Comparing interpretation systems

Interpretation systems for genotypic resistance patterns may provide different results depending on the data used to establish the interpretation rules, or algorithms.

A US-French collaboration considered the differences between online algorithms and their rates and causes for comparative discordance. This study aims to establish a site where algorithms can evolve and converge through ongoing inter-algorithm assessments and be validated using clinical data. The group analysed genotypic sequences from over 2230 individuals between 1997 and 2000.1

They found:

  • Application of three rules-based algorithms resulted in 84% concordance in deciding whether an isolate was susceptible or resistant to a particular anti-HIV drug.
  • Four drugs – amprenavir, abacavir, ddI, and d4T – were responsible for two-thirds of the discordances.
  • Several common mutational patterns were responsible for most of the discordances.
  • Discordances between algorithms were less marked with use of three-stage SIR (sensitive, intermediate, resistant) scoring, compared with simple binary sensitive/resistant scoring.

A comparison of four algorithms – Stanford, geno2pheno, Retrogram 1.4 and TruGene – used a scoring system to assess the match between predictions of sensitivity and subsequent virological response in 131 consecutive patients. The study found that between 66 and 77% of responses to drugs were predicted correctly, and between 67 and 75% of therapeutic failures were also predicted correctly.2

See Resistance in non-B HIV subtypes for further information on the performance of assays in different HIV subtypes.

The GUESS study compared the ability of twelve experts to predict phenotypic resistance from genotypes by taking 50 genotypes from the Virco database. Large variations between drugs were seen. While 74% predicted 3TC sensitivity correctly, only 25 and 26% predicted abacavir and nelfinavir resistance correctly. The consensus on treatment recommendations was generally poorer for NRTIs, and lowest for ddI.3

Yet another study sought to establish the correlation between protease and reverse transcriptase mutations and antiviral drug resistance. The study also aims to generate models that predict phenotypic drug resistance from sequence information. Their geno2pheno approach models permutations of identified mutants and directs them along decision-tree classifiers that identify them as resistant or susceptible. These pathways provide a sophisticated analysis based on the juxtaposition of different mutations. For example, they cite a mutational pair, 41M and 77L as resistant, but when these are combined with 215T and 75T/V, they report the same virus as being susceptible.

The use of an algorithm to interpret genotypic resistance results has been associated with a superior virological response. After three months of salvage therapy, patients whose resistance results were assessed using a rules-based algorithm were more likely to have a viral load below 500 copies/ml, even after controlling for other factors such as number of new drugs and baseline viral load.4

References

  1. Shafer RW Online comparison of HIV-1 drug resistance algorithms identifies rates and causes of discordant interpretations. Antivir Ther 6: 101-102, 2001
  2. Ehret R et al. Predictive value of different drug resistance interpretation systems in therapy management of HIV-infected patients in daily routine. Antiviral Therapy 7: S77, 2002
  3. Zolopa AR et al. Accuracy, precision and consistency of expert HIV-1 genotype interpretation: an international comparison (the GUESS study). Antiviral Therapy 7: S97, 2002
  4. van Laethem K et al. A genotypic drug resistance interpretation algorithm that significantly predicts therapy response in HIV-1-infected patients. Antivir Ther 7: 123-129, 2002
Community Consensus Statement on Access to HIV Treatment and its Use for Prevention

Together, we can make it happen

We can end HIV soon if people have equal access to HIV drugs as treatment and as PrEP, and have free choice over whether to take them.

Launched today, the Community Consensus Statement is a basic set of principles aimed at making sure that happens.

The Community Consensus Statement is a joint initiative of AVAC, EATG, MSMGF, GNP+, HIV i-Base, the International HIV/AIDS Alliance, ITPC and NAM/aidsmap
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