Modeling the Transfer of Drug Resistance in Solid Tumors
ABC efflux transporters are a key factor leading to multidrug resistance in cancer. Overexpression of these transporters significantly decreases the efficacy of anti-cancer drugs. Along with selection and induction, drug resistance may be transferred between cells, which is the focus of this paper....
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description | ABC efflux transporters are a key factor leading to multidrug resistance in cancer. Overexpression of these transporters significantly decreases the efficacy of anti-cancer drugs. Along with selection and induction, drug resistance may be transferred between cells, which is the focus of this paper. Specifically, we consider the intercellular transfer of P-glycoprotein (P-gp), a well-known ABC transporter that was shown to confer resistance to many common chemotherapeutic drugs. In a recent paper, Durán et al. (Bull Math Biol 78(6):1218–1237,
2016
) studied the dynamics of mixed cultures of resistant and sensitive NCI-H460 (human non-small lung cancer) cell lines. As expected, the experimental data showed a gradual increase in the percentage of resistance cells and a decrease in the percentage of sensitive cells. The experimental work was accompanied with a mathematical model that assumed P-gp transfer from resistant cells to sensitive cells, rendering them temporarily resistant. The mathematical model provided a reasonable fit to the experimental data. In this paper, we develop a new mathematical model for the transfer of drug resistance between cancer cells. Our model is based on incorporating a resistance phenotype into a model of cancer growth (Greene et al. in J Theor Biol 367:262–277,
2015
). The resulting model for P-gp transfer, written as a system of integro-differential equations, follows the dynamics of proliferating, quiescent, and apoptotic cells, with a varying resistance phenotype. We show that this model provides a good match to the dynamics of the experimental data of Durán et al. (
2016
). The mathematical model shows a better fit when resistant cancer cells have a slower division rate than the sensitive cells. |
doi_str_mv | 10.1007/s11538-017-0334-x |
format | Article |
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2016
) studied the dynamics of mixed cultures of resistant and sensitive NCI-H460 (human non-small lung cancer) cell lines. As expected, the experimental data showed a gradual increase in the percentage of resistance cells and a decrease in the percentage of sensitive cells. The experimental work was accompanied with a mathematical model that assumed P-gp transfer from resistant cells to sensitive cells, rendering them temporarily resistant. The mathematical model provided a reasonable fit to the experimental data. In this paper, we develop a new mathematical model for the transfer of drug resistance between cancer cells. Our model is based on incorporating a resistance phenotype into a model of cancer growth (Greene et al. in J Theor Biol 367:262–277,
2015
). The resulting model for P-gp transfer, written as a system of integro-differential equations, follows the dynamics of proliferating, quiescent, and apoptotic cells, with a varying resistance phenotype. We show that this model provides a good match to the dynamics of the experimental data of Durán et al. (
2016
). The mathematical model shows a better fit when resistant cancer cells have a slower division rate than the sensitive cells.</description><identifier>ISSN: 0092-8240</identifier><identifier>EISSN: 1522-9602</identifier><identifier>DOI: 10.1007/s11538-017-0334-x</identifier><identifier>PMID: 28852953</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>ABC transporter ; Anticancer properties ; Apoptosis ; Cancer ; Cell Biology ; Differential equations ; Drug resistance ; Efflux ; Experimental data ; Glycoproteins ; Life Sciences ; Lung cancer ; Mathematical analysis ; Mathematical and Computational Biology ; Mathematical models ; Mathematics ; Mathematics and Statistics ; Multidrug resistance ; Multidrug resistant organisms ; Original Article ; P-Glycoprotein ; Resistance factors ; Solid tumors ; Tumor cell lines ; Tumors</subject><ispartof>Bulletin of mathematical biology, 2017-10, Vol.79 (10), p.2394-2412</ispartof><rights>Society for Mathematical Biology 2017</rights><rights>Bulletin of Mathematical Biology is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-643745ed98faf75f43d2502ef09c82fdec950cb0addbea7e54b9e3f5ec034d223</citedby><cites>FETCH-LOGICAL-c415t-643745ed98faf75f43d2502ef09c82fdec950cb0addbea7e54b9e3f5ec034d223</cites><orcidid>0000-0003-2972-4857</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11538-017-0334-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11538-017-0334-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28852953$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Becker, Matthew</creatorcontrib><creatorcontrib>Levy, Doron</creatorcontrib><title>Modeling the Transfer of Drug Resistance in Solid Tumors</title><title>Bulletin of mathematical biology</title><addtitle>Bull Math Biol</addtitle><addtitle>Bull Math Biol</addtitle><description>ABC efflux transporters are a key factor leading to multidrug resistance in cancer. Overexpression of these transporters significantly decreases the efficacy of anti-cancer drugs. Along with selection and induction, drug resistance may be transferred between cells, which is the focus of this paper. Specifically, we consider the intercellular transfer of P-glycoprotein (P-gp), a well-known ABC transporter that was shown to confer resistance to many common chemotherapeutic drugs. In a recent paper, Durán et al. (Bull Math Biol 78(6):1218–1237,
2016
) studied the dynamics of mixed cultures of resistant and sensitive NCI-H460 (human non-small lung cancer) cell lines. As expected, the experimental data showed a gradual increase in the percentage of resistance cells and a decrease in the percentage of sensitive cells. The experimental work was accompanied with a mathematical model that assumed P-gp transfer from resistant cells to sensitive cells, rendering them temporarily resistant. The mathematical model provided a reasonable fit to the experimental data. In this paper, we develop a new mathematical model for the transfer of drug resistance between cancer cells. Our model is based on incorporating a resistance phenotype into a model of cancer growth (Greene et al. in J Theor Biol 367:262–277,
2015
). The resulting model for P-gp transfer, written as a system of integro-differential equations, follows the dynamics of proliferating, quiescent, and apoptotic cells, with a varying resistance phenotype. We show that this model provides a good match to the dynamics of the experimental data of Durán et al. (
2016
). The mathematical model shows a better fit when resistant cancer cells have a slower division rate than the sensitive cells.</description><subject>ABC transporter</subject><subject>Anticancer properties</subject><subject>Apoptosis</subject><subject>Cancer</subject><subject>Cell Biology</subject><subject>Differential equations</subject><subject>Drug resistance</subject><subject>Efflux</subject><subject>Experimental data</subject><subject>Glycoproteins</subject><subject>Life Sciences</subject><subject>Lung cancer</subject><subject>Mathematical analysis</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Multidrug resistance</subject><subject>Multidrug resistant organisms</subject><subject>Original Article</subject><subject>P-Glycoprotein</subject><subject>Resistance factors</subject><subject>Solid tumors</subject><subject>Tumor cell lines</subject><subject>Tumors</subject><issn>0092-8240</issn><issn>1522-9602</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kF1LwzAUhoMobk5_gDdS8MabaD6aNrmU-QkTQed1aJuT2dE1M2lh_nszOkUEr87Fed73HB6ETim5pITkV4FSwSUmNMeE8xRv9tCYCsawygjbR2NCFMOSpWSEjkJYkphRXB2iEZNSMCX4GMknZ6Cp20XSvUMy90UbLPjE2eTG94vkBUIduqKtIKnb5NU1tUnm_cr5cIwObNEEONnNCXq7u51PH_Ds-f5xej3DVUpFh7OU56kAo6QtbC5syg0ThIElqpLMGqiUIFVJCmNKKHIQaamAWwEV4alhjE_QxdC79u6jh9DpVR0qaJqiBdcHTRXnistM0oie_0GXrvdt_G5LSa5YRmWk6EBV3oXgweq1r1eF_9SU6K1WPWjVUaveatWbmDnbNfflCsxP4ttjBNgAhLhqF-B_nf639QtFkYHd</recordid><startdate>20171001</startdate><enddate>20171001</enddate><creator>Becker, Matthew</creator><creator>Levy, Doron</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SS</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</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>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2972-4857</orcidid></search><sort><creationdate>20171001</creationdate><title>Modeling the Transfer of Drug Resistance in Solid Tumors</title><author>Becker, Matthew ; Levy, Doron</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-643745ed98faf75f43d2502ef09c82fdec950cb0addbea7e54b9e3f5ec034d223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>ABC transporter</topic><topic>Anticancer properties</topic><topic>Apoptosis</topic><topic>Cancer</topic><topic>Cell Biology</topic><topic>Differential equations</topic><topic>Drug resistance</topic><topic>Efflux</topic><topic>Experimental data</topic><topic>Glycoproteins</topic><topic>Life Sciences</topic><topic>Lung cancer</topic><topic>Mathematical analysis</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Multidrug resistance</topic><topic>Multidrug resistant organisms</topic><topic>Original Article</topic><topic>P-Glycoprotein</topic><topic>Resistance factors</topic><topic>Solid tumors</topic><topic>Tumor cell lines</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Becker, Matthew</creatorcontrib><creatorcontrib>Levy, Doron</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>MEDLINE - Academic</collection><jtitle>Bulletin of mathematical biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Becker, Matthew</au><au>Levy, Doron</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling the Transfer of Drug Resistance in Solid Tumors</atitle><jtitle>Bulletin of mathematical biology</jtitle><stitle>Bull Math Biol</stitle><addtitle>Bull Math Biol</addtitle><date>2017-10-01</date><risdate>2017</risdate><volume>79</volume><issue>10</issue><spage>2394</spage><epage>2412</epage><pages>2394-2412</pages><issn>0092-8240</issn><eissn>1522-9602</eissn><abstract>ABC efflux transporters are a key factor leading to multidrug resistance in cancer. Overexpression of these transporters significantly decreases the efficacy of anti-cancer drugs. Along with selection and induction, drug resistance may be transferred between cells, which is the focus of this paper. Specifically, we consider the intercellular transfer of P-glycoprotein (P-gp), a well-known ABC transporter that was shown to confer resistance to many common chemotherapeutic drugs. In a recent paper, Durán et al. (Bull Math Biol 78(6):1218–1237,
2016
) studied the dynamics of mixed cultures of resistant and sensitive NCI-H460 (human non-small lung cancer) cell lines. As expected, the experimental data showed a gradual increase in the percentage of resistance cells and a decrease in the percentage of sensitive cells. The experimental work was accompanied with a mathematical model that assumed P-gp transfer from resistant cells to sensitive cells, rendering them temporarily resistant. The mathematical model provided a reasonable fit to the experimental data. In this paper, we develop a new mathematical model for the transfer of drug resistance between cancer cells. Our model is based on incorporating a resistance phenotype into a model of cancer growth (Greene et al. in J Theor Biol 367:262–277,
2015
). The resulting model for P-gp transfer, written as a system of integro-differential equations, follows the dynamics of proliferating, quiescent, and apoptotic cells, with a varying resistance phenotype. We show that this model provides a good match to the dynamics of the experimental data of Durán et al. (
2016
). The mathematical model shows a better fit when resistant cancer cells have a slower division rate than the sensitive cells.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>28852953</pmid><doi>10.1007/s11538-017-0334-x</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-2972-4857</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | ABC transporter Anticancer properties Apoptosis Cancer Cell Biology Differential equations Drug resistance Efflux Experimental data Glycoproteins Life Sciences Lung cancer Mathematical analysis Mathematical and Computational Biology Mathematical models Mathematics Mathematics and Statistics Multidrug resistance Multidrug resistant organisms Original Article P-Glycoprotein Resistance factors Solid tumors Tumor cell lines Tumors |
title | Modeling the Transfer of Drug Resistance in Solid Tumors |
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