3D-QSAR studies on thieno[3,2-d]pyrimidines as phosphodiesterase IV inhibitors

QSAR models reveal new EPAC-selective allosteric modulators

Exchange proteins directly activated by cAMP (EPAC) are guanine nucleotide exchange factors for the small GTPases, Rap1 and Rap2. They regulate several physiological functions and mitigation of their activity has been suggested as a possible treatment for multiple diseases such as cardiomyopathy, diabetes, chronic pain, and cancer. Several EPAC-specific modulators have been developed, however studies that quantify their structure-activity relationships are still lacking. Here we propose a quantitative structure-activity relationship (QSAR) model for a series of EPAC-specific compounds. The model demonstrated high reproducibility and predictivity and the predictive ability of the model was tested against a series of compounds that were unknown to the model. The compound with the highest predicted affinity was validated experimentally through fluorescence-based competition assays and NMR experiments revealed its mode of binding and mechanism of action as a partial agonist. The proposed QSAR model can, therefore, serve as an effective screening tool to identify promising EPAC-selective drug leads with enhanced potency.

Synthesis of thiophene and N-substituted thieno[3,2-d] pyrimidine derivatives as potent antitumor and antibacterial agents

A novel series of carbamothioylamino-benzene-sulfonamide-thiophene-carboxylates 4a-c and thieno[3,2-d]pyrimidin-2-yl-amino-benzene-sulfonamides 5a-c were synthesized in a series of synthetic steps and were used as key intermediates for the synthesis of thienotriazolopyrimidine-benzene-sulfonamide derivatives 6a-c and 7a-c. Thieno[3,2-d]pyrimidinones (8 and 9) were also prepared. Compound 9 was used as an intermediate for the synthesis of imidazole/1,2,4-triazole and tetrazine functionalized thieno[3,2-d]pyrimidine derivatives (10-12). Pyrrole derivatives/pyrrolopyrimidine/pyrrolotriazolopyrimidine functionalized thiophenes (15-19) were also synthesized. Structures of the newly synthesized compounds were established by elemental analysis and spectral data. Most of the newly synthesized compounds were evaluated for their in vitro activity against three human tumor cell lines, namely, liver cancer (HepG-2), colon cancer (HT-29) and lung cancer (NCI-H460), using doxorubicin as standard. Compounds 16 (GI50 = 0.02, 0.04 and 0.06 μmol L-1, resp.) and 19b (GI50 = 0.02, 0.03 and 0.05 μmol L-1, resp.) showed higher activity against all cell lines than doxorubicin. Most of the compounds were also screened for antibacterial activity using ciprofloxacin as standard drug. Compounds 4b and 6b, both containing benzenesulfonamide linked to N-, 10 bearing imidazole moiety, and 15 and 19b,c with a thiophene-2-carboxylic acid chain, exhibited high activity against Gram-positive and Gram-negative bacteria.

Pharmacophore generation and atom based 3D-QSAR of quinoline derivatives as selective phosphodiesterase 4B inhibitors

Reported PDE4B inhibitors were used to design QSAR based pharmacophore model. Using developed pharmacophore model, virtual screening was performed followed by cross-docking to identify novel PDE4B specific inhibitors.

The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models

The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure-activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ([Formula: see text] or q(2)) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger-Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger-Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.

α-Aminophosphonates as novel anti-leishmanial chemotypes: synthesis, biological evaluation, and CoMFA studies

α-Aminophosphonates have been identified as novel anti-leishmanial chemotypes against theL. donovanipromastigote with low μM range activity.

Novel thieno[2,3-d]pyrimidines: their design, synthesis, crystal structure analysis and pharmacological evaluation

Novel thieno[2,3-d]pyrimidines containing a cyclohexane ring fused with a six- or five-membered heterocyclic moiety along with a benzylic nitrile were designed as potential inhibitors of PDE4. Expeditious synthesis of these compounds was carried out via a multi-step sequence consisting of a few key steps such as Gewald reaction, Dieckmann type cyclisation and Krapcho decarboxylation. This newly developed strategy involved construction of the thienopyrimidine ring followed by the cyclohexanone moiety and subsequently the fused heterocyclic ring. A number of thieno[2,3-d]pyrimidine based derivatives were synthesized using this method some of which showed promising PDE4B inhibitory properties. One of them was tested for PDE4D inhibition in vitro and dose dependent inhibition of TNF-α. A few selected molecules were docked into the PE4B protein the results of which showed good overall correlations to their observed PDE4B inhibitory properties in vitro. The crystal structure analysis of representative compounds along with hydrogen bonding patterns and molecular arrangement present within the molecule is described.

Design and synthesis of novel thiophenecarbohydrazide, thienopyrazole and thienopyrimidine derivatives as antioxidant and antitumor agents

2-Amino-5-acetyl-4-methyl-thiophene-3-carboxylic acid ethyl ester (1) and 5-acetyl-2-amino-4-methylthiophene-3-carbohydrazide (2) were synthesized and used as starting materials for the synthesis of new series of 1-(5-amino-4-(3,5-dimethyl-1H-pyrazole-1-carbonyl)-3-methylthiophen-2-yl) ethanone (3a), 1-(5-amino-4-(4-chloro-3,5-dimethyl-1H-pyrazole-1-carbonyl)-3-methylthiophen-2-yl) ethanone (3b), 1-(4-methyl-2-amino-5-acetylthiophene-3-carbonyl)pyrazolidine-3,5-dione (4), (Z)-N'-(4-methyl-2-amino-5-acetylthiophene-3-carbonyl) formohydrazonic acid (5a), (Z)-ethyl-N'-4-methyl-2-amino-5-acetylthiophene-3-carbonylformo hydrazonate (5b), 6-acetyl-3-amino-2,5-dimethylthieno[2,3-d]pyrimidin-4(3H)-one (8), 5-methyl-3-amino-2-mercapto-6-acetylthieno [2,3-d]pyrimidin-4(3H)-one (10) and 5-methyl-6-acetyl-2-thioxo-2,3-dihydrothieno[2,3-d]pyrimidin-4(1H)-one (12) as potential antioxidant and antitumor agents. Pharmacological tests showed that compounds 6a, 6b, 8, 10 and 12 exhibited significant antitumor and antioxidant activity.

Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method

Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors

We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

Design and synthesis of 3-pyrazolyl-thiophene, thieno[2,3-d]pyrimidines as new bioactive and pharmacological activities

Two series of 5-ethyl-2-amino-3-pyrazolyl-4-methylthiophenecarboxylate and 2-thioxo-N(3)-aminothieno[2,3-d]pyrimidines were prepared from 3,5-diethyl-2-amino-4-methylthio-phenecaboxylate and evaluated as anti-inflammatory, analgesic and ulcerogenic activities. Among the compounds studied, compounds which containing the substituted hydrazide at C-3 position 7, 16, and 17a showed more potent anti-inflammatory and analgesic activities than the standard drug (Indomethacin and Aspirin), along without ulcerogenity. While compounds 2, 5, 9, 10, and 11c showed moderate activities. Some of the newly synthesized compounds have good to excellent antimicrobial activity.

Comparative molecular similarity indices analysis (CoMSIA) studies of 1,2-naphthoquinone derivatives as PTP1B inhibitors

Protein tyrosine phosphatase-1B (PTP1B) has been demonstrated to play a key role in the negative signalling pathway of insulin. Potent and orally active PTP1B inhibitors are considered to be promising pharmacological agents for the treatment of type-2 diabetes and resistance to weight gain. CoMSIA studies have been preformed on 1,2-naphthoquinone derivatives that are reported to be potential non-peptidic inhibitors of PTP1B. For the selection of dataset to develop the model, the reported molecules were subjected to property filters and segregated into training and test set. As the crystal structure of PTP1B-naphthoquinone derivative is not known, the most active molecule was subjected to simulated annealing dynamics method and the lowest energy conformer was reminimised and considered as the bioactive conformation. Database-inertial alignment was followed for aligning the molecules. Different CoMSIA models were built to get the best related field.

Three-dimensional quantitative structure (3-D QSAR) activity relationship studies on imidazolyl and N-pyrrolyl heptenoates as 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitors by comparative molecular similarity indices analysis (CoMSIA)

A comparative molecular similarity indices analysis (CoMSIA) of a set of 29 imidazolyl and N-pyrrolyl heptenoates have been performed to find out the structural requirements for 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitory activity. The HMG like side chain, a common moiety of statins, was used to align the molecules. The results guide to design new chemical entities with high potency.

3D-QSAR studies of indole derivatives as phosphodiesterase IV inhibitors

The 3D-QSAR studies of some indole derivatives as phosphodiesterase (PDE) type IV inhibitors was performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The global minimum energy conformer of the template molecule, 3 the most active molecule of the series, was obtained by simulated annealing method and used to build the structures of the molecules in the dataset. The CoMFA model produced statistically significant results with cross-validated and conventional correlation coefficients of 0.494 and 0.986 respectively. The combination of steric, electrostatic and hydrophobic fields in CoMSIA gave the best results with cross-validated and conventional correlation coefficients of 0.541 and 0.967 respectively. The predictive ability of CoMFA and CoMSIA were determined using a test set of seven indole derivatives giving predictive correlation coefficients of 0.56 and 0.59 respectively indicating good predictive power. Further, the robustness of the models was verified by bootstrapping analysis. Based upon the information derived from CoMFA and CoMSIA, we have identified some key features that may be used to design new indole derivatives and predict their PDE IV affinities prior to synthesis.

3D-QSAR illusions

3D-QSAR is typically used to construct models (1) to predict activities, (2) to illustrate significant regions, and (3) to provide insight into possible interactions. To the contrary, examples are described herein which make it clear that the predictivity of such models remains elusive, that so-called significant regions are subject to the vagaries of alignment, and that the nature of possible interactions heavily depends on the eye of the beholder. Although great strides have been made in the imaginative use of 3D-descriptors, 3D-QSAR remains largely a retrospective analytical tool. The arbitrary nature of both the alignment paradigm and atom description lends itself to capricious models, which in turn can lead to distorted conclusions. Despite these illusionary pitfalls, predictions can be enhanced when the test set is bounded by the descriptor space represented in the training set. Interpretation of significant interaction regions becomes more meaningful when alignment is constrained by a binding site. Correlations obtained with a variety of atom descriptors suggest choosing useful ones, in particular, in guiding synthetic effort.

3D-QSAR studies of pyruvate dehydrogenase kinase inhibitors based on a divide and conquer strategy

PDHK is a highly specific enzyme, which inhibits PDC thereby reducing the conversion of pyruvate to AcetylCoA leading to increased glucose and lactate level contributing to various pathological disease states. 3D-QSAR CoMFA studies were performed on diverse PDHK inhibitors based on maximum common substructural alignments of different classes of molecules with the selected reference molecule using a divide and conquer strategy. Statistically robust CoMFA model was obtained with a cross-validated correlation coefficient of 0.561 and conventional correlation coefficient of 0.990. Predictive correlation coefficient r2(pred) was found to be 0.875.