title: Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool.
abstract: 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.
authors.affiliation: Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Pironti
authors.affiliation: Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany.
authors.forename: Nico
authors.initials: N
authors.lastname: Pfeifer
authors.affiliation: Medizinisches Infektiologiezentrum Berlin, Berlin, Germany.|Medizinisches Labor Stendal, Stendal, Germany.
authors.forename: Hauke
authors.initials: H
authors.lastname: Walter
authors.affiliation: Clinic for Gastroenterology, Hepatology, and Infectiology, University Clinic of Düsseldorf, Düsseldorf, Germany.
authors.forename: Björn-Erik O
authors.initials: BO
authors.lastname: Jensen
authors.affiliation: Department of Medical Biotechnology, University of Siena, Siena, Italy.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.affiliation: Laboratorio de Biologia Molecular, LMCBM, SPC, HEM - Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal.|Centro de Investigacao Interdisciplinar Egas Moniz (CiiEM), Instituto Superior de Ciencias da Saude Sul, Caparica, Portugal.
authors.forename: Perpétua
authors.initials: P
authors.lastname: Gomes
authors.affiliation: Institute for Virology, University Clinic of Cologne, Cologne, Germany.
authors.forename: Rolf
authors.initials: R
authors.lastname: Kaiser
authors.affiliation: Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Lengauer
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology* / Q000627:therapeutic use; D019540:Area Under Curve; D016208:Databases, Factual; D024882:Drug Resistance, Viral / Q000187:drug effects*; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D020407:Internet; D008954:Models, Biological; D015233:Models, Statistical; D010641:Phenotype; D012372:ROC Curve
chemical_list: D019380:Anti-HIV Agents