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1.
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
2.
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
3.
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.].

4.
J Chem Inf Model ; 61(6): 2641-2647, 2021 06 28.
Artigo em Inglês | MEDLINE | ID: mdl-34032436

RESUMO

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.


Assuntos
COVID-19 , Descoberta de Drogas , Algoritmos , Metodologias Computacionais , Humanos , Aprendizado de Máquina , Teoria Quântica , SARS-CoV-2 , Máquina de Vetores de Suporte
5.
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.

6.
ACS Infect Dis ; 7(2): 406-420, 2021 02 12.
Artigo em Inglês | MEDLINE | ID: mdl-33434015

RESUMO

Schistosomiasis is a chronic and painful disease of poverty caused by the flatworm parasite Schistosoma. Drug discovery for antischistosomal compounds predominantly employs in vitro whole organism (phenotypic) screens against two developmental stages of Schistosoma mansoni, post-infective larvae (somules) and adults. We generated two rule books and associated scoring systems to normalize 3898 phenotypic data points to enable machine learning. The data were used to generate eight Bayesian machine learning models with the Assay Central software according to parasite's developmental stage and experimental time point (≤24, 48, 72, and >72 h). The models helped predict 56 active and nonactive compounds from commercial compound libraries for testing. When these were screened against S. mansoni in vitro, the prediction accuracy for active and inactives was 61% and 56% for somules and adults, respectively; also, hit rates were 48% and 34%, respectively, far exceeding the typical 1-2% hit rate for traditional high throughput screens.


Assuntos
Descoberta de Drogas , Schistosoma mansoni , Animais , Teorema de Bayes , Larva , Aprendizado de Máquina
7.
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
8.
J Ethnopharmacol ; 267: 113533, 2021 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-33137433

RESUMO

ETHNOPHARMACOLOGICAL RELEVANCE: Secondary metabolites play a critical role in plant defense against disease and are of great importance to ethnomedicine. Bacterial efflux pumps are active transport proteins that bacterial cells use to protect themselves against multiple toxic compounds, including many antimicrobials. Efflux pump inhibitors from plants can block these efflux pumps, increasing the potency of antimicrobial compounds. This study demonstrates that efflux pump inhibition against the Gram-positive bacterial pathogen Staphylococcus aureus is widespread in extracts prepared from individual species throughout the land plant lineage. It therefore suggests a general mechanism by which plants used by indigenous species may be effective as a topical treatment for some bacterial infections. AIM OF THE STUDY: The goal of this research was to evaluate the distribution of efflux pump inhibitors in nine plant extracts with an ethnobotanical use suggestive of an antimicrobial function for the presence of efflux pump inhibitory activity against Staphylococcus aureus. MATERIALS AND METHODS: Plants were collected, dried, extracted, and vouchers submitted to the Herbarium of the University of North Carolina Chapel Hill (NCU). The extracts were analyzed by quantitative mass spectrometry (UPLC-MS) to determine the presence and concentration of flavonoids with known efflux pump inhibitory activity. A mass spectrometry-based assay was employed to measure efflux pump inhibition for all extracts against Staphylococcus aureus. The assay relies on UPLC-MS measurement of changes in ethidium concentration in the spent culture broth when extracts are incubated with bacteria. RESULTS: Eight of these nine plant extracts inhibited toxic compound efflux at concentrations below the MIC (minimum inhibitory concentration) value for the same extract. The most active extracts were those prepared from Osmunda claytoniana L. and Pinus strobes L., which both demonstrated IC50 values for efflux inhibition of 19 ppm. CONCLUSIONS: Our findings indicate that efflux pump inhibitors active against Staphylococcus aureus are common in land plants. By extension, this activity is likely to be important in many plant-derived antimicrobial extracts, including those used in traditional medicine, and evaluation of efflux pump inhibition may often be valuable when studying natural product efficacy.


Assuntos
Antibacterianos/farmacologia , Proteínas de Bactérias/antagonistas & inibidores , Sistemas de Secreção Bacterianos/efeitos dos fármacos , Moduladores de Transporte de Membrana/farmacologia , Proteínas de Membrana Transportadoras/efeitos dos fármacos , Plantas Medicinais , Staphylococcus aureus/efeitos dos fármacos , Antibacterianos/isolamento & purificação , Proteínas de Bactérias/metabolismo , Moduladores de Transporte de Membrana/isolamento & purificação , Proteínas de Membrana Transportadoras/metabolismo , Testes de Sensibilidade Microbiana , Fitoterapia , Plantas Medicinais/química , Plantas Medicinais/classificação , Staphylococcus aureus/metabolismo
9.
Mol Pharmacol ; 99(2): 147-162, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33262250

RESUMO

Equilibrative nucleoside transporters (ENTs) 1 and 2 facilitate nucleoside transport across the blood-testis barrier (BTB). Improving drug entry into the testes with drugs that use endogenous transport pathways may lead to more effective treatments for diseases within the reproductive tract. In this study, CRISPR/CRISPR-associated protein 9 was used to generate HeLa cell lines in which ENT expression was limited to ENT1 or ENT2. We characterized uridine transport in these cell lines and generated Bayesian models to predict interactions with the ENTs. Quantification of [3H]uridine uptake in the presence of the ENT-specific inhibitor S-(4-nitrobenzyl)-6-thioinosine (NBMPR) demonstrated functional loss of each transporter. Nine nucleoside reverse-transcriptase inhibitors and 37 nucleoside/heterocycle analogs were evaluated to identify ENT interactions. Twenty-one compounds inhibited uridine uptake and abacavir, nevirapine, ticagrelor, and uridine triacetate had different IC50 values for ENT1 and ENT2. Total accumulation of four identified inhibitors was measured with and without NBMPR to determine whether there was ENT-mediated transport. Clofarabine and cladribine were ENT1 and ENT2 substrates, whereas nevirapine and lexibulin were ENT1 and ENT2 nontransported inhibitors. Bayesian models generated using Assay Central machine learning software yielded reasonably high internal validation performance (receiver operator characteristic > 0.7). ENT1 IC50-based models were generated from ChEMBL; subvalidations using this training data set correctly predicted 58% of inhibitors when analyzing activity by percent uptake and 63% when using estimated-IC50 values. Determining drug interactions with these transporters can be useful in identifying and predicting compounds that are ENT1 and ENT2 substrates and can thereby circumvent the BTB through this transepithelial transport pathway in Sertoli cells. SIGNIFICANCE STATEMENT: This study is the first to predict drug interactions with equilibrative nucleoside transporter (ENT) 1 and ENT2 using Bayesian modeling. Novel CRISPR/CRISPR-associated protein 9 functional knockouts of ENT1 and ENT2 in HeLa S3 cells were generated and characterized. Determining drug interactions with these transporters can be useful in identifying and predicting compounds that are ENT1 and ENT2 substrates and can circumvent the blood-testis barrier through this transepithelial transport pathway in Sertoli cells.


Assuntos
Acetatos/farmacologia , Didesoxinucleosídeos/farmacologia , Transportador Equilibrativo 1 de Nucleosídeo/genética , Transportador Equilibrativo 2 de Nucleosídeo/genética , Nevirapina/farmacologia , Ticagrelor/farmacologia , Uridina/análogos & derivados , Uridina/metabolismo , Teorema de Bayes , Transporte Biológico , Sistemas CRISPR-Cas , Linhagem Celular , Interações Medicamentosas , Transportador Equilibrativo 1 de Nucleosídeo/metabolismo , Transportador Equilibrativo 2 de Nucleosídeo/metabolismo , Técnicas de Inativação de Genes , Células HeLa , Humanos , Aprendizado de Máquina , Tioinosina/análogos & derivados , Tioinosina/farmacologia , Uridina/farmacologia
10.
Mol Pharm ; 18(1): 403-415, 2021 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-33325717

RESUMO

Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies, we and others have applied multiple machine learning algorithms and modeling metrics and, in some cases, compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and in comparison of our proprietary software Assay Central with random forest, k-nearest neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (three layers). Model performance was assessed using an array of fivefold cross-validation metrics including area-under-the-curve, F1 score, Cohen's kappa, and Matthews correlation coefficient. Based on ranked normalized scores for the metrics or datasets, all methods appeared comparable, while the distance from the top indicated that Assay Central and support vector classification were comparable. Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case. If anything, Assay Central may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay Central performance, although support vector classification seems to be a strong competitor. We also applied Assay Central to perform prospective predictions for the toxicity targets PXR and hERG to further validate these models. This work appears to be the largest scale comparison of these machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors, and machine learning algorithms and further refine the methods for evaluating and comparing such models.


Assuntos
Descoberta de Drogas/métodos , Algoritmos , Teorema de Bayes , Bases de Dados Factuais , Árvores de Decisões , Humanos , Aprendizado de Máquina , Redes Neurais de Computação , Estudos Prospectivos , Software , Máquina de Vetores de Suporte
11.
Environ Sci Technol ; 54(23): 15546-15555, 2020 12 01.
Artigo em Inglês | MEDLINE | ID: mdl-33207874

RESUMO

Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition of this enzyme can occur with drugs and natural products as well as environmental chemicals. Therefore, predicting potential endocrine disruption via exogenous chemicals requires that aromatase inhibition be considered in addition to androgen and estrogen pathway interference. Bayesian machine learning methods can be used for prospective prediction from the molecular structure without the need for experimental data. Herein, the generation and evaluation of multiple machine learning models utilizing different sources of aromatase inhibition data are described. These models are applied to two test sets for external validation with molecules relevant to drug discovery from the public domain. In addition, the performance of multiple machine learning algorithms was evaluated by comparing internal five-fold cross-validation statistics of the training data. These methods to predict aromatase inhibition from molecular structure, when used in concert with estrogen and androgen machine learning models, allow for a more holistic assessment of endocrine-disrupting potential of chemicals with limited empirical data and enable the reduction of the use of hazardous substances.


Assuntos
Aromatase , Aprendizado de Máquina , Androgênios , Inibidores da Aromatase , Teorema de Bayes , Estudos Prospectivos
12.
Environ Sci Technol ; 54(21): 13690-13700, 2020 11 03.
Artigo em Inglês | MEDLINE | ID: mdl-33085465

RESUMO

The androgen receptor (AR) is a target of interest for endocrine disruption research, as altered signaling can affect normal reproductive and neurological development for generations. In an effort to prioritize compounds with alternative methodologies, the U.S. Environmental Protection Agency (EPA) used in vitro data from 11 assays to construct models of AR agonist and antagonist signaling pathways. While these EPA ToxCast AR models require in vitro data to assign a bioactivity score, Bayesian machine learning methods can be used for prospective prediction from molecule structure alone. This approach was applied to multiple types of data corresponding to the EPA's AR signaling pathway with proprietary software, Assay Central. The training performance of all machine learning models, including six other algorithms, was evaluated by internal 5-fold cross-validation statistics. Bayesian machine learning models were also evaluated with external predictions of reference chemicals to compare prediction accuracies to published results from the EPA. The machine learning model group selected for further studies of endocrine disruption consisted of continuous AC50 data from the February 2019 release of ToxCast/Tox21. These efforts demonstrate how machine learning can be used to predict AR-mediated bioactivity and can also be applied to other targets of endocrine disruption.


Assuntos
Aprendizado de Máquina , Receptores Androgênicos , Androgênios , Teorema de Bayes , Estudos Prospectivos , Estados Unidos
13.
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.

14.
Antiviral Res ; 182: 104908, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32798602

RESUMO

We have recently identified three molecules (tilorone, quinacrine and pyronaridine tetraphosphate) which all demonstrated efficacy in the mouse model of infection with mouse-adapted Ebola virus (EBOV) model of disease and had similar in vitro inhibition of an Ebola pseudovirus (VSV-EBOV-GP), suggesting they interfere with viral entry. Using a machine learning model to predict lysosomotropism these compounds were evaluated for their ability to possess a lysosomotropic mechanism in vitro. We now demonstrate in vitro that pyronaridine tetraphosphate is an inhibitor of Lysotracker accumulation in lysosomes (IC50 = 0.56 µM). Further, we evaluated antiviral synergy between pyronaridine and artesunate (Pyramax®), which are used in combination to treat malaria. Artesunate was not found to have lysosomotropic activity in vitro and the combination effect on EBOV inhibition was shown to be additive. Pyramax® may represent a unique example of the repurposing of a combination product for another disease.


Assuntos
Antivirais/farmacologia , Artesunato/uso terapêutico , Reposicionamento de Medicamentos , Ebolavirus/efeitos dos fármacos , Lisossomos/efeitos dos fármacos , Naftiridinas/uso terapêutico , Quinacrina/uso terapêutico , Tilorona/uso terapêutico , Antivirais/uso terapêutico , Combinação de Medicamentos , Sinergismo Farmacológico , Células HeLa , Doença pelo Vírus Ebola/tratamento farmacológico , Doença pelo Vírus Ebola/virologia , Humanos , Células MCF-7 , Aprendizado de Máquina , Internalização do Vírus/efeitos dos fármacos
15.
Sci Rep ; 10(1): 12982, 2020 07 31.
Artigo em Inglês | MEDLINE | ID: mdl-32737414

RESUMO

Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to chordoma patients, a drug repurposing strategy represents an attractive approach. Drugs that have already advanced through human clinical safety trials have the potential to be approved more quickly than de novo discovered medicines on new targets. We have taken two strategies to enable this: (1) generated and validated machine learning models of chordoma inhibition and screened compounds of interest in vitro. (2) Tested combinations of approved kinase inhibitors already being individually evaluated for chordoma. Several published studies of compounds screened against chordoma cell lines were used to generate Bayesian Machine learning models which were then used to score compounds selected from the NIH NCATS industry-provided assets. Out of these compounds, the mTOR inhibitor AZD2014, was the most potent against chordoma cell lines (IC50 0.35 µM U-CH1 and 0.61 µM U-CH2). Several studies have shown the importance of the mTOR signaling pathway in chordoma and suggest it as a promising avenue for targeted therapy. Additionally, two currently FDA approved drugs, afatinib and palbociclib (EGFR and CDK4/6 inhibitors, respectively) demonstrated synergy in vitro (CI50 = 0.43) while AZD2014 and afatanib also showed synergy (CI50 = 0.41) against a chordoma cell in vitro. These findings may be of interest clinically, and this in vitro- and in silico approach could also be applied to other rare cancers.


Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/farmacologia , Cordoma/tratamento farmacológico , Reposicionamento de Medicamentos , Aprendizado de Máquina , Protocolos de Quimioterapia Combinada Antineoplásica/química , Benzamidas/agonistas , Benzamidas/farmacologia , Linhagem Celular Tumoral , Cordoma/metabolismo , Cordoma/patologia , Humanos , Morfolinas/agonistas , Morfolinas/farmacologia , Proteínas de Neoplasias/antagonistas & inibidores , Proteínas de Neoplasias/metabolismo , Piperazinas/agonistas , Piperazinas/farmacologia , Piridinas/agonistas , Piridinas/farmacologia , Pirimidinas/agonistas , Pirimidinas/farmacologia
16.
Environ Sci Technol ; 54(19): 12202-12213, 2020 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-32857505

RESUMO

The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.


Assuntos
Disruptores Endócrinos , Receptores de Estrogênio , Teorema de Bayes , Disruptores Endócrinos/toxicidade , Aprendizado de Máquina , Estudos Prospectivos
17.
Mol Pharm ; 17(7): 2628-2637, 2020 07 06.
Artigo em Inglês | MEDLINE | ID: mdl-32422053

RESUMO

Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank database, which classifies DILI severity and potential. These classifications have been used by various research groups in generating computational predictions for this type of liver injury. Recently, groups from Pfizer and AstraZeneca have collated DILI in vitro data and physicochemical properties for compounds that can be used along with data from the FDA to build machine learning models for DILI. In this study, we have used these data sets, as well as the Biopharmaceutics Drug Disposition Classification System data set, to generate Bayesian machine learning models with our in-house software, Assay Central. The performance of all machine learning models was assessed through both the internal 5-fold cross-validation metrics and prediction accuracy of an external test set of compounds with known hepatotoxicity. The best-performing Bayesian model was based on the DILI-concern category from the DILIRank database with an ROC of 0.814, a sensitivity of 0.741, a specificity of 0.755, and an accuracy of 0.746. A comparison of alternative machine learning algorithms, such as k-nearest neighbors, support vector classification, AdaBoosted decision trees, and deep learning methods, produced similar statistics to those generated with the Bayesian algorithm in Assay Central. This study demonstrates machine learning models grouped in a tool called MegaTox that can be used to predict early-stage clinical compounds, as well as recent FDA-approved drugs, to identify potential DILI.


Assuntos
Doença Hepática Induzida por Substâncias e Drogas/etiologia , Fígado/efeitos dos fármacos , Aprendizado de Máquina , Algoritmos , Teorema de Bayes , Biofarmácia , Simulação por Computador , Bases de Dados Factuais , Bases de Dados de Produtos Farmacêuticos , Aprovação de Drogas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Fígado/patologia , Software
18.
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
19.
Phytochem Lett ; 20: 54-60, 2017 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-28736584

RESUMO

The study presented herein constitutes an extensive investigation of constituents in Hydrastis canadensis L. (Ranunculaceae) leaves. It describes the isolation and identification of two previously unknown compounds, 3,4-dimethoxy-2-(methoxycarbonyl)benzoic acid (1) and 3,5,3'-trihydroxy-7,4'-dimethoxy-6,8-C-dimethyl-flavone (2), along with the known compounds (±)-chilenine (3), (2R)-5,4'-dihydroxy-6-C-methyl-7-methoxy-flavanone (4), 5,4'-dihydroxy-6,8-di-C-methyl-7-methoxy-flavanone (5), noroxyhydrastinine (6), oxyhydrastinine (7) and 4',5'-dimethoxy-4-methyl-3'-oxo-(1,2,5,6-tetrahydro-4H-1,3-dioxolo-[4',5':4,5]-benzo[1,2-e]-1,2-oxazocin)-2-spiro-1'-phtalan (8). Compounds 3-8 have been reported from other sources, but this is the first report of their presence in H. canadensis extracts. A mass spectrometry based assay was employed to demonstrate bacterial efflux pump inhibitory activity against Staphylococcus aureus for 2, with an IC50 value of 180 ± 6 µM. This activity in addition to that of other bioactive compounds such as flavonoids and alkaloids, may explain the purported efficacy of H. canadensis for treatment of bacterial infections. Finally, this report includes high mass accuracy fragmentation spectra for all compounds investigated herein which were uploaded into the Global Natural Products Social molecular networking library and can be used to facilitate their future identification in H. canadensis or other botanicals.

20.
Chem Sci ; 8(12): 8443-8450, 2017 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-29619192

RESUMO

Current chemotherapeutic dosing strategies are limited by the toxicity of anticancer agents and therefore rely on multiple low-dose administrations. As an alternative, we describe a novel sustained-release, biodegradable polymeric nanocarrier as a single administration replacement of multi-dose paclitaxel (PTX) treatment regimens. The first synthesis of poly(1,2-glycerol carbonate)-graft-succinic acid-paclitaxel (PGC-PTX) is described, and its use enables high, controlled PTX loadings of up to 74 wt%. Moreover, the polymer backbone is composed of biocompatible building blocks-glycerol and carbon dioxide. When formulated as nanoparticles (NPs), PGC-PTX NPs exhibit PTX concentrations >15 mg mL-1, sub-100 nm diameters, narrow dispersity, storage stability for up to 6 months, and sustained and controlled PTX release kinetics over an extended period of 70 days. A safely administered single dose of PGC-PTX NPs contains more PTX than the median lethal dose of standard PTX. In murine models of peritoneal carcinomatosis, in which the clinical implementation of multi-dose intraperitoneal (IP) treatment regimens is limited by catheter-related complications, PGC-PTX NPs exhibit improved safety at high doses, tumor localization, and efficacy even after a single IP injection, with comparable curative effect to PTX administered as a multi-dose IP treatment regimen.

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