-time selectivity, potent, reversible and durable serine protease dipeptidyl peptidase IV enzyme is compared to DPP-8 and DPP-9) inhibitor, which has been developed as an alternative second-line to metformin in place of a sulphonylurea. Alogliptin has been observed to increase and prolong the action of incretin hormone by inhibiting the DPP-IV enzyme activity. Alogliptin has been observed to well absorb and show low plasma protein binding, which displays slow-binding properties to DPP-IV enzyme. The X-ray crystallography studies have been revealed that Alogliptin binds to DPP-IV active site by non-covalently and provides sustained reduction of plasma DPP-IV activity as well as lowering of blood glucose, in drug-naive patients with T2DM and inadequate glycemic control, once daily oral dosing regimen with varying levels of doses ranging from 25-800 mg. Alogliptin is approved as monotherapy and in combination with alpha-glucosidase & thiazolidinediones. The 26 week clinical study of Alogliptin revealed that Alogliptin doesn't increase the weight and well tolerated. In the present review, we have tried to cover biology of DPP-IV, molecular chemistry, chemical characterization, crystal polymorphic information, interaction studies, commercial synthesis, current patent status, adverse effects and clinical status of Alogliptin giving emphasis on the medicinal chemistry aspect.