scholarly journals Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

scholarly journals Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies

2019 ◽  
Vol 20 (5) ◽  
pp. 1023 ◽  
Author(s):  
Margherita Lapillo ◽  
Tiziano Tuccinardi ◽  
Adriano Martinelli ◽  
Marco Macchia ◽  
Antonio Giordano ◽  
...  
Keyword(s):  
Small Molecule ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Protein Targets ◽  
Drug Candidates ◽  
Fishing Strategies ◽  
Benchmark Database ◽  
Speed Up ◽  
Actual Target ◽  
Target Fishing

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.

Natural Products with tandem Anti-inflammatory, Immunomodulatory and Anti-SARS-CoV/2 effects: A Drug Discovery Perspective against SARS-CoV-2

2021 ◽  
Vol 28 ◽  
Author(s):  
Luana N. O. Leal da Cunha ◽  
Tiago Tizziani ◽  
Gabriella B. Souza ◽  
Monalisa A. Moreira ◽  
José S. S. Neto ◽  
...  
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
In Silico ◽  
Biological Activities ◽  
Cysteine Proteases ◽  
Drug Candidate ◽  
Plant Metabolites ◽  
Drug Candidates ◽  
In Silico Studies ◽  
Anti Inflammatory

Background: COVID-19 is still causing victims with long-term health consequences, mass deaths, and collapsing healthcare systems around the world. The disease has no efficient drugs. However, previous studies revealed that SARS-CoV-2 and SARS-CoV have 96% and 86.5% similarities in cysteine proteases (3CLpro) and papain-like protease (PLpro) sequences, respectively. This resemblance could be significant in the search for drug candidates with antiviral effects against SARS-CoV-2. Objective: This paper is a compilation of natural products that inhibit SARS-CoV 3CLpro and PLpro and, concomitantly, reduce inflammation and/or modulate the immune system as a perspective strategy for COVID-19 drug discovery. It also presents in silico studies performed on these selected natural products using SARS-CoV-2 3CLpro and PLpro as targets to propose a list of hit compounds. Method: The plant metabolites were selected in the literature based on their biological activities on SARS-CoV proteins, inflammatory mediators, and immune response. The consensus docking analysis was performed using four different packages. Results: Seventy-nine compounds reported in the literature with inhibitory effects on SARS-CoV proteins were reported as anti-inflammatory agents. Fourteen of them showed in previous studies immunomodulatory effects. Five and six of these compounds showed significant in silico consensus as drug candidates that can inhibit PLpro and 3CLpro, respectively. Our findings corroborated recent results reported on anti-SARS-CoV-2 in the literature. Conclusion: This study revealed that amentoflavone, rubranoside B, savinin, psoralidin, hirsutenone, and papyriflavonol A are good drug candidate for the search of antibiotics against COVID-19.

Understanding Membrane Protein Drug Targets in Computational Perspective

2019 ◽  
Vol 20 (5) ◽  
pp. 551-564 ◽  
Author(s):  
Jianting Gong ◽  
Yongbing Chen ◽  
Feng Pu ◽  
Pingping Sun ◽  
Fei He ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Membrane Protein ◽  
Drug Target ◽  
Drug Targets ◽  
Computational Models ◽  
Drug Repurposing ◽  
Protein Targets ◽  
Target Interaction ◽  
Alternative Understanding

Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

scholarly journals SELDI ProteinChip®Array Technology: Protein-Based Predictive Medicine and Drug Discovery Applications

2003 ◽  
Vol 2003 (4) ◽  
pp. 237-241 ◽  
Author(s):  
Guru Reddy ◽  
Enrique A. Dalmasso
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Treatment Options ◽  
Drug Effects ◽  
Proteome Analysis ◽  
Predictive Medicine ◽  
Protein Drug ◽  
Proteinchip Array ◽  
Drug Candidates ◽  
Array Technology

Predictive medicine, utilizing the ProteinChip®Array technology, will develop through the implementation of novel biomarkers and multimarker patterns for detecting disease, determining patient prognosis, monitoring drug effects such as efficacy or toxicity, and for defining treatment options. These biomarkers may also serve as novel protein drug candidates or protein drug targets. In addition, the technology can be used for discovering small molecule drugs or for defining their mode of action utilizing protein-based assays. In this review, we describe the following applications of the ProteinChip Array technology: (1) discovery and identification of novel inhibitors of HIV-1 replication, (2) serum and tissue proteome analysis for the discovery and development of novel multimarker clinical assays for prostate, breast, ovarian, and other cancers, and (3) biomarker and drug discovery applications for neurological disorders.

scholarly journals Translational and Clinical Pharmacology Considerations in Drug Repurposing for X-linked Adrenoleukodystrophy-A Rare Peroxisomal Disorder

2021 ◽  
Author(s):  
Julianne Tieu ◽  
Siddhee Sahasrabudhe ◽  
Paul Orchard ◽  
James Cloyd ◽  
Reena Kartha
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Pharmacokinetics And Pharmacodynamics ◽  
Drug Candidates ◽  
Target Patient Population ◽  
Site Of Action ◽  
Repurposed Drugs ◽  
Gait Disturbances ◽  
Spastic Quadriparesis

X-linked adrenoleukodystrophy (X-ALD) is an inherited, neurodegenerative rare disease that can result in devastating symptoms of blindness, gait disturbances, and spastic quadriparesis due to progressive demyelination. Typically, the disease progresses rapidly, causing death within the first decade of life. With limited treatments available, efforts to determine an effective therapy that can alter disease progression or mitigate symptoms have been undertaken for many years, particularly through drug repurposing. Repurposing has generally been guided through clinical experience and small trials. At this time, none of the drug candidates have been approved for use, which may be due, in part, to the lack of pharmacokinetic/pharmacodynamic (PK/PD) information on the repurposed medications in the target patient population. Greater consideration for the disease pathophysiology, drug pharmacology, and potential drug-target interactions, specifically at the site of action, would improve drug repurposing and facilitate development. Although there is a good understanding of X-ALD pathophysiology, the absence of information on drug targets, pharmacokinetics, and pharmacodynamics hinders the repurposing of drugs for this condition. Incorporating advanced translational and clinical pharmacological approaches in preclinical studies and early stages clinical trials will improve the success of repurposed drugs for X-ALD as well as other rare diseases.

Development and Advances of Drugs for Cancer Theranostics – PART-IV

2021 ◽  
Vol 21 (18) ◽  
pp. 1644-1644
Author(s):  
Lian-Shun Feng
Keyword(s):  
Drug Discovery ◽  
Side Effects ◽  
Anticancer Activity ◽  
Anticancer Drug ◽  
Anticancer Agents ◽  
Drug Repurposing ◽  
Biological Properties ◽  
Drug Candidates ◽  
Pharmacokinetic Profiles ◽  
Hybrid Molecules

Cancer, a highly heterogeneous disease at intra/inter patient levels, is one of the most serious threats to human health across the world [1, 2]. Notwithstanding the noteworthy advances in its treat-ment, the morbidity and mortality of cancer are projected to grow for a long period, and the global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020 [3]. Accordingly, there is a constant need to explore novel anticancer agents. <p> There are several strategies to discover novel anticancer candidates: (1) new lead hits or candidates from natural resources [4] whichexhibit various biological properties and are a rich source of com-pounds in drug discovery due to the structural and mechanistic diversity, and more than 60% anti-cancer agents can be traced to a natural product; (2) Molecular hybridization is one of the most prom-ising strategies for the discovery of novel anticancer drug candidates since hybrid molecules have the potential to bind multiple targets or to enhance the effect through acting with another bio-target or to counterbalance the side effects caused by the other part of the hybrid [5]; (3) Dimerization is a useful tool to develop novel anticancer drug candidates with enhanced biological activity, reduced side effects and improved pharmacokinetic profiles [6]; (4) Drug repurposing strategy is is an attractive strategy and has been approved, along with non-anticancer macrolide drugs for the treatment of cancer, for anticancer drug discovery since toxicity and pharmacokinetic profiles have already been estab-lished [7]. <p> Heterocycles coumarin, β-lactone, macrolide and triazole are useful anticancer pharmacophores since their derivatives could exert the anticancer activity through diverse mechanisms, inclusive of inhibition of aromatase, carbonic anhydrase, ki-nase, P-glycoprotein, sulfatase, telomerase, vascular endothelial growth factor receptor 2 and tubulin [8-11]. In particular, nat-ural-derived coumarin, β-lactone and macrolide derivatives are important sources of new anticancer lead hits/candidates; mac-rolide repurposed drugs can circumvent high cost and long-time associated with traditional drug discovery strategies; couma-rin, β-lactone and macrolide hybrids as well as bis-triazole compounds have the potential to enhance the anticancer activity, overcome drug resistance, reduce the side effects and improve pharmacokinetic profiles.

scholarly journals Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease

2020 ◽  
Vol 13 (11) ◽  
pp. dmm044040 ◽  
Author(s):  
Katie Lloyd ◽  
Stamatia Papoutsopoulou ◽  
Emily Smith ◽  
Philip Stegmaier ◽  
Francois Bergey ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Nuclear Translocation ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Peritoneal Macrophages ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Aseptic Conditions ◽  
Novel Drug

ABSTRACTInflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required.This article has an associated First Person interview with the joint first authors of the paper.

scholarly journals Machine Learning Methods in Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5277
Author(s):  
Lauv Patel ◽  
Tripti Shukla ◽  
Xiuzhen Huang ◽  
David W. Ussery ◽  
Shanzhi Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Drug Candidates ◽  
Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

scholarly journals Polypharmacological study of Ceritinib using a structure based in silico approach

Bionatura ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 836-840
Author(s):  
Tammanna R. Sahrawat ◽  
Prabhjeet Kaur Kaur
Keyword(s):  
Side Effects ◽  
Binding Sites ◽  
In Silico ◽  
Protein Tyrosine Kinase ◽  
Drug Repurposing ◽  
Aurora Kinase ◽  
Specific Protein ◽  
Binding Energies ◽  
Aurora Kinase B ◽  
Anaplastic Lymphoma

Drug repurposing has gained mass recognition over the past few years as it has paved new therapeutic applications for already approved FDA drugs. It focuses on finding new molecular targets of drugs for medical uses different than the one originally proposed. Ceritinib, an Anaplastic Lymphoma Kinase (ALK) inhibitor is given orally in the treatment of non-small cell lung cancer (NSCLC). This treatment has been reported to be associated with a number of side effects such as hyperglycemia, convulsion, pneumonitis etc. The side effects are usually due to the unintended interaction of the drug with other protein targets. In silico polypharmacological studies of Ceritinib suggests that it binds to multiple targets other than the intended one which may largely be due to different proteins possessing similar binding sites. ProBis server was used to retrieve probable off-targets of Ceritinib based on presence of structurally similar protein binding sites as that of ALK. Ceritinib was found to bind effectively to three proteins namely Lymphocyte Cell-Specific Protein-Tyrosine Kinase, Tropomyosin receptor kinase B and Aurora kinase B having favorable binding energies and inhibition constants, with no reported side-effects as compared to their marketed drugs. Therefore, it is concluded from the present study that Ceritinib may act as an effective therapeutic target against its polypharmacological targets.

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