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Cancer is group of related diseases characterized by uncontrolled cell division.

Introduction

Cancer is group of related diseases characterized by uncontrolled cell division. Normally, cells proliferate only in response to injury immune response or in a few cases to replace cells that have undergone apoptotic cell death. Cancer involves both oncogene and tumor suppressor genes. Oncogenes promote cancer that is switched on by a mutation. On the other hand, tumor suppressor genes prevent cancer unless switched off by a mutation. In general, mutation in both types of genes is necessary. A mutation limited to oncogene would be suppressed by normal mitosis control (J Phesse, 2009). Tubulin is a heterodimeric structural protein consisting of a and ß subunits, each approximately 55KD in size (Kevin, G. 2000). The a and ß tubulin are bound virtually in all nucleated cells and they possess a high degree of homology (40-50%) to each other. Microtubules are cylindrical organelles of varying length with overall diameter of 25 nm, and a central hollow core of approximately 5 nm in diameter. Structurally, microtubules are composed of tubulin heterodimers. Tubulin bind with GTP energy rich guanosine triphosphate, and through a sequence of events that are still not fully understood, tubulin polymerize into microtubules. Microtubules are essential components of cell structure and are involved in many important cellular process including mitosis, morphogenesis, intracellular transport and secretion. The binding of small molecules and other proteins termed microtubule associated protein (MAPS) to tubulin can result in the stabilization or destabilization of microtubule formation (Oakley, B. R. 1995). Three distinct small molecule binding sites are known to date, for the tubulin system, and well characterized for these ligands. These are the colchicines site and vinca alkaloids domain both located on monomeric unpolymerized a, b-tubulin, and the taxoid site on the polymerized microtubule. Ligands which interact at taxoid site (such as paclitaxel, taxol and epothilones) stabilize the microtubule, while ligands interacting at the vinca domain (such as vinblastine and vincristine) or the colchicines site (such as colchicines and Combretastatin A-4) disrupt the formation of microtubules. These ligands affect the dynamic instability of the microtubule system and ultimately halt cellular division (Eva Nogales, 2001). Combretastatin A-4 is the most potent antimitotic agent from the combretastatin family (Fulvia Orsini, 2008). The first member of the series, combretastatin was isolated, and its structure was elucidated by Pettit and co-workers in 1982 from the South African tree Combretum caffrum (Pettit, 2000). CombretatstatinA-4 has been identified as competitive inhibitor of the colchicines binding region on this protein (Lin, C. M., 1989). The relatively simple structure and high affinity of CA-4 for the colchicine binding site has led to the synthesis and subsequent evaluation of a large number of CA-4 analogues. SAR work on combretastatins indicates that a cis ethylene linkage between the two-aryl rings leads to the greatest activity. Further studies have shown the importance of the 3,4,5-trimethoxy substituents on a ring A of CA-4 and 3'hydroxy group on ring B of CA-4 are indispensable for potent cytotoxicity (Chaudhary, 2007). It mainly examined the substituents on the olefin site and the ring B. CA-4 has been entered into clinical trials, but unfortunately these trials have met limited success due to the drug's poor solubility (Thomas Nielsen, 2008). Consequently, a more soluble phosphate prodrug of CA-4 is currently undergoing evaluation. Thus, novel compounds derived from the CA-4 core continue to hold interest as potential therapeutic targets. CA-4 is an organic molecule, it is biaryl connected by an ethylene bridge. Restricted rotation about the olefinic bridge seems crucial in this molecule. Pettit and co-workers have managed to add water-solubility to these natural products by replacing the phenolic hydrogen atom with phosphate groups (Alessandra Cirla, 2003). CA-4 has shown the ability to shut down tumor vasculature at 10% of maximum tolerated dose without affecting the normal vasculature. CA-4 represents a new class of therapeutic compounds (vascular disrupting agents) that functions by binding to tubulin. It is useful in disease conditions or pathologies, such as cancer, where an abnormal growth of blood vessels is an essential component to disease and its progression. CA-4 has shown ability in both pre clinical animal model and clinical trials to reduce blood flow in tumors (Patterson, D. 2007). Tumor or Cancer needs a large amount of blood supply to grow. CA-4 prodrug kills tumour or cancer cells by attacking the blood vessels that supply them with oxygen and nutrients. After intravenous infusion, the prodrug rapidly spreads throughout the patient's bloodstream. It is then converted into active compounds (CA-4), which enters the endothelial cells that line the blood vessels. In tumors these cells are immature and thus particularly sensitive to Combretastatin's effects compared to the endothelial cell in normal tissues. Once inside the endothelial cells, combretastatin destroys the internal skeleton of the cells and changes their shape from flat to round, effectively plugging the capillaries that feed the tumors. This theory was demonstrated in phase 1 clinical trial of patients bearing a variety of solid tumors, in which a statistically significant reduction in blood flow was seen within the tumors four to six hours after the infusion (Graham G. 1997). Tubulin protein is a major target for anticancer drug discovery. As a result, antimitotic agents constitute an important class of the current anticancer drugs. Hundreds of tubulin inhibitors, naturally occurring, semisynthetic or synthetic, have been reported. Among these, CA-4 is one of the most potent antimitotic agents. It shows strong cytotoxicity and tubulin depolymerization inhibitory activities (Jordan A, 1998). In this paper different 3D-QSAR method, CoMFA (Cramer III, 1986) was applied to investigate the correlation between tubulin polymerization inhibitory activities of a set of CA-4 analogues. The widely used CoMFA is based on the assumption that the interactions between the receptor and its ligands are primarily non covalent in nature but shape dependent. Therefore, a QSAR may be derived from sampling the steric (Lennard-Jones) and electrostatic (Coulombic) fields surrounding a set of ligands and correlating the differences in those fields to biological activity. Partial least square (PLS) analysis, with a cross validation procedure, was employed to select relevant components from the large set of CoMFA data to build up the best QSAR equation. The results from this study might be helpful to design new and more potent antimitotic (anticancer) inhibitors. The contour maps derived from both the CoMFA model permitted an understanding of the steric, electrostatic requirements for ligand binding. As consequence, the structural variations in the training set that give rise to variations in the molecular fields at particular regions of the space are correlated to biological activities serving as a guide to the design of novel inhibitors.

Materials and Methods

Data set

All data were normalized to an experimental IC50 for Combretastatin A-4 from the literature ( Nguyen-Hai Nam, 2003). Structurally these compounds can be classified into three groups (see table-1). i) A-ring modified analogues, ii) B-ring modified analogues iii) aminocombretastatins analogues and iv) heterocyclic analogues. Twenty eight analogues of CA-4 listed in Table 1 were tested for tubulin inhibitory polymerization activity by tubulin inhibitory polymerization assay using human solid tumour cell lines (Helge Prinz, 2002). Reported inhibitory activity (pIC50) in mM was used as a dependant variable in our studies. From a total of twenty-eight compounds of CA-4 derivatives a training set was created with twenty twocompounds and the remaining six compounds were used as the test set. Selection of test set molecules were made by considering the fact that, test set molecules represent range of biological activity similar to the training set. Thus the test set is the true representative of the training set.

Molecular structure

The molecular structures of CA-4 analogues were constructed and modeled using Gauss View (Gaussian 98, 2003) and SYBYL 6.9(SYBYL 6.9, 1699). The gasteiger charge set (Clark, M., 1989) was used on the ligands, and full optimization was performed to minimize each structure using the standard Tripos force field (SYBYL, ver. 6.9, 1998).

Alignment

The molecules were superimposed using the atom-based alignment in the SYBYL software. Compound CA-4 (Figure 1) was used as a template for the manual alignment. The atom C1, C4 of aromatic ring B, C1, C2 olefinic double bond and C2, C5 of aromatic ring A of the reference structure were used in the RMS fitting procedure for each molecule in the database

CoMFA Set Up

The steric and electrostatic potential fields for CoMFA were calculated at each lattice intersection of a regularly spaced 2.0 ?. The Van der Waals potentials (Lennard-Jones 6-12) and columbic term that represent steric and electrostatic fields respectively were calculated using tripos force field. A sp3 carbon atom with Van der Waals radius of 1.52 ? and +1.0 charges was served as the probe atom to calculate steric and electrostatic fields. The steric and electrostatic contributions were truncated to ± 30 kcal/mol and electrostatic contributions were ignored at lattice intersection with maximum steric interactions.

Calculation and Validation

Partial least square (PLS) methodology was used for all 3D QSAR analysis (R. D. III. Crammer, 1998). Column filtering was set to 2.0 kcal/mol to speed up the analysis and reduce the noise. The CoMFA descriptors were used as independent variables, and pIC50 values were used as dependent variables in partial least-squares regression analysis to derive 3D-QSAR models using the standard implemented in the SYBYL package. The predictive value of the models was evaluated first by leave-one-out (LOO) cross-validations (Wold, S. 1978). The cross-validated coefficient, (q2), was calculated using equation- 1.


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