Oyeladun Rhoda Oyewole

Bio: Oyeladun Rhoda Oyewole is an academic researcher. The author has contributed to research in topic(s): Pyrazole. The author has an hindex of 1, co-authored 1 publication(s) receiving 3 citation(s).

A DFT-Based QSAR and Molecular Docking Studies on Potent Anti-Colon Cancer Activity of Pyrazole Derivatives

Abstract: Pyrazole derivatives have been described as a group of compounds with various biological activities including anticancer effect. Therefore, a set of twenty Pyrazole based compounds which had been previously shown to be active against human colon cancer cell (HT29) are use in the study. These compounds were optimized using Density Functional Theory (DFT) for the calculations of molecular descriptors that related the bioactivity of these compounds to their structures. The developed quantitative structure activity relation (QSAR) was validated, and it showed the reliability and acceptability of the model. The in silico simulations were carried out on the twenty Pyrazole based compounds with colon cancer cell line, HT29 (PDB ID: 2N8A) using Autodock vina software. The docked complexes were validated and enumerated based on the AutoDock Scoring function to pick out the best inhibitors based on docked Energy. The analysis of the ligand-receptor complexes showed that H-bonds played a prominent role in the binding and posed stability of the ligand in the ligandreceptor complexes. The binding free energy, ΔG calculated ranged from 6.10 kcal/mol – 8.20 Kcal/mol.

Dataset on the DFT-QSAR, and docking approaches for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against human esophageal carcinoma (EC-109).

Abstract: The investigation of the novel hybrid, 1, 2, 3-triazole moiety combined with pyrimidine derivatives against human esophageal carcinoma is an unexplored field of theoretical/computational chemistry. Also, the development of new drugs still remains a major challenge, cost-intensive and time-consuming, thus making the computational approach now a hot topic due to its ability to hasten up and aid the process of drug designs. Here, the use of the quantum chemical method via density functional theory (DFT) was employed in calculating molecular descriptors for developing the quantitative structure-activity relation (QSAR) model which predicts bioactivity of the selected 1, 2, 3-triazole-pyrimidine derivatives. Quantum chemical method implemented in Spartan 14, was used in calculating the molecular descriptors. The obtained results were imputed into Gretl and SPSS (software package for social sciences) to generate a novel QSAR model equation for human esophageal carcinoma (EC-109) through multiple linear regression. The relationship between the experimental and predicted inhibition efficiency (IC50) of 1,2,3-triazole-pyrimidine with EC-109 was calculated which gives good correlation results. QSAR was validated using CV.R2 and R a 2 . Fitting value (R2) of 0.999 with an adjusted fitting value ( R a 2 ) of 0.995 was obtained and the result of validating QSAR performance gave CV.R2 and R a 2 value that is greater than 0.6, signifying its appropriateness and dependability. Molecular docking through simulation using Discovery Studio 4.1, Autodock Tool 1.5.6 and AutodockVina 1.1.2 was also carried out to calculate the free energy of ligand-receptor interactions as well as ligand conformation in the receptor-binding site. The results obtained revealed the presence of hydrogen bond interaction of the ligands with the amino acids residue in the binding sites of the receptor. Conformation of the ligands was essential property for binding ligand with the receptor. Critical examination and the correlations between the IC50 and binding energy showed the activeness of ligand conformation in the gouge of the receptor with binding energy greater than the 5-fluorouracil (5- Fu) that was used as the standard compound.

In silico toxicity as a tool for harm reduction: A study of new psychoactive amphetamines and cathinones in the context of criminal science.

Abstract: The emergence of new psychoactive substances (NPS) has raised many issues in the context of law enforcement and public drug policies. In this scenario, interdisciplinary studies are crucial to the decision-making process in the field of criminal science. Unfortunately, information about how NPS affect people's health is lacking even though knowledge about the toxic potential of these substances is essential: the more information about these drugs, the greater the possibility of avoiding damage within the scope of a harm reduction policy. Traditional analytical methods may be inaccessible in the field of forensic science because they are relatively expensive and time-consuming. In this sense, less costly and faster in silico methodologies can be useful strategies. In this work, we submitted computer-calculated toxicity values of various amphetamines and cathinones to an unsupervised multivariate analysis, namely Principal Component Analysis (PCA), and to the supervised techniques Soft Independent Modeling of Class Analogy and Partial Least Square-Discriminant Analysis (SIMCA and PLS-DA) to evaluate how these two NPS groups behave. We studied how theoretical and experimental values are correlated by PLS regression. Although experimental data was available for a small amount of molecules, correlation values reproduced literature values. The in silico method efficiently provided information about the drugs. On the basis of our findings, the technical information presented here can be used in decision-making regarding harm reduction policies and help to fulfill the objectives of criminal science.

Synthetic fentanyls evaluation and characterization by infrared spectroscopy employing in silico methods

Abstract: Non-pharmaceutical fentanyls (NPFs) are synthetic substances analogous to fentanyl that have not been approved for medical use. Their clandestine synthesis involves chemical modification of an existing drug, which may cause unpredictable pharmacological effects. New structures are emerging rapidly, and there is a lack of information for characterization. It makes their chemical identification difficult. In this scenario, in silico methods have become an alternative to study these systems. Here, we have applied factorial design to decide the best conditions to perform quantum calculations to obtain the infrared spectra of 46 seized NPFs. A multivariate classification was used to establish the main spectral characteristics of these substances. Similarities between theoretical and experimental spectra were determined through vibrational frequencies comparison, Kullback-Leibler divergence, and SIMCA evaluation. The in silico methods provided valuable information about illegal substances and proved helpful in predict the spectroscopic properties of new fentanyl analogous drugs.