scholarly journals Rapid Identification of Potential Drug Candidates from Multi-Million Compounds’ Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5593
Author(s):  
Katalin Szilágyi ◽  
Beáta Flachner ◽  
István Hajdú ◽  
Mária Szaszkó ◽  
Krisztina Dobi ◽  
...  
Keyword(s):  
Similarity Search ◽  
Pharmacophore Model ◽  
Cost Effective ◽  
Rapid Identification ◽  
Drug Candidates ◽  
Hit Rate ◽  
Physico Chemical ◽  
Cost Effective Approach ◽  
3D Methods

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds’ databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

scholarly journals Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254374
Author(s):  
Emiko Desvaux ◽  
Antoine Hamon ◽  
Sandra Hubert ◽  
Cheïma Boudjeniba ◽  
Bastien Chassagnol ◽  
...  
Keyword(s):  
Lung Inflammation ◽  
Drug Targets ◽  
Cost Effective ◽  
Economic Consequences ◽  
Network Computing ◽  
Drug Candidates ◽  
Therapeutic Drugs ◽  
Cost Effective Approach ◽  
Related Proteins ◽  
Airways Inflammation

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1β, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

scholarly journals The utility of yeast as a tool for cell-based, target-directed high-throughput screening

Parasitology ◽  
2013 ◽  
Vol 141 (1) ◽  
pp. 8-16 ◽  
Author(s):  
J. L. NORCLIFFE ◽  
E. ALVAREZ-RUIZ ◽  
J. J. MARTIN-PLAZA ◽  
P. G. STEEL ◽  
P. W. DENNY
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Neglected Tropical Diseases ◽  
Cost Effective ◽  
Tropical Diseases ◽  
Protein Targets ◽  
Biochemical Assays ◽  
Cost Effective Approach ◽  
Cellular Context

SUMMARYMany Neglected Tropical Diseases (NTDs) have recently been subject of increased focus, particularly with relation to high-throughput screening (HTS) initiatives. These vital endeavours largely rely of two approaches, in vitro target-directed screening using biochemical assays or cell-based screening which takes no account of the target or targets being hit. Despite their successes both of these approaches have limitations; for example, the production of soluble protein and a lack of cellular context or the problems and expense of parasite cell culture. In addition, both can be challenging to miniaturize for ultra (u)HTS and expensive to utilize. Yeast-based systems offer a cost-effective approach to study and screen protein targets in a direct-directed manner within a eukaryotic cellular context. In this review, we examine the utility and limitations of yeast cell-based, target-directed screening. In particular we focus on the currently under-explored possibility of using such formats in uHTS screening campaigns for NTDs.

scholarly journals Database-Centric Method for Automated High-Throughput Deconvolution and Analysis of Kinetic Antibody Screening Data

2017 ◽  
Vol 22 (5) ◽  
pp. 547-556
Author(s):  
R. Paul Nobrega ◽  
Michael Brown ◽  
Cody Williams ◽  
Chris Sumner ◽  
Patricia Estep ◽  
...  
Keyword(s):  
High Throughput ◽  
Molecular Mechanisms ◽  
Automated Analysis ◽  
Rapid Identification ◽  
Poor Quality ◽  
Quality Data ◽  
Kinetic Measurements ◽  
Storage And Retrieval ◽  
Drug Candidates

The state-of-the-art industrial drug discovery approach is the empirical interrogation of a library of drug candidates against a target molecule. The advantage of high-throughput kinetic measurements over equilibrium assessments is the ability to measure each of the kinetic components of binding affinity. Although high-throughput capabilities have improved with advances in instrument hardware, three bottlenecks in data processing remain: (1) intrinsic molecular properties that lead to poor biophysical quality in vitro are not accounted for in commercially available analysis models, (2) processing data through a user interface is time-consuming and not amenable to parallelized data collection, and (3) a commercial solution that includes historical kinetic data in the analysis of kinetic competition data does not exist. Herein, we describe a generally applicable method for the automated analysis, storage, and retrieval of kinetic binding data. This analysis can deconvolve poor quality data on-the-fly and store and organize historical data in a queryable format for use in future analyses. Such database-centric strategies afford greater insight into the molecular mechanisms of kinetic competition, allowing for the rapid identification of allosteric effectors and the presentation of kinetic competition data in absolute terms of percent bound to antigen on the biosensor.

scholarly journals Combined Application of Bacteriophages and Carvacrol in the Control of Pseudomonas syringae pv. actinidiae Planktonic and Biofilm Forms

2020 ◽  
Vol 8 (6) ◽  
pp. 837
Author(s):  
Peien Ni ◽  
Lei Wang ◽  
Bohan Deng ◽  
Songtao Jiu ◽  
Chao Ma ◽  
...  
Keyword(s):  
Pseudomonas Syringae ◽  
Phage Therapy ◽  
Combined Treatment ◽  
Cost Effective ◽  
Resistance Development ◽  
Combined Application ◽  
Cost Effective Approach ◽  
Alternative Approach ◽  
Actinidia Spp

Pseudomonas syringae pv. actinidiae (Psa) is the causative agent of the bacterial canker of kiwifruit (Actinidia spp.). Phage therapy has been suggested as a viable alternative approach to controlling this disease, but its efficacy is limited by the emergence of phage-resistant mutants. Carvacrol is an essential oil that may be useful for the control of Psa. Combination therapies can be used to overcome resistance development. Here, the combination of phages (single phage suspensions of phages PN05 and PN09, and a cocktail of both phages) and carvacrol was investigated in controlling Psa planktonic and biofilm forms in vitro. The phage therapy alone (with phages PN05 and PN09), and the carvacrol alone (minimum inhibitory concentration 2.0 mg/mL), inhibited Psa growth, but the combined effect of both therapies was more effective. The phages alone effectively inhibited Psa growth for 24 h, but Psa regrowth was observed after this time. The carvacrol (2.0 mg/mL) alone prevented the biofilm formation for 48 h, but did not destroy the pre-formed biofilms. The combined treatment, phages and carvacrol (2.0 mg/mL), showed a higher efficacy, preventing Psa regrowth for more than 40 h. In conclusion, the combined treatment with phages and carvacrol may be a promising, environment-friendly and cost-effective approach to controlling Psa in the kiwifruit industry.

scholarly journals Pharmacophore Investigations of Potential COVID-19 RNA Polymerase (Rdrp) Inhibitors: Repurposing FDA-Approved Drugs

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Valentina L. Kouznetsova ◽  
Caroline Kellogg ◽  
Aidan Zhang ◽  
Mahidhar Tatineni ◽  
Mark A. Miller ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Hydrophobic Interactions ◽  
Pharmacophore Model ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
Promising Target ◽  
Approved Drugs ◽  
Fda Approved Drugs

Authors: Valentina L. Kouznetsova, Caroline Kellogg, Aidan Zhang, Mahidhar Tatineni, Mark A. Miller, Igor F. Tsigelny Background: SARS-CoV-2 has caused tens of millions of infections worldwide and millions of deaths. Currently, no effective treatment has been identified against the virus. Of its viral proteins, the RNA-dependent RNA polymerase (RdRp) is a promising target for drug design because of its importance in the replication of the virus. Material and Methods: After the identification of an RdRp pocket site based on the crystal structure of the RdRp– nsp7–nsp8 complex and the triphosphate form of remdesivir (PDB ID: 7BV2), we created a pharmacophore model consisting of 11 different features. These features include two acceptors, three donors, one acceptor and donor, three donor or acceptor, and one hydrophobic; an excluded volume of R=1.1 Å was also added. We then ran a pharmacophore search on our conformational database (DB) of approximately 2500 FDA-approved drugs and 600 000 conformations to identify potential drug-candidates. To determine the drugs that bound the best, we conducted multi conformational docking of these results to the previously identified pocket site. Results: The pharmacophore search found 315 different potential inhibitors of RdRp, of which 85 were chosen based on the number of H-bonds and hydrophobic interactions in the best docking pose. Several of the drugs selected, including ritonavir, dasatinib, imatinib, and sofosbuvir, have previously been shown to be effective against other viruses. Conclusions: These findings highlight compounds that could lead to both in vitro and in vivo studies to identify potential treatments against SARS-CoV-2.

scholarly journals When GWAS meets the Connectivity Map: drug repositioning for seven psychiatric disorders

2016 ◽  
Author(s):  
Hon-Cheong So ◽  
Carlos K.L. Chau ◽  
Wan-To Chiu ◽  
Kin-Sang Ho ◽  
Cho-Pong Lo ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Developmental Process ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Association Studies ◽  
Cost Effective ◽  
Genome Wide Association Studies ◽  
Connectivity Map ◽  
Drug Candidates ◽  
Cost Effective Approach

AbstractOur knowledge of disease genetics has advanced rapidly during the past decade, with the advent of high-throughput genotyping technologies such as genome-wide association studies (GWAS). However, few methodologies were developed and systemic studies performed to identify novel drug candidates utilizing GWAS data. In this study we focus on drug repositioning, which is a cost-effective approach to shorten the developmental process of new therapies. We proposed a novel framework of drug repositioning by comparing GWAS-imputed transcriptome with drug expression profiles from the Connectivity Map. The approach was applied to 7 psychiatric disorders. We discovered a number of novel repositioning candidates, many of which are supported by preclinical or clinical evidence. We found that the predicted drugs are significantly enriched for known psychiatric medications, or therapies considered in clinical trials. For example, drugs repurposed for schizophrenia are strongly enriched for antipsychotics (p = 4.69E-06), while those repurposed for bipolar disorder are enriched for antipsychotics (p = 2.26E-07) and antidepressants (p = 1.17E-05). These findings provide support to the usefulness of GWAS signals in guiding drug discoveries and the validity of our approach in drug repositioning. We also present manually curated lists of top repositioning candidates for each disorder, which we believe will serve as a useful resource for researchers.

scholarly journals Molecular Informatics of Trypanothione Reductase of Leishmania major Reveals Novel Chromen-2-One Analogues as Potential Leishmanicides

2021 ◽  
Author(s):  
Samuel K. Kwofie ◽  
Gabriel B. Kwarko ◽  
Emmanuel Broni ◽  
Michael B. Adinortey ◽  
Michael D. Wilson
Keyword(s):  
Leishmania Major ◽  
Homology Modelling ◽  
Pharmacophore Model ◽  
Binding Energies ◽  
Dimensional Structure ◽  
Trypanothione Reductase ◽  
Pharmacophore Modelling ◽  
Drug Candidates ◽  
Dynamics Simulations

Trypanothione reductase (TR), a flavoprotein oxidoreductase is an important therapeutic target for leishmaniasis. Ligand-based pharmacophore modelling and molecular docking were used to predict selective inhibitors against TR. Homology modelling was employed to generate a three-dimensional structure of Leishmania major trypanothione reductase (LmTR). A pharmacophore model used to screen a natural compound library generated 42 hits, which were docked against the LmTR protein. Compounds with lower binding energies were evaluated via in silico pharmacological profiling and bioactivity. Four compounds emerged as potential leads comprising Karatavicinol (7-[(2E,6E,10S)-10,11-dihydroxy-3,7,11-trimethyldodeca-2,6-dienoxy]chromen-2-one), Marmin (7-[(E,6R)-6,7-dihydroxy-3,7-dimethyloct-2-enoxy]chromen-2-one), Colladonin (7-[[(4aS)-6-hydroxy-5,5,8a-trimethyl-2-methylidene-3,4,4a,6,7,8-hexahydro-1H-naphthalen-1-yl]methoxy]chromen-2-one), and Pectachol (7-[(6-hydroxy-5,5,8a-trimethyl-2-methylidene-3,4,4a,6,7,8-hexahydro-1H-naphthalen-1-yl)methoxy]-6,8-dimethoxychromen-2-one) with good binding energies of −9.4, −9.3, 8.8, and −8.5 kcal/mol, respectively. These compounds bound effectively to the FAD domain of the protein with some critical residues including Asp35, Thr51, Lys61, Tyr198, and Asp327. Furthermore, molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) computations corroborated their strong binding. The compounds were also predicted to possess anti-leishmanial activity. The molecules serves as templates for the design of potential drug candidates and can be evaluated in vitro with optimistic results in producing plausible attenuating infectivity in macrophages.

scholarly journals Rifampicin: biotransformation study using the fungus Cunninghamella elegans and monitoring through UHPLC-MS

2020 ◽  
Vol 4 (1) ◽  
pp. 44-48
Author(s):  
Rafaela Sponchiado ◽  
Julia Sorrentino ◽  
Letícia M Cordenonsi ◽  
Alexandre M Fuentefria ◽  
Martin Steppe ◽  
...  
Keyword(s):  
Microbial Transformation ◽  
Cost Effective ◽  
Efficient Production ◽  
Cunninghamella Elegans ◽  
C Elegans ◽  
Drug Candidates ◽  
Drug Biotransformation ◽  
Cost Effective Method ◽  
Chromatographic Profile

Drug biotransformation studies appear as an alternative to pharmacological investigations of metabolites, development of new drug candidates with reduced investment and most efficient production. The objective of this study was to evaluate the capacity of biotransformation of Rifampicin (RIF) by the filamentous fungus Cunninghamella elegans as a microbial model of mammalian metabolism. In 120 h, C. elegans transformed the drug into the following two metabolites: rifampicin quinone and novel metabolite. The products of rifampicin formed in vitro were monitored by HPLC-PDA, being identified through UHPLC–QTOF/MS. Metabolites were characterized according to their chromatographic profile, mass fragments and UV spectral data. The major metabolic pathways of rifampicin transformed by the fungus were oxidation, demethylation and mono-oxidation. The microbial transformation of RIF showed the potential of Cunninghamella species to produce RIF metabolites. This process can be used for a cost effective method for both known and unknown metabolite production.

scholarly journals The Synergy between Organ-on-a-Chip and Artificial Intelligence for the Study of NAFLD: From Basic Science to Clinical Research

Biomedicines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 248
Author(s):  
Francesco De Chiara ◽  
Ainhoa Ferret-Miñana ◽  
Javier Ramón-Azcón
Keyword(s):  
Artificial Intelligence ◽  
Multiorgan Failure ◽  
Adult Population ◽  
Scoring Systems ◽  
Cost Effective ◽  
Legal Regulation ◽  
Full Potential ◽  
Alcoholic Fatty Liver ◽  
Drug Candidates

Non-alcoholic fatty liver affects about 25% of global adult population. On the long-term, it is associated with extra-hepatic compliances, multiorgan failure, and death. Various invasive and non-invasive methods are employed for its diagnosis such as liver biopsies, CT scan, MRI, and numerous scoring systems. However, the lack of accuracy and reproducibility represents one of the biggest limitations of evaluating the effectiveness of drug candidates in clinical trials. Organ-on-chips (OOC) are emerging as a cost-effective tool to reproduce in vitro the main NAFLD’s pathogenic features for drug screening purposes. Those platforms have reached a high degree of complexity that generate an unprecedented amount of both structured and unstructured data that outpaced our capacity to analyze the results. The addition of artificial intelligence (AI) layer for data analysis and interpretation enables those platforms to reach their full potential. Furthermore, the use of them do not require any ethic and legal regulation. In this review, we discuss the synergy between OOC and AI as one of the most promising ways to unveil potential therapeutic targets as well as the complex mechanism(s) underlying NAFLD.

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