Multilocus genetic interactions and response to efavirenz-containing regimens: an adult AIDS clinical trials group study.
ABSTRACT For the HIV-1 reverse transcriptase inhibitor efavirenz, variant drug transporter gene ABCB1 may predict virologic response but not plasma efavirenz exposure. Conversely, variant drug metabolizing enzyme gene CYP2B6 predicts greater plasma efavirenz exposure but not virologic response. We examined whether long-term responses to efavirenz, and/or plasma efavirenz exposure, are better predicted by multilocus genetic interactions than by individual polymorphisms.
We studied antiretroviral-naïve study participants randomized to receive efavirenz (with or without nelfinavir) plus two nucleoside analogues in study ACTG 384, and who had DNA available for analysis. Participants were followed up for up to 3 years. Nine single nucleotide polymorphisms in ABCB1, CYP2B6, CYP3A4, CYP3A5 and CYP2C19 were identified. Gene-gene interactions were identified using multifactor dimensionality reduction.
Among 340 efavirenz recipients, higher efavirenz AUC24 h values were associated with a single locus model involving CYP2B6 516G>T (73% accuracy; P<0.001). This was also the best model among blacks (69% accuracy; P<0.001), whereas among whites the best model involved a gene-gene interaction between CYP2B6 516G>T and ABCB1 2677G>T (82% accuracy, P<0.001). Among 155 participants who received efavirenz without nelfinavir, virologic failure was associated with a two-locus interaction between ABCB1 2677G>T and CYP2B6 516G>T (65% accuracy, P<0.001). Toxicity failure was best predicted by an interaction between ABCB1 2677G>T and ABCB1 3435C>T (71% accuracy, P<0.001).
Multilocus genetic interactions between variant drug metabolism and transporter genes may predict efavirenz pharmacokinetics and treatment responses. This finding may have implications for better individualizing antiretroviral therapy.
- SourceAvailable from: David Samuels[show abstract] [hide abstract]
ABSTRACT: Introduction Mitochondrial function influences T cell dynamics and is affected by mitochondrial DNA (mtDNA) variation. We previously reported an association between African mtDNA haplogroup L2 and less robust CD4 cell recovery on antiretroviral therapy (ART) in non-Hispanic black ACTG 384 subjects. We explored whether additional T cell parameters in this cohort differed by mtDNA haplogroup. Methods ACTG 384 randomized ART-naïve subjects to two different nucleoside regimens with efavirenz, nelfinavir, or both. CD4 and CD8 memory and activation markers were available at baseline and week 48 on most subjects. mtDNA sequencing was performed on whole blood DNA, and haplogroups were determined. We studied non-Hispanic black subjects with HIV RNAPLoS ONE 08/2012; 7(8). · 3.73 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: OBJECTIVE:: CYP2B6 variation predicts pharmacokinetic characteristics of its substrates. Consideration for underlying genetic structure is critical to protect against spurious associations with the highly polymorphic CYP2B6 gene. DESIGN:: The effect of CYP2B6 variation on response to its substrates, nonnucleoside reverse transcriptase inhibitors (NNRTIs), was explored in the Women's Interagency HIV Study. METHODS:: Five putative functional polymorphisms were tested for associations with virologic suppression within 1 year after NNRTI initiation in women naive to antiretroviral agents (n = 91). Principal components were generated to control for population substructure. Logistic regression was used to test the joint effect of rs3745274 and rs28399499, which together indicate slow, intermediate, and extensive metabolizers. RESULTS:: Rs3745274 was significantly associated with virologic suppression [odds ratio = 3.61, 95% confidence interval (CI) 1.16-11.22, P trend = 0.03]; the remaining polymorphisms tested were not significantly associated with response. Women classified as intermediate and slow metabolizers were 2.90 (95% CI 0.79-12.28) and 13.44 (95% CI 1.66 to infinity) times as likely to achieve virologic suppression compared to extensive metabolizers after adjustment for principal components (P trend = 0.005). Failure to control for genetic ancestry resulted in substantial confounding of the relationship between the metabolizer phenotype and treatment response. CONCLUSION:: The CYP2B6 metabolizer phenotype was significantly associated with virologic response to NNRTIs; this relationship would have been masked by simple adjustment for self-reported ethnicity. Given the appreciable genetic heterogeneity that exists within self-reported ethnicity, these results exemplify the importance of characterizing underlying genetic structure in pharmacogenetic studies. Further follow-up of the CYP2B6 metabolizer phenotype is warranted, given the potential clinical importance of this finding.AIDS (London, England) 08/2012; 26(16):2097-2106. · 4.91 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Aim: This article evaluates which genetic factors are involved in CNS toxicity related to long-term treatment with efavirenz (EFV) standard doses and their relationship with plasma concentrations. Patients & methods: A total of 119 HIV-positive patients, in which 1350 EFV plasma concentrations, 68 SNPs and 14 EFV-related adverse effects (AEs) were analyzed. Results: Overall, 32.77% of patients reported CNS toxicity and 8.40% had concentrations above the therapeutic range. A correlation was mainly found between patients with global CNS AEs and high EFV maximum steady-state plasma concentration (p = 1.47 × 10(-6)). A preliminary analysis confirmed that CYP2B6*6 (516G>T and 785A>G) was the most highly correlated (p = 0.005) with AEs and high plasma concentrations. In a second analysis adjusting for maximum steady-state plasma concentration, suggestive genetic associations were found between BCRP 421C>A, MRP1 816G>A, 5-HT2A 102C>T and different AEs. Conclusion: The finding of the involvement of these SNPs in EFV toxicity opens the door for further studies to confirm their validity and for their application in the future clinical practice. Original submitted 18 February 2013; Revision submitted 17 May 2013.Pharmacogenomics 07/2013; 14(10):1167-78. · 3.86 Impact Factor