This post hoc analysis of ACQUIRE (NCT00559585) explored the effect of baseline body mass index (BMI) on the pharmacokinetics of and clinical response to subcutaneous (SC) or intravenous (IV) abatacept in patients with rheumatoid arthritis (RA). ACQUIRE was a phase 3b, 6-month, double-blind, double-dummy study in which patients with RA were randomized (1:1) to SC (fixed - dose; 125 mg/week) or IV (weight-tiered; ~ 10 mg/kg/month) abatacept plus methotrexate. In this analysis, minimum abatacept plasma concentration (Cmin) was measured at 3 and 6 months, and clinical remission over 6 months was assessed by Disease Activity Score 28 (C-reactive protein; DAS28 [CRP], < 2.6), Simplified Disease Activity Index (SDAI, ≤ 3.3), and Clinical Disease Activity Index (CDAI, ≤ 2.8). Data were stratified by baseline BMI (underweight/normal, < 25 kg/m2; overweight, 25 to < 30 kg/m2; obese, ≥ 30 kg/m2) and administration route. Of the 1456/1457 patients for whom baseline BMIs were available, 526 (36%; SC 265, IV 261) patients were underweight/normal, 497 (34%; SC 249, IV 248) were overweight, and 433 (30%; SC 221, IV 212) were obese. Median Cmin abatacept concentration was ≥ 10 μg/mL (efficacy threshold) at 3 and 6 months in > 90% of patients across BMI groups with both administration routes. DAS28 (CRP), SDAI, and CDAI remission rates at 6 months were similar across BMI groups and 95% confidence intervals overlapped at all time points in both separate and pooled SC/IV analyses. Therapeutic concentrations of abatacept and clinical remission rates using stringent criteria were similar across patient BMIs and administration routes.

Objectives Evaluate abatacept retention over 2 years in the AbataCepT In rOutiNe clinical practice (ACTION) study. Method ACTION was an international, observational study of patients with moderate-to-severe rheumatoid arthritis (RA) who initiated intravenous abatacept. Crude abatacept retention rates over 2 years were estimated using Kaplan-Meier analyses in biologic-naive and -failure patients. Clinically relevant risk factors and significant prognostic factors for retention were evaluated using a Cox proportional hazards multivariable model. Results Overall, 2350/2364 enrolled patients were evaluable; 673 (28.6%) were biologic naive and 1677 (71.4%) had prior biologic failure (1 biologic, 728/1677 [43.4%]; ≥ 2 biologics, 949/1677 [56.6%]). Abatacept retention rate (95% confidence interval [CI]) at 2 years was 47.9% (45.7, 50.0): 54.5% (50.4, 58.3) for biologic-naive vs 45.2% (42.7, 47.7) for biologic-failure patients (log-rank P < 0.001). For patients with 1 and ≥ 2 prior biologic failures, respectively, retention rates (95% CI) were 50.2% (46.3, 53.9) vs 41.3% (38.0, 44.6; log-rank P < 0.001). Main reasons for discontinuation (biologic-naive vs biologic-failure, respectively) were lack of efficacy (61.4 vs 67.7%) and safety (21.3 vs 21.2%). Rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) double positivity versus negativity were predictive of higher retention in both biologic-naive (hazard ratio [HR] [95% CI] 0.71 [0.53, 0.96]; P = 0.019) and biologic-failure patients (HR [95% CI] 0.76 [0.62, 0.94]; P = 0.035). Conclusions Abatacept initiation as earlier vs later line of therapy in RA may achieve higher 2-year retention rates. RF and anti-CCP seropositivity could predict increased abatacept retention, irrespective of treatment line. Trial registration NCT02109666

BackgroundHIV-infected cells in semen facilitate viral transmission. We studied the establishment of HIV reservoirs in semen and blood during PHI, along with systemic immune activation and the impact of early cART.MethodsPatients in the ANRS-147-OPTIPRIM trial received two years of early cART. Nineteen patients of the trial were analyzed, out of which 8 had acute PHI (WB ≤1 Ab). We quantified total cell-associated (ca) HIV-DNA in blood and semen and HIV-RNA in blood and semen plasma samples, collected during PHI and at 24 months of treatment.ResultsAt enrollment, HIV-RNA load was higher in blood than in semen (median 5.66 vs 4.22 log10 cp/mL, p<0.0001). Semen HIV-RNA load correlated strongly with blood HIV-RNA load (r = 0.81, p = 0.02, the CD4 cell count (r = -0.98, p<0.0001), and the CD4/CD8 ratio (r = -0.85, p<0.01) in acute infection but not in later stages of PHI. Median blood and seminal cellular HIV-DNA levels were 3.59 and 0.31 log10cp/106 cells, respectively. HIV-DNA load peaked in semen later than in blood and then correlated with blood IP10 level (r = 0.62, p = 0.04). HIV-RNA was undetectable in blood and semen after two years of effective cART. Semen HIV-DNA load declined similarly, except in one patient who had persistently high IP-10 and IL-6 levels and used recreational drugs.ConclusionsHIV reservoir cells are found in semen during PHI, with gradual compartmentalization. Its size was linked to the plasma IP-10 level. Early treatment purges both the virus and infected cells, reducing the high risk of transmission during PHI.Clinical trials registrationNCT01033760

Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21.3% of patients were underexposed (AUC24h < 666 mg.h/L) and 31.9% of patients were overexposed (Cmin > 24.3 mg/L) on the first TDM occasion. The BestDose model performed slightly better than the model in Tucuxi (ME = −0.13 ± 5.16 vs. −1.90 ± 6.99 mg/L, p < 0.001), but overall results were in agreement between the two models. A significant proportion of patients exhibited underexposure or overexposure to daptomycin after the initial dosage, which supports TDM. The two models may be useful for model-informed precision dosing.

Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520; 0.646] and 0.621 [0.563; 0.679] using the kNN and 0.565 [0.500; 0.631] and 0.617 [0.561; 0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554; 0.675] and 0.549 [0.495; 0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.