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Clinical utility of HIV-1 genotyping and expert advice: the Havana trial

Tural, Cristinaa; Ruiz, Lidiaa; Holtzer, Christopherb; Schapiro, Jonathanc; Viciana, Pompeyod; González, Juane; Domingo, Peref; Boucher, Charlesg,h; Rey-Joly, C.a; Clotet, Bonaventura*; and the Havana Study Group

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Author Information

From the aHIV Clinical Unit and IrsiCaixa Retrovirology Laboratory, Hospital Universitari Germans Trias i Pujol, Universitat Autónoma de Barcelona (UAB), Badalona, Spain, bVisible Genetics, Paris, France, the cUniversity of California, San Francisco Stanford University, Stanford, California, USA, the dHospital Virgen del Rocío, Sevilla, the eHospital La Paz, Madrid, the fHospital Sant Pau, Barcelona, Spain, gVirology Networks. Utrecht and the hUniversity of Utrecht. The Netherlands. *See Appendix.

Correspondence to B. Clotet, MD, PhD, HIV Clinical Unit and IrsiCaixa Retrovirology Laboratory, Hospital Universitari ‘Germans Trias i Pujol', 08916 Badalona. Barcelona. Spain. Tel: +34 93 465 6374; fax: +34 93 4657602; e-mail: bclotet@ns.hugtip.scs.es

Received: 8 June 2001;

revised: 13 August 2001; accepted: 30 August 2001.

Sponsorship: The study was supported, in part, through a grant from Visible Genetics, Europe S.A.

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Abstract

Objective: To determine whether HIV-1 genotyping and expert advice add additional short-term virologic benefit in guiding antiretroviral changes in HIV+ drug-experienced patients.

Design: A two factorial (genotyping and expert advice), randomized, open label, multi-center trial. The patients were stratified according to the number of treatment failures.

Patients and methods: HIV-1 infected patients on stable antiretroviral therapy who presented virological failure were included into the study. Genotypic testing was performed by using TrueGene HIV Genotyping kit and the results were interpreted by a software package (RetroGram®, version 1.0). An expert advisory committee suggested the new therapeutic approach based on clinical information alone or on clinical information plus HIV-1 genotyping results. Plasma HIV-1 RNA load, CD4+ cell count and adverse events were recorded at baseline and every 12 weeks.

Results: A total of 326 patients were included. The baseline CD4+ cell count and plasma HIV-1 RNA were 387 (± 224) × 106 cells/l and 4 (± 1) log10 respectively. The proportion of patients with plasma HIV-1 RNA < 400 copies/ml at 24 weeks differed between genotyping and no genotyping arms (48.5 and 36.2%, P < 0.05). Factors associated with a higher probability of plasma HIV-1 RNA < 400 copies/ml were HIV-1 genotyping [odds ratio (OR), 1.7; 95% confidence interval (CI), 1.1–2.8;P = 0.016] and the expert advice in patients failing to a second-line antiretroviral therapy (OR, 3.2; 95% CI, 1.2–8.3;P = 0.016).

Conclusions: HIV-1 genotyping interpreted by a software package improves the virological outcome when it is added to the clinical information as a basis for decisions on changing antiretroviral therapy. The expert advice also showed virologic benefit in the second failure group.

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Introduction

Virologic failure of antiretroviral (ARV) medications during therapy for HIV infection has many potential causes. A result and a possible cause of this failure is the development of mutations associated with reduced viral susceptibility to ARV medications. The relationship between resistance-related mutations and virologic outcome have been tentatively explored by the use of HIV-1 genotypic and phenotypic assays. Resistance to all available ARV medications and cross resistance between some agents have been demonstrated and may limit options [1–20].

There is retrospective evidence for a link between genetic mutations and virologic failure in vivo [4–11]. In addition, there have been a number of studies that have prospectively demonstrated the virologic benefits of HIV-1 genotyping in clinical settings [12–15]. These studies provided preliminary proof of concept, and in addition they have extended the acceptance of HIV genotyping in clinical settings and have helped stimulate the publication of guidelines recommending the use or consideration of resistance testing [16–20]. However, many questions regarding the clinical utility of HIV resistance testing remain to be answered.

There are some data demonstrating that expert advice may contribute to improve virologic outcomes with the clinical use of HIV-1 genotyping [12], however there is a lack of information on the role of expert advice in standard of care practice. Previous studies have shown that, in the setting of treating HIV, the experience of the clinician can have a direct impact on the clinical and cost outcomes of therapy [21–23]. Given the complexity of the clinical use of genotypic resistance testing [24–27], this is a likely case where expert interpretation may be of greater impact. Previous studies did not attempt to differentiate the benefits of resistance testing and expert advice [12,13]. This study investigates the influence that HIV-1 genotyping with software interpretation, and expert advice, each have on virologic outcomes in ARV-experienced patients.

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Patients and methods

Patients

HIV-1-infected patients were screened and enrolled from 13 hospitals in 10 cities in Spain. The study protocol was approved by the institutional review boards or ethic committees from all participating study sites. Written, informed consent was obtained from all study participants prior to randomization.

The inclusion criteria were: to have plasma HIV-1 RNA ≥ 1000 copies/ml and to be on stable ARV therapy combination for more than 6 months. Patients were excluded if they had substantial ARV-related adverse events history, poor adherence or active drug abuse was reported by the treating physician.

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Trial design

This randomized, open label, multi-centre trial evaluated the utility of the genotyping testing and expert advice in guiding changes of ARV therapy in individuals with virologic failure.

The primary endpoint was the proportion of patients with plasma HIV-1 RNA load (pVL) < 400 copies/ml at 24 weeks. The secondary endpoint was the change in pVL at 12 and 24 weeks from baseline.

Consecutive eligible patients were enrolled. This was a factorial study with two randomizations: genotyping (G+) versus no genotyping (G−) and expert advice (EA+) versus no expert advice (EA−), stratified by whether the patient had one (first failure group), two (second failure group) or three or more (third failure group) treatment failures.

This double randomization led to the definition of four treatment groups:

1.  genotyping without expert advice (G+/EA−)

2.  genotyping and expert advice (G+/EA+)

3.  no genotyping and no expert advice (G−/EA−)

4.  no genotyping with expert advice (G−/EA+).

Patients were randomly assigned in a 1 : 1 ratio to the two levels of each factor. Randomization was carried out by the co-ordinating centre (Fig. 1).

Fig. 1
Fig. 1
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Genotyping groups (groups 1 and 2)

The genotyping test results were interpreted by a software (RetroGram®, version 1.0; Virology Networks, Utrecht, The Netherlands) which reported the description of all the reverse transcriptase and protease substitutions found and a ranking of the available drugs within each group depending on the pattern of mutation. This report was sent to the physician in less than 4 weeks after the randomization date. In addition to the genotyping results and interpretations provided, changes in therapy were managed according to the best clinical judgement and based on most recent published guidelines [28].

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Expert advice groups (groups 2 and 4)

The expert advisory committee was made up of four clinicians and two virologists all with more than 10 years experience in the area of HIV clinical care or HIV-specific virology in a HIV Unit taking care of more than 2000 patients. This committee had information about the past pharmacological history, CD4 cell count and pVL evolution with previous treatment approaches, drug adverse events and adherence capability of the patient. All recommendations for therapy took into account the information provided (including the software interpretations of the genotyping test results when the patient was assigned to a G+ treatment group), panel member experience and published guidelines. The expert advisory committee decision was sent to the physician in less than 4 weeks after the randomization date.

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Control Group (group 3)

Patients assigned to group 3 were managed according to the best clinical judgement and based on most recent published guidelines [28].

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Study monitoring and enrolment

At baseline, pVL and CD4+ cell counts were taken along with demographic, previous pVL and CD4+ cell count, drug adverse event history, a subjective adherence assessment by the treating provider, concomitant medications and previous ARV history. Every 12 weeks the patients were asked to return for a pVL, CD4+ cell count, routine hematological and biochemical parameters and an assessment of adverse events. Physicians were allowed to change therapy if no virologic success was achieved (decrease from baseline < 0.5 log10) at 12 weeks of follow-up.

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Software interpretation of genotypic results

The RetroGram® software accepts a list of substitutions in the protease and reverse transcriptase genes with respect to the NL4-3 reference strain. The application contains approximately 200 rules relating substitutions on the HIV-genome to reported effects on drug response. The rules are based on scientific literature on HIV-resistance and references associated with individual rules are available for display to the user. Based on the rules, the drugs are reported to the physician at one of the five suitability levels: (A) can be used; (B) consider if no class A drug is available; (C) consider use if no class A or B drugs are available; (D) consider if no class A, B or C drugs are available; and (U) unranked, insufficient data available. The drugs are then ranked within each group depending on the pattern of substitutions, with the drugs least likely to be associated with clinical resistance appearing at the top of each of the three lists.

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Laboratory measurements

Plasma HIV-1 RNA load and genotyping resistance testing were obtained from the same baseline plasma sample.

Viral RNA was extracted using the QIAamp viral RNA mini Kit (Quiagen, Barcelona, Spain) following the manufacturer's instructions. Plasma HIV RNA load was measured by the standard method used in each participating centre [either Amplicor® (Roche Diagnostic Systems, Barcelona, Spain) or NASBA® (Organon-Tecknika, Barcelona, Spain)].

Genotypic resistance testing was performed in the coordinating centre. All genotyping samples were sequenced on the TRUGENE ‘HIV-1 Genotyping Kit and the OpenGene’ (Visible Genetics, Madrid, Spain) automated DNA sequencing system. Briefly, the entire protease gene (codons 1–99) and codons 37–248 of the reverse transcriptase gene of HIV-1 were sequenced. Viral RNA was isolated from a plasma sample and converted to 1.3 kb cDNA. The cDNA was then amplified by polymerase chain reaction (PCR) in a single tube reverse transcriptase-PCR reaction. These reaction products were then added, without purification, to a set of sequencing reaction tubes. Sequencing were carried out using CLIP®, a DNA sequencing technique for direct sequencing of small quantities of amplified templates. Reaction tubes were prepared containing two oligonucleotide primers, each labelled with a different fluorescent dye. When hybridized to the cDNA, the primers were oriented to allow chain extension towards each other (one primer on the sense strand and one on the anti-sense strand). The reaction tubes also contained all reagents necessary for chain-extension, along with one chain-terminating ddNTP per tube. The reaction was initiated with the addition of the sample and a thermostable DNA polymerase. The vast majority of extension products ended with a chain-terminating nucleotide, although a minority of the reaction products were extended far enough to serve as a template for hybridization of the opposite primer. The reaction proceeded through 30 cycles. Upon completion of the cycling program a STOP solution (formamide + dye) was added to each reaction tube. The reaction products were then loaded onto the MicroCel® (Visible Genetics) cassette and electrophoresed. The products were then detected by a laser based detection system and analyzed using supplied software (GeneObjects®; Visible Genetics). The sequence was compared to a known, reference HIV-1 sequence (LAV-1) for determination of mutational patterns.

The classification of primary mutations for the statistical analysis was based on that made by Hirsch et al. [27].

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Antiretroviral treatments

During the trial there were six nucleoside reverse transcriptase inhibitors (NRTI), (didanosine, lamivudine, stavudine, abacavir, zalcitabine and zidovudine), two non-nucleoside reverse transcriptase inhibitors (NNRTI) (nevirapine and efavirenz), four protease inhibitors (PI) (indinavir, ritonavir, nelfinavir and saquinavir) and one miscellaneous drug (hydroxyurea) available for routine use in Spain. At the time of study enrolment ritonavir was only available in a liquid formulation.

Selection of the ARV medications used was ultimately decided by the practising physician with the additional information provided by the study (expert recommendations and HIV-1 genotyping results).

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Statistical analysis

The number of patients enrolled provide 80% power to detect a treatment difference of 50% in percentage of patients with pVL < 400 copies/ml for the two factorial comparisons, using a 0.05 level test.

Primary analysis included all randomized patients (intention to treat analysis, ITT) and the last observation carried forward (LOCF) method was used to impute the information at week 24 for missing values. Per protocol (PP) analysis was performed as a secondary analysis and included all randomized patients with at least one measurement with the initial ARV approach.

The proportion of patients with pVL < 400 copies/ml, genotypic data, drug-related adverse events and ARV drugs used were tested using χ2 analysis. All demographic data were tested using one way analysis of variance (ANOVA).

A repeated-measures ANOVA analysis was performed to compare the independent groups of patients when each of whom were subjected to repeated measurements of the same response variable (pVL and CD4 cell count) at three periodic visits (baseline, 3 and 6 months). Scheffe's test for multiple comparison was used for comparing each pair of group means. When statistically significant differences were detected among groups, a two-way ANOVA analysis was performed to test the visit effects at each group of treatment. Plasma HIV-1 RNA load values at 12 and 24 weeks that were below the limit of detection for each assay were transformed to 399 copies/ml before the analysis. Normality and variance homogeneity were assumed in the previous analysis. The significance level was fixed to 0.05.

A logistic regression was performed to identify factors associated with pVL < 400 copies/ml at 24 weeks follow-up. Associations of categorical independent variables with pVL < 400 copies/ml were assessed with χ2 tests when variables or an interaction reached statistical significance. Failure group, HIV-1 genotyping, expert advice and all-possible two-way interactions between these variables were eligible for entry into a model. Using the method of maximum likelihood, estimated coefficients and their standard errors were calculated. After initial assessment of the performance of the model, variables and interactions were eliminated from the model, one at time, based on likelihood ratio tests eliminating non-significant (P > 0.10) variables and interactions.

The statistical software used was the SAS system release 6.12 for Windows (SAS Institute Inc., Cary, North Carolina, USA).

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Results

Enrolment took place between March 1999 and February 2000. A total of 326 patients were randomized into the study (Fig. 1). A lower than expected number of patients were included in first and second failure groups due to the fact that genotyping became an useful tool for managing HIV-infected patients during the recruitment period, and the relative low rate of virologic failure in first and second groups.

Baseline characteristics were similar across all randomization groups (Table 1). Patients in the third failure group had a significantly higher pVL at baseline than those in the first or second failure groups [3.9 (± 0.8) log10, 3.9 (± 0.7) log10 and 4.2 (± 0.8) log10, respectively;P < 0.05]. In addition, the third failure group patients were on ARV treatment and on PI-containing regimens for a significantly longer time [2.4 (± 1.7), 3.5 (± 1.7) and 5.4 (± 2.0) years on ARV therapy;P < 0.0001; and 1.1 (± 1.1), 1.8 (± 1.1) and 2.4 (± 0.8) years on PI-containing regimens, P < 0.0001 for the first, second and third failure groups].

Table 1
Table 1
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Patients in the G+ arms, had a mean of 1.8 (± 1.4) primary mutations in the retrotranscriptase (RT) gene and 0.9 (± 1.0) primary mutations in the protease (P) gene per patient. There was a significant difference in the mean number of primary mutations between failure groups (mean RT primary mutations: 0.9 (± 0.9), 1.5 (± 1.2) and 2.2 (± 1.5) in the first, second and third failure groups, P < 0.0001; mean P primary mutations: 0.4 (± 0.5), 0.5 (± 0.6) and 1.3 (± 1.1) in the first, second and third failure groups, P < 0.0001). Overall, patients had a high prevalence of 190A (6.0%), 44D (7.5%), Y181C (11.9%), 118I (14.2); K103N (15.7%), K70R (17.2%), T215Y (34.3%) and M184V (55.2%) in the RT gene and D30N (5.2%), M84V (10.4%), 46I (17.9%), V82A (20.9%), and L90M (29.9%) in the P gene.

Expert advice treatment recommendations were followed by 81% of the patients randomized to EA+ arms. Prescribed therapies included a mean of 4 (± 0.9) ARV drugs across all study subjects. Of note, the number of drugs prescribed varied between failure groups [3.4 (± 0.6), 3.8 (± 0.7) and 4.4 (± 0.9) in the first, second and third groups respectively, P < 0.0001)]. However, no differences were observed between genotype or expert advice randomized arms (G+, 4 (± 0.9) versus G−, 4 (± 0.8); EA+, 4.1 (± 0.9) versus EA−, 3.9 (± 0.9)]. Moreover, the proportion of patients receiving efavirenz and a double PI-containing regimen including ritonavir differed between failure groups: (first, 21.8%; second, 35.9%; third, 51.3%;P < 0.0001 for the double PI-containing regimen and first, 18.7%; second, 26.9%; third, 37.5%;P = 0.013 for efavirenz) and between expert advice arms (EA+, 53.3% EA−, 30.6%P < 0.0001 for the double PI-containing regimen and EA+, 23.9% versus EA−, 38.6%;P = 0.0041 for efavirenz).

A sub-analysis within the third failure group showed differences in the mean number of prescribed drugs between expert advice arms [EA+, 4.5 (± 0.9) and EA−, 4.2 (± 1);P = 0.038] but not between the genotyping arms [G+, 4.35 (± 1) versus G−, 4.41 (± 0.1)] and in the proportion of patients receiving a double PI-containing regimen including ritonavir between the expert advice arms (EA+, 64.1% versus EA−, 39.1%;P = 0.0007).

At 24 weeks the proportion of patients who showed pVL < 400 copies/ml differed between ARV stratification groups in the ITT analysis (P < 0.0001). Likewise, differences were found in the percentage of patients with pVL < 400 copies/ml between genotyping arms (P < 0.05) but not between expert advice arms (Fig. 2).

Fig. 2
Fig. 2
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A multivariate analysis including genotyping, expert advice, stratification group as independent variables, and all possible two-way interactions between them stated that the factors associated with a higher probability of pVL < 400 copies/ml at 24 weeks follow-up were HIV-1 genotyping and the expert advice in patients failing to a second line ARV therapy; whereas patients in the third failure group had a higher probability of virologic failure (Table 2).

Table 2
Table 2
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A significant pVL decrease from baseline was observed between failure groups and the two genotype arms in the combined 12 and 24 week analysis (P < 0.05) but not between expert advice arms (Fig. 3).

Fig. 3
Fig. 3
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Further exploratory analysis in the third failure group showed that there was no significant difference in the proportion of patients with plasma HIV-1 RNA < 400 copies/ml at 24 weeks among patients randomized to receive genotype or not and those randomized to receive expert advice or not (G+, 37.9% versus G−, 24.7%; EA+, 29.3% versus EA−, 33.7%). However, in this third failure group significant differences were observed in the mean decrease of pVL in the combined at 12 and 24 week analysis between genotype randomized arms [G+ mean decrease of −0.84 (± 0.9) and −0.7 (± 0.9) at 12 and 24 weeks versus G− mean decrease of −0.7 (± 0.7) and −0.5 (± 0.7), P = 0.039] but not between EA randomized arms [EA+ mean decrease of −0.80 (± 0.8) and −0.66 (± 0.8) at 12 and 24 weeks versus EA− mean decrease of −0.80 (± 0.8) and −0.66 (± 0.8)]. No significant differences were observed in the first and second failure groups.

In addition, we also evaluated the main endpoint of the Havana trial by a PP analysis. At 24 weeks significant differences were found in the proportion of patients with pVL < 400 copies/ml between stratification groups (P < 0.0001), genotyping arms (P < 0.05) and expert advice arms (P < 0.05) (Fig. 2).

A multivariate analysis including genotyping, expert advice and failure group as independent variables, showed that genotyping [odds ratio (OR), 1.92; 95% confidence interval (CI), 1.16–3.17;P = 0.011] and expert advice (expert advice: OR, 2.13; 95% CI, 1.3–3.53;P = 0.003) were associated with a higher probability of pVL < 400 copies/ml at 24 weeks whereas the third failure group was negatively associated with pVL < 400 copies/ml at 24 weeks (OR, 0.30; 95% CI, 0.16–0.59;P = 0.0004).

The proportions of patients that dropped out from the study between baseline and week 12 were 12.8% due to drug adverse events that required a change in the initial approach and 1.8% because of loss to follow-up. The distribution of those 48 patients among failure groups and the two randomization arms was similar.

Twenty eight (8.5%) patients were lost to follow-up following the 12 week endpoint, twenty two (6.7%) experienced a drug-related adverse event, ten (3%) changed therapy because of virologic failure and two (0.6%) died. No differences were observed in the proportion of patients that discontinued the initial approach between the 12 and 24 weeks of follow-up among failure groups or genotype or expert advice randomized arms.

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Discussion

In the present study, we have found a significantly better virologic outcome (percentage of patients with pVL < 400 copies/ml and decrease in pVL from baseline) at 6 months follow-up, in those patients randomized to guide the change of therapy according to the genotypic-resistance testing results than in those who received a change in therapy based on standard of care. In addition, in a multivariate analysis, we observed in the second failure group a beneficial effect of the expert advice recommendations for achieving undetectable pVL. Finally, in the third failure group, significant differences were found in the mean reduction of pVL in those randomized to a genotype arm (ITT).

When we analysed the results without considering those lost to follow-up during the first 12 weeks, (PP analysis) the outcome supports HIV-1 genotyping and expert advice recommendations as being of virologic benefit at both 12 and 24 weeks of follow-up.

As has been shown previously in both retrospective and some prospective trials, we also found that genotyping was superior to no genotyping in virologic outcomes at 24 weeks [12–14]. However, not all the studies aimed to show the benefit of resistance testing have been successful and some of them have not demonstrated any improvement in the virologic outcome when using either genotype or phenotype [15,29].

An important feature of this study was the demonstration by PP analysis and in the second failure group, by ITT, of virologic benefit from the use of expert advice recommendations for changes in ARV therapy. Previous studies relating to HIV therapy had shown differences in the treatment approaches provided by experienced HIV providers, cost savings and the potential for improved outcomes [12,21–23]. However, this is the first trial to show a direct virologic benefit from the incorporation of expert advice into the clinical care of HIV infected patients. The randomization of patients to EA+ and EA− arms in the Havana study, as well as G+ and G−, allowed us, in the PP analysis, to determine that both genotypic testing and expert advice can contribute individually to improved virologic outcomes. As it is hard to determine whether these effects were additive or synergistic, it may be prudent at this time to consider both these interventions as independently beneficial to patient management.

It is difficult to establish if the influence of the expert committee advice in the proportion of patients with HIV-1 RNA < 400 copies/ml in the Havana trial was due to their higher knowledge on resistance issues or to their excellence in the overall management of HIV-1 infection. However, the lack of significance of the interaction between genotype and expert advice in the multivariate analysis and the current limitations in the ARV drug arsenal to overcome resistance once it is acquired (lack of drugs with alternative resistance patterns or drug families with alternative sites of action) suggests that the impact of the expert committee recommendations on our study results could be primarily due to their overall experience in the management of HIV infection.

The complexity of HIV disease and antiretroviral treatment, the rapidly updating guidelines to HIV patient management and the specialized nature of HIV therapy are all potential causes for the benefit of expert advice. The challenges of incorporating resistance- testing data into routine patient management may magnify these needs.

All patients receiving genotypic analysis had result interpretations provided by a specialized software program. Such software does not propose a specific therapeutic strategy to follow, but establishes a ranking of suitability of the different drugs based on the existing pattern of resistance mutations. The individualized information for each drug, allows the clinician to select the most suitable treatment considering other clinical variables (adverse effects, toxicity, adherence, etc), that are as relevant as drug resistance for the tailoring of salvage therapies.

Our results provide evidence that even where expert advice is not available, genotyping with automated interpretation is superior to no testing. As no patients received genotyping without software interpretation, the relative contribution of automated interpretation cannot be determined.

Although Gart[12], Viradapt[13] and the Havana studies have all demonstrated the utility of genotyping for the design of salvage therapy in the short-term; inherent differences between the three studies make the comparison of their respective results difficult.

The Gart study incorporated expert advice into the interpretation of genotyping results but it was impossible to separate out the effects that genotyping and expert advice had independently, as all patients received both interventions. Another aspect that makes comparisons of the results difficult is the different follow-up periods (8 weeks in the Gart study and 24 weeks in the Havana) [12].

As compared to the Viradapt trial [13] the Havana study had a greater proportion of patients below limit of detection, whereas the Viradapt trial had a greater difference between the two arms at 6 months (Viradapt: 32% genotyping versus 14% standard of care, difference 18% Havana: 48.5% genotyping, 36.2 % non-genotyping, difference 12.3%). The differences seen may be due to sample sizes, differences in treatments available and differences in genotype interpretation or statistical analysis [13]. In addition, the results from the genotyping arm in the Havana trial may be influenced by the software interpretation of the genotype results and by the interpretation by the expert committee to a greater or lesser degree.

The difference in the time periods when the Gart, Viradapt and Havana studies were developed is relevant. This is mainly due to the progressive knowledge acquired by clinicians on drug resistance management and the prognostic factors of virologic response not related only to the intrinsic potency of antiretrovirals (adherence, therapeutic drug monitoring, etc) [14,29].

The appropriate use of HIV genotypic resistance testing in clinical practice will require better understanding of its utility in different populations. The Gart, Viradapt and Narval studies failed to address this issue [12,13,15]. However, in the present study we found significant improvement of the virologic outcome in the reduction of pVL when therapeutic choice was guided by genotyping in the most drug-experienced group (three or more combination therapy failures).

Although further work is required to better define this issue, it may not be appropriate now to assume genotyping is of reduced benefit in highly drug experienced patients [16].

The importance of expert interpretation of resistance assay results highlighted in this trial gives a particular sense of urgency to increased access to experts as well as training of clinicians in the area of HIV-1 resistance. As the widespread use of resistance testing in general and specifically genotyping becomes more common, there may be a patient benefit to education efforts directed towards the training of the HIV-treating clinicians to become familiar and comfortable with the area of interpretation of genotypes.

In summary, the Havana trial has demonstrated that both genotyping interpreted by a software and expert advice may improve short-term virologic outcome in HIV-infected patients. The relative utility of genotypic testing in different drug experienced population needs further defining. Our findings provide further support for the widespread use of resistance testing in pre-treated patients to achieve optimal care.

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Acknowledgements

The authors gratefully acknowledge Richard Haubrich and Anne M. Been for the critical review of the manuscript and Marià Sust, Montserrat Perez, Anna Muñoz, Rosa Lamarca and Margarida Garcia for performing and reviewing the statistical analysis.

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Appendix

Members of the Havana study group (local coordinators in parenthesis)

Hospital Universitari Germans Trias I Pujol (Badalona): (C. Tural, L. Ruiz), J. Romeu, G. Sirera, E. Negredo, J. Martinez-Picado, E. Ferrer, A. Bonjoch, A. Jou, J. Miranda, J.C. Martinez, A. Ballesteros, B. Clotet. Hospital de Sant Pau (Barcelona): (P. Domningo), J.M. Guardiola, A. Sambeat, J. Cadafalch, M. Fusté. Hospital Vall d'Hebron (Barcelona): (I. Ruiz), C. Azuaje, E. Ribera, I. Ocaña, A. Pahissa. Hospital Virgen del Rocio (Sevilla): (P. Viciana). Hospital La Paz (Madrid): (J. Gonzalez), J. Ma Peña, J.R. Arribas. Hospital Mutua de Terrassa (Terrassa): (D. Dalmau), J. Martinez Lacasa. Hospital de Mar (Barcelona): (A. Guelar), H. Knobel. Hospital General de Vic (Vic): (J. Vilaró). Hospital Universitari Joan XXIII (Tarragona): (F. Vidal), C. Richart, J. Peraire, C. Viladés. Hospital Nuestra Señora de Aranzazu (San Sebastián): (J.A. Iribarren). Hospital Sant Jaume de Calella (Calella): (J.M. Llibre). Hospital de Palmós (Palmós): (A. Masabeu). Consorci Hospitalari Parc Taulí (Sabadell): (M. Cervantes), M. Sala, F. Segura. Cited Here...

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Retrospective analysis of antiretroviral HIV treatment success based on medical history or guided by the reverse hybridisation LiPA HIV genotyping system
Alvarez, M; Garcia, F; Martinez, NM; Quero, JH; Louwagie, J; De Brauwer, A; Maroto, MC
Journal of Medical Virology, 73(2): 151-157.
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Journal of Medical Virology, 74(1): 8-15.
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Antiviral Therapy
Predictors of the virological response to a change in the antiretroviral treatment regimen in HIV-1-infected patients enrolled in a randomized trial comparing genotyping, phenotyping and standard of care (Narval trial, ANRS 088)
Vray, M; Meynard, JL; Dalban, C; Morand-Joubert, L; Clavel, F; Brun-Vezinet, FO; Peytavin, G; Costagliola, D; Girard, PM
Antiviral Therapy, 8(5): 427-434.

Antiviral Therapy
Role of the inhibitory quotient in HIV therapy
Hoefnagel, JGM; Koopmans, PP; Burger, DM; Schuurman, R; Galama, JM
Antiviral Therapy, 10(8): 879-892.

Labmedicine
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Wagner, TA; Frenkel, LM
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Antiviral Research
Algorithms for the interpretation of HIV-1 genotypic drug resistance information
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Plos Medicine
Should we include connection domain mutations of HIV-1 reverse transcriptase in HIV resistance testing?
Gotte, M
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Jaids-Journal of Acquired Immune Deficiency Syndromes
Limited evolution of HIV antiretroviral drug resistance-associated mutations during the performance of drug resistance testing
Birch, C; Middleton, T; Hales, G; Cooper, D; Law, M; Crowe, S; Hoy, J; Emery, S
Jaids-Journal of Acquired Immune Deficiency Syndromes, 32(1): 57-61.

Journal of Infectious Diseases
Genotype and phenotype at baseline and at failure in human immunodeficiency virus-infected antiretroviral-naive patients in a randomized trial comparing zidovudine and lamivudine plus nelfinavir or nevirapine
Ferrer, E; Podzamczer, D; Arnedo, M; Fumero, E; McKenna, P; Rinehart, A; Perez, JL; Barbera, MJ; Pumarola, T; Gatell, JM; Gudiol, F
Journal of Infectious Diseases, 187(4): 687-690.

Deutsche Medizinische Wochenschrift
Antiretroviral therapy of HIV infection. German-Austrian guidelines (July 2002)
Brockmeyer, NH
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Antiviral Research
Protease inhibitor resistance in HIV-infected patients: Molecular and clinical perspectives
Martinez-Cajas, JL; Wainberg, MA
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New England Journal of Medicine
HIV drug resistance - A chink in the armor
Hirsch, MS
New England Journal of Medicine, 347(6): 438-439.

Journal of Medical Virology
Modifications and substitutions of the RNA extraction module in the ViroSeq (TM) HIV-1 genotyping system version 2: Effects on sensitivity and complexity of the assay
Sturmer, M; Berger, A; Doerr, HW
Journal of Medical Virology, 71(4): 475-479.
10.1002/jmv.10527
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Clinical Infectious Diseases
Accuracy, precision, and consistency of expert HIV type 1 genotype interpretation: An international comparison (The GUESS Study)
Zolopa, AR; Lazzeroni, LC; Rinehart, A; Vezinet, FB; Clavel, F; Collier, A; Conway, B; Gulick, RM; Holodniy, M; Perno, CF; Shafer, RW; Richman, DD; Wainberg, MA; Kuritzkes, DR
Clinical Infectious Diseases, 41(1): 92-99.

Antiviral Therapy
Phenotype or virtual phenotype for choosing antiretroviral therapy after failure: a prospective, randomized study
Perez-Elias, MJ; Garcia-Arato, I; Munoz, V; Santos, I; Sanz, J; Abraira, V; Arribas, JR; Gonzalez, J; Moreno, A; Dronda, F; Antela, A; Pumares, M; Marti-Belda, P; Casado, JL; Geijo, P; Moreno, S
Antiviral Therapy, 8(6): 577-584.

Clinical Infectious Diseases
Comparison of a rule-based algorithm with a phenotype-based algorithm for the interpretation of HIV genotypes in guiding salvage regimens in HIV-infected patients by a randomized clinical trial: The mutations and salvage study
Gianotti, N; Mondino, V; Rossi, MC; Chiesa, E; Mezzaroma, I; Ladisa, N; Guaraldi, G; Torti, C; Tarquini, P; Castelli, P; Di Carlo, A; Boeri, E; Keulen, W; Mc Kenna, P; Lazzarin, A
Clinical Infectious Diseases, 42(): 1470-1480.

Deutsche Medizinische Wochenschrift
Antiretroviral therapy of HIV infection - German-Austrian recommendations
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Antiviral Research
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Paredes, R; Clotet, B
Antiviral Research, 85(1): 245-265.
10.1016/j.antiviral.2009.09.015
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Hiv Medicine
Factors associated with virological response in HIV-infected patients failing antiretroviral therapy: a prospective cohort study
Fournier, S; Chaffaut, C; Maillard, A; Loze, B; Lascoux, C; Gerard, L; Timsit, J; David, F; Bergmann, JF; Oksenhendler, E; Sereni, D; Chevret, S; Molina, JM
Hiv Medicine, 6(2): 129-134.

Enfermedades Infecciosas Y Microbiologia Clinica
Resistance genotyping in patients with therapeutic failure
Cabrera, C; Clotet, B
Enfermedades Infecciosas Y Microbiologia Clinica, 24(4): 219-221.

Enfermedades Infecciosas Y Microbiologia Clinica
Clinical use of HIV-1 resistance genotyping. Predictive factors of poor virological evolution in salvage treatments
Riera-Jaume, M; Penaranda-Vera, M; Ribas-Blanco, MA; Murillas-Angoiti, J; Campins, A; Salas-Aparicio, A; Leyes-Garcia, M; Pareja-Bezares, A; Perez, JL; Villalonga-Pieras, C
Enfermedades Infecciosas Y Microbiologia Clinica, 24(4): 225-231.

Journal of Infectious Diseases
Improved interpretation of genotypic changes in the HIV-1 reverse transcriptase coding region that determine the virological response to didanosine
De Luca, A; Di Giambenedetto, S; Trotta, MP; Colafigli, M; Prosperi, M; Ruiz, L; Baxter, J; Clevenbergh, P; Cauda, R; Perno, CF; Antinori, A
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Clinical Infectious Diseases
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Liu, TF; Shafer, RW
Clinical Infectious Diseases, 42(): 1608-1618.

Japanese Journal of Infectious Diseases
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Fujisaki, S; Fujisaki, S; Ibe, S; Asagi, T; Itoh, T; Yoshida, S; Koike, T; Oie, M; Kondo, M; Sadamasu, K; Nagashima, M; Gatanaga, H; Matsuda, M; Ueda, M; Masakane, A; Hata, M; Mizogami, Y; Mori, H; Minami, R; Okada, K; Watanabe, K; Shirasaka, T; Oka, S; Sugiura, W; Kaneda, T
Japanese Journal of Infectious Diseases, 60(): 113-117.

AIDS Patient Care and Stds
Utility of repeat genotypic resistance testing and clinical response in patients with three class resistance and virologic treatment failure
Badri, SM; Adeyemi, OM; Max, BE; Hota, BN; Barker, DE
AIDS Patient Care and Stds, 21(8): 544-550.
10.1089/apc.2006.0156
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Cochrane Database of Systematic Reviews
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Humphreys, EH; Hernandez, LB; Rutherford, GW
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Journal of Virology
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Journal of Antimicrobial Chemotherapy
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Journal of Antimicrobial Chemotherapy, 61(1): 206-209.
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Jaids-Journal of Acquired Immune Deficiency Syndromes
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Parkin, N; Chappey, C; Maroldo, L; Bates, M; Hellmann, NS; Petropoulos, CJ
Jaids-Journal of Acquired Immune Deficiency Syndromes, 31(2): 128-136.
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Journal of the American Medical Informatics Association
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Journal of Clinical Microbiology
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Campo, RE; Lichtenberger, PN; Rosa, I; Suarez, G; Rivera, FA; Rodriguez, AE; Jayaweera, DT; Wahlay, NA; Kolber, MA
Journal of Clinical Microbiology, 41(7): 3376-3378.
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Scandinavian Journal of Infectious Diseases
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De Luca, A; Antinori, A; Di Giambenedetto, S; Cingolani, A; Colafigli, M; Perno, CF; Cauda, R
Scandinavian Journal of Infectious Diseases, 35(): 29-34.
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Journal of Infectious Diseases
Mutations in HIV-1 reverse transcriptase potentially associated with hypersusceptibility to nonnucleoside reverse-transcriptase inhibitors: Effect on response to efavirenz-based therapy in an urban observational cohort
Tozzi, V; Zaccarelli, M; Narciso, P; Trotta, MP; Ceccherini-Silberstein, F; De Longis, P; D'Offizi, G; Forbici, F; D'Arrigo, R; Boumis, E; Bellagamba, R; Bonfigli, S; Carvelli, C; Antinori, A; Perno, CF
Journal of Infectious Diseases, 189(9): 1688-1695.

Antiviral Therapy
Current status and future prospects of therapeutic drug monitoring and applied clinical pharmacology in antiretroviral therapy
Boffito, M; Acosta, E; Burger, D; Fletcher, CV; Flexner, C; Garaffo, R; Gatti, G; Kurowski, M; Perno, CF; Peytavin, G; Regazzi, M; Back, D
Antiviral Therapy, 10(3): 375-392.

Clinical Infectious Diseases
Should resistance testing be performed for treatment-naive HIV-infected patients? A cost-effectiveness analysis
Sax, PE; Islam, R; Walensky, RP; Losina, E; Weinstein, MC; Goldie, SJ; Sadownik, SN; Freedberg, KA
Clinical Infectious Diseases, 41(9): 1316-1323.

AIDS Reviews
Predictors of virologic response to ritonavir-boosted protease inhibitors
Marcelin, AG; Flandre, P; Peytavin, G; Calvez, V
AIDS Reviews, 7(4): 225-232.

Antiviral Therapy
Three-year clinical outcomes of resistance genotyping and expert advice: extended follow-up of the Argenta trial
De Luca, A; Di Giambenedetto, S; Cingolani, A; Bacarelli, A; Ammassari, A; Cauda, R
Antiviral Therapy, 11(3): 321-327.

Archives of Pharmacal Research
Antiretroviral therapy 2006: Pharmacology, applications, and special situations
Samuel, R; Bettiker, R; Suh, B
Archives of Pharmacal Research, 29(6): 431-458.

Clinical Infectious Diseases
Antiretroviral drug resistance testing in adult HIV-1 infection: 2008 recommendations of an International AIDS Society-USA panel
Hirsch, MS; Gunthard, HF; Schapiro, JM; Brun-Vezinet, F; Clotet, B; Hammer, SM; Johnson, VA; Kuritzkes, DR; Mellors, JW; Pillay, D; Yeni, PG; Jacobsen, DM; Richman, DD
Clinical Infectious Diseases, 47(2): 266-285.
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Journal of Clinical Virology
Predictors of successful genotype-guided antiretroviral therapy in treatment-experienced individuals over calendar years: A cohort study
Bracciale, L; Fanti, I; Di Giambenedetto, S; Colafigli, M; Prosperi, M; Bacarelli, A; Santangelo, R; Cattani, P; Cauda, R; De Luca, A
Journal of Clinical Virology, 46(3): 290-294.
10.1016/j.jcv.2009.07.013
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Journal of Clinical Virology
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Kantor, R; Katzenstein, D
Journal of Clinical Virology, 29(3): 152-159.
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Expert Review of Molecular Diagnostics
HIV-1 genotyping: comparison of two commercially available assays
Sturmer, M; Berger, A; Preiser, W
Expert Review of Molecular Diagnostics, 4(3): 281-291.

Antiviral Therapy
The effect of number of mutations and of drug-class sparing on virological response to salvage genotype-guided antiretroviral therapy
Ciancio, BC; Trotto, MP; Lorenzini, P; Forbici, F; Visco-Comandini, U; Gori, C; Bonfigli, S; Bellocchi, MC; Sette, P; D'Arrigo, R; Tozzi, V; Zaccarelli, M; Boumis, E; Norciso, P; Perno, CF; Antinori, A
Antiviral Therapy, 8(6): 611-616.

Antiviral Therapy
Cost effectiveness analysis of routine use of genotypic antiretroviral resistance testing after failure of antiretroviral treatment for HIV
Corzillius, M; Muhlberger, N; Sroczynski, G; Jaeger, H; Wasem, J; Siebert, U
Antiviral Therapy, 9(1): 27-36.

Antiviral Therapy
HIV drug resistance testing: is the evidence really there?
Dunn, DT; Gibb, DM; Babiker, AG; Green, H; Darbyshire, JH; Weller, IVD
Antiviral Therapy, 9(5): 641-648.

Antiviral Therapy
Updated European recommendations for the clinical use of HIV drug resistance testing
Vandamme, AM; Sonnerborg, A; Ait-Khaled, M; Albert, J; Asjo, B; Bacheler, L; Banhegyi, D; Boucher, C; Brun-Vezinet, F; Camacho, R; Clevenbergh, P; Clumeck, N; Dedes, N; De Luca, A; Doerr, HW; Faudon, JL; Gatti, G; Gerstoft, J; Hall, WW; Hatzakis, A; Hellmann, N; Horban, A; Lundgren, JD; Kempf, D; Miller, M; Miller, V; Myers, TW; Nielsen, C; Opravil, M; Palmisano, L; Perno, CF; Phillips, A; Pillay, D; Pumarola, T; Ruiz, L; Salminen, M; Schapiro, J; Schmidt, B; Schmit, JC; Schuurman, R; Shulse, E; Soriano, V; Staszewski, S; Vella, S; Youle, M; Ziermann, R; Perrin, L
Antiviral Therapy, 9(6): 829-848.

Clinics in Laboratory Medicine
HIV-1 genotypic and phenotypic resistance
Hanna, GJ
Clinics in Laboratory Medicine, 22(3): 637-+.
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Journal of Clinical Virology
Evaluation of the UPA HIV-1 RT assay version 1: comparison of sequence and hybridization based genotyping systems
Sturmer, M; Morgenstern, B; Staszewski, S; Doerr, HW
Journal of Clinical Virology, 25(): S65-S72.
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Journal of Antimicrobial Chemotherapy
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Moyle, G
Journal of Antimicrobial Chemotherapy, 51(2): 213-217.
10.1093/jac/dkg066
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Journal of Infectious Diseases
Comparison between rules-based human immunodeficiency virus type 1 genotype interpretations and real or virtual phenotype: Concordance analysis and correlation with clinical outcome in heavily treated patients
Torti, C; Quiros-Roldan, E; Keulen, W; Scudeller, L; Lo Caputo, S; Boucher, C; Castelli, F; Mazzotta, F; Pierotti, P; Been-Tiktak, AM; Buccoliero, G; De Gennaro, M; Carosi, G; Tinelli, C
Journal of Infectious Diseases, 188(2): 194-201.

European Journal of Medical Research
Antiretroviral therapy of HIV infection German-Austrian recommendations (July 2002)
Arasteh, K; Bader, A; Berg, T; Bogner, J; Brockhaus, W; Brockmeyer, NH; Brodt, HR; Busch, H; Doerr, HW; Dupke, S; Esser, S; Fleckenstein, B; Goebel, FD; Golz, J; Grosch-Worner, I; Gurtler, L; Hartmann, M; Helm, EB; Hoffmann, C; Jager, H; Jablonowski, H; Jarke, J; Kern, P; Knechten, H; Korner, T; Korn, K; Kramer, A; Krausslich, HG; Kurowski, M; Marcus, U; Moll, A; Pfeil, B; Plettenberg, A; Rasokat, H; Rieger, A; Rockstroh, J; Ruf, B; Rump, JA; Salzberger, B; Schafberger, A; Schedel, I; Schmied, B; Schmidt, B; Schmidt, RE; Schofer, H; Schrappe, M; Staszewski, S; Stellbrink, HJ; Stoehr, A; Stoll, M; Tschachler, E; Uberla, K; van Lunzen, J; Vetter, N; Vielhaber, B; Zangerle, R
European Journal of Medical Research, 8(6): 257-274.

Medizinische Klinik
Drug resistance in antiretroviral therapy of HIV infection
Oette, M; Haussinger, D
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Antiviral Therapy
A randomized controlled trial of genotypic HIV drug resistance testing in HIV-1-infected children: The PERA (PENTA 8) trial
Green, H; Gibb, DM; Compagnucci, A; Giacomet, V; de Rossi, A; Harper, L; Saidi, Y; Castelli-Gattinara, G; Pillay, D; Babiker, AG; Aboulker, JP; Lyall, H; T Bacheler, L; Walker, AS; Debre, M; Rosso, R; Burger, DM; Della Negra, M; David, DT; Giaquinto, C
Antiviral Therapy, 11(7): 857-867.

Hiv Medicine
The long-term benefits of genotypic resistance testing in patients with extensive prior antiretroviral therapy: a model-based approach
Yazdanpanah, Y; Vray, M; Meynard, J; Losina, E; Weinstein, MC; Morand-Joubert, L; Goldie, SJ; Hsu, HE; Walensky, RP; Dalban, C; Sax, P; Girard, P; Freedberg, K
Hiv Medicine, 8(7): 439-450.

Hiv Medicine
Prediction of phenotypic susceptibility to antiretroviral drugs using physiochemical properties of the primary enzymatic structure combined with artificial neural networks
Kjer, J; Hoj, L; Fox, Z; Lundgren, JD
Hiv Medicine, 9(8): 642-652.
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AIDS Reviews
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de Mendoza, C; Anta, L; Garcia, F; Perez-Elias, J; Gutierrez, F; Llibre, JM; Menendez-Arias, L; Dalmau, D; Soriano, V
AIDS Reviews, 11(1): 39-51.

Journal of Virological Methods
Evaluation of two human immunodeficiency virus-1 genotyping systems: ViroSeq (TM) 2.0 and an in-house method
Saravanan, S; Vidya, M; Balakrishanan, P; Kumarasamy, N; Solomon, SS; Solomon, S; Kantor, R; Katzenstein, D; Ramratnarn, B; Mayer, KH
Journal of Virological Methods, 159(2): 211-216.
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Antiviral Therapy
A genotypic drug resistance interpretation algorithm that significantly predicts therapy response in HIV-1-infected patients
Van Laethem, K; De Luca, A; Antinori, A; Cingolani, A; Perno, CF; Vandamme, AM
Antiviral Therapy, 7(2): 123-129.

Journal of Virology
Sequencing-Based detection of low-frequency human immunodeficiency virus type 1 drug-resistant mutants by an RNA/DNA heteroduplex generator-tracking assay
Kapoor, A; Jones, M; Shafer, RW; Rhee, SY; Kazanjian, P; Delwart, EL
Journal of Virology, 78(): 7112-7123.
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AIDS
Limited benefit of antiretrovial resistance testing in treatment-experienced patients: a meta-analysis
Panidou, ET; Trikalinos, TA; Ioannidis, JPA
AIDS, 18(): 2153-2161.

European Journal of Medical Research
German-Austrian recommendations for the antiretroviral therapy of HIV-infection (Status May 2004)
Salzberger, B; Marcus, U; Vielhaber, B; Arasteh, K; Golz, J; Brockmeyer, NH; Rockstroh, J
European Journal of Medical Research, 9(): 491-504.

Journal of Clinical Microbiology
Evidence for preferential genotyping of a minority human immunodeficiency virus population due to primer-template mismatching during PCR-based amplification
Martinez-Picado, J; Morales-Lopetegi, K; Villena, C; Gutierrez, C; Izquierdo, N; Marfil, S; Clotet, B; Ruiz, L
Journal of Clinical Microbiology, 43(1): 436-438.
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Antiviral Therapy
Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes
De Luca, A; Vendittelli, M; Boldini, F; Di Giambenedetto, S; Trotta, MP; Cingolani, A; Bacarelli, A; Gori, C; Perno, CF; Antinori, A; Ulivi, G
Antiviral Therapy, 9(4): 583-593.

Antimicrobial Agents and Chemotherapy
Discordances between interpretation algorithms for genotypic resistance to protease and reverse transcriptase inhibitors of human immunodeficiency virus are subtype dependent
Snoeck, J; Kantor, R; Shafer, RW; Van Laethem, K; Deforche, K; Carvalho, AP; Wynhoven, B; Soares, MA; Cane, P; Clarke, J; Pillay, C; Sirivichayakul, S; Ariyoshi, K; Holguin, A; Rudich, H; Rodrigues, R; Bouzas, MB; Brun-Vezinet, F; Reid, C; Cahn, P; Brigido, LF; Grossman, Z; Soriano, V; Sugiura, W; Tanuri, A; Harrigan, RP; Camacho, R; Schapiro, JM; Katzenstein, D; Vandamme, AM
Antimicrobial Agents and Chemotherapy, 50(2): 694-701.
10.1128/AAC.50.2.694-701.2006
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Cellular and Molecular Life Sciences
Overcoming HIV drug resistance through rational drug design based on molecular, biochemical, and structural profiles of HIV resistance
Yin, PD; Das, D; Mitsuya, H
Cellular and Molecular Life Sciences, 63(): 1706-1724.
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Antiviral Therapy
A computer-based system to aid in the interpretation of plasma concentrations of antiretrovirals for therapeutic drug monitoring
Goicoechea, M; Vidal, A; Capparelli, E; Rigby, A; Kemper, C; Diamond, C; Witt, MD; Haubrich, R
Antiviral Therapy, 12(1): 55-62.

Reviews in Medical Virology
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Qadir, MI; Malik, SA
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Clinical Microbiology Reviews
Genotypic testing for human immunodeficiency virus type 1 drug resistance
Shafer, RW
Clinical Microbiology Reviews, 15(2): 247-+.
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Journal of Clinical Microbiology
Sources and magnitude of intralaboratory variability in a sequence-based genotypic assay for human immunodeficiency virus type 1 drug resistance
Galli, RA; Sattha, B; Wynhoven, B; O'Shaughnessy, MV; Harrigan, PR
Journal of Clinical Microbiology, 41(7): 2900-2907.
10.1128/JCM.41.7.2900-2907.2003
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Scandinavian Journal of Infectious Diseases
Theme 1 - Genotypic resistance tests in the management of the HIV-infected patient at virological failure
Aceti, A; Carosi, G; Torti, C
Scandinavian Journal of Infectious Diseases, 35(): 61-66.
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Brazilian Journal of Infectious Diseases
Genotype testing and antiretroviral resistance profiles from HIV-1 patients experiencing therapeutic failure in Northeast Brazil
Medeiros, MS; Arruda, EAG; Guerrant, RL; Brown, C; Hammarskjold, ML; Rekosh, D; Lima, AAM
Brazilian Journal of Infectious Diseases, 11(4): 390-394.

AIDS Research and Human Retroviruses
Genotypic susceptibility scores and HIV type 1 RNA responses in treatment-experienced subjects with HIV type 1 infection
Anderson, JA; Jiang, HY; Ding, X; Petch, L; Journigan, T; Fiscus, SA; Haubrich, R; Katzenstein, D; Swanstrom, R; Gulick, RM
AIDS Research and Human Retroviruses, 24(5): 685-694.
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Irish Journal of Medical Science
Successful virological outcome in an HIV-infected individual with a three-class resistant variant and an insertion in the protease genome with a Tipranavir based regimen
Panagopoulos, P; Paraskevis, D; Antoniadou, A; Papadopoulos, A; Poulakou, G; Hatzakis, A; Giamarellou, H
Irish Journal of Medical Science, 179(2): 305-307.
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European Journal of Medical Research
Radata - Implementation of resistance analysis and expert advice for optimized HAART switches in general practice of HIV-infected individuals via a Compiling internet presence
Paech, V; Lorenzen, T; Stoehr, A; Plettenberg, A
European Journal of Medical Research, 7(7): 323-329.

Microbiologica
Temporal change in the use of genotypic resistance testing over the years 1999-2003
Santoro, M; Ceccherini-Silberstein, F; Gori, C; Svicher, V; Forbici, F; Bellocchi, MC; d'Arrigo, R; Bertoli, A; Giannella, S; Trotta, MP; Bonfigli, S; Antinori, A; Perno, CF
Microbiologica, 27(2): 141-144.

Antiviral Therapy
Variability in the interpretation of transmitted genotypic HIV-1 drug resistance and prediction of virological outcomes of the initial HAART by distinct systems
De Luca, A; Cozzi-Lepri, A; Perno, CF; Balotta, C; Di Giambenedetto, S; Poggio, A; Pagano, G; Tositti, G; Piscopo, R; Del Forno, A; Chiodo, F; Magnanio, G; Monforte, AD
Antiviral Therapy, 9(5): 743-752.

Proceedings of the National Academy of Sciences of the United States of America
Mechanism for nucleoside analog-mediated abrogation of HIV-1 replication: Balance between RNase H activity and nucleotide excision
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Keywords:

HIV-1; genotypic resistance; expert advice; viral load; antiretroviral therapy; virological failure; resistance mutations

© 2002 Lippincott Williams & Wilkins, Inc.

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