Relationship between antiretroviral plasma concentration and emergence of HIV-1 resistance mutations at treatment failure
ABSTRACT The relationship between antiretroviral pharmacokinetic exposure and acquisition of human immunodeficency virus-1 (HIV-1) drug resistance mutations (DRM) is not fully understood. The aim of this study was to investigate whether antiretroviral plasma concentration could predict the emergence of DRM at treatment failure.
The study cohort comprised retrospectively selected patients with failing antiretroviral regimens for whom a protease inhibitor (PI) or non-nucleoside reverse transcriptase inhibitor (NNRTI) trough concentration measurement (TDM) had been obtained before failure, a genotypic resistance test (GRT1) had been performed before the TDM, and a genotypic resistance test (GRT2) had been performed at therapeutic failure. Drug levels were classified as undetectable/detectable or subtherapeutic/therapeutic according to limits of quantification of a high-performance liquid chromatography-ultraviolet assay or pre-defined efficacy thresholds, respectively. The number of DRM acquired at treatment failure was evaluated by comparing the results of the GRT2 and GRT1.
A total of ten and 57 failure episodes occurred among our patients on NNRTI-based and PI-based regimens, respectively, and included in the evaluation. PI concentration was subtherapeutic in 28.1% of patients, among which the levels were undetectable in 21.1%. Twenty-five (43.9%) patients acquired at least one new PI-DRM according to the GRT2. Patients with undetectable PI levels showed a lower emergence of PI-DRM (minor + major) than those with detectable levels (8.3 vs. 53.3%, p = 0.007). Multivariate analysis confirmed that undetectable PI levels were independent negative predictors of DRM selection. NNRTI measurements were subtherapeutic in 2/10 (20%) patients. NNRTI-DRM were acquired by all patients regardless of NNRTI levels.
A PI measurement showing undetectable drug levels prior to treatment failure predicted the lack of emergence of PI-DRM at failure. These results suggest that PI levels can help clinicians interpret the reasons for treatment failure and guide the type of interventions needed.
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ABSTRACT: Introduction In order to assess the significance of drug levels measured in intensive care medicine, clinical and forensic toxicology, as well as for therapeutic drug monitoring, it is essential that a comprehensive collection of data is readily available. Therefore, it makes sense to offer a carefully referenced compilation of therapeutic and toxic plasma concentration ranges, as well as half-lives, of a large number of drugs and other xenobiotics for quick and comprehensive information. Methods Data have been abstracted from original papers and text books, as well as from previous compilations, and have been completed with data collected in our own forensic and clinical toxicology laboratory. The data presented in the table and corresponding annotations have been developed over the past 20 years and longer. A previous compilation has been completely revised and updated. In addition, more than 170 substances, especially drugs that have been introduced to the market since 2003 as well as illegal drugs, which became known to cause intoxications, were added. All data were carefully referenced and more than 200 new references were included. Moreover, the annotations providing details were completely revised and more than 100 annotations were added. Results For nearly 1,000 drugs and other xenobiotics, therapeutic ("normal") and, if data were available, toxic and comatose-fatal blood-plasma concentrations and elimination half-lives were compiled in a table. Conclusions In case of intoxications, the concentration of the ingested substances and/or metabolites in blood plasma better predicts the clinical severity of the case when compared to the assumed amount and time of ingestion. Comparing and contrasting the clinical case against the data provided, including the half-life, may support the decision for or against further intensive care. In addition, the data provided are useful for the therapeutic monitoring of pharmacotherapies, to facilitate the diagnostic assessment and monitoring of acute and chronic intoxications, and to support forensic and clinical expert opinions.Critical care (London, England) 07/2012; 16(4):R136. DOI:10.1186/cc11441 · 4.48 Impact Factor
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ABSTRACT: Efavirenz is mainly metabolized by cytochrome P450 2B6 (CYP2B6). This study aimed to examine the frequencies of CYP2B6 and the association between CYP2B6 polymorphisms and plasma efavirenz concentrations in an HIV-1 infected Thai population. Mid-dose plasma efavirenz concentration was determined at 12 weeks following the initiation of an antiretroviral therapy (tenofovir, lamivudine and efavirenz) in 100 Thai adults with HIV-1 infection using high-performance liquid chromatography. Candidate CYP2B6 polymorphisms (c.64C>T, c.499C>G, c.516G>T, c.785A>G, c.1375A>G, c.1459C>T) were conducted by real-time PCR-based allelic discrimination. The most frequent polymorphism among this cohort was the CYP2B6 c.785A>G and c.516G>T, which had a frequency of 0.36 and 0.32, respectively. From the observed, two single nucleotide polymorphisms (SNPs) (c.516G>T and c.785A>G) were significantly associated with high efavirenz plasma levels (P < 0.05). The most frequent haplotypic combinations were *1/*6, *1/*1, *1/*2 and *6/*6 at a frequency of 42.0%, 32.0%, 8.0% and 7.0%, respectively. Increased plasma concentrations of efavirenz were present in individuals with CYP2B6 *6/*6 (7.210 mg/L; interquartile range (IQR), 5.020-9.260) when compared to those with CYP2B6*1/*1 (1.570 mg/L; IQR, 1.295-2.670), P < 0.001. In our study, the impact of SNPs, which are correlated with high dose of efavirenz plasma concentrations, was found. The genetic configuration of SNPs, which are associated with high plasma efavirenz levels, may be useful in optimizing the efavirenz dose that is used in HIV-1 infected patients.Drug Metabolism and Pharmacokinetics 02/2013; 28(5). DOI:10.2133/dmpk.DMPK-12-RG-120 · 2.57 Impact Factor
- Pharmacogenomics 07/2013; 14(9):999-1001. DOI:10.2217/pgs.13.69 · 3.22 Impact Factor