Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 23
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
ACS Med Chem Lett ; 12(11): 1832-1839, 2021 Nov 11.
Artigo em Inglês | MEDLINE | ID: mdl-34795874

RESUMO

Calcium and integrin binding protein 1 (CIB1) is a small, intracellular protein recently implicated in survival and proliferation of triple-negative breast cancer (TNBC). Considering its interactions with PAK1 and downstream signaling, CIB1 has been suggested as a potential therapeutic target in TNBC. As such, CIB1 has been the focus of inhibitor discovery efforts. To overcome issues of potency and stability in previously reported CIB1 inhibitors, we deploy mRNA display to discover new cyclic peptide inhibitors with improved biophysical properties and cellular activity. We advance UNC10245131, a cyclic peptide with low nanomolar affinity and good selectivity for CIB1 over other EF-hand domain proteins and improved permeability and stability over previously identified linear peptide inhibitor UNC10245092. Unlike UNC10245092, UNC10245131 lacks cytotoxicity and does not affect downstream signaling. Despite this, UNC10245131 is a potent ligand that could aid in clarifying roles of CIB1 in TNBC survival and proliferation and other CIB1-associated biological phenotypes.

2.
J Chem Inf Model ; 61(9): 4224-4235, 2021 09 27.
Artigo em Inglês | MEDLINE | ID: mdl-34387990

RESUMO

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.


Assuntos
COVID-19 , SARS-CoV-2 , Teorema de Bayes , Humanos , Aprendizado de Máquina , Simulação de Acoplamento Molecular
3.
Curr Opin Chem Biol ; 65: 74-84, 2021 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-34274565

RESUMO

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.

4.
J Chem Inf Model ; 61(8): 3804-3813, 2021 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-34286575

RESUMO

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 µM and CC50 24 µM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.


Assuntos
Febre Amarela , Infecção por Zika virus , Zika virus , Animais , Antivirais/farmacologia , Descoberta de Drogas , Aprendizado de Máquina , Vírus da Febre Amarela
5.
ACS Omega ; 6(24): 16253, 2021 Jun 22.
Artigo em Inglês | MEDLINE | ID: mdl-34179670

RESUMO

[This corrects the article DOI: 10.1021/acsomega.0c05591.].

6.
ACS Omega ; 6(11): 7454-7468, 2021 Mar 23.
Artigo em Inglês | MEDLINE | ID: mdl-33778258

RESUMO

Severe acute respiratory coronavirus 2 (SARS-CoV-2) is a newly identified virus that has resulted in over 2.5 million deaths globally and over 116 million cases globally in March, 2021. Small-molecule inhibitors that reverse disease severity have proven difficult to discover. One of the key approaches that has been widely applied in an effort to speed up the translation of drugs is drug repurposing. A few drugs have shown in vitro activity against Ebola viruses and demonstrated activity against SARS-CoV-2 in vivo. Most notably, the RNA polymerase targeting remdesivir demonstrated activity in vitro and efficacy in the early stage of the disease in humans. Testing other small-molecule drugs that are active against Ebola viruses (EBOVs) would appear a reasonable strategy to evaluate their potential for SARS-CoV-2. We have previously repurposed pyronaridine, tilorone, and quinacrine (from malaria, influenza, and antiprotozoal uses, respectively) as inhibitors of Ebola and Marburg viruses in vitro in HeLa cells and mouse-adapted EBOV in mice in vivo. We have now tested these three drugs in various cell lines (VeroE6, Vero76, Caco-2, Calu-3, A549-ACE2, HUH-7, and monocytes) infected with SARS-CoV-2 as well as other viruses (including MHV and HCoV 229E). The compilation of these results indicated considerable variability in antiviral activity observed across cell lines. We found that tilorone and pyronaridine inhibited the virus replication in A549-ACE2 cells with IC50 values of 180 nM and IC50 198 nM, respectively. We used microscale thermophoresis to test the binding of these molecules to the spike protein, and tilorone and pyronaridine bind to the spike receptor binding domain protein with K d values of 339 and 647 nM, respectively. Human Cmax for pyronaridine and quinacrine is greater than the IC50 observed in A549-ACE2 cells. We also provide novel insights into the mechanism of these compounds which is likely lysosomotropic.

7.
ACS Omega ; 6(4): 3186-3193, 2021 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-33553934

RESUMO

Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1. This enzyme catalyzes the removal of sialic acid moieties from glycoproteins and glycolipids. Therefore, the defective or deficient protein leads to the buildup of sialylated glycoproteins as well as several characteristic symptoms of sialidosis including visual impairment, ataxia, hepatomegaly, dysostosis multiplex, and developmental delay. In this study, we used a bibliometric tool to generate links between lysosomal storage disease (LSD) targets and existing bioactivity data that could be curated in order to build machine learning models and screen compounds in silico. We focused on sialidase as an example, and we used the data curated from the literature to build a Bayesian model which was then used to score compound libraries and rank these molecules for in vitro testing. Two compounds were identified from in vitro testing using microscale thermophoresis, namely sulfameter (K d 2.15 ± 1.02 µM) and mexenone (K d 8.88 ± 4.02 µM), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.

8.
Chem Res Toxicol ; 34(5): 1296-1307, 2021 05 17.
Artigo em Inglês | MEDLINE | ID: mdl-33400519

RESUMO

Acetylcholinesterase (AChE) is an important drug target in neurological disorders like Alzheimer's disease, Lewy body dementia, and Parkinson's disease dementia as well as for other conditions like myasthenia gravis and anticholinergic poisoning. In this study, we have used a combination of high-throughput screening, machine learning, and docking to identify new inhibitors of this enzyme. Bayesian machine learning models were generated with literature data from ChEMBL for eel and human AChE inhibitors as well as butyrylcholinesterase inhibitors (BuChE) and compared with other machine learning methods. High-throughput screens for the eel AChE inhibitor model identified several molecules including tilorone, an antiviral drug that is well-established outside of the United States, as a newly identified nanomolar AChE inhibitor. We have described how tilorone inhibits both eel and human AChE with IC50's of 14.4 nM and 64.4 nM, respectively, but does not inhibit the closely related BuChE IC50 > 50 µM. We have docked tilorone into the human AChE crystal structure and shown that this selectivity is likely due to the reliance on a specific interaction with a hydrophobic residue in the peripheral anionic site of AChE that is absent in BuChE. We also conducted a pharmacological safety profile (SafetyScreen44) and kinase selectivity screen (SelectScreen) that showed tilorone (1 µM) only inhibited AChE out of 44 toxicology target proteins evaluated and did not appreciably inhibit any of the 485 kinases tested. This study suggests there may be a potential role for repurposing tilorone or its derivatives in conditions that benefit from AChE inhibition.


Assuntos
Antivirais/farmacologia , Inibidores da Colinesterase/farmacologia , Tilorona/farmacologia , Acetilcolinesterase/metabolismo , Animais , Antivirais/química , Inibidores da Colinesterase/química , Relação Dose-Resposta a Droga , Electrophorus , Humanos , Aprendizado de Máquina , Simulação de Acoplamento Molecular , Estrutura Molecular , Relação Estrutura-Atividade , Tilorona/química
9.
bioRxiv ; 2020 Dec 02.
Artigo em Inglês | MEDLINE | ID: mdl-33299990

RESUMO

SARS-CoV-2 is a newly identified virus that has resulted in over 1.3 M deaths globally and over 59 M cases globally to date. Small molecule inhibitors that reverse disease severity have proven difficult to discover. One of the key approaches that has been widely applied in an effort to speed up the translation of drugs is drug repurposing. A few drugs have shown in vitro activity against Ebola virus and demonstrated activity against SARS-CoV-2 in vivo . Most notably the RNA polymerase targeting remdesivir demonstrated activity in vitro and efficacy in the early stage of the disease in humans. Testing other small molecule drugs that are active against Ebola virus would seem a reasonable strategy to evaluate their potential for SARS-CoV-2. We have previously repurposed pyronaridine, tilorone and quinacrine (from malaria, influenza, and antiprotozoal uses, respectively) as inhibitors of Ebola and Marburg virus in vitro in HeLa cells and of mouse adapted Ebola virus in mouse in vivo . We have now tested these three drugs in various cell lines (VeroE6, Vero76, Caco-2, Calu-3, A549-ACE2, HUH-7 and monocytes) infected with SARS-CoV-2 as well as other viruses (including MHV and HCoV 229E). The compilation of these results indicated considerable variability in antiviral activity observed across cell lines. We found that tilorone and pyronaridine inhibited the virus replication in A549-ACE2 cells with IC 50 values of 180 nM and IC 50 198 nM, respectively. We have also tested them in a pseudovirus assay and used microscale thermophoresis to test the binding of these molecules to the spike protein. They bind to spike RBD protein with K d values of 339 nM and 647 nM, respectively. Human C max for pyronaridine and quinacrine is greater than the IC 50 hence justifying in vivo evaluation. We also provide novel insights into their mechanism which is likely lysosomotropic.

10.
ACS Omega ; 5(46): 29935-29942, 2020 Nov 24.
Artigo em Inglês | MEDLINE | ID: mdl-33251429

RESUMO

Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) and is a deadly disease resulting in the deaths of approximately 1.5 million people with 10 million infections reported in 2018. Recently, a key condensation step in the synthesis of mycolic acids was shown to require ß-ketoacyl-ACP synthase (KasA). A crystal structure of KasA with the small molecule DG167 was recently described, which provided a starting point for using computational structure-based approaches to identify additional molecules binding to this protein. We now describe structure-based pharmacophores, docking and machine learning studies with Assay Central as a computational tool for the identification of small molecules targeting KasA. We then tested these compounds using nanoscale differential scanning fluorimetry and microscale thermophoresis. Of note, we identified several molecules including the Food and Drug Administration (FDA)-approved drugs sildenafil and flubendazole with K d values between 30-40 µM. This may provide additional starting points for further optimization.

11.
ACS Omega ; 5(41): 26551-26561, 2020 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-33110983

RESUMO

Alzheimer's disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine-threonine kinase glycogen synthase kinase 3 beta (GSK3ß), which is a proline-directed serine-threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3ß consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3ß (avg IC50 = 97.3 nM) and GSK3α (IC50 = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening.

12.
Pharm Res ; 37(7): 127, 2020 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-32529312

RESUMO

PURPOSE: Individuals with the rare genetic disorder Pitt Hopkins Syndrome (PTHS) do not have sufficient expression of the transcription factor 4 (TCF4) which is located on chromosome 18. TCF4 is a basic helix-loop-helix E protein that is critical for the normal development of the nervous system and the brain in humans. PTHS patients lacking sufficient TCF4 frequently display gastrointestinal issues, intellectual disability and breathing problems. PTHS patients also commonly do not speak and display distinctive facial features and seizures. Recent research has proposed that decreased TCF4 expression can lead to the increased translation of the sodium channel Nav1.8. This in turn results in increased after-hyperpolarization as well as altered firing properties. We have recently identified through a drug repurposing screen an FDA approved dihydropyridine calcium antagonist nicardipine used to treat angina, which inhibited Nav1.8. METHODS: We have now performed behavioral testing in groups of 10 male Tcf4(± ) PTHS mice dosing by oral gavage at 3 mg/kg once a day for 3 weeks using standard methods to assess sociability, nesting, fear conditioning, self-grooming, open field and test of force. RESULTS: Nicardipine returned this spectrum of behavioral deficits in the Tcf4(± ) PTHS mouse model to WT levels and resulted in statistically significant results. CONCLUSIONS: These in vivo results in the well characterized Tcf4(± ) PTHS mice may suggest the potential to test this already approved drug further in a clinical study with PTHS patients or suggest the potential for use off label under compassionate use with their physician.


Assuntos
Bloqueadores dos Canais de Cálcio/química , Canais de Cálcio/metabolismo , Di-Hidropiridinas/química , Reposicionamento de Medicamentos/métodos , Hiperventilação/tratamento farmacológico , Deficiência Intelectual/tratamento farmacológico , Nicardipino/química , Animais , Controle Comportamental , Bloqueadores dos Canais de Cálcio/farmacologia , Modelos Animais de Doenças , Descoberta de Drogas , Facies , Medo/efeitos dos fármacos , Regulação da Expressão Gênica/efeitos dos fármacos , Humanos , Masculino , Camundongos , Canal de Sódio Disparado por Voltagem NAV1.8/metabolismo , Nicardipino/farmacologia , Fenótipo , Habilidades Sociais , Fator de Transcrição 4/genética
13.
ACS Chem Biol ; 15(6): 1505-1516, 2020 06 19.
Artigo em Inglês | MEDLINE | ID: mdl-32383857

RESUMO

Calcium and integrin binding protein 1 (CIB1) is an EF-hand-containing, small intracellular protein that has recently been implicated in cancer cell survival and proliferation. In particular, CIB1 depletion significantly impairs tumor growth in triple-negative breast cancer (TNBC). Thus, CIB1 is a potentially attractive target for cancer chemotherapy that has yet to be validated by a chemical probe. To produce a probe molecule to the CIB1 helix 10 (H10) pocket and demonstrate that it is a viable target for molecular intervention, we employed random peptide phage display to screen and select CIB1-binding peptides. The top peptide sequence selected, UNC10245092, was produced synthetically, and binding to CIB1 was confirmed by isothermal titration calorimetry (ITC) and a time-resolved fluorescence resonance energy transfer (TR-FRET) assay. Both assays showed that the peptide bound to CIB1 with low nanomolar affinity. CIB1 was cocrystallized with UNC10245092, and the 2.1 Å resolution structure revealed that the peptide binds as an α-helix in the H10 pocket, displacing the CIB1 C-terminal H10 helix and causing conformational changes in H7 and H8. UNC10245092 was further derivatized with a C-terminal Tat-derived cell penetrating peptide (CPP) to demonstrate its effects on TNBC cells in culture, which are consistent with results of CIB1 depletion. These studies provide a first-in-class chemical tool for CIB1 inhibition in cell culture and validate the CIB1 H10 pocket for future probe and drug discovery efforts.


Assuntos
Proteínas de Ligação ao Cálcio/antagonistas & inibidores , Sequência de Aminoácidos , Calorimetria/métodos , Linhagem Celular Tumoral , Descoberta de Drogas , Humanos , Interações Hidrofóbicas e Hidrofílicas , Conformação Proteica
14.
Drug Discov Today ; 25(5): 928-941, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32320852

RESUMO

In the past decade we have seen two major Ebola virus outbreaks in Africa, the Zika virus in Brazil and the Americas and the current pandemic of coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There is a strong sense of déjà vu because there are still no effective treatments. In the COVID-19 pandemic, despite being a new virus, there are already drugs suggested as active in in vitro assays that are being repurposed in clinical trials. Promising SARS-CoV-2 viral targets and computational approaches are described and discussed. Here, we propose, based on open antiviral drug discovery approaches for previous outbreaks, that there could still be gaps in our approach to drug discovery.


Assuntos
Antivirais/farmacologia , Antivirais/uso terapêutico , Betacoronavirus/efeitos dos fármacos , Infecções por Coronavirus/tratamento farmacológico , Descoberta de Drogas , Pneumonia Viral/tratamento farmacológico , Enzima de Conversão de Angiotensina 2 , Animais , Betacoronavirus/metabolismo , COVID-19 , Chlorocebus aethiops , Simulação por Computador , Reposicionamento de Medicamentos , Doença pelo Vírus Ebola/tratamento farmacológico , Humanos , Técnicas In Vitro , Coronavírus da Síndrome Respiratória do Oriente Médio , Simulação de Acoplamento Molecular , Pandemias , Peptidil Dipeptidase A/metabolismo , Vírus da SARS , SARS-CoV-2 , Síndrome Respiratória Aguda Grave/tratamento farmacológico , Glicoproteína da Espícula de Coronavírus/metabolismo , Células Vero , Infecção por Zika virus/tratamento farmacológico
15.
Int J Biol Macromol ; 136: 493-502, 2019 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-31216447

RESUMO

Cellulases are essential enzymatic components for the transformation of plant biomass into fuels, renewable materials and green chemicals. Here, we determined the crystal structure, pattern of hydrolysis products release, and conducted molecular dynamics simulations of the major endoglucanase from the Xanthomonas campestris pv. campestris (XccCel5A). XccCel5A has a TIM barrel fold with the catalytic site centrally placed in a binding groove surrounded by aromatic side chains. Molecular dynamics simulations show that productive position of the substrate is secured by a network of hydrogen bonds in the four main subsites, which differ in details from homologous structures. Capillary zone electrophoresis and computational studies reveal XccCel5A can act both as endoglucanase and licheninase, but there are preferable arrangements of substrate regarding ß-1,3 and ß-1,4 bonds within the binding cleft which are related to the enzymatic efficiency.


Assuntos
Celulase/química , Celulase/metabolismo , Simulação de Dinâmica Molecular , Oligossacarídeos/metabolismo , Xanthomonas campestris/enzimologia , Domínio Catalítico , Cristalografia por Raios X , Hidrólise
17.
Nat Mater ; 18(5): 435-441, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31000803

RESUMO

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.


Assuntos
Biologia Computacional/métodos , Aprendizado de Máquina , Algoritmos , Teorema de Bayes , Simulação por Computador , Desenho de Fármacos , Desenvolvimento de Medicamentos , Descoberta de Drogas , Reposicionamento de Medicamentos , Humanos , Nanomedicina , Redes Neurais de Computação , Máquina de Vetores de Suporte , Tecnologia Farmacêutica/tendências
18.
Drug Discov Today ; 24(5): 1139-1147, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30885676

RESUMO

Recent estimates suggest close to one million people per year die globally owing to HIV-related illnesses. Therefore, there is still a need to identify new targets to develop future treatments. Many of the more recently identified targets are host-related and these might be more difficult for the virus to develop drug resistance to. In addition, there are virus-related targets (capsid and RNAse H) that have yet to be exploited clinically. Several of the newer targets also address virulence factors, virus latency or target persistence. The targets highlighted in this review could represent the next generation of viable candidates for drug discovery projects as well as continue the search for a cure for this disease.


Assuntos
Fármacos Anti-HIV/farmacologia , Infecções por HIV/metabolismo , Terapia de Alvo Molecular , Animais , Fármacos Anti-HIV/uso terapêutico , Descoberta de Drogas , Infecções por HIV/tratamento farmacológico , Humanos
19.
Nature ; 564(7736): 439-443, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30405246

RESUMO

Stimulator of interferon genes (STING) is a receptor in the endoplasmic reticulum that propagates innate immune sensing of cytosolic pathogen-derived and self DNA1. The development of compounds that modulate STING has recently been the focus of intense research for the treatment of cancer and infectious diseases and as vaccine adjuvants2. To our knowledge, current efforts are focused on the development of modified cyclic dinucleotides that mimic the endogenous STING ligand cGAMP; these have progressed into clinical trials in patients with solid accessible tumours amenable to intratumoral delivery3. Here we report the discovery of a small molecule STING agonist that is not a cyclic dinucleotide and is systemically efficacious for treating tumours in mice. We developed a linking strategy to synergize the effect of two symmetry-related amidobenzimidazole (ABZI)-based compounds to create linked ABZIs (diABZIs) with enhanced binding to STING and cellular function. Intravenous administration of a diABZI STING agonist to immunocompetent mice with established syngeneic colon tumours elicited strong anti-tumour activity, with complete and lasting regression of tumours. Our findings represent a milestone in the rapidly growing field of immune-modifying cancer therapies.


Assuntos
Benzimidazóis/química , Benzimidazóis/farmacologia , Neoplasias do Colo/tratamento farmacológico , Neoplasias do Colo/imunologia , Desenho de Fármacos , Proteínas de Membrana/agonistas , Animais , Benzimidazóis/administração & dosagem , Benzimidazóis/uso terapêutico , Humanos , Ligantes , Proteínas de Membrana/imunologia , Camundongos , Modelos Moleculares , Nucleotídeos Cíclicos/metabolismo
20.
Data Brief ; 7: 1430-1437, 2016 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-27761505

RESUMO

Loss-of-function mutation V290M in the ligand-binding domain of the peroxisome proliferator activated receptor γ (PPARγ) is associated with a ligand resistance syndrome (PLRS), characterized by partial lipodystrophy and severe insulin resistance. In this data article we discuss an X-ray diffraction dataset that yielded the structure of PPARγ LBD V290M mutant refined at 2.3 Šresolution, that allowed building of 3D model of the receptor mutant with high confidence and revealed continuous well-defined electron density for the partial agonist diclofenac bound to hydrophobic pocket of the PPARγ. These structural data provide significant insights into molecular basis of PLRS caused by V290M mutation and are correlated with the receptor disability of rosiglitazone binding and increased affinity for corepressors. Furthermore, our structural evidence helps to explain clinical observations which point out to a failure to restore receptor function by the treatment with a full agonist of PPARγ, rosiglitazone.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...