Loading…

TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model

Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2020-04, Vol.15 (4), p.e0231499-e0231499
Main Authors: Lai, Xiulan, Hao, Wenrui, Friedman, Avner
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c526t-954a31c924365218c805c698f4a71e3ca80382023579f5bee13a1892a01abadb3
cites cdi_FETCH-LOGICAL-c526t-954a31c924365218c805c698f4a71e3ca80382023579f5bee13a1892a01abadb3
container_end_page e0231499
container_issue 4
container_start_page e0231499
container_title PloS one
container_volume 15
creator Lai, Xiulan
Hao, Wenrui
Friedman, Avner
description Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1, a drug that enhances the activity of anti-cancer T cells. Based on results in experimental melanoma, it is shown, by a mathematical model, that resistances to anti-PD-1 can be significantly reduced by combining it with anti-TNF-α. The model is used to simulate the efficacy of the combined therapy with different range of doses, different initial tumor volume, and different schedules. In particular, it is shown that under a course of treatment with 3-week cycles where each drug is injected in the first day of either week 1 or week 2, injecting anti-TNF-α one week after anti-PD-1 is the most effective schedule in reducing tumor volume.
doi_str_mv 10.1371/journal.pone.0231499
format article
fullrecord <record><control><sourceid>proquest_plos_</sourceid><recordid>TN_cdi_plos_journals_2392432487</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_d5682fab55c5443287b38810fecb4283</doaj_id><sourcerecordid>2393044532</sourcerecordid><originalsourceid>FETCH-LOGICAL-c526t-954a31c924365218c805c698f4a71e3ca80382023579f5bee13a1892a01abadb3</originalsourceid><addsrcrecordid>eNptUs1u1DAYtBCIloU3QBCJSy_Z-jdxOCBVLf0RFXAoZ-uL82XXqyRe7ASJx-JFeCa83bRqUS-2Zc-MZ-wh5C2jSyZKdrzxUxigW279gEvKBZNV9YwcskrwvOBUPH-wPiCvYtxQqoQuipfkQCQ4rVRxSL7cfD3P__7J3LB2tRt9yAI2k8WYNWFa5QGjiyMMFrPRZzCMLv9-lrOP2UnWw7jGNDgLXdb7BrvX5EULXcQ387wgP84_35xe5tffLq5OT65zq3gx5pWSIJituBSF4kxbTZUtKt1KKBkKC5oKzVMiVVatqhGZAKYrDpRBDU0tFuT9Xnfb-Wjmd4iGi50kl7pMiKs9ovGwMdvgegi_jQdnbjd8WBkIyXmHplGF5i3USlklE12XtdCa0RZtLbkWSevTfNtU99hYHMYA3SPRxyeDW5uV_2VKVlKudmaOZoHgf04YR9O7aLHrYEA_3foWVEqVPmVBPvwHfTqd3KNs8DEGbO_NMGp23bhjmV03zNyNRHv3MMg96a4M4h-IPLW0</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2392432487</pqid></control><display><type>article</type><title>TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model</title><source>PubMed Central Free</source><source>ProQuest - Publicly Available Content Database</source><creator>Lai, Xiulan ; Hao, Wenrui ; Friedman, Avner</creator><contributor>Wodarz, Dominik</contributor><creatorcontrib>Lai, Xiulan ; Hao, Wenrui ; Friedman, Avner ; Wodarz, Dominik</creatorcontrib><description>Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1, a drug that enhances the activity of anti-cancer T cells. Based on results in experimental melanoma, it is shown, by a mathematical model, that resistances to anti-PD-1 can be significantly reduced by combining it with anti-TNF-α. The model is used to simulate the efficacy of the combined therapy with different range of doses, different initial tumor volume, and different schedules. In particular, it is shown that under a course of treatment with 3-week cycles where each drug is injected in the first day of either week 1 or week 2, injecting anti-TNF-α one week after anti-PD-1 is the most effective schedule in reducing tumor volume.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0231499</identifier><identifier>PMID: 32310956</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Animals ; Anticancer properties ; Antigens ; Antineoplastic Combined Chemotherapy Protocols - pharmacology ; Apoptosis ; Biology and Life Sciences ; Cancer therapies ; Cell death ; Chemotherapy ; Computer simulation ; Cytokines ; Cytotoxicity ; Dendritic cells ; Drug resistance ; Drug Resistance, Neoplasm - drug effects ; Immunotherapy ; Kinases ; Ligands ; Lymphocytes ; Lymphocytes T ; Mathematical analysis ; Mathematical models ; Medicine and Health Sciences ; Melanoma ; Mice ; Models, Theoretical ; Mutation ; Neoplasm Recurrence, Local - drug therapy ; Neoplasm Recurrence, Local - metabolism ; Partial differential equations ; PD-1 protein ; Physical Sciences ; Programmed Cell Death 1 Receptor - antagonists &amp; inhibitors ; Proteins ; Schedules ; Tumor Burden - drug effects ; Tumor Necrosis Factor-alpha - antagonists &amp; inhibitors ; Tumor necrosis factor-α ; Tumors</subject><ispartof>PloS one, 2020-04, Vol.15 (4), p.e0231499-e0231499</ispartof><rights>2020 Lai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Lai et al 2020 Lai et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-954a31c924365218c805c698f4a71e3ca80382023579f5bee13a1892a01abadb3</citedby><cites>FETCH-LOGICAL-c526t-954a31c924365218c805c698f4a71e3ca80382023579f5bee13a1892a01abadb3</cites><orcidid>0000-0002-2764-8937</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2392432487/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2392432487?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32310956$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Wodarz, Dominik</contributor><creatorcontrib>Lai, Xiulan</creatorcontrib><creatorcontrib>Hao, Wenrui</creatorcontrib><creatorcontrib>Friedman, Avner</creatorcontrib><title>TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1, a drug that enhances the activity of anti-cancer T cells. Based on results in experimental melanoma, it is shown, by a mathematical model, that resistances to anti-PD-1 can be significantly reduced by combining it with anti-TNF-α. The model is used to simulate the efficacy of the combined therapy with different range of doses, different initial tumor volume, and different schedules. In particular, it is shown that under a course of treatment with 3-week cycles where each drug is injected in the first day of either week 1 or week 2, injecting anti-TNF-α one week after anti-PD-1 is the most effective schedule in reducing tumor volume.</description><subject>Animals</subject><subject>Anticancer properties</subject><subject>Antigens</subject><subject>Antineoplastic Combined Chemotherapy Protocols - pharmacology</subject><subject>Apoptosis</subject><subject>Biology and Life Sciences</subject><subject>Cancer therapies</subject><subject>Cell death</subject><subject>Chemotherapy</subject><subject>Computer simulation</subject><subject>Cytokines</subject><subject>Cytotoxicity</subject><subject>Dendritic cells</subject><subject>Drug resistance</subject><subject>Drug Resistance, Neoplasm - drug effects</subject><subject>Immunotherapy</subject><subject>Kinases</subject><subject>Ligands</subject><subject>Lymphocytes</subject><subject>Lymphocytes T</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Melanoma</subject><subject>Mice</subject><subject>Models, Theoretical</subject><subject>Mutation</subject><subject>Neoplasm Recurrence, Local - drug therapy</subject><subject>Neoplasm Recurrence, Local - metabolism</subject><subject>Partial differential equations</subject><subject>PD-1 protein</subject><subject>Physical Sciences</subject><subject>Programmed Cell Death 1 Receptor - antagonists &amp; inhibitors</subject><subject>Proteins</subject><subject>Schedules</subject><subject>Tumor Burden - drug effects</subject><subject>Tumor Necrosis Factor-alpha - antagonists &amp; inhibitors</subject><subject>Tumor necrosis factor-α</subject><subject>Tumors</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUs1u1DAYtBCIloU3QBCJSy_Z-jdxOCBVLf0RFXAoZ-uL82XXqyRe7ASJx-JFeCa83bRqUS-2Zc-MZ-wh5C2jSyZKdrzxUxigW279gEvKBZNV9YwcskrwvOBUPH-wPiCvYtxQqoQuipfkQCQ4rVRxSL7cfD3P__7J3LB2tRt9yAI2k8WYNWFa5QGjiyMMFrPRZzCMLv9-lrOP2UnWw7jGNDgLXdb7BrvX5EULXcQ387wgP84_35xe5tffLq5OT65zq3gx5pWSIJituBSF4kxbTZUtKt1KKBkKC5oKzVMiVVatqhGZAKYrDpRBDU0tFuT9Xnfb-Wjmd4iGi50kl7pMiKs9ovGwMdvgegi_jQdnbjd8WBkIyXmHplGF5i3USlklE12XtdCa0RZtLbkWSevTfNtU99hYHMYA3SPRxyeDW5uV_2VKVlKudmaOZoHgf04YR9O7aLHrYEA_3foWVEqVPmVBPvwHfTqd3KNs8DEGbO_NMGp23bhjmV03zNyNRHv3MMg96a4M4h-IPLW0</recordid><startdate>20200420</startdate><enddate>20200420</enddate><creator>Lai, Xiulan</creator><creator>Hao, Wenrui</creator><creator>Friedman, Avner</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</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>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2764-8937</orcidid></search><sort><creationdate>20200420</creationdate><title>TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model</title><author>Lai, Xiulan ; Hao, Wenrui ; Friedman, Avner</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-954a31c924365218c805c698f4a71e3ca80382023579f5bee13a1892a01abadb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Animals</topic><topic>Anticancer properties</topic><topic>Antigens</topic><topic>Antineoplastic Combined Chemotherapy Protocols - pharmacology</topic><topic>Apoptosis</topic><topic>Biology and Life Sciences</topic><topic>Cancer therapies</topic><topic>Cell death</topic><topic>Chemotherapy</topic><topic>Computer simulation</topic><topic>Cytokines</topic><topic>Cytotoxicity</topic><topic>Dendritic cells</topic><topic>Drug resistance</topic><topic>Drug Resistance, Neoplasm - drug effects</topic><topic>Immunotherapy</topic><topic>Kinases</topic><topic>Ligands</topic><topic>Lymphocytes</topic><topic>Lymphocytes T</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Melanoma</topic><topic>Mice</topic><topic>Models, Theoretical</topic><topic>Mutation</topic><topic>Neoplasm Recurrence, Local - drug therapy</topic><topic>Neoplasm Recurrence, Local - metabolism</topic><topic>Partial differential equations</topic><topic>PD-1 protein</topic><topic>Physical Sciences</topic><topic>Programmed Cell Death 1 Receptor - antagonists &amp; inhibitors</topic><topic>Proteins</topic><topic>Schedules</topic><topic>Tumor Burden - drug effects</topic><topic>Tumor Necrosis Factor-alpha - antagonists &amp; inhibitors</topic><topic>Tumor necrosis factor-α</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lai, Xiulan</creatorcontrib><creatorcontrib>Hao, Wenrui</creatorcontrib><creatorcontrib>Friedman, Avner</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</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 &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science 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>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agriculture Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest - Publicly Available Content Database</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>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lai, Xiulan</au><au>Hao, Wenrui</au><au>Friedman, Avner</au><au>Wodarz, Dominik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-04-20</date><risdate>2020</risdate><volume>15</volume><issue>4</issue><spage>e0231499</spage><epage>e0231499</epage><pages>e0231499-e0231499</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1, a drug that enhances the activity of anti-cancer T cells. Based on results in experimental melanoma, it is shown, by a mathematical model, that resistances to anti-PD-1 can be significantly reduced by combining it with anti-TNF-α. The model is used to simulate the efficacy of the combined therapy with different range of doses, different initial tumor volume, and different schedules. In particular, it is shown that under a course of treatment with 3-week cycles where each drug is injected in the first day of either week 1 or week 2, injecting anti-TNF-α one week after anti-PD-1 is the most effective schedule in reducing tumor volume.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32310956</pmid><doi>10.1371/journal.pone.0231499</doi><orcidid>https://orcid.org/0000-0002-2764-8937</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2020-04, Vol.15 (4), p.e0231499-e0231499
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_2392432487
source PubMed Central Free; ProQuest - Publicly Available Content Database
subjects Animals
Anticancer properties
Antigens
Antineoplastic Combined Chemotherapy Protocols - pharmacology
Apoptosis
Biology and Life Sciences
Cancer therapies
Cell death
Chemotherapy
Computer simulation
Cytokines
Cytotoxicity
Dendritic cells
Drug resistance
Drug Resistance, Neoplasm - drug effects
Immunotherapy
Kinases
Ligands
Lymphocytes
Lymphocytes T
Mathematical analysis
Mathematical models
Medicine and Health Sciences
Melanoma
Mice
Models, Theoretical
Mutation
Neoplasm Recurrence, Local - drug therapy
Neoplasm Recurrence, Local - metabolism
Partial differential equations
PD-1 protein
Physical Sciences
Programmed Cell Death 1 Receptor - antagonists & inhibitors
Proteins
Schedules
Tumor Burden - drug effects
Tumor Necrosis Factor-alpha - antagonists & inhibitors
Tumor necrosis factor-α
Tumors
title TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T07%3A00%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=TNF-%CE%B1%20inhibitor%20reduces%20drug-resistance%20to%20anti-PD-1:%20A%20mathematical%20model&rft.jtitle=PloS%20one&rft.au=Lai,%20Xiulan&rft.date=2020-04-20&rft.volume=15&rft.issue=4&rft.spage=e0231499&rft.epage=e0231499&rft.pages=e0231499-e0231499&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0231499&rft_dat=%3Cproquest_plos_%3E2393044532%3C/proquest_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c526t-954a31c924365218c805c698f4a71e3ca80382023579f5bee13a1892a01abadb3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2392432487&rft_id=info:pmid/32310956&rfr_iscdi=true