Pharmacophore generation and atom-based 3D-QSAR of N-iso-propyl pyrrole-based derivatives as HMG-CoA reductase inhibitors

Background

Coronary heart disease continues to be the leading cause of mortality and a significant cause of morbidity and account for nearly 30% of all deaths each year worldwide. High levels of cholesterol are an important risk factor for coronary heart disease. The blockage of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase activity by small molecule inhibitors has been shown to inhibit hypercholesterolemia. Herein, we describe the development of effective and robust pharmacophore model and the structure–activity relationship studies of 43N-iso-propyl pyrrole-based derivatives previously reported for HMG-CoA reductase inhibition.

Results

A 5-point pharmacophore model was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D quantitative structure–activity relationship analysis (3D-QSAR) model for the studied dataset. The obtained 3D-QSAR model has an excellent correlation coefficient value (r ²?=?0.96) along with good statistical significance as shown by high Fisher ratio (F?=?143.2). The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient (q ²?=?0.672). Further, pharmacophoric model was employed for virtual screening to identify four potential HMG-CoA reductase inhibitors.

Conclusions

The QSAR model suggests that electron-withdrawing character is crucial for the HMG-CoA reductase inhibitory activity. In addition to the electron-withdrawing character, hydrogen bond--donating groups, hydrophobic and negative ionic groups positively contribute to the HMG-CoA reductase inhibition. These findings provide a set of guidelines for designing compounds with better HMG-CoA reductase inhibitory potential.     

Atom based 3D-QSAR study of 1,4-benzodiazepine-2-ones as potent anti-trypanosomal agents and its validation

In the investigation, a robust pharmacophore model to investigate structure–activity relationship of 1,4-benzodiazepine-2-ones derivatives was developed for human African trypanosomiasis (HAT) using PHASE module of Schrodinger software. The pharmacophore model consists of five contours such as two aromatic rings (R), one hydrophobic hydrogen (H) and two hydrogen-bond acceptor (A) with discrete geometries. The generated pharmacophore model was used to derive a predictive atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) model for the studied data set. The obtained 3D-QSAR model has an excellent correlation coefficient value (R ² = 0.97) along with good statistical significance as shown by high Fisher ratio (F = 216.80). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (Q ² = 0.80). To confirm the validity of the model further calculation of the enrichment factor and percentage recovery studies were calculated, which confirm the validity of the model. The findings of the QSAR study provide a set of guidelines for designing compounds with better HAT inhibitory activity.     

3D-QSAR studies of some tetrasubstituted pyrazoles as COX-II inhibitors

Pharmacophore mapping studies were undertaken for a series of molecules belonging to tetrasubstituted pyrazoles as canine COX-II inhibitors. A six point pharmacophore with 3 hydrogen bond acceptors (A), one hydrophobic group (H) and two aromatic rings (R) as pharmacophoric feature was developed. The pharmacophoric hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.958. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.852 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore model describe the key structure-activity relationship of COX-II inhibitors, can predict their activities, and can thus be used to design novel inhibitors.     

Three-dimensional quantitative structure-activity relationship analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach

A 3D pharmacophore model able to quantitatively predict inhibition constants was derived for a series of inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR), a validated target for antimalarial therapy. The data set included 52 inhibitors, with 23 of these comprising the training set and 29 an external test set. The activity range, expressed as Ki, of the training set molecules was from 0.3 to 11 300 nM. The 3D pharmacophore, generated with the HypoGen module of Catalyst 4.7, consisted of two hydrogen bond donors, one positive ionizable feature, one hydrophobic aliphatic feature, and one hydrophobic aromatic feature and provided a 3D-QSAR model with a correlation coefficient of 0.954. Importantly, the type and spatial location of the chemical features encoded in the pharmacophore were in full agreement with the key binding interactions of PfDHFR inhibitors as previously established by molecular modeling and crystallography of enzyme-inhibitor complexes. The model was validated using several techniques, namely, Fisher's randomization test using CatScramble, leave-one-out test to ensure that the QSAR model is not strictly dependent on one particular compound of the training set, and activity prediction in an external test set of compounds. In addition, the pharmacophore was able to correctly classify as active and inactive the dihydrofolate reductase and aldose reductase inhibitors extracted from the MDDR database, respectively. This test was performed in order to challenge the predictive ability of the pharmacophore with two classes of inhibitors that target very different binding sites. Molecular diversity of the data sets was finally estimated by means of the Tanimoto approach. The results obtained provide confidence for the utility of the pharmacophore in the virtual screening of libraries and databases of compounds to discover novel PfDHFR inhibitors.     

Pharmacophore-based 3D-QSAR study of fungal chitin synthase inhibitors

To design new compounds with enhanced activity against the fungal chitin synthase enzyme, 3D-pharmacophore models were generated and QSAR study was carried out on 44 novel homoallylamines and related compounds, nikkomycin, maleimide, chalcones, and quinolin-2-one derivatives. A three-point pharmacophore with two hydrophobic (H) and one aromatic ring (R) as pharmacophore features was developed by PHASE module of Schrodinger molecular modeling suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² of 0.84 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² of 0.63 and Pearson-R value of 0.82 for a randomly chosen test set of nine compounds. The 3D-QSAR model explains the structure–activity relationship of these compounds which may help in the design and development of novel fungal chitin synthase inhibitors.     

Pharmacophore modeling of some novel indole β-diketo acid and coumarin-based derivatives as HIV integrase inhibitors

To design new chemotypes with enhanced potencies against the HIV integrase enzyme, 3D pharmacophore models were generated and QSAR study was carried out on 44 novel indole β-diketo acid derivatives and coumarin-based Inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A) and three aromatic rings (R) as pharmacophore features was developed by PHASE module of Schrodinger suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² = 0.81 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² = 0.69 for a randomly chosen test set of eight compounds. The 3D-QSAR plots illustrated insights into the structure activity relationship of these compounds which may helps in the design and development of novel integrase inhibitors.     

Pharmacophore modeling and 3D-QSAR studies of leucettines as potent Dyrk2 inhibitors

Dyrk family enzymes are essential components of important signaling casades in the pathophysiology of cancer and Alzheimer’s disease. Especially, Dyrk2 biological expression levels regulate key signaling processes in these diseases. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of leucettine analogs possessing Dyrk2 inhibitory activity. Developed pharmacophore model contains four hydrogen bond acceptors (A) and one hydrophobic aromatic ring (R). These are crucial molecular fingerprints which predict binding efficacy of high affinity and low affinity ligands to the Dyrk2 enzyme. These pharmacophoric features point toward key structural requirements of leucettines for potent Dyrk2 inhibition. Furthermore, a biological correlation between pharmacophore hypothesis-based 3D-QSAR variables and functional fingerprints of leucettines responsible for the receptor binding was observed. Alignment of the developed model with Dyrk2 crystal structure indicated importance of A3 and A4 H-bond accetor sites, which are involved in the important interactions with Leu231A and Lys178A residues of the active site. Excellent statistical results of QSAR model such as good correlation coefficient (r ² > 0.95), higher F value (F > 106), and excellent predictive power (Q ² > 0.7) with low standard deviation (SD < 0.2) strongly suggest that the developed model is good for the future prediction of Dyrk2 inhibitory activity of new leucettine analogs.     

Pharmacophore modeling and 3D QSAR analysis of isothiazolidinedione derivatives as PTP1B inhibitors

The article describes the development of a robust pharmacophore model and investigation of structure activity relationship analysis of 56 isothiazolidinedione derivatives reported as PTP1B inhibitors. A six-point pharmacophore model consisting of four aromatic rings (R), one hydrogen bond donor (D) and one hydrogen bond acceptor (A) with discrete geometries as pharmacophoric features was developed and the generated pharmacophore model was used to derive a predictive 3D QSAR model for the studied dataset. The obtained 3D QSAR model has an excellent correlation coefficient value (r ² = 0.98) along with good statistical significance as shown by a high Fisher ratio (F = 428.60). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (q ² = 0.62). The QSAR model suggests that hydrophobic aromatic character is crucial for the PTP1B inhibitory activity at the R-15 site.     

Pharmacophore modeling, 3D-QSAR, and molecular docking study on naphthyridine derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1

The 3-phosphoinositide-dependent protein kinase-1 (PDK1) is an imminent target for discovering novel anticancer drugs. In order to understand the structure–activity correlation of naphthyridine-based PDK-1 inhibitors, we have carried out a combined pharmacophore, three-dimensional quantitative structure–activity relationship (3D-QSAR), and molecular docking studies. The study has resulted in six point pharmacophore models with four hydrogen bond acceptors (A), one hydrogen bond donor (D), and one aromatic ring (R) are used to derive a predictive atom-based 3D-QSAR model. The generated 3D-QSAR model shows that the alignment has good correlation coefficient for the training set compounds which comprises the values of R ² = 0.96, SD = 0.2, and F = 198.2. Test set compounds shows Q ² = 0.84, RMSE = 0.56, and Pearson-R = 0.84. The external validation was carried out to validate the predicted QSAR model which shows good predictive power of $ r_{m}^{2} $  = 0.83 and k = 1.01, respectively. The external validation results also confirm the fitness of the model. The results indicated that, atom-based 3D-QSAR models and further modifications in PDK1 inhibitors via pharmacophore hypothesis are rational for the prediction of the activity of new inhibitors in prospect of drug design.     

Pharmacophore modeling studies on N-hydroxyphenyl acrylamides and N-hydroxypyridin-2-yl-acrylamides as inhibitor of human cancer leukemia K562 cells

In order to understand the essential structural features for inhibitors of human cancer leukemia K562 cells, three-dimensional pharmacophore hypotheses were built on the basis of a set of inhibitors of human cancer leukemia K562 selected from literature using PHASE program. Five point pharmacophore with two hydrogen bond acceptor (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophoric features were developed. Among them, the pharmacophore hypothesis AADRR 62 yielded a statistically significant 3D-QSAR model with as R ² value 0.883 and Q ² value 0.528 and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.765 was observed between experimental and predicted activity values of test set molecules.     

3D-QSAR and pharmacophore model study on aryl diphenolic azoles as estrogen receptor-β ligands

Pharmacophore and three-dimensional quantitative structure–activity relationship (3D-QSAR) model of 106 aryl diphenolic azoles as estrogen receptor-β ligands were proposed. The best pharmacophore model, five-site model including two hydrogen bond donors and three aromatic rings, has been established. The biological activity values (pIC₅₀) and classification (active or inactive) of compounds were predicted by 3D-QSAR model. The model correlation coefficient (R ²) is 0.9623. The classification accuracies in predicting for training, test, and total datasets are 97.5, 84.6, and 94.3 %, respectively. The 3D-QSAR model maps revealed that hydrogen bond donor, hydrophobic, and electron-withdrawing fields would be the essential factors for designing new ligands with higher biological activity. The results indicate that the combination of pharmacophore and 3D-QSAR analyses could be a powerful modeling tool for finding novel estrogen receptor-β ligands.     

Pharmacophore modeling and 3D-QSAR studies of galloyl benzamides as potent P-gp inhibitors

P-gp transporter regulates key ADME of drugs in MDR condition. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of galloyl benzamides analogs possessing P-gp inhibitory activity. Developed pharmacophore model contains two hydrogen-bond acceptors (A), one hydrophobic (H), one hydrogen-bond donor (D) and two aromatic rings (R). These are crucial molecular fingerprints which predict binding efficacy of high-affinity and low-affinity ligands to the P-gp efflux pump. These pharmacophoric features point toward key structural requirements of galloyl benzamides for potent P-gp inhibition. Furthermore, a biological correlation 3D-QSAR variants and functional fingerprints of P-gp responsible for the receptor binding were observed. Alignment of the developed model with P-gp crystal structure indicated importance of A2 and A4 H-bond acceptor sites, which are involved in the important interactions with Glu530 and His690 residues of the active site. Excellent statistical results of QSAR model such as good correlation coefficient (r ² > 0.95), higher F value (F > 205) and excellent predictive power (Q ² > 0.6) with low standard deviation (SD < 0.2) strongly suggest that the developed model is good for the future prediction of P-gp inhibitory activity of new galloyl benzamide analogs.     

P45017α抑制剂──17位取代甾体化合物的三维定量构效关系

目的　建立P450_17α的17位取代甾体抑制剂的三维定量构效关系,为设计新的、更有效的抑制剂提供理论依据。方法和结果　利用比较分子力场方法,建立了P450_17α抑制剂的三维定量构效关系模型。交叉验证回归系数R2CV、非交叉验证回归系数R2 和标准偏差SEE分别为0.538,0.799和0.257。说明该系列化合物分子周围立体场和静电场的分布与生物活性间有良好的相关性。用该模型对本室合成的3个化合物进行活性预测,结果与实测值相符。结论　所得模型支持了假设的抑制剂作用机理和作用模型。所得CoMFA模型有一定的预测能力,可用来指导设计新的P450_17α抑制剂     

A combined approach based on 3D pharmacophore and docking for identification of new aurora A kinase inhibitors

Aurora kinase A is involved in multiple mitotic events in cell cycle and has been identified as a major regulator of centrosome function in mitosis. Aurora A has been found to be over-expressed in many tumor types including breast, lung, colon, ovarian, pancreatic and glial cells. Thus, inhibition of aurora A can be a potential target in oncology. A five-point pharmacophore was generated using PHASE for a set of aurora A inhibitors reported in literature. The generated pharmacophore yielded statistically significant 3D-QSAR model, with a correlation coefficient r ² of 0.936 and q ² of 0.703. The pharmacophore indicated that presence of two aromatic ring features (R), two acceptor features (A) and one donor feature (D) is necessary for potent inhibitory activity. The database screening was done initially by use of pharmacophore followed by an interaction-based selection using docking. Twelve hits with satisfactory pharmacokinetic properties have been identified.     

Computation of pharmacophore models for the prediction of mitogen-activated protein kinase activated protein kinase-2 inhibitory activity of pyrrolopyridines

This article is an attempt to formulate the three-dimensional pharmacophore modelling of pyrrolopyridine derivatives inhibiting mitogen-activated protein kinase activated protein kinase-2 (MK₂). To understand the essential structural features for MK₂ inhibitors, pharmacophore hypothesis were built on the basis of a set of known MK₂ inhibitors selected from literature using PHASE program. Three pharmacophore models with one hydrogen-bond acceptor (A), two hydrogen-bond donors (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features were developed. Amongst them the pharmacophore hypothesis ADDHR1 yielded a statistically significant 3D-QSAR model with 0.926 as R ² value and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.882 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore was expected to be useful for the design of selective MK₂ inhibitors.     

Ligand-based 3D-QSAR analysis and virtual screening in exploration of new scaffolds as Plasmodium falciparum glutathione reductase inhibitors

Plasmodium falciparum glutathione reductases involved in redox homeostasis pathway of parasite are found to be the most emerging target in the treatment of malaria. In the present study, a 3D-QSAR pharmacophore model was developed, based on twenty-three 1,4-naphthoquinone derivatives reported previously with marked inhibition against glutathione reductase (GR). The pharmacophore model development and 3D-QSAR analysis was carried out by PHASE program. The hypothesis with best survival score was found to be AAHRR. Thus the resulting pharmacophore model contained two aromatic rings, a hydrophobic and two hydrogen-bond acceptor sites. A statistically reliable model with good predictive power (r ² = 0.8155, q ² = 0.7054, average r _m ²  = 0.745) was obtained. Using these pharmacophore features, we screened a library of 214,029 compounds (Asinex Database) to find potential ligands that could inhibit the PFGR protein. The compounds then subjected to a number of filters of virtual screening workflow of Schrödinger software. Here, we report the best seven compounds based on their docking scores and mode of interactions. The aromatic ring, hydrophobic group and hydrogen-bond acceptor effects contribute to the inhibitory activity. Binding interaction of the inhibitors can further provide the information regarding the role of different features in ligands responsible for linkage with receptor. Both compound 1 and screened hit with highest docking score (lead-1) found to interact with ASN 278, LYS 32, GLU 31, GLU 277, ASP 275, THR 38, LYS 151 within same binding pocket of PFGR enzyme. The backbone structural scaffolds of these seven lead compounds obtained after screening could serve as building blocks when designing drug-like molecules for inhibition of P. falciparum GR.     

Pharmacophore and docking-based hierarchical virtual screening for the designing of aldose reductase inhibitors: synthesis and biological evaluation

A set of 54 studied flavonoid inhibitors of aldose reductase (ALR2) enzyme has been utilized for pharmacophore modeling and 3D-QSAR analysis using “PHASE” program of Schrödinger software. The generated pharmacophore model (AADRR.1109) was challenged to screen “PHASE” database to identify new ALR2 inhibitors. The retrieved hits were employed for docking analysis and pharmacokinetic parameter calculation to obtain orally active molecules. To predict the activity of final retrieved hits, 3D-QSAR model was developed, and the best model was selected on the basis of various statistical parameters (R _train ² 0.719; Q _test ² 0.647 and SD 0.663). Totally five screened molecules which showed better enhanced predicted activity were synthesized and evaluated for in vitro ALR2 inhibitory activity. All tested molecules showed ALR2 inhibitory activity (IC₅₀) below 40 µM. Additionally, the free radical scavenging potential of synthesized molecules was also determined which played a useful role to control the progression of diabetic complications. All molecules showed good antioxidant potential, thus the designed molecules, in future, could be explored to ameliorate the development of diabetic complications.