High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of <i>Staphylococcus aureus</i>

Dalecki, Alex G.; Zorn, Kimberley M.; Clark, Alex M.; Ekins, Sean; Narmore, Whitney; Tower, Nichole A.; Rasmussen, Lynn; Bostwick, Robert; Kutsch, Olaf; Wolschendorf, Frank

doi:10.1039/c8mt00342d

Cited by 31 publications

(37 citation statements)

References 60 publications

(71 reference statements)

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“…The Assay Central software has been previously described (23)(24)(25)(26)(27)(28)(29)(30)(31)(32) which uses the source code management system Git to gather and store structure-activity datasets collated in Molecular Notebook (Molecular Materials Informatics, Inc. in Montreal, Canada). The output is a high-quality dataset and a Bayesian model using extendedconnectivity fingerprints of maximum diameter 6 (ECFP6) descriptors.…”

Section: Lysosomotropic Machine Learning Modelmentioning

confidence: 99%

Repurposing Pyramax® for the Treatment of Ebola Virus Disease: Additivity of the Lysosomotropic Pyronaridine and Non-Lysosomotropic Artesunate

Lane¹,

Dyall²,

Luke³

et al. 2020

Preprint

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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 inhibit via a lysosomotropic mechanism in vitro. We now demonstrate in vitro that pyronaridine tetraphosphate is an inhibitor of Lysotracker accumulation in lysosomes (IC 50 = 0.56 μM). Further, we evaluated 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.

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Section: Lysosomotropic Machine Learning Modelmentioning

confidence: 99%

Repurposing Pyramax® for the Treatment of Ebola Virus Disease: Additivity of the Lysosomotropic Pyronaridine and Non-Lysosomotropic Artesunate

Lane¹,

Dyall²,

Luke³

et al. 2020

Preprint

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“…Machine learning models for chordoma drug discovery. Several recently published studies of compounds screened against chordoma cell lines 20,21 were used to generate Bayesian machine learning models with our Assay Central software 10,12,[22][23][24][25][26][27][28][29] . In one published chordoma study 1097 compounds were screened against 3 chordoma cell lines (U-CH1, U-CH2, MUG-Chor1) and 27 had chordoma selective cytotoxicity 20 and many of these were EGFR inhibitors.…”

Section: Resultsmentioning

confidence: 99%

“…These datasets underwent curation to remove problematic molecules before model building as described elsewhere 10,12,[22][23][24][25][26][27][28][29] . We utilized Assay Central which has been previously described in detail 10,12,[22][23][24][25][26][27][28][29] to prepare and merge datasets collated in Molecular Notebook 60 , as well as generate Bayesian models using ECFP6 descriptors 61,62 . Briefly, the Assay Central project includes automated workflows for curating well-defined structure-activity datasets that employ a set of rules for the detection of problematic data (i.e.…”

Section: Model Namementioning

confidence: 99%

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Anderson

Havener

Zorn

et al. 2020

Sci Rep

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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 (ic 50 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 (CI 50 = 0.43) while AZD2014 and afatanib also showed synergy (ci 50 = 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. Chordoma is a rare cancer that occurs in the bones of the skull base and spine which is part of a larger class of tumors known as sarcomas. Chordoma tumors develop from cells of the notochord, an embryonic structure that facilitates development of the spine 1. The notochord disappears when the fetus is about 8 weeks old, but some notochord cells remain in the bones of the spine and skull base 2. This is a rare occurrence, but when they do, these cells can turn into chordoma. A chordoma tumor usually grows slowly without symptoms for years before diagnosis, which is often in the 5 th and 6 th decades of life (although it can occur at any age). Studies have demonstrated that skull base chordomas are observed more often in children, whilst spinal chordomas are more frequently observed later in life 2,3. It has also been described that when chordomas metastasize they frequently distribute to the lungs, liver, bones, or lymph nodes. This occurs in 30 to 40 percent of people where the tumor metastasizes to other parts of the body 2. At this point in time there are no known environmental, dietary or lifestyle risk factors for this rare type of cancer. Chordomas often occur at random with no direct inherited genetic trait, however familial cases can be caused by duplications of the brachyury gene 4. A SNP in the brachyury gene occurs in 95 percent of people with this tumor 5,6 , and furthermore, chordomas have ...

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“…In 88 order to evaluate the model performance on an external testing set, a total of 30 89 molecules was collated from different studies 11,21-25 . The Assay Central TM software (AC) has been previously described 19,[26][27][28][29][30][31][32][33][34] . AC 93 employs a series of rules for the detection of problem data for automated structure 94 standardization to generate high-quality data sets and Bayesian machine learning 95 models capable of predicting potential bioactivity for proposed compounds.…”

Section: Data Curation 80mentioning

confidence: 99%

Machine Learning Models Identify Inhibitors of SARS-CoV-2

Gawriljuk

Zin

Foil

et al. 2020

Preprint

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27With the ongoing SARS-CoV-2 pandemic there is an urgent need for the 28 discovery of a treatment for the coronavirus disease . Drug repurposing is 29 one of the most rapid strategies for addressing this need and numerous compounds 30 have been selected for in vitro testing by several groups already. These have led to a 31 growing database of molecules with in vitro activity against the virus. Machine learning 32 models can assist drug discovery through prediction of the best compounds based on 33 previously published data. Herein we have implemented several machine learning 34 methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data 35and used them to prioritize additional FDA approved compounds for in vitro testing 36 selected from our in-house compound library. From the compounds predicted with a 37 Bayesian machine learning model, CPI1062 and CPI1155 showed antiviral activity in 38HeLa-ACE2 cell-based assays and represent potential repurposing opportunities for 39 COVID-19. This approach can be greatly expanded to exhaustively virtually screen 40 available molecules with predicted activity against this virus as well as a prioritization 41 tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for 42 SARS-CoV-2 is available at www.assaycentral.org. 43 44 45 46 47 129 Applicability and Reliability Domain Assessment 130In order to check if it is valid to apply the model for compounds being predicted 131 and how reliable the predictions are, an applicability and reliability domain assessment 132 was performed. First, the compound applicability within the model is assessed 133 comparing its similarity with the model's data using both molecular and fingerprint 134 descriptors. If the molecule satisfies both criteria it is considered within the applicability 135 domain and goes to the reliability domain assessment. 136

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High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus

Abstract: One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with antibiotic activity only in the presence of copper.

Cited by 31 publications

References 60 publications

Repurposing Pyramax® for the Treatment of Ebola Virus Disease: Additivity of the Lysosomotropic Pyronaridine and Non-Lysosomotropic Artesunate

Repurposing Pyramax® for the Treatment of Ebola Virus Disease: Additivity of the Lysosomotropic Pyronaridine and Non-Lysosomotropic Artesunate

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Machine Learning Models Identify Inhibitors of SARS-CoV-2

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