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A computational diffusion model to study antibody transport within reconstructed tumor microenvironments
Antibodies revolutionized cancer treatment over the past decades. Despite their successfully application, there are still challenges to overcome to improve efficacy, such as the heterogeneous distribution of antibodies within tumors. Tumor microenvironment features, such as the distribution of tumor...
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Published in: | BMC bioinformatics 2020-11, Vol.21 (1), p.529-529, Article 529 |
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description | Antibodies revolutionized cancer treatment over the past decades. Despite their successfully application, there are still challenges to overcome to improve efficacy, such as the heterogeneous distribution of antibodies within tumors. Tumor microenvironment features, such as the distribution of tumor and other cell types and the composition of the extracellular matrix may work together to hinder antibodies from reaching the target tumor cells. To understand these interactions, we propose a framework combining in vitro and in silico models. We took advantage of in vitro cancer models previously developed by our group, consisting of tumor cells and fibroblasts co-cultured in 3D within alginate capsules, for reconstruction of tumor microenvironment features.
In this work, an experimental-computational framework of antibody transport within alginate capsules was established, assuming a purely diffusive transport, combined with an exponential saturation effect that mimics the saturation of binding sites on the cell surface. Our tumor microenvironment in vitro models were challenged with a fluorescent antibody and its transport recorded using light sheet fluorescence microscopy. Diffusion and saturation parameters of the computational model were adjusted to reproduce the experimental antibody distribution, with root mean square error under 5%. This computational framework is flexible and can simulate different random distributions of tumor microenvironment elements (fibroblasts, cancer cells and collagen fibers) within the capsule. The random distribution algorithm can be tuned to follow the general patterns observed in the experimental models.
We present a computational and microscopy framework to track and simulate antibody transport within the tumor microenvironment that complements the previously established in vitro models platform. This framework paves the way to the development of a valuable tool to study the influence of different components of the tumor microenvironment on antibody transport. |
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In this work, an experimental-computational framework of antibody transport within alginate capsules was established, assuming a purely diffusive transport, combined with an exponential saturation effect that mimics the saturation of binding sites on the cell surface. Our tumor microenvironment in vitro models were challenged with a fluorescent antibody and its transport recorded using light sheet fluorescence microscopy. Diffusion and saturation parameters of the computational model were adjusted to reproduce the experimental antibody distribution, with root mean square error under 5%. This computational framework is flexible and can simulate different random distributions of tumor microenvironment elements (fibroblasts, cancer cells and collagen fibers) within the capsule. The random distribution algorithm can be tuned to follow the general patterns observed in the experimental models.
We present a computational and microscopy framework to track and simulate antibody transport within the tumor microenvironment that complements the previously established in vitro models platform. This framework paves the way to the development of a valuable tool to study the influence of different components of the tumor microenvironment on antibody transport.</description><identifier>ISSN: 1471-2105</identifier><identifier>EISSN: 1471-2105</identifier><identifier>DOI: 10.1186/s12859-020-03854-2</identifier><identifier>PMID: 33203360</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>3D in vitro cancer models ; Alginates ; Alginic acid ; Algorithms ; Antibodies ; Antibodies - metabolism ; Antibody diffusion ; Binding sites ; Biological transport ; Cancer ; Cell Count ; Cell Line, Tumor ; Cell surface ; Collagen ; Computational modelling ; Computer applications ; Computer Simulation ; Computer-generated environments ; Development and progression ; Diffusion ; Diffusion models ; Extracellular matrix ; Fibers ; Fibroblasts ; Fluorescence ; Fluorescence microscopy ; Humans ; Influence ; Laser microscopy ; Light sheet fluorescence microscopy ; Light sheets ; Methods ; Microenvironments ; Microscope and microscopy ; Microscopy ; Neoplasms - pathology ; Oncology, Experimental ; Protein Transport ; Saturation ; Stochastic Processes ; Tumor cells ; Tumor microenvironment ; Tumor Microenvironment - immunology ; Tumors ; Viral antibodies</subject><ispartof>BMC bioinformatics, 2020-11, Vol.21 (1), p.529-529, Article 529</ispartof><rights>COPYRIGHT 2020 BioMed Central Ltd.</rights><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c597t-8fcdc52322f9b23d999be133373203ab214c495acc81f18e1ac84bc337cdd083</citedby><cites>FETCH-LOGICAL-c597t-8fcdc52322f9b23d999be133373203ab214c495acc81f18e1ac84bc337cdd083</cites><orcidid>0000-0002-0237-5805</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672975/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2461851769?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33203360$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cartaxo, Ana Luísa</creatorcontrib><creatorcontrib>Almeida, Jaime</creatorcontrib><creatorcontrib>Gualda, Emilio J</creatorcontrib><creatorcontrib>Marsal, Maria</creatorcontrib><creatorcontrib>Loza-Alvarez, Pablo</creatorcontrib><creatorcontrib>Brito, Catarina</creatorcontrib><creatorcontrib>Isidro, Inês A</creatorcontrib><title>A computational diffusion model to study antibody transport within reconstructed tumor microenvironments</title><title>BMC bioinformatics</title><addtitle>BMC Bioinformatics</addtitle><description>Antibodies revolutionized cancer treatment over the past decades. Despite their successfully application, there are still challenges to overcome to improve efficacy, such as the heterogeneous distribution of antibodies within tumors. Tumor microenvironment features, such as the distribution of tumor and other cell types and the composition of the extracellular matrix may work together to hinder antibodies from reaching the target tumor cells. To understand these interactions, we propose a framework combining in vitro and in silico models. We took advantage of in vitro cancer models previously developed by our group, consisting of tumor cells and fibroblasts co-cultured in 3D within alginate capsules, for reconstruction of tumor microenvironment features.
In this work, an experimental-computational framework of antibody transport within alginate capsules was established, assuming a purely diffusive transport, combined with an exponential saturation effect that mimics the saturation of binding sites on the cell surface. Our tumor microenvironment in vitro models were challenged with a fluorescent antibody and its transport recorded using light sheet fluorescence microscopy. Diffusion and saturation parameters of the computational model were adjusted to reproduce the experimental antibody distribution, with root mean square error under 5%. This computational framework is flexible and can simulate different random distributions of tumor microenvironment elements (fibroblasts, cancer cells and collagen fibers) within the capsule. The random distribution algorithm can be tuned to follow the general patterns observed in the experimental models.
We present a computational and microscopy framework to track and simulate antibody transport within the tumor microenvironment that complements the previously established in vitro models platform. This framework paves the way to the development of a valuable tool to study the influence of different components of the tumor microenvironment on antibody transport.</description><subject>3D in vitro cancer models</subject><subject>Alginates</subject><subject>Alginic acid</subject><subject>Algorithms</subject><subject>Antibodies</subject><subject>Antibodies - metabolism</subject><subject>Antibody diffusion</subject><subject>Binding sites</subject><subject>Biological transport</subject><subject>Cancer</subject><subject>Cell Count</subject><subject>Cell Line, Tumor</subject><subject>Cell surface</subject><subject>Collagen</subject><subject>Computational modelling</subject><subject>Computer applications</subject><subject>Computer Simulation</subject><subject>Computer-generated environments</subject><subject>Development and progression</subject><subject>Diffusion</subject><subject>Diffusion models</subject><subject>Extracellular matrix</subject><subject>Fibers</subject><subject>Fibroblasts</subject><subject>Fluorescence</subject><subject>Fluorescence microscopy</subject><subject>Humans</subject><subject>Influence</subject><subject>Laser microscopy</subject><subject>Light sheet fluorescence microscopy</subject><subject>Light sheets</subject><subject>Methods</subject><subject>Microenvironments</subject><subject>Microscope and microscopy</subject><subject>Microscopy</subject><subject>Neoplasms - pathology</subject><subject>Oncology, Experimental</subject><subject>Protein Transport</subject><subject>Saturation</subject><subject>Stochastic Processes</subject><subject>Tumor cells</subject><subject>Tumor microenvironment</subject><subject>Tumor Microenvironment - immunology</subject><subject>Tumors</subject><subject>Viral antibodies</subject><issn>1471-2105</issn><issn>1471-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1vEzEQXSEQLYU_wAFZ4gKHLf7ctS9IUQU0UiUk6N3y-iNxtGsH29vSf4_TpKVByAePZt68see9pnmL4DlCvPuUEeZMtBDDFhLOaIufNaeI9qjFCLLnT-KT5lXOGwhRzyF72ZwQgiEhHTxt1gug47Sdiyo-BjUC452bc43BFI0dQYkgl9ncARWKH2INSlIhb2Mq4NaXtQ8gWR1DLmnWxRpQ5ikmMHmdog03PsUw2VDy6-aFU2O2bw73WXP99cv1xWV79f3b8mJx1Wom-tJyp41mmGDsxICJEUIMFhFC-t2T1YAR1VQwpTVHDnGLlOZ00LWujYGcnDXLPa2JaiO3yU8q3cmovLxPxLSSKhWvRyuNo4OrG-SmZ7QjnTID1dh0mjtsITaV6_OeazsPkzW6fiOp8Yj0uBL8Wq7ijey7HoueVYIPB4IUf802Fzn5rO04qmDjnCWmXdUR4Q5V6Pt_oJs4pyrIAcVQ34m_qJWqH_DBxTpX70jlomOQciHIbuz5f1D1GFtlicE6X_NHDR-PGiqm2N9lpeac5fLnj2Ms3mOrvjkn6x73gaDc2VLubSmrLeW9LSWuTe-ebvKx5cGH5A_wEd6W</recordid><startdate>20201117</startdate><enddate>20201117</enddate><creator>Cartaxo, Ana Luísa</creator><creator>Almeida, Jaime</creator><creator>Gualda, Emilio J</creator><creator>Marsal, Maria</creator><creator>Loza-Alvarez, Pablo</creator><creator>Brito, Catarina</creator><creator>Isidro, Inês A</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0237-5805</orcidid></search><sort><creationdate>20201117</creationdate><title>A computational diffusion model to study antibody transport within reconstructed tumor microenvironments</title><author>Cartaxo, Ana Luísa ; Almeida, Jaime ; Gualda, Emilio J ; Marsal, Maria ; Loza-Alvarez, Pablo ; Brito, Catarina ; Isidro, Inês A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c597t-8fcdc52322f9b23d999be133373203ab214c495acc81f18e1ac84bc337cdd083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>3D in vitro cancer models</topic><topic>Alginates</topic><topic>Alginic acid</topic><topic>Algorithms</topic><topic>Antibodies</topic><topic>Antibodies - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cartaxo, Ana Luísa</au><au>Almeida, Jaime</au><au>Gualda, Emilio J</au><au>Marsal, Maria</au><au>Loza-Alvarez, Pablo</au><au>Brito, Catarina</au><au>Isidro, Inês A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A computational diffusion model to study antibody transport within reconstructed tumor microenvironments</atitle><jtitle>BMC bioinformatics</jtitle><addtitle>BMC Bioinformatics</addtitle><date>2020-11-17</date><risdate>2020</risdate><volume>21</volume><issue>1</issue><spage>529</spage><epage>529</epage><pages>529-529</pages><artnum>529</artnum><issn>1471-2105</issn><eissn>1471-2105</eissn><abstract>Antibodies revolutionized cancer treatment over the past decades. Despite their successfully application, there are still challenges to overcome to improve efficacy, such as the heterogeneous distribution of antibodies within tumors. Tumor microenvironment features, such as the distribution of tumor and other cell types and the composition of the extracellular matrix may work together to hinder antibodies from reaching the target tumor cells. To understand these interactions, we propose a framework combining in vitro and in silico models. We took advantage of in vitro cancer models previously developed by our group, consisting of tumor cells and fibroblasts co-cultured in 3D within alginate capsules, for reconstruction of tumor microenvironment features.
In this work, an experimental-computational framework of antibody transport within alginate capsules was established, assuming a purely diffusive transport, combined with an exponential saturation effect that mimics the saturation of binding sites on the cell surface. Our tumor microenvironment in vitro models were challenged with a fluorescent antibody and its transport recorded using light sheet fluorescence microscopy. Diffusion and saturation parameters of the computational model were adjusted to reproduce the experimental antibody distribution, with root mean square error under 5%. This computational framework is flexible and can simulate different random distributions of tumor microenvironment elements (fibroblasts, cancer cells and collagen fibers) within the capsule. The random distribution algorithm can be tuned to follow the general patterns observed in the experimental models.
We present a computational and microscopy framework to track and simulate antibody transport within the tumor microenvironment that complements the previously established in vitro models platform. This framework paves the way to the development of a valuable tool to study the influence of different components of the tumor microenvironment on antibody transport.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>33203360</pmid><doi>10.1186/s12859-020-03854-2</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-0237-5805</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 3D in vitro cancer models Alginates Alginic acid Algorithms Antibodies Antibodies - metabolism Antibody diffusion Binding sites Biological transport Cancer Cell Count Cell Line, Tumor Cell surface Collagen Computational modelling Computer applications Computer Simulation Computer-generated environments Development and progression Diffusion Diffusion models Extracellular matrix Fibers Fibroblasts Fluorescence Fluorescence microscopy Humans Influence Laser microscopy Light sheet fluorescence microscopy Light sheets Methods Microenvironments Microscope and microscopy Microscopy Neoplasms - pathology Oncology, Experimental Protein Transport Saturation Stochastic Processes Tumor cells Tumor microenvironment Tumor Microenvironment - immunology Tumors Viral antibodies |
title | A computational diffusion model to study antibody transport within reconstructed tumor microenvironments |
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