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Antimicrobial Agents and Chemotherapy, April 2007, p. 1473-1480, Vol. 51, No. 4
0066-4804/07/$08.00+0     doi:10.1128/AAC.00481-06
Copyright © 2007, American Society for Microbiology. All Rights Reserved.

Interpretation of Genotype and Pharmacokinetics for Resistance to Fosamprenavir-Ritonavir-Based Regimens in Antiretroviral-Experienced Patients{triangledown}

Isabelle Pellegrin,1,{dagger}* Dominique Breilh,2,{dagger} Gaelle Coureau,4 Sébastien Boucher,1 Didier Neau,3 Patrick Merel,1 Denis Lacoste,3 Hervé Fleury,1 Marie-Claude Saux,2 Jean-Luc Pellegrin,3 Estibaliz Lazaro,3 François Dabis,4 Rodolphe Thiébaut,4 for the ANRS Co3 Aquitaine Cohort {ddagger}

Departments of Virology,1 Clinical Pharmacokinetics and Pharmacy,2 Internal Medicine and Infectious Diseases,3 INSERM U593, Bordeaux University Hospital, Bordeaux, France4

Received 19 April 2006/ Returned for modification 31 October 2006/ Accepted 12 January 2007


   ABSTRACT
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion.
 References
 
In this study, named the Zephir study (Telzir-pharmacokinetics), 121 antiretroviral-experienced human immunodeficiency virus (HIV) patients failing on highly active antiretroviral therapy (HAART) were included in a prospective cohort and received a fosamprenavir-ritonavir (700 mg/100 mg twice a day)-based regimen. The impact of baseline HIV type 1 (HIV-1) mutations, pharmacokinetic (PK) parameters, and genotype inhibitory quotient (GIQ) on the virological response at week 12 (W12) was assessed. HIV reverse transcriptase and protease were sequenced at W0. The response at W12 was defined as <2.3 log10 HIV-1 RNA copies/ml or a virus load decrease of ≥1 log10 copies/ml. W4 amprenavir PK were determined by high-performance liquid chromatography. Patients had a median of nine previous treatments over 8 years. Median W0 values were as follows: 295 CD4+/µl, 4.4 log10 HIV-1 RNA copies/ml, and 6 protease- and 5 nucleotide reverse transcription inhibitor-related mutations. Respective values for minimum concentration of drug in serum (Cmin) and area under the concentration-time curve (AUC) from 0 to 24 h were 1,400 ng/ml and 35 mg·h/ml. At W12, 52% of the patients were successes, with a median decrease of –0.7 log10 HIV-1 RNA copies/ml. The Zephir mutation score included 12 IAS protease mutations associated with poorer virological response: L10I/F/R/V, L33F, M36I, M46I/L, I54L/M/T/V, I62V, L63P, A71I/L/V/T, G73A/C/F/T, V82A/F/S/T, I84V, L90M, and polymorphism mutations I13V, L19I, K55R, and L89M. Comparing <4 versus ≥4 mutations, HIV-1 RNA decreases were –2.3 log10 copies/ml versus –0.1 log10 copies/ml (P < 10–4) with 93% versus 19% successes (P < 10–4), respectively. This score predicted W12 failure with 94% sensitivity, versus 31% for the ANRS 2005 algorithm. Cmin (<1,600 ng/ml), AUC (<40 mg·h/ml), and GIQ (<300) values were associated with failure (all P values were <10–4). The need to test genotype-based algorithms using different patient databases before their implementation in clinical practice is highlighted. Specific mutations, PK and GIQ, provide relevant information for monitoring fosamprenavir-ritonavir-based HAART.


   INTRODUCTION
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusion.
 References
 
Pharmacokinetic (PK) parameters and virological characteristics have been associated with virological responses to highly active antiretroviral therapy (HAART) (2, 9, 13, 19, 20, 28, 29, 30, 39). The human immunodeficiency virus type 1 (HIV-1) protease inhibitor (PI) fosamprenavir (FPV)—an amprenavir (APV) prodrug—is rapidly hydrolyzed during absorption into its APV parent form by cellular phosphatases in the gut epithelium (Lexiva [prescribing information]; GlaxoSmithKline, Research Triangle Park, NC). APV undergoes CYP450-based catabolism in the gastrointestinal tract and liver and has been shown to inhibit and induce different isoforms of this system (6, 15). Coadministration of ritonavir, a potent CYP450 inhibitor, decreases APV elimination (34), resulting in clinically significant increases of APV exposure (10, 14, 33); plasma APV levels are similarly boosted when ritonavir is coadministered with FPV (41).

Results of PK studies conducted with healthy individuals (41) and HIV-1-infected patients (43) showed that boosting a 700-mg dose of FPV with 100 mg of ritonavir (FPV/r) twice a day (BID) increased the APV minimum concentration in serum (Cmin) by four- to sixfold compared with that of FPV alone, without substantially increasing its maximum concentration in serum (Cmax). This boost has the potential to enhance treatment potency, particularly against drug-resistant HIV-1 strains.

FPV safety and efficacy have been demonstrated with antiretroviral-naive and treatment-experienced patients. In treatment-naive patients (12, 32), FPV, ritonavir-boosted or unboosted, met primary end points of noninferiority against nelfinavir. FPV/r was compared to lopinavir-ritonavir with treatment-experienced patients (7). Noninferiority was not achieved, but similar numbers of patients achieved viral suppression when FPV/r was given BID.

Most of the current genotype interpretation algorithms for FPV/r have been adapted from results obtained with boosted or unboosted APV. In accordance with the findings on APV-ritonavir (APV/r) reported by Marcelin et al. (20), the French National Agency for AIDS Research (ANRS) guidelines (8; http://hivfrenchresistance.org/) proposed a clinical cutoff of ≥6 mutations (ANRS 2005 score) among the following set, 10F/I/V, 20 M/R, 35D, 41K, 54V, 63P, 82A/F/S/T, 84V, V32I I47V, or I50V alone, to predict failure for patients on FPV/r-containing HAART. Elston et al. (11) identified protease codons associated with a poorer response to FPV/r at week 12 (W12), without establishing a clinical cutoff due to the small sample size in categories with high numbers of mutations.

At present, few data on the efficacy, resistance, and pharmacological determinants of the response to FPV/r-containing HAART for PI-experienced patients are available. In this study, named the Zephir study (Telzir-pharmacokinetics), we analyzed the probability of failure according to baseline genotypes and PK data for a prospective cohort of HIV-1-infected patients included because of the initiation of an FPV/r-based regimen. The aims were to define a clinically relevant virus genotype profile to interpret FPV/r resistance (Zephir mutation score), to assess the impact of protease polymorphism mutations, and to evaluate the usefulness of combining plasma APV concentrations with the genotype score to enhance the predictability of failure on FPV/r-containing HAART for PI-experienced patients (3, 27). Finally, we evaluated the emergence of additional protease gene mutations in failing patients.


   MATERIALS AND METHODS
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusion.
 References
 
Study population. The ANRS Co3 Aquitaine Cohort is a prospective hospital-based cohort of HIV-1-infected patients under routine clinical management that was initiated in 1987 in the Bordeaux University Hospitals and four other public hospitals in Aquitaine, southwestern France (38). Clinical, biological, and therapeutic information have been collected prospectively at each visit. One hundred twenty-one of these HIV-1-infected, PI-experienced patients in virological failure on HAART (plasma HIV-1 RNA level, >1.7 log10 copies/ml) were enrolled consecutively in the Zephir observational study because they started (week zero [W0]) FPV/r (700 mg/100 mg, BID)-containing HAART between August 2004 and May 2005. The patients were monitored at W0, W4, W12, and W24. Blood samples were drawn for HIV-1 RNA determination and CD4+-cell counts at each follow-up visit and for plasma APV PK evaluation at W4.

Virological outcomes. Virological success was defined as plasma HIV-1 RNA of <2.3 log10 copies/ml or a virus load decrease of ≥1 log10 copies/ml, quantitated using the CobasTaqman HIV assay (Roche Diagnostics, Basel, Switzerland), within the first 12 weeks on the FPV/r regimen. When both criteria were not achieved, patients were considered to have failed therapy. Secondary efficacy goals included a plasma HIV-1 RNA level change from baseline, the percentage of patients with an HIV-1 RNA level of <1.7 log10 copies/ml and CD4+-cell counts through week 12.

Genotype resistance testing: reverse transcriptase and protease sequence analyses. Plasma HIV-1 RNA obtained prior to inclusion and at failure were used to sequence reverse transcriptase (codons 1 to 240) and protease (codons 1 to 99) genes, as described previously (26). The sequences were analyzed using CEQ software version 6.0 (Beckman Coulter, Inc., Fullerton, CA) and reported as amino acid changes with respect to the wild-type virus HXB2 sequence. If the encoded amino acid was a mixture of wild-type and mutant amino acids, the mutation was considered to be present at the codon of interest. Major and minor protease mutations were defined according to the International AIDS Society (IAS)-USA panel (17) (http://www.iasusa.org); polymorphism mutations were amino acid substitutions at codons 1 to 99, except those in the International AIDS Society (IAS) list.

Relationship between number of protease mutations and failure. First, we analyzed the impact of each protease gene mutation on virological outcome by comparing the failure rates according to the presence of each one. All positions were analyzed when the HIV-1 protease sequences from ≥10% of patients differed from the HXB2 sequence at that codon. All mutations associated with failure at a P value of 0.003 (Bonferroni correction for 16 univariate tests) were retained to construct the score. Pertinently, because all but one significant mutation (K20M/R) were associated with a crude P value of <10–3, the definition of statistical level did not markedly influence the selection of a mutation. The score was then defined as the number of protease mutations at baseline. The final multivariable model adjusted for potential confounding factors was selected according to a backward selection procedure. Because the correlation between the number of mutations and the virological response was not continuous, we conducted a change point analysis (4). The threshold yielding the best multivariable model likelihood was retained. Corrected odds ratios (OR) and P values for the probability of failure according to the optimal thresholds were calculated using a twofold cross-validation (22). The ANRS 2005 mutation score, which applied a threshold of <6 versus ≥6 mutations (8) (Table 1), was also tested with the present study population. In an attempt to identify a relationship between failure and susceptibility to HAART, the genotype sensitivity score, representing the sum of genotype sensitivities to all the drugs prescribed in the new regimen (the number of baseline nucleotide reverse transcription inhibitor [NRTI] mutations and susceptibilities to FPV/r and coprescribed antiretroviral drugs), was calculated according to the ANRS 2005 rules (8).


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  		TABLE 1. APV, APV/r, and FPV/r genotype algorithms, comprising specific mutation sets to be considered in relationship to clinical responses based upon previously published observations and the Zephir study

 
Determination of plasma APV concentrations. Blood was drawn to determine plasma APV concentrations at the PK steady state at least 4 weeks after starting FPV/r therapy. Minimum (Cmin) and maximum (Cmax) serum drug concentrations, corresponding to around 12 h and 3 h after FPV/r ingestion, respectively, were measured using a validated high-performance liquid chromatography method and UV detection after specific treatment of the samples (2). Bayesian PK estimators of FPV/r levels from plasma APV-boosted concentrations were obtained from a previous PK population study (16, 42) and used to determine the individual PK areas under the concentration-time curve from 0 to 24 h (AUC0-24) for the patients included in the Zephir observational cohort. The genotype inhibitory quotient (GIQ) was calculated as the ratio of the steady-state plasma APV Cmin value at W4 to the number of baseline FPV/r-specific protease mutations selected in the Zephir mutation score.

Statistical analyses. Analyses were performed using SAS 8.0 (SAS Institute, Inc., Cary, NC). The characteristics were compared between groups using the Pearson's chi-square or Fisher's exact test for qualitative variables and Wilcoxon's test for quantitative variables. We used the nonparametric Spearman's correlation coefficient to test relationships between quantitative variables. Multivariable models adjusted for demographic, immunovirological variables and previous antiretroviral therapies were constructed using logistic regressions. Goodness-of-fit of each final multivariable model was checked using the Hosmer-Lemeshow chi-square test. Distributions are described as medians (25th percentile; 75th percentile), unless stated otherwise.


   RESULTS
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusion.
 References
 
Baseline patient characteristics. The baseline characteristics of the 121 patients enrolled in this observational cohort study are reported in Table 2. Among the 121 patients, 108 had virus load determinations at W12 and all the PK-parameter values and 100 had virus load measurements at W12 and genotype resistance testing. Patients switched from lopinavir-ritonavir to FPV/r to limit gastrointestinal disorders, to attenuate lipid abnormalities, or because of an expected stronger effect of FPV/r guided by the interpretation of the genotypic data. The nucleoside analogues most frequently coprescribed with FPV/r were tenofovir disoproxil fumarate (DF) (37%) and lamivudine or emtricitabine (75%). Other antiretroviral drugs prescribed in combination with FPV/r at baseline were as follows: zidovudine (n = 27; 22%); abacavir (n = 31; 26%); a second PI among saquinavir (n = 3; 2.5%), lopinavir (n = 12; 10%), and atazanavir (n = 3, 2.5%); and enfuvirtide (n = 17, 14%). According to the ANRS 2005 algorithm, 16% of patients were considered to harbor HIV-1 strains genotypically resistant to FPV/r prior to enrollment.


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  		TABLE 2. Baseline characteristics of the 121 Zephir cohort patients prescribed FPV/r-based HAART

 
Responses to the FPV/r-containing regimens. At W12, 56 (52%) patients were considered successes, defined as having an HIV-1 RNA level of <2.3 log10 copies/ml (43%; n = 46) or a virus load decrease of ≥1 log10 copies/ml from baseline (9%; n = 10). The secondary efficacy goals were met by 29% of the patients (n = 31), i.e., undetectable virus load (<1.7 log10 copies/ml). The overall median (25th percentile; 75th percentile) HIV-1 RNA level decrease from baseline was –0.7 (–2.5; +0.04) log10 copies/ml, while the concomitant median CD4+ count rise was +34 (–9; +117) cells/µl. The overall median HIV-1 RNA level decline from baseline was –0.7 (–2.5; +0.04) log10 copies/ml, while the concomitant CD4+ count increase was +34 (–9; +117) cells/µl.

Identification of baseline genotype mutations that limit the W12 response to FPV/r. According to our univariable analysis of the Zephir cohort, 12 protease mutations from the IAS list were associated (P ≤ 0.003) with failure in FPV/r-containing HAART. Among the polymorphism mutations detected for ≥10% of our patients, four amino acid substitutions were associated with virological outcome (Tables 1 and 3). The number of Zephir score mutations and the response were significantly associated (OR = 1.8 for each additional mutation; P < 10–4) and correlated with the extent of virus load decline at W12 ({rho} = 0.59; P < 10–4). The best clinical cutoff for FPV/r genotype resistance mutations was 4 mutations (Fig. 1A): for <4 versus ≥4 mutations, the HIV-1 RNA load for W12 minus that for W0 (25th percentile; 75th percentile) decreased by –2.3 (–3.2; –1.0) versus –0.1 (–0.5; +0.3) log10 copies/ml (P < 10–4), with 93% mounting successful responses versus 19% failures (P < 10–4), respectively. The same mutation score cutoff was defined when the four polymorphism mutations were included in the mutation set, with similar virus load changes and response percentages (Fig. 1B). Among the eight patients whose isolates carried the V32I plus I47V mutations, seven failed at W12 and all had >4 Zephir score mutations. Pertinently, ≥4 Zephir score mutations (with or without polymorphism mutations) predicted failure at W12 with 94% (95% confidence interval [CI] = 83 to 99%) sensitivity, 78% (95% CI = 65 to 89%) specificity, 81% (95% CI = 68 to 90%) positive predictive value, and 93% (95% CI = 81 to 99%) negative predictive value, whereas the ANRS 2005 rules (≥6 mutations) predicted failure at W12 with 31% (95% CI = 18 to 45%) sensitivity, 98% (95% CI = 90 to 100%) specificity, 94% (95% CI = 70 to 100%) positive predictive value, and a negative predictive value of 60% (95% CI = 48 to 70%) (Fig. 1C).


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  		TABLE 3. Amino acid substitutions in HIV-1 protease (codons 1 to 99) associated with virological failure (>2.3 log10 HIV-1 RNA copies/ml or virus load decrease of <1 log10 copies/ml) with FPV/r-containing regimen at W12

 

Figure 1
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  		FIG. 1. Virological responses (with success defined as a plasma HIV-1 RNA level of <2.3 log10 copies/ml or a virus load decrease of ≥1 log10 copies/ml) and delta virus load (log10 copies/ml) between weeks 12 and 0 with FPV/r-based HAART, according to the number of mutations (M) from the Zephir set without (A) or with (B) polymorphism mutations or (C) with the ANRS 2005 score. The number of patients (Pt) is indicated under M.

 
Analysis of PK parameters. Median (25th percentile; 75th percentile) plasma APV Cmin and Cmax values determined at W4 were 1,400 (800; 1,800) and 4,650 (3,600; 6,150) ng/ml, respectively, and the AUC0-24 value was 35 (27; 45) mg·h/liter. In unadjusted analyses, Cmin, Cmax, and AUC0-24 values were significantly associated with failure at W12 (all P values were <10–4). The most discriminatory efficacy threshold estimates were 1,600 ng/ml and 40 mg·h/liter for plasma APV Cmin and AUC, respectively. The plasma APV Cmin value was <1,600 ng/ml for 61 (56%) patients, 19 (31%) of whom responded successfully, whereas 80% of the patients with plasma APV Cmin values of ≥1,600 ng/ml were successes. Comparing Cmin values of <1,600 to those of ≥1,600 ng/ml, the HIV-1 RNA load for W12 minus that for W0 decreased by –0.2 (–1.1; +0.2) versus –2.1 (–3.2; –0.5) log10 copies/ml (P < 10–4). The median GIQ was 320 (110; 1,200). Virological failure at W12 was strongly associated with this GIQ (P < 10–4). Comparing results for GIQ of <300 with those for GIQ of ≥300, the HIV-1 RNA load for W12 minus that for W0 decreased, respectively, by –0.03 (–0.4; +0.3) versus –2.2 (–3.1; –0.9) log10 copies/ml (P < 10–4), with 10% of patients mounting a successful response versus 90% failures (P < 10–4), respectively. In our cohort, Cmin values of <40 mg·h/liter, AUC0-24 values of <1,600 ng/ml, and GIQ values of <300 predicted failure with, respectively, sensitivities of 81% (95% CI = 67 to 90%), 94% (95% CI = 84 to 99%), and 90% (95% CI = 78 to 97%) and specificities of 66% (95% CI = 52 to 78%), 57% (95% CI = 43 to 70%), and 90% (95% CI = 79 to 97%).

PK parameters were significantly modified when tenofovir DF was coadministered with FPV/r. Forty-five of our patients took tenofovir DF concomitantly. Median Cmin [25th percentile; 75th percentile] (1,200 [600; 1,600] versus 1,550 [900; 1,800] ng/ml; P = 0.06), Cmax (4,000 [3,200; 5,200] versus 5,100 [3,600; 6,250] ng/ml; P = 0.02), and AUC0-24 (31 [23; 38] versus 38 [28; 46] mg·h/liter; P = 0.02) values were significantly lower for patients on FPV/r and tenofovir DF than for the 76 patients taking FPV/r without it.

Adjusted analysis. Univariable analysis selected eight variables associated with failure at W12 (P < 0.25): CDC stage (stage C vs. other clinical stages), baseline CD4+ count (<200 vs. ≥200 cells/µl), number of previous treatment regimens (≥7 vs. <7), CD4+ count nadir (<50 vs. ≥50 cells/µl), number of previous PI (≥3 vs. <3), previous nonnucleoside reverse transcriptase inhibitors (NNRTI), APV-to-FPV switch, and tenofovir DF coprescription. These variables were introduced in a multivariable logistic-regression model to test the independence of the association between the Zephir mutation score or PK parameters and failure at W12. A Zephir mutation score of ≥4 vs. a score of <4 remained independently associated with failure at W12 (OR = 30.4; P < 10–4) (Table 4). The three PK parameters remained independently associated with failure at W12: Cmin (OR = 4.0; P = 0.01), GIQ (OR = 45.8; P < 10–4), and AUC (OR = 15.9; P = 10–3). When the Zephir set and AUC values were included in the same multivariable model (Table 4, model 5), both variables were independently associated with failure (OR = 33.2 and P < 10–4; OR = 28.3 and P = 10–3). Moreover, according to the Akaike's information criterion (the lower the value, the better), that model was also the best one. When OR and P values were corrected using a twofold cross-validation, the adjusted effects of Zephir mutation score, Cmin, GIQ, and AUC were still statistically significant (OR = 12.1 and P < 10–4; OR = 2.9 and P = 0.05; OR = 30.8 and P < 10–4; and OR = 5.6, P = 10–3, respectively).


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  		TABLE 4. Multivariable analyses of factors associated with virological failure on FPV/r-containing HAART at W12

 
Robustness analyses for these four determinants of failure gave similar results when patients who switched from APV to FVP or those treated with another PI were excluded (respectively, Zephir mutation score, OR = 24.7 and P < 10–4; OR = 23.4 and P < 10–4; Cmin, OR = 5.5 and P = 0.005; OR = 5.4 and P = 0.003; GIQ, OR = 50.6 and P < 10–4; OR = 28.9 and P < 10–4; AUC, OR = 43.2 and P = 10–3; OR = 17.2 and P < 10–4).

Evolution of the protease genotype in patients failing on FPV/r therapy. Paired-baseline and last-visit (W12 to W24) genotypes were available for 59 patients whose virus loads remained >2.3 log10 copies/ml throughout follow-up. Among these 59 paired samples, 21/59 (36%) patients, with a total of 4 to 10 protease mutations at baseline, had not developed any additional protease mutations at the time of failure despite having ongoing virological replication; but three of them had developed additional NRTI-, NNRTI-, or enfuvirtide resistance-related mutations. In the 38 (64%) remaining patients, mutations were selected that had previously been associated with APV resistance (V32I I47V, n = 3; M46I/L, n = 7; I50V, n = 1; I54L/V, n = 5; V82A/F/S/T, n = 2; I84V, n = 2; L90M, n = 3) or associated secondary mutations (L10I/F/V, n = 3; K20M/R, n = 2; L24I, n = 2; L33F, n = 5; E35D, n = 3; M36I, n = 1; R41K, n = 2; F53L, n = 3; I62V, n = 5; L63P, n = 2; A71I/L/V/T, n = 2; L89M, n = 7). The I50V mutation, associated with lower susceptibility to FPV/r (16), emerged in one patient on a background of multiresistant protease (codons 10, 24, 41, 46, 54, 63, 71, 82, and 90). NRTI-, NNRTI-, or enfuvirtide resistance-related mutations appeared concomitantly for 10, 5, and 3 patients, respectively.


   DISCUSSION
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusion.
 References
 
In this study, we investigated factors, including baseline genotype variables and PK parameters, which might contribute to the subsequent virological success or failure of the FPV/r-containing HAART in an observational cohort study based on clinical practice. The emergence of antiretroviral resistance in samples from subjects who subsequently failed on these regimens was assessed.

We identified 12 amino acid substitutions among those on the IAS list that were associated with failure with FPV/r therapy and four polymorphism mutations. Pertinently, failure was associated with the number of mutations, and a clinical cutoff of ≥4 mutations yielded a significantly poorer response, even after adjustment for other potential confounding factors. The most important differences between the Zephir and ANRS 2005 sets were the inclusion of primary mutations at codons 46 and 90 in the Zephir score. These two mutations, known to impact on the response to APV, were also retained in the mutation sets of Elston et al. (11) and the previously used ANRS 2001-2002 score (9). The Zephir mutation score provided better sensitivity for predicting failure at W12 than the ANRS 2005 rules (94% versus 31%) when applied to the present study population. In most previous studies, the amino acid positions in the protease gene were not analyzed, which could explain why new mutations, like I13V, L19I/Q/V, K55R, or L89M in the Zephir score, could be identified as being involved in the poorer response to FPV/r HAART (L89M was also retained in the work of Elston et al.). The results of other recent studies strongly suggest that other mutations beyond those currently known to be associated with PI resistance should be considered to define precise algorithms able to predict resistance to antiretroviral drugs (5, 23, 36, 44; Stanford University, HIV Drug-Resistance Database [http://hivdb.stanford.edu/]). However, their exact role in PI resistance has not yet been precisely established, and few studies demonstrated the relevance of taking polymorphism mutations into consideration to establish genotype algorithms (24, 30, 36, 40).

Here we showed that Zephir mutation score clinical cutoffs, sensitivity, and specificity in predicting failure remained unchanged, regardless of whether polymorphism mutations were included or not. Pertinently, considering these mutations might render interpretation of the data more confusing, as illustrated in Fig. 1B: two patients with six mutations at baseline were considered successful responders, while their genotypes predicted failure because of three or four polymorphism mutations, leading to the interpretation of a "resistant" genotype. In our opinion, every mutation selected by any statistical analysis should not be accorded the same weight. Indeed, the current approach constitutes one of the limits of using algorithms to interpret genotypes. Nothing allows us to think that polymorphism mutation 13, 19, or 55 carries the same weight as L90M, a primary mutation recognized as being responsible for most cross-resistance to PI. When only the number of mutations from among a given set present at baseline is considered, without specifying which might predict failure, the same impact is accorded each mutation included in the set. This drawback becomes even more acute as the proportion of polymorphism mutations retained increases compared to that of the resistance mutations selected under the pressure of in vivo or in vitro exposure to antiviral drugs. Finally, the potential interactions existing between mutations are extremely difficult to evaluate with available means and are not considered in these scores.

The isolates from 64% of the failing patients developed resistance, previously described with the use of APV/r for PI-experienced patients (21), for naive patients on FPV/r (35), or through alternative pathways involving substitutions commonly observed with other PI and additional NRTI-, NNRTI-, and/or enfuvirtide-related resistance mutations. We showed that resistance could occur even when the genetic barrier of the regimen is deeply eroded by the presence of high numbers of resistance-related mutations at baseline and by the potentially poor exposure to at least some components of the antiretroviral therapy.

Very few data on FPV/r PK are available, especially for multiexperienced patients. The FPV/r once-daily regimen reached a median APV Cmin value of 1,430 ng/ml for the naive patients included in the SOLO study (12). A median APV Cmin value obtained with treatment-experienced patients on FPV/r BID in the CONTEXT study (11) or when associated with saquinavir (1) was 1,670 or 1,264 ng/ml, respectively, compared to 1,400 ng/ml for the Zephir cohort. In contrast to those studies on FPV/r, we found a strong association between Cmin and AUC values for failure at W12, in accordance with findings of authors who used APV/r (14). Furthermore, in our study, AUC was a better predictor of failure than Cmin. Pertinently, the combination of APV Cmin value and genotype score, expressed as GIQ, was highly predictive of failure. Moreover, the independence of AUC values and the Zephir set or GIQ demonstrates the contribution of evaluating genotypes and PK data. Considering that 56% of the patients' Cmin values did not reach the efficacy threshold of 1,600 ng/ml, we demonstrated the high level of relevance of therapeutic drug monitoring with such multiexperienced, multiresistant patients. Plasma APV levels need to be adapted to achieve optimal PK values and thereby avoid virus evolution towards high-level resistance to FPV/r and cross-resistance. The development of such algorithms, combining virus mutations and PI trough levels, should continue to be validated in prospective clinical trials.

Contradictory results were recently reported for the impact on PI exposure when tenofovir DF is coadministered (18, 25, 30, 31, 37). The Zephir cohort data supported a significant correlation among tenofovir DF coprescription, APV PK parameters, and failure at W12 for this population of experienced patients. We showed that the APV Cmin (–23%), Cmax (–22%), and AUC (–19%) values decreased significantly upon tenofovir DF adjunction. In our study, in which 56% of patients did not achieve target plasma APV concentrations, a small APV Cmin decrease could possibly have affected the virological response. But the results of another study indicated that an 11% APV Cmin decrease (31) had no clinical relevance. However, those authors reported data on a small number of patients and compared APV PK parameters with historical data. The mechanism of such interactions could partly be explained by APV properties of PK metabolic induction.


   Conclusion.
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion.
References
 
These findings highlight the need to apply genotype-based algorithms to interpret the impact of mutations on the virological outcome using different patient databases before their implementation in routine clinical practice. FPV/r-specific mutations, plasma APV AUC and Cmin values, and the GIQ provide relevant information for monitoring FPV/r-based HAART.


   ACKNOWLEDGMENTS
 
Genotyping was funded by an unrestricted grant from GlaxoSmithKline (Marly-le-Roi, France).

We thank all the physicians who participated in this study (see below). We thank Chantal Leduc, Dominique Hanou, Monique Descamp, Pascal Bonot, Hélène Diaz, and Danièle Jacquemart for excellent technical assistance and Janet Jacobson for editing our English.

Contributing members of the ANRS Co3 Aquitaine Cohort are as follows. Scientific committee: J. Beylot, M. Dupon, M. Longy-Boursier, J. L. Pellegrin, J. M. Ragnaud, and R. Salamon (Chair). Scientific coordination: G. Chêne, F. Dabis (coordinator), S. Lawson-Ayayi, C. Lewden, and R. Thiébaut. Medical coordination: N. Bernard, M. Dupon, D. Lacoste, D. Malvy, J. F. Moreau, P. Mercié, P. Morlat, D. Neau, J. L. Pellegrin, and J. M. Ragnaud. Data management and statistical analysis: E. Balestre, L. Dequae-Merchadou, and V. Lavignolle-Aurillac. Technical team: M. J. Blaizeau, M. Decoin, S. Delveaux, D. Dutoit, C. Hanappier, L. Houinou, S. Labarrère, G. Palmer, D. Touchard, and B. Uwamaliya.

Participating hospital departments (participating physicians): Bordeaux University Hospitals, J. Beylot (N. Bernard, M. Bonarek, F. Bonnet, D. Lacoste, P. Morlat, and R. Vatan), P. Couzigou, H. Fleury (M. E. Lafon, B. Masquelier, and I. Pellegrin), M. Dupon (H. Dutronc, F. Bocquentin, and S. Lafarie), J. L. Pellegrin (O. Caubet, E. Lazaro C. Nouts, and J. F. Viallard), M. Longy-Boursier (D. Malvy, P. Mercié, T. Pistonne, and C. Receveur), J. F. Moreau (P. Blanco), and J. M. Ragnaud (C. Cazorla, D. Chambon, C. De La Taille, D. Neau, and A. Ochoa); Dax Hospital, P. Loste (L. Caunègre); Bayonne Hospital, F. Bonnal (S. Farbos and M. C. Gemain); Libourne Hospital, J. Ceccaldi (S. Tchamgoué); and Mont-de-Marsan Hospital, S. de Witte.


   FOOTNOTES
 
* Corresponding author. Mailing address: Laboratoire de Virologie, Hôpital Pellegrin, place Amélie Raba-Léon, 33076 Bordeaux Cedex, France. Phone: 33 5 5679 5510. Fax: 33 5 5679 5673. E-mail: isabelle.pellegrin{at}chu-bordeaux.fr Back

{triangledown} Published ahead of print on 12 February 2007. Back

{dagger} The first two authors contributed equally to this study. Back

{ddagger} For composition of the ANRS Co3 Aquitaine Cohort, see Acknowledgments. Back


   REFERENCES
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion.
 References
 

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Antimicrobial Agents and Chemotherapy, April 2007, p. 1473-1480, Vol. 51, No. 4
0066-4804/07/$08.00+0     doi:10.1128/AAC.00481-06
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