Various studies have evaluated the performance characteristics of different GISs in predicting susceptibility to first-generation PIs by the use of independent data sets (8
). However, the performance of GISs with an independent data set for darunavir and tipranavir has rarely been evaluated. Independent data sets are important for the evaluation of GISs, as a score created on the basis of a given data set will always perform well due to some degree of model overfitting. Even if a portion of the data set is used as a training data set and the remainder is used as a validation data set, the similarities of the treatment histories and other patient characteristics between the training and the validation data sets will likely also lead to the overly good performance of a given scoring system.
Within our collection of highly PI-resistant HIV isolates, the GISs performed well in predicting phenotypic susceptibility to darunavir. However, the Stanford HIV database discrete score performed less well, as it appeared that the intermediate resistance category was too broad and included isolates with a wide range of fold changes. The suggestion that there was a misclassification in the intermediate group is supported by the findings presented in Fig. , in which there is a wide range of phenotypes in the intermediate category, and also by the fact that the numerical score performed quite well and had an R2 value of 0.68, consistent with the R2 values of the other GISs tested. The Stanford HIV database numerical score is readily available on the website when one enters a genotype into the web-based system, but clinicians frequently focus only on the discrete score when interpreting a genotype. Given these results, we would encourage clinicians to be sure to review the numerical score when deciding on the use of darunavir. Additionally, as a result of the findings of this study and others, the authors of the Stanford HIV database have revised their scores for darunavir to attempt to better predict darunavir susceptibility.
We found that the ability of the GISs to interpret tipranavir susceptibility was generally poor. GISs make qualitative judgments of susceptibility on the basis of the available clinical and laboratory data. As new results become available, the system is updated to incorporate the new data. The relative lack of publicly available clinical information on tipranavir may help explain the poor performance of rules-based GISs. Another possible explanation is that the development of genotypic resistance to tipranavir may be inherently more complex than the development of genotypic resistance to darunavir because several diverse pathways can lead to resistance to tipranavir. For instance, IAS-USA lists 21 resistance-conferring mutations for tipranavir and 11 for darunavir (13
). With this complexity, it may be more difficult to create a rules-based algorithm for tipranavir. Our finding is consistent with the findings of others, who have also found that GISs do not perform well in evaluating tipranavir susceptibility (22
The superior performance of the Vircotype for predicting phenotypic resistance to tipranavir was surprising. In previous studies, the virtual phenotype has not outperformed rules-based GISs in predicting the virologic response or phenotypic resistance (9
). However, Virco's access to a large pool of proprietary data for tipranavir may have allowed the Vircotype to have superior performance. In addition, the algorithm that Virco uses to correlate genotypic resistance to phenotypic resistance may be superior to rules-based algorithms in predicting phenotypic resistance for a drug with as complex a pattern of resistance as tipranavir.
We found the presence of I84V and V82T and the lack of L10F to be associated with relative susceptibility to darunavir compared to the level of susceptibility to tipranavir. The importance of V82T as an important predictor of relative darunavir susceptibility is not surprising, as V82T is a signature mutation for resistance to tipranavir and has not been associated with decreased darunavir susceptibility (7
). On the basis of the tipranavir manufacturer's score, the presence of V82T alone is sufficient for reduced tipranavir susceptibility (10
). I84V has been associated with decreased susceptibility to both darunavir and tipranavir in clinical studies (4a
), but in a number of scoring systems, I84V is given more weight in tipranavir resistance scores than in darunavir resistance scores (4a
). I84V was also shown to be one of the first mutations to emerge during in vitro
passage experiments with tipranavir (4a
). L10F is a relatively common PI-associated mutation and has not been considered an important mutation conferring resistance to darunavir or tipranavir. The lack of the L10F mutation was unexpectedly associated with relative darunavir susceptibility, and its relevance should be confirmed by additional studies. It may be that the L10F is a proxy for other resistance-associated mutations that together affected the relative susceptibilities of these two new-generation PIs.
Not surprisingly, the presence of I54L was an important predictor of relative tipranavir susceptibility. It has been associated with an improved virologic response to tipranavir and has been given a negative weighting (the inverse of resistance) within the tipranavir manufacturer's score, while it not considered an important mutation for darunavir resistance (4a
). The emergence of V32I as an important mutation predicting relative tipranavir susceptibility is also consistent with the findings of previous work (4a
). On the other hand, I47V has been associated with decreased responses to both darunavir and tipranavir, and its validity as a predictor of relative tipranavir susceptibility should be explored within other independent data sets. Again, this mutation may simply be a marker for other associated mutations that are present within our cohort.
The validation of GISs can be performed through genotype-clinical outcome correlation studies or with correlations with phenotypic resistance testing, as was done in this study. We did not have access to clinical data, which was a limitation of the study. Another limitation of the study is the limited number of clinical isolates that were available, which may not have allowed us to detect some important mutations for darunavir and tipranavir resistance.
Genotypic resistance testing does not routinely sequence gag
, while the Antivirogram phenotype assay incorporates only a portion of the C terminus of gag
. Mutations in gag
not only have been associated with restored replicative capacity but also have been independently associated with protease resistance (3
). The clinical utility of evaluating gag
during resistance testing has not been established, and since the currently available interpretation algorithms do not include gag
mutations, we cannot speculate how the results of our study would have varied had these mutations been included. Nonetheless, our results do reflect the results that can be obtained by using the interpretation tools currently available to the clinician.
In patients harboring virus with extensive resistance to PIs, the choice of the PI to be used for salvage therapy is usually darunavir or tipranavir. By generating rules for predicting relative darunavir susceptibility versus relative tipranavir susceptibility, we attempt to provide guidance to clinicians for when one drug may be more active than the other, although additional studies are needed to validate the mutations selected by our models. Additionally, the activities of other drugs in the background regimen (especially etravirine, which cannot be coadministered with tipranavir) and the tolerability of the drugs will also be a factor in the selection of the appropriate PI (www.accessdata.fda.gov/drugsatfda_docs/label/2009/022187s002lbl.pdf
). Furthermore, with the availability of a new class of agents, for some, the requirement for PIs in salvage therapy is less obvious (24
In conclusion, for the set of isolates with high-level resistance to PIs evaluated in the present study, it was reassuring that the majority retained susceptibility to both darunavir and tipranavir. GISs effectively predict susceptibility to darunavir. However, other than use of the Vircotype, GISs cannot be relied upon to predict phenotypic susceptibility to tipranavir, but specific mutations may predict which isolates have relative tipranavir susceptibility over relative darunavir susceptibility. However, due to darunavir's ease of use and the fact that it retains activity even against highly resistant isolates, the role of tipranavir in salvage therapy will remain limited and tipranavir might be considered for use only in patients harboring the subgroup of isolates with the pattern of mutations that favor the use of tipranavir over darunavir.