Suchergebnisse

No funding was received specifically for this study or for the preparation of this article. Elin M. Svensson is supported by PanACEA, which is part of the European and Developing Countries Clinical Trials Partnership (EDCTP) 2 programme supported by the European Union (Grant number TRIA2015-1102-PanACEA). ; Background and ObjectiveThis study aimed to develop and evaluate a population pharmacokinetic model and limited sampling strategy for isoniazid to be used in model-based therapeutic drug monitoring. MethodsA population pharmacokinetic model was developed based on isoniazid and acetyl-isoniazid pharmacokinetic data from seven studies with in total 466 patients from three continents. Three limited sampling strategies were tested based on the available sampling times in the dataset and practical considerations. The tested limited sampling strategies sampled at 2, 4, and 6 h, 2 and 4 h, and 2 h after dosing. The model-predicted area under the concentration–time curve from 0 to 24 h (AUC24) and the peak concentration from the limited sampling strategies were compared to predictions using the full pharmacokinetic curve. Bias and precision were assessed using the mean error (ME) and the root mean square error (RMSE), both expressed as a percentage of the mean model-predicted AUC24 or peak concentration on the full pharmacokinetic curve. ResultsPerformance of the developed model was acceptable and the uncertainty in parameter estimations was generally low (the highest relative standard error was 39% coefficient of variation). The limited sampling strategy with sampling at 2 and 4 h was determined as most suitable with an ME of 1.1% and RMSE of 23.4% for AUC24 prediction, and ME of 2.7% and RMSE of 23.8% for peak concentration prediction. For the performance of this strategy, it is important that data on both isoniazid and acetyl-isoniazid are used. If only data on isoniazid are available, a limited sampling strategy using 2, 4, and 6 h can be employed with an ME of 1.7% and RMSE of 20.9% for AUC24 prediction, and ME of 1.2% and RMSE of 23.8% for peak concentration prediction. ConclusionsA model-based therapeutic drug monitoring strategy for personalized dosing of isoniazid using sampling at 2 and 4 h after dosing was successfully developed. Prospective evaluation of this strategy will show how it performs in a clinical therapeutic drug monitoring setting. ; Publisher PDF ; Peer reviewed

Abstract
IntroductionWhen protease inhibitor (PI)‐based second‐line ART fails, guidelines recommend drug resistance testing and individualized third‐line treatment. However, PI‐resistant viral strains are rare and drug resistance testing is costly. We investigated whether less costly PI‐exposure testing can be used to select those patients who would benefit most from drug resistance testing.MethodsWe performed a retrospective analysis of South African adults living with HIV experiencing failure of ritonavir‐boosted‐lopinavir (LPV/r)‐based second‐line ART for whom drug resistance testing results were available. We included patients who received plasma‐based drug resistance testing at a central South African reference laboratory in 2017 and patients who received dried blood spots (DBS)‐based drug resistance testing at a rural South African clinic between 2009 and 2017. PI‐exposure testing was performed on remnant plasma or DBS using liquid chromatography mass spectrometry (LCMS). Additionally, a low‐cost immunoassay was used on plasma. Population genotypic drug resistance testing of the pol region was performed on plasma and DBS using standard clinical protocols.ResultsSamples from 544 patients (494 plasma samples and 50 DBS) were included. Median age was 41.0 years (IQR: 33.3 to 48.5) and 58.6% were women. Median HIV‐RNA load was 4.9 log10 copies/mL (4.3 to 5.4). Prevalence of resistance to the NRTI‐backbone was 70.6% (349/494) in plasma samples and 56.0% (28/50) in DBS. Major PI‐resistance mutations conferring high‐level resistance to LPV/r were observed in 26.7% (132/494) of plasma samples and 12% (6/50) of DBS. PI‐exposure testing revealed undetectable LPV levels in 47.0% (232/494) of plasma samples and in 60.0% (30/50) of DBS. In pooled analysis of plasma and DBS samples, detectable LPV levels had a sensitivity of 90% (84% to 94%) and a negative predictive failure of 95% (91% to 97%) for the presence of major LPV/r resistance.ConclusionsPI‐exposure testing revealed non‐adherence in half of patients experiencing failure on second‐line ART and accurately predicted the presence or absence of clinically relevant PI resistance. PI‐exposure testing constitutes a novel screening strategy in patients with virological failure of ART that can differentiate between different underlying causes of therapy failure and may allow for more effective use of limited resources available for drug resistance testing.