Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia.

In an attempt to understand the applicability of various animal models to dyslipidemia in humans and to identify improved preclinical models for target discovery and validation for dyslipidemia, we measured comprehensive plasma lipid profiles in 24 models. These included five mouse strains, six other nonprimate species, and four nonhuman primate (NHP) species, and both healthy animals and animals with metabolic disorders. Dyslipidemic humans were assessed by the same measures. Plasma lipoprotein profiles, eight major plasma lipid fractions, and FA compositions within these lipid fractions were compared both qualitatively and quantitatively across the species. Given the importance of statins in decreasing plasma low-density lipoprotein cholesterol for treatment of dyslipidemia in humans, the responses of these measures to simvastatin treatment were also assessed for each species and compared with dyslipidemic humans. NHPs, followed by dog, were the models that demonstrated closest overall match to dyslipidemic humans. For the subset of the dyslipidemic population with high plasma triglyceride levels, the data also pointed to hamster and db/db mouse as representative models for practical use in target validation. Most traditional models, including rabbit, Zucker diabetic fatty rat, and the majority of mouse models, did not demonstrate overall similarity to dyslipidemic humans in this study.

Analysis of randomized comparative clinical trial data for personalized treatment selections.

Suppose that under the conventional randomized clinical trial setting, a new therapy is compared with a standard treatment. In this article, we propose a systematic, 2-stage estimation procedure for the subject-level treatment differences for future patient's disease management and treatment selections. To construct this procedure, we first utilize a parametric or semiparametric method to estimate individual-level treatment differences, and use these estimates to create an index scoring system for grouping patients. We then consistently estimate the average treatment difference for each subgroup of subjects via a nonparametric function estimation method. Furthermore, pointwise and simultaneous interval estimates are constructed to make inferences about such subgroup-specific treatment differences. The new proposal is illustrated with the data from a clinical trial for evaluating the efficacy and toxicity of a 3-drug combination versus a standard 2-drug combination for treating HIV-1-infected patients.

Effect of ACE insertion/deletion and 12 other polymorphisms on clinical outcomes and response to treatment in the LIFE study.

OBJECTIVE: This pharmacogenetics substudy from the Losartan Intervention for Endpoint reduction in Hypertension study in patients with hypertension and left ventricular hypertrophy (LVH) treated with the angiotensin receptor blocker losartan versus the beta-blocker atenolol for 4.8 years tested whether the insertion/deletion (I/D) polymorphism of the angiotensin-converting enzyme (ACE) gene and 12 other previously well-characterized polymorphisms of hypertension susceptibility genes affected blood pressure reduction, heart rate reduction, cardiovascular events, and/or response to treatment. These polymorphisms were chosen because they could affect blood pressure control or the pharmacological action of losartan or atenolol. METHODS: We genotyped 3503 patients, 1774 on losartan and 1729 on atenolol. RESULTS: ACE and the 12 other genotypes did not affect the reduction in systolic blood pressure, diastolic blood pressure, pulse pressure, mean arterial pressure, or heart rate, or treatment differences between losartan and atenolol on these endpoints, as assessed by general linear models. Also, ACE and the 12 other genotypes did not affect risk of the primary composite endpoint or its components stroke, myocardial infarction, and cardiovascular death, or treatment differences between losartan and atenolol on these endpoints, as assessed by Cox proportional hazards models including baseline Framingham risk score and LVH. CONCLUSION: ACE insertion/deletion and 12 other polymorphisms of hypertension susceptibility genes did not affect blood pressure reduction, heart rate reduction, or cardiovascular events in patients with hypertension and LVH, or treatment differences between losartan and atenolol on these endpoints. These results suggest that the observed effects of losartan versus atenolol in the Losartan Intervention for Endpoint reduction in hypertension study do not depend on ACE and 12 other polymorphisms of hypertension susceptibility genes.

ACE gene polymorphism and losartan treatment in type 2 diabetic patients with nephropathy.

Losartan treatment reduced renal outcomes in proteinuric patients with type 2 diabetes in the Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan (RENAAL) study. It is unknown whether an insertion (I)/deletion (D) polymorphism in the angiotensin I-converting enzyme (ACE) gene predicts renal outcomes and death and influences the effect of losartan in these patients. Pharmacogenetic analyses were performed comparing losartan with placebo administered with conventional blood pressure-lowering therapy in 1435 (95%) of the 1513 RENAAL study patients. The primary endpoint was the composite of doubling of baseline serum creatinine concentration, end-stage renal disease (ESRD) or death. Cox regression models were stratified on baseline proteinuria and included treatment, geographic region, ACE/ID genotype, and treatment x genotype interaction. Within the placebo group, subjects with the ID or DD genotype were more likely than those with the II genotype to reach the composite endpoint (by 17.5% and 38.1%, respectively, P = 0.029) or its individual components. Within the losartan group, genotype did not correlate with reaching the composite endpoint. Compared with placebo, however, losartan reduced the risk of reaching the composite endpoint by 5.8% (95% confidence interval, -23.3, 28.0), 17.6% (3.8, 29.4), and 27.9% (7.0, 44.1) among those with the II, ID, and DD genotypes, respectively. Similar trends were demonstrated for the individual endpoints. In conclusion, proteinuric type 2 diabetic patients with the D allele of the ACE gene have an unfavorable renal prognosis, which can be mitigated and even improved by losartan.

Effect of rofecoxib on the pharmacokinetics of chronically administered oral contraceptives in healthy female volunteers.

The effect of rofecoxib, a highly selective cyclooxygenase (COX)-2 inhibitor, on the pharmacokinetics of ethinyl estradiol (EE) and norethindrone (NET), two common components of a combination oral contraceptive product, was examined. A double-blind, two-period crossover study was conducted in 18 healthy women who received ORTHO-NOVUM 1/35, a combination of EE (35 microg) and NET (1 mg), concurrently for 14 days with either 175 mg rofecoxib or matching placebo during two consecutive menstrual cycles. Plasma was sampled for EE, NET, sex hormone binding globulin (SHBG), and albumin. The AUC(0-24 h) geometric mean ratio (GMR: rofecoxib/placebo) with corresponding 90% confidence interval (CI) of EE and NET was 1.13 (1.06, 1.19) and 1.18 (1.13, 1.24), respectively. The Cmax GMR of EE and NET was 1.06 (0.98, 1.16) and 1.04 (0.99, 1.09), respectively. In each case, the 90% CIs satisfied the predefined bioequivalence limits of (0.80, 1.25). Measures of SHBG and albumin and routine clinical and laboratory safety parameters showed no clinically meaningful changes. The addition of rofecoxib to the oral contraceptive was not associated with any clinically important changes in EE or NET pharmacokinetics and thus would not be anticipated to influence the efficacy of this contraceptive regimen.

Pharmacokinetics of etoricoxib in patients with renal impairment.

The effect of renal insufficiency on the pharmacokinetics of etoricoxib, a selective inhibitor of cyclooxygenase-2, was examined in 23 patients with varying degrees of renal impairment (12 moderate [creatinine clearance between 30 and 50 mL/min/1.73 m2], 5 severe [creatinine clearance below 30 mL/min/1.73 m2], and 6 with end-stage renal disease requiring hemodialysis) following administration of single 120-mg oral doses of etoricoxib. Even the most severe renal impairment was found to have little effect on etoricoxib pharmacokinetics. The low recovery of etoricoxib in dialysate (less than 6% of the dose) supports that hemodialysis also has little effect on etoricoxib pharmacokinetics, and binding of etoricoxib to plasma proteins was generally unaffected by renal disease. Single doses of etoricoxib were generally well tolerated by patients with renal impairment. Based on pharmacokinetic considerations, dosing adjustments are not necessary for patients with any degree of renal impairment. However, because patients with advanced renal disease (creatinine clearance below 30 mL/min/1.73 m2) are likely to be very sensitive to any further compromise of renal function, and there is no long-term clinical experience in these patients, the use of etoricoxib is not recommended in patients with advanced renal disease.

Strategic applications of gene expression: from drug discovery/development to bedside.

Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.