Line Labbé

Pharmacogenetics of Long-Term Responses to Antiretroviral Regimens Containing Efavirenz and/or Nelfinavir: An Adult AIDS Clinical Trials Group Study

BACKGROUND Efavirenz and nelfinavir are metabolized by cytochrome P-450 (CYP) 2B6 and CYP2C19, respectively, with some involvement by CYP3A. Nelfinavir is a substrate for P-glycoprotein, which is encoded by MDR1. The present study examined associations between genetic variants and long-term responses to treatment. METHODS Adult AIDS Clinical Trials Group study 384 randomized antiretroviral-naive subjects to receive efavirenz and/or nelfinavir plus 2 nucleoside analogues, with follow-up lasting up to 3 years. Population pharmacokinetics were estimated from a nonlinear mixed-effects model. Polymorphisms in CYP2B6, CYP2C19, CYP3A4, CYP3A5, and MDR1 were characterized. RESULTS The 504 participants in the genetic study included 340 efavirenz recipients and 348 nelfinavir recipients (184 of the 504 participants received both efavirenz and nelfinavir). Of the participants, 49% were white, 31% were black, and 19% were Hispanic. Plasma exposure to efavirenz and nelfinavir in each population was significantly associated with the polymorphisms CYP2B6 516G-->T and CYP2C19 681G-->A, respectively. Among efavirenz recipients, the MDR1 position 3435 TT genotype was associated with decreased likelihood of virologic failure and decreased emergence of efavirenz-resistant virus but not with plasma efavirenz exposure. Among nelfinavir recipients, a trend toward decreased virologic failure was associated with the polymorphism CYP2C19 681G-->A. CONCLUSIONS Genetic variants predict plasma exposure to efavirenz and nelfinavir, and they may predict virologic failure and/or emergence of drug-resistant virus. These associations with treatment responses must be validated in other studies.

Population Pharmacokinetics and Pharmacodynamics of Efavirenz, Nelfinavir, and Indinavir: Adult AIDS Clinical Trial Group Study 398

ABSTRACT The present population pharmacokinetic (PK) and pharmacodynamic (PD) study modeled the effects of covariates including drug adherence and the coadministration of protease inhibitors (PIs) on the pharmacokinetics of efavirenz (EFV) and the relationship between EFV exposure and virological failure in patients who failed initial PI treatment in Adult AIDS Clinical Trial Group (AACTG) study 398. We also report on the population PKs of the PIs nelfinavir (NFV) and indinavir (IDV). AACTG study 398 patients received EFV, amprenavir, adefovir dipivoxil, and abacavir and were randomized to take, in addition, one of the following: NFV, IDV, saquinavir (SQV), or placebo. The PK databases consisted of 531 EFV concentrations (139 patients), 219 NFV concentrations (75 patients), and 66 IDV concentrations (11 patients). Time to virological failure was ascertained for all patients in the PK databases. PK data were fit with a population PK model that assumed exclusive hepatic elimination (the well-stirred model). Notable findings with respect to EFV PK and PD are as follows. (i) The hepatic clearance of EFV is unaltered by NFV, IDV, or SQV coadministration. (ii) The hepatic clearance of EFV appears to be 28% higher in white non-Hispanics than in African Americans and Hispanics (P = 0.03). (iii) Higher adherence scores (as measured with the Medication Event Monitoring System) are associated with marginally increased levels of exposure to EFV. (iv) In patients with no prior experience with nonnucleoside reverse transcriptase inhibitors (NNRTIs), a given percent increase in the oral clearance (CL/F) of EFV is associated with a greater percent increase in the hazard of virological failure (P < 0.0003). Among NNRTI-experienced patients, however, hazard is relatively uncorrelated with EFV CL/F.

Modeling and simulation of adherence: Approaches and applications in therapeutics

Partial adherence with a prescribed or randomly assigned dose gives rise to unintended variability in actual drug exposure in clinical practice and during clinical trials. There are tremendous costs associated with incomplete and/or improper drug intake—to both individual patients and society as a whole. Methodology for quantifying the relation between adherence, exposure and drug response is an area of active research. Modeling and statistical approaches have been useful in evaluating the impact of adherence on therapeutics and in addressing the challenges of confounding and measurement error which arise in this context. This paper reviews quantitative approaches to using adherence information in improving therapeutics. It draws heavily on applications in the area of HIV pharmacology.

Application and Impact of Population Pharmacokinetics in the Assessment of Antiretroviral Pharmacotherapy

Population pharmacokinetics has been an important technique used to explore and define relevant sources of variation in drug exposure and response in patient populations. This has been especially true in the area of antiretroviral therapy where the assurance of adequate and sustained drug exposure of multiple agents is highly correlated with therapeutic success. Population pharmacokinetic analyses across the four drug classes and 20 US FDA-approved products used to treat HIV have been published to date. The published reports were predominantly based on actual clinical trials conducted in HIV-infected patients with one or more agents administered.Modelling and simulation approaches have been used in the evaluation of antiretroviral agent outcomes incorporating problematic design and analysis factors such as sparse plasma sampling, data imbalance and censored data. Additional benefits of population modelling approaches applied to the investigation of antiretroviral agents include the ability to assess dosing compliance, understanding and quantifying drug-drug interactions in order to select dosing regimens and the screening of new drug candidates. Pharmacokinetic/pharmacodynamic models have been used to characterise the relationship between drug exposure and virological and immunological response, and to predict clinical outcome. These models offer the best opportunity for individualising and optimising patient therapy, particularly when adjusted for adherence/compliance.The impact of population pharmacokinetics in the area of antiretroviral therapy can be directly assessed by its role in the validation of surrogate markers such as viral RNA load, therapeutic drug monitoring and the management of individual patient outcomes via exposure-toxicity relationships. Each of these population pharmacokinetic outcomes has contributed to the current regulatory environment, specifically in the area of accelerated approval of new antiretroviral agents.

Effect of coadministration of nelfinavir, indinavir, and saquinavir on the pharmacokinetics of amprenavir

Pharmacokinetic interactions are expected when human immunodeficiency virus (HIV) protease inhibitors are coadministered because many are both substrates forand inhibitors of CYP3A4. The goal of this model‐based pharmacokinetic analysis was to describe the differences observed in amprenavir pharmacokinetics among treatment arms in the Adult AIDS Clinical Trial Group (AACTG) study protocol 398 and to propose mechanisms toaccount for them.

Limited sampling strategies for monitoring tacrolimus in pediatric liver transplant recipients.

Objective: To develop and validate limited sampling strategies (LSSs) for tacrolimus in pediatric liver transplant recipients. Methods: Thirty-six 12-hour pharmacokinetic profiles from 28 pediatric liver transplant recipients (0.4-18.5 years) were collected. Tacrolimus concentrations were measured by immunoassay and area under the curve (AUC0-12) was determined by trapezoidal rule. LSSs consisting of 1, 2, 3, or 4 concentration-time points were developed using multiple regression analysis. Eight promising models (2 per category) were selected based on the following criteria: r2 ≥ 0.90, inclusion of trough concentration (C0), and time points within 4 hours postdose. The predictive performance of these LSSs was evaluated in an independent set of data by measuring the mean prediction error and the root mean squared prediction error. Results: Five models including 2-4 time points predicted AUC0-12 with a ±15% error limit. Bias (mean prediction error) and precision (root mean squared prediction error) of LSS involving C0, C1, and C4 (AUCpredicted = 9.30 + 3.69 × C0 + 2.19 × C1 + 4.69 × C4) were −4.98% and 8.29%, respectively. Among single time point LSSs, the model using C0 had a poor correlation with AUC0-12 (r2 = 0.53), whereas the one with C4 had the highest correlation with tacrolimus exposure (r2 = 0.84). Conclusions: Trough concentration is a poor predictor of tacrolimus AUC0-12 in pediatric liver transplant recipients. However, LSSs using 2-4 concentration-time points obtained within 4 hours postdose provide a reliable and convenient method to predict tacrolimus exposure in this population. The proposed LSSs represent an important step that will allow the undertaking of prospective trials aiming to better define tacrolimus target AUC in pediatric liver transplant recipients and to determine whether AUC-guided monitoring is superior to C0-based monitoring in terms of efficacy and safety.

Increased Risk of Myocardial Infarction Associated With Angiotensin‐Converting Enzyme Gene Polymorphism Is Age Dependent

The angiotensin‐converting enzyme (ACE) gene is a candidate genetic locus for coronary artery disease (CAD). Studies investigating the relationship between the ACE—insertion/deletion (I/D) gene polymorphism and myocardial infarction (MI) have been inconsistent. The authors hypothesized that age may be an important modulating factor in this relationship. ACE‐I/D allele and genotype distribution was determined in 3 groups: 104 men with a first MI at a young age (≤45 years old), 271 healthy young men (≤30 years old), and 28 healthy elderly men (>65 years old). All participants were French descendants from Quebec City, Canada. Frequency distribution of the ACE alleles and genotypes was similar among the healthy young, the healthy elderly, and the MI patients (P > .05). However, when considering the age at the time of the MI (≤40, ≤35, or ≤30 years old), a significant age‐dependent effect with the prevalence of the ACE‐DD genotype was found, as it increased by 22%, 61%, and 157%, respectively, compared with the healthy young group (P < .05). Similar observations were obtained versus the healthy elderly men (P < .05). The ACE‐I/D polymorphism seems to be a genetic risk factor for MI in young men and becomes an important modulator of MI risk at a young age.;

Novel Population Pharmacokinetic Method Compared to the Standard Noncompartmental Approach to Assess Bioequivalence of Iron Gluconate Formulations

PURPOSE Iron-containing products are atypical in terms of their pharmacokinetic properties because iron is only removed by plasma sampling and is non-linear. This study aims to present a novel way of assessing the relative bioavailability of two sodium ferric gluconate complex (SFGC) formulations and compare this approach to a standard previously published noncompartmental approach. METHODS Data were from open-label, randomized, single-dose studies (Study 1 was parallel whereas Study 2 was crossover). Subjects with low but normal iron levels were infused IV SFGC in sucrose by GeneraMedix Inc. and/or Ferrlecit ® Injection (Watson Laboratories Inc.). In Study 1 (n=240), 125 mg was infused over 10 minutes. In Study 2 (n=29), 62.5 mg was infused over 30 minutes. Samples were assayed for total iron (TI) and transferrin-bound iron (TBI) over 36 hours (Study 1) or 72 hours (Study 2) post-dose. Studies 1 and 2 used standard noncompartmental analysis. Study 2 also used population PK (PPK) analyses with ADAPT 5®. The final model predicted SFGC area-under-the-curve (AUCpred) and maximal concentration (Cmaxpred). Analyses of variance was conducted on ln-transformed PK parameters. Ratios of means and 90% confidence intervals (CIs) were estimated. Bioequivalence was demonstrated if values were within 80-125%. RESULTS For Study 1, ratios and 90% CIs for TI baseline-corrected Cmax and AUC0-36 were 100.4 (96.5 - 104.5) and 99.7 (94.2 - 105.5). For TBI, results for TI baseline-corrected Cmax and AUC0-36 were 86.8 (82.7 - 91.1) and 92.4 (85.6 - 99.7). For Study 2, a multi-compartmental model simultaneously described the PK of TI, TBI and SFGC. Ratios and 90% CIs for SFGC Cmaxpred and AUCpred were 89.9 (85.9 - 94.0) and 89.7 (85.7 - 93.9), while ratios and 90% CI obtained from the noncompartmental analysis of Study 2 did not meet BE criteria because of low power. CONCLUSIONS Both the standard and PPK modeling approach suggested bioequivalence between the iron products. However, with the PPK method, less subjects were required to meet study objectives compared to the standard noncompartmental approach which required considerably more subjects (29 vs 240).