BACKGROUND
management of antiviral resistance is an important consideration in the treatment of hiv- <dig> patients with antiviral drugs  <cit> .
facing high viral loads and fast replication rates, a combination of multiple drugs is needed to suppress viral replication so that the viral load in the plasma becomes undetectable.
hiv- <dig> has a high mutation rate, and in conjunction with the large intra-host population and fast generation time  <cit> , the virus is able to develop resistance mutations quickly.
therefore, a strict adherence to the treatment is regarded as crucial in the prevention of suboptimal drug concentrations and subsequent viral replication.
as part of the management of antiviral treatment, genotypic resistance testing is recommended when starting or switching treatment  <cit> .
when virological failure is detected timely and a genotypic resistance test performed immediately, in many cases the test shows that the virus has developed resistance but not to all drugs in the regimen  <cit> .
some drugs, such as the currently used non-nucleoside reverse transcriptase inhibitors, have a low genetic barrier to resistance since only a single nucleotide mutation is required to completely loose drug activity.
by contrast, other drugs  require an ordered accumulation of multiple mutations to confer resistance, and thus have a higher genetic barrier to resistance.
at treatment failure, the virus is more likely to have developed resistance against the drug with the lower genetic barrier  <cit> .
however, the actual genetic barrier is not merely the number of mutations needed to confer resistance, since the likelihood of a mutation is not uniform due to evolutionary restrictions.
a mutation must also be considered in the context of in vivo fitness, reflecting the combination of phenotypic resistance and intrinsic replication capacity.
epistatic fitness interactions between mutations may alter the prevalence of a mutation depending on the presence of another mutation.
genotypic resistance testing aims at uncovering mutational patterns in the virus and interpreting their impact on drug resistance.
this interpretation is difficult because of the complexity of resistance patterns, the existence of cross-resistance and resensitization, and the high natural variation of hiv- <dig>  ideally, a genotypic resistance test not only helps in selecting a treatment regimen that will immediately inhibit viral replication, but also in selecting a treatment with a high genetic barrier to resistance, and thus a durable response.
therefore, not only the contribution to resistance of detected mutations, but also their impact on lowering the genetic barrier towards resistance should be considered.
because there is no readily available measure for the genetic barrier , the impact of many mutations and mutational patterns on genetic barrier is not well understood.
with a few exceptions, such as the so-called revertants at reverse transcriptase position  <dig>  <cit> , the clinical relevance of a supposedly decreased genetic barrier has not been shown.
a lower genetic barrier not only poses a higher long-term risk for failure in case of nonoptimal adherence, but may also impact treatment options available at failure, under the assumption that development of resistance at treatment failure is more likely for drugs with a lower genetic barrier, and because of the extensive cross-resistance within drug classes.
the extensive natural variation within the hiv- <dig> main group  is not believed to impact drug susceptibility substantially  <cit> .
still, this variation may affect the genetic barrier to resistance for some drugs, even in treatment-naive patients, and this could in principle be predicted from the genotype  <cit> .
several studies have indeed suggested that the presence of polymorphisms, known as minor mutations, impact virological outcome  <cit> .
however, these studies usually lacked statistical power to assign the effect on virological outcome to the presence of particular polymorphisms because of the small prevalence of many polymorphisms, and the confounding effect of adherence.
in previous work, we presented a method to estimate a fitness landscape experienced by the virus during treatment, and applied this in the context of the protease inhibitor nelfinavir   <cit> .
simulated evolution from a baseline sequence, over such a fitness landscape, together with a criterion for resistance, allows the estimation of the individualized genetic barrier to resistance.
in the present study, we investigate the association of the individualized genetic barrier with development of resistance at failure, as predicted by an expert rule-based genotypic interpretation system, in patients fully susceptible to nfv at baseline.
we also explore genotypic factors that impact this estimated genetic barrier for viruses predicted to be fully susceptible to nfv.
RESULTS
predicting development of nfv resistance at treatment failure
the final longitudinal data set included  <dig> protease sequence pairs with a subtype distribution largely dominated by subtype b .
a neighbor-joining phylogenetic tree constructed from the baseline sequences revealed no intra-subtype clustering according to data source .
at treatment failure, the rega algorithm predicted full nfv resistance , i.e.
with gssnfv =  <dig>  in  <dig> cases  and intermediate nfv resistance , i.e.
with gssnfv =  <dig> , in  <dig> cases .
in these pairs, genotypic susceptibility to nfv treatment as estimated in vivo fitness value and estimates of the simulated genetic barrier to resistance were computed from the baseline sequence .
despite the fact that each patient was predicted at baseline to be fully susceptible to nfv by a genotypic interpretation system , we observed variation in estimated fitness under nfv treatment at baseline as well as substantial variation in estimated genetic barrier to nfv resistance .
the genotypic susceptibility of the virus to the remaining drugs in the combination, predicted by rega, was high.
for most patients , the activity score for the combination excluding nfv  summed up to ≥  <dig>  which suggests that the majority of the nfv-based regimens was potent enough at the time of therapy initiation.
the median time to treatment failure was  <dig> months.
description of model variables in a longitudinal data set for  <dig> patients, which were fully susceptible to nfv at baseline.
data are median  for log estimated fitness (), genetic barrier estimates , duration between baseline and follow-up sample , the backbone activity  and the subtype distribution .
the results of the univariable analysis are shown in table  <dig>  a lower estimated genetic barrier, both in terms of mutations  per additional mutation, p = .02) or in terms of generations  per  <dig> more generations, p = .01) and lower activity of the other drugs in the combination , p <  <dig> ) were associated with a higher risk of developing nfv resistance at treatment failure.
estimated fitness under nfv selective pressure, duration on therapy or subtype b virus were not associated with nfv resistance development.
univariable association of factors at baseline with risk of nelfinavir  resistance development at treatment failure: fitness under nfv treatment , expected number of mutations to nfv resistance (), expected number of generations to nfv resistance (), time between baseline and follow-up sequence , the activity of the other drugs in the combination  and the subtype distribution .
these significant associations remained in the multivariable analysis .
a lower genetic barrier in terms of mutations  per additional mutation, p = .02) or in terms of generations  per  <dig> more generations, p = .0075) associated significantly with an increased risk for developing nfv resistance at treatment failure.
also a lower backbone activity , respectively p < .
<dig> and p = .001) was independently indicative for acquiring nfv resistance.
a multivariable logistic regression model is shown for development of nelfinavir  resistance at treatment failure starting from the baseline genotype based on the expected number of mutations to nfv resistance () in the upper table and based on the expected number of generations to nfv resistance () in the lower table.
analyses are corrected for duration between baseline and follow-up sequence , fitness under nfv treatment , the activity score of the combination excluding nfv  and the subtype distribution .
the two measures for genetic barrier were highly correlated , p < 10-16), since each additional mutation in general requires extra evolutionary time to evolve.
this also explains why the results were similar when using  versus .
genotypic correlates of estimated genetic barrier
to investigate contributions of protease mutations and polymorphisms on the predicted genetic barrier, evolution was simulated for a virtual cohort of  <dig> patients on nfv treatment, and for each simulation, the number of generations gr to develop nfv resistance was recorded.
because the estimated fitness landscape only models intra-subtype variation for each subtype, this analysis was done only using hiv- <dig> subtype b, the most prevalent subtype in our data set.
phylogenetic reconstruction indicated interspersion of multiple lineages of sequences sampled in portugal.
therefore, separation of sequences in the tree conditioned on the center of data collection could not be established .
a step-wise model search was performed to identify a best linear model for log gr, which thus included the independent, multiplicative contributions of single mutations.
in the final model,  <dig> mutations  independently decreased the genetic barrier , while  <dig> mutations  increased the genetic barrier .
although figure  <dig> indicates contributions of pro-tease mutations to the genetic barrier with a fixed extent, these values resulted from averaging over the entire population  and, since only independent and individual mutational contributions were considered, as well over mutations epistatically interacting with the respective mutation listed .
as such, these findings do not contradict with the observation that the genetic context contributes to fitness in the landscape, and consequently to the genetic barrier to resistance.
for example, mutation 71v was present in  <dig> isolates , of which  <dig>  selected 30n as first mutation and  <dig>   <dig> m .
baseline sequences lacking this mutation only selected in  <dig>  and in  <dig> cases  30n and  <dig> m respectively.
on the other hand,  <dig> isolates harboured mutation  <dig> d and 30n was only selected in  <dig>  and  <dig> m  zero cases, compared to  <dig>  and  <dig>  for isolates lacking  <dig> d.
for several of the mutations that contributed to a decreased genetic barrier  and one mutation that increased genetic barrier , predicted selection by the fitness landscape model was shown previously to correlate with observed evolution in longitudinal data from patients on nfv treatment  <cit> .
thus, for these mutations, the fitness function modeled interactions with polymorphisms or other resistance mutations that affects their selection.
rega considers ten mutations  to contribute to resistance as minor mutations.
eleven mutations that were predicted to decrease genetic barrier , and five mutations that were predicted to increase the genetic barrier  are not included in the rules for nfv resistance in rega.
some of these mutations have been described previously in relation to resistance to nfv or other protease inhibitors: mutations 36i and 77i are polymorphisms that are involved in nfv resistance  <cit> ; mutation 45r has recently been associated with nfv treatment  <cit> ; mutations 13v, 36i/v, 45r, 72v, 75i and 77i are associated with protease inhibitor treatment  <cit>  and mutation 13v has been associated with reduced response to tipranavir  <cit> .
mutation 89i has been linked to treatment failure in several non-b subtypes, where the wild-type is  <dig> m  <cit> .
89i/m are rare mutations in subtype b, and the model indicates that in subtype b, they increase the genetic barrier to resistance because they are reverted to the wild type , although the same model correctly predicts selection of 89i during nfv treatment in other subtypes  <cit> .
discussion
in this study, we evaluated retrospectively the association of genotypic information contained in the baseline genotype with the risk of developing nfv resistance at treatment failure, when treated with a nfv containing regimen, in longitudinal sequence pairs.
the baseline sequences were interpreted using an estimated fitness function for hiv- <dig> under nfv selective pressure, which was used to compute the estimated fitness  and two measures of genetic barrier: the expected number of mutations  or generations  to evolve a mutational pattern that is considered by rega as causing resistance to nfv.
as expert resistance interpretation system, rega was chosen because it has been clinically evaluated for prediction of treatment outcome  <cit> .
this will allow us to investigate if the fitness landscape could be used to predict treatment options available at treatment failure as predicted by rega.
both in univariable and multivariable analyses, a lower genetic barrier was found to increase the risk for developing nfv resistance at treatment failure.
such estimated genetic barrier may provide unique and useful information to a clinician contemplated a change of treatment, allowing to take into account available therapy options in case of subsequent treatment failure.
this is, to our knowledge, the first proof of direct clinical impact of  genetic barrier on risk of development of resistance at treatment failure.
with the goal of life-long treatment, options at treatment failure are taken into consideration at start of treatment, and therefore current hiv- <dig> treatment guidelines take into account proper drug sequencing and the sparing of inhibitor classes  <cit> .
an individualized prediction of resistance development at treatment failure may therefore contribute to a more informed treatment choice.
noteworthy, a lower activity of the regimen accompanying nfv, predicted by rega, was associated with an increased risk of nfv resistance at therapy failure.
this association can be expected since a suboptimal, less potent regimen may favor evolution and development of nfv resistance more easily.
the accuracy of the predictions may be further improved by using the genetic barrier to resistance for the other drugs in the combination, instead of a susceptibility score.
the association of a lower genetic barrier with an increased risk for resistance development at failure implies indirectly that the estimated genetic barrier could also be predictive for long-term treatment response.
indeed, these results show that a lower genetic barrier facilitates resistance development, and may therefore be expected to increase as well the risk for treatment failure because of resistance development under non-optimal adherence.
although the rega system for genotypic resistance interpretation also scores the presence of several minor resistance mutations as intermediate resistance , in this analysis only patients were included for which rega predicted full susceptibility to nfv at baseline.
assuming that viral fitness during treatment depends on the susceptibility of the virus to the drug, log  can be considered an in vivo resistance phenotype.
the restriction of the study to patients predicted susceptible may explain why viral fitness, visualized by the virus position in the landscape, did not relate to the emergence of nfv resistance.
overall, these results provide additional indication that the estimated fitness landscape may outperform an expert system for prediction of treatment outcome, in particular for patients who are considered fully susceptible by the expert system  <cit> .
although clinical response in terms of viral load measurements was not available for these patients, the availability of a follow-up genotype is indicative of treatment failure.
by requesting a genotypic test, the clinician presumed failure of the current regimen, and successful genotyping implied high enough viremia.
we previously evaluated the performance of this landscape to predict virological outcome in a clinical cohort of patients, starting with a combination of zidovudine , lamivudine  and nfv.
differently from the current study, patients were not required to be fully susceptible to nfv.
a higher genetic barrier was significantly associated with higher viral load reduction on short term and with lower odds of virological failure on long term  <cit> .
for this analysis, sequence data were combined that originated from different geographic locations.
genotypic variation was accounted for by adding hiv- <dig> subtype to the model.
additionally, phylogenetic analysis did not unveil geographical withinsubtype sequence differences.
by pooling data from multiple sources,  variables, besides epidemiology, could differ between patient groups and influence resistance development.
the objective of the fitness estimation procedure was not to predict resistance development as such, but to quantify the influence of mutational patterns on viral fitness under drug selective pressure and eventually to predict virus evolution under this pressure.
resistant virus was defined by an independent interpretation algorithm.
as measures of "time", we considered the number of mutations or simulated generations.
the actual time to therapy failure is besides the evolutionary distance under drug selective pressure , a function of the rate at which hiv- <dig> will bridge this distance .
next to drug activity, the potency of treatment is the outcome of different parameters.
though information on patient-specific parameters  or on management of hiv- <dig> infection is missing, these parameters most likely do not influence the actual evolutionary distance to resistance, but do affect drug potency and subsequently the time to therapy failure.
hence, the time between therapy initiation and failure was included as a variable to correct for  variables that influence the amount of virus evolution tolerated.
to obtain an insight into the contributions of mutations and polymorphisms towards estimated genetic barrier to nfv resistance in isolates susceptible to nfv, we simulated resistance evolution during nfv treatment in subtype b sequences from a large virtual clinical patient cohort.
a total of  <dig> mutations and polymorphisms were identified that independently contribute to the genetic barrier to nfv resistance .
because of the combined use of the fitness landscape with an expert system, a mutation may influence the estimated genetic barrier either because it contributes to resistance , or because it influences, in the fitness function, the selection of mutations that contribute to resistance, or both.
a number of mutations  are not included in the rules for nfv resistance.
therefore, each of these mutations contributes to a lower  estimated genetic barrier through their inclusion in the fitness landscape model, where they cause a faster  selection of resistance mutations that are considered by rega.
the mechanism for the contribution to a lower genetic barrier of mutations  which are considered by rega to contribute to resistance , may be because of their inclusion in the rule for predicting resistance in rega, or because of an influence on selection of  resistance mutations in the fitness landscape model, or both.
the contribution of a mutation to viral fitness is highly dependent on the genetic background, and a mutation with an impact on the genetic barrier was identified by the model conditioned on the presence of polymorphic variation.
despite the recruitment of only subtype b sequences, and phylogenetic analysis that indicated distributed sequences among the tree, intra-subtype variation, as a consequence of founder effects, is inevitable and has also been reported  <cit> .
a total of  <dig> mutations listed in figure  <dig> differed significantly  in prevalence between the two patients groups .
however, these mutations still contributed significantly to the genetic barrier when the analysis was restricted to data source, highlighting the role of sequence variability.
application of the same methodology to another subtype b dataset may conceivably not identify exactly the same set of mutations, given that genotypic  variation exists within a subtype.
these findings argue the usefulness of the genetic barrier to predict resistance development, and the influence of the genetic background on this parameter.
knowledge extracted from this analysis could be used to enhance prediction of therapy outcome.
as evolutionary simulator of the hiv- <dig> intrahost population, an ideal wright-fisher model of molecular evolution was assumed, which is a well accepted model for evolution in a finite population.
a number of assumptions were implemented to reduce the  complexity of the model .
the model did not include recombination.
these simplifications may be avoided with availability of a more accurate, but also more computationally demanding simulator.
although recombination can speed up resistance accumulation, the fitness landscape attempts to capture the selective advantage of mutational patterns under drug selective pressure, what is not expected to be influenced by recombination.
CONCLUSIONS
in conclusion, we have demonstrated for the first time the existence of intra-patient variation in genetic barrier to resistance  in patients considered fully susceptible by an expert system.
the estimated genetic barrier not only reflects the amount of genetic change needed for resistance, but also takes into account the influence of virus genetic background, evolutionary constraints as well as the relative impact of a mutation on the in vivo fitness.
we found that a lower individualized genetic barrier was associated with a higher risk for development of resistance at treatment failure.
the genetic barrier to resistance, estimated at baseline, may uncover more information predictive for developing resistance than currently used genotypic algorithms.

 management of antiviral resistance is an important consideration in the treatment of hiv- patients with antiviral drugs. facing high viral loads and fast replication rates, a combination of multiple drugs is needed to suppress viral replication so that the viral load in the plasma becomes undetectable. hiv- has a high mutation rate, and in conjunction with the large intra-host population and fast generation time, the virus is able to develop resistance mutations quickly. therefore, a strict adherence to the treatment is regarded as crucial in the prevention of suboptimal drug concentrations and subsequent viral replication. as part of the management of antiviral treatment, genotypic resistance testing is recommended when starting or switching treatment. when virological failure is detected timely and a genotypic resistance test performed immediately, in many cases the test shows that the virus has developed resistance but not to all drugs in the regimen. some drugs, such as the currently used non-nucleoside reverse transcriptase inhibitors, have a low genetic barrier to resistance since only a single nucleotide mutation is required to completely loose drug activity. by contrast, other drugs require an ordered accumulation of multiple mutations to confer resistance, and thus have a higher genetic barrier to resistance. at treatment failure, the virus is more likely to have developed resistance against the drug with the lower genetic barrier. however, the actual genetic barrier is not merely the number of mutations needed to confer resistance, since the likelihood of a mutation is not uniform due to evolutionary restrictions. a mutation must also be considered in the context of in vivo fitness, reflecting the combination of phenotypic resistance and intrinsic replication capacity. epistatic fitness interactions between mutations may alter the prevalence of a mutation depending on the presence of another mutation. genotypic resistance testing aims at uncovering mutational patterns in the virus and interpreting their impact on drug resistance. this interpretation is difficult because of the complexity of resistance patterns, the existence of cross-resistance and resensitization, and the high natural variation of hiv- ideally, a genotypic resistance test not only helps in selecting a treatment regimen that will immediately inhibit viral replication, but also in selecting a treatment with a high genetic barrier to resistance, and thus a durable response. therefore, not only the contribution to resistance of detected mutations, but also their impact on lowering the genetic barrier towards resistance should be considered. because there is no readily available measure for the genetic barrier, the impact of many mutations and mutational patterns on genetic barrier is not well understood. with a few exceptions, such as the so-called revertants at reverse transcriptase position, the clinical relevance of a supposedly decreased genetic barrier has not been shown. a lower genetic barrier not only poses a higher long-term risk for failure in case of nonoptimal adherence, but may also impact treatment options available at failure, under the assumption that development of resistance at treatment failure is more likely for drugs with a lower genetic barrier, and because of the extensive cross-resistance within drug classes. the extensive natural variation within the hiv- main group is not believed to impact drug susceptibility substantially. still, this variation may affect the genetic barrier to resistance for some drugs, even in treatment-naive patients, and this could in principle be predicted from the genotype. several studies have indeed suggested that the presence of polymorphisms, known as minor mutations, impact virological outcome. however, these studies usually lacked statistical power to assign the effect on virological outcome to the presence of particular polymorphisms because of the small prevalence of many polymorphisms, and the confounding effect of adherence. in previous work, we presented a method to estimate a fitness landscape experienced by the virus during treatment, and applied this in the context of the protease inhibitor nelfinavir. simulated evolution from a baseline sequence, over such a fitness landscape, together with a criterion for resistance, allows the estimation of the individualized genetic barrier to resistance. in the present study, we investigate the association of the individualized genetic barrier with development of resistance at failure, as predicted by an expert rule-based genotypic interpretation system, in patients fully susceptible to nfv at baseline. we also explore genotypic factors that impact this estimated genetic barrier for viruses predicted to be fully susceptible to nfv.  predicting development of nfv resistance at treatment failure the final longitudinal data set included protease sequence pairs with a subtype distribution largely dominated by subtype b. a neighbor-joining phylogenetic tree constructed from the baseline sequences revealed no intra-subtype clustering according to data source. at treatment failure, the rega algorithm predicted full nfv resistance, i.e. with gssnfv = in cases and intermediate nfv resistance, i.e. with gssnfv =, in cases. in these pairs, genotypic susceptibility to nfv treatment as estimated in vivo fitness value and estimates of the simulated genetic barrier to resistance were computed from the baseline sequence. despite the fact that each patient was predicted at baseline to be fully susceptible to nfv by a genotypic interpretation system, we observed variation in estimated fitness under nfv treatment at baseline as well as substantial variation in estimated genetic barrier to nfv resistance. the genotypic susceptibility of the virus to the remaining drugs in the combination, predicted by rega, was high. for most patients, the activity score for the combination excluding nfv summed up to ≥ which suggests that the majority of the nfv-based regimens was potent enough at the time of therapy initiation. the median time to treatment failure was months. description of model variables in a longitudinal data set for patients, which were fully susceptible to nfv at baseline. data are median for log estimated fitness (), genetic barrier estimates, duration between baseline and follow-up sample, the backbone activity and the subtype distribution. the results of the univariable analysis are shown in table a lower estimated genetic barrier, both in terms of mutations per additional mutation, p =.02) or in terms of generations per more generations, p =.01) and lower activity of the other drugs in the combination, p < ) were associated with a higher risk of developing nfv resistance at treatment failure. estimated fitness under nfv selective pressure, duration on therapy or subtype b virus were not associated with nfv resistance development. univariable association of factors at baseline with risk of nelfinavir resistance development at treatment failure: fitness under nfv treatment, expected number of mutations to nfv resistance (), expected number of generations to nfv resistance (), time between baseline and follow-up sequence, the activity of the other drugs in the combination and the subtype distribution. these significant associations remained in the multivariable analysis. a lower genetic barrier in terms of mutations per additional mutation, p =.02) or in terms of generations per more generations, p =.0075) associated significantly with an increased risk for developing nfv resistance at treatment failure. also a lower backbone activity, respectively p <. and p =.001) was independently indicative for acquiring nfv resistance. a multivariable logistic regression model is shown for development of nelfinavir resistance at treatment failure starting from the baseline genotype based on the expected number of mutations to nfv resistance () in the upper table and based on the expected number of generations to nfv resistance () in the lower table. analyses are corrected for duration between baseline and follow-up sequence, fitness under nfv treatment, the activity score of the combination excluding nfv and the subtype distribution. the two measures for genetic barrier were highly correlated, p < 10-16), since each additional mutation in general requires extra evolutionary time to evolve. this also explains why the results were similar when using versus. genotypic correlates of estimated genetic barrier to investigate contributions of protease mutations and polymorphisms on the predicted genetic barrier, evolution was simulated for a virtual cohort of patients on nfv treatment, and for each simulation, the number of generations gr to develop nfv resistance was recorded. because the estimated fitness landscape only models intra-subtype variation for each subtype, this analysis was done only using hiv- subtype b, the most prevalent subtype in our data set. phylogenetic reconstruction indicated interspersion of multiple lineages of sequences sampled in portugal. therefore, separation of sequences in the tree conditioned on the center of data collection could not be established. a step-wise model search was performed to identify a best linear model for log gr, which thus included the independent, multiplicative contributions of single mutations. in the final model, mutations independently decreased the genetic barrier, while mutations increased the genetic barrier. although figure indicates contributions of pro-tease mutations to the genetic barrier with a fixed extent, these values resulted from averaging over the entire population and, since only independent and individual mutational contributions were considered, as well over mutations epistatically interacting with the respective mutation listed. as such, these findings do not contradict with the observation that the genetic context contributes to fitness in the landscape, and consequently to the genetic barrier to resistance. for example, mutation 71v was present in isolates, of which selected 30n as first mutation and m. baseline sequences lacking this mutation only selected in and in cases 30n and m respectively. on the other hand, isolates harboured mutation d and 30n was only selected in and m zero cases, compared to and for isolates lacking d. for several of the mutations that contributed to a decreased genetic barrier and one mutation that increased genetic barrier, predicted selection by the fitness landscape model was shown previously to correlate with observed evolution in longitudinal data from patients on nfv treatment. thus, for these mutations, the fitness function modeled interactions with polymorphisms or other resistance mutations that affects their selection. rega considers ten mutations to contribute to resistance as minor mutations. eleven mutations that were predicted to decrease genetic barrier, and five mutations that were predicted to increase the genetic barrier are not included in the rules for nfv resistance in rega. some of these mutations have been described previously in relation to resistance to nfv or other protease inhibitors: mutations 36i and 77i are polymorphisms that are involved in nfv resistance; mutation 45r has recently been associated with nfv treatment; mutations 13v, 36i/v, 45r, 72v, 75i and 77i are associated with protease inhibitor treatment and mutation 13v has been associated with reduced response to tipranavir. mutation 89i has been linked to treatment failure in several non-b subtypes, where the wild-type is m. 89i/m are rare mutations in subtype b, and the model indicates that in subtype b, they increase the genetic barrier to resistance because they are reverted to the wild type, although the same model correctly predicts selection of 89i during nfv treatment in other subtypes. discussion in this study, we evaluated retrospectively the association of genotypic information contained in the baseline genotype with the risk of developing nfv resistance at treatment failure, when treated with a nfv containing regimen, in longitudinal sequence pairs. the baseline sequences were interpreted using an estimated fitness function for hiv- under nfv selective pressure, which was used to compute the estimated fitness and two measures of genetic barrier: the expected number of mutations or generations to evolve a mutational pattern that is considered by rega as causing resistance to nfv. as expert resistance interpretation system, rega was chosen because it has been clinically evaluated for prediction of treatment outcome. this will allow us to investigate if the fitness landscape could be used to predict treatment options available at treatment failure as predicted by rega. both in univariable and multivariable analyses, a lower genetic barrier was found to increase the risk for developing nfv resistance at treatment failure. such estimated genetic barrier may provide unique and useful information to a clinician contemplated a change of treatment, allowing to take into account available therapy options in case of subsequent treatment failure. this is, to our knowledge, the first proof of direct clinical impact of genetic barrier on risk of development of resistance at treatment failure. with the goal of life-long treatment, options at treatment failure are taken into consideration at start of treatment, and therefore current hiv- treatment guidelines take into account proper drug sequencing and the sparing of inhibitor classes. an individualized prediction of resistance development at treatment failure may therefore contribute to a more informed treatment choice. noteworthy, a lower activity of the regimen accompanying nfv, predicted by rega, was associated with an increased risk of nfv resistance at therapy failure. this association can be expected since a suboptimal, less potent regimen may favor evolution and development of nfv resistance more easily. the accuracy of the predictions may be further improved by using the genetic barrier to resistance for the other drugs in the combination, instead of a susceptibility score. the association of a lower genetic barrier with an increased risk for resistance development at failure implies indirectly that the estimated genetic barrier could also be predictive for long-term treatment response. indeed, these results show that a lower genetic barrier facilitates resistance development, and may therefore be expected to increase as well the risk for treatment failure because of resistance development under non-optimal adherence. although the rega system for genotypic resistance interpretation also scores the presence of several minor resistance mutations as intermediate resistance, in this analysis only patients were included for which rega predicted full susceptibility to nfv at baseline. assuming that viral fitness during treatment depends on the susceptibility of the virus to the drug, log can be considered an in vivo resistance phenotype. the restriction of the study to patients predicted susceptible may explain why viral fitness, visualized by the virus position in the landscape, did not relate to the emergence of nfv resistance. overall, these results provide additional indication that the estimated fitness landscape may outperform an expert system for prediction of treatment outcome, in particular for patients who are considered fully susceptible by the expert system. although clinical response in terms of viral load measurements was not available for these patients, the availability of a follow-up genotype is indicative of treatment failure. by requesting a genotypic test, the clinician presumed failure of the current regimen, and successful genotyping implied high enough viremia. we previously evaluated the performance of this landscape to predict virological outcome in a clinical cohort of patients, starting with a combination of zidovudine, lamivudine and nfv. differently from the current study, patients were not required to be fully susceptible to nfv. a higher genetic barrier was significantly associated with higher viral load reduction on short term and with lower odds of virological failure on long term. for this analysis, sequence data were combined that originated from different geographic locations. genotypic variation was accounted for by adding hiv- subtype to the model. additionally, phylogenetic analysis did not unveil geographical withinsubtype sequence differences. by pooling data from multiple sources, variables, besides epidemiology, could differ between patient groups and influence resistance development. the objective of the fitness estimation procedure was not to predict resistance development as such, but to quantify the influence of mutational patterns on viral fitness under drug selective pressure and eventually to predict virus evolution under this pressure. resistant virus was defined by an independent interpretation algorithm. as measures of "time", we considered the number of mutations or simulated generations. the actual time to therapy failure is besides the evolutionary distance under drug selective pressure, a function of the rate at which hiv- will bridge this distance. next to drug activity, the potency of treatment is the outcome of different parameters. though information on patient-specific parameters or on management of hiv- infection is missing, these parameters most likely do not influence the actual evolutionary distance to resistance, but do affect drug potency and subsequently the time to therapy failure. hence, the time between therapy initiation and failure was included as a variable to correct for variables that influence the amount of virus evolution tolerated. to obtain an insight into the contributions of mutations and polymorphisms towards estimated genetic barrier to nfv resistance in isolates susceptible to nfv, we simulated resistance evolution during nfv treatment in subtype b sequences from a large virtual clinical patient cohort. a total of mutations and polymorphisms were identified that independently contribute to the genetic barrier to nfv resistance. because of the combined use of the fitness landscape with an expert system, a mutation may influence the estimated genetic barrier either because it contributes to resistance, or because it influences, in the fitness function, the selection of mutations that contribute to resistance, or both. a number of mutations are not included in the rules for nfv resistance. therefore, each of these mutations contributes to a lower estimated genetic barrier through their inclusion in the fitness landscape model, where they cause a faster selection of resistance mutations that are considered by rega. the mechanism for the contribution to a lower genetic barrier of mutations which are considered by rega to contribute to resistance, may be because of their inclusion in the rule for predicting resistance in rega, or because of an influence on selection of resistance mutations in the fitness landscape model, or both. the contribution of a mutation to viral fitness is highly dependent on the genetic background, and a mutation with an impact on the genetic barrier was identified by the model conditioned on the presence of polymorphic variation. despite the recruitment of only subtype b sequences, and phylogenetic analysis that indicated distributed sequences among the tree, intra-subtype variation, as a consequence of founder effects, is inevitable and has also been reported. a total of mutations listed in figure differed significantly in prevalence between the two patients groups. however, these mutations still contributed significantly to the genetic barrier when the analysis was restricted to data source, highlighting the role of sequence variability. application of the same methodology to another subtype b dataset may conceivably not identify exactly the same set of mutations, given that genotypic variation exists within a subtype. these findings argue the usefulness of the genetic barrier to predict resistance development, and the influence of the genetic background on this parameter. knowledge extracted from this analysis could be used to enhance prediction of therapy outcome. as evolutionary simulator of the hiv- intrahost population, an ideal wright-fisher model of molecular evolution was assumed, which is a well accepted model for evolution in a finite population. a number of assumptions were implemented to reduce the complexity of the model. the model did not include recombination. these simplifications may be avoided with availability of a more accurate, but also more computationally demanding simulator. although recombination can speed up resistance accumulation, the fitness landscape attempts to capture the selective advantage of mutational patterns under drug selective pressure, what is not expected to be influenced by recombination.  in conclusion, we have demonstrated for the first time the existence of intra-patient variation in genetic barrier to resistance in patients considered fully susceptible by an expert system. the estimated genetic barrier not only reflects the amount of genetic change needed for resistance, but also takes into account the influence of virus genetic background, evolutionary constraints as well as the relative impact of a mutation on the in vivo fitness. we found that a lower individualized genetic barrier was associated with a higher risk for development of resistance at treatment failure. the genetic barrier to resistance, estimated at baseline, may uncover more information predictive for developing resistance than currently used genotypic algorithms. 
we investigated the association of estimated genetic barrier with risk of development of nfv resistance at virological failure, in patients that were predicted fully susceptible to nfv at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure, per additional mutation needed, p =.02). if similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure. failure on highly active anti-retroviral treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. in general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. by simulation of evolution over this landscape, the individualized genetic barrier to nfv resistance may be estimated for an isolate. thus, variation in individualized genetic barrier to nfv resistance may impact effective treatment options available after treatment failure. previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by hiv- under nelfinavir treatment.
 failure on highly active anti-retroviral treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. in general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by hiv- under nelfinavir treatment. by simulation of evolution over this landscape, the individualized genetic barrier to nfv resistance may be estimated for an isolate.  we investigated the association of estimated genetic barrier with risk of development of nfv resistance at virological failure, in patients that were predicted fully susceptible to nfv at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure, per additional mutation needed, p =.02).  thus, variation in individualized genetic barrier to nfv resistance may impact effective treatment options available after treatment failure. if similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure. 