HIV-GRADE: a publicly available, rules-based drug resistance interpretation algorithm integrating bioinformatic knowledge

Background: Genotypic drug resistance testing provides essential information for guiding treatment in HIV-infected patients. It may either be used for identifying patients with transmitted drug resistance or to clarify reasons for treatment failure and to check for remaining treatment options. While different approaches for the interpretation of HIV sequence information are already available, no other available rules-based systems specifically have looked into the effects of combinations of drugs. HIV-GRADE (Genotypischer Resistenz Algorithmus Deutschland) was planned as a countrywide approach to establish standardized drug resistance interpretation in Germany and also to introduce rules for estimating the influence of mutations on drug combinations. The rules for HIV-GRADE are taken from the literature, clinical follow-up data and from a bioinformatics-driven interpretation system (geno2pheno([resistance])). HIV-GRADE presents the option of seeing the rules and results of other drug resistance algorithms for a given sequence simultaneously.

Methods: The HIV-GRADE rules-based interpretation system was developed by the members of the HIV-GRADE registered society. For continuous updates, this expert committee meets twice a year to analyze data from various sources. Besides data from clinical studies and the centers involved, published correlations for mutations with drug resistance and genotype-phenotype correlation data information from the bioinformatic models of geno2pheno are used to generate the rules for the HIV-GRADE interpretation system. A freely available online tool was developed on the basis of the Stanford HIVdb rules interpretation tool using the algorithm specification interface. Clinical validation of the interpretation system was performed on the data of treatment episodes consisting of sequence information, antiretroviral treatment and viral load, before and 3 months after treatment change. Data were analyzed using multiple linear regression.

Results: As the developed online tool allows easy comparison of different drug resistance interpretation systems, coefficients of determination (R(2)) were compared for the freely available rules-based systems. HIV-GRADE (R(2) = 0.40), Stanford HIVdb (R(2) = 0.40), REGA algorithm (R(2) = 0.36) and ANRS (R(2) = 0.35) had a very similar performance using this multiple linear regression model.

Conclusion: The performance of HIV-GRADE is comparable to alternative rules-based interpretation systems. While there is still room for improvement, HIV-GRADE has been made publicly available to allow access to our approach regarding the interpretation of resistance against single drugs and drug combinations.