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Usefulness of monitoring HIV drug resistance and adherence in individuals failing highly active antiretroviral therapy: a randomized study (ARGENTA)

Cingolani, Antonellaa; Antinori, Andreab; Rizzo, Maria Gabriellaa; Murri, Ritaa; Ammassari, Adrianaa; Baldini, Francescoa; Di Giambenedetto, Simonaa; Cauda, Robertoa; De Luca, Andreaa

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Potent antiretroviral therapeutic combinations have changed the natural history of HIV-1 infection from an almost invariably fatal to a chronic disease [1]. As antiviral agents are unable to eradicate infection [2], prolonged clinical benefit requires the most continuous and sustained suppression of viral replication. Although clinical trials report suppression of HIV replication in 60–90% of subjects by highly active antiretroviral (HAART) combinations, in clinical practice less than half of the patients reach undetectable HIV-RNA levels in plasma [3,4]. This is due to a number of reasons, including the presence of a high proportion of patients undergoing suboptimal treatments that were available before the current regimens, incomplete adherence to prescribed therapies, individual differences in drug metabolism, or drug–drug interactions causing patients to fail to reach adequate levels of the prescribed antiretroviral agents, and side-effects of antiviral drugs [5–7]. This leads invariably to the emergence of HIV-1 strains that become resistant to the prescribed drugs. Drug resistance is associated with the selection of viral mutants that are able to replicate under the pressure of a specific antiviral agent. Some specific mutations in HIV-1 reverse transcriptase and protease have been associated with resistance to drugs both in vitro and in vivo [8–10]. Rapid and standardized nucleic acid sequencing assays have been developed and used to address the prevalence and clinical significance of drug-resistant HIV-1 in retrospective studies [11–18]. Two published randomized studies and another two studies presented in preliminary form have so far tried to establish whether the systematic search for these mutations could be useful in the selection of more effective treatments in patients from the clinical practice showing virological failure to potent combination HIV therapy. Results have apparently been contradictory: two studies showed a short-term benefit that was partly reduced over time in one, a third study showed a minimal beneficial effect of genotypic resistance-guided treatment decisions, and the fourth yielded independent significant advantages from both genotyping and the application of expert advice [19–22]. In order to clarify further the clinical usefulness of genotypic resistance assays according to the level of patient-reported adherence, we performed a randomized, controlled study. The present paper contains results of the final 6 months follow-up from the AntiRetroviral Genotypic resistance and patiENT-reported Adherence study (ARGENTA).


Patients and study design

ARGENTA was designed as a randomized, open, controlled, single-centre study. Inclusion criteria were: to have been at least 2 months on a highly active antiretroviral regimen, definition used for the concomitant use of three or more antiretroviral agents, and to have: (i) either a plasma viral load greater than 2000 copies/ml in at least two consecutive determinations; or (ii) less than 1 log reduction of HIV RNA more than 2 months after the start of the last regimen. All patients found to be eligible were consecutively randomly assigned 1 : 1 to receive treatment decisions based on the evaluation of treatment history, clinical picture and standard immunological and virological parameters, according to the standard of care (the SOC arm), or treatment decisions based on the standard of care with the additional information of a genotypic resistance assay (the genotype arm, G). Treatment decisions in both arms were discussed by the same panel composed each time of the treating physician, and at least two experts in the interpretation of genotypic resistance results. Patients in the SOC arm, who 3 months after changing antiretroviral therapy did not have at least a 1 log10 reduction in plasma HIV-RNA levels and whose HIV-RNA did not fall below 2000 copies/ml were allowed, if required by the treating physician, to receive a genotypic resistance assay. The primary endpoints of the study were the proportion of patients with less than 500 HIV-RNA copies/ml of plasma at months 3 and 6. Changes in plasma HIV-RNA levels and CD4 lymphocyte counts from baseline at months 3 and 6 were also analysed.

The study was approved by the local Ethics Committee. All patients gave written, informed consent.

Virological and immunological assays

The resistance genotypes were assessed in all patients using the TruGene assay (version 2.0, Visible Genetics Inc., Toronto, Ontario, Canada), according to the instructions given by the manufacturer. Briefly, a 1.3 kb sequence from the pol region encompassing the entire protease gene and the major part of the reverse transcriptase gene of HIV-1 was reverse transcribed and polymerase chain reaction amplified in a single tube. Bidirectional sequencing ladders were generated from complementary DNA amplicons by CLIP sequencing technology and were detected in a MicroCell (Visible Genetics, Inc.), using the MicroGene Clipper (Visible Genetics, Inc.) automated sequencer. Chromatograms for the sequencing reactions were analysed using GeneObjects software (Visible Genetics), assembled and compared with the wild-type HIV-1HXB2 sequence using GeneLibrarian (Visible Genetics). The accuracy of all sequences was verified by manual proofreading. Sequences were then compared with a database of known mutations associated with drug resistance, to detect which were present in each clinical sample. Mixtures of wild-type and mutant bases were considered to be mutant for resistance interpretation.

Plasma HIV-RNA concentrations were measured using a branched DNA assay with a detection limit of 50 copies/ml (Quantiplex 3.0, Chiron, Emeryville, CA, USA). Peripheral blood CD4 lymphocyte counts were performed using standard flow cytometry.

Definition of active drug during the study and of sensitivity score

In taking treatment decisions for the G arm, the panel took into consideration the above-mentioned criteria for the standard of care plus the results of the genotypic assay. An antiretroviral drug was defined as ‘active’ if there was no evidence of genotypic resistance to that agent. The presence of genotypic resistance was defined by the detection of at least one primary mutation associated with resistance to a particular agent. Initially, investigators received a report of mutations interpreted according to a resistance table released by the International AIDS Society – USA panel in June 1998 [9]. Starting from October 1999, after 120 patients were randomly assigned, the interpretation table was updated with data available from the literature by the manufacturer of the assay, and the investigators received the list of mutations with the interpretation given in Table 1 (Visible Genetics interpretation system, hiv.gnl, September 1999). Additional expert advice was used throughout the study: this was not based on a specific algorithm, but the reports from the manufacturer of the assay were re-interpreted taking into account the contribution of primary and secondary mutations, continuously evolving knowledge in this field according to emerging new data in the scientific literature and patients’ history.

Table 1
Table 1:
List of amino acid substitutions used for the interpretation of HIV-1 drug resistance.

After completing the trial, the number of active drugs was re-calculated on the basis of the interpretation system given by the manufacturer of the assay. A sensitivity score of 1 was given to each drug in the treatment regimen that was not associated with any resistance mutations identified as primary in Table 1. The number of active drugs at 3 months and at 6 months were calculated on the last regimen patients were given and the last genotype results before these dates. In addition, as part of the post-study analysis, the genotypic sensitivity score from the Resistance Collaborative Group, was applied to patients’ genotypes and salvage regimens, as described by the authors [15].

Assessment of patients’ adherence to the antiretroviral regimen

Patients from both groups were asked to complete an already validated questionnaire on adherence to antiretroviral drugs as well as on the determinants of non-adherence [23]. The self-administered questionnaire was completed at baseline. Patients were informed that the treating physician would not have read the completed questionnaire, which was collected by the nursing staff in a closed envelope. The questionnaire contained three items specifically focused on adherence whose details have been previously published [23]. For this study, based on our previous findings, we chose to analyse the first item, in which patients were asked when they had missed their last medication dose, and the possible options were ‘yesterday', ‘last week', ‘before 2–3 weeks', ‘before 3–4 weeks'. A dichotomization of the level of adherence was performed: the subjects reporting the answers ‘yesterday’ and ‘last week’ were classified as non-adherent, whereas the patients indicating one of the other possible options were classified as adherent.

Statistical analysis

Unless specified, all analyses were performed by intention-to-treat criterion. HIV-RNA copy numbers were log-transformed before calculations. The time of enrolment of a given patient in the trial was defined as the time between the randomization of the first patient to the time when that patient was randomly assigned. The t-test was used to compare continuous and the Fisher's exact test or chi square test were employed to compare categorical variables. Correlation between continuous variables was analysed by linear regression. Bivariate and multivariable logistic regression was used to analyse the independent association of variables with virological endpoints using odds ratios with 95% confidence intervals. Only variables with P values of less than 0.10 at bivariate analysis were included in the multivariate models. Because of the limited number of outcomes compared with the high number of variables, two separate multivariate models were used to analyse predictors of 3 month virological response. All analyses were performed using the SPSS version 8.0 software package (SPSS Inc., Chicago, IL, USA).


Baseline patient characteristics

From April 1999 to February 2000, 174 patients were randomly assigned: 85 to arm G and 89 to SOC. The characteristics of patients at baseline are summarized in Table 2. Although the majority of parameters were balanced between the arms, there was a significantly greater proportion of injecting drug users in the SOC arm and a greater number of resistance-associated mutations in the genotype arm (see Table 3). When the main individual baseline resistance mutations were analysed, there was a significantly greater proportion of patients in the G arm showing amino acid changes at codon 215 (reverse transcriptase gene) and a greater proportion of patients with both mutations at codons 82 and 90 (protease gene) (see Table 3). There was a trend towards a greater number of total secondary mutations in the G arm at baseline (see Table 3, P = 0.06), with a trend towards a greater mean mumber of secondary mutations to nucleoside reverse transcriptase inhibitors (NRTI) in the G arm (P = 0.09) and no difference in secondary mutations to non-nucleoside reverse transcriptase inhibitors (NN RTI) and protease inhibitors (PI) between arms. Total resistance mutations (primary plus secondary) to individual drug classes did not differ between groups. There was a lower baseline number of total resistance mutations in the control arm than in the genotype arm, and a slightly greater mean number of active drugs in the control arm (1.9 versus 1.7;P = 0.08). Patients with a pre-randomization history of ever having HIV-RNA levels below 500 copies/ml (23% of the sample size) had a smaller mean baseline number of total resistance mutations (5.6 versus 7.9;P < 0.001).

Table 2
Table 2:
Baseline patients characteristics.
Table 3
Table 3:
Baseline prevalence of resistance mutations.

Patient-reported adherence

The adherence questionnaire was introduced into the study from June 1999 and was given to 150 patients at baseline. Of these, 15 refused to fill it in and another eight were unable to complete it without assistance. Therefore, 127 questionnaires (73% of the total patient sample) were reliably completed and were used for the analysis. Baseline characteristics between those who did and those who did not complete the questionnaire were homogeneous (not shown). According to the employed categorization of self-reported adherence, 43% of patients were classified as non-adherent. There was no baseline difference between the study arms, with 45.8% in the SOC arm and 42.7% in the G arm classified as non-adherent.

Treatments after randomization and sensitivity score

At the beginning of the study, the antiretroviral agents registered in Italy were five NRTI (zidovudine, didanosine, zalcitabine, stavudine and lamivudine), one NNRTI (nevirapine) and four PI (saquinavir hard gel, indinavir, ritonavir and nelfinavir). Efavirenz became available at the centre by an unlimited early access programme in March 1999; abacavir was available for a limited number of patients in the expanded access programme from July 1999 and was approved in January 2000.

Panel decisions were applied to the patient in 144 out of 174 cases (83%): 67 out of 85 (79%) in the genotype arm and 77 out of 89 (87%) in the control arm (P = 0.18). The reasons for not prescribing the drugs decided by the consensus panel were patient's concern about potential toxicity and physician-estimated patient non-adherence. The mean number of drugs employed in the study arms after randomization was 3.3 in the SOC arm and 3.4 in the G arm (P = 0.72). In the G arm, the mean number of new antiretroviral drugs employed was not significantly greater than in the control arm (1.1 compared with 0.7, P = 0.16). There was a trend towards a more frequent use of a new antiretroviral class in the genotype arm (29% compared with 18% in the control arm, P = 0.08). The number of active drugs during follow-up did not differ between randomization arms: at 3 months the mean number of active drugs was 2.3 and 2.1 in the G and SOC arm, respectively (P = 0.20), at 6 months the mean numbers were 2.3 and 2.2, respectively (P = 0.32). Also using the Resistance Collaborative Group interpretation algorithm, there was no significant difference in the genotypic sensitivity scores between study arms (mean genotype sensitivity score 1.8 and 1.7 in G and SOC, respectively). Nevertheless, the genotype-guided group showed a more prominent mean increase from the baseline number of active drugs at 3 months (+0.61 compared with +0.22 in the controls, P = 0.004) and at 6 months (+0.69 versus +0.3, respectively, P = 0.017). There was no correlation between the time of enrolment in the trial and the number of new drugs (P = 0.12) or the number of active drugs (P = 0.52) in the salvage regimen employed in both study arms. Only five patients in the control arm received a genotype between month 3 and month 6.

Virological outcomes

The frequency of primary study endpoints in both treatment arms are shown in Fig. 1 and Table 3. Using an intent-to-treat approach with missing values considered failures, at 3 months 12% in the control arm and 27% in the genotype arm had HIV-RNA levels below 500 copies/ml (P = 0.01). At 6 months, the relative proportions were 17 and 21%, respectively (P = 0.47).

Fig. 1.
Fig. 1.:
Virological responses according to study randomization expressed as a proportion of patients reaching less than 500 copies/ml of HIV RNA. Continuous lines indicate the genotype arm, dashed lines the control arm; ▪ results from on-treatment analysis, difference between arms P = 0.014 at 3 months and P = 0.356 at 6 months; ▿ intent-to-treat analysis using the missing value equals the failure principle, difference between arms P = 0.014 at 3 months and P = 0.467 at 6 months.

By intent-to-treat analysis, using the last observation carried forward, the mean change from baseline viral load at 3 months was −0.38 (SD ± 0.96) log10 copies/ml for those assigned to standard of care and −0.62 (SD ± 1.16) log10 copies/ml for those assigned to genotype-guided treatment (P = 0.12). Mean changes at 6 months were −0.39 (SD ± 1.04) log10 and −0.57 (SD ± +1.09) log10, respectively (P = 0.28).

In the group of patients with baseline viral loads of less than 4 log copies/ml, the advantage of the genotype information over the standard of care was particularly evident (see Table 4).

Table 4
Table 4:
Patients with plasma HIV-RNA levels below 500 copies/ml at 3 and 6 months according to randomization group and baseline characteristics.

Considering the overall patient population (G plus SOC), the proportion reaching HIV-RNA levels of less than 500 copies/ml at 3 months was 23% in those failing their first or second HAART and 10% in those failing three or more HAART regimens [odds ratio (OR) 2.79, 95% confidence interval (CI) 0.86–10.05;P = 0.06]; the proportions were 23 and 5% at 6 months, respectively (OR 6.14, 95% CI 1.34–38.95;P = 0.007). The effect of genotyping was also analysed in the subgroups of patients failing their first or second HAART regimen and in the subgroup failing three or more regimens (see Table 4).

The frequency of virological response at 3 months differed in relation to the patient-reported adherence category, with 21 out of 73 (29%) adherent patients and seven out of 54 (13%) non-adherent patients achieving HIV-RNA levels below 500 copies/ml. The responses according to adherence category in the different randomization arms are reported in Table 4. Adherent patients in the G arm had a significantly greater probability of 3 month virological success than non-adherent patients in the SOC arm (OR 5.80, 95% CI 1.07–41.41;P = 0.02), also in the SOC arm adherent patients had a greater probability of 3 month success than non-adherent patients (OR 4.17, 95% CI 0.70–31.84;P = 0.07).

Immunological outcomes

Using an intent-to-treat last observation carried forward analysis, in the G arm, the mean changes from baseline CD4 cell counts were +9 (95% CI −18–+27) cells/μl and +15 (95% CI −10–+39) cells/μl at months 3 and 6, respectively. In the SOC arm, changes were +19 (95% CI −2–+39) cells/μl and +22 (95% CI −4–+49) cells/μl, respectively. There was no significant difference between the study arms.

When the patient-reported adherence category was used to analyse CD4 cell responses, the mean CD4 cell count changes at 3 months were +50 cells/μl in adherent patients and −12 cells/μl in non-adherent patients (P < 0.01), at 6 months the changes were +62 cells/μl in adherent patients and −13 cells/μl in non-adherent patients (P < 0.01) (see Fig. 2).

Fig. 2.
Fig. 2.:
CD4 cell responses according to (a) randomization arm, continuous line indicates the genotype arm, dashed line indicates the control arm; (b) self-reported adherence category, continuous line indicates adherent patients, dashed line indicates non-adherent patient.

Predictors of virological success

Using bivariate logistic regression, we found the following factors showing association with virological response (HIV-RNA levels below 500 copies/ml) at 3 months: belonging to the transmission category of injecting drug users (OR 0.39, 95% CI 0.15–1.00;P = 0.05); a previous history of an HIV-RNA level of less than 500 copies/ml (OR 3.07, 1.36–6.90;P = 0.006); a greater number of experienced HAART regimens (for each more, OR 0.61, 95% CI 0.40–0.94;P = 0.023); a greater baseline viral load (for each log unit increase OR 0.45, 95% CI 0.23–0.90, P = 0.022); patient-reported non-adherence (OR 0.37, 95% CI 0.14–0.95, P = 0.038); the presence of all active drugs in the combination, according to the definition of primary resistance given in Table 1 (OR 4.36, 95% CI 0.96–19.86, P = 0.055); the presence of protease substitution L90M (OR 0.25, 95% CI 0.09–0.68, P = 0.007); and the total (primary plus secondary) number of PI mutations (for each PI mutation more, OR 0.86, 95% CI 0.74–1.01, P = 0.061). The total number of resistance mutations was not associated with virological response (P = 0.196). The time of enrolment in the trial showed an association with virological response (for each month more from trial initiation, OR 1.12, 95% CI 1.00–1.27, P = 0.057): this was observed only in the G arm (OR 1.19, 95% CI 1.01–1.40, P = 0.03), and not in the SOC arm (OR 1.06, 95% CI 0.87–1.30, P = 0.54). A greater number of active drugs was not significantly predictive of the virological success at 3 months (for each drug more, OR 1.37, 95% CI 0.90–2.10, P = 0.140), even when using the interpretation algorithm from the Resistance Collaborative Group [15] (for each unit sensitivity score increase, OR 1.34, 95% CI 0.89–2.03, P = 0.162).

As a result of the limited number of outcomes, we used two split multivariate models to detect the independent predictors of 3 month virological success (see Table 5). In a first model, we adjusted the randomization arm with variables that were unbalanced at baseline and showed significant odds of virological success at bivariate analysis, together with adherence-related factors. Results showed that being assigned to G and having a previous history of undetectable viral load independently predicted virological success, whereas patient-reported non-adherence independently predicted virological failure. In a second model, the randomization arm was adjusted with the baseline viral load, the number of previous HAART regimens, and the presence of all active drugs in the combination. The number of previous regimens, the baseline viral load and being assigned to G was independently predictive of the 3 month virological outcome (see Table 5). Finally, when analysing all covariates in a single model, being assigned to G retained a significant association with 3 month virological success (OR 2.67, 95% CI 1.09–6.54;P = 0.03).

Table 5
Table 5:
Predictors of virological success (HIV RNA < 500 copies/ml) at 3 and 6 months.

The variables associated with virological success at 6 months at the bivariate analysis were: a previous history of an HIV-RNA level of less than 500 copies/ml (OR 3.87, 1.71–8.74;P = 0.001); a greater number of experienced HAART regimens (for each regimen more, OR 0.51, 95% CI 0.31–0.83;P = 0.006); a greater baseline viral load (for each log unit more, OR 0.40, 95% CI 0.16–0.71, P = 0.004); the presence of protease substitution L90M (OR 0.15, 95% CI 0.04–0.52, P = 0.003); the total (primary plus secondary) number of PI mutations (for each PI mutation more, OR 0.76, 95% CI 0.64–0.92, P = 0.003); and the total number of resistance mutations (for each mutation more, OR 0.87, 95% CI 0.77–0.97, P = 0.016). The number of active drugs, the randomization arm and patient-reported adherence were not associated with the 6 month virological outcome.

In the multivariate analysis, a previous history of undetectable viral load, the baseline viral load and the number of previously experienced HAART regimens remained the only variables independently predictive of the 6 month virological outcome (see Table 5).


This randomized study confirms the usefulness of genotype-guided treatment decisions in patients failing combination antiretroviral therapy. At 3 months, there was a significantly greater proportion of patients with HIV-RNA levels below 500 copies/ml in the G arm compared with the SOC arm. Multivariate analysis confirmed genotype information as being independently predictive of virological success. Nevertheless, the advantage of genotype information was lost after 6 months. These findings partly confirm results from previous randomized studies, indicating that resistance testing helps choose more effective salvage regimens in patients failing previous antiretroviral regimens. The VIRADAPT study showed that the treatment adaptation with the results from a genotypic assay allowed a better control of HIV replication up to 6 months over the standard of care [19]. The CPCRA 046 study showed a significantly more profound viral inhibition in the G arm at 12 weeks, but the advantage tended to be reduced over time [20].

Some peculiarities that help interpret the partly discrepant results also distinguish the present study from other randomized trials. First, patients from published studies were less pretreated than patients from the present study. The proportion of patients failing two or more HAART regimens was greater (57%), with 25% failing three to seven regimens. Moreover, both the VIRADAPT study and the CPCRA 046 trial included fewer than 3% or no patients with previous NNRTI experience, whereas 41% of patients enrolled in the present trial had previous exposure to NNRTI, and 38% showed major baseline mutations conferring resistance to this class of drugs. Given the strong cross-resistance among NNRTI, this fact precludes the use of a whole new class of antiretroviral agents in choosing salvage therapy for these patients, which would be a determinant factor for a successful treatment modification [24]. Furthermore, there was a larger proportion of L90M at baseline than in the VIRADAPT study; the presence of this primary mutation in the protease gene confers resistance or partial cross-resistance to all four PI available during the study period [25,26]. Finally, in contrast with CPCRA 046, in which the G arm received expert advice whereas the group assigned to SOC did not, in the present study treatment decisions were taken by the same panel in both randomization groups, therefore the difference between groups relied solely on the adjunctive information of the resistance assay.

This trial was conducted at a single site, and the fact that treatment decisions were taken by the same panel could have led to a contamination of knowledge from the G arm to the SOC arm, so that the benefit conferred by the genotype information could have been reduced as the trial continued and new patients were enrolled. Nevertheless, we found no correlation between the time of randomization and the use of new drugs or the number of active drugs in the salvage regimen in either arm. Furthermore, contrary to the formulated hypothesis, we found that a later time of enrolment was associated with a greater probability of virological success in the G but not in the SOC arm. This finding argues against the possibility of cross-arm contamination, and suggests that genotype-guided decisions could have been improved by increasing expert knowledge about genotypic resistance interpretation over time.

The fact that the advantage of using genotype information did not persist over time was not caused by the fact that the SOC arm could also receive genotype information in the case of a lack of response after the first 3 months, in fact such access was subject to specific request by the treating physician and only five patients in the control group did actually receive genotyping after this timepoint; the results did not change after excluding these patients from the analysis (not shown). Possible further reasons for the lack of a persistent virological advantage of the G arm could be the greater prevalence of baseline resistance mutations than in the control arm, in particular at the key codon 215 from the reverse transcriptase gene, involved in resistance to zidovudine and to some extent stavudine, and at codons 82 and 90 from the protease gene, involved together in viral resistance to all four PI available at the time of the study [9,24,25]. These characteristics further indicated that these patients had few residual treatment options, limiting the clinical usefulness of resistance genotyping. This is confirmed by the finding that a greater number of previously failed HAART regimens was also independently predictive of virological failure. In patients who have failed most if not all available agents, resistance assays will hardly indicate more effective alternative treatment opportunities [27]. Preliminary results from a large, randomized trial in a setting of heavily pretreated individuals (NARVAL, ANRS 088) [21] also indicated that the virological advantage of genotype-driven therapy over SOC is marginal, in agreement with the findings from this study.

An important finding was that patient-reported adherence was an important factor influencing the virological and immunological responses to salvage therapy in both the G and in the control arm. Patients showing the best control of viral replication were those receiving genotyping and reporting adherence at baseline. On the other hand, those patients in the control group reporting non-adherence had the worst virological responses and those with the mixed characteristics (adherents from the control arm and non-adherents from the G arm) achieved intermediate virological inhibition. Moreover, on multivariable analysis, non-adherence was independently associated with a reduced probability of viral inhibition. The importance of adherence in predicting the virological response was already established in naive patients [5,28,29]. This study underscores the importance of adherence in the context of genotype-driven salvage therapy. More effective salvage strategies must first take into account adherence optimization when necessary, and subsequently resistance-based adaptation.

We also found that a history of virological inhibition before study entry predicted subsequent virological success: this might be interpreted both as a history of previous adherence to HAART, which might be more easily maintained over time and therefore predict better adherence to the new salvage regimen, as well as a lower probability of covert or archived resistance mutations, as shown partly by the lower mean number of total resistance mutations in this group. Patients randomly assigned with lower viral loads had a greater chance of reaching virological suppression, particularly when assigned to the genotype group. This suggests that, when a genotype combined with treatment history indicates that a new option is available with a relevant number of presumably active drugs, an early switch at low HIV-RNA levels should be encouraged.

In agreement with previous studies, despite the virological advantage of genotyping, CD4 cell responses did not differ between randomization groups. Although it can be hypothesized that patients had already reached substantial CD4 cell repopulation before study entry and that CD4 cell responses might take longer, this finding cannot be completely explained at this time. It can be speculated that the reduced viral fitness of resistant strains or reduced pathogenicity might compensate for the difference in viral inhibition observed [29,30]. Interestingly, there was a strong, significant difference in CD4 cell responses according to the category of patient-reported adherence. Patients reporting adherence at baseline showed a significant increase in CD4 cell counts both at 3 and 6 months, whereas patients reporting non-adherence showed a slight reduction in the mean CD4 cell count level over time. Contrary to virological responses, significantly better immunological responses in adherent patients were maintained over time. This phenomenon might have different explanations. In patients harbouring drug-resistant HIV, a sufficient selective pressure by antiretroviral agents might help reduce CD4 cell killing more effectively than reducing HIV replication, either by selecting less pathogenic viruses or by some direct inhibitory action of PI on apoptosis [31] (W. Malorni, et al., in preparation).


Virological responses in patients on salvage HIV therapy were significantly improved by the guide of a genotypic resistance test in a population of heavily experienced subjects, but this effect was lost over time. Responses were negatively influenced by patient-reported non-adherence, by the greater number of failed HAART regimens and by a greater viral load, whereas a previous history of viral inhibition on HAART was predictive of better virological responses. Although CD4 cell responses were not influenced by the genotype guide, these were extremely sensitive to patient adherence.

In order to optimize further the response to salvage therapy, genotypic resistance assays should be accompanied by a careful assessment and appropriate implementation of patient adherence. More information on the correct interpretation of genotypic mutations should also help improve the usefulness of genotypic resistance testing [32].


1. Palella FJ, Delaney KM, Moorman AC. et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. N Engl J Med 1998, 338: 853–860.
2. Finzi D, Blankson J, Siliciano JD. et al. Latent infection of CD4+ T cells provides a mechanism for lifelong persistence of HIV-1, even in patients on effective combination therapy. Nat Med 1999, 5: 512–517.
3. Deeks S, Hecht FM, Swanson M. et al. HIV RNA and CD4 cell count response to protease inhibitor therapy in an urban AIDS clinic: response to both initial and salvage therapy. AIDS 1999, 13: F35–F44.
4. Fatkenheuer G, Theisen A, Rockstroh J. et al. Virological treatment failure of protease inhibitor therapy in an unselected cohort of HIV-infected patients. AIDS 1997, 11: F113–F116.
5. Lucas GM, Chaisson RE, Moore RD. Highly active antiretroviral therapy in a large urban clinic: risk factors for virologic failure and adverse drug reactions. Ann Intern Med 1999, 131: 81–87.
6. Bangsberg DR, Hecht FM, Charlebois ED. et al. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. AIDS 2000, 14: 357–366.
7. Durant J, Clevenbergh P, Garraffo R. et al. Importance of protease inhibitor plasma levels in HIV-infected patients treated with genotypic-guided therapy: pharmacological data from the Viradapt Study. AIDS 2000, 14: 1333–1339.
8. Hirsch MS, Conway B, D'Aquila RT. et al. Antiretroviral drug resistance testing in adult HIV-1 infection. JAMA 1998, 279: 1984–1991.
9. Hirsch MS, Brun-Vezinet F, D'Aquila RT. et al. Antiretroviral drug resistance testing in adult HIV-1 infection: recommendations of an International AIDS Society – USA Panel. JAMA 2000, 283: 2417–2426.
10. Shafer RW, Jung DR, Betts BJ, Xi Y, Gonzales MJ. Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucl Acids Res 2000, 28: 346–348.
11. Pillay D, Taylor S, Richman DD. Incidence and impact of resistance against approved antiretroviral drugs. Rev Med Virol 2000, 10: 231–253.
12. Miller V, Ait-Khaled M, Stone C. et al. HIV-1 reverse transcriptase (RT) genotype and susceptibility to RT inhibitors during abacavir monotherapy and combination therapy. AIDS 2000, 14: 163–171.
13. Harrigan PR, Montaner JS, Hogg RS. et al. Baseline HIV drug resistance profile predicts response to ritonavir/saquinavir protease inhibitors therapy in a community setting. AIDS 1999, 19: 1863–1871.
14. Zolopa AR, Shafer RW, Warford A. et al. HIV-1 genotypic resistance patterns predict response to saquinavir-ritonavir therapy in patients in whom previous protease inhibitor therapy had failed. Ann Intern Med 1999, 131: 813–821.
15. DeGruttola V, Dix L, D'Aquila R. et al. The relation between baseline HIV drug resistance and response to antiretroviral therapy: re-analysis of retrospective and prospective studies using a standardized data analysis plan. Antivir Ther 2000, 5: 41–48.
16. Lorenzi P, Opravil M, Hirschel B. et al. Impact of drug resistance mutations on virologic response to salvage therapy.Swiss HIV Cohort Study. AIDS 1999, 13: F17–F21.
17. Kuritzkes DR, Sevin A, Young B. et al. Effect of zidovudine resistance mutations on virologic response to treatment with zidovudine–lamivudine–ritonavir: genotypic analysis of human immunodeficiency virus type 1 isolates from AIDS clinical trials group protocol 315.ACTG Protocol 315 Team. J Infect Dis 2000, 181: 491–497.
18. Tamalet C, Pasquier C, Yahi N. et al. Prevalence of drug resistant mutants and virological response to combination therapy in patients with primary HIV-1 infection. J Med Virol 2000, 61: 181–186.
19. Durant J, Clevenbergh P, Halfon P. et al. Drug-resistance genotyping in HIV-1 therapy: the VIRADAPT randomised controlled study. Lancet 1999, 353: 2195–2199.
20. Baxter JD, Mayers BL, Wentworth DN. et al. A randomized study of antiretroviral management based on plasma genotypic resistance testing in patients failing therapy. AIDS 2000, 14: F83–F93.
21. Meynard JL, Vray M, Morand-Joubert L. et al. Impact of treatment guided by phenotypic or genotypic resistance tests on the response to antiretroviral therapy: a randomized trial (NARVAL, ANRS 088). Antivir Ther 2000, 5 (Suppl. 3) : 67.67.
22. Tural C, Ruiz L, Holtzer C, et al.Utility of HIV genotyping and clinical expert advice – the Havana Trial. In:Program and abstracts of the 8th Conference on Retroviruses and Opportunistic Infections. Chicago, 2001 [Abstract 434].
23. Murri R, Ammassari A, Gallicano K. et al. Patient-reported nonadherence to HAART is related to protease inhibitors levels. J Acquir Immunodefic Syndr 2000, 24: 123–128.
24. Weidle PJ, Lichtenstein KA, Moorman AC. et al. Factors associated with the successful modification of antiretroviral therapy. AIDS 2000, 14: 491–497.
25. Lawrence J, Schapiro J, Winters M. et al. Clinical resistance patterns and responses to two sequential protease inhibitor regimens in saquinavir and reverse transcriptase inhibitor-experienced persons. J Infect Dis 1999, 179: 1356–1364.
26. Churchill DR, Pym AS, Galpin S. et al. The Rabbit study: ritonavir and saquinavir in combination in saquinavir-experienced and previously untreated patients. AIDS Res Hum Retroviruses 1999, 15: 1181–1189.
27. Harrigan PR, Coté H. Clinical utility of testing human immunodeficiency virus for drug resistance. Clin Infect Dis 2000, 30 (Suppl 2) : S117–S122.
28. Montaner JS, Reiss P, Cooper D. et al. A randomized, double-blind trial comparing combinations of nevirapine, didanosine, and zidovudine for HIV-infected patients: the INCAS Trial.Italy, The Netherlands, Canada and Australia Study. JAMA 1998, 279: 930–937.
29. Haubrich RH, Little SJ, Currier JS. et al. The value of patient-reported adherence to antiretroviral therapy in predicting virologic and immunologic response.California Collaborative Treatment Group. AIDS 1999, 13: 1099–1107.
30. Erickson JW, Gulnik SV, Markowitz M. Protease inhibitors.Resistance, cross-resistance, fitness and the choice of initial and salvage therapies. AIDS 1999, 13 (Suppl A) : S189–S204.
31. Stoddart C, Liegler TJ, Mammano F. et al. Impaired replication of protease inhibitor-resistant HIV-1 in human thymus. Nat Med 2001, 7: 712–718.
32. De Luca A, Cingolani A, Rizzo MG. et al. Prediction of treatment outcomes by different interpretation systems (IS) for baseline genotypic resistance in a cohort of patients on salvage HIV therapy. Antivir Ther 2001, 6 (Suppl 1) : S33.S33.

Drug resistance; genotypic resistance; adherence; antiretroviral-therapy; salvage therapy

© 2002 Lippincott Williams & Wilkins, Inc.