Shovanlal Gayen

Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques

Phosphodiesterase 1 (PDE1) is a potential target for a number of neurodegenerative disorders such as Schizophrenia, Parkinson’s and Alzheimer’s diseases. A number of pyrazolo[3,4-d]pyrimidine PDE1 inhibitors were subjected to different molecular modelling techniques [such as regression-based quantitative structure-activity relationship (QSAR): multiple linear regression, support vector machine and artificial neural network; classification-based QSAR: Bayesian modelling and Recursive partitioning; Monte Carlo based QSAR; Open3DQSAR; pharmacophore mapping and molecular docking analyses] to get a detailed knowledge about the physicochemical and structural requirements for higher inhibitory activity. The planarity of the pyrimidinone ring plays an important role for PDE1 inhibition. The N-methylated function at the 5th position of the pyrazolo[3,4-d]pyrimidine core is required for interacting with the PDE1 enzyme. The cyclopentyl ring fused with the parent scaffold is necessary for PDE1 binding potency. The phenylamino substitution at 3rd position is crucial for PDE1 inhibition. The N2-substitution at the pyrazole moiety is important for PDE1 inhibition compared to the N1-substituted analogues. Moreover, the p-substituted benzyl side chain at N2-position helps to enhance the PDE1 inhibitory profile. Depending on these observations, some new molecules are predicted that may possess better PDE1 inhibition.

First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington’s disease: A proposal to chemists!

Huntingtons disease (HD) is caused by mutation of huntingtin protein (mHtt) leading to neuronal cell death. The mHtt induced toxicity can be rescued by inhibiting the kynurenine monooxygenase (KMO) enzyme. Therefore, KMO is a promising drug target to address the neurodegenerative disorders such as Huntingtons diseases. Fiftysix arylpyrimidine KMO inhibitors are structurally explored through regression and classification based multi-QSAR modeling, pharmacophore mapping and molecular docking approaches. Moreover, ten new compounds are proposed and validated through the modeling that may be effective in accelerating Huntingtons disease drug discovery efforts.

Comparative validated molecular modeling of p53-HDM2 inhibitors as antiproliferative agents.

Tumor suppressor protein p53 regulates the cell cycle and inhibits tumor growth. It is inactivated by mutation or binding with human double minute 2 (HDM2) protein. The HDM2 is a promising target for treatment of p53 protein related cancers. Molecular modeling techniques such as 2D-QSAR, pharmacophore mapping and 3D-QSAR analyses were performed on 155 structurally diverse HDM2 inhibitors to understand structural and physicochemical requirements for higher activity. The linear and spline 2D-QSAR models were developed through multiple linear regression and genetic functional algorithm methods. The 2D-QSAR models suggested that number of fluorine, chlorine, tertiary nitrogen atoms as well as donor feature, stereogenic centers and higher value of solvent accessible surface area are important features in defining activity. Monte Carlo method was applied to generate QSAR models that determined structural indicators (alerts) for increase or decrease of the biological activity. Ligand-based pharmacophore mapping showed importance of two hydrophobic, one hydrophobic aromatic, one ring aromatic and one donor features. The structure-based pharmacophore model demonstrated significance of two hydrophobic, one ring aromatic and two acceptor features. The pharmacophore (ligand) aligned structures were subjected to 3D-QSAR analyses. The structure-based pharmacophore was also used for pharmacophore restraint molecular docking to analyze ligand-receptor interactions and for adjudging predictability as well as validation of different modeling techniques. These comparative molecular modeling techniques may help to design novel HDM2 inhibitors.

Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches

Abstract Schizophrenia is a complex disorder of thinking and behaviour (0.3−0.7% of the population is affected). The over-expression of phosphodiesterase 10A (PDE10A) enzyme may be a potential target for schizophrenia and Huntington’s disease. Because 3D QSAR analysis is one of the most frequently used modelling techniques, in the present study, five different 3D QSAR tools, namely CoMFA, CoMSIA, kNN-MFA, Open3DQSAR and topomer CoMFA methods, were used on a dataset of pyrimidine-based PDE10A inhibitors. All developed models were validated internally and externally. The non-commercial Open3DQSAR produced the best statistical results amongst 3D QSAR tools. The structural interpretations obtained from different methods were thoroughly analysed and were justified on the basis of information obtained from the crystal structure. Information from one method was mostly validated by the results of other methods and vice versa. In the current work, the use of multiple tools in the same analysis revealed more complete information about the structural requirements of these compounds. On the basis of the observations of the 3D QSAR studies, 12 new compounds were designed for better PDE10A inhibitory activity. The current investigation may help in further designing new PDE10A inhibitors with promising activity.

Quantitative Structure-Activity Relationship Study on Some Azidopyridinyl Neonicotinoid Insecticides for Their Selective Affinity Towards the Drosophila Nicotinic Receptor Over Mammalian a4ß2 Receptor Using Electrotopological State Atom Index

Neonicotinoids are the most important class of synthetic insecticides increasingly used in agriculture and veterinary medicine. Fundamental differences between the nicotinic acetylcholine receptors (nAChRs) of insects and mammals confer remarkable selectivity of the neonicotinoids at insect nAChR over mammalian nAChR. To identify pharmacophoric requirements of azidopyridinyl neonicotinoids for their efficacy and selectivity towards the insect nAChR over the mammalian one, quantitative structure-activity relationship (QSAR) study was performed using electrotopological state atom (ETSA) indices. This study clearly showed that nitroimines, nitromethylenes, and cyanoimines are more selective to Drosophila nAChR and safe for human being, whereas N-substituted imines have affinity to mammalian receptor. Pharmacophore mapping for both the activities was done.

Insight into the Structural Requirements of Theophylline-Based Aldehyde Dehydrogenase lAl (ALDHlAl) Inhibitors Through Multi-QSAR Modeling and Molecular Docking Approaches.

Over expression of aldehyde dehydrogenase (ALDH1A1) is one of the vital hallmarks of the self-renewal and differentiational cancer stem cells (CSCs). Till now, no selective ALDH1A1 inhibitor is commercially available in the market. So there is an urgent need to explore some novel molecules which can selectively inhibit ALDH1A1 to combat cancer. Presently, our work deals with the development of QSAR models of some theophylline-based molecules by conventional 2D-QSAR, hologram QSAR (HQSAR), and Bayesian classification modeling. The descriptors identified from these QSAR models give avenues to modulate the structure of theophylline-based compounds to a desirable biological end point. Molecular docking study reveals the selectivity of these molecules towards ALDH1A1 (PDB: 4WP7) and important binding residues (GLY 125, 458; THR 129; TRP 178; TYR 297; PHE 171, 466; VAL 174, 460; MET 175; HIS 293 etc.) for the interaction with the receptors. The current study may help to design novel compounds as selective ALDH1A1 inhibitors.

An integrated ligand-based modelling approach to explore the structure-property relationships of influenza endonuclease inhibitors

Influenza endonuclease plays important role in the viral transcription and translation processes. Inhibition of endonuclease enzyme may be an interesting choice to restrict influenza infection. This current study deals with validated multi-chemometric modelling approaches namely regression-based and classification-based quantitative structure-activity relationships (QSARs), hologram QSAR, comparative molecular similarity analysis (CoMSIA), Open3DQSAR study and pharmacophore mapping to identify the structural and physicochemical requirements along with the chemico-biological interactions of pyridinones and pyranones for anti-endonuclease activity. The results suggest that the pyridinone scaffold is more preferable than the pyranone ring. The keto function at 4th position and aryl tetrazole substitution at 1st position of the parent moiety may be important for endonuclease inhibition. Hydroxyl group at 5th position of the parent ring may act as hydrogen bond acceptor feature. The steric substituent is suitable at 2nd position whereas hydrophobic substitution is found to be unfavourable at this position. Bulky hydrophobic substituents are not preferred at the 3rd position of the parent moiety. The information revealed from these integrated ligand-based modelling methods may provide useful informations for designing newer potential anti-influenza agents in future.

Modelling the cytotoxic activity of pyrazolo-triazole hybrids using descriptors calculated from the open source tool “PaDEL-descriptor”

Abstract In this study, we developed QSAR models for the anti-proliferative activity of pyrazolo-triazole hybrids [(1-benzyl-1H-1,2,3-triazol-4-yl)(1,3-diphenyl-1H-pyrazol-4-yl) methanone] on human brain cancer (U87MG), lung cancer (A549), prostate cancer (PC-3), and colon cancer (HT-29) cell lines. We employed K-means cluster analysis to split the data sets. Statistically robust models were generated [pIC50 (U87MG): R = 0.873, Q2 = 0.554, pIC50 (A549): R = 0.879, Q2 = 0.637, pIC50 (PC3): R = 0.953; Q2 = 0.850; pIC50 (HT-29): R = 0.962, Q2 = 0.891; The reliability of these models was confirmed by acceptable validation parameters, and these models also satisfied the Golbraikh and Tropsha acceptable model criteria. The QSAR study highlighted the atomic feature and molecular descriptors, information content descriptors, and topological and constitutional descriptors that affect anti-cancer activity.

An Integrated Multi-QSAR Modeling Approach for Designing Knoevenagel- Type Indoles with Enhancing Cytotoxic Profiles

BACKGROUND Unconventional Knoevenagel-type indoles have been the topic of interest of many synthetic chemists because of its promising efficacy in different diseases including cancer. OBJECTIVE To explore the structural requirements of Knoevenagel-type cytotoxic indoles for higher efficacy. METHODS Multi-QSAR modeling (MLR, ANN, SVM, Bayesian classification, HQSAR and Topomer CoMFA) was performed on these analogs. RESULTS All these modeling techniques were validated individually and interpreted with the experimental SAR observations. Phenyl or p-methoxyphenyl substitution at 2nd position, electron withdrawing groups (such as sulphonyl, cyano etc.) at 3rd position and methoxy substation at 5th position of the indole scaffold may favor cytotoxicity. Eight new indole molecules were predicted from the developed QSAR models. CONCLUSION These newly designed compounds may bind to the colchicine binding site of the tubulin protein as suggested by the molecular docking study.

Pharmacoinformatics study of Piperolactam A from Piper betle root as new lead for non steroidal anti fertility drug development

Fertility control is a burning problem all over the world to regulate population overflow and maintain ecological balance. This study is an in-silico approach to explore a non-steroidal lead as contraceptive agent in order to avoid several contraindications generated by steroidal analogues. Piperolactam A, an aristolactam isolated from Piper betle Linn. showed binding affinity towards estrogen and progesterone receptor as -8.9 and -9.0Kcal/mol (inhibition constant Ki=0.294μM and 0.249μM) respectively which is even larger than that of reported antagonists such as Rohitukine and OrgC (binding affinity -8.7 and -8.4Kcal/mol; Ki 0.443μM and 0.685μM respectively). The binding site exploration displayed more hydrogen bonding of Piperolactam A (His 524, Leu 346, Thr 347) than Rohitukine and OrgC (Leu 718) with associated receptors which was further confirmed by molecular dynamics simulations. The drug-likeliness of the compound has been proved from its tally with Lipinskys Rule of Five and lowered toxicity such as cardiac toxicity, liver toxicity, mutagenicity and ecological toxicity. Endocrine disruptome and later docking guided molecular simulations revealed that Piperolactam A has weaker binding affinity and/or lower probability of binding with nuclear receptors especially hERG and cytochrome P450. The high Caco-2 permeability suggested more bioavailability hence more therapeutic efficacy of the drug.