1
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Hanson BS, Hailemariam A, Yang Y, Mohamed F, Donati GL, Baker D, Sacchettini J, Cai JJ, Subashchandrabose S. Identification of a copper-responsive small molecule inhibitor of uropathogenic Escherichia coli. J Bacteriol 2024:e0011224. [PMID: 38856220 DOI: 10.1128/jb.00112-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/10/2024] [Indexed: 06/11/2024] Open
Abstract
Urinary tract infections (UTIs) are a major global health problem and are caused predominantly by uropathogenic Escherichia coli (UPEC). UTIs are a leading cause of prescription antimicrobial use. Incessant increase in antimicrobial resistance in UPEC and other uropathogens poses a serious threat to the current treatment practices. Copper is an effector of nutritional immunity that impedes the growth of pathogens during infection. We hypothesized that copper would augment the toxicity of select small molecules against bacterial pathogens. We conducted a small molecule screening campaign with a library of 51,098 molecules to detect hits that inhibit a UPEC ΔtolC mutant in a copper-dependent manner. A molecule, denoted as E. coli inhibitor or ECIN, was identified as a copper-responsive inhibitor of wild-type UPEC strains. Our gene expression and metal content analysis results demonstrate that ECIN works in concert with copper to exacerbate Cu toxicity in UPEC. ECIN has a broad spectrum of activity against pathogens of medical and veterinary significance including Acinetobacter baumannii, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus. Subinhibitory levels of ECIN eliminate UPEC biofilm formation. Transcriptome analysis of UPEC treated with ECIN reveals induction of multiple stress response systems. Furthermore, we demonstrate that L-cysteine rescues the growth of UPEC exposed to ECIN. In summary, we report the identification and characterization of a novel copper-responsive small molecule inhibitor of UPEC.IMPORTANCEUrinary tract infection (UTI) is a ubiquitous infectious condition affecting millions of people annually. Uropathogenic Escherichia coli (UPEC) is the predominant etiological agent of UTI. However, UTIs are becoming increasingly difficult to resolve with antimicrobials due to increased antimicrobial resistance in UPEC and other uropathogens. Here, we report the identification and characterization of a novel copper-responsive small molecule inhibitor of UPEC. In addition to E. coli, this small molecule also inhibits pathogens of medical and veterinary significance including Acinetobacter baumannii, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus.
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Affiliation(s)
- Braden S Hanson
- Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Amanuel Hailemariam
- Department of Biochemistry and Biophysics, College of Agriculture and Life Sciences, Texas A&M University, College Station, Texas, USA
| | - Yongjian Yang
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Faras Mohamed
- Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - George L Donati
- Department of Chemistry, Wake Forest University, Winston-Salem, North Carolina, USA
| | - Dwight Baker
- Department of Biochemistry and Biophysics, College of Agriculture and Life Sciences, Texas A&M University, College Station, Texas, USA
| | - James Sacchettini
- Department of Biochemistry and Biophysics, College of Agriculture and Life Sciences, Texas A&M University, College Station, Texas, USA
| | - James J Cai
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Sargurunathan Subashchandrabose
- Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
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2
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Rasmussen L, Sanders S, Sosa M, McKellip S, Nebane NM, Martinez-Gzegozewska Y, Reece A, Ruiz P, Manuvakhova A, Zhai L, Warren B, Curry A, Zeng Q, Bostwick JR, Vinson PN. A high-throughput response to the SARS-CoV-2 pandemic. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2024:100160. [PMID: 38761981 DOI: 10.1016/j.slasd.2024.100160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/24/2024] [Accepted: 05/06/2024] [Indexed: 05/20/2024]
Abstract
Four years after the beginning of the COVID-19 pandemic, it is important to reflect on the events that have occurred during that time and the knowledge that has been gained. The response to the pandemic was rapid and highly resourced; it was also built upon a foundation of decades of federally funded basic and applied research. Laboratories in government, pharmaceutical, academic, and non-profit institutions all played roles in advancing pre-2020 discoveries to produce clinical treatments. This perspective provides a summary of how the development of high-throughput screening methods in a biosafety level 3 (BSL-3) environment at Southern Research Institute (SR) contributed to pandemic response efforts. The challenges encountered are described, including those of a technical nature as well as those of working under the pressures of an unpredictable virus and pandemic.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Ling Zhai
- Southern Research, Birmingham, AL, USA
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3
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Linciano P, Quotadamo A, Luciani R, Santucci M, Zorn KM, Foil DH, Lane TR, Cordeiro da Silva A, Santarem N, B Moraes C, Freitas-Junior L, Wittig U, Mueller W, Tonelli M, Ferrari S, Venturelli A, Gul S, Kuzikov M, Ellinger B, Reinshagen J, Ekins S, Costi MP. High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents. J Med Chem 2023; 66:15230-15255. [PMID: 37921561 PMCID: PMC10683024 DOI: 10.1021/acs.jmedchem.3c01322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/14/2023] [Accepted: 10/18/2023] [Indexed: 11/04/2023]
Abstract
Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.
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Affiliation(s)
- Pasquale Linciano
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Antonio Quotadamo
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Rosaria Luciani
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Matteo Santucci
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Kimberley M. Zorn
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H. Foil
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R. Lane
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Anabela Cordeiro da Silva
- Institute
for Molecular and Cell Biology, 4150-180 Porto, Portugal
- Instituto
de Investigaçao e Inovaçao em Saúde, Universidade do Porto and Institute for Molecular
and Cell Biology, 4150-180 Porto, Portugal
| | - Nuno Santarem
- Institute
for Molecular and Cell Biology, 4150-180 Porto, Portugal
- Instituto
de Investigaçao e Inovaçao em Saúde, Universidade do Porto and Institute for Molecular
and Cell Biology, 4150-180 Porto, Portugal
| | - Carolina B Moraes
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), 13083-970 Campinas, São Paulo, Brazil
| | - Lucio Freitas-Junior
- Brazilian
Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), 13083-970 Campinas, São Paulo, Brazil
| | - Ulrike Wittig
- Scientific
Databases and Visualization Group and Molecular and Cellular Modelling
Group, Heidelberg Institute for Theoretical
Studies (HITS), D-69118 Heidelberg, Germany
| | - Wolfgang Mueller
- Scientific
Databases and Visualization Group and Molecular and Cellular Modelling
Group, Heidelberg Institute for Theoretical
Studies (HITS), D-69118 Heidelberg, Germany
| | - Michele Tonelli
- Department
of Pharmacy, University of Genoa, Viale Benedetto XV n.3, 16132 Genoa, Italy
| | - Stefania Ferrari
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Alberto Venturelli
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
- TYDOCK
PHARMA S.r.l., Strada
Gherbella 294/b, 41126 Modena, Italy
| | - Sheraz Gul
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Maria Kuzikov
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Bernhard Ellinger
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Jeanette Reinshagen
- Fraunhofer
Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases
CIMD, Schnackenburgallee
114, D-22525 Hamburg, Germany
| | - Sean Ekins
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Maria Paola Costi
- Department
of Life Sciences, University of Modena and
Reggio Emilia, Via Campi 103, 41125 Modena, Italy
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4
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Andrews RM, Bollar GE, Giattina AS, Dalecki AG, Wallace Jr JR, Frantz L, Eschliman K, Covarrubias-Zambrano O, Keith JD, Duverger A, Wagner F, Wolschendorf F, Bossmann SH, Birket SE, Kutsch O. Repurposing sunscreen as an antibiotic: zinc-activated avobenzone inhibits methicillin-resistant Staphylococcus aureus. Metallomics 2023; 15:mfad049. [PMID: 37653446 PMCID: PMC10478290 DOI: 10.1093/mtomcs/mfad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/05/2023] [Indexed: 09/02/2023]
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a major healthcare concern with associated healthcare costs reaching over ${\$}$1 billion in a single year in the USA. Antibiotic resistance in S. aureus is now observed against last line of defense antibiotics, such as vancomycin, linezolid, and daptomycin. Unfortunately, high throughput drug discovery approaches to identify new antibiotics effective against MRSA have not resulted in much tangible success over the last decades. Previously, we demonstrated the feasibility of an alternative drug discovery approach, the identification of metallo-antibiotics, compounds that gain antibacterial activity only after binding to a transition metal ion and as such are unlikely to be detected in standard drug screens. We now report that avobenzone, the primary active ingredient of most sunscreens, can be activated by zinc to become a potent antibacterial compound against MRSA. Zinc-activated avobenzone (AVB-Zn) potently inhibited a series of clinical MRSA isolates [minimal inhibitory concentration (MIC): 0.62-2.5 µM], without pre-existing resistance and activity without zinc (MIC: >10 µM). AVB-Zn was also active against clinical MRSA isolates that were resistant against the commonly used zinc-salt antibiotic bacitracin. We found AVB-Zn exerted no cytotoxicity on human cell lines and primary cells. Last, we demonstrate AVB-Zn can be deployed therapeutically as lotion preparations, which showed efficacy in a mouse wound model of MRSA infection. AVB-Zn thus demonstrates Zn-activated metallo-antibiotics are a promising avenue for future drug discovery.
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Affiliation(s)
- Rachel M Andrews
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Gretchen E Bollar
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - A Sophia Giattina
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Alex G Dalecki
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - John R Wallace Jr
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Leah Frantz
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kayla Eschliman
- Department of Chemistry, Kansas State University, Kansas City, KS, USA
| | - Obdulia Covarrubias-Zambrano
- Department of Chemistry, Kansas State University, Kansas City, KS, USA
- Department of Cancer Biology, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Johnathan D Keith
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Alexandra Duverger
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Frederic Wagner
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Frank Wolschendorf
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Stefan H Bossmann
- Department of Chemistry, Kansas State University, Kansas City, KS, USA
- Department of Cancer Biology, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Susan E Birket
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Olaf Kutsch
- School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
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5
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Ayon NJ. High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery. Metabolites 2023; 13:625. [PMID: 37233666 PMCID: PMC10220967 DOI: 10.3390/metabo13050625] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 04/29/2023] [Accepted: 05/01/2023] [Indexed: 05/27/2023] Open
Abstract
Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.
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Affiliation(s)
- Navid J Ayon
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
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6
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Liao Y, Cao P, Luo L. Identification of Novel Arachidonic Acid 15-Lipoxygenase Inhibitors Based on the Bayesian Classifier Model and Computer-Aided High-Throughput Virtual Screening. Pharmaceuticals (Basel) 2022; 15:1440. [PMID: 36422570 PMCID: PMC9695033 DOI: 10.3390/ph15111440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/11/2022] [Accepted: 11/16/2022] [Indexed: 08/29/2023] Open
Abstract
Ferroptosis is an iron-dependent lipid peroxidative form of cell death that is distinct from apoptosis and necrosis. ALOX15, also known as arachidonic acid 15-lipoxygenase, promotes ferroptosis by converting intracellular unsaturated lipids into oxidized lipid intermediates and is an important ferroptosis target. In this study, a naive Bayesian machine learning classifier with a structure-based, high-throughput screening approach and a molecular docking program were combined to screen for three compounds with excellent target-binding potential. In the absorption, distribution, metabolism, excretion, and toxicity characterization, three candidate molecules were predicted to exhibit drug-like properties. The subsequent molecular dynamics simulations confirmed their stable binding to the targets. The findings indicated that the compounds exhibited excellent potential ALOX15 inhibitor capacity, thereby providing novel candidates for the treatment of inflammatory ischemia-related diseases caused by ferroptosis.
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Affiliation(s)
- Yinglin Liao
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China
| | - Peng Cao
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Lianxiang Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang 524023, China
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7
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Puhl AC, Gao ZG, Jacobson KA, Ekins S. Machine Learning for Discovery of New ADORA Modulators. Front Pharmacol 2022; 13:920643. [PMID: 35814244 PMCID: PMC9257522 DOI: 10.3389/fphar.2022.920643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/30/2022] [Indexed: 01/12/2023] Open
Abstract
Adenosine (ADO) is an extracellular signaling molecule generated locally under conditions that produce ischemia, hypoxia, or inflammation. It is involved in modulating a range of physiological functions throughout the brain and periphery through the membrane-bound G protein-coupled receptors, called adenosine receptors (ARs) A1AR, A2AAR, A2BAR, and A3AR. These are therefore important targets for neurological, cardiovascular, inflammatory, and autoimmune diseases and are the subject of drug development directed toward the cyclic adenosine monophosphate and other signaling pathways. Initially using public data for A1AR agonists we generated and validated a Bayesian machine learning model (Receiver Operator Characteristic of 0.87) that we used to identify molecules for testing. Three selected molecules, crisaborole, febuxostat and paroxetine, showed initial activity in vitro using the HEK293 A1AR Nomad cell line. However, radioligand binding, β-arrestin assay and calcium influx assay did not confirm this A1AR activity. Nevertheless, several other AR activities were identified. Febuxostat and paroxetine both inhibited orthosteric radioligand binding in the µM range for A2AAR and A3AR. In HEK293 cells expressing the human A2AAR, stimulation of cAMP was observed for crisaborole (EC50 2.8 µM) and paroxetine (EC50 14 µM), but not for febuxostat. Crisaborole also increased cAMP accumulation in A2BAR-expressing HEK293 cells, but it was weaker than at the A2AAR. At the human A3AR, paroxetine did not show any agonist activity at 100 µM, although it displayed binding with a Ki value of 14.5 µM, suggesting antagonist activity. We have now identified novel modulators of A2AAR, A2BAR and A3AR subtypes that are clinically used for other therapeutic indications, and which are structurally distinct from previously reported tool compounds or drugs.
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Affiliation(s)
- Ana C. Puhl
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, United States,*Correspondence: Ana C. Puhl, ; Sean Ekins,
| | - Zhan-Guo Gao
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Kenneth A. Jacobson
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, United States,*Correspondence: Ana C. Puhl, ; Sean Ekins,
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8
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Chalcones from Angelica keiskei (ashitaba) inhibit key Zika virus replication proteins. Bioorg Chem 2022; 120:105649. [PMID: 35124513 PMCID: PMC9187613 DOI: 10.1016/j.bioorg.2022.105649] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 01/21/2022] [Accepted: 01/25/2022] [Indexed: 12/25/2022]
Abstract
Zika virus (ZIKV) is a dangerous human pathogen and no antiviral drugs have been approved to date. The chalcones are a group of small molecules that are found in a number of different plants, including Angelica keiskei Koidzumi, also known as ashitaba. To examine chalcone anti-ZIKV activity, three chalcones, 4-hydroxyderricin (4HD), xanthoangelol (XA), and xanthoangelol-E (XA-E), were purified from a methanol-ethyl acetate extract from A. keiskei. Molecular and ensemble docking predicted that these chalcones would establish multiple interactions with residues in the catalytic and allosteric sites of ZIKV NS2B-NS3 protease, and in the allosteric site of the NS5 RNA-dependent RNA-polymerase (RdRp). Machine learning models also predicted 4HD, XA and XA-E as potential anti-ZIKV inhibitors. Enzymatic and kinetic assays confirmed chalcone inhibition of the ZIKV NS2B-NS3 protease allosteric site with IC50s from 18 to 50 µM. Activity assays also revealed that XA, but not 4HD or XA-E, inhibited the allosteric site of the RdRp, with an IC50 of 6.9 µM. Finally, we tested these chalcones for their anti-viral activity in vitro with Vero cells. 4HD and XA-E displayed anti-ZIKV activity with EC50 values of 6.6 and 22.0 µM, respectively, while XA displayed relatively weak anti-ZIKV activity with whole cells. With their simple structures and relative ease of modification, the chalcones represent attractive candidates for hit-to-lead optimization in the search of new anti-ZIKV therapeutics.
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9
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Schmalstig AA, Zorn KM, Murci S, Robinson A, Savina S, Komarova E, Makarov V, Braunstein M, Ekins S. Mycobacterium abscessus drug discovery using machine learning. Tuberculosis (Edinb) 2022; 132:102168. [PMID: 35077930 PMCID: PMC8855326 DOI: 10.1016/j.tube.2022.102168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/30/2021] [Accepted: 01/14/2022] [Indexed: 01/22/2023]
Abstract
The prevalence of infections by nontuberculous mycobacteria is increasing, having surpassed tuberculosis in the United States and much of the developed world. Nontuberculous mycobacteria occur naturally in the environment and are a significant problem for patients with underlying lung diseases such as bronchiectasis, chronic obstructive pulmonary disease, and cystic fibrosis. Current treatment regimens are lengthy, complicated, toxic and they are often unsuccessful as seen by disease recurrence. Mycobacterium abscessus is one of the most commonly encountered organisms in nontuberculous mycobacteria disease and it is the most difficult to eradicate. There is currently no systematically proven regimen that is effective for treating M. abscessus infections. Our approach to drug discovery integrates machine learning, medicinal chemistry and in vitro testing and has been previously applied to Mycobacterium tuberculosis. We have now identified several novel 1-(phenylsulfonyl)-1H-benzimidazol-2-amines that have weak activity on M. abscessus in vitro but may represent a starting point for future further medicinal chemistry optimization. We also address limitations still to be overcome with the machine learning approach for M. abscessus.
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Affiliation(s)
- Alan A. Schmalstig
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Sebastian Murci
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Andrew Robinson
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Svetlana Savina
- Research Center of Biotechnology RAS, Moscow, 119071, Russia
| | - Elena Komarova
- Research Center of Biotechnology RAS, Moscow, 119071, Russia
| | - Vadim Makarov
- Research Center of Biotechnology RAS, Moscow, 119071, Russia
| | - Miriam Braunstein
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, North Carolina, 27606, USA.,Corresponding author: Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, North Carolina, 27606, USA.
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10
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Gawriljuk VO, Zin PPK, Puhl AC, Zorn KM, Foil DH, Lane TR, Hurst B, Tavella TA, Costa FTM, Lakshmanane P, Bernatchez J, Godoy AS, Oliva G, Siqueira-Neto JL, Madrid PB, Ekins S. Machine Learning Models Identify Inhibitors of SARS-CoV-2. J Chem Inf Model 2021; 61:4224-4235. [PMID: 34387990 DOI: 10.1021/acs.jcim.1c00683] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
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.
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Affiliation(s)
- Victor O Gawriljuk
- São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100-Santa Angelina, São Carlos, São Paulo 13563-120, Brazil
| | - Phyo Phyo Kyaw Zin
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Ana C Puhl
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Brett Hurst
- Institute for Antiviral Research, Utah State University, Logan, Utah 84322-5600, United States.,Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, Utah 84322-4815, United States
| | - Tatyana Almeida Tavella
- Laboratory of Tropical Diseases-Prof. Dr. Luiz Jacinto da Silva, Department of Genetics, Evolution, Microbiology and Immunology, University of Campinas-UNICAMP, Campinas, São Paulo, Brazil
| | - Fabio Trindade Maranhão Costa
- Laboratory of Tropical Diseases-Prof. Dr. Luiz Jacinto da Silva, Department of Genetics, Evolution, Microbiology and Immunology, University of Campinas-UNICAMP, Campinas, São Paulo, Brazil
| | - Premkumar Lakshmanane
- Department of Microbiology and Immunology, University of North Carolina School of Medicine, Chapel Hill North Carolina 27599, United States
| | - Jean Bernatchez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, California 92093, United States
| | - Andre S Godoy
- São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100-Santa Angelina, São Carlos, São Paulo 13563-120, Brazil
| | - Glaucius Oliva
- São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100-Santa Angelina, São Carlos, São Paulo 13563-120, Brazil
| | - Jair L Siqueira-Neto
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, California 92093, United States
| | - Peter B Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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11
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Chen HY, Bohlen JF, Maher BJ. Molecular and Cellular Function of Transcription Factor 4 in Pitt-Hopkins Syndrome. Dev Neurosci 2021; 43:159-167. [PMID: 34134113 DOI: 10.1159/000516666] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/20/2021] [Indexed: 11/19/2022] Open
Abstract
Transcription factor 4 (TCF4, also known as ITF2 or E2-2) is a type I basic helix-loop-helix transcription factor. Autosomal dominant mutations in TCF4 cause Pitt-Hopkins syndrome (PTHS), a rare syndromic form of autism spectrum disorder. In this review, we provide an update on the progress regarding our understanding of TCF4 function at the molecular, cellular, physiological, and behavioral levels with a focus on phenotypes and therapeutic interventions. We examine upstream and downstream regulatory networks associated with TCF4 and discuss a range of in vitro and in vivo data with the aim of understanding emerging TCF4-specific mechanisms relevant for disease pathophysiology. In conclusion, we provide comments about exciting future avenues of research that may provide insights into potential new therapeutic targets for PTHS.
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Affiliation(s)
- Huei-Ying Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA,
| | - Joseph F Bohlen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA
| | - Brady J Maher
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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12
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Urbina F, Zorn KM, Brunner D, Ekins S. Comparing the Pfizer Central Nervous System Multiparameter Optimization Calculator and a BBB Machine Learning Model. ACS Chem Neurosci 2021; 12:2247-2253. [PMID: 34028255 DOI: 10.1021/acschemneuro.1c00265] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Fabio Urbina
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7545, United States
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniela Brunner
- PsychoGenics, 215 College Road, Paramus, New Jersey 07652, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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13
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Donlin MJ, Lane TR, Riabova O, Lepioshkin A, Xu E, Lin J, Makarov V, Ekins S. Discovery of 5-Nitro-6-thiocyanatopyrimidines as Inhibitors of Cryptococcus neoformans and Cryptococcus gattii. ACS Med Chem Lett 2021; 12:774-781. [PMID: 34055225 DOI: 10.1021/acsmedchemlett.1c00038] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/31/2021] [Indexed: 12/27/2022] Open
Abstract
Opportunistic infections from pathogenic fungi present a major challenge to healthcare because of a very limited arsenal of antifungal drugs, an increasing population of immunosuppressed patients, and increased prevalence of resistant clinical strains due to overuse of the few available antifungals. Cryptococcal meningitis is a life-threatening opportunistic fungal infection caused by one of two species in the Cryptococcus genus, Cryptococcus neoformans and Cryptococcus gattii. Eighty percent of cryptococcosis diseases are caused by C. neoformans that is endemic in the environment. The standard of care is limited to old antifungals, and under a high standard of care, mortality remains between 10 and 30%. We have identified a series of 5-nitro-6-thiocyanatopyrimidine antifungal drug candidates using in vitro and computational machine learning approaches. These compounds can inhibit C. neoformans growth at submicromolar levels, are effective against fluconazole-resistant C. neoformans and a clinical strain of C. gattii, and are not antagonistic with currently approved antifungals.
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Affiliation(s)
- Maureen J. Donlin
- Edward A. Doisy Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine, St. Louis, Missouri 63104, United States
- Institute for Drug and Biotherapeutic Development, Saint Louis University, St. Louis, Missouri 63103, United States
| | - Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina 27606, United States
| | - Olga Riabova
- Department of Biology, Saint Louis University, St. Louis, Missouri 63103, United States
| | - Alexander Lepioshkin
- Department of Biology, Saint Louis University, St. Louis, Missouri 63103, United States
| | - Evan Xu
- Edward A. Doisy Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine, St. Louis, Missouri 63104, United States
| | - Jeffrey Lin
- Department of Biology, Saint Louis University, St. Louis, Missouri 63103, United States
| | - Vadim Makarov
- Research Center of Biotechnology RAS, 119071 Moscow, Russia
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina 27606, United States
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14
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Lima CS, Mottin M, de Assis LR, Mesquita NCDMR, Sousa BKDP, Coimbra LD, Santos KBD, Zorn KM, Guido RVC, Ekins S, Marques RE, Proença-Modena JL, Oliva G, Andrade CH, Regasini LO. Flavonoids from Pterogyne nitens as Zika virus NS2B-NS3 protease inhibitors. Bioorg Chem 2021; 109:104719. [PMID: 33636437 PMCID: PMC8227833 DOI: 10.1016/j.bioorg.2021.104719] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 01/26/2021] [Accepted: 02/02/2021] [Indexed: 12/18/2022]
Abstract
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.
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Affiliation(s)
- Caroline Sprengel Lima
- Laboratory of Antibiotics and Chemotherapeutics (LAQ), Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University (Unesp), São José do Rio Preto, SP, Brazil
| | - Melina Mottin
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, Brazil
| | - Leticia Ribeiro de Assis
- Laboratory of Antibiotics and Chemotherapeutics (LAQ), Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University (Unesp), São José do Rio Preto, SP, Brazil
| | | | - Bruna Katiele de Paula Sousa
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, Brazil
| | - Lais Durco Coimbra
- Brazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP, Brazil
| | - Karina Bispo-Dos- Santos
- Laboratory of Emerging Viruses (LEVE), Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, United States
| | - Rafael V C Guido
- Institute of Physics of São Carlos, University of São Paulo, São Carlos, SP, Brazil
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, United States
| | - Rafael Elias Marques
- Brazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP, Brazil
| | - José Luiz Proença-Modena
- Laboratory of Emerging Viruses (LEVE), Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Glaucius Oliva
- Institute of Physics of São Carlos, University of São Paulo, São Carlos, SP, Brazil
| | - Carolina Horta Andrade
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, Brazil.
| | - Luis Octavio Regasini
- Laboratory of Antibiotics and Chemotherapeutics (LAQ), Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University (Unesp), São José do Rio Preto, SP, Brazil.
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15
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Delpe-Acharige A, Zhang M, Eschliman K, Dalecki A, Covarrubias-Zambrano O, Minjarez-Almeida A, Shrestha T, Lewis T, Al-Ibrahim F, Leonard S, Roberts R, Tebeje A, Malalasekera AP, Wang H, Kalubowilage M, Wolschendorf F, Kutsch O, Bossmann SH. Pyrazolyl Thioureas and Carbothioamides with an NNSN Motif against MSSA and MRSA. ACS OMEGA 2021; 6:6088-6099. [PMID: 33718700 PMCID: PMC7948249 DOI: 10.1021/acsomega.0c04513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/20/2021] [Indexed: 05/27/2023]
Abstract
A novel series of copper-activatable drugs intended for use against methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant S. aureus (MRSA) were synthesized, characterized, and tested against the MSSA strain Newman and the MRSA Lac strain (a USA300 strain), respectively. These drugs feature an NNSN structural motif, which enables the binding of copper. In the absence of copper, no activity against MSSA and MRSA at realistic drug concentrations was observed. Although none of the novel drug candidates exhibits a stereocenter, sub-micromolar activities against SA Newman and micromolar activities against SA Lac were observed in the presence, but not in the absence, of bioavailable copper. Copper influx is a component of cellular response to bacterial infections, which is often described as nutritional immunity.
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Affiliation(s)
- Anjana Delpe-Acharige
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Man Zhang
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Kayla Eschliman
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Alex Dalecki
- Department
of Medicine, University of Alabama at Birmingham, 845 19th Street S, Birmingham, Alabama 35294, United States
| | | | | | - Tejaswi Shrestha
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Tanji Lewis
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Fatimah Al-Ibrahim
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Sophia Leonard
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Riana Roberts
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Anteneh Tebeje
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Aruni P. Malalasekera
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Hongwang Wang
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Madumali Kalubowilage
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
| | - Frank Wolschendorf
- Department
of Medicine, University of Alabama at Birmingham, 845 19th Street S, Birmingham, Alabama 35294, United States
| | - Olaf Kutsch
- Department
of Medicine, University of Alabama at Birmingham, 845 19th Street S, Birmingham, Alabama 35294, United States
| | - Stefan H. Bossmann
- Department
of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States
- Department
of Cancer Biology, The University of Kansas
Cancer Center, 3901 Rainbow
Blvd, Kansas City, Kansas 66160, United States
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16
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Zorn KM, Sun S, McConnon CL, Ma K, Chen EK, Foil DH, Lane TR, Liu LJ, El-Sakkary N, Skinner DE, Ekins S, Caffrey CR. A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules. ACS Infect Dis 2021; 7:406-420. [PMID: 33434015 PMCID: PMC7887754 DOI: 10.1021/acsinfecdis.0c00754] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
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.
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Affiliation(s)
- Kimberley M. Zorn
- Collaborations Pharmaceuticals, 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Shengxi Sun
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
| | - Cecelia L. McConnon
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
| | - Kelley Ma
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
| | - Eric K. Chen
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
| | - Daniel H. Foil
- Collaborations Pharmaceuticals, 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R. Lane
- Collaborations Pharmaceuticals, 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Lawrence J. Liu
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
| | - Nelly El-Sakkary
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
| | - Danielle E. Skinner
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Conor R. Caffrey
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093-0021, United States
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17
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Vignaux PA, Minerali E, Lane TR, Foil DH, Madrid PB, Puhl AC, Ekins S. The Antiviral Drug Tilorone Is a Potent and Selective Inhibitor of Acetylcholinesterase. Chem Res Toxicol 2021; 34:1296-1307. [PMID: 33400519 DOI: 10.1021/acs.chemrestox.0c00466] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
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.
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Affiliation(s)
- Patricia A Vignaux
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Eni Minerali
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Peter B Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Ana C Puhl
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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18
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Lane TR, Foil DH, Minerali E, Urbina F, Zorn KM, Ekins S. Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery. Mol Pharm 2020; 18:403-415. [PMID: 33325717 DOI: 10.1021/acs.molpharmaceut.0c01013] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Eni Minerali
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Fabio Urbina
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7545, United States
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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19
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Zorn KM, Foil DH, Lane TR, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. Comparing Machine Learning Models for Aromatase (P450 19A1). ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:15546-15555. [PMID: 33207874 PMCID: PMC8194505 DOI: 10.1021/acs.est.0c05771] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
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.
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Affiliation(s)
- Kimberley M. Zorn
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Daniel H. Foil
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Thomas R. Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Wendy Hillwalker
- Global Product Safety, SC Johnson and Son, Inc., Racine, WI, USA
| | | | - Frank Jones
- Global Product Safety, SC Johnson and Son, Inc., Racine, WI, USA
| | | | | | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
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20
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Vignaux P, Minerali E, Foil DH, Puhl AC, Ekins S. Machine Learning for Discovery of GSK3β Inhibitors. ACS OMEGA 2020; 5:26551-26561. [PMID: 33110983 PMCID: PMC7581251 DOI: 10.1021/acsomega.0c03302] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/25/2020] [Indexed: 05/08/2023]
Abstract
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 (GSK3β), 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 GSK3β 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 GSK3β (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.
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Affiliation(s)
- Patricia
A. Vignaux
- Collaborations Pharmaceuticals,
Inc., 840 Main Campus
Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Eni Minerali
- Collaborations Pharmaceuticals,
Inc., 840 Main Campus
Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H. Foil
- Collaborations Pharmaceuticals,
Inc., 840 Main Campus
Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Ana C. Puhl
- Collaborations Pharmaceuticals,
Inc., 840 Main Campus
Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals,
Inc., 840 Main Campus
Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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21
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Zorn KM, Foil DH, Lane TR, Russo DP, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:12202-12213. [PMID: 32857505 PMCID: PMC8194504 DOI: 10.1021/acs.est.0c03982] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
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.
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Affiliation(s)
- Kimberley M Zorn
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H Foil
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey 08102, United States
| | - Wendy Hillwalker
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - David J Feifarek
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Frank Jones
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - William D Klaren
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Ashley M Brinkman
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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22
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Lane TR, Dyall J, Mercer L, Goodin C, Foil DH, Zhou H, Postnikova E, Liang JY, Holbrook MR, Madrid PB, Ekins S. Repurposing Pyramax®, quinacrine and tilorone as treatments for Ebola virus disease. Antiviral Res 2020; 182:104908. [PMID: 32798602 PMCID: PMC7425680 DOI: 10.1016/j.antiviral.2020.104908] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/03/2020] [Accepted: 08/04/2020] [Indexed: 12/15/2022]
Abstract
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.
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Affiliation(s)
- Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Julie Dyall
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Luke Mercer
- Cambrex, 3501 Tricenter Blvd, Suite C, Durham, NC, 27713, USA
| | - Caleb Goodin
- Cambrex, 3501 Tricenter Blvd, Suite C, Durham, NC, 27713, USA
| | - Daniel H Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Huanying Zhou
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | | | - Janie Y Liang
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Michael R Holbrook
- Integrated Research Facility, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Peter B Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
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23
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Anderson E, Havener TM, Zorn KM, Foil DH, Lane TR, Capuzzi SJ, Morris D, Hickey AJ, Drewry DH, Ekins S. Synergistic drug combinations and machine learning for drug repurposing in chordoma. Sci Rep 2020; 10:12982. [PMID: 32737414 PMCID: PMC7395084 DOI: 10.1038/s41598-020-70026-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 07/20/2020] [Indexed: 12/18/2022] Open
Abstract
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.
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Affiliation(s)
- Edward Anderson
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tammy M Havener
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Daniel H Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Stephen J Capuzzi
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dave Morris
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Anthony J Hickey
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- RTI International, Research Triangle Park, NC, USA
| | - David H Drewry
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Sean Ekins
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA.
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24
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Repurposing the Dihydropyridine Calcium Channel Inhibitor Nicardipine as a Na v1.8 Inhibitor In Vivo for Pitt Hopkins Syndrome. Pharm Res 2020; 37:127. [PMID: 32529312 DOI: 10.1007/s11095-020-02853-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 06/05/2020] [Indexed: 12/20/2022]
Abstract
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.
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25
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Crawford CL, Dalecki AG, Perez MD, Schaaf K, Wolschendorf F, Kutsch O. A copper-dependent compound restores ampicillin sensitivity in multidrug-resistant Staphylococcus aureus. Sci Rep 2020; 10:8955. [PMID: 32488067 PMCID: PMC7265353 DOI: 10.1038/s41598-020-65978-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 03/09/2020] [Indexed: 12/11/2022] Open
Abstract
Multi-drug resistant Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA), has become a worldwide, major health care problem. While initially restricted to clinical settings, drug resistant S. aureus is now one of the key causative agents of community-acquired infections. We have previously demonstrated that copper dependent inhibitors (CDIs), a class of antibiotics that are only active in the presence of copper ions, are effective bactericidal agents against MRSA. A second-generation CDI, APT-6K, exerted bactericidal activity at nanomolar concentrations. At sub-bactericidal concentrations, it effectively synergized with ampicillin to reverse drug resistance in multiple MRSA strains. APT-6K had a favorable therapeutic index when tested on eukaryotic cells (TI: > 30) and, unlike some previously reported CDIs, did not affect mitochondrial activity. These results further establish inhibitors that are activated by the binding of transition metal ions as a promising class of antibiotics, and for the first time, describe their ability to reverse existing drug resistance against clinically relevant antibiotics.
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Affiliation(s)
- Cameron L Crawford
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Alex G Dalecki
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mildred D Perez
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kaitlyn Schaaf
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Frank Wolschendorf
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Olaf Kutsch
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
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26
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Ekins S, Gerlach J, Zorn KM, Antonio BM, Lin Z, Gerlach A. Repurposing Approved Drugs as Inhibitors of K v7.1 and Na v1.8 to Treat Pitt Hopkins Syndrome. Pharm Res 2019; 36:137. [PMID: 31332533 DOI: 10.1007/s11095-019-2671-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 07/10/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE Pitt Hopkins Syndrome (PTHS) is a rare genetic disorder caused by mutations of a specific gene, transcription factor 4 (TCF4), located on chromosome 18. PTHS results in individuals that have moderate to severe intellectual disability, with most exhibiting psychomotor delay. PTHS also exhibits features of autistic spectrum disorders, which are characterized by the impaired ability to communicate and socialize. PTHS is comorbid with a higher prevalence of epileptic seizures which can be present from birth or which commonly develop in childhood. Attenuated or absent TCF4 expression results in increased translation of peripheral ion channels Kv7.1 and Nav1.8 which triggers an increase in after-hyperpolarization and altered firing properties. METHODS We now describe a high throughput screen (HTS) of 1280 approved drugs and machine learning models developed from this data. The ion channels were expressed in either CHO (KV7.1) or HEK293 (Nav1.8) cells and the HTS used either 86Rb+ efflux (KV7.1) or a FLIPR assay (Nav1.8). RESULTS The HTS delivered 55 inhibitors of Kv7.1 (4.2% hit rate) and 93 inhibitors of Nav1.8 (7.2% hit rate) at a screening concentration of 10 μM. These datasets also enabled us to generate and validate Bayesian machine learning models for these ion channels. We also describe a structure activity relationship for several dihydropyridine compounds as inhibitors of Nav1.8. CONCLUSIONS This work could lead to the potential repurposing of nicardipine or other dihydropyridine calcium channel antagonists as potential treatments for PTHS acting via Nav1.8, as there are currently no approved treatments for this rare disorder.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA.
| | - Jacob Gerlach
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina, 27606, USA
| | - Brett M Antonio
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
| | - Zhixin Lin
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
| | - Aaron Gerlach
- Icagen, Inc., 4222 Emperor Blvd, Durham, North Carolina, 27703, USA
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27
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Hunsaker EW, Franz KJ. Emerging Opportunities To Manipulate Metal Trafficking for Therapeutic Benefit. Inorg Chem 2019; 58:13528-13545. [PMID: 31247859 DOI: 10.1021/acs.inorgchem.9b01029] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The indispensable requirement for metals in life processes has led to the evolution of sophisticated mechanisms that allow organisms to maintain dynamic equilibria of these ions. This dynamic control of the level, speciation, and availability of a variety of metal ions allows organisms to sustain biological processes while avoiding toxicity. When functioning properly, these mechanisms allow cells to return to their metal homeostatic set points following shifts in the metal availability or other stressors. These periods of transition, when cells are in a state of flux in which they work to regain homeostasis, present windows of opportunity to pharmacologically manipulate targets associated with metal-trafficking pathways in ways that could either facilitate a return to homeostasis and the recovery of cellular function or further push cells outside of homeostasis and into cellular distress. The purpose of this Viewpoint is to highlight emerging opportunities for chemists and chemical biologists to develop compounds to manipulate metal-trafficking processes for therapeutic benefit.
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Affiliation(s)
- Elizabeth W Hunsaker
- Department of Chemistry , Duke University , French Family Science Center, 124 Science Drive , Durham , North Carolina 27708 , United States
| | - Katherine J Franz
- Department of Chemistry , Duke University , French Family Science Center, 124 Science Drive , Durham , North Carolina 27708 , United States
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