Letters in Drug Design & Discovery - Volume 20, Issue 2, 2023

Novel technology has led to advanced approaches and understandings of viral biology, and the advent in previous years has raised the possibility of determination of mechanisms of viral replication and infection, trans-species adaption, and disease. The outbreak of Coronavirus 2019 (COVID-19) has become a global life-threatening concern recently. The war against COVID19 has now reached the most critical point, whereby it has caused worldwide social and economic disruption. Unfortunately, limited knowledge persists among the community regarding the biology of SARS-CoV-2 infection. The present review will summarize the basic life cycle and replication of the well-studied coronaviruses, identifying the unique characteristics of coronavirus biology and highlighting critical points where research has made significant advances that might represent targets for antivirals or vaccines. Areas where rapid progress has been made in SARS-CoV research have been highlighted. Additionally, an overview of the efforts dedicated to an effective vaccine for this novel coronavirus, particularly different generations of vaccines, which has crippled the world, has also been discussed. Areas of concern for research in coronavirus replication, genetics, and pathogenesis have been explained as well. Speedy evaluation of multiple approaches to elicit protective immunity and safety is essential to curtail unwanted immune potentiation, which plays an important role in the pathogenesis of this virus. Hope is to provide a glimpse into the current efforts, and the progress is made with reference to Coronaviruses and how the community can work together to prevent and control coronavirus infection now and in the future.

Background: The free carboxylic acid group in the mefenamic acid (MFA) structure plays a potential role in developing various neuromuscular side effects after MFA administration. In this study, the hydroxypropyl promoiety was added to the carboxylic acid group of MFA in an attempt to reduce the neuromuscular side effects of MFA and improve its therapeutic effects. Methods: Hydroxypropylester of MFA (HPEMA) was synthesized and subjected to various in vivo investigations compared to MFA. The neuromuscular toxicity was conducted following high doses administration in mice and was evaluated at various measuring parameters, such as the percentage of catalepsy, clonic-tonic seizure, and death. In addition, the anti-inflammatory and anti-nociceptive effects of HPEMA were evaluated in the carrageenan-induced paw edema test and acetic acid-induced writhing test, respectively. Results: The findings of this study reveal that the percentage of catalepsy, clonic-tonic seizure, and death is significantly lower in mice treated with HPEMA than in those treated with equimolar doses of MFA. In addition, treatment with HPEMA caused a comparable anti-inflammatory activity in the carrageenaninduced paw edema test and a significantly higher anti-nociceptive effect in the acetic acid-induced writhing test than the MFA treatment. Conclusion: This study’s findings suggest that HPEMA is a promising prodrug for MFA.

Background: Cancer is the most prevalent disease seen nowadays. Thymidine phosphorylase (TP) is an angiogenic enzyme that is overexpressed in many solid tumors. Over the years, Thymidine phosphorylase has emerged as a novel target for anticancer drug development as an inhibitor. Objective: To design novel oxadiazole-isatin pharmacophore-containing molecules and explore their structural requirements related to the anticancer activity. Methods: Pharmacophore optimisation was carried out for oxadiazole-isatin hybrid molecules using molecular modeling studies (2D and 3D QSAR). Further, the new chemical entities were designed using the combilib tool of V life software. To have a better understanding of the binding interactions, the newly designed molecules were docked. To achieve a drug-like pharmacokinetic profile, molecules were also tested for ADME prediction. Results: Two-Dimensional Quantitative Structure-Activity Relationship (2D-QSAR) model was generated using the multiple regression method with r² = 0.84 and q² = 0.76. Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) model was obtained by simulated annealing k nearest near (SA kNN) method with q² = 0.8099. Molecular docking studies showed promising results. Compound 5 was found to be with the best dock score and the best fit to the active site pocket of the thymidylate phosphorylase enzyme. The compounds have notable absorption, distribution, metabolism, and excretion (ADME) properties that can be predicted to assure a drug-like pharmacokinetic profile. Conclusion: One of the most successful and fast-increasing methodologies is molecular modeling. It not only aids in the prediction of specific target compounds but also aids in the cost reduction of valuable substances. The successful use of molecular modeling was done in this study, with caution taken to avoid any chance co-relation. Optimised pharmacophore was obtained and new chemical entities were designed. Docking studies revealed that Compound 5 has shown better H-bond interaction with Lys 221 and Thr 151 with bond distances 2.0 ° and 1.8 ° which is the most active molecule. ADME tests discovered that the majority of the newly designed compounds were within a reasonable range as required in a druglike pharmacokinetic profile. Molecules 2, 4, 5, 6 can be considered as a lead for future synthesis and biological screening.

Background: Machine learning has become an essential tool for drug research to generate pertinent structural information to design drugs with higher biological activities. Quantitative structureactivity relationship (QSAR) is considered one technique. QSAR study involves two main steps: first is the generation of descriptors, and the second is building and validating the models. Aim: By using a Python program language for building the QSAR model of pyrazoline derivatives, the data were collected from diverse literature for the inhibition of Mycobacterium tuberculosis. Pyrazoline, a small molecule scaffold, could block the biosynthesis of mycolic acids, resulting in mycobacteria death and leading to anti-tubercular drug discovery. Methods: We have developed a new Python script that effectively uses CDK descriptor as the independent variable and anti-tubercular bioactivity as the dependent variable in building and validating the best QSAR model. The built QSAR model was further cross-validated by using the external test set compounds. Then, the three algorithms, viz. multiple linear regression, support vector machine, and partial least square classifiers, were used to differentiate and compare their r2 values. Results: Our generated QSAR model via an open-source python program predicted well with external test set compounds. The generated statistical model afforded the ordinary least squares (OLS) regression as R² value of 0.514, F value of 5.083, the adjusted R² value of 0.413, and std. error of 0.092. Moreover, the multiple linear regression showed the R² value of 0.5143, reg.coef_ of, -0.07795 (PC1), 0.01619 (PC2), 0.03763 (PC3), 0.07849 (PC4), -0.09726 (PC5), and reg.intercept_ of 4.8324. The performance of the model was determined by the support vector machine classifier of sklearn, module and it provided a model score of 0.5901. Further, the model performance was supported by a partial least square regression, and it showed the R² value of 0.5901. The model performance was validated, and the model predicted similar values when compared to that of the train set, and the plotted linear curve between the predicted and actual pMIC50 value showed all data to fall over the middle linear line. Conclusion: We have found that the model score obtained using this script via three diverse algorithms correlated well, and there was not much difference between them; the model may be useful in the design of a similar group of pyrazoline analogs as anti-tubercular agents.

Background: The fusion of the secretory signal peptide to the N-terminal of a polypeptide’s amino acid sequence is an attractive technique for the secretory production of heterologous proteins. On the other hand, applying computational analysis may be beneficial in overcoming the barriers of trial-anderror approaches in detecting proper signal sequences. As the scope of this study, the most probable effective properties of 30 signal sequences for the extracellular production of recombinant human interferon-gamma (rhIFN-γ) were analysed. Methods: Online available web server, SignalP5.0, was used to predict signal peptides’ probability, most likely translocation pathways, and cleavage site location. The physicochemical features of signal peptides and rhIFN-γ were assessed by the ProtParam tool, and the solubility of protein was predicted by SOLpro. Results: Finally, 12 high probable signal peptides, including OmpC, PhoE, AnsB, and OmpA, were theoretically detected with ideal solubility probabilities and almost balanced physicochemical properties; hopes to be helpful in future experimental studies for the secretion of rhIFN-γ. Conclusion: The experimental analysis is required to validate the in silico results and focus on in-lab affecting factors such as cultivation methods and conditions.

Background: Thymidine Phosphorylase (TP) is an imperative target for cancer researchers. In the current research, quantitative structure-activity relationship (QSAR) models were demonstrated to identify new TP inhibitors. Objective: The main objective is to perform a QSAR study on a series of 19 derivatives of thiobarbituric acid and new molecules designed and dock to check potency and efficacy for anticancer activity. Methods: Multiple linear regression analysis (MLR) was used to establish a two-dimensional quantitative structure-activity relationship (2D-QSAR) with regression coefficient values of 0.9781, 0.9513, and 0.9819 for the training set (r²), leave-one-out (LOO) dependent internal regression (q²), and external test set regression (r² _pred), respectively. Three-dimensional quantitative structure-activity relationship (3DQSAR) model, obtained by using the simulated annealing k nearest neighbour (SA-KNN) method (q2 = 0.7880). Newly designed molecules were subjected to docking studies with 7-deazaxanthine taken as standard. Results: Molecular modelling, structure-based drug design and docking study analysis were performed. The new chemical entities (NCE’s) designed, docked towards targeted receptor and show good results as compared to the standard 7-deazaxanthine. It was found that these molecules bind similar amino acid pocket regions as that of standard. Molecules bind at the active site of TP enzyme involving H bond interactions with shorter distances showed greater affinity. At last, the oral bioavailability and toxic effect were evaluated as absorption, distribution, metabolism, and elimination (ADME) studies by computational means of the Qikprop tool of Schrodinger. Conclusion: One of the most successful and fast-increasing methodologies is molecular modelling. It not only aids in the prediction of specific target compounds but also aids in the cost reduction of valuable substances. QSAR and docking study was performed, and most of the molecules have shown good dock scores. Based on these results, NCE’s for anticancer activity were successfully designed and analysed in this research work which will be helpful for effective drug synthesis with less toxicity in the future. Others: 2D QSAR model was generated by three methods, and the best one was selected for further study. NCEs were planned based on descriptors such as topological, electrostatic, steric, and hydrophobic substitutions around the core.

Background: Polycystic ovarian syndrome (PCOS) is defined by excessive production and/or secretion of androgenic hormones in women. This disease has a complicated nature, so its control is difficult and challenging. Therefore, many women use complementary therapies to support medical treatment, one of which is the consumption of mint plants. Objectives: This study aimed to characterize the chemical composition of peppermint (Mentha piperita L.) and spearmint (Mentha spicata L.) oils, to assess the binding of constituents of the oils to the androgen receptor as well as their pharmacokinetic features. Methods: The essential oils were isolated by water distillation and then analyzed using GC-MS and GCFID. Thereafter, in silico binding studies were performed between the main volatile constituents and human androgen receptors using Autodock Vina. Besides, the pharmacokinetic properties of the selected compounds were evaluated using SwissADME. Results: GC analyses showed the presence of 19 and 23 constituents out of the total components (accounting for 94.7% and 97.6%, respectively), with carvone (73.0%), and menthone (33.1%) and menthol (29.3%) as the major compounds in spearmint and peppermint oils, respectively. Moreover, molecular docking studies revealed that carvone has the lowest binding energy to the androgen receptor. On the other hand, all tested compounds finally exhibited favorable pharmacokinetic parameters. Conclusion: The present study virtually indicated that the main volatile constituent in the spearmint oil, i.e., carvone, could probably cause a beneficial effect on PCOS.

Background: Breast cancer ranks first in women and is the second most common type of cancer overall. It is the most important barrier to the rise of life expectancy, globally affecting disease modalities. Cyclooxygenase-2 (COX-2) has become a prominent hallmark as an inhibition target for breast cancer, and this therapeutic target for anti-inflammatory drugs regulates cell proliferation, angiogenesis, tumor growth and apoptosis. There is a need to explore new anti-cancerous drugs for searching the best possible hit candidates for cancer treatment. The computer-aided drug design approach was conducted to discover the new alternative COX-2 inhibitors. Objective: The research framework of this study is to identify new potent inhibitors for the COX-2 receptor using computer-aided drug design. Methods: In the present investigation, an in-silico approach was used to screen with the best established three biological databases (Zinc15, ChemSpider and BindingDB) and docked against the COX-2 protein structure (PDB ID: 5IKR). Molecular docking was carried out using the Schrodinger Maestro suite. The compounds were filtered out based on their physicochemical, ADMET, and other drug-like properties. Several computational approaches such as molecular docking, binding free energy calculation, ADMET analysis, protein-ligand interaction and MD simulation were performed to determine the suitability of correct ligands for the selected COX-2 target. Results: The two ligands showed relatively better binding affinities (-10.028 kcal/mol for compound A and -10.007 kcal/mol for ZINC000048442590) than the standard (-9.751 kcal/mol). These compounds followed Lipinski’s rule and drug-likeness index, which exhibited a good predicted therapeutic druggability profile. The interaction of the protein-ligand complex correlates with the COX-2 receptor. The MD simulation of the protein-ligand complex showed good stability in the time period of 10ns. Conclusion: It is the first study in which two new compounds ZINC000048442590 and compound A were found to be highly promising with active potential in inhibiting cyclooxygenase-2 enzyme could be effective as the potential drug candidates for breast cancer against COX-2 protein. Hopefully, in the future, these compounds as anti-inflammatory drug molecules could be used as new templates for the development of anticancer agents.

Background: Infections caused by helminth parasites are the main cause of economic losses in the livestock industry worldwide. The rapid resistance acquired by different parasites against commercially available drugs motivates the search, design, and development of new compounds capable of overcoming this situation. Previously, our group reported the novel hybrid valerolactam-fenbendazole (VALFBZ) compound with in vitro anthelmintic activity and good ex vivo parasite permeation. Objective: This study aimed at optimizing the novel hybrid VAL-FBZ compound synthesis and scaling up to the multigram order necessary for in vivo assays. Methods: For the hybrid VAL-FBZ synthesis, a convergent strategy was utilized. To obtain the benzimidazole core, widely available fenbendazole and L-Ornithine hydrochloride synthesis were used. The key step was the coupling reaction, for which an inexpensive coupling agent of the uronium salt family was used. Optimization was carried out by minimizing the risks and costs of upscaling at the multigram level. Results: In the first stage, the precursors of Valerolactam and Benzimidazole cores were synthesized on a decagram scale to obtain better results than previous reports. Also, the coupling reaction was carried out using HBTU to obtain VAL-FBZ with above 99% HPLC purity, and an overall yield of 48%. The successful synthesis was carried out without performing chromatographic purification in any step to minimize a few risks for the operator. Conclusion: Successfully, an efficient multigram and economic process is developed.

Objective: Rheumatoid Arthritis (RA) is made when the synovial tissues and joints are destroyed by the inflammation refection, especially the chronic inflammation. The RA-FLS was treated with Oxymatrine, and the influence of miR-146a and TRAF6 /JNK pathway was explored. Methods: Oxymatrine -treated RA-FLS were harvested to detect cell viability by CCK-8. The expression of miR-146a was detected by qRT-PCR. The expression of IRAK1, TRAF6, JNK1, and p-JNK1 was obtained by Western blot. Results: The optimum oxymatrine concentration inhibiting RA-FLS was 4mg/ml at 48h. The expression of miR-146a at 48h and 72h was higher than 0 and 24h in RA-FLS treated with 4mg/ml oxymatrine. IRAK1, TRAF6, and p-JNK at 48h and 72h were lower than 0 and 24h in RA-FLS treated with 4mg/ml oxymatrine. When the miR-146a was inhibited, the expression of miR-146a was very low in the miR- 146a inhibitor group. No matter whether oxymatrine existed, the expression of IRAK1, TRAF6, and p- JNK in the miR-146a inhibitor group with or without oxymatrine was higher than the mock group, blank group, and only oxymatrine added group. The cell viability in the miR-146a inhibitor group and oxymatrine + miR-146a inhibitor group was higher than in the other groups. When IRAK1 was over expressed, the expression of miR-146a in the oxymatrine + IRAK1 overexpression group was higher than in the IRAK1 overexpression group. However, The expression of IRAK1, TRAF6, and p-JNK1 in the IRAK1 overexpression group with or without oxymatrine was higher than the pcDNA3.1 group, blank group, and only oxymatrine added group. The cell viability in the IRAK1 overexpression group and oxymatrine + IRAK1 overexpression group was higher than in the other groups. Conclusion: Oxymatrine can inhibit RA-FLS proliferation via miR146a and IRAK1/TRAF6/JNK1 axis. Hence, oxymatrine may be a drug or adjuvant drug to treat RA in the future.

Objective: Hepatocellular carcinoma (HCC) is a widely occurring cancer ranking second in humans, with an incidence rate of approximately 1.6% per year in India. Experimental analysis of the Boeravinones or the Rotenoids classification of compounds present in the roots of the Boerhaavia diffusa Linn plant has shown a wide range of anti-cancer activity against liver hepatoblastoma. Methods: Boeravinone B (BB) was screened from widely available Boeravinone A-E compounds based on a maximum drug-likeness score using Lipinski’s rule Five. BB was checked for anti-HCC activity by binding with the five receptors of VEGF, EGF, BCl2, Caspase-3 and Caspase-9 when compared with Sorafenib through molecular docking. GROMACS was used for simulating molecular dynamics. Results: BB has shown a negative maximum internal energy score of -8.04, -8.42, -6.66, -8.33 and -7.74 Kcal/mol when compared to Sorafenib’s internal energy score of -6.55, -7.12, -4.05, -5.48 and -6.12 Kcal/mol for VEGFR, EGFR, BCl2, Caspase-3 and Caspase-9 respectively. Simulation using GROMACS has revealed that RMSD results BB forms a more stable complex with the Caspase-3 and EGFR after 19s and 15s of simulation time. RMSF analysis has characterized local changes on 170-190 residues and 860- 900 residues in C-alpha atoms of BB-Caspase-3 and BB-EGFR complexes revealed protein flexibility. Conclusion: MMPBSA score of BB docked Caspase-3 and EGFR complexes were found to be -62.178 and -42.84 KJ/mol.