Current Computer - Aided Drug Design - Volume 17, Issue 2, 2021

Background: Breast cancer (BC) is a leading cause of cancer-related deaths in women next to skin cancer. Estrogen receptors (ERs) play an important role in the progression of BC. Current anticancer agents have several drawbacks such as serious side effects and the emergence of resistance to chemotherapeutic drugs. As coumarins possess minimum side effects along with multidrug reversal activity, it has a tremendous ability to regulate a diverse range of cellular pathways that can be explored for selective anticancer activity. Objectives: Synthesis and evaluation of new coumarin analogues for anti-proliferative activity on human breast cancer cell line MCF-7 along with exploration of binding interaction of the compounds for ER-α target protein by molecular docking. Methods: In this study, the anti-proliferative activity of C-3 substituted coumarins analogues (1-17) has been evaluated against estrogen receptor-positive MCF-7 breast cancer cell lines. Molecular interactions and ADME study of the compounds were analyzed by using Schrodinger software. Results: Among the synthesized analogues, 12 and 13 show good antiproliferative activity with IC50 values 1 and 1.3 μM, respectively. Molecular docking suggests a remarkable binding pose of all the seventeen compounds. Compounds 12 and 13 were found to exhibit a docking score of -4.10 kcal/mol and -4.38 kcal/mol, respectively. Conclusion: Compounds 12 and 13 showed the highest activity followed by 1 and 5. ADME properties of all compounds were in the acceptable range. The active compounds can be taken for lead optimization and mechanistic interventions for their in vivo study in the future.

Objective: To screen the selected phytochemicals against diabetes by docking studies in comparison with experimental analysis. Methods: Ethanol crude extract was obtained from the leaves of C.intybus and its chemical compounds were identified using GC- MS. Docking studies were carried out for selected phytochemicals to find the binding affinity and H-bond interaction using Schrodinger suite. Dynamic simulations were carried out for protein-ligand complex up to 50ns using desmond OPLS AA forcefield and α- Amylase and α- Glucosidase assay were carried for the ethanolic extract to infer its inhibition. Results: Four compounds were chosen for induced fit docking based on the docking score and glide energy obtained from GLIDE-XP docking. The compounds were docked with the protein target human aldose reductase (PDB ID: 2FZD) for checking the anti-diabetic nature. The molecular dynamics simulations were carried out for the most favorable compounds and stability was checked during the simulations. The ethanol extract exhibits significant α-amylase and α-glucosidase inhibitory activities with an IC50 value of 38μg and 88μg dry extract, respectively, and well compared with standard acarbose drug. The antimicrobial activity was also carried out for various extracts (Chloroform, Ethyl acetate, and Ethanol) of the same (C. intybus) screened against four selected human pathogens. Compared to other solvent extracts, ethanol and chloroform extracts show better inhibition and their minimal inhibitory concentration (MIC) value has been calculated. Conclusion: In silico studies and in vitro studies reveals that C. intybus plant compounds have more potent for treating diabetes.

Aim: To generate and validate predictive models for blood-brain permeation (BBB) of CNS molecules using the QSPR approach. Background: Prediction of molecules crossing BBB remains a challenge in drug delivery. Predictive models are designed for the evaluation of a set of preclinical drugs which may serve as alternatives for determining BBB permeation by experimentation. Objective: The objective of the present study was to generate QSPR models for the permeation of CNS molecules across BBB and its validation using existing in-house leads. Methods: The present study envisaged the determination of the set of molecular descriptors which are considered significant correlative factors for BBB permeation property. Quantitative Structure- Property Relationship (QSPR) approach was followed to describe the correlation between identified descriptors for 45 molecules and highest, moderate and least BBB permeation data. The molecular descriptors were selected based on drug-likeness, hydrophilicity, hydrophobicity, polar surface area, etc. of molecules that served the highest correlation with BBB permeation. The experimental data in terms of log BB were collected from available literature, subjected to 2D-QSPR model generation using a regression analysis method like Multiple Linear Regression (MLR). Results: The best QSPR model was Model 3, which exhibited regression coefficient as R²= 0.89, F = 36; Q²= 0.7805 and properties such as polar surface area, hydrophobic hydrophilic distance, electronegativity, etc., which were considered key parameters in the determination of the BBB permeability. The developed QSPR models were validated with in-house 1,5-benzodiazepines molecules and correlation studies were conducted between experimental and predicted BBB permeability. Conclusion: The QSPR model 3 showed predictive results that were in good agreements with experimental results for blood-brain permeation. Thus, this model was found to be satisfactory in achieving a good correlation between selected descriptors and BBB permeation for benzodiazepines and tricyclic compounds.

Background: Hospital-acquired (HA) infections are caused due to E. coli, which is resistant to multiple drugs particularly to fluoroquinolone class of drugs. Urinary tract infections (UTI) affects people in the community and hospitals. 150 million people per annum are suffering from UTI worldwide. Methods: In this present study, we designed 36 novel coumarin derivatives, also we predicted pharmacokinetic and toxicity parameters. Docking studies were also carried out and all the compounds were evaluated for antibacterial activity against resistant quinolone E. coli strain ATCC 25922. It was interesting to note that the introduction of electron-withdrawing group on the aromatic ring resulted in compounds with an increased antibacterial activity, which is observed in compound 6 (with 4-nitro substitution), compound 23 (chloro) and compound 30 (chloro, nitro). Results: From the MIC results, it was observed that compounds 6, 23 and 30 showed higher activity with 0.5μg/ml, 0. 12 μg/ml, 0.5 μg/ml respectively. Docking studies were performed with the active site of DNA gyrase (PDB ID: 4CKK). The maximum binding energy was found to be -10.7 Kcal/mol. Conclusion: From the study, it was found that 3 compounds were potentially active against quinolone- resistant E. coli strains. This study can further be extended for in vivo evaluation.

Background: Considering the interesting role in the peptidoglycan biosynthesis pathway, the enzyme UDP-N-acetylglucosamine enolpyruvyl transferase is an attractive target to develop new antibacterial agents. It catalyzes the first key step of this pathway and its inhibition leads to bacterial cell death. Fosfomycin is known as the natural inhibitor of MurA. Objective: The study aimed to introduce new inhibitors of MurA by virtual screening of different chemical compounds libraries, and test the best scored “virtual hits” against three pathogenic bacteria: Escherichia coli, Bacillus subtilis and Staphylococcus aureus. Methods: A virtual screening of the structural analogues of fosfomycin downloaded from the Pub- Chem database was performed. Moreover, French National Chemical Library and ZINC database were also utilized to identify new structures different from fosfomycin. FlexX was the software used for this study. The antibacterial testing was divided into two methods: disk diffusion and broth dilution. Results: A set of virtual hits was found to have better energy score than that of fosfomycin, seven of them were tested in vitro. In addition, the disk diffusion method explored four compounds that exhibited antibacterial activity: CID-21680357 (fosfomycin analogue), AB-00005001, ZINC04658565, and ZINC901335. The testing was continued by broth dilution method for both compounds CID-21680357 and ZINC901335 to determine their minimum inhibitory concentrations, and ZINC901335 had the best value with 457μg/ml against Staphylococcus aureus. Conclusion: Four compounds were found and proven in silico and in vitro to have antibacterial activity, namely CID-21680357, AB-00005001, ZINC04658565, and ZINC901335.

Introduction: Short bowel syndrome (SBS) is a disabling condition that occurs following the loss of substantial portions of the intestine, leading to inadequate absorption of nutrients and fluids. Teduglutide is the only drug that has been FDA-approved for long-term treatment of SBS. This medicine exerts its biological effects through binding to the GLP-2 receptor. Methods: The current study aimed to use computational mutagenesis approaches to design novel potent analogues of teduglutide. To this end, the constructed teduglutide-GLP2R 3D model was subjected to the alanine scanning mutagenesis where ARG²⁰, PHE²², ILE²³, LEU²⁶, ILE²⁷ and LYS³⁰ were identified as the key amino acids involved in ligand-receptor interaction. In order to design potent teduglutide analogues, using MAESTROweb machine learning method, the residues of teduglutide were virtually mutated into all naturally occurring amino acids and the affinity improving mutations were selected for further analysis using PDBePISA methodology which interactively investigates the interactions established at the interfaces of macromolecules. Results: The calculations resulted in D15I, D15L, D15M and N24M mutations, which can improve the binding ability of the ligand to the receptor. The final evaluation of identified mutations was performed by molecular dynamics simulations, indicating that D15I and D15M are the most reliable mutations to increase teduglutide affinity towards its receptor. Conclusion: The findings in the current study may facilitate designing more potent teduglutide analogues leading to the development of novel treatments in short bowel syndrome.

Objective: Azole antifungal agents, which are widely used as antifungal antibiotics, inhibit cytochrome P450 sterol 14α-demethylase (CYP51). Nearly all azole antifungal agents are Nsubstituted azoles. In addition, an azolylphenalkyl pharmacophore is uniquely shared by all azole antifungals. Due to the importance of nitrogen atom of azoles (N-3 of imidazole and N-4 of triazole) in coordination with heme in the binding site of the enzyme, here a group of N- un-substituted azoles in which both nitrogen are un-substituted was reported. Materials and Methods: Designed compounds were synthesized by the reaction of imidazole-4- carboxaldehyde with appropriate arylamines and subsequently reduced to desired amine derivatives. Antifungal activity against Candida albicans and Saccharomyces cervisiae was done using a broth micro-dilution assay. Docking studies were done using AutoDock. Results: Antimicrobial evaluation revealed that some of these compounds exhibited moderate antimicrobial activities against tested pathogenic fungi, wherein compounds 3, 7, and 8 were potent. Docking studies propose that all of the prepared azoles interacted with 14α-DM, wherein azoleheme coordination played the main role in drug-receptor interaction. Conclusion: Our results offer some useful references for molecular design performance or modification of this series of compounds as a lead compound to discover new and potent antimicrobial agents.

Background: IC50 is one of the most important parameters of a drug. But, it is very difficult to predict this value of a new compound without experiment. There are only a few QSAR based methods available for IC50 prediction, which is also highly dependable on a huge number of known data. Thus, there is an immense demand for a sophisticated computational method of IC50 prediction in the field of in silico drug designing. Objective: Recently developed quantum computation based method of IC50 prediction by Bag and Ghorai requires an affordable known data. In present research work, further development of this method is carried out such that the requisite number of known data being minimal. Methods: To retrench the cardinal data span and shrink the effects of variant biological parameters on the computed value of IC50, a relative approach of IC50 computation is pursued in the present method. To predict an approximate value of IC50 of a small molecule, only the IC50 of a similar kind of molecule is required for this method. Results: The present method of IC50 computation is tested for both organic and organometallic compounds as HIV-1 capsid A inhibitor and cancer drugs. Computed results match very well with the experiment. Conclusion: This method is easily applicable to both organic and organometallic compounds with acceptable accuracy. Since this method requires only the dipole moments of an unknown compound and the reference compound, IC50 based drug search is possible with this method. An algorithm is proposed here for IC50 based drug search.

Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea is a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. Background: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in the database and identified. Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in silico peptide mass fingerprinting. Methods: The calculation of in silico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from the MASCOT online server. Anticancer probability is calculated by iACP server, docking of active peptides is done by CABS-dock the server. Results: The anti-cancer peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De-novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the active chain, the human leukemia protein docking with peptides shows the accurate binding. Conclusion: These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

Background: Heart attack predominantly occurs during the last phase of sleep and early morning hours, causing millions of death worldwide. Hydrochlorothiazide (HCTZ) is a recommended drug for the prevention of heart disease, but its long action (>4 h) dosage form is lacking in the commercial market and development of modified-release formulation may have industrial significance. Regulatory agencies emphasize Quality by Design based approach for product development to entrust quality in the product. Objective: The current research aimed to develop a quality product profile of HCTZ modifiedrelease tablets (MRT; ∼14 h) by applying Response Surface Methodology using the computational QbD approach. Methods: Three independent factors were identified by qualitative and quantitative risk assessment. Statistical terms like p-value, lack of fit, the sum of square, R-squared value, model F value, and linear equations were determined. Graphical tools like normal plot of residual, residual vs predicted plot and box cox plot were used to verify the model selection. The graphical relationship among the critical, independent variables was represented using the Contour plot and 3-D surface plot. Design space was identified by designing an overlay plot using response surface design. Results: Excellent correlation was observed between actual and predicted values. Similarity Factor (F2) of reproducible trials was 78 and 79, and content uniformity was 100.9% and 100.4%. Average weight, hardness, thickness, diameter, and friability were within acceptable limits. Conclusion: QbD approach, along with a quality risk management tool, provided an efficient and effective paradigm to build quality MRT of HCTZ.

Background: Tuberculosis is one of the leading causes of deaths due to infectious disease worldwide. There is an urgent need for developing new drugs due to the rising incidents of drug resistance. Previously, triazole molecules showing antitubercular activity, were reported. Various computational tools pave the way for a rational approach to understanding the structural importance of these compounds in inhibiting the growth of Mycobacterium Tuberculosis. Objective: The aim of this study is to develop and compare two different QSAR models based on a set of previously reported triazole molecules and use the best one for gaining structural insights into those molecules. Methods: In this current study, two separate models were made with CoMFA and CoMSIA descriptors based on a dataset of triazole molecules showing antitubercular activity. Several one dimensional (1D) descriptors were added to each of the models and the validation results and contour data generated from them were compared. The best model was analysed to give a detailed understanding of the triazole molecules and their role in the antitubercular activity. Results: The r², q², predicted r² and SEP (Standard error of prediction) for the CoMFA model were 0.866, 0.573, 0.119 and 0.736 respectively and for the CoMSIA model, the r², q², predicted r² and SEP were calculated to be 0.998, 0.634, 0.013 and 0.869 respectively. Although both the QSAR models produced acceptable internal and external validation scores, but the CoMSIA results were significantly better. The CoMSIA contours also provided a better match than CoMFA with most of the features of the active compound 30b. Hence, the CoMSIA model was chosen and its contours were explored for gaining structural insights into the triazole molecules. Conclusion: The CoMSIA contours helped us understand the role of several atoms and groups of the triazole molecules in their biological activity. The possibilities for substitution in the triazole compounds that would enhance the activity were also analyzed. Thus, this study paves the way for designing new antitubercular drugs in future.

Tuberculosis (TB) is a major cause of mortality and illness as reported by the W.H.O in 2019. The WHO report also mentioned the fact that about 10.0 million people fell ill with tuberculosis in the year 2018. Hydrazide–hydrazones having azomethine group (–NH–N=CH–) connected with carbonyl group is reported for the number of bioactivities like anti-inflammatory, anticonvulsant, anticancer, antiviral and antiprotozoal. Objective: The objective of our current study is to design and synthesise more potent hydrazide– hydrazones, containing anti-tubercular agents. Methods: In the current study, we synthesized 10 hydrazones (3a-3j) by stirring corresponding benzohydrazides (2) with substituted aldehydes (1a-j) in ethanol as a solvent and acetic acid as a catalyst at room temperature. All synthesized compounds were characterized by various spectroscopic techniques including elemental analysis, ultraviolet–visible spectroscopy, fluorescence, fourier- transform infrared spectroscopy and nuclear magnetic resonance spectroscopy. Compounds (3a-3j) were tested for in vitro anti-TB activity using Microplate Alamar Blue Assay (MABA). Results: All our synthesized compounds (3a-3j) were found to be potent against Mycobacteria tuberculosis (H37RV strain) with MIC (minimum inhibitory concentrations) values of 3.125-50 μg/mL. The hydrazide CO-NH protons in (3a-j) compounds are highly deshielded and showed broad singlet at 9.520-9.168 ppm. All the compounds were found to have more intense emission in the 416 – 429 nm regions and strong absorption in the regions of 316 – 327 nm. Synthesized compounds were also tested for in silico analysis using different software for their Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) analysis. All the compounds were found to be in silico non-carcinogenic. Conclusion: It will be worth saying that our in silico and in vitro approaches used in the current study will become a guide for medicinal chemists to make structural modifications and synthesize more effective and potent hydrazone containing anti-tubercular agents.

Background: Diosmin is a flavonoid obtained from the citrus fruits of the plants. Diosmin has blood lipid lowering activities, antioxidant activity, enhances venous tone and microcirculation, protects capillaries, mainly by reducing systemic oxidative stress. Objective: The present study demonstrates the potential of Diosmin against the enzymes aldose reductase, α-glucosidase, and α-amylase involved in diabetes and its complications by in vitro evaluation and reverse molecular docking studies. Methods: The assay of aldose reductase was performed by using NADPH as starting material and DL-Glyceraldehyde as a substrate. DNS method was used for alpha amylase inhibition and in alpha glucosidase inhibitory activity p-nitrophenyl glucopyranoside (pNPG) was used as substrate. The reverse molecular docking studies was performed by using Molegro software (MVD) with grid resolution of 30 Å. Results: Diosmin shows potent inhibitory effect against aldose reductase (IC50:333.88±0.04 μg/mL), α-glucosidase (IC50:410.3±0.01 μg/mL) and α-amylase (IC50: 404.22±0.02 μg/mL) respectively. The standard drugs shows moderate inhibitory activity for enzymes. The MolDock Score of Diosmin was -224.127 against aldose reductase, -168.17 against α-glucosidase and - 176.013 against α-amylase respectively, which was much higher than standard drugs. Conclusion: From the result it was concluded that diosmin was a potentially inhibitor of aldose reductase, alpha amylase and alpha glucosidase enzymes then the standard drugs and it will be helpful in the management of diabetes and its complications. This will also be benevolent to decrease the socio economical burden on the middle class family of the society.

Background: In this report, we consider a data set, which consists of 310 Zika virus genome sequences taken from different continents, Africa, Asia and South America. The sequences, which were compiled from GenBank, were derived from the host cells of different mammalian species (Simiiformes, Aedes opok, Aedes africanus, Aedes luteocephalus, Aedes dalzieli, Aedes aegypti, and Homo sapiens). Methods: For chemometrical treatment, the sequences have been represented by sequence descriptors derived from their graphs or neighborhood matrices. The set was analyzed with three chemometrical methods: Mahalanobis distances, principal component analysis (PCA) and self organizing maps (SOM). A good separation of samples with respect to the region of origin was observed using these three methods. Results: Study of 310 Zika virus genome sequences from different continents. To characterize and compare Zika virus sequences from around the world using alignment-free sequence comparison and chemometrical methods. Conclusion: Mahalanobis distance analysis, self organizing maps, principal components were used to carry out the chemometrical analyses of the Zika sequence data. Genome sequences are clustered with respect to the region of origin (continent, country). Africa samples are well separated from Asian and South American ones.

Background: Casein Kinase 2 (CK2) is a ubiquitous cellular serine-threonine kinase with broad spectrum of substrates. This enzyme is widely expressed in eukaryotic cells and is overexpressed in different human cancers. Thus, the inhibition of CK2 can induce the physiological process of apoptosis leading to tumor cell death. Objectives: Selecting natural inhibitors toward the target enzyme using database mining. Methods: With our continuous effort to discover new compounds with CK2 inhibitory effect, several commercial natural databases were searched using molecular modeling approach and the selected compounds were evaluated in vitro. Results: Three compounds were selected as candidates and evaluated in vitro using CK2 holoenzyme, their effect on three cancer cell lines was determined. The selected candidates were weak inhibitors toward the target enzyme, only one compound showed moderate effect on cell viability. Conclusion: Several natural databases were screened, compounds were selected and tested in vitro. Despite the unexpected low inhibitory activity of the tested compounds, this study can help in directing the search of potent CK2 inhibitors and better understand the binding requirements of the ATP competitive inhibitors.