Development of CoMFA, advance CoMFA and CoMSIA models in pyrroloquinazolines as thrombin receptor antagonist

Discovery of novel ALK2 inhibitors of pyrazolo-pyrimidines: A computational study

ALK2 is a serine/threonine kinase, involved in different signaling pathways and associated with cell proliferation and differentiation. The present study includes development of pharmacophore, 3-D QSAR, docking and virtual screening studies on 30 different pyrazolo[1,5-a]pyrimidine derivatives. The pharmacophore study provides ARRR_2 hypothesis with four different features essential for ALK2 kinase inhibitory activity. The 3 D-QSAR study determined the statistically significant model by using partial least-square regression (PLS) method with R² value of 0.9711 and Q² value of 0.6846. Validation of 3 D-QSAR has been performed by LOO cross-validation method where with R²CV value of 0.56. The virtual screening study on ZINC database provides compounds such as ZINC66091638, ZINC43524105, ZINC19458227 and ZINC72441013 involved good binding interactions (docking scores -8.91, -7.40, -8.43, and -9.47, respectively) with ALK2 kinase (PDB ID: 3Q4U). The docking study of pyrazolo-pyrimidines derivatives found potent compounds, 7i, 13r, 13d, and 21 with docking scores -9.83, -9.75, -9.76, and -9.75, respectively. The important interactions with amino acid residues were HID 286, ASN341. ADME properties further assist to provide important structural features of ALK2 kinase. The present study may be help to medicinal scientists in the direction to develop potent inhibitors against ALK2 kinase.Communicated by Ramaswamy H. Sarma.

Computational Approaches for the Design of Novel Anticancer Compounds Based on Pyrazolo[3,4-d]pyrimidine Derivatives as TRAP1 Inhibitor

In the present in-silico study, various computational techniques were applied to determine potent compounds against TRAP1 kinase. The pharmacophore hypothesis DHHRR_1 consists of important features required for activity. The 3D QSAR study showed a statistically significant model with R² = 0.96 and Q² = 0.57. Leave one out (LOO) cross-validation (R² CV = 0.58) was used to validate the QSAR model. The molecular docking study showed maximum XP docking scores (−11.265, −10.532, −10.422, −10.827, −10.753 kcal/mol) for potent pyrazole analogs (42, 46, 49, 56, 43), respectively, with significant interactions with amino acid residues (ASP 594, CYS 532, PHE 583, SER 536) against TRAP1 kinase receptors (PDB ID: 5Y3N). Furthermore, the docking results were validated using the 100 ns MD simulations performed for the selected five docked complexes. The selected inhibitors showed relatively higher binding affinities than the TRAP1 inhibitor molecules present in the literature. The ZINC database was used for a virtual screening study that screened ZINC05297837, ZINC05434822, and ZINC72286418, which showed similar binding interactions to those shown by potent ligands. Absorption, distribution, metabolism, and excretion (ADME) analysis showed noticeable results. The results of the study may be helpful for the further development of potent TRAP1 inhibitors

CoMFA, CoMSIA and HQSAR Analysis of 3-aryl-3-ethoxypropanoic Acid Derivatives as GPR40 Modulators

BACKGROUND

Human GPR40 receptor, also known as free fatty-acid receptor 1, is a Gprotein- coupled receptor that binds long chain free fatty acids to enhance glucose-dependent insulin secretion. In order to improve the resistance and efficacy, computational tools were applied to a series of 3-aryl-3-ethoxypropanoic acid derivatives. A relationship between the structure and biological activity of these compounds, was derived using a three-dimensional quantitative structure-activity relationship (3D-QSAR) study using CoMFA, CoMSIA and two-dimensional QSAR study using HQSAR methods.

METHODS

Building the 3D-QSAR models, CoMFA, CoMSIA and HQSAR were performed using Sybyl-X software. The ratio of training to test set was kept 70:30. For the generation of 3D-QSAR model three different alignments were used namely, distill, pharmacophore and docking based alignments. Molecular docking studies were carried out on designed molecules using the same software.

RESULTS

Among all the three methods used, Distill alignment was found to be reliable and predictive with good statistical results. The results obtained from CoMFA analysis q2, r2cv and r2 pred were 0.693, 0.69 and 0.992 respectively and in CoMSIA analysis q2, r2cv and r2pred were 0.668, 0.648 and 0.990. Contour maps of CoMFA (lipophilic and electrostatic), CoMSIA (lipophilic, electrostatic, hydrophobic, and donor) and HQSAR (positive & negative contribution) provided significant insights i.e. favoured and disfavoured regions or positive & negative contributing fragments with R1 and R2 substitutions, which gave hints for the modifications required to design new molecules with improved biological activity.

CONCLUSION

3D-QSAR techniques were applied for the first time on the series 3-aryl-3- ethoxypropanoic acids. All the models (CoMFA, CoMSIA and HQSAR) were found to be satisfactory according to the statistical parameters. Therefore such a methodology, whereby maximum structural information (from ligand and biological target) is explored, gives maximum insights into the plausible protein-ligand interactions and is more likely to provide potential lead candidates has been exemplified from this study.

Two Rationally Identified Novel Glitazones Reversed the Behavioral Dysfunctions and Exhibited Neuroprotection Through Ameliorating Brain Cytokines and Oxy-Radicals in ICV-LPS Neuroinflammatory Rat Model

The present study has planned to evaluate the neuroprotective activity of two novel glitazones in a neuroinflammatory rat model. Two novel glitazones were selected from an in-house virtual library of glitazones based on their docking scores against peroxisome proliferator-activated receptor-gamma (PPAR-γ) protein and other parameters studied in in silico computational studies. Initially, an acute oral toxicity study was carried out for glitazones in rats to assess the toxicity profile and to determine the therapeutic range for neuroprotective evaluation. Prior to induction of neuroinflammation, the treatments with glitazones (G1 and G2) and standard pioglitazone were made for four consecutive days to respective groups. On the fifth day, the neuroinflammation was induced by intracerebroventricular (ICV) administration of lipopolysaccharides (LPS) (2 μg/μl) using stereotaxic apparatus. After 7 days, the rats were subjected to behavioral assessment followed by neurobiochemical evaluation and histopathological studies. The pre-treatment with glitazones at two dose levels (15 and 30 mg/kg) has significantly reversed behavioral dysfunctions. Glitazones have shown significant reduction in the levels of LPO, NO, TNF-α, and IL-1β and also increased the levels of antioxidant enzymes such as SOD, CAT, and GSH in the brain of LPS-administered rats. The neuroprotection exhibited by two novel glitazones is comparable with standard pioglitazone. The PPAR-γ-dependent amelioration of cytokines and oxy-radicals released by novel glitazones during neuroinflammatory conditions may be attributed to the reversal of behavioral dysfunctions through preventing the degeneration of neurons in major regions of the brain.

Synthesis of novel 1,5-disubstituted pyrrolo[1,2-a]quinazolines and their evaluation for anti-bacterial and anti-oxidant activities

A new series of 1,5-disubstituted pyrrolo[1,2-a]quinazoline derivatives were prepared from 2-chloro-4-substituted quinazolines, propargyl alcohol, and secondary amines through novel multi-component reactions.

The chemistry and pharmacology of privileged pyrroloquinazolines

The advent of next-generation sequencing (NGS) technology has plummeted the cost of whole genome sequencing, which has provided a long list of putative drug targets for a variety of diseases ranging from infectious diseases to cancers. The majority of these drug targets are still awaiting high-quality small molecule ligands to validate their therapeutic potential and track their druggability. Screening compound libraries based on privileged scaffolds is an efficient strategy to identify potential ligands to distinct biological targets. 7H-Pyrrolo[3,2-f]quinazoline (PQZ) is a potential privileged heterocyclic scaffold with diverse pharmacological properties. A number of biological targets have been identified for different derivatives of PQZ. This review summarized the synthetic strategies to access the chemical space associated with PQZ and discussed their unique biological profiles.

Understanding the Structural Requirements of Hybrid (S)-6-((2-(4-Phenylpiperazin-1-yl)ethyl)(propyl)amino)-5,6,7,8-tetrahydronaphthalen-1-ol and its Analogs as D2/D3 Receptor Ligands: A Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Investigation

To gain insights into the structural requirements for dopamine D2 and D3 agonists in the treatment of Parkinson's disease (PD) and to elucidate the basis of selectivity for D3 over D2 (D2/D3), 3D quantitative structure-activity relationship (3D QSAR) investigations using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods were performed on a series of 45 structurally related D2 and D3 dopaminergic ligands. Two alignment methods (atom-based and flexible) and two charge calculation methods (Gasteiger-Hückel and AM1) were used in the present study. Overall, D2 affinity and selectivity (D2/D3) models performed better with r²_cv values of 0.71 and 0.63 for CoMFA and 0.71 and 0.79 for CoMSIA, respectively. The corresponding predictive r² values for the CoMFA and CoMSIA models were 0.92 and 0.86 and 0.91 and 0.78, respectively. The CoMFA models generated using flexible alignment outperformed the models with the atom-based alignment in terms of relevant statistics and interpretability of the generated contour maps while CoMSIA models obtained using atom-based alignment showed superiority in terms of internal and external predictive abilities. The presence of carbonyl group (C=O) attached to the piperazine ring and the hydrophobic biphenyl ring were found to be the most important features responsible for the D3 selectivity over D2. This study can be further utilized to design and develop selective and potent dopamine agonists to treat PD.

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q²) of 0.602 and 0.618, respectively, and conventional coefficients (r²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r² pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.

Synthesis and evaluation of platelet aggregation inhibitory activity of some 3-phenyl-pyrroloquinazolinones

A series of 3-phenyl-2,7-dihydro-1H-pyrrolo[3,2-f]quinazolin-1-one derivatives (3-PPyQZ) was synthesized starting from 5-amino-indoles, via condensation with N-ethoxycarbonylthiobenzamides followed by thermal cyclization. On the basis of their structural analogy with reported anti-thrombin pyrroloquinazolines, the derivatives were first tested for their capacity to inhibit platelet aggregation. Some of them had in vitro inhibitory effects on collagen and thrombin-induced aggregation in the micromolar range, and much higher inhibition than that shown by some phenyl-pyrroloquinolinones. Experiments to determine the mechanism of action of the most potent inhibitor (compound 18) indicated that it acts in at least two sites: one preceding the agonist-induced increase of cytosolic [Ca(2+)], and one following this step of the platelet activation cascade. The compound also inhibited thrombin-evoked protein-Tyr-phosphorylation. Although it is premature to draw definitive conclusions, the present results indicate that 3-PPyQZ structure, with the quite potent inhibitor of platelet aggregation compound 18, might constitute a starting point for the synthesis of potential anti-thrombosis agents.

Studies of H4R antagonists using 3D-QSAR, molecular docking and molecular dynamics

Three-dimensional quantitative structure-activity relationship studies were performed on a series of 88 histamine receptor 4 (H4R) antagonists in an attempt to elucidate the 3D structural features required for activity. Several in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), molecular docking, and molecular dynamics (MD), were carried out. The results show that both the ligand-based CoMFA model (Q (2) = 0.548, R (ncv) (2)  = 0.870, R (pre) (2)  = 0.879, SEE = 0.410, SEP = 0.386) and the CoMSIA model (Q (2) = 0.526, R (ncv) (2) =0.866, R (pre) (2)  = 0.848, SEE = 0.416, SEP = 0.413) are acceptable, as they show good predictive capabilities. Furthermore, a combined analysis incorporating CoMFA, CoMSIA contour maps and MD results shows that (1) compounds with bulky or hydrophobic substituents at positions 4-6 in ring A (R2 substituent), positively charged or hydrogen-bonding (HB) donor groups in the R1 substituent, and hydrophilic or HB acceptor groups in ring C show enhanced biological activities, and (2) the key amino acids in the binding pocket are TRP67, LEU71, ASP94, TYR95, PHE263 and GLN266. To our best knowledge, this work is the first to report the 3D-QSAR modeling of these H4R antagonists. The conclusions of this work may lead to a better understanding of the mechanism of antagonism and aid in the design of new, more potent H4R antagonists.

Studies of new fused benzazepine as selective dopamine D3 receptor antagonists using 3D-QSAR, molecular docking and molecular dynamics

In recent years, great interest has been paid to the development of compounds with high selectivity for central dopamine (DA) D3 receptors, an interesting therapeutic target in the treatment of different neurological disorders. In the present work, based on a dataset of 110 collected benzazepine (BAZ) DA D3 antagonists with diverse kinds of structures, a variety of in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), homology modeling, molecular docking and molecular dynamics (MD) were carried out to reveal the requisite 3D structural features for activity. Our results show that both the receptor-based (Q(2) = 0.603, R(2) (ncv) = 0.829, R(2) (pre) = 0.690, SEE = 0.316, SEP = 0.406) and ligand-based 3D-QSAR models (Q(2) = 0.506, R(2) (ncv) =0.838, R(2) (pre) = 0.794, SEE = 0.316, SEP = 0.296) are reliable with proper predictive capacity. In addition, a combined analysis between the CoMFA, CoMSIA contour maps and MD results with a homology DA receptor model shows that: (1) ring-A, position-2 and R(3) substituent in ring-D are crucial in the design of antagonists with higher activity; (2) more bulky R(1) substituents (at position-2 of ring-A) of antagonists may well fit in the binding pocket; (3) hydrophobicity represented by MlogP is important for building satisfactory QSAR models; (4) key amino acids of the binding pocket are CYS101, ILE105, LEU106, VAL151, PHE175, PHE184, PRO254 and ALA251. To our best knowledge, this work is the first report on 3D-QSAR modeling of the new fused BAZs as DA D3 antagonists. These results might provide information for a better understanding of the mechanism of antagonism and thus be helpful in designing new potent DA D3 antagonists.

QSAR Studies on andrographolide derivatives as α-glucosidase inhibitors

Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validation r² (0.731) and standard error (0.225) illustrated that the 2D-QSAR model was able to identify the important molecular fragments and the cross-validation r² (0.794) and standard error (0.127) demonstrated that the 3D-QSAR model was capable of exploring the spatial distribution of important fragments. The obtained results suggested that proposed combination of 2D and 3D QSAR models could be useful in predicting the α-glucosidase inhibiting activity of andrographolide derivatives.

Enaminones 8: CoMFA and CoMSIA studies on some anticonvulsant enaminones

3D-QSAR studies comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 26 structurally diverse subcutaneous pentylenetetrazol (scPTZ) active enaminone analogues, previously synthesized in our laboratory. CoMFA and CoMSIA were employed to generate models to define the specific structural and electrostatic features essential for enhanced binding to the putative GABA receptor. The 3D-QSAR models demonstrated a reliable ability to predict the CLogP of the active anticonvulsant enaminones, resulting in a q(2) of 0.558 for CoMFA, and a q(2) of 0.698 for CoMSIA. The outcomes of the contour maps for both models provide detailed insight for the structural design of novel enaminone derivatives as potential anticonvulsant agents.

Internet resources in GPCR modelling

G-Protein coupled receptors (GPCRs), one of the most important families of drug targets, belong to the super family of integral membrane proteins characterized by seven transmembrane helices. Because they are difficult to crystallize, the three dimensional structure of these receptors have not yet been determined by X-ray crystallography, except one. In the absence of a 3-D structure, in-silico approaches for solving the structure of this class of proteins are widely used and provide valuable information for structure based drug design. There are several web servers and computer programs available that automate the modelling process of GPCRs. Some of these include Modeller, Swiss-Model server, Homer, etc. Using these tools reliable homology models of human histamine H1 receptor (HRH1) and thrombin receptor (PAR-1) have been generated which explain the binding mode of the standard antagonists of these receptors and may be useful in designing their novel antagonists.

Effect of steric molecular field settings on CoMFA predictivity

Steric molecular field can be represented in a number of ways in comparative molecular field analysis (CoMFA). This study aimed to investigate whether the choice of steric molecular field settings significantly influences the predictive performance of CoMFA and, if so, which is the best. The three-dimensional quantitative structure activity relationship (3D-QSAR) models based on Lennard-Jones, indicator, parabolic and Gaussian steric fields were compared using 28 datasets taken from the literature. The analysis of the predictive ability of these models (cross validated R(2)) indicates that steric fields in which the value drops off quickly with distance (i.e. Lennard-Jones and indicator fields) tend to perform better than the Gaussian version, which has a slower and smoother decrease. Furthermore, depending on the steric field type used, the field sampling density (i.e. grid spacing) has a variable influence on the predictive ability of the models generated.

3D QSAR Studies on Protein Tyrosine Phosphatase 1B Inhibitors: Comparison of the Quality and Predictivity among 3D QSAR Models Obtained from Different Conformer-Based Alignments

A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.

Systematic Statistical Comparison of Comparative Molecular Similarity Indices Analysis Molecular Fields for Computer-Aided Lead Optimization

Comparative molecular similarity indices analysis (CoMSIA) is a 3D quantitative structure-activity relationship technique used to determine structural and electronic features influencing biological activity. This proves particularly useful for facilitating lead optimization projects. This study aimed to compare CoMSIA models produced using different subsets of the CoMSIA molecular fields (steric, electrostatic, hydrophobic, hydrogen-bond donor, and hydrogen-bond acceptor) in a systematic and statistically valid manner. A total of 23 data sets sourced from the literature were used to compare molecular field contribution and model predictivity using leave-one-out cross-validated R2 values. Predictive ability varied in a highly statistically significant manner depending on the set of CoMSIA molecular fields used. In general, the greater the number of CoMSIA molecular fields included in the analysis, the better the model predictivity was. There is great redundancy in the information contained in the different CoMSIA molecular fields. When all five CoMSIA molecular fields are included, the hydrophobic and electrostatic fields had the largest and the steric field the smallest contribution. Data sets were clustered into four groups on the basis of the utility of molecular field sets to generate predictive models.