Comparative QSAR study of tyrosine kinase inhibitors

Outliers in SAR and QSAR: 4. effects of allosteric protein-ligand interactions on the classical quantitative structure-activity relationships

Effects of allosteric interactions on the classical structure-activity relationship (SAR) and quantitative SAR (QSAR) have been investigated. Apprehending the outliers in SAR and QSAR studies can improve the quality, predictability, and use of QSAR in designing unknown compounds in drug discovery research. We explored allosteric protein-ligand interactions as a possible source of outliers in SAR/QSAR. We used glycogen phosphorylase as an example of a protein that has an allosteric site. Examination of the ligand-bound x-ray crystal structures of glycogen phosphorylase revealed that many inhibitors bound at more than one binding site. The results of QSAR analyses of the inhibitors included a QSAR that recognized an outlier bound at a distinctive allosteric binding site. The case provided an example of constructive use of QSAR identifying outliers with alternative binding modes. Other allosteric QSARs that captured our attention were the inverted parabola/bilinear QSARs. The x-ray crystal structures and the QSAR analyses indicated that the inverted parabola QSARs could be associated with the conformational changes in the allosteric interactions. Our results showed that the normal parabola, as well as the inverted parabola QSARs, can describe the allosteric interactions. Examination of the ligand-bound X-ray crystal structures of glycogen phosphorylase revealed that many inhibitors bound at more than one binding site. The results of QSAR analyses of the inhibitors included a QSAR that recognized an outlier bound at a distinctive allosteric binding site.

One-Pot Cascade Heterocyclization of γ- and β-Ketomalononitriles to 2,4-Dichloro-Substituted Pyrano[2,3- d]pyrimidines and Furo[2,3- d]pyrimidines Mediated by Triphosgene and Triphenylphosphine Oxide

A one-pot cascade heterocyclization strategy has been developed for the synthesis of 2,4-dichloro-substituted pyrano[2,3- d]pyrimidines and furo[2,3- d]pyrimidines from linear γ- and β-ketomalononitriles using triphosgene and triphenylphosphine oxide. The reaction afforded synthetic useful products with moderate to good yields, bypassing the conventional harsh conditions of chlorination. The mechanistic study revealed that the reaction proceeded with a non-isocyanate route, and the second step may conduct in a triphenylphosphine oxide-catalyzed manner.

CNS Anticancer Drug Discovery and Development Conference White Paper

Following the first CNS Anticancer Drug Discovery and Development Conference, the speakers from the first 4 sessions and organizers of the conference created this White Paper hoping to stimulate more and better CNS anticancer drug discovery and development. The first part of the White Paper reviews, comments, and, in some cases, expands on the 4 session areas critical to new drug development: pharmacological challenges, recent drug approaches, drug targets and discovery, and clinical paths. Following this concise review of the science and clinical aspects of new CNS anticancer drug discovery and development, we discuss, under the rubric "Accelerating Drug Discovery and Development for Brain Tumors," further reasons why the pharmaceutical industry and academia have failed to develop new anticancer drugs for CNS malignancies and what it will take to change the current status quo and develop the drugs so desperately needed by our patients with malignant CNS tumors. While this White Paper is not a formal roadmap to that end, it should be an educational guide to clinicians and scientists to help move a stagnant field forward.

Cheminfomatic-based Drug Discovery of Human Tyrosine Kinase Inhibitors

Receptor Tyrosine Kinases (RTKs) are essential components for regulating cell-cell signaling and communication events in cell growth, proliferation, differentiation, survival and metabolism. Deregulation of RTKs and their associated signaling pathways can lead to a wide variety of human diseases such as immunodeficiency, diabetes, arterosclerosis, psoriasis and cancer. Thus RTKs have become one of the most important drug targets families in recent decade. Pharmaceutical companies have dedicated their research efforts towards the discovery of small-molecule inhibitors of RTKs, many of which had been approved by the U.S. Food and Drug Administration (US FDA) or are currently in clinical trials. The great successes in the development of small-molecule inhibitors of RTKs are largely attributed to the use of modern cheminformatic approaches to identifying lead scaffolds. Those include the quantitative structure-activity relationship (QSAR) modeling, as well as the structure-, and ligand-based pharmacophore modeling techniques in this case. Herein we inspected the literature thoroughly in an effort to conduct a comparative analysis of major findings regarding the essential structure-activity relationships (SARs)/pharmacophore features of known active RTK inhibitors, most of which were collected from cheminformatic modeling approaches.

In silico QSAR studies of anilinoquinolines as EGFR inhibitors

Members of the epidermal growth factor receptor (EGFR) family of proteins are frequently overactive in solid tumors. A relatively new therapeutic approach to inhibit the kinase activity is the use of ATP-competitive small molecules. In silico techniques were employed to identify the key interactions between inhibitors and their protein receptors. A series of EGFR inhibitory anilinoquinolines was studied within the framework of hologram quantitative structure activity relationship (HQSAR), density functional theory (DFT)-based QSAR, and three-dimensional (3D) QSAR (CoMFA/CoMSIA). The HQSAR analysis implied that substitutions at certain sites on the inhibitors play an important role in EGFR inhibition. DFT-based QSAR results suggested that steric and electronic interactions contributed significantly to the activity. Ligand-based 3D-QSAR and receptor-guided 3D-QSAR analyses such as CoMFA and CoMSIA techniques were carried out, and the results corroborated the previous two approaches. The 3D QSAR models indicated that steric and hydrophobic interactions are dominant, and that substitution patterns are an important factor in determining activity. Molecular docking was helpful in identifying a bioactive conformer as well as a plausible binding mode. The docked geometry-based CoMFA model with steric and electrostatic fields effect gave q(2) = 0.66, r(2) = 0.94 with r(2) (predictive) = 0.72. Similarly, CoMSIA with hydrophobic field gave q(2) = 0.59, r(2) = 0.85 with r(2) (predictive) = 0.63. Bulky groups around site 3 of ring "C", and hydrophilic and bulky groups at position 6 of ring "A" are desirable, with a hydrophobic and electron-donating group at site 7 of ring "A" being helpful. Accordingly, potential EGFR inhibitors may be designed by modification of known inhibitors.

QSAR of aromatic substances: EGFR inhibitory activity of quinazoline analogues

The flip regression procedure that we used earlier for handling the xanthones system has been applied to phenylaminoquinazoline analogues. It is known that the substituents at the 6- and 7- positions of the polycyclic system have been identified as the most important structural features. The steric as well as the electrostatic interactions proved to be the most important for the inhibitory effect. In this contribution it is shown that the orientation of nodes in their occupied pi orbitals, and also the energies of these orbitals explains a further large portion of the variance in their inhibitory activity.

Possible allosteric interactions of monoindazole-substituted P2 cyclic urea analogues with wild-type and mutant HIV-1 protease

Our ongoing efforts to understand the difference in the binding pattern of HIV-1 protease inhibitor (HIVPI) with the wild-type and mutant HIV-1 protease (HIVPR) and to provide mechanistic insight are continued further. We report here the results of a recent quantitative structure-activity relationship (QSAR) study on monoindazole-substituted P2 analogues of cyclic urea HIVPIs. The QSAR models revealed an inverted parabolic relationship between biological activity and calculated molar refractivity (CMR). That is, biological activity first decreases with increase in CMR and at a certain minimum point (inversion point) it suddenly changes and increases with further increase in CMR. CMR is a measure of volume-dependent-polarizability and is an indication of the polar interactions between ligand and receptor. The results seem to be best rationalized by larger molecules inducing a change in a receptor unit that allows for a new mode of interaction. Similar QSAR models were also observed for the biological activity of these molecules tested against a panel of mutant viruses including mutant strains with single amino acid substitution (I84V), double amino acid substitutions (I84V/V82F), and multiple amino acid changes corresponding to mutations observed in clinical isolates of patients treated with Ritonavir((R)). Interestingly the inversion points for these mutant strains were found larger than for wild-type. The subtle but significant difference in the inversion point indicates change in the shape and size of the binding pocket. Earlier QSAR studies have shown that the correlation of biological activity with an inverted parabola is an indicative of the 'allosteric interaction' of the ligands with the receptor. This report presents a detail analysis of these observations.

2D Autocorrelation, CoMFA, and CoMSIA modeling of protein tyrosine kinases' inhibition by substituted pyrido[2,3-d]pyrimidine derivatives

2D Autocorrelation, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of substituted pyrido[2,3-d]pyrimidine derivatives to correlate platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR), and c-Src tyrosine kinases' inhibition with 2D and 3D structural properties of 22 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling each protein tyrosine kinase (PTK) inhibitory activity were selected by genetic algorithm (GA) and multiple linear regression (MLR) approach. The 2D autocorrelation space brings different descriptors for each PTK inhibition and suggests the atomic properties relevant for the inhibitors to interact with each PTK active site. CoMFA and CoMSIA were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the hydrophobic and H-bond donor fields around them.

Design, synthesis, antitumor evaluations and molecular modeling studies of novel 3,5-substituted indolin-2-one derivatives

AIM

To design and synthesize a novel class of antitumor agents, featuring the 3, 5-substituted indolin-2-one framework.

METHODS

Based on enzyme binding features of (Z)-SU5402, introducing a beta-pyrrole group at the 3-position of the indolin- 2-one core, a series of novel 3,5-substituted indolin-2-ones were designed and synthesized. Four human carcinoma cell lines of A-431, A-549, MDA-MB-468, and Autosomal Dominant Polycystic Kidney disease were chosen for the cell proliferation assay.

RESULTS

Twenty new compounds (1a-t) with E configuration have been designed, synthesized and bioassayed. Their structural features were determined by nuclear magnetic resonance (NMR) spectra, low- and high-resolution mass spectra, and confirmed by X-ray crystallography. Although the enzyme assay showed a weak inhibition effect against the epidermal growth factor receptor, vascular endothelial growth factor receptor, fibroblast growth factor receptor and platelet-derived growth factor receptor tyrosine kinases, the cell-based antitumor activity was promising. Compounds 1 g and 1 h showed higher inhibitory activity toward the A-549 and MDA-MB-468 cell lines with IC(50 ) of 0.065-9.4 micromol/L.

CONCLUSION

This study provides a new template for further development of potent antitumor drugs.

QSAR analysis of tyrosine kinase inhibitor using modified ant colony optimization and multiple linear regression

Quantitative structure-activity relationship (QSAR) models of inhibiting action of some analogues of 4-(3-bromoanilino)-6,7-dimethoxyquinazoline on epidermal growth factor receptor tyrosine kinase were constructed using modified ant colony optimization (ACO) method. As a comparison to this method, the evolutionary algorithm (EA) was also tested. It has been demonstrated that the modified ACO is a useful tool for variable selection comparable to EA. In the selected descriptors, electronic descriptor sigma(Y)(-) is the most important descriptor in predicting EGFR inhibitory activity. Electron-donating groups such as Y-substituents enhance the activity as evident by negative sigma(Y)(-). In addition, for quinazoline substituents, nitro group has a large deactivating effect.

QSAR studies on 1-phenylbenzimidazoles as inhibitors of the platelet-derived growth factor

This work is devoted to the development of quantitative structure-activity relationship (QSAR) models of the biological activity of 123 1-phenylbenzimidazoles as inhibitors of the PDGF receptor. The molecular features are represented by chemical descriptors that have been calculated on geometrical, topological, quantum mechanical, and electronic basis by using CODESSA PRO. The obtained models, linear (multilinear regression) and nonlinear (artificial neural network), are aimed to link the structures to their reported activity log 1/IC50. The former model can be used for physico-chemical interpretation, while the latter possesses a superior predictive ability.

Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of PDGFR inhibitors

A QSAR modeling study has been done with a set of 79 piperazyinylquinazoline analogues which exhibit PDGFR inhibition. Linear regression and nonlinear computational neural network models were developed. The regression model was developed with a focus on interpretative ability using a PLS technique. However, it also exhibits a good predictive ability after outlier removal. The nonlinear CNN model had superior predictive ability compared to the linear model with a training set error of 0.22 log(IC50) units (R2 = 0.93) and a prediction set error of 0.32 log(IC50) units (R2 = 0.61). A random forest model was also developed to provide an alternate measure of descriptor importance. This approach ranks descriptors, and its results confirm the importance of specific descriptors as characterized by the PLS technique. In addition the neural network model contains the two most important descriptors indicated by the random forest model.

Design of EGFR kinase inhibitors: a ligand-based approach and its confirmation with structure-based studies

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 100 anilinoquinazolines, inhibiting epidermal growth factor receptor (EGFR) kinase. The studies included molecular field analysis (MFA) and receptor surface analysis (RSA). The cross-validated r2 (r2cv) values are 0.81 and 0.79 for MFA and RSA, respectively. The predictive ability of these models was validated by 28 test set molecules. The results of the best QSAR model were further compared with structure-based investigations using docking studies with the crystal structure of EGFR kinase domain. The results helped to understand the nature of substituents at the 6- and 7-positions, thereby providing new guidelines for the design of novel inhibitors.

Quantitative structure–activity relationships (QSAR): studies of inhibitors of tyrosine kinase

A quantitative structure-activity relationship (QSAR) study of the 1-phenylbenzimidazoles as inhibitors of the platelet-derived growth factor receptor (PDGFR) was performed. Some new electronic parameters Q(o), Q(m) and Q(p) are suggested for characterizing the effect of substituents. Many other descriptors are also used which are selected by evolution algorithm (EA) using modified Cp as objective function proposed by the present authors. The descriptor Q(m) is shown to be an important variable to express effect of substituents. The variable selection shows that spatial descriptors are most important variables revealing important properties of the inhibitors. Electron-releasing substitutes at 5-position and the absence of bulky groups at 4,7-positions of the parent structure can enhance inhibitor activity. Principal component analysis is performed to classify this series of compounds.

Allosteric interactions and QSAR: on the role of ligand hydrophobicity

A study of a very large database of QSAR (9100) has uncovered a few unusual examples where as one increases the hydrophobicity of the members of a set of congeners, activity decreases until at a certain point, activity begins to increase. Obviously a change in mechanism is involved. The only way we have found to rationalize this unusual event is by a change in the structure of the receptor. We have found this to occur with hemoglobin, a substance first used to define allosteric reactions.

Searching for allosteric effects via QSAR. Part II

Allosteric interactions have in the past been established by means of X-ray crystallography or careful study of a single molecule at a variety of concentrations. Here we report a method for using QSAR to establish a change in reaction mechanism by establishing an inversion point. That is, as polarizability of a member of a congeneric set of compounds is increased (as measured by CMR), activity at first decreases until, at the inversion, activity turns around and increases. Out of 23 examples, 14 have inversion points of 10+/-1. This includes a wide variety of receptors such as thrombin, 5-HT, dopamine, and tyrosine kinase acting with a variety of ligands.