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1.
Mol Pharmacol ; 100(6): 548-557, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34503974

RESUMO

Equilibrative nucleoside transporters (ENTs) are present at the blood-testis barrier (BTB), where they can facilitate antiviral drug disposition to eliminate a sanctuary site for viruses detectable in semen. The purpose of this study was to investigate ENT-drug interactions with three nucleoside analogs, remdesivir, molnupiravir, and molnupiravir's active metabolite, ß-d-N4-hydroxycytidine (EIDD-1931), and four non-nucleoside molecules repurposed as antivirals for coronavirus disease 2019 (COVID-19). The study used three-dimensional pharmacophores for ENT1 and ENT2 substrates and inhibitors and Bayesian machine learning models to identify potential interactions with these transporters. In vitro transport experiments demonstrated that remdesivir was the most potent inhibitor of ENT-mediated [3H]uridine uptake (ENT1 IC50: 39 µM; ENT2 IC50: 77 µM), followed by EIDD-1931 (ENT1 IC50: 259 µM; ENT2 IC50: 467 µM), whereas molnupiravir was a modest inhibitor (ENT1 IC50: 701 µM; ENT2 IC50: 851 µM). Other proposed antivirals failed to inhibit ENT-mediated [3H]uridine uptake below 1 mM. Remdesivir accumulation decreased in the presence of 6-S-[(4-nitrophenyl)methyl]-6-thioinosine (NBMPR) by 30% in ENT1 cells (P = 0.0248) and 27% in ENT2 cells (P = 0.0054). EIDD-1931 accumulation decreased in the presence of NBMPR by 77% in ENT1 cells (P = 0.0463) and by 64% in ENT2 cells (P = 0.0132), which supported computational predictions that both are ENT substrates that may be important for efficacy against COVID-19. NBMPR failed to decrease molnupiravir uptake, suggesting that ENT interaction is likely inhibitory. Our combined computational and in vitro data can be used to identify additional ENT-drug interactions to improve our understanding of drugs that can circumvent the BTB. SIGNIFICANCE STATEMENT: This study identified remdesivir and EIDD-1931 as substrates of equilibrative nucleoside transporters 1 and 2. This provides a potential mechanism for uptake of these drugs into cells and may be important for antiviral potential in the testes and other tissues expressing these transporters.

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.
J Pharmacol Exp Ther ; 379(1): 96-107, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34253645

RESUMO

In the wake of the COVID-19 pandemic, drug repurposing has been highlighted for rapid introduction of therapeutics. Proposed drugs with activity against SARS-CoV-2 include compounds with positive charges at physiologic pH, making them potential targets for the organic cation secretory transporters of kidney and liver, i.e., the basolateral organic cation transporters, OCT1 and OCT2; and the apical multidrug and toxin extruders, MATE1 and MATE2-K. We selected several compounds proposed to have in vitro activity against SARS-CoV-2 (chloroquine, hydroxychloroquine, quinacrine, tilorone, pyronaridine, cetylpyridinium, and miramistin) to test their interaction with OCT and MATE transporters. We used Bayesian machine learning models to generate predictions for each molecule with each transporter and also experimentally determined IC50 values for each compound against labeled substrate transport into CHO cells that stably expressed OCT2, MATE1, or MATE2-K using three structurally distinct substrates (atenolol, metformin and 1-methyl-4-phenylpyridinium) to assess the impact of substrate structure on inhibitory efficacy. For the OCTs substrate identity influenced IC50 values, although the effect was larger and more systematic for OCT2. In contrast, inhibition of MATE1-mediated transport was largely insensitive to substrate identity. Unlike MATE1, inhibition of MATE2-K was influenced, albeit modestly, by substrate identity. Maximum unbound plasma concentration/IC50 ratios were used to identify potential clinical DDI recommendations; all the compounds interacted with the OCT/MATE secretory pathway, most with sufficient avidity to represent potential DDI issues for secretion of cationic drugs. This should be considered when proposing cationic agents as repurposed antivirals. SIGNIFICANCE STATEMENT: Drugs proposed as potential COVID-19 therapeutics based on in vitro activity data against SARS-CoV-2 include compounds with positive charges at physiological pH, making them potential interactors with the OCT/MATE renal secretory pathway. We tested seven such molecules as inhibitors of OCT1/2 and MATE1/2-K. All the compounds blocked transport activity regardless of substrate used to monitor activity. Suggesting that plasma concentrations achieved by normal clinical application of the test agents could be expected to influence the pharmacokinetics of selected cationic drugs.


Assuntos
Antivirais/farmacologia , Proteínas de Transporte de Cátions Orgânicos/metabolismo , SARS-CoV-2/efeitos dos fármacos , Animais , Compostos de Benzalcônio/farmacologia , Células CHO , Cetilpiridínio/farmacologia , Cloroquina/análogos & derivados , Cloroquina/farmacologia , Cricetinae , Cricetulus , Naftiridinas/farmacologia , Proteínas de Transporte de Cátions Orgânicos/efeitos dos fármacos , Quinacrina/farmacologia , Tilorona/farmacologia
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
6.
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.].

7.
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
8.
ACS Chem Neurosci ; 12(12): 2247-2253, 2021 06 16.
Artigo em Inglês | MEDLINE | ID: mdl-34028255

RESUMO

The ability to calculate whether small molecules will cross the blood-brain barrier (BBB) is an important task for companies working in neuroscience drug discovery. For a decade, scientists have relied on relatively simplistic rules such as Pfizer's central nervous system multiparameter optimization models (CNS-MPO) for guidance during the drug selection process. In parallel, there has been a continued development of more sophisticated machine learning models that utilize different molecular descriptors and algorithms; however, these models represent a "black box" and are generally less interpretable. In both cases, these methods predict the ability of small molecules to cross the BBB using the molecular structure information on its own without in vitro or in vivo data. We describe here the implementation of two versions of Pfizer's algorithm (Pf-MPO.v1 and Pf-MPO.v2) and compare it with a Bayesian machine learning model of BBB penetration trained on a data set of 2296 active and inactive compounds using extended connectivity fingerprint descriptors. The predictive ability of these approaches was compared with 40 known CNS active drugs initially used by Pfizer as their positive set for validation of the Pf-MPO.v1 score. 37/40 (92.5%) compounds were predicted as active by the Bayesian model, while only 30/40 (75%) received a desirable Pf-MPO.v1 score ≥4 and 33/40 (82.5%) received a desirable Pf-MPO.v2 score ≥4, suggesting the Bayesian model is more accurate than MPO algorithms. This also indicates machine learning models are more flexible and have better predictive power for BBB penetration than simple rule sets that require multiple, accurate descriptor calculations. Our machine learning model statistics are comparable to recent published studies. We describe the implications of these findings and how machine learning may have a role alongside more interpretable methods.


Assuntos
Barreira Hematoencefálica , Sistema Nervoso Central , Teorema de Bayes , Fármacos do Sistema Nervoso Central , Aprendizado de Máquina
9.
Drug Metab Dispos ; 49(7): 479-489, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33980604

RESUMO

Equilibrativenucleoside transporters (ENTs) participate in the pharmacokinetics and disposition of nucleoside analog drugs. Understanding drug interactions with the ENTs may inform and facilitate the development of new drugs, including chemotherapeutics and antivirals that require access to sanctuary sites such as the male genital tract. This study created three-dimensional pharmacophores for ENT1 and ENT2 substrates and inhibitors using Kt and IC50 data curated from the literature. Substrate pharmacophores for ENT1 and ENT2 are distinct, with partial overlap of hydrogen bond donors, whereas the inhibitor pharmacophores predominantly feature hydrogen bond acceptors. Mizoribine and ribavirin mapped to the ENT1 substrate pharmacophore and proved to be substrates of the ENTs. The presence of the ENT-specific inhibitor 6-S-[(4-nitrophenyl)methyl]-6-thioinosine (NBMPR) decreased mizoribine accumulation in ENT1 and ENT2 cells (ENT1, ∼70% decrease, P = 0.0046; ENT2, ∼50% decrease, P = 0.0012). NBMPR also decreased ribavirin accumulation in ENT1 and ENT2 cells (ENT1: ∼50% decrease, P = 0.0498; ENT2: ∼30% decrease, P = 0.0125). Darunavir mapped to the ENT1 inhibitor pharmacophore and NBMPR did not significantly influence darunavir accumulation in either ENT1 or ENT2 cells (ENT1: P = 0.28; ENT2: P = 0.53), indicating that darunavir's interaction with the ENTs is limited to inhibition. These computational and in vitro models can inform compound selection in the drug discovery and development process, thereby reducing time and expense of identification and optimization of ENT-interacting compounds. SIGNIFICANCE STATEMENT: This study developed computational models of human equilibrative nucleoside transporters (ENTs) to predict drug interactions and validated these models with two compounds in vitro. Identification and prediction of ENT1 and ENT2 substrates allows for the determination of drugs that can penetrate tissues expressing these transporters.

10.
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.

11.
Bioorg Chem ; 109: 104719, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33636437

RESUMO

Although the widespread epidemic of Zika virus (ZIKV) and its neurological complications are well-known there are still no approved drugs available to treat this arboviral disease or vaccine to prevent the infection. Flavonoids from Pterogyne nitens have already demonstrated anti-flavivirus activity, although their target is unknown. In this study, we virtually screened an in-house database of 150 natural and semi-synthetic compounds against ZIKV NS2B-NS3 protease (NS2B-NS3p) using docking-based virtual screening, as part of the OpenZika project. As a result, we prioritized three flavonoids from P. nitens, quercetin, rutin and pedalitin, for experimental evaluation. We also used machine learning models, built with Assay Central® software, for predicting the activity and toxicity of these flavonoids. Biophysical and enzymatic assays generally agreed with the in silico predictions, confirming that the flavonoids inhibited ZIKV protease. The most promising hit, pedalitin, inhibited ZIKV NS2B-NS3p with an IC50 of 5 µM. In cell-based assays, pedalitin displayed significant activity at 250 and 500 µM, with slight toxicity in Vero cells. The results presented here demonstrate the potential of pedalitin as a candidate for hit-to-lead (H2L) optimization studies towards the discovery of antiviral drug candidates to treat ZIKV infections.


Assuntos
Antivirais/farmacologia , Proteínas não Estruturais Virais/antagonistas & inibidores , Proteínas Virais/antagonistas & inibidores , Zika virus/metabolismo , Animais , Antivirais/química , Sobrevivência Celular/efeitos dos fármacos , Chlorocebus aethiops , Inibidores Enzimáticos/química , Inibidores Enzimáticos/farmacologia , Flavonas/farmacologia , Aprendizado de Máquina , Modelos Moleculares , Simulação de Acoplamento Molecular , Conformação Proteica , Quercetina/farmacologia , Rutina/farmacologia , Serina Endopeptidases , Células Vero
12.
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
13.
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
14.
Eur J Med Chem ; 211: 113014, 2021 Feb 05.
Artigo em Inglês | MEDLINE | ID: mdl-33218683

RESUMO

Viruses are obligate intracellular parasites and have evolved to enter the host cell. To gain access they come into contact with the host cell through an initial adhesion, and some viruses from different genus may use heparan sulfate proteoglycans for it. The successful inhibition of this early event of the infection by synthetic molecules has always been an attractive target for medicinal chemists. Numerous reports have yielded insights into the function of compounds based on the dispirotripiperazine scaffold. Analysis suggests that this is a structural requirement for inhibiting the interactions between viruses and cell-surface heparan sulfate proteoglycans, thus preventing virus entry and replication. This review summarizes our current knowledge about the early history of development, synthesis, structure-activity relationships and antiviral evaluation of dispirotripiperazine-based compounds and where they are going in the future.


Assuntos
Antivirais/farmacologia , Desenho de Fármacos , Piperazinas/farmacologia , Compostos de Espiro/farmacologia , Vírus/efeitos dos fármacos , Antivirais/química , Proteoglicanas de Heparan Sulfato/antagonistas & inibidores , Proteoglicanas de Heparan Sulfato/metabolismo , Estrutura Molecular , Piperazinas/química , Compostos de Espiro/química , Vírus/metabolismo
15.
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
16.
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
17.
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.

18.
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
19.
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
20.
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
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