scholarly journals Computational Study of the Therapeutic Potential of Novel Heterocyclic Derivatives against SARS-CoV-2

COVID ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 757-774
Author(s):  
Benjamin Ayodipupo Babalola ◽  
Tosin Emmanuel Adetobi ◽  
Oluwamayowa Samuel Akinsuyi ◽  
Otunba Ahmed Adebisi ◽  
Elizabeth Oreoluwa Folajimi
Keyword(s):  
Binding Affinity ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Computational Study ◽  
Docking Simulation ◽  
Therapeutic Interventions ◽  
Binding Motif ◽  
Therapeutic Benefits ◽  
Heterocyclic Derivatives ◽  
Inhibitory Potential

Severe Acute Respiratory Syndrome Coronavirus- 2 (SARS-CoV-2), including the recently reported severe variant B.1.617.2, has been reported to attack the respiratory tract with symptoms that may ultimately lead to death. While studies have been conducted to evaluate therapeutic interventions against the virus, this study evaluated the inhibitory potential of virtually screened novel derivatives and structurally similar compounds towards SARS-CoV-2 via a computational approach. A molecular docking simulation of the inhibitory potentials of the compounds against the SARS-CoV-2 drug targets—main protease (Mpro), spike protein (Spro), and RNA-dependent RNA polymerase (RdRp)—were evaluated and achieved utilizing AutoDock Vina in PyRx workspace. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of these compounds were assessed using SwissADME and ADMETLab servers. All the compounds displayed high binding affinities for the SARS-CoV-2 drug targets. However, the C13 exhibited the highest binding affinity for the drug targets, Spro and RdRp, while C15 exhibited the highest binding affinity for Mpro. The compounds interacted with the LEU A:271, LEU A:287, ASP A:289, and LEU A:272 of Mpro and the HIS A:540, PRO A:415, PHE A:486, and LEU A:370 of the Spro receptor binding motif and some active site amino acids of RdRp. The compounds also possess a favourable ADMET profile and showed no tendency towards hERG inhibition, hepatotoxicity, carcinogenicity, mutagenicity, or drug-liver injury. These novel compounds could offer therapeutic benefits against SARS-CoV-2, and wet laboratory experiments are necessary to further validate the results of this computational study.

scholarly journals Screening of Chloroquine, Hydroxychloroquine and Its Derivatives for Their Binding Affinity to Multiple SARS-CoV-2 Protein Drug Targets

2020 ◽  
Author(s):  
Mallikarjuna Nimgampalle ◽  
Vasudharani Devanthan ◽  
Ambrish Saxena
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Viral Proteins ◽  
Protein Drug ◽  
Potential Treatment ◽  
Health Authorities ◽  
Derivatives Of

Recently Chloroquine and its derivative Hydroxychloroquine have garnered enormous interest amongst the clinicians and health authorities’ world over as a potential treatment to contain COVID-19 pandemic. The present research aims at investigating the therapeutic potential of Chloroquine and its potent derivative Hydroxychloroquine against SARS-CoV-2 viral proteins. At the same time we have screened some chemically synthesized derivatives of Chloroquine and compared their binding efficacy with chemically synthesized Chloroquine derivatives through <i>in silico</i>approaches. For the purpose of the study, we have selected some essential viral proteins and enzymes implicated in SARS-CoV-2 replication and multiplication as putative drug targets.<br>

Outwitting an Old Neglected Nemesis: A Review on Leveraging Integrated Data-Driven Approaches to Aid in Unraveling of Leishmanicides of Therapeutic Potential

2020 ◽  
Vol 20 (5) ◽  
pp. 349-366 ◽  
Author(s):  
Samuel K. Kwofie ◽  
Emmanuel Broni ◽  
Bismark Dankwa ◽  
Kweku S. Enninful ◽  
Gabriel B. Kwarko ◽  
...  
Keyword(s):  
Drug Targets ◽  
Therapeutic Potential ◽  
Treatment Options ◽  
Organometallic Compounds ◽  
Leishmania Species ◽  
Data Driven ◽  
Treatment Regimens ◽  
Inhibitory Potential ◽  
Novel Algorithms ◽  
Almost All

The global prevalence of leishmaniasis has increased with skyrocketed mortality in the past decade. The causative agent of leishmaniasis is Leishmania species, which infects populations in almost all the continents. Prevailing treatment regimens are consistently inefficient with reported side effects, toxicity and drug resistance. This review complements existing ones by discussing the current state of treatment options, therapeutic bottlenecks including chemoresistance and toxicity, as well as drug targets. It further highlights innovative applications of nanotherapeutics-based formulations, inhibitory potential of leishmanicides, anti-microbial peptides and organometallic compounds on leishmanial species. Moreover, it provides essential insights into recent machine learning-based models that have been used to predict novel leishmanicides and also discusses other new models that could be adopted to develop fast, efficient, robust and novel algorithms to aid in unraveling the next generation of anti-leishmanial drugs. A plethora of enriched functional genomic, proteomic, structural biology, high throughput bioassay and drug-related datasets are currently warehoused in both general and leishmania-specific databases. The warehoused datasets are essential inputs for training and testing algorithms to augment the prediction of biotherapeutic entities. In addition, we demonstrate how pharmacoinformatics techniques including ligand-, structure- and pharmacophore-based virtual screening approaches have been utilized to screen ligand libraries against both modeled and experimentally solved 3D structures of essential drug targets. In the era of data-driven decision-making, we believe that highlighting intricately linked topical issues relevant to leishmanial drug discovery offers a one-stop-shop opportunity to decipher critical literature with the potential to unlock implicit breakthroughs.

scholarly journals Antimalarial Flavonoid-Glycoside from Acacia pennata with Inhibitory Potential Against PfDHFR-TS: An In-silico Study

2021 ◽  
Vol 12 (4) ◽  
pp. 4871-4887
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Enzyme Inhibitor ◽  
Docking Simulation ◽  
Flavonoid Glycoside ◽  
Flavonoid Glycosides ◽  
Discovery Studio ◽  
Antimalarial Agents ◽  
In Silico Studies ◽  
Inhibitory Potential

Drug resistance, toxicity, and adverse effects of current antimalarial drugs have mandated the need to search for newer antimalarial agents. The present study aims to identify promising flavonoid-glycosides (FGs) from Acacia pennata as possible antimalarial agents effective against PfDHFR-TS (PDB ID: 3DGA) by in-silico studies. The co-crystal inhibitor (RJ1) of PfDHFR-TS was used as the reference standard compound. A compound library of 17 FGs reported to be isolated from A. pennata was prepared and subjected to molecular docking simulation studies. PyRx 0.8 and AutoDock Vina revealed Pinocembrin-7-O-β-D-glucopyranoside (FG17) as the best PfDHFR-TS inhibitor with a binding affinity of -10.4 kcal/mol and -10.8 kcal/mol, respectively. In both methods, FG17 has a better binding affinity than the co-crystal inhibitor, RJ1 (-7.9 kcal/mol). The docking protocols were validated by RMSD calculation with Discovery Studio Visualizer software (2020). FG17 interacted with amino acids ALA16, LEU40, SER167, GLY41, GLY44, MET55, PHE58, ILE112, LEU119, GLY166, and TYR170 at the active binding site of PfDHFR-TS. Further, FG17 was computed as a non-toxic, bioavailable, synthetically accessible compound and a better enzyme inhibitor than RJ1. Hence, we conclude that FG17 may be used as a lead scaffold to design antimalarial agents against PfDHFR-TS in the future.

scholarly journals PolypharmDB, a Deep Learning-Based Resource, Quickly Identifies Repurposed Drug Candidates for COVID-19

2020 ◽  
Author(s):  
Dar'ya S. Redka ◽  
Stephen S. MacKinnon ◽  
Melissa Landon ◽  
Andreas Windemuth ◽  
Naheed Kurji ◽  
...  
Keyword(s):  
Deep Learning ◽  
Small Molecule ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Host Cells ◽  
Therapeutic Interventions ◽  
Clinical Environment ◽  
Drug Candidates ◽  
Small Molecule Drugs

<p>There is an immediate need to discover treatments for COVID-19, the pandemic caused by the SARS-CoV-2 virus. Standard small molecule drug discovery workflows that start with library screens are an impractical path forward given the timelines to discover, develop, and test clinically. To accelerate the time to patient testing, here we explored the therapeutic potential of small molecule drugs that have been tested to some degree in a clinical environment, including approved medications, as possible therapeutic interventions for COVID-19. Motivating our process is a concept termed polypharmacology, i.e. off-target interactions that may hold therapeutic potential. In this work, we used Ligand Design, our deep learning drug design platform, to interrogate the polypharmacological profiles of an internal collection of small molecule drugs with federal approval or going through clinical trials, with the goal of identifying molecules predicted to modulate targets relevant for COVID-19 treatment. Resulting from our efforts is PolypharmDB, a resource of drugs and their predicted binding of protein targets across the human proteome. Mining PolypharmDB yielded molecules predicted to interact with human and viral drug targets for COVID-19, including host proteins linked to viral entry and proliferation and key viral proteins associated with the virus life-cycle. Further, we assembled a collection of prioritized approved drugs for two specific host-targets, TMPRSS2 and cathepsin B, whose joint inhibition was recently shown to block SARS-CoV-2 virus entry into host cells. Overall, we demonstrate that our approach facilitates rapid response, identifying 30 prioritized candidates for testing for their possible use as anti-COVID drugs. Using the PolypharmDB resource, it is possible to identify repurposed drug candidates for newly discovered targets within a single work day. We are making a complete list of the molecules we identified available at no cost to partners with the ability to test them for efficacy, in vitro and/or clinically.</p><div><br></div>

scholarly journals Screening of Chloroquine, Hydroxychloroquine and Its Derivatives for Their Binding Affinity to Multiple SARS-CoV-2 Protein Drug Targets

2020 ◽  
Author(s):  
Mallikarjuna Nimgampalle ◽  
Vasudharani Devanthan ◽  
Ambrish Saxena
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Viral Proteins ◽  
Protein Drug ◽  
Potential Treatment ◽  
Health Authorities ◽  
Derivatives Of

Recently Chloroquine and its derivative Hydroxychloroquine have garnered enormous interest amongst the clinicians and health authorities’ world over as a potential treatment to contain COVID-19 pandemic. The present research aims at investigating the therapeutic potential of Chloroquine and its potent derivative Hydroxychloroquine against SARS-CoV-2 viral proteins. At the same time we have screened some chemically synthesized derivatives of Chloroquine and compared their binding efficacy with chemically synthesized Chloroquine derivatives through <i>in silico</i>approaches. For the purpose of the study, we have selected some essential viral proteins and enzymes implicated in SARS-CoV-2 replication and multiplication as putative drug targets.<br>

scholarly journals PolypharmDB, a Deep Learning-Based Resource, Quickly Identifies Repurposed Drug Candidates for COVID-19

2020 ◽  
Author(s):  
Dar'ya S. Redka ◽  
Stephen S. MacKinnon ◽  
Melissa Landon ◽  
Andreas Windemuth ◽  
Naheed Kurji ◽  
...  
Keyword(s):  
Deep Learning ◽  
Small Molecule ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Host Cells ◽  
Therapeutic Interventions ◽  
Clinical Environment ◽  
Drug Candidates ◽  
Small Molecule Drugs

<p>There is an immediate need to discover treatments for COVID-19, the pandemic caused by the SARS-CoV-2 virus. Standard small molecule drug discovery workflows that start with library screens are an impractical path forward given the timelines to discover, develop, and test clinically. To accelerate the time to patient testing, here we explored the therapeutic potential of small molecule drugs that have been tested to some degree in a clinical environment, including approved medications, as possible therapeutic interventions for COVID-19. Motivating our process is a concept termed polypharmacology, i.e. off-target interactions that may hold therapeutic potential. In this work, we used Ligand Design, our deep learning drug design platform, to interrogate the polypharmacological profiles of an internal collection of small molecule drugs with federal approval or going through clinical trials, with the goal of identifying molecules predicted to modulate targets relevant for COVID-19 treatment. Resulting from our efforts is PolypharmDB, a resource of drugs and their predicted binding of protein targets across the human proteome. Mining PolypharmDB yielded molecules predicted to interact with human and viral drug targets for COVID-19, including host proteins linked to viral entry and proliferation and key viral proteins associated with the virus life-cycle. Further, we assembled a collection of prioritized approved drugs for two specific host-targets, TMPRSS2 and cathepsin B, whose joint inhibition was recently shown to block SARS-CoV-2 virus entry into host cells. Overall, we demonstrate that our approach facilitates rapid response, identifying 30 prioritized candidates for testing for their possible use as anti-COVID drugs. Using the PolypharmDB resource, it is possible to identify repurposed drug candidates for newly discovered targets within a single work day. We are making a complete list of the molecules we identified available at no cost to partners with the ability to test them for efficacy, in vitro and/or clinically.</p><div><br></div>

Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Targets ◽  
Computational Models ◽  
Factor Xa ◽  
Coagulation Factor ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

The Potential of Milk-derived Exosomes for Drug Delivery

2020 ◽  
Vol 17 ◽  
Author(s):  
Shuyuan Li ◽  
Yue Tang ◽  
Yushun Dou
Keyword(s):  
Drug Delivery ◽  
Oral Delivery ◽  
Therapeutic Potential ◽  
Current Knowledge ◽  
Biological Fluids ◽  
Drug Loading ◽  
Drug Carriers ◽  
Current Application ◽  
Therapeutic Benefits ◽  
Loading Methods

Background: Exosomes, one of the extracellular vesicles, are widely present in all biological fluids and play an important role in intercellular communication. Because of its hydrophobic lipid bilayer and aqueous hydrophilic core structure, it is considered a possible alternative to liposome drug delivery systems. Not only do they protect the cargo like liposomes during delivery, they are less toxic and better tolerated. However, due to the lack of sources and methods for obtaining enough exosomes, the therapeutic application of exosomes as drug carriers is limited. Methods: A literature search was performed using the ScienceDirect and PubMed electronic databases to obtain information from published literature on milk exosomes related to drug delivery. Results: Here, we briefly reviewed the current knowledge of exosomes, expounded the advantages of milk-derived exosomes over other delivery vectors, including a higher yield, the oral delivery characteristic and additional therapeutic benefits. The purification and drug loading methods of milk exosomes, and the current application of milk exosomes were also introduced. Conclusion: The emergence of milk-derived exosomes is expected to break through the limitations of exosomes as therapeutic carriers of drugs. We hope to raise awareness of the therapeutic potential of milk-derived exosomes as a new drug delivery system.

2,4-Dichloro-5-[(N-aryl/alkyl)sulfamoyl]benzoic Acid Derivatives: In Vitro Antidiabetic Activity, Molecular Modeling and In silico ADMET Screening

2019 ◽  
Vol 15 (2) ◽  
pp. 186-195 ◽  
Author(s):  
Samridhi Thakral ◽  
Vikramjeet Singh
Keyword(s):  
Molecular Docking ◽  
Benzoic Acid ◽  
Inhibitory Activity ◽  
In Silico ◽  
Computational Study ◽  
Antidiabetic Activity ◽  
Standard Drug ◽  
Benzoic Acid Derivatives ◽  
In Silico Admet ◽  
Inhibitory Potential

Background: Postprandial hyperglycemia can be reduced by inhibiting major carbohydrate hydrolyzing enzymes, such as α-glucosidase and α-amylase which is an effective approach in both preventing and treating diabetes. Objective: The aim of this study was to synthesize a series of 2,4-dichloro-5-[(N-aryl/alkyl)sulfamoyl] benzoic acid derivatives and evaluate α-glucosidase and α-amylase inhibitory activity along with molecular docking and in silico ADMET property analysis. Method: Chlorosulfonation of 2,4-dichloro benzoic acid followed by reaction with corresponding anilines/amines yielded 2,4-dichloro-5-[(N-aryl/alkyl)sulfamoyl]benzoic acid derivatives. For evaluating their antidiabetic potential α-glucosidase and α-amylase inhibitory assays were carried out. In silico molecular docking studies of these compounds were performed with respect to these enzymes and a computational study was also carried out to predict the drug-likeness and ADMET properties of the title compounds. Results: Compound 3c (2,4-dichloro-5-[(2-nitrophenyl)sulfamoyl]benzoic acid) was found to be highly active having 3 fold inhibitory potential against α-amylase and 5 times inhibitory activity against α-glucosidase in comparison to standard drug acarbose. Conclusion: Most of the synthesized compounds were highly potent or equipotent to standard drug acarbose for inhibitory potential against α-glucosidase and α-amylase enzyme and hence this may indicate their antidiabetic activity. The docking study revealed that these compounds interact with active site of enzyme through hydrogen bonding and different pi interactions.

Effects of Extracellular Vesicles Derived from Mesenchymal Stem/Stromal Cells on Liver Diseases

2019 ◽  
Vol 14 (5) ◽  
pp. 442-452 ◽  
Author(s):  
Wenjie Zheng ◽  
Yumin Yang ◽  
Russel Clive Sequeira ◽  
Colin E. Bishop ◽  
Anthony Atala ◽  
...  
Keyword(s):  
Regenerative Medicine ◽  
Extracellular Vesicles ◽  
Stromal Cells ◽  
Liver Diseases ◽  
Therapeutic Potential ◽  
Mechanisms Of Action ◽  
Therapeutic Effects ◽  
Chronic Liver Injury ◽  
Mediated Effects ◽  
Therapeutic Benefits

Therapeutic effects of Mesenchymal Stem/Stromal Cells (MSCs) transplantation have been observed in various disease models. However, it is thought that MSCs-mediated effects largely depend on the paracrine manner of secreting cytokines, growth factors, and Extracellular Vesicles (EVs). Similarly, MSCs-derived EVs also showed therapeutic benefits in various liver diseases through alleviating fibrosis, improving regeneration of hepatocytes, and regulating immune activity. This review provides an overview of the MSCs, their EVs, and their therapeutic potential in treating various liver diseases including liver fibrosis, acute and chronic liver injury, and Hepatocellular Carcinoma (HCC). More specifically, the mechanisms by which MSC-EVs induce therapeutic benefits in liver diseases will be covered. In addition, comparisons between MSCs and their EVs were also evaluated as regenerative medicine against liver diseases. While the mechanisms of action and clinical efficacy must continue to be evaluated and verified, MSCs-derived EVs currently show tremendous potential and promise as a regenerative medicine treatment for liver disease in the future.

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