Influence of ABCB1, ABCC1, ABCC2, and ABCG2 haplotypes on the cellular exposure of nelfinavir in vivo

OBJECTIVES: The human immunodeficiency virus protease inhibitor nelfinavir is substrate of polyspecific drug transporters encoded by ABCB1 (P-glycoprotein), ABCC1 (MRP1) and ABCC2 (MRP2), and an inhibitor of BCRP, encoded by ABCG2. Genetic polymorphism in these genes may be associated with changes in transport function. METHODS: A comprehensive evaluation of single nucleotide polymorphisms (39 SNPs in ABCB1, 7 in ABCC1, 27 in ABCC2, and 16 in ABCG2), and inferred haplotypes was done to assess possible associations of genetic variants with cellular exposure of nelfinavir in vivo. Analysis used peripheral mononuclear cells from individuals receiving nelfinavir (n=28). Key results were re-examined in a larger sample size (n=129) contributing data on plasma drug levels. RESULTS AND CONCLUSIONS: There was no significant association between cellular nelfinavir area under the curve (AUC) and SNPs or haplotypes at ABCC1, ABCC2, ABCG2. There was an association with cellular exposure for two loci in strong linkage disequilibrium: ABCB1 3435C>T; AUCTT>AUCCT>AUCCC (ratio 2.1, 1.4, 1, Ptrend=0.01), and intron 26 +80T>C; AUCCC> AUCCT > AUCTT (ratio 2.4, 1.3, 1, Ptrend=0.006). Haplotypic analysis using tagging SNPs did not improve the single SNP association values.

Treatment with the HIV protease inhibitor Nelfinavir triggers the unfolded protein response and may overcome proteasome inhibitor resistance of multiple myeloma in combination with bortezomib: a phase I trial (SAKK 65/08).

Downregulation of the unfolded protein response mediates proteasome inhibitor resistance in Multiple Myeloma.The Human Immunodeficieny Virus protease inhibitor nelfinavir activates the unfolded protein response in vitro. We determined dose limiting toxicity and recommended dose for phase II of nelfinavir in combination with the proteasome inhibitor bortezomib. 12 patients with advanced hematological malignancies were treated with nelfinavir (2500 - 5000 mg/d p.o., d 1-14, 3+3 dose escalation) and bortezomib (1.3 mg/m2, d 1, 4, 8, 11; 21 day cycles). A run in phase with nelfinavir monotherapy allowed pharmakokinetic/pharmakodynamic assessment of nelfinavir in the presence or absence of concomittant bortezomib. Endpoints included dose limiting toxicity, activation of the unfolded protein response, proteasome activity, toxicity and response to trial treatment. Nelfinavir 2 x 2500 mg was the recommended phase II dose identified. Nelfinavir alone significantly upregulated expression of proteins related to the unfolded protein response in peripheral blood mononuclear cells and inhibited proteasome activity. Of 10 evaluable patients in the dose escalation cohort, 3 achieved a partial response, 4 stable disease for ≥ 2 cycles, while 3 had progressive disease as best response. In an exploratory extension cohort with 6 relapsed, bortezomib-refractory, lenalidomide-resistant myeloma patients treated at the recommended phase II dose, 3 reached a partial response, 2 a minor response and one progressive disease. The combination of nelfinavir with bortezomib is safe and shows promising signals for activity in advanced, bortezomib-refractory MM. Induction of the unfolded protein response by nelfinavir may overcome the biological features of proteasome inhibitor resistance. (Trial registration NCT01164709).

Orosomucoid (alpha1-acid glycoprotein) plasma concentration and genetic variants: effects on human immunodeficiency virus protease inhibitor clearance and cellular accumulation

BACKGROUND AND OBJECTIVE: Protease inhibitors are highly bound to orosomucoid (ORM) (alpha1-acid glycoprotein), an acute-phase plasma protein encoded by 2 polymorphic genes, which may modulate their disposition. Our objective was to determine the influence of ORM concentration and phenotype on indinavir, lopinavir, and nelfinavir apparent clearance (CL(app)) and cellular accumulation. Efavirenz, mainly bound to albumin, was included as a control drug. METHODS: Plasma and cells samples were collected from 434 human immunodeficiency virus-infected patients. Total plasma and cellular drug concentrations and ORM concentrations and phenotypes were determined. RESULTS: Indinavir CL(app) was strongly influenced by ORM concentration (n = 36) (r2 = 0.47 [P = .00004]), particularly in the presence of ritonavir (r2 = 0.54 [P = .004]). Lopinavir CL(app) was weakly influenced by ORM concentration (n = 81) (r2 = 0.18 [P = .0001]). For both drugs, the ORM1 S variant concentration mainly explained this influence (r2 = 0.55 [P = .00004] and r2 = 0.23 [P = .0002], respectively). Indinavir CL(app) was significantly higher in F1F1 individuals than in F1S and SS patients (41.3, 23.4, and 10.3 L/h [P = .0004] without ritonavir and 21.1, 13.2, and 10.1 L/h [P = .05] with ritonavir, respectively). Lopinavir cellular exposure was not influenced by ORM abundance and phenotype. Finally, ORM concentration or phenotype did not influence nelfinavir (n = 153) or efavirenz (n = 198) pharmacokinetics. CONCLUSION: ORM concentration and phenotype modulate indinavir pharmacokinetics and, to a lesser extent, lopinavir pharmacokinetics but without influencing their cellular exposure. This confounding influence of ORM should be taken into account for appropriate interpretation of therapeutic drug monitoring results. Further studies are needed to investigate whether the measure of unbound drug plasma concentration gives more meaningful information than total drug concentration for indinavir and lopinavir.

Are plasma levels valid surrogates for cellular concentrations of antiretroviral drugs in HIV-infected patients?

Total plasma concentrations are currently measured for therapeutic drug monitoring of HIV protease inhibitors (PIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs). However, the pharmacological target of antiretroviral drugs reside inside cells. To study the variability of their cellular accumulation, and to determine to which extent total plasma concentrations (TPC) correlate with cellular concentrations (CC), plasma and peripheral blood mononuclear cells (PBMCs) were simultaneously collected at single random times after drug intake from 133 HIV infected patients. TPC levels were analysed by high-performance liquid chromatography with ultraviolet detection and CC by LC-MS/MS from peripheral blood mononuclear cells. The best correlations between TPC and CC were observed for nelfinavir (NFV, slope=0.93, r=0.85), saquinavir (SQV, slope=0.76, r=0.80) and lopinavir (LPV, slope=0.87, r=0.63). By contrast, TPC of efavirenz (EFV) exhibited a moderate correlation with CC (slope=0.69, r=0.58), while no correlation was found for nevirapine (NVP, slope=-0.3, r=0.1). Interindividual variability in the CC/TPC ratio was lower for protease inhibitors (coefficients of variation 76%, 61%, and 80% for SQV, NFV and LPV, respectively) than for nonnucleoside reverse transcriptase inhibitors (coefficients of variation 101% and 318%, for EFV and NVP). As routine CC measurement raises practical difficulties, well-correlated plasma concentrations (ie, NFV, SQV and LPV) can probably be considered as appropriate surrogates for cellular drug exposure. For drugs such as EFV or NVP, there may be room for therapeutic drug monitoring improvement using either direct CC determination or other predictive factors such as genotyping of transporters or metabolizing enzyme genes.

Rates of disease progression according to initial highly active antiretroviral therapy regimen: a collaborative analysis of 12 prospective cohort studies

BACKGROUND: No large clinical end-point trials have been conducted comparing regimens among human immunodeficiency virus type 1-positive persons starting antiretroviral therapy. We examined clinical progression according to initial regimen in the Antiretroviral Therapy Cohort Collaboration, which is based on 12 European and North American cohort studies. METHODS: We analyzed progression to death from any cause and to AIDS or death (AIDS/death), comparing efavirenz (EFV), nevirapine (NVP), nelfinavir, idinavir, ritonavir (RTV), RTV-boosted protease inhibitors (PIs), saquinavir, and abacavir. We also compared nucleoside reverse-transcriptase inhibitor pairs: zidovudine/lamivudine (AZT/3TC), stavudine (D4T)/3TC, D4T/didanosine (DDI), and others. RESULTS: A total of 17,666 treatment-naive patients, 55,622 person-years at risk, 1,617 new AIDS events, and 895 deaths were analyzed. Compared with EFV, the adjusted hazard ratio (HR) for AIDS/death was 1.28 (95% confidence interval [CI], 1.03-1.60) for NVP, 1.31 (95% CI, 1.01-1.71) for RTV, and 1.45 (95% CI, 1.15-1.81) for RTV-boosted PIs. For death, the adjusted HR for NVP was 1.65 (95% CI, 1.16-2.36). The adjusted HR for death for D4T/3TC was 1.35 (95% CI, 1.14-1.59), compared with AZT/3TC. CONCLUSIONS: Outcomes may vary across initial regimens. Results are observational and may have been affected by bias due to unmeasured or residual confounding. There is a need for large, randomized, clinical end-point trials.

Gilbert syndrome and the development of antiretroviral therapy-associated hyperbilirubinemia

BACKGROUND: Unconjugated hyperbilirubinemia results from Gilbert syndrome and from antiretroviral therapy (ART) containing protease inhibitors. An understanding of the interaction between genetic predisposition and ART may help to identify individuals at highest risk for developing jaundice. METHODS: We quantified the contribution of UGT1A1*28 and ART to hyperbilirubinemia by longitudinally modeling 1386 total bilirubin levels in 96 human immunodeficiency virus (HIV)-infected individuals during a median of 6 years. RESULTS: The estimated average bilirubin level was 8.8 micromol/L (0.51 mg/dL). Atazanavir increased bilirubin levels by 15 mu mol/L (0.87 mg/dL), and indinavir increased bilirubin levels by 8 micromol/L (0.46 mg/dL). Ritonavir, lopinavir, saquinavir, and nelfinavir had no or minimal effect on bilirubin levels. Homozygous UGT1A1*28 increased bilirubin levels by 5.2 micromol/L (0.3 mg/dL). As a consequence, 67% of individuals homozygous for UGT1A1*28 and receiving atazanavir or indinavir had > or =2 episodes of hyperbilirubinemia in the jaundice range (>43 micromol/L [>2.5 mg/dL]), versus 7% of those with the common allele and not receiving either of those protease inhibitors (P<.001). Efavirenz resulted in decreased bilirubin levels, which is consistent with the induction of UDP-glucuronosyltransferase 1A1. CONCLUSIONS: Genotyping for UGT1A1*28 before initiation of ART would identify HIV-infected individuals at risk for hyperbilirubinemia and decrease episodes of jaundice.

Lipid profiles for antiretroviral-naive patients starting PI- and NNRTI-based therapy in the Swiss HIV cohort study

BACKGROUND: Blood lipid abnormalities in patients on highly active antiretroviral therapy (HAART) have been associated with exposure to protease inhibitors (PIs), particularly ritonavir. First therapy with a non-nucleoside reverse transcriptase inhibitor (NNRTI) leads to relatively favourable lipid profiles. We report on medium-term lipid profiles (up to 5 years) for antiretroviral-naive patients starting NNRTI- and PI-based HAART in the Swiss HIV Cohort Study. METHODS: Since April 2000, blood samples taken at visits scheduled every 6 months have been analysed for cholesterol and triglyceride concentrations. For 1065 antiretroviral-naive patients starting HAART after April 2000, we estimated changes in concentration over time using multivariate linear regression with adjustment for baseline covariates, use of lipid-lowering drugs and whether the sample was taken in a fasting state. RESULTS: Non-high density lipoprotein (HDL) cholesterol levels increase with increasing exposure to either PI- or NNRTI-based therapy, HDL cholesterol levels increase and triglyceride levels decrease with increasing exposure to NNRTI-based therapy, whereas triglyceride levels increase with increasing exposure to PI-based therapy. Between NNRTI-based therapies, there is a slight difference in triglyceride levels, which tend to increase with increasing exposure to efavirenz and to decrease with increasing exposure to nevirapine. Of the three common PI-based therapies, nelfinavir appears to have a relatively favourable lipid profile, with little change with increasing exposure. Of the other two PI therapies, lopinavir with ritonavir has a more favourable profile than indinavir with ritonavir, with smaller increases in both non-HDL cholesterol and triglycerides and an increase in HDL cholesterol. Increasing exposure to abacavir is associated with a decrease in the level of triglycerides. CONCLUSION: In general, NNRTI-based therapy is associated with a more favourable lipid profile than PI-based therapy, but different PI-based therapies are associated with very different lipid profiles. Nelfinavir appears to have a relatively favourable lipid profile. Of the two boosted PI therapies, lopinavir appears to have a more favourable lipid profile than indinavir.

Does short-term virologic failure translate to clinical events in antiretroviral-naïve patients initiating antiretroviral therapy in clinical practice?

OBJECTIVE: To determine whether differences in short-term virologic failure among commonly used antiretroviral therapy (ART) regimens translate to differences in clinical events in antiretroviral-naïve patients initiating ART. DESIGN: Observational cohort study of patients initiating ART between January 2000 and December 2005. SETTING: The Antiretroviral Therapy Cohort Collaboration (ART-CC) is a collaboration of 15 HIV cohort studies from Canada, Europe, and the United States. STUDY PARTICIPANTS: A total of 13 546 antiretroviral-naïve HIV-positive patients initiating ART with efavirenz, nevirapine, lopinavir/ritonavir, nelfinavir, or abacavir as third drugs in combination with a zidovudine and lamivudine nucleoside reverse transcriptase inhibitor backbone. MAIN OUTCOME MEASURES: Short-term (24-week) virologic failure (>500 copies/ml) and clinical events within 2 years of ART initiation (incident AIDS-defining event, death, and a composite measure of these two outcomes). RESULTS: Compared with efavirenz as initial third drug, short-term virologic failure was more common with all other third drugs evaluated; nevirapine (adjusted odds ratio = 1.87, 95% confidence interval (CI) = 1.58-2.22), lopinavir/ritonavir (1.32, 95% CI = 1.12-1.57), nelfinavir (3.20, 95% CI = 2.74-3.74), and abacavir (2.13, 95% CI = 1.82-2.50). However, the rate of clinical events within 2 years of ART initiation appeared higher only with nevirapine (adjusted hazard ratio for composite outcome measure 1.27, 95% CI = 1.04-1.56) and abacavir (1.22, 95% CI = 1.00-1.48). CONCLUSION: Among antiretroviral-naïve patients initiating therapy, between-ART regimen, differences in short-term virologic failure do not necessarily translate to differences in clinical outcomes. Our results should be interpreted with caution because of the possibility of residual confounding by indication.

Lipodystrophy and weight changes: data from the Swiss HIV Cohort Study, 2000-2006

BACKGROUND AND OBJECTIVES: Combination antiretroviral therapy (cART) is changing, and this may affect the type and occurrence of side effects. We examined the frequency of lipodystrophy (LD) and weight changes in relation to the use of specific drugs in the Swiss HIV Cohort Study (SHCS). METHODS: In the SHCS, patients are followed twice a year and scored by the treating physician as having 'fat accumulation', 'fat loss', or neither. Treatments, and reasons for change thereof, are recorded. Our study sample included all patients treated with cART between 2003 and 2006 and, in addition, all patients who started cART between 2000 and 2003. RESULTS: From 2003 to 2006, the percentage of patients taking stavudine, didanosine and nelfinavir decreased, the percentage taking lopinavir, nevirapine and efavirenz remained stable, and the percentage taking atazanavir and tenofovir increased by 18.7 and 22.2%, respectively. In life-table Kaplan-Meier analysis, patients starting cART in 2003-2006 were less likely to develop LD than those starting cART from 2000 to 2002 (P<0.02). LD was quoted as the reason for treatment change or discontinuation for 4% of patients on cART in 2003, and for 1% of patients treated in 2006 (P for trend <0.001). In univariate and multivariate regression analysis, patients with a weight gain of >or=5 kg were more likely to take lopinavir or atazanavir than patients without such a weight gain [odds ratio (OR) 2, 95% confidence interval (CI) 1.3-2.9, and OR 1.7, 95% CI 1.3-2.1, respectively]. CONCLUSIONS: LD has become less frequent in the SHCS from 2000 to 2006. A weight gain of more than 5 kg was associated with the use of atazanavir and lopinavir.