A cohort study of treatment-experienced HIV-1-infected patients treated with raltegravir: factors associated with virological response and mutations selected at failure.

This study aimed to identify factors associated with virological response (VR) to raltegravir (RAL)-containing regimens in 468 treatment-experienced but integrase inhibitor-naive HIV-1 patients receiving a RAL-containing regimen. VR was defined at Month 6 (M6) as HIV-1 RNA viral load (VL) <50 copies/mL. The impacts on VR of baseline integrase mutations, VL, CD4 count, genotypic sensitivity score for nucleoside reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors and protease inhibitors, and the number of new antiretrovirals used for the first time associated with RAL were investigated. For patients with VL >50 copies/mL at M6, integrase mutations selected were characterised. Median baseline VL was 4.2 log(10)copies/mL (IQR 3.3-4.9 log(10) copies/mL) and CD4 count was 219 cells/mm(3) (IQR 96-368 cells/mm(3)). At M6, 71% of patients were responders. In multivariate analysis, baseline VL and CD4 count and ≥ 2 new antiretrovirals among darunavir, etravirine, maraviroc and enfuvirtide were associated with VR to RAL. Neither HIV-1 subtype nor baseline integrase polymorphisms were associated with VR to RAL. Among 63 failing patients at M6, selection of ≥ 1 change in the integrase gene was observed in 49 (77.8%), and 27/63 (42.9%) were considered as RAL-associated resistance mutations. Factors independently associated with the occurrence of ≥ 1 RAL-associated resistance mutation were VL at failure >3 log(10) and having no new drugs associated with RAL. RAL showed great potency in treatment-experienced patients. The number of new drugs associated with RAL was an important factor associated with VR. HIV-1 subtype and baseline integrase polymorphisms do not influence the RAL VR.

Scoring methods for building genotypic scores: an application to didanosine resistance in a large derivation set.

BACKGROUND: Several attempts have been made to determine HIV-1 resistance from genotype resistance testing. We compare scoring methods for building weighted genotyping scores and commonly used systems to determine whether the virus of a HIV-infected patient is resistant. METHODS AND PRINCIPAL FINDINGS: Three statistical methods (linear discriminant analysis, support vector machine and logistic regression) are used to determine the weight of mutations involved in HIV resistance. We compared these weighted scores with known interpretation systems (ANRS, REGA and Stanford HIV-db) to classify patients as resistant or not. Our methodology is illustrated on the Forum for Collaborative HIV Research didanosine database (N = 1453). The database was divided into four samples according to the country of enrolment (France, USA/Canada, Italy and Spain/UK/Switzerland). The total sample and the four country-based samples allow external validation (one sample is used to estimate a score and the other samples are used to validate it). We used the observed precision to compare the performance of newly derived scores with other interpretation systems. Our results show that newly derived scores performed better than or similar to existing interpretation systems, even with external validation sets. No difference was found between the three methods investigated. Our analysis identified four new mutations associated with didanosine resistance: D123S, Q207K, H208Y and K223Q. CONCLUSIONS: We explored the potential of three statistical methods to construct weighted scores for didanosine resistance. Our proposed scores performed at least as well as already existing interpretation systems and previously unrecognized didanosine-resistance associated mutations were identified. This approach could be used for building scores of genotypic resistance to other antiretroviral drugs.

Switching to darunavir/ritonavir 800/100 mg once-daily containing regimen maintains virological control in fully suppressed pre-treated patients infected with HIV-1.

The objective of this study was to evaluate the switch to once-daily darunavir/ritonavir 800/100 mg in treatment-experienced patients with suppressed HIV-1 replication on a twice-daily ritonavir-boosted protease-inhibitor (bid PI/r) containing regimen, that is in a setting where genotypic resistance test cannot be performed. In this open label, non-comparative, multicenter study, patients on a bid PI/r-containing triple combination, with suppressed viral replication, were switched to once-daily darunavir/r 800/100 mg containing triple combination. The primary endpoint was the proportion of patients with plasma HIV-RNA < 50 copies/ml 24 weeks after the switch. Intensive darunavir pharmacokinetic evaluation was performed at Week 4 (W4) in 11 patients. Eighty-five patients were enrolled. All had HIV-RNA < 50 copies/ml at screening with a pre-exposure to a median of 2 PI/r (1-5). By intent-to-treat analysis (missing = failure), 78/85 patients (92%, 95% CI [83;96]) maintained an HIV-RNA < 50 copies/ml at W24. Seven patients experienced protocol-defined treatment failure between baseline and W24: Two had confirmed low-level viral rebound, one discontinued study treatment for adverse event, three withdrew their consent, and one was lost to follow-up. By on-treatment analysis, 78/80 patients (97%, 95% CI [91;99]) maintained an HIV-RNA < 50 copies/ml at W24. Results were similar at Week 48. The median area under the darunavir plasma concentration-time curve measured in 11 patients was 61,380 ng hr/ml; darunavir median trough concentration 1,340 ng/ml and darunavir half-life was 12.2 hr. Tolerability of once-daily darunavir/r 800/100 mg was excellent. Optimally suppressed, treatment-experienced patients can switch safely from a twice-daily PI/r regimen to a once-daily darunavir/r 800/100 mg containing regimen.

Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data.

Background. Many statistical models have been tested to predict phenotypic or virological response from genotypic data. A statistical framework called Super Learner has been introduced either to compare different methods/learners (discrete Super Learner) or to combine them in a Super Learner prediction method. Methods. The Jaguar trial is used to apply the Super Learner framework. The Jaguar study is an "add-on" trial comparing the efficacy of adding didanosine to an on-going failing regimen. Our aim was also to investigate the impact on the use of different cross-validation strategies and different loss functions. Four different repartitions between training set and validations set were tested through two loss functions. Six statistical methods were compared. We assess performance by evaluating R(2) values and accuracy by calculating the rates of patients being correctly classified. Results. Our results indicated that the more recent Super Learner methodology of building a new predictor based on a weighted combination of different methods/learners provided good performance. A simple linear model provided similar results to those of this new predictor. Slight discrepancy arises between the two loss functions investigated, and slight difference arises also between results based on cross-validated risks and results from full dataset. The Super Learner methodology and linear model provided around 80% of patients correctly classified. The difference between the lower and higher rates is around 10 percent. The number of mutations retained in different learners also varys from one to 41. Conclusions. The more recent Super Learner methodology combining the prediction of many learners provided good performance on our small dataset.

Effect of antiretroviral drugs on the quality of semen.

The aim of this cross-sectional study was to determine which antiretroviral drugs (ARVs) are associated with changes in the characteristics of semen and the impact of these ARVs according to their score penetration into the male genital compartment. Data from 144 men infected with HIV-1 enrolled in an Assisted Reproductive Technology program were analyzed retrospectively. A seminal penetration score of ARV was based on the available literature. The nonparametric Kruskal-Wallis test and chi-square test were used. There was no difference on sperm parameters between NRTI, NNRTI, or PI regimen. In patients receiving NRTIs or PIs no differences were observed between antiretrovirals of these classes. However, in patients receiving NNRTIs, nevirapine (n = 22) was associated with a higher percentage of progressively motile spermatozoa (P < 0.0001) versus efavirenz (n = 38) as well as vitality (P = 0.0004). No relationship was observed between semen quality and the penetration score. NRTIs and PIs were not associated with any semen changes. Nevirapine was associated with a better quality of semen versus efavirenz. It would be of interest to validate, improve and test our penetration score in a prospective study.

Relative contributions of baseline patient characteristics and the choice of statistical methods to the variability of genotypic resistance scores: the example of didanosine.

BACKGROUND: Our aim was to investigate the respective role of statistical methodology and patients' baseline characteristics in the selection of mutations included in genotypic resistance scores. METHODS: As an example, the FORUM database on didanosine including 1453 patients was used. We split this population into four samples based on countries of enrolment (France n = 474, Italy n = 440, USA/Canada n = 219, others n = 320). We used both a continuous outcome measure (the viral load reduction at week 8) and a binary outcome measure (viral load decline at week 8 <0.6 log(10) or > or =0.6 log(10)) and both parametric and non-parametric methods for each outcome. RESULTS: Overall, 61 distinct mutations were selected by at least one method in at least one data set. The variability due to baseline characteristics varies from 79% to 88%, i.e. for a given method applied to the four data sets >80% of the mutations are selected only once. The variability induced by the methodology varies from 49% to 56%, i.e. for a given data set approximately 50% of the mutations are selected by at least two methods. CONCLUSIONS: Baseline patient characteristics contribute more than the choice of statistical method to the variability of the mutations to be included in the genotypic resistance scores.