Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool.
Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We train...
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doaj-222610729284488d9917eed9fc7529152020-11-25T00:02:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01124e017499210.1371/journal.pone.0174992Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool.Alejandro PirontiNico PfeiferHauke WalterBjörn-Erik O JensenMaurizio ZazziPerpétua GomesRolf KaiserThomas LengauerAntiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.http://europepmc.org/articles/PMC5386274?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alejandro Pironti Nico Pfeifer Hauke Walter Björn-Erik O Jensen Maurizio Zazzi Perpétua Gomes Rolf Kaiser Thomas Lengauer |
spellingShingle |
Alejandro Pironti Nico Pfeifer Hauke Walter Björn-Erik O Jensen Maurizio Zazzi Perpétua Gomes Rolf Kaiser Thomas Lengauer Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. PLoS ONE |
author_facet |
Alejandro Pironti Nico Pfeifer Hauke Walter Björn-Erik O Jensen Maurizio Zazzi Perpétua Gomes Rolf Kaiser Thomas Lengauer |
author_sort |
Alejandro Pironti |
title |
Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. |
title_short |
Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. |
title_full |
Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. |
title_fullStr |
Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. |
title_full_unstemmed |
Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. |
title_sort |
using drug exposure for predicting drug resistance - a data-driven genotypic interpretation tool. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2017-01-01 |
description |
Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs. |
url |
http://europepmc.org/articles/PMC5386274?pdf=render |
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