Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database

J Infect Dis. 2009 Apr 1;199(7):999-1006. doi: 10.1086/597305.


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.

Conclusion: 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

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Anti-HIV Agents / administration & dosage*
  • Anti-HIV Agents / pharmacology*
  • Decision Support Systems, Clinical*
  • Drug Therapy, Combination
  • Genetic Predisposition to Disease
  • Genotype
  • HIV Infections / drug therapy*
  • HIV-1 / genetics*
  • Humans
  • Predictive Value of Tests
  • ROC Curve
  • Reproducibility of Results
  • Retrospective Studies
  • Software


  • Anti-HIV Agents