title: Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database.
abstract: BACKGROUND
  Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure.
  
  
  METHODS
  We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega.
  
  
  RESULTS
  The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed.
  
  
  CONCLUSIONS
  Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org.
authors.affiliation: Max Planck Institute for Informatics, Saarbrücken, University of Cologne, Cologne, Germany. altmann@mpi-inf.mpg.de
authors.forename: André
authors.initials: A
authors.lastname: Altmann
authors.forename: Martin
authors.initials: M
authors.lastname: Däumer
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
authors.forename: Yardena
authors.initials: Y
authors.lastname: Peres
authors.forename: Eugen
authors.initials: E
authors.lastname: Schülter
authors.forename: Joachim
authors.initials: J
authors.lastname: Büch
authors.forename: Soo-Yon
authors.initials: SY
authors.lastname: Rhee
authors.forename: Anders
authors.initials: A
authors.lastname: Sönnerborg
authors.forename: W Jeffrey
authors.initials: WJ
authors.lastname: Fessel
authors.forename: Robert W
authors.initials: RW
authors.lastname: Shafer
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.forename: Rolf
authors.initials: R
authors.lastname: Kaiser
authors.forename: Thomas
authors.initials: T
authors.lastname: Lengauer
mesh_terms: D019380:Anti-HIV Agents / Q000008:administration & dosage* / Q000494:pharmacology*; D020000:Decision Support Systems, Clinical; D004359:Drug Therapy, Combination; D020022:Genetic Predisposition to Disease; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy*; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012984:Software
chemical_list: D019380:Anti-HIV Agents