Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents

Oxazolidinones exemplified by eprezolid and linezolid are a new class of antibacterials that are active against Gram positive and anaerobic bacteria including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE) and vancomycin resistant enterococci (VRE). In an effort to have a better antibacterial agent in the oxazolidinone class, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of tricyclic oxazolidinones. 3D-QSAR studies were performed using the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) procedures. These studies were performed using 42 compounds; the QSAR model was developed using a training set of 33 compounds. The predictive ability of the QSAR model was assessed using a test set of 9 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.975 and 0.940 for CoMFA and CoMSIA respectively; similarly, cross-validated coefficient q(2) values of 0.523 and 0.557 for CoMFA and CoMSIA, respectively, were obtained. The CoMFA 3D-QSAR model performed better than the CoMSIA model.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

The 3-D QSAR study of anticancer 1-N-substituted imidazo- and pyrrolo-quinoline-4,9-dione derivatives by CoMFA and CoMSIA

The 3-D QSAR analysis with new imidazo- and pyrrolo-quinolinedione derivatives was conducted by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). When crossvalidation value (q(2)) is 0.844 at four components, the Pearson correlation coefficient (r(2)) of the CoMFA is 0.964. In the CoMSIA, q(2) is 0.709 at six components and r(2) is 0.969. Unknown samples were analyzed, using QSAR analyzed results from the CoMFA and CoMSIA methods. Excellent agreement was obtained between, with an error range of 0.01-0.15 the calculated values and measured in vitro cytotoxic activities against human lung A-549 cancer cell lines.     

Three-dimensional quantitative structure-activity relationship analyses of piperidine-based CCR5 receptor antagonists

The CCR5 chemokine receptor has recently been found to play a crucial role in the viral entry stage of HIV infection and has therefore become an attractive potential target for anti-HIV therapeutics. On the other hand, the lack of CCR5 crystal structure data has impeded the development of structure-based CCR5 antagonist design. In this paper, we compare two three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) methods: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) on a series of piperidine-based CCR5 antagonists as an alternative approach to investigate the interaction between CCR5 antagonists and their receptor. Superimposition of antagonist structures was performed using two alignment rules: atomic/centroid rms fit and rigid body field fit techniques. The 3D QSAR models were derived from a training set of 72 compounds, and were found to have predictive capability for a set of 19 holdout test compounds. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to biological activity in this series of compounds. Further analyses of these interaction-field contour maps also showed a high level of internal consistency.     

CoMFA and CoMSIA 3D QSAR analysis on N1-arylsulfonylindole compounds as 5-HT6 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series of N(1)-arylsulfonylindole compounds as 5-HT(6) antagonists. Evaluation of 20 compounds served to establish the models. The lowest energy conformer of compound 1 obtained from random search was used as template for alignment. The best predictions were obtained with CoMFA standard model (q2 = 0.643, r2 = 0.939 ) and with CoMSIA combined steric, electrostatic, hydrophobic, and hydrogen bond acceptor fields (q2 = 0.584, r2 = 0.902 ). Both the models were validated by an external test set of eight compounds giving satisfactory predictive r2 values of 0.604 and 0.654, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps can be used for further design of specific 5-HT(6) antagonists.     

3D-QSAR study of 8-azabicyclo[3.2.1] octane analogs antagonists of cholinergic receptor

3D-QSAR models of Comparative of Molecular Field Analysis (CoMFA) and Comparative of Molecular Similarities Indices Analysis (CoMSIA) of 20 8-azabicyclo[3.2.1] octane (potent muscarinic receptor blocker) was performed. These benztropine analogs were optimized using ligand based alignment method. The conventional ligand-based 3D-QSAR studies were performed based on the low energy conformations employing database alignment rule. The ligand-based model gave q² value 0.819 and 0.810 and r² value 0.991 and 0.988 for CoMFA and CoMSIA, respectively, and the predictive ability of the model was validated. Results indicate that the CoMFA and CoMSIA models could be reliable model which may be used in the design of novel muscarinic antagonists as leads.     

3D QSAR studies on T-type calcium channel blockers using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of isoxazolyl compounds as a potent T-type calcium channel blockers. A set of 24 structurally similar compounds served to establish the model. Four different conformations of the most active compound were used as template structures for the alignment, three of which were obtained from Catalyst pharmacophore modeling and one by using SYBYL random search option. All CoMFA and CoMSIA models gave cross-validated r(2) (q(2)) value of more than 0.5 and conventional r(2) value of more than 0.85. The predictive ability of the models was validated by an external test set of 10 compounds, which gave satisfactory pred r(2) values ranging from 0.577 to 0.866 for all models. Best predictions were obtained with CoMFA std model of Conformer no: 3 alignment (q(2)=0.756, r(2)=0.963), giving predictive r(2) value of 0.866 for the test set. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands accounting for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, hydrophobic and hydrogen bonding fields.     

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists

Two 3D-QSAR methods--CoMFA and CoMSIA--were applied to a set of 38 angiotensin receptor (AT1) antagonists. The conformation and alignment of molecules were obtained by a novel method - consensus dynamics. The representation of biological activity, partial charge formalism, absolute orientation of the molecules in the grid, and grid spacing were also studied for their effect on the CoMFA models. The models were thoroughly validated through trials using scrambled activities and bootstrapping. The best CoMFA model had a cross-validated correlation coefficient ( q2) of 0.632, which improved with "region focusing" to 0.680. This model had a "predictive" r2 of 0.436 on a test series that was unique and with little representation in the training set. Although the "predictive" r2 of the best CoMSIA model, which included steric, electrostatic, and hydrogen bond acceptor fields was higher than that of the best CoMFA model, the other statistical parameters like q2, r2, F value, and s were unsatisfactory. The contour maps generated using the best CoMFA model were used to identify the structural features important for biological activity in these compounds.     

3D-QSAR CoMFA, CoMSIA studies on substituted ureas as Raf-1 kinase inhibitors and its confirmation with structure-based studies

Three-dimensional quantitative structure activity relationship (3D-QSAR) analyses were carried out on 91 substituted ureas in order to understand their Raf-1 kinase inhibitory activities. The studies include Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Models with good predictive abilities were generated with the cross validated r2 (r2cv) values for CoMFA and CoMSIA being 0.53 and 0.44, respectively. The conventional r2 values are 0.93 and 0.87 for CoMFA and CoMSIA, respectively. In addition, a homology model of Raf-1 was also constructed using the crystal structure of the kinase domain of B-Raf isoform with one of the most active Raf-1 inhibitors (48) inside the active site. The ATP binding pocket of Raf-1 is virtually similar to that of B-Raf. Selected ligands were docked in the active site of Raf-1. Molecule 48 adopts an orientation similar to that inside the B-Raf active site. The 4-pyridyl group bearing amide substituent is located in the adenosine binding pocket, and anchored to the protein through a pair of hydrogen bonds with Cys424 involving ring N-atom and amide NH group. The results of best 3D-QSAR model were compared with structure-based studies using the Raf-1 homology model. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as Raf-1 inhibitors. Based on these results, novel molecules with improved activity can be designed.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of human thymidine kinase 1 substrates

Thymidine kinase 1 (TK1) is a key target for antiviral and anticancer chemotherapy. Three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques was applied to analyze the phosphorylation capacity of a series of 31 TK1 substrates. The optimal predictive CoMFA model with 26 molecules provided the following values: cross-validated r(2) (q(2))=0.651, non-cross-validated r(2)=0.980, standard error of estimate (s)=0.207, F=129.3. For the optimal CoMSIA model the following values were found: q(2)=0.619, r(2)=0.994, s=0.104, F=372.2. The CoMSIA model includes steric, electrostatic, and hydrogen bond donor fields. The predictive capacity of both models was successfully validated by calculating known phosphorylation rates of five TK1 substrates that were not included in the training set. Contour maps obtained from CoMFA and CoMSIA models correlated with the experimentally developed SAR.     

Docking-based CoMFA and CoMSIA analyses of tetrahydro-β-carboline derivatives as type-5 phosphodiesterase inhibitors

Tetrahydro-β-carboline derivatives (THBCs) have been identified as a class of potent Type-5 Phosphodiesterase (PDE5) inhibitors, showing benefits for the treatment of erectile dysfunction and also bearing anticancer properties. A computational strategy based on molecular docking studies, followed by docking-based Comparative Molecular Fields Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA), has been used to elucidate the atomic details of the PDE5/THBC interactions and to identify the most important features impacting the THBC PDE5 inhibitory activity. The final CoMSIA model resulted to be the more predictive, showing r(ncv)(2) = 0.96, r(cv)(2) = 0.688, SEE = 0.248, F = 104.800, and r(2)(pred) = 0.78. The results allowed us to obtain useful information for the design of new THBC analogues, potentially acting as PDE5 inhibitors, and to predict their potency prior to synthesis.     

Design and preliminary structure–activity relationship of redox-silent semisynthetic tocotrienol analogues as inhibitors for breast cancer proliferation and invasion

Vitamin E (VE) is a generic term that represents a family of compounds composed of various tocopherol and tocotrienol isoforms. Tocotrienols display potent anti-angiogenic and antiproliferative activities. Redox-silent tocotrienol analogues also display potent anticancer activity. The ultimate objective of this study was to develop semisynthetically C-6-modified redox-silent tocotrienol analogues with enhanced antiproliferative and anti-invasive activities as compared to their parent compound. Examples of these are carbamate and ether analogues of α-, γ-, and δ-tocotrienols (1–3). Various aliphatic, olefinic, and aromatic substituents were used. Steric limitation, electrostatic, hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) properties were varied at this position and the biological activities of these derivatives were tested. Three-dimensional quantitative structure–activity relationship (3D QSAR) studies were performed using Comparative Molecular Field (CoMFA) and Comparative Molecular Similarity Indices Analyses (CoMSIA) to better understand the structural basis for biological activity and guide the future design of more potent VE analogues.     

3D-QSAR study of ring-substituted quinoline class of anti-tuberculosis agents

A 3D-QSAR analysis of a new class of ring-substituted quinolines with anti-tuberculosis activity has been carried out by three methods-Comparative Molecular Field Analysis (CoMFA), CoMFA with inclusion of a hydropathy field (HINT), and Comparative Molecular Similarity Indices Analysis (CoMSIA). The conformation of the molecules was generated using a simulated annealing protocol and they were superimposed using features common to the set with database alignment (SYBYL) and field fit methods. Several statistically significant CoMFA, CoMFA with HINT, and CoMSIA models were generated. Prediction of the activity of a set of test molecules was the best for the CoMFA model generated with database alignment. Based upon the information contained in the CoMFA model, we have identified some novel features that can be incorporated into the quinoline framework to improve the activity.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

Models of the pharmacophoric pattern and affinity trend of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate derivatives as σ1 ligands

Sigma-1 (σ₁) affinities of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate (MAPCC) derivatives were modelled by the genetic algorithm with linear assignment of hypermolecular alignment of datasets (GALAHAD) and the comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) methods. GALAHAD was used for deriving the 3D pharmacophore pattern that encompasses the most potent σ₁ ligands within this series. Five MAPCC derivatives with a high σ₁ affinity were used for deriving this model. The obtained model included a nitrogen atom, the hydrophobes and the hydrogen bond acceptor features; it was able to identify other potent σ₁ ligands. On the other hand, CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 17 compounds, after which they were evaluated for predictive ability with additional five compounds. The best QSAR model was obtained by using CoMSIA, including steric, electrostatic and hydrophobic fields, and had a good predictive quality according to both internal and external validation criteria. In general, the models described herein provide meaningful information relevant for the rational design of new σ₁ ligands.     

3D–QSAR studies of orvinol analogs as κ-opioid agonists

Orvinols are potent analgesics that target opioid receptors. However, their analgesic mechanism remains unclear and no significant preference for subtype opioid receptor has been achieved. In order to find new orvinols that target the κ-receptor, comparative 3D–QSAR studies were performed on 26 orvinol analogs using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best predictions for the κ-receptor were obtained with the CoMFA standard model (q ²=0.686, r ²=0.947) and CoMSIA model combined steric, electrostatic, hydrophobic, and hydrogen bond donor/acceptor fields (q ²=0.678, r ²=0.914). The models built were further validated by a test set made up of seven compounds, leading to predictive r ² values of 0.672 for CoMFA and 0.593 for CoMSIA. The study could be helpful for designing and prepare new category κ-agonists from orvinols.      

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

Cyclic nucleotide phosphodiesterase IV (PDE IV) inhibitors find utility in asthma and Chronic Obstructive Pulmonary Disease (COPD) therapy. A series of 29 thieno[3,2-d]pyrimidines with affinity for PDE IV was subjected to three dimensional quantitative structure activity relationship (3D-QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Both CoMFA and CoMSIA provided statistically valid models with good correlative and predictive power. The incorporation of hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields showed insignificant improvement in CoMSIA model. The 3D-QSAR models provide information for predicting the affinity of related compounds and designing more potent inhibitors.     

3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.     

Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1

Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r2, q2(loo) and r2(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r2(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.