Perspective Chapter: Environmental Assessment on the Effect of Chemical Waste from Dyeing Industries in Zaria

The research study was done on groundwater obtained from waste wells, well around the waste wells and wells about 5 km from the dyeing sites of Zaria, in Kaduna state of Nigeria. The objectives were to assess the status of pollution on local dyeing areas, the occupational hazard associated with this activity and the impact on the residents of the area. Parameters such as pH, coli form bacteria, conductivity, colour, DO, BOD, COD, chlorides, total available nitrogen, cadmium, chromium, lead, mercury and alkalinity were determined and statistical analysis carried out to evaluate the Significant difference of pollutants in the area studied

QSAR MODELLING OF SOME ANTICANCER PGI50 ACTIVITY ON HL-60 CELL LINES

QSAR (2D and 3D) studies were performed on a series of CAMPTOTHECIN derivatives using Material Studio software (accelrys). QSAR study performed on 102 analogues of which 90 were used in the training set and the rest 22 considered for the test set.  QSAR study performed using Genetic function approximation (GFA). GFA method came out with good correlation coefficient 0.837 , cross-validated coefficient 0.792  and R2Test of 0.9408. A highly predictive and statistically significant model was generated. The QSAR models were found to accurately predict the anticancer activity of structurally diverse test set compounds and to yield reliable clues for further optimization of the of CAMPTOTHECIN derivatives in the data set

Theoretical modelling and molecular docking simulation evaluating 3-aryl-5-(alkyl-thio)-1 H-1,2,4-triazoles derivatives as potent anti-tubercular agents against MTB CYP121 receptor

This work applies a Quantitative Structure Activity Relationship (QSAR) model, developed with the help of the QSARIN program, to predict the bioactivities of both active and dormant Mycobacterium tuberculosis (mtb) inhibitors. Four QSAR models were constructed using a molecular docking study on a series of Nitrophenyltriazole compounds retrieved from the literature, with two models specifically designed for the active and dormant Mtb H37Ra strain, and the other two for the dormant and active M bovis strain. The models were validated and the applicability domain was also presented. In silico molecular docking studies, molecular dynamics and ADMET study were undertaken to interpret the potential mechanisms of antimycobacterial activity of the bioactive Nitrophenyltriazole derivatives (NPT) with two different mtb receptors. Compound 5 was found to be a potent inhibitor of the growth of Mycobacterium tuberculosis H37Rv. Molecular dynamics simulation of compound 5 and MTB InhA (4OHU) complex showed that the NPT binds with residues SER123, Lys165, Phe149, Tyr158 and Asp148, which are essential for the transformation of MTB Inh

Quantitative structure–activity relationship study on potent anticancer compounds against MOLT-4 and P388 leukemia cell lines

A quantitative structure–activity relationship (QSAR) study was carried out on 112 anticancer compounds to develop a robust model for the prediction of anti-leukemia activity (pGI50) against MOLT-4 and P388 leukemia cell lines. The Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models that relate the structural features to the biological activities. The final equations consist of 15 and 10 molecular descriptors calculated using the paDEL molecular descriptor software. The GA-MLRA analysis showed that the Conventional bond order ID number of order 1 (piPC1), number of atomic composition (nAtomic), and Largest absolute eigenvalue of Burden modified matrix – n 7/weighted by relative mass (SpMax7_Bhm) play a significant role in predicting the anticancer activities of these compounds. The best QSAR model for MOLT-4 was obtained with R2 value of 0.902, Q2LOO = 0.881 and R2pred = 0.635, while for P388 cell line R2 = 0.904, Q2LOO = 0.856 and R2pred = 0.670. The Y-scrambling/randomization validation also confirms the statistical significance of the models. These models are expected to be useful for predicting the inhibitory activity (pGI50) against MOLT-4 and P388 leukemia cell lines

Estimation of Sulphur Containing Amino Acids in Soybean Products in Nigeria

Abstract Five different samples of soybean products (Raw Soybeans Seed (RSS), Local Soybean Powdered Milk (LSPM), Local Soybean Liquid Milk (LSLM), Vita-milk and Chi-soymilk) were used for the analysis of sulphur containing amino acids (methionine and cysteine) contents. Qualitative analysis using thin layer chromatography technique was carried out on the soybean products to detect the presence of sulphur containing amino acids while spectrophotometric method involving methionine and cysteine standards were used to quantitatively estimate the two amino acids in the soybean products ). An increase in both methionine and cysteine concentrations for Chi-soymilk and Vita-milk suggests that these soybean products were enriched with these amino acids during processing, hence can effectively replenish the lost sulphur amino acids in the body due to the action of trypsin inhibitor present in soybeans

Homology modeling and molecular docking simulation of some novel imidazo[1,2-a]pyridine-3-carboxamide (IPA) series as inhibitors of Mycobacterium tuberculosis

Abstract Background Tuberculosis (TB) remains a serious global health challenge that is caused by Mycobacterium tuberculosis and has killed numerous people. This necessitated the urgent need for the hunt and development of more potent drugs against the fast-emerging extensively drug-resistant (XDR) and multiple-drug-resistant (MDR) M. tuberculosis strains. Mycobacterium tuberculosis cytochrome b subunit of the cytochrome bc1 complex (QcrB) was recognized as a potential drug target in M. tuberculosis (25618/H37Rv) for imidazo[1,2-a]pyridine-3-carboxamides whose crystal strucuture is not yet reported in the Protein Data Bank (PDB). The concept of homology modeling as a powerful and useful computational method can be applied, since the M. tuberculosis QcrB protein sequence data are available. Results The homology model of QcrB protein in M. tuberculosis was built from the X-ray structure of QcrB in M. smegmatis as a template using the Swiss-Model online workspace. The modeled protein was assessed, validated, and prepared for the molecular docking simulation of 35 ligands of N-(2-phenoxy)ethyl imidazo[1,2-a] pyridine-3-carboxamide (IPA) to analyze their theoretical binding affinities and modes. The docking results showed that the binding affinity values ranged from − 6.5 to − 10.1 kcal/mol which confirms their resilience potency when compared with 6.0kcal/mol of isoniazid standard drug. However, ligands 2, 7, 22, 26, and 35 scored higher binding affinity values of − 9.60, − 9.80, − 10.10, − 10.00, and − 10.00 kcal/mol, and are respectively considered as the best ligands among others with better binding modes in the active site of the modeled QcrB protein. Conclusion The information derived in this research revealed some potential hits and paved a route for structure-based drug discovery of new hypothetical imidazo pyridine amide analogs as anti-tubercular drug candidates

A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach

Development of more potent antituberculosis agents is as a result of emergence of multidrug resistant strains of M. tuberculosis. Novel compounds are usually synthesized by trial approach with a lot of errors, which is time consuming and expensive. QSAR is a theoretical approach, which has the potential to reduce the aforementioned problem in discovering new potent drugs against M. tuberculosis. This approach was employed to develop multivariate QSAR model to correlate the chemical structures of the 2,4-disubstituted quinoline analogues with their observed activities using a theoretical approach. In order to build the robust QSAR model, Genetic Function Approximation (GFA) was employed as a tool for selecting the best descriptors that could efficiently predict the activities of the inhibitory agents. The developed model was influenced by molecular descriptors: AATS5e, VR1_Dzs, SpMin7_Bhe, TDB9e, and RDF110s. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.9265, adjusted correlation coefficient (R2 adj) value of 0.9045, and leave-one-out cross-validation coefficient (Q_cv∧2) value of 0.8512, while the external validation test was found to have (R2 test) of 0.8034 and Y-randomization coefficient (cR_p∧2) of 0.6633. The proposed QSAR model provides a valuable approach for modification of the lead compound and design and synthesis of more potent antitubercular agents

Virtual molecular docking study of some novel carboxamide series as new anti-tubercular agents

A virtual docking simulation study was performed on thirty-five newly discovered compounds of N-(2-phenoxy) ethyl imidazo[1,2-a] pyridine-3-carboxamide (IPA), to explore their theoretical binding energy and pose with the active sites of the Mycobacterium tuberculosis target (DNA gyrase). The chemical structures of the compounds were drawn correctly with ChemDraw Ultra software, and then geometrically optimized at DFT level of theory with Spartan 14 software package. Consequently, the docking analysis was carried out using Molegro Virtual Docker (MVD). Five complexes (Complex 5, 24, 25, 33 and 35) with high binding energy were selected to examine their binding pose with the active sites of the protein. The docking results suggested a good MolDock score (≥ -90 kcal/mol) and Protein-Ligand ANT System (PLANTS) score (≥ -60 kcal/mol) which depicted that the compounds can efficiently bind with the active sites of the target. However, compound 5 has the best binding pose with the MolDock score of -140.476 kcal/mol which formed three hydrogen bond interactions with the Gln 538, Ala 531, and Ala 533 amino acid residues. This research gives a firsthand theoretical knowledge to improve the binding efficiency of these compounds with the target

QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer against Leukemia cell line (K562)

This research applied Quantitative Structure Activity Relationship (QSAR) technique in developing a Multiple-Linear Regression (MLR) model using Genetic Functional Algorithm (GFA) method in selecting relevant molecular descriptors from the structures of 24 C14-urea tetrandrine compounds. Firstly, the compounds were optimized at Density Functional Theory (DFT) level using Becke’s three-parameter Lee-Yang-Parr hybrid functional (B3LYP) with the 6-31G* basis set in the Spartan 14 Version 1.1.4 software. The molecular descriptors were calculated using Padel- software, and the results were divided in to training and test set. A model was built from the training set with internal validation parameter R2train as 0.910403. The external validation of the model was carried out using the test set compounds with validation parameter R2test as 0.6443 which passed the criteria for acceptability of a QSAR model globally. The coefficient of determination (2) parameter was calculated as 0.819296 which is greater than 0.5, this affirms that the generated model is robust. Furthermore, AST4p, GATS8v and MLFER are the descriptors in the model with positive mean effect of 0.089972855, 0.909814859 and 0.000212286 respectively. This study inferred that there will be positive influence on the inhibitory concentrations when the each descriptor value increase