Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models

Natural anthraquinone compound emodin as a novel inhibitor of aurora A kinase: A pilot study

Aurora kinase A (AURKA) carries out an essential role in proliferation and involves in cisplatin resistance in various cancer cells. Overexpression of AURKA is associated with the poor prognosis of cancer patients. Thus, AURKA has been considered as a target for cancer therapy. Developing AURKA inhibitors became an important issue in cancer therapy. A natural compound emodin mainly extracted from rhubarbs possesses anti-cancer properties. However, the effect of emodin on AURKA has never been investigated. In the present study, molecular docking analysis indicated that emodin interacts with AURKA protein active site. We also found nine emodin analogues from Key Organic database by using ChemBioFinder software. Among that, one analogue 8L-902 showed a similar anti-cancer effect as emodin. The bindings of emodin and 8L-902 on AURKA protein were confirmed by cellular thermal shift assay. Furthermore, emodin inhibited the AURKA kinase activity in vitro and enhanced the cisplatin-DNA adduct level in a resistant ovarian cancer cell line. It seems that emodin may have the potential to inhibit cancer cell growth and enhance cisplatin therapy in cancer with resistance. Collectively, our finding reveals a novel AURKA inhibitor, emodin, which may be vulnerable to ovarian cancer therapy in the future.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.

Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density Functional Theory-Based Reactivity Descriptors

Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models. The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in modeling effective QSAR/QSPR/QSTR is highlighted.

Scaffold and cell line based approaches for QSAR studies on anticancer agents

Importance of 2D QSAR in drug discovery, lower number of descriptors containing models shows best statistical parameters, number of involved scaffolds in models affects the statistical values.

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.

Synthesis and biological evaluation of 1,7,8,8a-tetrahydro-3H-oxazolo[3,4-a]pyrazin-6(5H)-ones as antitumoral agents

A series of 1,7,8,8a-tetrahydro-3H-oxazolo[3,4-a]pyrazin-6(5H)-ones has been synthesized by an intramolecular, palladium(II) catalyzed, aminooxygenation of alkenyl ureas, readily available from glycine allylamides as starting materials. Biological tests showed that the obtained compounds are endowed with an interesting antitumoral activity against two human thyroid cancer cell lines, namely FTC-133 and 8305C, by promoting the apoptotic pathway and DNA fragmentation.

NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database

Plant-derived molecules have been highly valued by biomedical researchers and pharmaceutical companies for developing drugs, as they are thought to be optimized during evolution. Therefore, we have collected and compiled a central resource Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database (NPACT, http://crdd.osdd.net/raghava/npact/) that gathers the information related to experimentally validated plant-derived natural compounds exhibiting anti-cancerous activity (in vitro and in vivo), to complement the other databases. It currently contains 1574 compound entries, and each record provides information on their structure, manually curated published data on in vitro and in vivo experiments along with reference for users referral, inhibitory values (IC(50)/ED(50)/EC(50)/GI(50)), properties (physical, elemental and topological), cancer types, cell lines, protein targets, commercial suppliers and drug likeness of compounds. NPACT can easily be browsed or queried using various options, and an online similarity tool has also been made available. Further, to facilitate retrieval of existing data, each record is hyperlinked to similar databases like SuperNatural, Herbal Ingredients' Targets, Comparative Toxicogenomics Database, PubChem and NCI-60 GI(50) data.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.