Structure-activity relationships of the antimalarial agent artemisinin. 6. The development of predictive in vitro potency models using CoMFA and HQSAR methodologies

Artemisinin (1) is a unique sesquiterpene peroxide occurring as a constituent of Artemisia annua L. Because of the effectiveness of Artemisinin in the treatment of drug-resistant Plasmodium falciparum and its rapid clearance of cerebral malaria, development of clinically useful semisynthetic drugs for severe and complicated malaria (artemether, artesunate) was prompt. However, recent reports of fatal neurotoxicity in animals with dihydroartemisinin derivatives such as artemether have spawned a renewed effort to develop nontoxic analogues of artemisinin. In our effort to develop more potent, less neurotoxic agents for the oral treatment of drug-resistant malaria, we utilized comparative molecular field analysis (CoMFA) and hologram QSAR (HQSAR), beginning with a series of 211 artemisinin analogues with known in vitro antimalarial activity. CoMFA models were based on two conformational hypotheses: (a) that the X-ray structure of artemisinin represents the bioactive shape of the molecule or (b) that the hemin-docked conformation is the bioactive form of the drug. In addition, we examined the effect of inclusion or exclusion of racemates in the partial least squares (pls) analysis. Databases derived from the original 211 were split into chiral (n = 157), achiral (n = 34), and mixed databases (n = 191) after leaving out a test set of 20 compounds. HQSAR and CoMFA models were compared in terms of their potential to generate robust QSAR models. The r(2) and q(2) (cross-validated r(2)) were used to assess the statistical quality of our models. Another statistical parameter, the ratio of the standard error to the activity range (s/AR), was also generated. CoMFA and HQSAR models were developed having statistically excellent properties, which also possessed good predictive ability for test set compounds. The best model was obtained when racemates were excluded from QSAR analysis. Thus, CoMFA of the n = 157 database gave excellent predictions with outstanding statistical properties. HQSAR did an outstanding job in statistical analysis and also handled predictions well.     

Elucidating the inhibiting mode of AHPBA derivatives against HIV-1 protease and building predictive 3D-QSAR models.

The Lamarckian genetic algorithm of AutoDock 3.0 has been used to dock 27 3(S)-amino-2(S)-hydroxyl-4-phenylbutanoic acids (AHPBAs) into the active site of HIV-1 protease (HIVPR). The binding mode was demonstrated in the aspects of the inhibitor's conformation, subsite interaction, and hydrogen bonding. The data of geometrical parameters (tau(1), tau(2), and tau(3) listed in Table 2) and root mean square deviation values as compared with the known inhibitor, kni272,(28) show that both kinds of inhibitors interact with HIVPR in a very similar way. The r(2) value of 0.860 indicates that the calculated binding free energies correlate well with the inhibitory activities. The structural and energetic differences in inhibitory potencies of AHPBAs were reasonably explored. Using the binding conformations of AHPBAs, consistent and highly predictive 3D-QSAR models were developed by performing CoMFA, CoMSIA, and HQSAR analyses. The reasonable r(corss)(2) values were 0.613, 0.530, and 0.717 for CoMFA, CoMSIA, and HQSAR models, respectively. The predictive ability of these models was validated by kni272 and a set of nine compounds that were not included in the training set. Mapping these models back to the topology of the active site of HIVPR leads to a better understanding of vital AHPBA-HIVPR interactions. Structural-based investigations and the final 3D-QSAR results provide clear guidelines and accurate activity predictions for novel HIVPR inhibitors.     

Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: predicting the estrogenic activity of xenoestrogens

Federal legislation has resulted in the two-tiered in vitro and in vivo screening of some 80 000 structurally diverse chemicals for possible endocrine disrupting effects. To maximize efficiency and minimize expense, prioritization of these chemicals with respect to their estrogenic disrupting potential prior to this time-consuming and labor-intensive screening process is essential. Computer-based quantitative structure-activity relationship (QSAR) models, such as those obtained using comparative molecular field analysis (CoMFA), have been demonstrated as useful for risk assessment in this application. In general, however, CoMFA models to predict estrogenicity have been developed from data sets with limited structural diversity. In this study, we constructed CoMFA models based on biological data for a structurally diverse set of compounds spanning eight chemical families. We also compared two standard alignment schemes employed in CoMFA, namely, atom-fit and flexible field-fit, with respect to the predictive capabilities of their respective models for structurally diverse data sets. The present analysis indicates that flexible field-fit alignment fares better than atom-fit alignment as the structural diversity of the data set increases. Values of log(RP), where RP = relative potency, predicted by the final flexible field-fit CoMFA models are in good agreement with the corresponding experimental values. These models should be effective for predicting the endocrine disrupting potential of existing chemicals as well as prospective and newly prepared chemicals before they enter the environment.     

Predictive 3D-QSAR and HQSAR model generation of isocitrate lyase (ICL) inhibitors by various alignment methods combined with docking study

Isocitrate lyase (ICL) is one of the most important targets in the treatment of Mycobacterium tuberculosis. In this study a diverse set of 2-benzanilide derivatives were aligned by two different methods for CoMFA, CoMSIA, and HQSAR analysis. The best CoMFA model was obtained with the internal validation value (q ²) of 0.730 and conventional coefficient (r ²) of 0.944. Various CoMSIA models were generated and cross-validated. The best cross-validation coefficient (q ²) value was found to be statistically satisfactory (0.688). Both the models were validated by test set of 10 compounds with satisfactory prediction value of (r ² _pred ) 0.725 and 0.631 for CoMFA and CoMSIA, respectively. Cross-validation coefficient value (q ²) of 0.694 and r ² of 0.856 were obtained for HQSAR study. The docking study reveals that large hydrophobic pockets occupy R substitutions of these compounds. An electronically negative surface is observed near R₁ substitution. The results of the 3D-QSAR analysis corroborate with the molecular docking results, and our findings will serve as a basis for further development of better allosteric inhibitors of ICL inhibitors against M. tuberculosis.     

3D-QSAR model of flavonoids binding at benzodiazepine site in GABAA receptors

With flavone as a structural template, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies and ab initio calculations were performed on a series of flavonoids. A reasonable pharmacophore model was built through CoMFA, CoMSIA, and HQSAR analyses and electrostatic potential calculations. A plausible binding mode for flavonoids with GABA(A) receptors was rationalized. On the basis of the commonly recognized binding site, the specific S1 and S2 subsites relating to substituent positions were proposed. The different binding affinities could be explained according to the frontier orbitals and electrostatic potential (ESP) maps. The ESP could be used as a novel starting point for designing more selective BZ-binding-site ligands.     

QSAR analysis on PfPK7 inhibitors using HQSAR, CoMFA, and CoMSIA

Plasmodium falciparum protein kinase 7 (PfPK7) is an important drug target for the development of anti-malarial treatment. In this study, hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of imidazopyridazine derivatives of PfPK7 inhibitors. The best HQSAR model was obtained using atoms, connection, donor, and acceptor as fragment distinction parameter with fragment size (4–7) using a hologram length of 353 and 6 components (q ² = 0.770, r ² = 0.964). The receptor-guided alignment has produced better statistical results for both CoMFA (q ² = 0.590, r ² = 0.986) and CoMSIA (q ² = 0.735, r ² = 0.988). The predictive ability of the developed models was further validated by a test set of eight compounds. HQSAR contribution map identified the presence of phenyl ring and cyclohexane moiety makes positive contribution for activity. Furthermore, CoMFA and CoMSIA contour maps suggested that additional bulky groups in cyclohexane moiety would increase the biological activity of PfPK7 inhibitors. Finally, these QSAR models were used to design new virtual molecules for imidazopyridazine derivatives and the results obtained from this study could be useful for further investigations.     

Topomer CoMFA: a design methodology for rapid lead optimization

To provide an objective QSAR methodology that might accelerate lead optimization, the CoMFA and topomer technologies have been merged, with surprisingly good results. A series of input structures are each broken into two or more fragments at central acyclic single bonds, while removing any core fragment structurally common to the entire series. Standard topomer 3D models are automatically constructed for each fragment, and a set of steric and electrostatic fields ("CoMFA column") is generated for each set of topomers. Application of "topomer CoMFA" to 15 3D-QSAR analyses taken from the literature (847 structures) were all successful, with an average q(2) of 0.520 (literature average q(2) = 0.636) and an average standard deviation of true prediction (SDEP) of 0.688 (literature average SDEP = 0.553) for 133 structures. Topomer CoMFA results are particularly promising as queries into virtual libraries already composed of topomer structures, to directly seek structures having increased potency. Accordingly, in 13 of the 15 such "topomer CoMFA searches" attempted, combinations of commercially offered fragments were retrieved that were predicted to be more potent than any structure described in the original publication (average predicted potency increase = 20 x), showing in principle how optimization could occur.     

Molecular docking and 3D-QSAR studies on gag peptide analogue inhibitors interacting with human cyclophilin A

The interaction of a series gag peptide analogues with human cyclophilin A (hCypA) have been studied employing molecular docking and 3D-QSAR approaches. The Lamarckian Genetic Algorithm (LGA) and divide-and-conquer methods were applied to locate the binding orientations and conformations of the inhibitors interacting with hCypA. Good correlations between the calculated interaction free energies and experimental inhibitory activities suggest that the binding conformations of these inhibitors are reasonable. A novel interaction model was identified for inhibitors 11, 15, and 17 whose N-termini were modified by addition of the deaminovaline (Dav) group and the C-termini of 15 and 17 were modified by addition of a benzyl group. Accordingly, two new binding sites (sites A and D in Figure 1) were revealed, which show a strong correlation with inhibitor potency and thus can be used as a starting point for new inhibitor design. In addition, two predictive 3D-QSAR models were obtained by CoMFA and CoMSIA analyses based on the binding conformations derived from the molecular docking calculations. The reasonable r(cross)(2) (cross-validated) values 0.738 and 0.762 were obtained for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by four peptide analogues test set. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding groove of hCypA. This indicates the reasonableness of the binding model of the inhibitors with hCypA. Considering all these results together with the valuable clues of binding from references published recently, reasonable pharmacophore elements have been suggested, demonstrating that the 3D-QSAR models about peptide analogue inhibitors are expected to be further employed in predicting activities of the novel compounds for inhibiting hCypA.     

First CoMFA characterization of vesamicol analogs as ligands for the vesicular acetylcholine transporter

Vesamicol derivatives are promising candidates as ligands for the vesicular acetylcholine transporter (VAChT) to enable in vivo imaging of cholinergic deficiencies if applied as positron emission tomography radiotracers. So far, optimization of the binding affinity of vesamicol-type ligands was hampered by the lack of respective quantitative structure-activity relationships. We developed the first quantitative model to predict, from molecular structure, the binding affinity of vesamicol-type ligands toward VAChT employing comparative molecular field analysis (CoMFA) for a set of 37 ligands, covering three different structural types (4-phenylpiperidine, spiro, and tropan derivatives of vesamicol). The prediction capability was assessed by leave-one-out cross-validation (LOO) and through leaving out and predicting 50% of the compounds selected such that both the training and the prediction sets cover almost the whole range of experimental data. The statistics indicate a significant prediction power of the models ( q (2) (LOO) = 0.66, q (2) (50% out) = 0.59-0.74). The discussion includes detailed analyses of CoMFA regions critical for ligand-VAChT binding, identifying structural implications for high binding affinity.     

Discovery of novel Bruton's tyrosine kinase inhibitors using a hybrid protocol of virtual screening approaches based on SVM model, pharmacophore and molecular docking

Bruton's tyrosine kinase has emerged as a potential target for the treatment for B-cell malignancies and autoimmune diseases. Discovery of Bruton's tyrosine kinase inhibitors has thus attracted much attention recently. In this investigation, we introduced a hybrid protocol of virtual screening methods including support vector machine model-based virtual screening, pharmacophore model-based virtual screening and docking-based virtual screening for retrieving new Bruton's tyrosine kinase inhibitors from commercially available chemical databases. Performances of the hybrid virtual screening approach were evaluated against a test set, which results showed that the hybrid virtual screening approach significantly shortened the overall screening time, and considerably increased the hit rate and enrichment factor compared with the individual method (SB-VS, PB-VS and DB-VS) or their combinations by twos. This hybrid virtual screening approach was then applied to screen several chemical databases including Specs (202,408 compounds) and Enamine (980,000 compounds) databases. Thirty-nine compounds were selected from the final hits and have been shifted to experimental studies.     

Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA–COMSIA approach

Chloroquine resistance is nowadays a great problem in malaria. Aurone derivatives were effective against chloroquine resistant parasite. Validated density functional theory (DFT)-based chemometric modeling, hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies were conducted on 35 aurone derivatives having antimalarial activity. 2D-QSAR models were developed on the training sets by Y-based ranking method. This model was validated on 50 pairs of the test and the training sets by k-Means cluster analysis method. HQSAR, CoMFA, and CoMSIA models were validated by standard techniques and each method validates the DFT-based 2D-QSAR study and in turn validates the earlier observed structural activity relationship data as well as each other. DFT-based 2D-QSAR model suggests that the increase of Mulliken charge at C₁₄ and HOMO density located on C₁₁ may be conducive to antimalarial activity. Ethyl group attached to C₁₄ and the increase of the value of chemical potential may be beneficial for antimalarial activity. Methoxy fragment is important for better antimalarial activity by HQSAR study. CoMFA analysis shows a favorable steric green region is located near C₁₄ whereas the unfavorable yellow region is far away from C₁₄. A large blue region located near C₁₄ indicates the positively charged groups are favorable at this position. CoMSIA steric features correlates well with the CoMFA steric features. CoMSIA study suggests the bulky hydrophobic substitution at C₁₄ is necessary for antimalarial activity. These results may be utilized to obtain potential antimalarial molecules.     

Three-dimensional QSAR and pharmacophore mapping of biphenyl benzoic acid derivatives as selective human β3-adrenergic receptor agonists

Molecular modeling studies were performed to develop a predictive common pharmacophore hypothesis (CPH) and use it for alignment in three-dimensional (3D) quantitative structure-activity relationship (QSAR) studies using CoMFA and CoMSIA, with a diverse set of 80 β₃-adrenergic receptor (β₃-AR) agonists. Using PHASE (Pharmacophore Alignment and Scoring Engine) six-point CPH with one acceptor, one negative charge, one positive charge, and three rings, features were derived for pharmacophore-based alignment of molecules. CPH was selected by correlating the observed and estimated activity for the training set and test set of molecules using partial least squares analysis. The validated pharmacophore hypothesis was used for alignment of molecules in CoMFA and CoMSIA model development. The models so generated showed a good “predictive” r ² value of 0.6635 and 0.8665 for CoMFA and CoMSIA, respectively. The 3D contour CoMFA/CoMSIA maps provided an interpretable explanation of SAR for the compounds and also permitted interesting conclusions about the substituent effects at different positions of the biphenyl benzoic acid derivatives. CPH can also provide a powerful template for virtual screening and design of new β₃-AR agonists.     

A 3D-QSAR study of catechol-O-methyltransferase inhibitors using CoMFA and CoMSIA

应用CoMFA方法和CoMSIA方法研究儿茶酚转甲基酶抑制剂的三维定量构效关系@艾纯芝$Lab of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Graduate School of the Chinese Academy of Sciences!457 Zhongshan Road, Dalian 116023, Liaoning,China