\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"10147",leadTitle:null,fullTitle:"Waste in Textile and Leather Sectors",title:"Waste in Textile and Leather Sectors",subtitle:null,reviewType:"peer-reviewed",abstract:"In this book in your hands, the relationship between the textile and leather sectors, and the environment is examined from many viewpoints. The book contains many different subjects, from sustainability in the textile and leather sectors to the effect of historical textiles on human health. It will be interesting for readers from many disciplines in science.I thank all the authors contributing to the book and I hope that it will be helpful to the readers.",isbn:"978-1-78985-244-8",printIsbn:"978-1-78985-243-1",pdfIsbn:"978-1-83880-497-8",doi:"10.5772/intechopen.90014",price:119,priceEur:129,priceUsd:155,slug:"waste-in-textile-and-leather-sectors",numberOfPages:220,isOpenForSubmission:!1,isInWos:null,hash:"36eb1ed7179e0790a029523c97f1df04",bookSignature:"Ayşegül Körlü",publishedDate:"September 9th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/10147.jpg",numberOfDownloads:3091,numberOfWosCitations:0,numberOfCrossrefCitations:4,numberOfDimensionsCitations:7,hasAltmetrics:0,numberOfTotalCitations:11,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 22nd 2019",dateEndSecondStepPublish:"February 17th 2020",dateEndThirdStepPublish:"April 17th 2020",dateEndFourthStepPublish:"July 6th 2020",dateEndFifthStepPublish:"September 4th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,editors:[{id:"255885",title:"Dr.",name:"Ayşegül",middleName:null,surname:"Körlü",slug:"aysegul-korlu",fullName:"Ayşegül Körlü",profilePictureURL:"https://mts.intechopen.com/storage/users/255885/images/system/255885.jpeg",biography:"Prof. Dr. Ayşegül Körlü received her Msc and PhD degrees in textile engineering from Ege University. She has been employed by the Department of Textile Engineering, Engineering Faculty, Ege University since 1988. Currently, her main research interests are sustainable and ecological finishing processes, pretreatment of natural fibers, functional textiles and occupational safety in textile industry. She has co-authored numerous publications and has taken part in the management of many national and international projects.",institutionString:"Ege University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Ege University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"296",title:"Textile Engineering",slug:"textile-engineering"}],chapters:[{id:"71356",title:"Antimicrobial Fabrics Impregnated with Ag Particles Included in Silica Matrices",doi:"10.5772/intechopen.91631",slug:"antimicrobial-fabrics-impregnated-with-ag-particles-included-in-silica-matrices",totalDownloads:220,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Katerine Igal and Patricia Vázquez",downloadPdfUrl:"/chapter/pdf-download/71356",previewPdfUrl:"/chapter/pdf-preview/71356",authors:[null],corrections:null},{id:"71611",title:"The Waste Problem of Antimicrobial Finishing",doi:"10.5772/intechopen.91863",slug:"the-waste-problem-of-antimicrobial-finishing",totalDownloads:200,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Candan Akca",downloadPdfUrl:"/chapter/pdf-download/71611",previewPdfUrl:"/chapter/pdf-preview/71611",authors:[null],corrections:null},{id:"71971",title:"Textile Wastes: Status and Perspectives",doi:"10.5772/intechopen.92234",slug:"textile-wastes-status-and-perspectives",totalDownloads:670,totalCrossrefCites:1,totalDimensionsCites:1,signatures:"Burçin Ütebay, Pinar Çelik and Ahmet Çay",downloadPdfUrl:"/chapter/pdf-download/71971",previewPdfUrl:"/chapter/pdf-preview/71971",authors:[null],corrections:null},{id:"72493",title:"Understanding Denim Recycling: A Quantitative Study with Lifecycle Assessment Methodology",doi:"10.5772/intechopen.92793",slug:"understanding-denim-recycling-a-quantitative-study-with-lifecycle-assessment-methodology",totalDownloads:491,totalCrossrefCites:0,totalDimensionsCites:1,signatures:"Sedef Uncu Akı, Cevza Candan, Banu Nergis and Neslihan Sebla Önder",downloadPdfUrl:"/chapter/pdf-download/72493",previewPdfUrl:"/chapter/pdf-preview/72493",authors:[null],corrections:null},{id:"72570",title:"Investigation of Sound Absorption Characteristics of Textile Materials Produced from Recycled Fibers",doi:"10.5772/intechopen.92792",slug:"investigation-of-sound-absorption-characteristics-of-textile-materials-produced-from-recycled-fibers",totalDownloads:300,totalCrossrefCites:1,totalDimensionsCites:1,signatures:"Nilgün Özdil, Gonca Özçelik Kayseri and Gamze Süpüren Mengüç",downloadPdfUrl:"/chapter/pdf-download/72570",previewPdfUrl:"/chapter/pdf-preview/72570",authors:[null],corrections:null},{id:"72686",title:"Innovation of Textiles through Natural By-Products and Wastes",doi:"10.5772/intechopen.93011",slug:"innovation-of-textiles-through-natural-by-products-and-wastes",totalDownloads:235,totalCrossrefCites:1,totalDimensionsCites:1,signatures:"Lorena Coelho, Ana Isabel Magalhães, Sara Fernandes, Patrícia Batista, Manuela Pintado, Pedro Faria, Catarina Costa, Bruna Moura, Augusta Marinho, Rosa Maria, Albertina Reis, Marta Carvalho, Mário Marques, Ângela Teles, José De Almeida Morgado, Maria Helena Vilaça, Jéssica Alexandra Pereira, Pedro José Magalhães, Ana Sofia Silva, Ricardo Jorge Silva, Mário Jorge Silva, Vera Lúcia Sá, Sandra Gabriela Ventura, João Silva Abreu, Joaquim Manuel Gaião, Raquel Rosa Mourão, Fernando Manuel Merino, Mónica Sofia Gonçalves and Regina Malgueiro",downloadPdfUrl:"/chapter/pdf-download/72686",previewPdfUrl:"/chapter/pdf-preview/72686",authors:[null],corrections:null},{id:"72365",title:"Value Addition to Leather Industry Wastes and By-Products: Hydrolyzed Collagen and Collagen Peptides",doi:"10.5772/intechopen.92699",slug:"value-addition-to-leather-industry-wastes-and-by-products-hydrolyzed-collagen-and-collagen-peptides",totalDownloads:400,totalCrossrefCites:0,totalDimensionsCites:2,signatures:"Ali Yorgancioglu, Bahri Başaran and Aykut Sancakli",downloadPdfUrl:"/chapter/pdf-download/72365",previewPdfUrl:"/chapter/pdf-preview/72365",authors:[null],corrections:null},{id:"71987",title:"Characterization of Grafted Acrylamide onto Pine Magnetite Composite for the Removal of Methylene Blue from Wastewater",doi:"10.5772/intechopen.92114",slug:"characterization-of-grafted-acrylamide-onto-pine-magnetite-composite-for-the-removal-of-methylene-bl",totalDownloads:142,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Kgomotso N.G. Mtshatsheni, Bobby E. Naidoo and Augustine E. Ofomaja",downloadPdfUrl:"/chapter/pdf-download/71987",previewPdfUrl:"/chapter/pdf-preview/71987",authors:[null],corrections:null},{id:"72463",title:"Wastewater Treatment Using Imprinted Polymeric Adsorbents",doi:"10.5772/intechopen.92386",slug:"wastewater-treatment-using-imprinted-polymeric-adsorbents",totalDownloads:176,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Burcu Okutucu",downloadPdfUrl:"/chapter/pdf-download/72463",previewPdfUrl:"/chapter/pdf-preview/72463",authors:[null],corrections:null},{id:"71457",title:"Considerations Regarding the Research for the Conservation of Heritage Textiles in Romania",doi:"10.5772/intechopen.91393",slug:"considerations-regarding-the-research-for-the-conservation-of-heritage-textiles-in-romania",totalDownloads:264,totalCrossrefCites:1,totalDimensionsCites:1,signatures:"Ilieș Dorina Camelia, Herman Grigore Vasile, Caciora Tudor, Ilieș Alexandru, Indrie Liliana, Wendt Jan, Axinte Anamaria, Diombera Mamadou, Lite Cristina, Berdenov Zharas and Albu Adina",downloadPdfUrl:"/chapter/pdf-download/71457",previewPdfUrl:"/chapter/pdf-preview/71457",authors:[null],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},relatedBooks:[{type:"book",id:"7431",title:"Textile Industry and Environment",subtitle:null,isOpenForSubmission:!1,hash:"be9d70201ab46060419025deb99c16f3",slug:"textile-industry-and-environment",bookSignature:"Ayşegül Körlü",coverURL:"https://cdn.intechopen.com/books/images_new/7431.jpg",editedByType:"Edited by",editors:[{id:"255885",title:"Dr.",name:"Ayşegül",surname:"Körlü",slug:"aysegul-korlu",fullName:"Ayşegül Körlü"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9203",title:"Chemistry and Technology of Natural and Synthetic Dyes and Pigments",subtitle:null,isOpenForSubmission:!1,hash:"126a19fe8435f744b10161895ed51116",slug:"chemistry-and-technology-of-natural-and-synthetic-dyes-and-pigments",bookSignature:"Ashis Kumar Samanta, Nasser S. Awwad and Hamed Majdooa Algarni",coverURL:"https://cdn.intechopen.com/books/images_new/9203.jpg",editedByType:"Edited by",editors:[{id:"42763",title:"Prof.",name:"Ashis Kumar",surname:"Samanta",slug:"ashis-kumar-samanta",fullName:"Ashis Kumar Samanta"}],equalEditorOne:{id:"145209",title:"Prof.",name:"Nasser",middleName:"S",surname:"Awwad",slug:"nasser-awwad",fullName:"Nasser Awwad",profilePictureURL:"https://mts.intechopen.com/storage/users/145209/images/system/145209.jpg",biography:'Dr. Nasser Awwad received his Ph.D. in inorganic and radiochemistry in 2000 from Ain Shams University and his Ph.D. at Sandia National Labs, New Mexico, USA, 2004. Nasser Awwad was an Associate Professor of Radiochemistry in 2006 and Professor of Inorganic and Radiochemistry in 2011. He has been a Professor at King Khalid University, Abha, KSA, from 2011 until now. He has published two chapters in the following books \\"Natural Gas - Extraction to End Use\\" and “Advances in Petrochemicals”. Pro Awwad has edited four books (Uranium, New trends in Nuclear Sciences, Lanthanides, and Nuclear Power Plants) and he has co-edited two books (“Chemistry and Technology of Natural and Synthetic Dyes and Pigments” and “Chromatography - Separation, Identification, and Purification Analysis”). He has also published 95 papers in ISI journals. He has supervised 4 Ph.D. and 18 MSc students in the field of radioactive and wastewater treatment. He has participated in 26 international conferences in South Korea, the USA, Lebanon, KSA, and Egypt. He has reviewed 2 Ph.D. and 13 MSc theses. He participated in 6 big projects with KACST at KSA and Sandia National Labs in the USA. He is a member of the Arab Society of Forensic Sciences and Forensic Medicine. He is a permanent member of the American Chemical Society, and a rapporteur of the Permanent Committee for Nuclear and Radiological Protection at KKU. He is Head of the Scientific Research and International Cooperation Unit, Faculty of Science, King Khalid University.',institutionString:"King Khalid University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"King Khalid University",institutionURL:null,country:{name:"Saudi Arabia"}}},equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],ofsBooks:[]},correction:{item:{id:"67322",slug:"corrigendum-to-sexual-dysfunction-in-patients-with-systemic-sclerosis",title:"Corrigendum to: Sexual Dysfunction in Patients with Systemic Sclerosis",doi:null,correctionPDFUrl:"https://cdn.intechopen.com/pdfs/67322.pdf",downloadPdfUrl:"/chapter/pdf-download/67322",previewPdfUrl:"/chapter/pdf-preview/67322",totalDownloads:null,totalCrossrefCites:null,bibtexUrl:"/chapter/bibtex/67322",risUrl:"/chapter/ris/67322",chapter:{id:"66966",slug:"sexual-dysfunction-in-patients-with-systemic-sclerosis",signatures:"Barbora Heřmánková",dateSubmitted:"July 16th 2018",dateReviewed:"April 5th 2019",datePrePublished:"May 3rd 2019",datePublished:null,book:{id:"8269",title:"New Insights into Systemic Sclerosis",subtitle:null,fullTitle:"New Insights into Systemic Sclerosis",slug:"new-insights-into-systemic-sclerosis",publishedDate:"September 18th 2019",bookSignature:"Michal Tomcik",coverURL:"https://cdn.intechopen.com/books/images_new/8269.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"193284",title:"Dr.",name:"Michal",middleName:null,surname:"Tomcik",slug:"michal-tomcik",fullName:"Michal Tomcik"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null}},chapter:{id:"66966",slug:"sexual-dysfunction-in-patients-with-systemic-sclerosis",signatures:"Barbora Heřmánková",dateSubmitted:"July 16th 2018",dateReviewed:"April 5th 2019",datePrePublished:"May 3rd 2019",datePublished:null,book:{id:"8269",title:"New Insights into Systemic Sclerosis",subtitle:null,fullTitle:"New Insights into Systemic Sclerosis",slug:"new-insights-into-systemic-sclerosis",publishedDate:"September 18th 2019",bookSignature:"Michal Tomcik",coverURL:"https://cdn.intechopen.com/books/images_new/8269.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"193284",title:"Dr.",name:"Michal",middleName:null,surname:"Tomcik",slug:"michal-tomcik",fullName:"Michal Tomcik"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null},book:{id:"8269",title:"New Insights into Systemic Sclerosis",subtitle:null,fullTitle:"New Insights into Systemic Sclerosis",slug:"new-insights-into-systemic-sclerosis",publishedDate:"September 18th 2019",bookSignature:"Michal Tomcik",coverURL:"https://cdn.intechopen.com/books/images_new/8269.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"193284",title:"Dr.",name:"Michal",middleName:null,surname:"Tomcik",slug:"michal-tomcik",fullName:"Michal Tomcik"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"8504",leadTitle:null,title:"Pectins",subtitle:"Extraction, Purification, Characterization and Applications",reviewType:"peer-reviewed",abstract:"This book deepens the study and knowledge on pectins, especially in the processes of extraction, purification, and characterization, in short its many and wide applications. Among the most prominent applications are the food, pharmaceutical, and other industries. The development of pectins has a very promising future with a marked annual increase and with a wide range of sources. As written above, this book will help its readers to expand their knowledge on this biopolymer with vast application in the industry worldwide.",isbn:"978-1-78984-072-8",printIsbn:"978-1-78984-071-1",pdfIsbn:"978-1-83968-550-7",doi:"10.5772/intechopen.78880",price:119,priceEur:129,priceUsd:155,slug:"pectins-extraction-purification-characterization-and-applications",numberOfPages:178,isOpenForSubmission:!1,hash:"ff1acef627b277c575a10b3259dd331b",bookSignature:"Martin Masuelli",publishedDate:"January 22nd 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8504.jpg",keywords:null,numberOfDownloads:6205,numberOfWosCitations:8,numberOfCrossrefCitations:7,numberOfDimensionsCitations:23,numberOfTotalCitations:38,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 12th 2018",dateEndSecondStepPublish:"November 26th 2018",dateEndThirdStepPublish:"January 25th 2019",dateEndFourthStepPublish:"April 15th 2019",dateEndFifthStepPublish:"June 14th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"99994",title:"Dr.",name:"Martin",middleName:"Alberto",surname:"Masuelli",slug:"martin-masuelli",fullName:"Martin Masuelli",profilePictureURL:"https://mts.intechopen.com/storage/users/99994/images/system/99994.jpg",biography:"Martin Masuelli is an Inv. Adj. of Instituto de Física Aplicada (INFAP) – CONICET – UNSL and Associate Professor at the National University of San Luis (UNSL), Argentina. He holds a Master’s degree and a Ph.D. and Master's thesis in Membrane Technology from the National University of San Luis. He is the Director of Physics Chemistry Service Laboratory at UNSL. He is an expert in polysaccharides and physics chemistry of macromolecules. He is an author or co-author of more than 30 peer-reviewed international publications, 8 book chapters, 75 communications in national and international congresses and editor of 8 books. He is a member of the Sociedad Argentina de Ciencia y Tecnología Ambiental, Asociación Argentina de Fisicoquímica y Química Inorgánica and Asociación Argentina de Tecnólogos Alimentarios. Since July 2013, he is the Editor in Chief and Founder of the Journal of Polymer and Biopolymers Physics Chemistry, Science and Education Publishing. He is on the Editorial Board of numerous journals.",institutionString:"National University of San Luis",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"National University of San Luis",institutionURL:null,country:{name:"Argentina"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"333",title:"Food Chemistry",slug:"food-science-food-chemistry"}],chapters:[{id:"69850",title:"Extraction and Characterization of Pectins From Peels of Criolla Oranges (Citrus sinensis): Experimental Reviews",slug:"extraction-and-characterization-of-pectins-from-peels-of-criolla-oranges-em-citrus-sinensis-em-exper",totalDownloads:771,totalCrossrefCites:1,authors:[{id:"99994",title:"Dr.",name:"Martin",surname:"Masuelli",slug:"martin-masuelli",fullName:"Martin Masuelli"}]},{id:"66671",title:"Extraction and Purification of Pectin from Agro-Industrial Wastes",slug:"extraction-and-purification-of-pectin-from-agro-industrial-wastes",totalDownloads:1560,totalCrossrefCites:0,authors:[null]},{id:"66458",title:"Pectin - Extraction, Purification, Characterization and Applications",slug:"pectin-extraction-purification-characterization-and-applications",totalDownloads:556,totalCrossrefCites:0,authors:[null]},{id:"65793",title:"Role of Pectin in Food Processing and Food Packaging",slug:"role-of-pectin-in-food-processing-and-food-packaging",totalDownloads:1878,totalCrossrefCites:5,authors:[null]},{id:"65638",title:"Pectins as Emulsifying Agent on the Preparation, Characterization, and Photocatalysis of Nano-LaCrO3",slug:"pectins-as-emulsifying-agent-on-the-preparation-characterization-and-photocatalysis-of-nano-lacro-su",totalDownloads:350,totalCrossrefCites:1,authors:[null]},{id:"66396",title:"Properties of Wine Polysaccharides",slug:"properties-of-wine-polysaccharides",totalDownloads:598,totalCrossrefCites:0,authors:[{id:"207851",title:"Dr.",name:"Zenaida",surname:"Guadalupe",slug:"zenaida-guadalupe",fullName:"Zenaida Guadalupe"},{id:"207853",title:"Dr.",name:"Belén",surname:"Ayestarán",slug:"belen-ayestaran",fullName:"Belén Ayestarán"},{id:"207854",title:"Dr.",name:"Leticia",surname:"Martínez-Lapuente",slug:"leticia-martinez-lapuente",fullName:"Leticia Martínez-Lapuente"}]},{id:"65895",title:"Flavonoids and Pectins",slug:"flavonoids-and-pectins",totalDownloads:497,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"278926",firstName:"Ivana",lastName:"Barac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/278926/images/8058_n.jpg",email:"ivana.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"2828",title:"Fiber Reinforced Polymers",subtitle:"The Technology Applied for Concrete Repair",isOpenForSubmission:!1,hash:"4922c593466cc822b281fe7cc7d7fef6",slug:"fiber-reinforced-polymers-the-technology-applied-for-concrete-repair",bookSignature:"Martin Alberto Masuelli",coverURL:"https://cdn.intechopen.com/books/images_new/2828.jpg",editedByType:"Edited by",editors:[{id:"99994",title:"Dr.",name:"Martin",surname:"Masuelli",slug:"martin-masuelli",fullName:"Martin Masuelli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1408",title:"Scientific, Health and Social Aspects of the Food Industry",subtitle:null,isOpenForSubmission:!1,hash:"e683dc398eabec0db3a88e891209a406",slug:"scientific-health-and-social-aspects-of-the-food-industry",bookSignature:"Benjamin Valdez",coverURL:"https://cdn.intechopen.com/books/images_new/1408.jpg",editedByType:"Edited by",editors:[{id:"65522",title:"Dr.",name:"Benjamin",surname:"Valdez",slug:"benjamin-valdez",fullName:"Benjamin Valdez"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5060",title:"Milk Proteins",subtitle:"From Structure to Biological Properties and Health Aspects",isOpenForSubmission:!1,hash:"4a7d2e5f38e97aaea90bb3fec55b3751",slug:"milk-proteins-from-structure-to-biological-properties-and-health-aspects",bookSignature:"Isabel Gigli",coverURL:"https://cdn.intechopen.com/books/images_new/5060.jpg",editedByType:"Edited by",editors:[{id:"175679",title:"Dr.",name:"Isabel",surname:"Gigli",slug:"isabel-gigli",fullName:"Isabel Gigli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6761",title:"Generation of Aromas and Flavours",subtitle:null,isOpenForSubmission:!1,hash:"32cb87c823ee53fcbff7ecb2e944d4b9",slug:"generation-of-aromas-and-flavours",bookSignature:"Alice Vilela",coverURL:"https://cdn.intechopen.com/books/images_new/6761.jpg",editedByType:"Edited by",editors:[{id:"181011",title:"Prof.",name:"Alice",surname:"Vilela",slug:"alice-vilela",fullName:"Alice Vilela"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8311",title:"Nutraceuticals",subtitle:"Past, Present and Future",isOpenForSubmission:!1,hash:"51994c7d3887b9ecd6926b4967a4fdfb",slug:"nutraceuticals-past-present-and-future",bookSignature:"María Chávarri Hueda",coverURL:"https://cdn.intechopen.com/books/images_new/8311.jpg",editedByType:"Edited by",editors:[{id:"150285",title:"Dr.",name:"María",surname:"Chávarri Hueda",slug:"maria-chavarri-hueda",fullName:"María Chávarri Hueda"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8625",title:"Whey",subtitle:"Biological Properties and Alternative Uses",isOpenForSubmission:!1,hash:"449a36f43c9a30ae4d43f9775599e8ac",slug:"whey-biological-properties-and-alternative-uses",bookSignature:"Isabel Gigli",coverURL:"https://cdn.intechopen.com/books/images_new/8625.jpg",editedByType:"Edited by",editors:[{id:"175679",title:"Dr.",name:"Isabel",surname:"Gigli",slug:"isabel-gigli",fullName:"Isabel Gigli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7332",title:"Some New Aspects of Colloidal Systems in Foods",subtitle:null,isOpenForSubmission:!1,hash:"0dd822267e027684bd3ff53da4f2ef41",slug:"some-new-aspects-of-colloidal-systems-in-foods",bookSignature:"Jafar M. Milani",coverURL:"https://cdn.intechopen.com/books/images_new/7332.jpg",editedByType:"Edited by",editors:[{id:"91158",title:"Associate Prof.",name:"Jafar",surname:"Milani",slug:"jafar-milani",fullName:"Jafar Milani"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6878",title:"Frontiers and New Trends in the Science of Fermented Food and Beverages",subtitle:null,isOpenForSubmission:!1,hash:"aaeaec7ab2b300434df9061448772e57",slug:"frontiers-and-new-trends-in-the-science-of-fermented-food-and-beverages",bookSignature:"Rosa Lidia Solís-Oviedo and Ángel de la Cruz Pech-Canul",coverURL:"https://cdn.intechopen.com/books/images_new/6878.jpg",editedByType:"Edited by",editors:[{id:"227052",title:"Dr.",name:"Rosa Lidia",surname:"Solís-Oviedo",slug:"rosa-lidia-solis-oviedo",fullName:"Rosa Lidia Solís-Oviedo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53039",title:"Going Small: Using Biophysical Screening to Implement Fragment Based Drug Discovery",doi:"10.5772/66423",slug:"going-small-using-biophysical-screening-to-implement-fragment-based-drug-discovery",body:'\n“Going small” with fragment‐based drug discovery (FBDD) denotes using low molecular weight compounds to probe a therapeutic target. This also includes using smaller tailored libraries and lower screening throughput in more carefully measured assays. This is a consequence of being reliant on biophysical technologies, as compared to classical high‐throughput screening (HTS) approaches performed in 384‐well plates that detect product formation. FBDD at its core is target‐based drug discovery, but the initial approach of fragment screening differs from standard lead‐like screening, which utilizes much larger higher molecular weight screening libraries.
\nIn theory, modern target‐based drug discovery screening libraries are designed to maximize coverage of chemical space. This is especially important for groups that use high‐throughput technologies (HTS) and screen against diverse targets. These target‐based programs tend to rely on screening the highest practical number of chemical entities from their screening libraries, sometimes accumulating millions of compounds [1]. However, with over 166 billion possible synthetically accessible organic molecules containing up to 17 heavy atoms (nonhydrogen) [2], even the biggest screening libraries cannot possibly statistically represent this vast chemical space [3].
\nFrom modeling described in a 2001 article, Hann and colleagues showed how higher molecular complexity (i.e., ligand size) significantly decreased the probability of protein‐site molecular recognition [4]. The authors outlined this as a primary shortcoming of the combinatorial chemistry/HTS approach to drug discovery and promoted the idea that screening smaller libraries with reduced complexity could be a complementary approach. Thus, by reducing compound complexity, FBDD evades the pitfall of scaffold bias which develops in large lead‐like screening libraries.
\nIn this chapter, the reader should come to appreciate how going small is an intrinsically orthogonal screening platform that is easily integrated with established biophysical techniques. The methods, examples, and citations discussed are intended to guide a newcomer to FBDD, specifically scientists who have some prior experience with drug screening principles.
Fragment‐based drug discovery started as a concept published in 1981 [5] by biochemist William P. Jencks, who characterized the binding affinities of molecules to proteins as being built from components. Citing several examples, he described how the balance of Gibbs‐free energy for a two‐component molecule binding to a protein could also be described through the equation
At present, NMR, surface plasmon resonance (SPR), thermal shift assay (TSA), isothermal titration calorimetry (ITC), and X‐ray crystallography (Sections 3.2–3.4, 4.1 and 4.2) are the most widely used techniques in FDBB. Given their respective throughput capacities NMR, SPR, and TSA are often used as the primary screening technology with ITC and X‐ray crystallography reserved as secondary screening. Figure 1 shows the effective ligand affinity coverage of each technique which partly demonstrates their utility with FBDD. All biophysical screening techniques work best in combination and individual hits need careful orthogonal validation. Crystallography is the gold standard as the information gained allows for rapid ligand advancement. However, its application as a primary screen is often impractical due to resource and time limitations. High concentration inhibitor biochemical or fluorescence polarization (FP) assays can be used in some cases for orthogonal validation of primary screening hits where crystallography is not an option.
The range of fragment affinities covered by the biophysical techniques described in this chapter. The techniques are ranked from the top downwards in order of their typical frequency of use in FBDD programs (SPR being a close second). NMR (yellow outline) is represented as a medium‐throughput method but can be low‐throughput based on the availability of protein and whether recycling is required. *Fluorescence polarization, technically a biochemical technique and highly dependent on probe affinity, is included for comparison but can be applied to fragment screening.
The workflow represented in Figure 2 shows how these techniques might be organized into a traditional screening paradigm. It is common to use some of these techniques in parallel, particularly at the secondary screening stage since there are always fewer compounds to evaluate. Pragmatically, scientists should obtain structural insights at this secondary stage to validate primary screening results. Structural information is preferred when deciding to progress a fragment to the hit generation stage (discussed further in Section 5.2).
An example of the typical FBDD screening workflow. The workflow assumes structural information by X‐ray or NMR. The hierarchy will not accurately depict the resources of all drug discovery programs. Dashed connections represent screening options at the primary and secondary screening stages. Arrows point to the results from each screen to be ranked or compared to those of other techniques. Fragments promoted to secondary screening will eventually require structural information to be progressed to hit generation. *The ITC technique is typically used for ranking fragment hits after secondary screening.
An obvious first step for any primary fragment screening is to resource compounds for the screen. However, wielding a proper fragment library as a tool for hit generation conflicts with traditional lead‐like screening methods. Central to the conflict is the regular use of high concentrations of compounds to accommodate expected low binding affinities. Practical pitfalls, such as compound aggregation, compound precipitation, dramatic pH changes, detector saturation, and nonspecific interactions, are a minor concern when nanomolar concentrations are used during screening of larger molecular weight molecules, but can become major issues when millimolar concentrations are used in fragment screening.
\nIn 2003, scientists at Astex Pharmaceuticals published a synopsis of their emerging fragment drug‐discovery program and noted that the average physical properties of their fragment hits fell conveniently within different orders of 3 (molecular weight <300, hydrogen bond donors ≤3, hydrogen bond acceptors ≤3, and ClogP ≤3) [10]. As a compliment to Lipinski\'s rule of 5 (RO5), the fragment rule of 3 (RO3) was a convenient target toward which chemical suppliers built fragment libraries from their existing stores. Ten years later, Astex Pharmaceuticals revised their position [11], stating that similar to the RO5 described by Lipinski, RO3 was more of a guideline and that their refined library consisted of fragments with less than 17 heteroatoms with molecular mass <230 Daltons. This exemplifies how well‐constructed fragment libraries should rely heavily on practicality to be effective tools and speaks to the need for additional scrutiny of (in order of importance) solubility, stability, and reactivity for effective fragment screening.
\nCommercial and nonprofit access to fragment libraries exists, and several examples have been characterized [12, 13]. A custom library allows for existing libraries to be used in addition to catalog resources with some tailoring based on specific cheminformatics principles, such as optimization of the representative chemical space, avoidance of nuisance compounds, and guidance with pharmacophore models. One early‐stage example of note is the Global Fragment Initiative (GFI) by Pfizer [14], wherein the library was built from compounds on hand, purchased, and synthesized. Each member of the GFI library was rigorously characterized and empirically tested for aqueous solubility up to 1 mM, with the intent of using the library for multiple biophysical techniques. How a fragment library is procured will depend on an acceptable balance of convenience and cost, but it is highly recommended that the end user have methods in place to reliably assess each compound for practical use at high concentrations. For an example of this workflow, see Figure 3.
A suggested library construction workflow for FBDD. Once compounds are obtained as dry stocks, the workflow proceeds left to right. Having redundancy built into the screening stocks helps rule out contamination or mishandling. It is presumed that fragments will eventually be used in NMR studies, therefore dissolved using deuterated solvents (d6‐DMSO and D2O). Rigid quality control is recommended to eliminate spoiled or misidentified compounds and repeated on hits or on the event of significant additions of fragments to the library.
Fragment hits can have limited traction toward chemical expansion or linking as a consequence of their size. As a safeguard, Merck strategically redesigned its general FBDD library to accommodate more structure‐activity relationships and to fill structural gaps by visual inspection [15]. The purposeful move away from diversity in its general library was a concerted effort of cheminformatics and crowdsourcing of medicinal chemists to gain pipeline traction. The strategy leads to larger general screening libraries and effectively restricts widespread application to appropriately equipped programs. Regardless, any FBDD program design must account for this pitfall and ensure the potential to develop fragment hits through chemistry or catalogs.
\nFinally, Pan‐Assay Interference Compounds (PAINS) are a well‐known classification of chemical entities to activity across multiple assays and proteins, and they have been thoroughly reviewed in regards to their practical impacts on FBDD [16], and related cheminformatics filters are available via the Internet [17]. The reduced chemical complexity of fragments does inherently diminish the number of “worst offenders” in its library and often bad fragments are quickly identified and triaged from screening libraries.
\nIn conclusion, for FBDD, it is prudent to prioritize highly soluble fragment libraries with a diversity of ring shapes that can match a broad range of hydrogen bonding interactions from the protein target. A minimalistic approach would be to only eliminate mostly predictable fragment “show stoppers,” containing toxicophores subject to xenobiotic metabolism, since it is often easy to scaffold hop in the early stages of FBDD to remove unwanted motifs.
Modern NMR spectroscopy is best known for enabling the three‐dimensional characterization of ordered molecular structures in solution and was the first technique to be used for fragment screening [6]. It is also one of the few biophysical techniques that can easily be switched between perspectives of the small molecule and the protein at run time. A growing list of NMR experiments used in fragment screening can help validate hits without using additional biophysical techniques.
\nSamples are prepared in situ by using automation (such as the Gilson GX‐271 in Figure 4) or by pipetting manually. One immediate benefit of the manual method is the ease with which a scientist can eliminate precipitated or turbid samples by optical analytics. Individual inspection of samples is time‐consuming but a must for any successful FBDD program, providing important feedback about the fragment library. Fragments must be dissolved in deuterated solvent (e.g., 99.9% d6‐DMSO, Cambridge Isotope Laboratories, Inc., USA) for programs using NMR at any stage (see Figure 3). Typical concentration ratios for test samples are 10:1 up to 30:1 fragment to protein in the chosen buffer (phosphate buffers being the most common). Prior knowledge of the Kd and stoichiometry is not required but can be used to tune concentrations to avoid unintended site saturation by an individual fragment, which is a general concern when screening fragment mixtures or performing competition experiments. Finally, the issue of whether or not to use surfactants (e.g., 0.05% Triton X100) to help eliminate false positives is best left to a case by case basis, but if used, the conditions should be consistent with orthogonal techniques, with the exception of crystallography.
Screening fragments by NMR may include the use of automated sample preparation and handling. Common NMR experiments used in FBDD programs are listed, categorized according to their use of the magnetization pathways indicated. For ligand‐detected experiments, additional pathways to the unbound ligand also exist. The white arrow indicates magnetization transfer from the protein to its bound ligand and is specific to the saturation transfer difference (STD) experiment.
Spectroscopy experiments that indicate binding from the fragment\'s perspective are structurally less informative but have a higher dynamic range than do protein‐detected experiments. Because significant cost savings can be made by using unlabeled protein, early‐stage, budget‐conscious programs may focus on using the ligand‐detected suite of experiments shown in Figure 4, often acquiring them in parallel for each sample. The saturation transfer difference (STD) experiment can provide a binding‐epitope map, as magnetization can only travel through the protein to the bound fragment [18]. The epitope map enables a scientist using unlabeled protein to identify the portions of the fragment in closest proximity to the protein and, conversely, the portions available for expansion or linking for hit generation. Specialized experiments, such as the interligand nuclear Overhauser effect and target immobilized NMR screening (ILOE [19] and TINS [20], respectively), are best reserved for the study of difficult proteins or competition experiments. From the protein perspective, several variants of the two‐dimensional HSQC experiment (typically 1H nuclei measured directly and X nuclei, indirectly) that helps to disperse the numerous signals in a protein target and depend on the protein isotopic enrichment strategy. TROSY (another HSQC variant) can be used for large, usually perdeuterated proteins but requires a high‐field instrument (i.e., 800 MHz and up); the discovery of methods to selectively label methyls [21] has simplified the resulting spectra and enabled screening on lower‐field instruments (e.g., 500 MHz) [22].
\nTo reduce protein consumption, NMR FBDD relies on the screening of equimolar fragment mixtures. This step is immediately followed by mixture dereplication, usually involving manual interpretation of spectra, although with careful sample preparation and a well‐curated fragment spectral database, hits can be identified by software (e.g., Mnova Suite, Mestrelab Research S.L.; ACD/Spectrus Suite, Advanced Chemistry Development, Inc.; Topspin, Bruker Inc.). Individual reference spectra are acquired during the quality control steps of the fragment library design, and mixtures are then designed to avoid spectral overlap and reactivity. The number of fragments used per mixture is not standardized; but, basic statistical principles support using as few fragments as possible while avoiding mixing acids with bases or nucleophiles with electrophiles. For 1H‐observed experiments, 5–7 compounds per mixture is a reasonable starting point. Validation of hits is accomplished by a second round of screening individual fragments. At the validation stage, the use of spectroscopy experiments that provide binding site information (e.g., HSQC, STD mapping, ILOE) is highly recommended if crystallography is not readily available. Any resulting structural information is crucial for promoting the fragment into the downstream processes of medicinal chemistry and hit generation.
\nWith the exception of WaterLOGSY, titration and analysis of the resulting signal of these NMR experiments can provide binding affinity scores for fragments with reasonable accuracy. In addition to the previously described STD experiment, 19F NMR screening by filtered transverse relaxation (T2), a filter also referred to as a Carr‐Purcell‐Meiboom‐Gill (CPMG) scheme, can be a powerful option if used in competition with a known fluorinated ligand having a Kd measured carefully by using more rigorous techniques (e.g., SPR, ITC, FP). The potential benefit is that one sample can be analyzed for fragments in competition with or, perhaps, having an allosteric contribution to binding, and binding affinity is back‐calculated relative to an internal nonbinding control or electric reference signal [23].
\nThere are relatively few drawbacks to using available NMR facilities in a FBDD program considering the method\'s ability to contribute to every aspect of the workflow, such as library quality control and hit generation. The two drawbacks that are most often cited are the speed of the screen from sample preparation to data analysis and the demands on protein production for screening by NMR.
Surface plasmon resonance shares the spotlight with NMR as a major screening technique for FBDD programs. The hurdles that come with using an immobilized protein for screening are counterbalanced by increased sensitivity and immediate access to kinetics data. Although absolute binding kinetics are not assured when dealing with weak affinities, for well‐optimized experiments, obtaining ka (binding), kd (dissociation) rates, and the KD (binding constant) value is certainly possible. Consequently, interpreting the resulting sensorgram can be challenging; but thankfully, the biosensor community has over 25 years of experiments to help set standards—hundreds just in 2009 [24]. With improvements in software to include experiment wizards, relatively new users can screen numerous fragments.
\nThese experiments generally have long lead in times, as protein immobilization chemistry and buffer conditions need to be carefully perfected prior to screening experiments. Setting aside the routine instrument maintenance and screen validation using appropriate controls, the traditional stages of each experiment have been buffer and compound preparation, target immobilization, start‐up samples (i.e., buffer match, blanks, and positive controls), fragment primary screen, data reduction and analysis, hit selection, and secondary dose response of hits.
\nSuccessful FBDD programs focus most practical attention on the preparation of the chip immobilization surface, ensuring stability between experiments. This focus on stability assumes that the loading conditions for the protein have been standardized and validated across multiple coupling methods, such as a covalent amine coupling [25] (to amine termini or lysine) or the less‐amenable (regarding FBDD) coupling by protein tags (e.g., biotinylation or poly‐histidine).
\nOnce the target is loaded, screening samples should be prepared as single‐point concentrations, for example 100 μM. The concentrations can be variable, but the expectation is that they are carefully prepared to avoid common problems such as precipitation and aggregate formation that may produce nonstoichiometric binding to the target. The use of detergents is allowable for the sake of the target but further complicates buffer matching in the reference channel. Troublesome fragments identified by their atypical sensorgrams are usually triaged from screening collections.
\nOnce the data have been collected, data reduction and normalization follows and requires some practice to prosecute these steps efficiently. An experienced scientist can perform a first pass of the entire data set to quickly exclude sensorgrams for which a reasonable curve fit is unlikely due to compound incompatibilities or systemic problems with the instrument (Figure 5). Next, data reduction can seem like a tedious process to simply “clean” the sensorgrams; but in addition to aligning the injection time points, it checks the soundness of the blank injections, which yield important problem‐solving data. Finally, the configuration of most instruments requires multiple runs to cover entire fragment libraries, so run‐to‐run variation naturally exists. Normalization seeks to enable the comparison of experimental responses, regardless of target density, binding activity, molecular weight, and buffer mismatch. The most expedient way to achieve this is by using the known concentration and binding affinities of the control compound injections to convert the response unit (RU) to a relative occupancy (θ), wherein 100% would be saturated binding (i.e., Req/Rmax).
Ideal (green) and unfortunate (red) sensorgrams simulate binding and nonbinding events during the SPR experiment. Aggregation is typically concentration‐dependent, whereas incorrect stoichiometry will not fit simple binding models. Relative occupancy (θ) of 100%, with a 1:1 stoichiometry indicated at the dashed line, represents how using high concentrations of fragments that appear to bind do so nonspecifically, binding weakly to multiple sites on the protein, chip surface, or nothing at all.
Recently developed methods have increased the throughput of dose response studies, providing kinetic information earlier in the screening workflow and practically eliminating the need for a secondary set of experiments for hits. For example, a nonbinding diluent (i.e., 20%, w/v sucrose) can be used to create a range of compound concentrations from the same sample [26], or individually prepared sample concentrations can be sequentially injected (e.g., “single cycle kinetics” or kinetic titration) [27]. Both methods save time by avoiding multiple regeneration steps. Further gains have been realized by using Taylor dispersion injections [28] in a longer flow path (e.g., OneStep™, SensiQ Technologies Inc.) to deliberately produce a gradient of analyte concentration flowing across the chip surface: by modeling [29] this dispersion from a known initial concentration, the same kinetic data can be obtained during the injection phase of the experiment.
Protein exists in a thermodynamic equilibrium between the folded and unfolded state. As the temperature of the system increases, the ratio of folded to unfolded protein shifts toward the unfolded state, making it possible to determine the temperature at which half of the protein is in the unfolded state. This point is referred to as the melting temperature of the protein (Tm). The thermal shift assay relies on ligand‐induced conformational stabilization, which is based on the energetic coupling of ligand binding and protein denaturation. In short, fragment binding alters the ratio of folded to unfolded protein by stabilizing the folded state (Figure 6). Adding a stabilizing fragment will shift the Tm, allowing for the calculation of ΔTmas an indicator of fragment binding [30–32]. Several methods available to measure the ratio of folded to unfolded protein in a given condition are: circular dichroism, infrared spectroscopy, differential scanning calorimetry, measurement of the intrinsic fluorescence of exposed tryptophan residues, and TSA, which is the most commonly applied technique. These methods were first adapted for use in TSA as a simple and inexpensive biophysical method for drug discovery in 2001 [30]. This work determined that ligand‐induced protein stabilization could be tracked with environmentally sensitive dyes over a range of experimental temperatures. In an aqueous environment, the dye is quenched, giving minimal quantum yield. As the protein is denatured in the increasing temperature, its hydrophobic core is exposed to react with the dye. This interaction measurably increases the quantum yield of the dye.
This figure demonstrates the theory associated with thermal shift interactions. Ligand binding causes stabilization of the protein in the more ordered folded state (represented in green). Thus, more thermal energy is required to move ligand stabilized protein from the folded to the unfolded state. Protein in the absence of ligand is represented in red. This protein requires less thermal energy to shift to the disordered state.
Thermal shift has since been revised and optimized for use as an economical fragment screening method. A typical TSA has minimal requirements for the quantity of the target protein. A pilot assay should be completed wherein the concentration of protein is altered over a given range, as running TSA with an excess of protein can saturate the detector. Alternatively, too little protein will give a flat curve and negatively affect the signal‐to‐noise ratio in the resulting data (Figure 7). Thermal shift assays are commonly completed with 1–10 μM target protein, which should be as pure as possible. Gross impurities within the sample protein could lead to multiple curves, reducing the accuracy of the resulting data.
The importance of protein concentration in TSA. As protein concentration moves above or below the optimal range for the assay the curves fail to accurately represent the system and will increase the error of melting temperature calculation.
In addition to protein, TSA requires an environmentally sensitive dye. Historically napthylamine sulfonic acid dyes (such as 1,8‐ANS, 2,6‐ANS, or 2,6‐TNS) were used for this purpose. Currently, SYPRO Orange is used more commonly as its fluorescence (Ex/Em of 300, 550/630 nm) is better adapted to rtPCR instruments. This dye is supplied from the vendor as a 5000X concentrate in DMSO. A pilot assay should be conducted to determine the appropriate concentration of SYPRO Orange for a given assay, as is done for protein concentration. In practice, many assays can be effectively run at a 5X concentration of SYPRO Orange. After the appropriate dye concentration has been established, a master mix of dye and buffer can be applied to the assay plate. At this point the plate is ready for the application of a fragment library.
\nAs in many fragment‐screening assays, the quality of the library is paramount. In the assay development stage, a pilot assay should be run in which the concentration of solubilizing agent is varied over a range to define any effects that the agent might have on the stability of the protein. If time and availability of fragments allow, then a screen of fragment alone should be performed to check for fluorescence in the absence of ligand; this additional screen could yield data that is useful for removing problem compounds within the library. With an appropriate fragment library, it is possible to apply fragment to the screening plates. This is typically accomplished by using a pin tool or similar device capable of accurately delivering small volumes. After the plate has been exposed to the fragment library, it should be sealed with a fluorescently inert plate seal to avoid sample evaporation during the course of the assay. The assay plate should then be centrifuged to remove any air pockets from the samples, as these might reduce the quality of the data.
\nIt has been established that TSA can be completed in a standard qPCR instrument [33]. The minimal requirement is the ability to evenly heat the samples over a suitable range of temperatures and record fluorescence. Deconvolution of resulting data output is variable based on instrumentation. This step can be time consuming and automation in data processing is helpful if large‐scale projects are planned. Results should be recorded as fluorescent units recorded at each temperature in each well. This information can then be moved into data analysis software (e.g., Graphpad Prism, Graphpad Software, Inc.). Plotting fluorescence units against temperature should result in a sigmoidal curve reflecting the folded and unfolded states of the protein over a range of temperatures (Figure 8a and b). The signal commonly drops after it has reached a plateau. This drop is the result of aggregation in the protein‐dye complex after denaturation [33]. Failure to remove data points resulting from this drop in signal can detrimentally affect subsequent curve fitting (Figure 8a and b). The Boltzmann equation can be adapted to calculate the exact Tm for each protein. Alternatively, it is possible to plot the derivative of the signal against temperature, recording the maximum of this derivative as the melting temperature (Figure 8c). The appropriate method for calculating the Tm can vary by target. Different methods should be tested to determine which calculation most accurately reflects the Tm of the experimental protein [32]. The thermal stability of a protein is increased to varying degrees when ligand is bound. The extent of this shift can vary greatly. In the case of fragment binding, thermal stabilization can be as little as 0.5 °C, making it crucial to establish the baseline stability of the protein within the experimental environment. It is then possible to establish the threshold of ΔTm for a positive result in fragment stabilization of the ligand. As a rule of thumb, the standard deviation should not be greater than 10% of the ΔTm [34].
Curve fitting. (a) In this figure fluorescence intensity is plotted against temperature in blue. Above 41°C the dye begins to denature causing a decrease in signal. Fitting the full curve with the Boltzman equation (shown in red) would give an inaccurate estimation of Tm. (b) In this figure fluorescence intensity is plotted against temperature in blue. The data here is trimmed as signal begins to decrease improving the curve fitting (shown in green). (c) In this figure the change in fluorescence intensity over the change in temperature is plotted against temperature in purple.
There are many benefits to using TSA for the initial biophysical screening of a fragment library. First, the assay does not rely on the biochemical activity of the target and can be performed with limited knowledge of the target\'s function, which is beneficial for FBLD because fragment binding often does not yield a measurable biochemical result. Additionally, TSA requires only a small amount of minimally stable protein whose thermal stability can be tracked in the presence and absence of the ligand [30]. Thermal shift assays can be completed by using widely available RT‐PCR instruments [33] and is relatively simple to perform with limited training, reducing the up‐front cost of implementing. This medium‐ to high‐throughput assay typically enables the testing of up to 384 compounds in only 30–40 min.
\nThermal shift assay is not a silver bullet per se and has some limitations and drawbacks. Traditional methods of assaying thermal shift will not work if a protein does not contain a hydrophobic core, as there will be nothing for the dye to differentially interact with when the target unfolds. Similarly, this assay will not produce valuable data if the surface of the protein is hydrophobic because the dye will fluoresce before the protein unfolds. Changing the dye used in the assay can mitigate these issues. The fluorescent readout of this assay also creates limitations. Some fragments commonly found in screening libraries fluoresce and interfere with the signal from SYPRO Orange. This phenomenon is readily evident upon inspection of the resulting data but requires a deconvolution step to avoid false‐positive or false‐negative results. Additionally, TSA does not provide accurate affinity data. However, a concentration versus ΔTm curve can be fit to generate an EC50 value that can estimate the range in which subsequent biochemical or biophysical assays can be more effective [34, 35]. With these limitations in mind, TSA can be a powerful tool for detecting fragment binding.
For the purposes of validation, a “good” fragment hit should be spatially described within a known target site via crystal structure, two‐dimensional NMR studies, or at least, a ligand epitope map. The structural information enables the chemical expansion or linking of fragments during hit generation.
\nHit rates in fragment‐based screening are typically high, frequently at least an order of magnitude higher than those of ligand‐based screening. Hits in the primary screen can be narrowed by using an orthogonal technique of comparable throughput for validation. Considering the numerous techniques available at the primary screening stage, the path to validation can be variable. Using an orthogonal approach to primary screening assumes that fragments that are hit by both techniques will translate successfully in secondary screening.
\nUsing multiple techniques for fragment primary screening may yield a diminishing return. For example, after solving 71 crystal structures from soaking 361 fragments and statistically comparing the results of other fragment screening techniques [36], one group found that nearly half of the 71 “good” fragment hits were missed by other techniques. When used in combination, hit validation was statistically worse, but this fact was heightened by the inclusion of hits that were originally missed by crystallography. Therefore, orthogonal primary screening with at least two techniques still achieves the goal of fragment hit validation. However, if the primary fragment screening techniques do not provide meaningful structure characterization, then NMR or crystallography is required in a secondary screening capacity.
Several companies, including Astex Pharmaceuticals, SGX Pharmaceuticals, Plexxikon, and Abbott, have effectively used structural biology in their fragment‐based lead discovery efforts. This valuable tool avoids the pitfalls of false‐positive results and nonspecific binding that may result from other fragment‐screening methods. Any fragment hit discovered via crystallography is inherently validated for the given target. Crystallography gives a clear picture of the fragment binding posture within the active site. This information can greatly facilitate the design of libraries based on the initial hit.
\nUsing crystallography as a method of fragment‐based lead discovery has some limitations. It has long been associated with slow throughput. Additionally, some targets, such as membrane‐associated proteins, do not readily lend themselves to crystallization. Crystallography often requires extensive and time‐consuming efforts to arrive at crystallization conditions suitable for fragment soaking experiments. Even if these conditions have been determined, ready access to a suitable beam line and expertise in crystallography and data reduction can be hurdles in the rest of the lead discovery process. Protein in crystallization conditions is in a crystal lattice, which does not completely reflect a physiological environment. This artificial environment can lead to artifacts in the data and an inaccurate picture of fragment binding. Although a crystal structure is rich in information, it does not reflect the potency or the biochemical activity of the bound fragment. With a wealth of structural information, it will not be possible to rank hits based on these criteria; orthogonal assays are critical for these purposes, and crystallography alone will not suffice.
\nThe process of generating a fragment structure typically follows a set path. First, protein must be purified. Then, crystallization conditions for the purified protein are determined. Crystals can then either be grown in the presence of a fragment or soaked into a preexisting crystal. The resulting crystals are flash frozen and used for data collection either in house or at a larger beam line. The data is analyzed to generate a three‐dimensional model of the fragment binding site within the target protein. This model can be used in iterative design efforts to grow the fragment into the binding site or link it to other fragments in neighboring sites.
\nPerhaps one of the most critical hurdles to successfully implementing structural biology into the FBLD process is having a suitable supply of the target protein. In most cases, protein used for X‐ray crystallography must be pure and in high yields. A typical screen for crystallization conditions is completed by using as much as 20 mg/ml of protein. If initial crystal screening efforts using native protein are not effective, then it may be necessary to modify the target via removal of mobile loop regions or trimming the terminal ends. Creating multiple variants is commonly a valuable step in generating robust high‐resolution structural data.
\nOnce protein is available with sufficient yield and purity, screening for crystallization conditions begins. This is typically performed as a high‐throughput screen with as many as 1000 set conditions in a single experiment. Many commercially available sparse matrix and additive crystallization screens use conditions that have historically yielded crystals. When these experiments yield a hit, the conditions can then be optimized to yield larger, highly reproducible crystals. Suitable crystals for fragment soaking should have fairly high resolution (<2 Å). Starting with a higher resolution structure improves resulting maps and increases the chances of producing an accurate model of fragment binding. If multiple crystallization conditions are available, it is best to choose the one that more closely represents physiological conditions, even at a slight cost to resolution. This trade‐off will result in fragments with best chance of advancement to be prioritized.
\nFragments suitable for X‐ray crystallography benefit from good solubility, as insoluble fragments have a low probability of yielding a structure with suitable occupancy of the ligand. Compounds are soaked at high concentrations to improve the chances of high occupancy within the structure. Given this fact, a fragment should have a solubility of at least 1 mM [37]. SGX pharmaceuticals benefited from generating a brominated fragment library, which enabled detection of anomalous scatter as an indication of successful soaking, streamlining the data collection process. To further increase throughput, fragments were soaked in mixtures composed of fragments with diverse shapes. Resulting structures could then be deconvoluted based on the shape of the ligand in the active site [36]. Fragment mixtures do run the risk of decreasing the effective concentration of individual fragments in the mixture because high fragment concentration contributes to high‐occupancy crystal structures, which can be detrimental to an experiment. In addition, these mixtures increase the chances of damaging the crystal in the soaking process, and fragments within the mix can interact with one another, skewing the results of the experiment. In one report, fragment mixtures resulted in 11 structures, whereas 20 structures resulted from individual soaking experiments [37]. These data suggest that if time and resources permit, soaking individual fragments is preferable to using mixtures.
\nIf all other factors fall into place, a data set is collected. Improper treatment of this data set can result in an inaccurate and misleading model of fragment binding. Methods of data reduction and refinement are highly variable, and model building is a continuously evolving process. Certain steps in model building are especially pertinent when dealing with fragments. When searching for ligand density in a map, it is tempting to perform a quick refinement, and presume the location of a ligand. This approach is especially hazardous when modeling small fragments. If the map is not of high enough quality when the search for ligands begins, it is possible that water, a poorly resolved side chain, or even highly conserved buffers could masquerade as bound fragments [38]. It is best to perform several rounds of refinement before the ligand hunt begins. Model in any waters and then refine a few more times [37]. If the fragment is bound, a convincing map should take shape, and the creation of an accurate model of the target protein bound to fragments should be possible.
\nAs technology progresses, many of the limitations of structural biology are being addressed. Most notably, throughput is being increased by incorporating automation. As this occurs, obtaining structural data is no longer the rate‐limiting step in lead development. In several cases, automation of this process has improved to the extent that structural studies are successfully being used as a primary screen. Although this approach might not yet be a feasible option in laboratories with limited resources and limited access to beam lines, it holds promise for an improved fragment screening process in the future.
Calorimetry measures the thermodynamics of a molecular interaction via observations of heat change in a reaction occurring in an adiabatic (thermodynamically closed) system [39]. In the context of drug discovery, the molecular interaction most commonly measured is the heat of binding of a small molecule to a protein target, although reaction kinetics can be measured under specific circumstances [40]. Measuring the heat associated with a molecular interaction allows direct measurement of the extent of breakage and formation of noncovalent interactions upon complex formation [39]. Using other methods, such as coupled reactions (e.g., product release) and fluorescent binding techniques, the change in enthalpy can only be inferred via the Van\'t Hoff relationship [41].
\nClassically, calorimetry has been applied to measurement of a binding interaction in two different ways: isothermal titration calorimetry, which measures heat release upon binding; and differential scanning calorimetry (DSC), which measures thermal stabilization of a protein due to binding of a small molecule. These methods offer a very detailed look at the thermodynamics of binding and have been used successfully in optimization after initial fragment hits have been identified. For the purposes of this chapter, discussion will be limited to ITC, as it is more directly applicable to FBDD. As a direct measurement of the heat of binding, ITC allows the researcher to remove the effects of fluorescent tags, antibody relationships, or coupling chemistry from the investigation of a binding relationship. Because ITC is a solution‐based method, surface physical effects that interfere with binding (often seen with SPR) are not an issue.
\nDirectly determining the thermodynamic components of overall binding allows a researcher to optimize a lead compound for a chosen target through specific binding interactions while minimizing off‐target effects that often derail drug discovery programs. When combined with X‐ray‐based binding information or applied to analysis of structure‐activity relationships, ITC can be a powerful tool in drug discovery. Overall binding of a small molecule to a target (as expressed by KD) can be broken down into enthalpic (specific interactions such as H‐bonding and π‐stacking) and entropic (nonspecific events such as bound water release and increases in conformational flexibility) components.
\nThe normal range of dissociation constants that can be measured by ITC is from 10 nM to 100 μM [41]. This range can be extended below 1 nM or above 1 mM by using displacement methods [42, 43], although a suitable displacement ligand (independently characterized) must be identified beforehand. Displacement ITC has not yet gained wide acceptance in drug discovery as of this writing, with most researchers reporting results of direct binding studies. Most fragments have binding affinities in the millimolar range, limiting the applicability of ITC as a screening method. In addition, large amounts of protein are typically required (usually 0.1–0.3 mg of protein per experiment, this moderate amount adds up for multiple samples and repeats). Each titration requires a moderate amount of time (45 min–1 h), but for a large number of samples, experiment time becomes a hurdle to using ITC as a screening method.
\nFor these reasons, ITC is usually brought in to the drug discovery process after screening, as part of hit validation and lead optimization [44]. Fewer compounds are involved, allowing more focus on the large amount of information provided by ITC [44–46]. After a compound of interest is identified, a small set of structurally similar compounds can be purchased or synthesized to gain insight into the nature of binding to a target [47, 48]. At this stage, ITC offers the most benefit, as small molecules can be identified that maximize enthalpic interactions and minimize entropic interactions with the target.
\nRecent research into high‐throughput calorimetry offer solutions to researchers wanting to incorporate calorimetry into an earlier stage in their screening cascade. Research into technologies in pursuit of the so‐called “lab on a chip” has led to the development of both microfluidic [49] and droplet‐based systems [50–52]. The droplet‐based system has been applied to both binding and kinetics.
Biochemical screening is not a typical choice for the primary screening of fragments but can be used to verify an inhibition of function, and inhibition by a known mechanism which may help discriminate fragments binding to alternative target sites.
\nFluorescence polarization‐based assays are an option in FBDD when preliminary information about a target, such as small molecules that bind, is known. FP assays are competition assays in that they indirectly measure the effect of a compound on binding of an enzyme to a fluorescent probe. A fluorescent probe ideally starts out as a small molecule that binds tightly to an enzyme with known stoichiometry. This small molecule is chemically modified by the addition of a fluorescent label via an aliphatic or polyol linker to generate a fluorescent probe. Signal readout, represented in millipolarization units (mP), is calculated by measuring the amount of plane‐polarized light passing through two filters (perpendicular and parallel to the plane of incident light) that remains after interaction with a solution containing probe and calculating the ratio of parallel to perpendicular light [53, 54]. In an FP assay, a plate reader measures the difference in relative tumbling between a free probe (high amount of tumbling, leading to more scattered emission, and thus low FP signal) and probe bound to a protein (low amount of tumbling, with more ordered emission relative to incident light, leading to high FP signal).
\nDepending on assay design, FP can be applied to either binding or activity assays, with ready‐to‐use kits available from BellBrook Labs [55] or Cayman Chemical [56]. Activity assays using FP rely on endpoint detection of binding to a probe, thus, are modified binding assays. For applications for which no kit is available, a small amount of synthesis can combine a small molecule of interest with a wide variety of synthetic fluorophores available from major vendors. When designing a probe for FP, resources such as the Molecular Probes Handbook [57] can be valuable.
\nFP is less commonly used in fragment screening than perhaps it should be. The range of binding affinities normally found in fragment libraries is in the high‐μM to low‐millimolar range, previously considered to be out of the range of FP when tight binding probes are used [58]. Although still somewhat limited in application by the need for a well‐characterized probe, FP offers an inexpensive way to screen large numbers of compounds and has been used for many years in conventional drug discovery programs. With the advent of fragment‐based techniques, FP is finding use both as a site directed primary [59, 60] and secondary [61–64] screening method, and has been used to validate new methods [65].
For fragments, it is important to see activity in more than one assay, but equivalent potency in assays is not so important, and is not the best way to determine which molecules to promote. Various combinations of advanced metrics (i.e., efficiencies) with empirically driven evaluations (e.g., PAINS, metabolic stability) can help scientists make informed decisions on hit progression. An outside example of the successful application of metrics is sabermetrics. Today widely used in baseball, but also heavily scrutinized and evolving, sabermetrics uses advanced statistics to define in‐game performance and improve decision‐making by managers. Just like a game manager, scientists have to be aware of the limitations and effects of following a metric\'s indication, being consistent about hit progression occasionally in the light of conflicting results. Some experts [66] suggest using ligand efficiencies that can be easily determined without a calculator to facilitate discussions. Several metric‐focused reviews are available to consult; one in particular covers a large number of reported hit‐lead programs [67] for a wider perspective.
\nFragment screening methods virtually ensure that most screens will produce multiple hits for any target. Thus, the challenge to the researcher is not identifying compounds that interfere with a specific enzyme but determining which of many is the best to carry forward. Several metrics have emerged to guide the selection of fragment lead molecules through the drug discovery process: these metrics combine physical properties (e.g., molecular weight, cLogP, polar surface area, number of H‐bond donors and acceptors) with potency data. The earliest of these was simply termed ligand efficiency (LE) and involved dividing the free energy (ΔG) of binding by the total number of nonhydrogen atoms in the molecule [68]. With the introduction of LE, researchers now havea relatively simple way to keep focused on the specificity of binding to the target, potentially avoiding downstream problems due to nonspecific binding [69].
\nAnother metric in wide use in FBDD is lipophilic ligand efficiency (LLE) [70], which takes into account the total lipophilicity and potency of a molecule (IC50, KD, Ki). A useful modification of LLE (LLEAstex) also controls for molecule size [71]. These statistical means of grading performance can support the early and late stages of the FBDD workflow and can be extended into progression analyses used during lead development (Figure 9). Which metric to use is up to the individual researcher and is based on the specific goals of the research program. Further reading to find an appropriate metric to use is recommended.
A typical progression analysis found in lead development can also include ligand efficiency metrics in a seamless fashion.
Fragments that progress to the hit‐generation stage typically do so with structural insight that either describes the fragment bound to its protein target or the binding epitope mapped onto the fragment. A typical downstream workflow for hit generation includes a path that is structure blind, but this is essentially a diversion from traditional target‐based discovery and may lead to an SAR bottleneck. The hit‐generation stage refers to acquisition and screening of larger nonfragment ligands, which are obtained by catalog or synthetically prepared. This workflow includes either a chemical elaboration of individual fragments or the linking of at least two fragments, which then requires some optimization of the linker between them.
\nThe hit generation phase is a practical place for virtual screening to be used to assist in fragment development. Method comparisons [72, 73] suggest that the currently available force fields and docking procedures based on lead‐like molecules will provide adequate results for fragments (i.e., better than randomized screening). However, the careful consideration of a scoring function to reliably discern weak interactions cannot be overemphasized. Scientists have recognized this distinction within FBDD and have sought to improve the scoring functions for fragments [74]. One notable viewpoint is that fragment elaboration or linking may be facilitated when binding poses are expressed as Gibbs energy [75].
Considering that FBDD has been attributed to at least two FDA drug approvals, and that the platform is relatively easy to integrate into existing technologies, many companies and academic groups have started their own fragment‐based discovery programs. The biophysical techniques each group uses will be dictated by the target, available facilities, and individual preferences of the investigators. Generally speaking, as long as the protein target has been successfully used with a technique in lead‐like screening and structural information is available, there are virtually no other major obstacles to generating new chemical matter for a given target‐based screening program. What remains are practical challenges, two of which bear repeating for those who are in the beginning stages of FBDD.
\nFirst, the key practical difference between lead‐like screening and fragment screening is the use of high concentrations of the fragments. The increased concentration impacts the compound library that is used and the clarity at which hits are delineated. Some suggestions have been made as to the optimal concentrations to use for a given technique. These are only suggestions and will likely change based on the system and techniques employed. With some practice, these procedures can be suitably optimized and need less attention going forward.
\nSecond, is the challenge of directing fragment build out and/or fragment linking chemistry which can be resource intensive. As such, medicinal chemists are aided by the use of a preferred ligand efficiency metric early in the FBDD process to assist the ranking of fragment hits. Certainly other empirical and nonempirical factors will influence the progression of fragments, but metrics will help organize the structure‐activity relationship which is a key driver of the expansion or linking of fragments during hit generation.
The authors would like to acknowledge the funding support from the National Institutes of Health grant R01AI110578, and the American Lebanese Syrian Associated Charities (ALSAC), St Jude Children\'s Research Hospital. We thank Cherise Guess, PhD, ELS, of the SJCRH Department of Scientific Editing for her assistance with editing.
Neuroblastoma is the most frequently diagnosed extracranial solid tumor in children. About 90% of cases occur in children less than 5 years old and it is rare in adults. Of cancer deaths in children, about 15% are due to neuroblastoma [1]. Chances of long-term survival, however, are less than 40% despite aggressive treatment [2].
MYC is an oncogenic transcription factor that is overexpressed in many types of cancer. MYC has been shown to directly upregulate a protumorigenic group of miRNAs and represses several suppressor miRNAs, thus contributing to tumorigenesis [3]. For example, MYC overexpression can upregulate the oncogenic miR-17-92 cluster, that are directly activated in lymphoma [4], and can also repress several suppressor miRNAs [3]. The MYC gene is amplified in various human cancers, including in lung carcinoma, breast carcinoma, and colon carcinoma [5].
Histone deacetylases affect gene expression by altering the histone acetylation status, and that as a consequence, HDAC overexpression contribute to tumorigenesis by affecting the expression of key mRNAs and miRNAs. HDACs are overexpressed in most cancers, leading to histone deacetylation, inhibition of growth- suppressive genes, and increased cell proliferation [6]. HDAC8 overexpression correlates with advanced neuroblastoma in patient tumor samples, and HDAC8 inhibition reduced cell proliferation and induced neuroblastoma cell differentiation [7]. HDAC inhibitors reduced the proliferation and induced the apoptosis of neuroblastoma cells in vitro and in vivo in mice [8, 9].
Given that both MYC and HDACs play an important role in the maintenance of the normal cellular physiological functions and that their overexpression is linked to neuroblastoma tumorigenesis, we asked whether the levels of both MYC and HDAC8 should be reduced to obtain significant inhibition of cell proliferation.
Our results demonstrate that miR-665 targets c-MYC and HDAC8 m RNA, miR-665 treatment also increased the percentage of cells in G1 phase and reduced the percentage of cells in S phase of the cell cycle. This is the first report to show that miR-665 is a suppressor miRNA directly targeting the 3’-UTR of c-MYC and HDAC8 in neuroblastoma [10].
We investigated the effects of small interfering RNAs (siRNAs) targeting HDAC8 and MYC in murine neuroblastoma cells. RNA interference is a process of posttranscriptional gene silencing in which a double stranded RNA inhibits gene expression in a sequence-dependent manner via degradation of the corresponding mRNA. siRNAs can be used as potent and specific tools for gene knockdown. Several laboratories have reported siRNA targeting of gene expression in cancer cells and the inhibition of cell proliferation in vitro and tumor growth in vivo [11, 12, 13, 14].
We reported that in vitro, single-agent siRNA HDAC8 or siRNA-MYC inhibited cell proliferation by 40–50%; however, treatment with the combination of siRNA MYC + siRNA-HDAC8 inhibited cell proliferation by 86% [10]. To further confirm these findings in an animal model, we set out to verify if tumor growth can be inhibited in a neuroblastoma xenograft mouse model when tumors are treated with a combination of siRNA-MYC and siRNA HDAC8. Our findings from this study show that the tumor growth was reduced by 80% following intratumoral delivery of a combination of siRNAs targeting both MYC and HDAC8 simultaneously [15].
Cell culture media, DMEM with high glucose (D6429), essential and non-essential amino acids (M5550, m7145), Bt2c AMP (D0627), the colorimetric Caspase 3 kit (Code CASP-3-C), and propidium iodide (P4170) were purchased from Sigma Aldrich, St. Louis. Fetal bovine serum (FBS) was purchased from Phenix Research Products, Candler, NC, USA. BD-Falcon tissue culture 96-well plates (353072) were purchased from BD Biosciences. The RNA extraction miRNeasy kit (Cat No. 217084) was purchased from Qiagen, Germantown, MD, USA. The MTS Cell. Titer 96 Aqueous One Solution (Cat # G3580) cell proliferation assay was purchased from Promega Biotechnology, Madison,WI, USA. The HDAC Kit (#K331–100) was purchased from BioVision, Inc. Co rning. 96-well EIA/RIA plates (CLS3369) were used for ELISA. Antibodies for HDAC 8, H-145 (sc11405), C MYC, C-19 (SC-786), acetylated Histones, Ac- H2B, Lys 5/12/15/20 (SC-8652), Ac-H3, lys9 (sc-8655), Ac-H4, lys16 (sc-8662), and siRNA for c-MYC (pool of 4 different siRNA duplexes, sc-29227) were purchased from Santa Cruz Biotechnology, Dallas, TX, USA. Negative control #2 siRNA (#4390846), siRNA-HDAC 8 (S88696) and Lipofectamine RNAi Max (#13778075) were purchased from Life Technologies/Ambion/ Invitrogen. Negative control miRNA Cel-miR-67 (#CN-001000) sequences based on C. elegans miRNA, mimic hsa-miR-665 (#C 301246–01), and transfection reagent Dharmafect Duo (#T2010–01) were purchased from Dharmacon. Luciferase expression plasmids with the 3’-UTR for HDAC8 (#S804229), C-MYC (#S804638), MYCN (Product No S807230), or empty vector without 3’-UTR (#S890005), and the LightSwitch luciferase assay kit (#32031, LS010) were purchased from Active Motif, CA, USA.
Mouse neuroblastoma cholinergic clonal cells (S20) were obtained from Dr. Marshall Nirenberg of The US National Institutes of Health (NIH). Cells were grown in monolayers in DMEM supplemented with essential and nonessential amino acids, penicillin/streptomycin, and 10% FBS at 37°C with 5% CO2 and humidity.
Neuroblastoma cells were plated in 96-well plates at 12x103 cells per well and After 48–72 h, cell viability was measured colorimetrically using the MTS Cell Titer 96 Aqueous One Solution. Samples were incubated at 37°C for 3–4 h and samples were read at 490 nm in a plate reader according to the manufacturer’s instructions.
For cell cycle analysis, 1x106 cells were plated in T25 flasks. After 48 h, cells were trypsinized, treated with 75% ethanol and 100ug/ml RNAse A, and then stained with propodium iodide (PI). Untreated and (20,000 cells/ sample) were analyzed for cell cycle distribution via flow cytometry at the Core lab of Children’s Cancer Center Hospital, Houston, TX, USA.
The effects of miR-665 or siRNA on cell proliferation were determined using reverse transfection. First, 100 nM negative control miRNA, miR-665, negative control siRNA, C-MYC siRNA, or HDAC8 siRNA was mixed with Lipofectamine RNAimax. This mixture was added to 12x103 cells, which were then plated in 96-well plates. After 48–72 h, cell viability was measured using MTS Cell Titer 96 Aqueous One Solution and incubated at 37°C for 3–4 h. Samples were read at 490 nm according to manufacturer’s instructions.
miRNA effects on the cell cycle were assessed using reverse transfection of cells with 100 nM negative control miRNA or miR-665 mimic plus Lipofectamine RNAimax. The transfection mixture was added to 1x106 cells, which were then plated in a T25 flask. After 48 h, cells were trypsinized, treated with 75% ethanol and 100ug/ml RNAse A, and then stained with PI. For cell cycle analysis, 20,000 cells/sample were analyzed via flow cytometry in the Core lab of Children’s Cancer Center Hospital, Houston, TX.
Cell extracts were prepared from untreated,, and miRNA transfected cells for target assays. miRNA-transfected cells were reverse transfected with 100 nM negative control miRNA, miR-665, negative control siRNA, c-MYC siRNA, or HDAC8 siRNA plus Lipofectamine RNAimax. Transfected cells were plated in T25 flasks. After 48–72 h, cell extracts were prepared in assay buffer as described by Khandelia, et al. [16]. Assay buffer consisted of 20 mM Tris–HCL pH 7.5, 150 mM NaCl, 5 mM EDTA, 10% glycerol, 1% Nonidet P40, and protease inhibitor cocktail from Sigma (P8340). Protein concentrations were determined using Pierce’s BCA Assay as per the manufacturer’s instructions.
miR-665 inhibit cell growth compared to untreated cells and cells treated with negative control miRNA. Assays were normalized using equal concentrations of protein (50–100 ug) from untreated, negative control miRNA-, and miR-665- treated cells in assessing total HDAC and Caspase 3 activity, and HDAC8 and c-MYC levels via ELISA.
Mouse neuroblastoma cells were transfected with 100 nM miR-665 mimic and negative control cel-miR-67. 48 h post-transfection, total RNA was extracted from three biological replicates per treatment using the Qiagen RNEasy mini kit. miR-665 was quantitated via realtime qPCR by Arraystar, Inc. (Rockville, MD, USA).
Real-time PCR was performed for each RNA sample to quantify miR-665 and the housekeeping gene, U6. According to the standard curve, mRNA concentrations in each sample are determined directly using Rotor-Gene Real-Time Analysis software v.6.0 and the 2∆∆Ct method.
Total HDAC activity was measured in 50–75ug of protein from cell extracts prepared from untreated, or negative control miRNA- or miR 665-transfected cells using the Biovision kit (#K331–100). Acetylated HDAC substrate and other reagents were added according to the manufacturer’s instructions and the final deacetylated product was read at 405 nm in a plate reader.
HDAC8 and c-MYC proteins were quantitated using cell extracts prepared from untreated or 1 mM Bt2cAMP treated cells, or negative control miRNA- or miR-665- transfected cells via ELISA. 100ug protein per sample was mixed with 0.02 M carbonate coating buffer (pH 9.5) and added to 96-well BD-Falcon ELISA plates.
Samples were incubated at 4°C for 15 h. Wells were blocked with 10% FBS in PBS, treated with antibodies (diluted 1:30) specific for HDAC8 (SC11405) or c-MYC (SC-798), and incubated at 37°C for 2 h. Samples were washed with PBS + 0.05% Tween, treated with goat anti-rabbit IgG.
HRP secondary antibody (diluted 1:500), and incubated at 37°C for 1 h. Wells were washed and treated with substrate TMB and incubated at room temperature for 30 min, and then the reaction was stopped with 2 N H2SO4. Samples were read at 450 nm in a plate reader.
Caspase 3 activity was measured in 50ug protein from untreated, Bt2cAMP-treated, or miR-665-transfected cells using Sigma Aldrich’s colorimeter kit (Code CASP3-C). 50ug protein was mixed with the peptide substrate, Ac-DEVD-pNA (p-nitroanilide), in the presence of 10 mM DTT. Caspase 3 hydrolyzes the substrate, releasing p-nitroaniline, which is read at 405 nm. The specificity of caspase 3 activity was determined in the presence of the inhibitor, Ac-DEVD- CHO.
HepG2 cells were used for miR-665 target validation, because miR-665 does not inhibit the growth of these cells. When mouse neuroblastoma cells were used for target validation, the negative control luciferase vector plasmid without any target 3’-UTR showed a 50% decrease in luciferase activity when co-transfected with miR-665 compared to negative control miRNA. This decrease in luciferase activity was non-specific and was caused by cell growth inhibition due to miR-665 transfection.
To validate miR-665 targets, HepG2 cells were grown for 24 h in a 96-well plate. Cells were then co transfected with 100 ng luciferase expression plasmids containing the 3’-UTR for HDAC8, c-MYC, or MYCN, or the empty vector without any target 3’UTR, plus 100 nM negative control miRNA or miR-665 with Dharmafect Duo transfection agent. After 48 h of cotransfection, luciferase activity was measured using the Active Motifs LightSwitch luciferase assay kit. Luminescence was read on a Molecular Devices Soft Max Pro5 luminometer.
Histone acetylation was quantified via ELISA in cell extracts prepared from cells transfected with negative control miRNA, miR-665, negative control siRNA, or HDAC8 siRNA. 100ug protein was mixed with 0.02 M carbonate coating buffer (pH 9.5), added to 96-well BD Falcon ELISA plates, and incubated at 4°C for 15 h. Wells were blocked with 10% FBS in PBS, treated with acetylated antibodies (diluted 1:30) for Ac H2B (Lys 5/12/15/20), Ac-H3 (lys9), or Ac-H4 (lys16), and incubated at 37°C for 2 h. Samples were washed with PBS + 0.05% Tween, treated with an appropriate HRP-conjugated secondary antibody (diluted 1:500), and incubated at 37°C for 1 h. Wells were washed and treated with substrate TMB and incubated at room temperature for 30 min, and then the reaction was stopped using 2 N H2SO4. Samples were read at 450 nm in a plate reader.
Mice experiments were performed with the approval of the institutional Animal Care and Use Committee, IACUC at Nanospectra Biosciences Inc. Houston, Texas.
A/J female mice six weeks old were purchased from Jackson Laboratory, Bar Harbor, Maine, USA. Murine neuroblastoma cells, 1x106 cells in DMEM media with 50% matrigel in 100 ul without fetal bovine serum and without antibiotics were subcutaneously injected on the right flanks. After 12 days, tumor growth can be seen and tumors were measured with a caliper.
When tumors reached 100 mm3 in size, mice were divided into two groups with 8–10 mice in each group.
Intratumoral delivery of siRNA.
siRNA-HDAC8 (S88696), Sense Sequence: (5′----3′).
CGACGGAAAUUUGACCGUAtt.
Antisense Sequence:
UACGGUCAAAUUUCCGUCGca.
siRNA-MYC (S70224), Sense Sequence: (5′------3′).
AGGUAGUGAUCCUCAAAAAtt.
Antisense Sequence: UUUUUGAGGAUCACUACCUtg.
Negative control #2 siRNA (#4390846), and Lipofectamine RNAi Max (#13778075) were purchased from Life Technologies/Ambion.
A total of 3 nmol Negative control siRNA or 3 nmol combinations of siRNA-MYC + siRNA-HDAC8 were mixed with Lipofectamine RNAi max (Liposome) in DMEM media without fetal bovine serum and without antibiotic. siRNA complexed with Lipofectamine in a volume of 30 ul was delivered into tumors by intratumoral injection every third day. Tumors were measured every second day with a caliper and mice were weighed every third day. Tumor volume was calculator with a formula, V = Length X width2/2. Experiment was stopped when the control tumors treated with negative control siRNA reached a tumor burden volume of 1200 mm3. Mice were euthanized by CO2 inhalation 2 days after last treatment with siRNA. Tumors were removed and weighed. Tumors were frozen in liquid nitrogen and stored at -80o C freezers until used for preparation of tumor extracts for ELISA.
Tumors treated with NC-siRNA or with combined siRNA-HDAC8 + siRNA-MYC were cut into small pieces and homogenized in assay buffer in a glass homogenizer. Assay buffer as described by Khandelia et al. [16], consisted of 20 mM Tris–HCL pH 7.5, 150 mM NaCl, 5 mM EDTA, 10% glycerol, 1% Nonidet P40, and protease inhibitor cocktail from Sigma (P8340). Protein concentrations were determined using Pierce’s BCA Assay as per the manufacturer’s instructions.
Error bars represent standard error of the mean (SEM) from 2 to 3 biological replicates from 3 to 5 independent experiments. P-values were calculated using T.Test (2 tailed, 3 samples, unequal variance) and p < 0.05 was considered statistically significant.
NB cells transfected with miR-665 show changes in cell morphology, lost the normal spindle shape and cells grew in clumps without processes compared to cells transfected with negative control miRNA (Figure 1A and B). Cell cycle analysis results show that miR-665 treated cells show an increase of 16% of cells in G1 phase of cell cycle and the cell number decreased by 18% in S phase compared to negative control miRNA transfected cells. miR-665 treatment did not affect the cells in G2 phase (Figure 1C). Cell viability decreased proportionally with the increasing concentration of miR-665, represented by black bars (Figure 1D).
miR-665 effects on cell proliferation (A) cells treated with 100 nM negative control miRNA and miR-665 (B) for 72 hr. were prepared for cell cycle distribution analysis. Propidium iodide stained cells were analyzed by FLOW cytometry (C) Several concentration of miR-665 effect on cell viability (D) STDEV was used for +/− standard error bar; data is from 2 independent experiments with 3 biological replicates for each experiment was used. (figures were printed from published article in “Oncotarget”, N.Prashad Vol 9, 33186–33201, 2018).
Computational algorithm prediction site TargetScan and miRanda (
Predicted binding sites for miR-665 in targets HDAC 8, c MYC and MYCN 3’ UTR. Computational prediction site miRanda (microRNA.org) predicted hsa-miR-665 targets 3’ UTR of HDAC 8 and the sequence alignment is presented in (A). miranda, also predicts that hsa-miR-665 targets MYCN 3’ UTR and the sequence alignment is presented in (B). miranda and Targetscan did not include 3’ UTR of C MYC as miR-665 target. Therefore, complimentary sequences between miR-665 and 3’ UTR of C MYC were compared at online pairwise sequence alignment site www.ebi.ac.uk. Sequence alignment is presented in (C). (D) miR-665 targets were validated by co transfection of 100 ng luciferase expression plasmids with 3’-UTR and 100 nM negative control miRNA and miR-665 into HepG2 cells. Empty vector without 3’ UTR was used as a control. After 48 hr., luciferase activity was measured and normalized luciferase activity is presented. Data is presented from 2 independent experiments with 3 biological replicates were used. STDEV was used for +/− standard error bar. (figures were printed from published article in “Oncotarget”, N.Prashad Vol 9, 33186–33201, 2018).
MYC is overexpressed in 30% of all human cancers and frequently predicts for a poor clinical outcome, and deregulated expression of MYC is a hallmark feature of cancer [3] miRanda and Targetscan did not include 3’ UTR of C MYC as miR-665 target. Therefore, Complimentary sequences between miR-665 and 3’ UTR of C MYC were compared at online pairwise sequence alignment site
First we measured total HDAC activity in the cell extracts prepared from negative control miRNA and miR-665 transfected cells in the presence of acetylated HDAC substrate Ac-Lys (Ac)-p NA and deacetylated end product was measured colorimetrically. HDAC 8 and c MYC proteins were measured with antibody in ELISA. The results show that total Pan HDAC activity was decreased by 40%, and HDAC 8 and c MYC proteins were decreased by 40% in miR-665 transfected cells compared to negative control miRNA treated cells (Figure 3A–C).
miR-665 effect on target HDAC 8, c MYC and MYCN cell extracts from100nM negative control miRNA and miR-665 transfected cells were used for the quantitation of; (A) total HDAC activity, (B) HDAC8 protein by ELISA, (C) c MYC protein by ELISA and (D) caspase 3 activity and specificity of caspase 3 enzyme activity was determined in the presence of inhibitor. Data is presented from 2 independent experiments with 3 biological replicates were used. STDEV was used for +/− standard error bar. (figures were printed from published article in “Oncotarget”, N.Prashad Vol 9, 33186–33201, 2018).
Next, we tested whether HDAC 8, c MYC and MYCN genes are the direct target of miR-665. In these experiments HepG2 cells were used because miR-665 does not affect the growth of these cells (our unpublished results). Luciferase reporter plasmids with 3’ UTR were cotransfected with negative control miRNA and miR-665 in HepG2 cells and after 48 hr. luciferase activity were measured in cell extracts. Results show a 40% decrease in luciferase activity of HDAC 8 3’ UTR, a 51% decrease in luciferase activity of c MYC 3’ UTR and a 50% decrease in luciferase activity of MYCN 3’UTR from the cells co transfected with miR-665 compared to the co transfection with negative control miRNA (Figure 2D). These results validate that m RNAs of HDAC 8, c MYC and MYCN are the direct targets of suppressor miR-665.
HDAC inhibitors induce the caspase 3-dependent apoptosis [8] Suppressor miR-34a increased the activation of caspase 3 and caused caspase dependent apoptosis in neuroblastoma cells [17, 18]. Caspase 3 is a critical part of apoptosis, and is required for the DNA fragmentation and for the typical morphological changes of cells undergoing apoptosis. We investigated the effect of miR-665 on the activation of caspase 3 and activity was measured by the hydrolysis of the peptide substrate attached to p-nitroanilid. Caspase 3 activity was measured in cell extracts prepared from cells transfected with negative control miRNA and miR-665. Results show that caspase 3 activity was increased by 2.5-fold in miR-665 transfected cells compared to negative control miRNA (Figure 3D). Specificity of the caspase 3 was determined by the addition of caspase 3 inhibitor TSA before the addition of substrate in the assay. The results show that inhibitor binds to caspase 3 and inhibited 90% of miR-665 activated caspase 3 activity (Figure 3D). These results show that mimic miR-665 activated caspase 3 in neuroblastoma cells, suggesting that miR-665 can inhibit cell growth and reduced viable cells by caspase 3 dependent apoptosis.
miR-665 levles were quantitated in neuroblastoma cells transfected with negative control miRNA and miR 665 using real time qPCR. Mouse neuroblastoma cells have very low levels of endogenous miR-665 (Figure 4A); however, miR-665 expression increased 848-fold in cells transfected with mimic miR-665 compared to cells transfected with the negative control miRNA, cel miR-67 (Figure 4A and B). miRNA levels reportedly increased by over 1000-fold in cells transfected with miR 200a [19]. Our results strongly indicate that miR-665 upregulation decreased MYC and HDAC8 expression, thus inhibiting proliferation and inducing apoptosis in mouse neuroblastoma cells.
Quantitation of miR-665 in transfected cells. miR-665 was quantitated via real-time qPCR normalized to the U6 gene from three biological replicates 48 h after transfection with negative control miRNA (cel-miR-67) or miR-665. From left, lane 1 and 14 (M), show DNA molecular weight ladder (A) lanes 2–7 (NC-1–NC3 and 665–1–665-3) show the U6 gene. Lanes 8–10 (NC-1–NC3) show miR-665 levels from cells transfected with negative control miRNA. Lanes 11–13 (665–1–665-3) show miR-665 levels from cells transfected with miR-665. The miR-665 fold increase in miR-665-transfected cells was quantitated using the 2-ΔΔCt method (B) miR665 levels are shown in cells transfected with negative control miRNA (black bar) and in miR-665-transfected cells (white). Error bars were calculated from the standard deviation from three biological replicates. *P < 0.54x10–6. (figures were printed from published article in “Oncotarget”, N.Prashad Vol 9, 33186–33201, 2018).
miRNA targets hundreds of m RNAs and suppresses their expression, however, siRNA targets a specific m RNA. In these experiments, siRNA for HDAC 8 (siRNA-HDAC 8) and siRNA for MYC (siRNA-c MYC) were used to substantiate the effects of miR-665 on neuroblastoma cells.
NB cells were transfected with negative control siRNA, siRNA-HDAC 8, siRNA-c MYC and the combination of siRNA-HDAC 8 + siRNA-c MYC. After 48 hr. of growth, cell viability was determined with CellTiter assay (Promega). SiRNA-HDAC8 inhibited 42% and siRNA-c MYC inhibited 55% of cell proliferation, however, the combination of both siRNAs inhibited 86% of the growth of the cells (Figure 5A). Therefore, the combination of siRNA-HDAC8 plus siRNA-c MYC was more targeted towards mRNA of HDAC8 and c MYC and caused more effective apoptosis and loss of cells. These results show that HDAC 8 and MYC are critical targets and inhibition of both targets is required for the inhibition of neuroblastoma.
siRNA effects on neuroblastoma cells.siRNA specific for HDAC8 (siRNA-HDAC8) or c-MYC (siRNA-c-MYC, a mixture of 4 siRNAs) were used to confirm the effects of miR665 in neuroblastoma cells. (A) siRNA effect on cell proliferation. Neuroblastoma cells were transfected with 50 nM siRNA-HDAC8, 100 nM siRNA-c-MYC, or both siRNAs together. Cell viability was measured via MTS assay. SEM bars represent the standard deviation from two independent experiments with three biological replicates each. *P < 0.005, **P < 0.001, ***P = 6.8x10–5. (B) Cell extracts from negative control siRNA- or siRNA-HDAC8-treated cells were used to quantify HDAC8 levels via ELISA. HDAC8 was down regulated in HDAC8-siRNA-transfected cells *P < 0.04. (C) Caspase 3 activity was quantified in cell extracts via Casp-3 kit. Caspase 3 activity increased in siRNA-HDAC8- and siRNA-c-MYC-transfected cells. SEM bars represent the standard deviation from two independent experiments with two biological replicates each. *P < 0.01, **P < 0.004. (figures were printed from published article in “Oncotarget”, N.Prashad Vol 9, 33186–33201, 2018).
HDAC 8 and c MYC proteins were quantitated by antibody in ELISA in cell extracts prepared from the cells transfected with negative control siRNA and siRNA-HDAC 8. The results show that siRNA-HDAC 8 transfection inhibited 40% of HDAC 8 proteins (Figure 5B) as well as inhibited 35% of MYC protein. Inhibition of MYC may be indirect effect of HDAC 8 inhibition. HDAC8 inhibition increases acetylation of histones and alters gene expression, thus decreasing MYC expression. Therefore, miR-665 represses the expression of c MYC both at the transcription and at the post transcription levels. siRNA-HDAC 8 and siRNA-c MYC substantiated the effects of miR-665 on neuroblastoma cells.
Caspase 3 activity was measured in the cell extracts prepared from cells transfected with negative control siRNA, siRNA-HDAC8 and siRNA-c MYC. The results show that siRNA-HADC8 increased the activity of caspase 3 by 1.8-fold and siRNA –c MYC increased the activity of caspase 3 by 2.5-fold compared to negative control siRNA (Figure 5C). Therefore, the results of siRNA effects substantiate the effects of miR-665 on the activation of caspase 3.
Our results show that miR-665 and siRNA-HADC8, decreased total HDAC activity and decrease HDAC 8 protein, therefore, we measured the acetylation of histones in the cell extracts and the results were compared among all treatments. MiR-665 transfected cells show increases in the acetylation of histones Ac-H2B by 25%, Ac-H3 by 40% and Ac-H4 by 50% compared to negative control miRNA transfected cells (Figure 6A). miR-665 acetylates predominantly H3 and H4 histones.
Histone acetylation. Cell extracts from negative control miRNA-, miR-665-, negative control siRNA-, or siRNA-HDAC8 treated cells were used to quantitate histone acetylation via ELISA. Data represent standard deviations from two independent experiments. Negative control miRNA (white) and miR-665 transfection increased histone acetylation (black) (A) *P < 0.01, **P < 0.01, ***P < 0.04. Negative control siRNA (white) and siRNA-HDAC8 increased histone acetylation (black) (B) *P < 0.008, **P < 0.04, ***P < 0.001.
Likewise, Si RNA-HDAC8 treated cells also show increases in the acetylation of histones Ac-H2B by 38%, acetylation of Ac-H3 by 58% and show higher acetylation of Ac-H4 by 2-fold (200%) compared to negative control siRNA treated cells (Figure 6B). siRNA-HDAC8 acetylated predominately AC-H4 and correlate with the results of miR-665.
Taken together, our results indicate that miR-665 targets c-MYC and HDAC8, decreasing their expression, increasing histone acetylation, and modulating expression of cell proliferation related genes. We propose a model (Figure 7)illustrating suppressor miR-665 involvement in the inhibition of neuroblastoma cell proliferation [10].
Proposed model illustrating how suppressor miR-665 targets c-MYC and HDAC8 to inhibit neuroblastoma cell proliferation and maintain cellular homeostasis. (figures were printed from published article in “Oncotarget”, N.Prashad Vol 9, 33186–33201, 2018).
When cells were treated with the combination of siRNA HDAC 8 + siRNA-MYC, cell proliferation was inhibited by 86% [10]. Therefore, HDAC 8 and MYC are critical targets and effective blockade of both targets is required to ensure a maximum inhibition of neuroblastoma cell proliferation.
On the basis of these results, we hypothesized that neuroblastoma tumor xenograft growth in mice can be inhibited when treated with the combination of siRNA-MYC + siRNA- HDAC8.
We explored the therapeutic effect of the combination of siRNA-HDAC8 + siRNA-MYC treatment on neuroblastoma tumor xenograft in mice. A total of 1x106 mouse neuroblastoma cells in 50% Matrigel were inoculated subcutaneously in 6 weeks old female A/J mice. Tumors were formed with an average volume of 100 mm3, 12 days after cells were inoculated. A 3 nmol negative control siRNA (NC-siRNA) or a 3 nmol combination of siRNA-HDAC + siRNA-MYC complexed with Lipofectamine RNAi max (Invitrogen) were inoculated into 10 tumors each by intratumoral injections every 3rd day. Tumor growth was measured every 2 days with a caliper and volume was calculated with the formula, length X width2/2. The growth of control tumors treated with NC-siRNA increased, however, the tumors treated with the combination of siRNA-HDAC8 + siRNA-MYC show inhibition of the growth of tumors (Figure 8A). The rates of tumor growth were significantly decreased when treated with combined siRNA-MYC + siRNA-HDAC8 compared to tumors treated with control negative siRNA.
(A) Effect of combination of siRNA on the growth of neuroblastoma tumor. Murine neuroblastoma cells, 1 × 106 cells in DMEM media with 50% matrigel in 100 ul were subcutaneously injected on the right flanks. After 12 days, tumor growth can be seen and tumors were measured with a caliper. When tumors reached 100 mm3 in size, mice were divided into two groups with 8 mice in each group. Negative control siRNA or combination of siRNA-MYC + siRNA-HDAC8 was mixed with Lipofectamine RNAi max (liposome) in DMEM media without fetal bovine serum and without antibiotic. siRNA complexed with Lipofectamine in a volume of 30 ul was delivered into tumors by intratumoral injection every third day. Tumors were measured every second day with a caliper and mice were weighed every third day. Tumor volume was calculator with a formula, V = length × width 2 /2. The numbers on X-axis show the number of siRNA treatments. Control tumor growth is shown by diamond (^) markers and the growth of tumors treated with siRNAs is shown by round (0) markers. SEM bars represent the standard deviation from 8 mice at each point P* < 0.031. (figure is printed from published article in “J. Cancer Biology and Therapeutics” N. Prashad V 6: 301–307 2020). (B) Mice with control and combination of siRNA treated tumors. Top row of mice with control tumors treated with NC siRNA and the bottom row of mice with tumors treated with the combination of siRNA-MYC + siRNA-HDAC. (C) Tumors from control and combination of siRNA treated mice. Top row representative control tumors treated with NC-siRNA and the bottom row representative tumors treated with the combination of siRNA-MYC + siRNA-HDAC8. (figure is printed from published article in “J. Cancer biology and therapeutics” N. Prashad V 6: 301–307 2020). (D) Weights of control and combination of siRNA treated tumors. Average of 8 control tumors was 1 gram and average of 8 tumors treated with combination of siRNA MYC + siRNA-HDAC8 was 0.186 gram. SEM bars represent the standard deviation from 8 tumors P** < 0.004.
All mice experiments were performed under IACUC approved animal study protocol.
Experiment was stopped when the control tumors reached an average volume of over 1200 mm3, then mice were euthanized by CO2 and tumors were removed and weighed. Pictures of mice with tumors were taken before tumors were removed (Figure 8B) and pictures of tumors removed and weighed are shown in (Figure 8C). The average wet weight of 8 control tumors treated with NC-siRNA and tumors treated with combination of siRNA-HDAC + siRNA-MYC is presented in Figure 8D. The average weight of tumors treated with the combination of siRNA-HDAC8 + siRNA-MYC was decreased by 5-fold [0.186 g] compared to average weight of control tumors [1 g] treated with NC-siRNA. Tumor xenograft experiment was repeated twice with 10 mice treated with NC-siRNA and 10 mice treated with a combination of siRNA-HDAC8 + siRNA-MYC.
Tumor targets HDAC 8 and MYC proteins were quantitated by ELISA in extracts prepared from tumors treated with negative control siRNA and combination of siRNA-HDAC 8 + siRNA-MYC treated tumors. The results indicate that targets HDAC 8 and MYC were decreased by 85% and 65% in tumors treated with the combination of siRNA-HDAC8 + siRNA-MYC compared to tumors treated with NC-siRNA (Figure 9A and B).
(A, B) Quantitation of Myc and Hdac8 proteins from control and combination of siRNA treated tumors. Tumor targets Hdac8 and Myc proteins were quantitated by ELISA in extracts prepared from 3 tumors treated with negative control siRNA and 3 tumors treated with the combination of siRNA-HDAC 8 + siRNA-MYC. Average targets Hdac8 (A) and Myc (B) proteins were decreased by 85% and 65% in tumors treated with the combination of siRNA-HDAC8 + siRNA-MYC compared to tumors treated with NC-siRNA. SEM bars represent the standard deviation from 3 tumors (P* < 0.030, P** < 0.01). (figures printed from the article published in “J Cancer Biol Therap,” N. Prashad 6(1): 301–307 (2020)).
The results indicate that a decrease in the tumor targets HDAC 8 and MYC caused the inhibition of the growth of tumors.
miRNAs are both oncogenic and tumor suppressors. In normal cells homeostasis is maintained by keeping equilibrium between oncogenic and suppressor miRNA. If this equilibrium is disrupted that can cause dysfunction with increases in oncogenic miRNA and decreases in suppressor miRNA. A decrease in a specific suppressor miRNA can cause overexpression of HDCAs, c MYC and MYCN which can alter gene expression and cause cancer. However, when suppressor miRNAs are added exogenously to these cells then these cells restore normal properties and show growth arrest and apoptosis Therefore, suppressor miRNAs seem to be critical in the maintenance of cellular homeostasis.
A decrease in suppressor miRNA can over express genes like c MYC, MYCN and HDACs and cause cancers. Over expression of c MYC and MYCN cause the down regulation of suppressor miRNAs. HDACs indirectly effect gene expression by the deacetylation of histones, therefore, this process can also effect the expression of miRNA.
Transfection of miR-665 into murine NB cells caused growth inhibition, cell cycle arrest, decreased total HDAC activity, decreased HDAC8 and MYC protein expression, activated caspase 3 and increased the acetylation of histones. miR-665 targets HDAC8, c MYC and MYCN oncogene and decreases their expression. These targets are validated by the co- transfection of luciferase reporter with target 3’ UTR and miRNA-665. Therefore, miRNA 665 directly targets HDAC 8, MYCN and c MYC in the inhibition of mouse neuroblastoma cells. This is the first report to show that miR-665 is a suppressor miRNA of mouse neuroblastoma.
In targeted therapy of cancer, critical genes and proteins involved in the tumorigenesis are identified and therapeutic agents’ miRNA and siRNA are used to inhibit the expression of target genes to inhibit the growth of cells in vitro and in vivo. SiRNA-mediated gene knockdown is much more potent and specific with only one mRNA target, whereas miRNA has multiple mRNA targets. siRNA therapeutic approach was used in gene targeting overexpressed cancer proteins in inhibiting cancer cell growth in vitro and inhibited tumor growth in vivo in the following mouse models: breast cancer mouse model, Glioma cells tumor and colon cancer tumor. MYCN, c-MYC, and HDAC8 may each contribute to neuroblastoma tumorigenesis. We reported that transfection of mimic suppressor miR-665 inhibited the expression of c-MYC and HDAC 8 and increased caspase 3 involved in apoptosis and inhibited the growth of neuroblastoma cells in vitro [10].
Our data also indicate that both c-MYC and HDAC 8 are critical targets and targeting these two targets with siRNA inhibited cell growth by 86% in vitro. The combination of siRNAs inhibited tumor growth in vivo by 80%, therefore, inhibiting more than one target is critical for the successful treatment of tumors in vivo.
Neuroblastoma is the most frequently diagnosed extracranial solid tumor in children. These tumors account for 15% of childhood deaths from cancer. Survival in one- year-old children is <30% despite aggressive therapies.
We used mouse neuroblastoma tumor model and identified MYC and HDAC8 are the critical targets in neuroblastoma tumorigenesis. Treatment of the tumors in mice with the combination of siRNA-MYC + siRNA-HDAC8 inhibited both the targets MYC and HDAC8 simultaneously and inhibited the growth of tumor by 80%. Therefore, inhibiting more than one target is critical for the successful treatment of tumors in vivo.
The author would like to thank Texas Children’s Hospital Flow Cytometry Core Facilities, Houston, Texas, for their help in performing cell cycle analysis and Dr. George Calin and associates at MD Anderson Cancer Center, Houston, Texas, for access to the luminometer plate reader.
The author declares that they have no conflicts of interest.
Ethics approval: Mice experiments were performed with the approval of the institutional Animal Care and Use Committee, IACUC at Nanospectra Biosciences Inc. Houston, Texas.
TMC Internist, Houston, Texas provided partial funding for the chemical supplies used in this project.
The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
",metaTitle:"Open Access Publishing Fees",metaDescription:"Open Access Publishing Fees",metaKeywords:null,canonicalURL:"/page/OA-publishing-fees",contentRaw:'[{"type":"htmlEditorComponent","content":"As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
\\n\\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
\\n\\nOAPF Publishing Options
\\n\\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\\n\\nServices included are:
\\n\\nSee our full list of services here.
\\n\\nWhat isn't covered by the Open Access Publishing Fee?
\\n\\nIf your manuscript:
\\n\\nYour Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\\n\\nOpen Access Funding
\\n\\nTo explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\\n\\nFor Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\\n\\nAdded Value of Publishing with IntechOpen
\\n\\nChoosing to publish with IntechOpen ensures the following benefits:
\\n\\nBenefits of Publishing with IntechOpen
\\n\\nAs a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
\n\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
\n\nOAPF Publishing Options
\n\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\n\nServices included are:
\n\nSee our full list of services here.
\n\nWhat isn't covered by the Open Access Publishing Fee?
\n\nIf your manuscript:
\n\nYour Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\nOpen Access Funding
\n\nTo explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\n\nFor Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\nAdded Value of Publishing with IntechOpen
\n\nChoosing to publish with IntechOpen ensures the following benefits:
\n\nBenefits of Publishing with IntechOpen
\n\n