scholarly journals GPCRs: Lipid-Dependent Membrane Receptors That Act as Drug Targets

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Amitabha Chattopadhyay
Keyword(s):  
Drug Targets ◽  
Membrane Lipids ◽  
Structural Information ◽  
Membrane Receptors ◽  
Future Research ◽  
Membrane Cholesterol ◽  
Drug Candidates ◽  
Basic Biology ◽  
Novel Drug

G protein-coupled receptors (GPCRs) are the largest class of molecules involved in signal transduction across cell membranes and represent major targets in the development of novel drug candidates in all clinical areas. Although there have been some recent leads, structural information on GPCRs is relatively rare due to the difficulty associated with crystallization. A specific reason for this is the intrinsic flexibility displayed by GPCRs, which is necessary for their functional diversity. Since GPCRs are integral membrane proteins, interaction of membrane lipids with them constitutes an important area of research in GPCR biology. In particular, membrane cholesterol has been reported to have a modulatory role in the function of a number of GPCRs. The role of membrane cholesterol in GPCR function is discussed with specific example of the serotonin1A receptor. Recent results show that GPCRs are characterized with structural motifs that preferentially associate with cholesterol. An emerging and important concept is oligomerization of GPCRs and its role in GPCR function and signaling. The role of membrane cholesterol in GPCR oligomerization is highlighted. Future research in GPCR biology would offer novel insight in basic biology and provide new avenues for drug discovery.

scholarly journals Sirtuin 1 and Sirtuin 3 in Granulosa Cell Tumors

2021 ◽  
Vol 22 (4) ◽  
pp. 2047
Author(s):  
Nina Schmid ◽  
Kim-Gwendolyn Dietrich ◽  
Ignasi Forne ◽  
Alexander Burges ◽  
Magdalena Szymanska ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Granulosa Cell ◽  
Granulosa Cells ◽  
Drug Targets ◽  
Growth Regulation ◽  
Sirtuin 1 ◽  
Ki 67 ◽  
Granulosa Cell Tumors ◽  
Novel Drug

Sirtuins (SIRTs) are NAD+-dependent deacetylases that regulate proliferation and cell death. In the human ovary, granulosa cells express sirtuin 1 (SIRT1), which has also been detected in human tumors derived from granulosa cells, i.e., granulosa cell tumors (GCTs), and in KGN cells. KGN cells are an established cellular model for the majority of GCTs and were used to explore the role of SIRT1. The SIRT1 activator SRT2104 increased cell proliferation. By contrast, the inhibitor EX527 reduced cell numbers, without inducing apoptosis. These results were supported by the outcome of siRNA-mediated silencing studies. A tissue microarray containing 92 GCTs revealed nuclear and/or cytoplasmic SIRT1 staining in the majority of the samples, and also, SIRT2-7 were detected in most samples. The expression of SIRT1–7 was not correlated with the survival of the patients; however, SIRT3 and SIRT7 expression was significantly correlated with the proliferation marker Ki-67, implying roles in tumor cell proliferation. SIRT3 was identified by a proteomic analysis as the most abundant SIRT in KGN. The results of the siRNA-silencing experiments indicate involvement of SIRT3 in proliferation. Thus, several SIRTs are expressed by GCTs, and SIRT1 and SIRT3 are involved in the growth regulation of KGN. If transferable to GCTs, these SIRTs may represent novel drug targets.

scholarly journals Mutation ofRv2887, amarR-Like Gene, Confers Mycobacterium tuberculosis Resistance to an Imidazopyridine-Based Agent

2015 ◽  
Vol 59 (11) ◽  
pp. 6873-6881 ◽  
Author(s):  
Kathryn Winglee ◽  
Shichun Lun ◽  
Marco Pieroni ◽  
Alan Kozikowski ◽  
William Bishai
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Targets ◽  
Efflux Pump ◽  
Transcriptional Regulator ◽  
Cross Resistance ◽  
Loss Of Function ◽  
Strong Activity ◽  
Content Type ◽  
Novel Drug

ABSTRACTDrug resistance is a major problem inMycobacterium tuberculosiscontrol, and it is critical to identify novel drug targets and new antimycobacterial compounds. We have previously identified an imidazo[1,2-a]pyridine-4-carbonitrile-based agent, MP-III-71, with strong activity againstM. tuberculosis. In this study, we evaluated mechanisms of resistance to MP-III-71. We derived three independentM. tuberculosismutants resistant to MP-III-71 and conducted whole-genome sequencing of these mutants. Loss-of-function mutations inRv2887were common to all three MP-III-71-resistant mutants, and we confirmed the role ofRv2887as a gene required for MP-III-71 susceptibility using complementation. The Rv2887 protein was previously unannotated, but domain and homology analyses suggested it to be a transcriptional regulator in the MarR (multiple antibiotic resistance repressor) family, a group of proteins first identified inEscherichia colito negatively regulate efflux pumps and other mechanisms of multidrug resistance. We found that two efflux pump inhibitors, verapamil and chlorpromazine, potentiate the action of MP-III-71 and that mutation ofRv2887abrogates their activity. We also used transcriptome sequencing (RNA-seq) to identify genes which are differentially expressed in the presence and absence of a functional Rv2887 protein. We found that genes involved in benzoquinone and menaquinone biosynthesis were repressed by functional Rv2887. Thus, inactivating mutations ofRv2887, encoding a putative MarR-like transcriptional regulator, confer resistance to MP-III-71, an effective antimycobacterial compound that shows no cross-resistance to existing antituberculosis drugs. The mechanism of resistance ofM. tuberculosisRv2887mutants may involve efflux pump upregulation and also drug methylation.

Developing novel chemical entities for the treatment of lysosomal storage disorders: an academic perspective

2015 ◽  
Vol 309 (12) ◽  
pp. F996-F999 ◽  
Author(s):  
James A. Shayman
Keyword(s):  
Drug Targets ◽  
Lysosomal Storage ◽  
Federal Drug Administration ◽  
Drug Candidates ◽  
Gaucher Disease Type 1 ◽  
Biotechnology Companies ◽  
Approved Drugs ◽  
Novel Drug ◽  
Early Drug

Historically, most Federal Drug Administration-approved drugs were the result of “in-house” efforts within large pharmaceutical companies. Over the last two decades, this paradigm has steadily shifted as the drug industry turned to startups, small biotechnology companies, and academia for the identification of novel drug targets and early drug candidates. This strategic pivot has created new opportunities for groups less traditionally associated with the creation of novel therapeutics, including small academic laboratories, for engagement in the drug discovery process. A recent example of the successful development of a drug that had its origins in academia is eliglustat tartrate, an oral agent for Gaucher disease type 1.

scholarly journals Integrative pharmacogenomics to infer large-scale drug taxonomy

2016 ◽  
Author(s):  
Nehme El-Hachem ◽  
Deena M.A. Gendoo ◽  
Laleh Soltan Ghoraie ◽  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Prior Information ◽  
Structural Information ◽  
Drug Efficacy ◽  
Basic Drug ◽  
Flexible Tool ◽  
Experimental Drugs ◽  
New Compounds ◽  
Novel Drug

ABSTRACTIdentification of drug targets and mechanism of action (MoA) for new and uncharacterlzed drugs is important for optimization of drug efficacy. Current MoA prediction approaches largely rely on prior information including side effects, therapeutic indication and/or chemo-informatics. Such information is not transferable or applicable for newly identified, previously uncharacterlzed small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary towards development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose a new integrative computational pharmacogenomlc approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that relies only on basic drug characteristics towards elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. We demonstrate that the DNF taxonomy succeeds in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. We highlight how the scalability of DNF facilitates identification of key drug relationships across different drug categories, and poses as a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on the compound MoA and potential insights for drug repurposlng.

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 Potential Therapeutic Strategies for Alzheimer’s Disease Targeting or Beyondβ-Amyloid: Insights from Clinical Trials

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Qiutian Jia ◽  
Yulin Deng ◽  
Hong Qing
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Drug Targets ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Therapeutic Approaches ◽  
Drug Candidates ◽  
Effective Drugs ◽  
Novel Drug

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with two hallmarks:β-amyloid plagues and neurofibrillary tangles. It is one of the most alarming illnesses to elderly people. No effective drugs and therapies have been developed, while mechanism-based explorations of therapeutic approaches have been intensively investigated. Outcomes of clinical trials suggested several pitfalls in the choice of biomarkers, development of drug candidates, and interaction of drug-targeted molecules; however, they also aroused concerns on the potential deficiency in our understanding of pathogenesis of AD, and ultimately stimulated the advent of novel drug targets tests. The anticipated increase of AD patients in next few decades makes development of better therapy an urgent issue. Here we attempt to summarize and compare putative therapeutic strategies that have completed clinical trials or are currently being tested from various perspectives to provide insights for treatments of Alzheimer’s disease.

scholarly journals Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using The CANDO Platform: Implications for Drug Design

2018 ◽  
Author(s):  
William Mangione ◽  
Ram Samudrala
Keyword(s):  
Drug Design ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Learning Approaches ◽  
Drug Candidates ◽  
Protein Library ◽  
Fold Reduction ◽  
Ligand Interactions ◽  
Novel Drug

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100–1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

New Molecular Targets and Strategies for Antimalarial Discovery

2019 ◽  
Vol 26 (23) ◽  
pp. 4380-4402 ◽  
Author(s):  
Anna Caroline Aguiar ◽  
Lorena R.F. de Sousa ◽  
Celia R.S. Garcia ◽  
Glaucius Oliva ◽  
Rafael V.C. Guido
Keyword(s):  
Structural Biology ◽  
Drug Targets ◽  
Structural Information ◽  
Molecular Targets ◽  
Major Health Problem ◽  
Post Translational Modifications ◽  
Major Health ◽  
Artemisinin Derivatives ◽  
Drug Candidates ◽  
Lead Compounds

Malaria remains a major health problem, especially because of the emergence of resistant P. falciparum strains to artemisinin derivatives. In this context, safe and affordable antimalarial drugs are desperately needed. New proteins have been investigated as molecular targets for research and development of innovative compounds with welldefined mechanism of action. In this review, we highlight genetically and clinically validated plasmodial proteins as drug targets for the next generation of therapeutics. The enzymes described herein are involved in hemoglobin hydrolysis, the invasion process, elongation factors for protein synthesis, pyrimidine biosynthesis, post-translational modifications such as prenylation, phosphorylation and histone acetylation, generation of ATP in mitochondrial metabolism and aminoacylation of RNAs. Significant advances on proteomics, genetics, structural biology, computational and biophysical methods provided invaluable molecular and structural information about these drug targets. Based on this, several strategies and models have been applied to identify and improve lead compounds. This review presents the recent progresses in the discovery of antimalarial drug candidates, highlighting the approaches, challenges, and perspectives to deliver affordable, safe and low single-dose medicines to treat malaria.

Potential Role of Endonuclease Inhibition and Other Targets in the Treatment of Influenza

2020 ◽  
Vol 21 (2) ◽  
pp. 202-211
Author(s):  
Doreen Szollosi ◽  
Ashley Bill
Keyword(s):  
Rna Polymerase ◽  
Drug Targets ◽  
Rna Virus ◽  
Neuraminidase Inhibitor ◽  
Viral Fusion ◽  
Dependent Endonuclease ◽  
Resistant Strains ◽  
Polymerase Basic 1 ◽  
Novel Drug

Background: Influenza is a single-stranded RNA virus that is highly contagious and infects millions of people in the U.S. annually. Due to complications, approximately 959,000 people were hospitalized and another 79,400 people died during the 2017-2018 flu season. While the best methods of prevention continue to be vaccination and hygiene, antiviral treatments may help reduce symptoms for those who are infected. Until recently, the only antiviral drugs in use have been the neuraminidase inhibitors: oseltamivir, zanamivir, and peramivir. Objective: We reviewed novel drug targets that can be used in the treatment of influenza, particularly in the case of neuraminidase inhibitor-resistant strains that may emerge. Results: More recently, a drug with a new mechanism of action has been approved. Baloxavir marboxil inhibits the influenza cap-dependent endonuclease that is needed for the virus to initiate replication within the host cell. This endonuclease target is within the polymerase acid (PA) subunit of RNA polymerase. Since the RNA-dependent RNA polymerase consists of two other subunits, polymerase basic 1 and 2, RNA polymerase has several targets that prevent viral replication. Other targets still under investigation include viral kinases, endocytosis, and viral fusion. Conclusion: Due to the possibility of viral mutations and resistance, it is important to have antivirals with different mechanisms available, especially in the case of a new pandemic strain. Several novel antivirals are within various stages of development and may represent new classes of treatments that can reduce symptoms and complications in those patients who may be at higher risk.

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