Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity
Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evo...
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description | Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems. |
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These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems.</description><identifier>ISSN: 2050-084X</identifier><identifier>EISSN: 2050-084X</identifier><identifier>DOI: 10.7554/eLife.69032</identifier><identifier>PMID: 34473060</identifier><language>eng</language><publisher>England: eLife Science Publications, Ltd</publisher><subject>Anti-HIV Agents - adverse effects ; Drug resistance ; Drug Resistance, Multiple, Viral - genetics ; Drug therapy ; Efavirenz ; Evolution ; Evolution, Molecular ; Evolutionary Biology ; Genetics and Genomics ; HIV ; HIV (Viruses) ; HIV Infections - drug therapy ; HIV Infections - genetics ; HIV Infections - virology ; HIV-1 - drug effects ; HIV-1 - genetics ; HIV-1 - pathogenicity ; Human immunodeficiency virus ; Humans ; Multidrug resistance ; Mutation ; Mutation - genetics ; Mutation Rate ; Selection, Genetic - genetics ; spatial heterogeneity ; temporal heterogeneity ; Viruses</subject><ispartof>eLife, 2021-09, Vol.10</ispartof><rights>2021, Feder et al.</rights><rights>COPYRIGHT 2021 eLife Science Publications, Ltd.</rights><rights>2021, Feder et al. 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These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems.</description><subject>Anti-HIV Agents - adverse effects</subject><subject>Drug resistance</subject><subject>Drug Resistance, Multiple, Viral - genetics</subject><subject>Drug therapy</subject><subject>Efavirenz</subject><subject>Evolution</subject><subject>Evolution, Molecular</subject><subject>Evolutionary Biology</subject><subject>Genetics and Genomics</subject><subject>HIV</subject><subject>HIV (Viruses)</subject><subject>HIV Infections - drug therapy</subject><subject>HIV Infections - genetics</subject><subject>HIV Infections - virology</subject><subject>HIV-1 - drug effects</subject><subject>HIV-1 - genetics</subject><subject>HIV-1 - pathogenicity</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Multidrug resistance</subject><subject>Mutation</subject><subject>Mutation - genetics</subject><subject>Mutation Rate</subject><subject>Selection, Genetic - genetics</subject><subject>spatial heterogeneity</subject><subject>temporal heterogeneity</subject><subject>Viruses</subject><issn>2050-084X</issn><issn>2050-084X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNptks9rFDEUgAdRbKk9eZcBTyKz5udkchFKsXZhQVAr3kKavMxm2ZmsSUbsf9_sbK1dMDnkkXzv473wquo1RgvBOfsAK-9g0UpEybPqlCCOGtSxn8-fxCfVeUobVJZgXYfly-qEMiYoatFpNdyMFmLKerR-7OudzhnimOrg6uvlj3qYttk3Nk59HSH5PWegzusYpn5dD8HCdmYzDLsQ9bYunjoViy_xnLaGIgw9jODz3avqhdPbBOcP51l1c_Xp--V1s_ryeXl5sWoMF21ujDASa46EFsiJtrWYMOAcYwvS3YKzlmjqJOkQQ9rRDmGGSSskocxaizp6Vi0PXhv0Ru2iH3S8U0F7NV-E2CsdszdbUG1nNBEIOyCWmdZqiaQUlLbYYsapLa6PB9duuh3AGhhzafRIevwy-rXqw2_VlaIkwUXw9kEQw68JUlabMMWx9K8Il5xgjgX_R_W6VOVHF4rMDD4ZddEKhjqE6J5a_Icq28LgTRjB-XJ_lPDuKKEwGf7kXk8pqeW3r8fs-wNrYkgpgntsEiO1nzY1T5uap63Qb57-yyP7d7boPeovz1o</recordid><startdate>20210902</startdate><enddate>20210902</enddate><creator>Feder, Alison F</creator><creator>Harper, Kristin N</creator><creator>Brumme, Chanson J</creator><creator>Pennings, Pleuni S</creator><general>eLife Science Publications, Ltd</general><general>eLife Sciences Publications Ltd</general><general>eLife Sciences Publications, Ltd</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>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2915-089X</orcidid><orcidid>https://orcid.org/0000-0001-8704-6578</orcidid></search><sort><creationdate>20210902</creationdate><title>Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity</title><author>Feder, Alison F ; 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These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems.</abstract><cop>England</cop><pub>eLife Science Publications, Ltd</pub><pmid>34473060</pmid><doi>10.7554/eLife.69032</doi><orcidid>https://orcid.org/0000-0003-2915-089X</orcidid><orcidid>https://orcid.org/0000-0001-8704-6578</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Anti-HIV Agents - adverse effects Drug resistance Drug Resistance, Multiple, Viral - genetics Drug therapy Efavirenz Evolution Evolution, Molecular Evolutionary Biology Genetics and Genomics HIV HIV (Viruses) HIV Infections - drug therapy HIV Infections - genetics HIV Infections - virology HIV-1 - drug effects HIV-1 - genetics HIV-1 - pathogenicity Human immunodeficiency virus Humans Multidrug resistance Mutation Mutation - genetics Mutation Rate Selection, Genetic - genetics spatial heterogeneity temporal heterogeneity Viruses |
title | Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity |
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