A Quantitative Structure Activity Relationship (QSAR) Study on Anthrapyrazoles anticancer agents
A quantitative structure–activity relationship (QSAR) study was carried out on The inability of anticancer drugs to mitigate cancer in some cancer cell lines. PDF | A Quantitative Structure-Activity Relationship (QSAR) study was carried out The inability of anticancer drugs to mitigate cancer in some. Agents that complex DNA have been established as one of the most effective classes of anticancer agents in clinical use today, with broad application against a.
This article has been cited by other articles in PMC. Abstract In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships QSAR modeling were performed on 35 diaryl urea derivative inhibitors of VEB-RAF. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors.
Furthermore, molecular docking was carried out to study the binding mode of the compounds.
A Quantitative Structure Activity Relationship (QSAR) Study on Anthrapyrazoles anticancer agents
Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site. Its contribution is in mitogen activated protein kinase MAPK signaling pathway, which conducts signals from membrane-based receptors to the nucleus to mediate cell proliferation, differentiation, and survival 2. Numerous cancers are related to the constitutive activation of the above signaling pathway 3. B-RAF is one of the isoforms of the RAF kinase family that can regulate multiple downstream molecules and is also regulated by a variety of signaling molecules 45.
Some small molecule RAF kinase inhibitors by diverse scaffolds such as ureas, urea bioisosteres, imidazoles, benzamides, oxindoles, and aza-stilbenes have emerged in the recent past decades 11 But diaryl urea have been most extensively investigated because of sorafenib success in clinical for renal and hepatocellular carcinoma 1314 It is of great importance to introduce computer-aided drug design CADD approach to accelerate the time-consuming process of conventional drug discovery Quantitative structure activity relationships QSAR and molecular docking are two of the helpful methods of CADD for drug design and prediction of drug activity 17 In QSAR large number of compounds are usually evaluated resulting in models that can predict the potency or activity of new or even non-synthesized compounds When the three-dimensional structure of the target protein is available or can be modeled, molecular docking is often used for screening of compound libraries.
Molecular docking predicts the conformation of a protein-ligand complex and calculates the binding affinity and investigates protein—ligand interactions 20 In this study aimed to develop a robust and accurate model for the inhibitory activity of inhibitors in order to design potential B-RAF kinase inhibitor.
We used different method to connect between structural parameters and B-RAF kinase inhibitory. The latter method was used to carry out non-linear mappings on the physicochemical and biological descriptors of the molecules. In Support vector machines, nonlinear kernel based functions were used to solve both regression and classification problems.
An advantage of this method is its reproducibility in data mapping Our aim in this study was to develop more examples of modeling based on this approach. Finally docking study was performed to suggest a binding mode for the inhibitors on B-RAF target.
Dataset and descriptor generation The dataset used in this study was taken from the work of Menard, et al. Chemical structure of 35 studied compounds is provided in Table 1. This set contains diarylurea derivatives with inhibition potency against B-RAF kinase. Along with the advancements in knowledge and technology, drug discovery and development also involve computational processes, 15 including quantitative structure—activity relationship QSAR analysis and molecular docking.
A QSAR model generates new compounds with better predicted biological activity, 16 which can then be developed as drug candidates. Molecular docking is based on a structure-based approach and computational method using mathematical algorithms the scoring function to evaluate the binding tightness between the docked compound and target protein receptor. Both QSAR analysis and molecular docking can be used individually or in combination. A previous study identified some polyhydroxyxanthone derivatives 1,6-dihydroxy- 1,3,7-trihydroxy- and 1,3,6,8-tetrahydroxyxanthones as potent in vitro anticancer agents.
The cytotoxic activity did not increase linearly with an increasing number of hydroxyl groups, which suggested that the position of the substituted group influenced the activity. The aim of this study was to evaluate these novel xanthones and determine the most prominent descriptor for cytotoxic activity to aid the development of more active anticancer agents.
We also investigated the most probable mechanisms of action of xanthone against cancer on the basis of common principles of cancer through in silico molecular docking. Materials and methods Tested compounds and cancer cell culture The synthesized xanthone compounds were the property of Yuanita Laboratory of Organic Chemistry of the Faculty of Mathematics and Natural Sciences, Gadjah Mada University and are listed in Table 1. Only confluent cells were used for the experiment.
QSAR, quantitative structure—activity relationship.
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The plates were read on a microplate enzyme-linked immunosorbent assay reader at nm. The percentage of viable cells was determined according to the following formula: The Austin Model 1 AM1 semiempirical method was applied for geometric optimization of each compound. Molecular docking was performed against a number of cancer pathologies the hallmarks of cancer as described earlier Table 2. Only the A-chain of the protein was extracted from each PDB file, and hydrogen atoms were added in the preparation process.
The results were saved in the. The downloaded native ligands of each enzyme were prepared by using Marvinsketch to configure them into two-dimensional 2D formats.
The pKa values were 7. The 10 conformers from each ligand were then saved in the. The most active xanthone compound was used as a test ligand. The 2D xanthone structure was constructed by using Marvinsketch 5. The binding pose with the best free binding energy the lowest score was considered as the best predictive binding position. Amino acid residues that formed hydrogen bonds with the redocking compound from Pymol visualization were compared with those that interacted with the crystal molecule.
The minor difference between the free binding energy of the native ligand and selected xanthone as well as the similarity of the amino acid residues of the hydrogen bonds suggested the possible mechanism underlying the anticancer activity of xanthone. Results and discussion Cytotoxic effects of xanthone derivatives on WiDR cancer cells and their selectivity Ten novel xanthones Table 1 were evaluated for their cytotoxicity against WiDR colorectal cancer and Vero normal cell lines by using the MTT method.
Doxorubicin was used as a positive control group because it had a nucleus structure similar to that of xanthones. Over the last 30 years, doxorubicin has become one of the most potent chemotherapeutic agents against many kinds of cancer.
Those existing antimalarial agents had a wide range of known anticancer properties that could be distinct from their antimalarial properties. Most of the naturally derived antimalarial agents were found to have anticancer properties, and nearly half of them entered the clinical phase of drug development. The potential anticancer activity in antimalarial agents might be contributed by the multifaceted nature of the cellular response to some antimalarials, such as artemisinin.
In the tumor selectivity analysis, we found that compound 5 exhibited high tumor selectivity with an SI of A compound with moderate cytotoxic activity could be selected for further laboratory investigation. A previous study has shown that the same compound 5 also exhibited good antimalarial activity, although its IC50 was slightly higher than that of the standard drug chloroquine.
QSAR analysis process Since colorectal cancer is one of the most frequent and deadly cancers, it is important to develop more potent agents against this cancer. Further structural optimization of some antitumor compounds needs QSAR analysis based on their cytotoxic effects.
Xanthone structures were geometrically optimized by using the AM1 semiempirical method.
[Full text] Biological activity, quantitative structure-activity relationship anal | DDDT
The AM1 method was selected because it is a simple geometrical optimization that requires no complex mathematical calculation, unlike the ab initio method.
This method is able to predict large molecules and multivalent compounds 38 with good accuracy. To determine the influence of a side chain on the cytotoxic activity of compounds, BuildQSAR was performed on a series of xanthones. Multiple regressions performed by using BuildQSAR correlate the physicochemical descriptors and the cytotoxic activity of xanthone derivatives.
This program automatically detects collinearity between descriptors, and only descriptors with non-collinearity are included in the regression equation. The use of BuildQSAR requires no statistical analysis because the analysis had already been included in the program.
Model 4 of the genetic algorithm method was similar to model 1 of the systematic research. Because of the closeness of R values among all models, determination of the best model could not be established only by comparing the R value.
QSAR, quantitative structure—activity relationship; u, dipole moment. Table 5 Rank of selected models Notes: This is the most important descriptor in a QSAR equation because such electrostatic interactions are driven by electrical charges in the molecule.
Thus, the electron charges are important in many physicochemical properties of compounds and widely used as chemical reactivity indices. This descriptor has a role in the molecule—receptor interaction. V represents the molecular volume that has a role in the binding free energies. This descriptor shows the affinity of a molecule to partition into the non-polar fraction eg, lipids instead of into the polar fraction water. The equation of model 6 is statistically the best; thus, all of the following discussions are based on model 6.
In this model, n represents the number of compounds contributed to build the model. The R value is the correlation coefficient; the closer R is to 1, the better the goodness of fit of the equation.