Pharmacokinetic-pharmacodynamic analysis of the static allodynia response to pregabalin and sildenafil in a rat model of neuropathic pain.

The objective of this study was to develop a pharmacokinetic-pharmacodynamic (PK-PD) model of the static allodynia response to pregabalin with and without sildenafil in a chronic constriction injury model of neuropathic pain. Six treatment groups were evaluated every 30 min for 6 h. Rats were treated with either 1) a saline infusion; 2) a 2-h pregabalin infusion at 4 mgxkg(-1)xh(-1); 3) a 2-h pregabalin infusion at 10 mgxkg(-1)xh(-1); 4) a 2.2-mg loading dose + 12 mgxkg(-1)xmin(-1) infusion of sildenafil; 5) a 2-h pregabalin infusion at 1.6 mgxkg(-1)xh(-1) with sildenafil; and 6) a 2-h infusion of pregabalin at 4 mgxkg(-1)xh with sildenafil. The static allodynia endpoint was modeled by using three population PD approaches: 1) the behavior of the injured paw using a three-category ordinal logistic regression model; 2) paw withdrawal threshold (PWT) (g) between the injured and uninjured paw using the Hill equation with a baseline function; and 3) the baseline normalized difference in PWT between the injured and uninjured paw. The categorical model showed a significant shift in the concentration-response relationship of pregabalin to lower concentrations with concomitant sildenafil. Likewise, the continuous PK-PD models demonstrated a reduction in the EC(50) of pregabalin necessary for PD response in the presence of sildenafil. The difference-transformed PD model resulted in a 54.4% (42.3-66.9%) decrease in EC(50), whereas the percentage-transformed PD model demonstrated a 53.5% (42.7-64.3%) shift. It is concluded from these studies that there is a synergistic PD interaction between pregabalin and sildenafil.

Population pharmacokinetic model of the pregabalin-sildenafil interaction in rats: application of simulation to preclinical PK-PD study design.

PURPOSE: Preliminary evidence has suggested a synergistic interaction between pregabalin and sildenafil for the treatment of neuropathic pain. The focus of this study was to determine the influence of sildenafil on the pharmacokinetics (PK) of pregabalin with the objective of informing the design of a quantitative pharmacodynamic (PD) study. METHODS: The pharmacokinetics were determined in rats following 2-hr intravenous infusions of pregabalin at doses of 4 mg/kg/hr and 10 mg/kg/hr with and without a sildenafil bolus (2.2 mg) and steady state infusion (12 mg/kg/hr for 6 h). This PK model was utilized in a preclinical trial simulation with the aim of selecting the optimal sampling strategy to characterize the PK-PD profile in a future study. Eight logistically feasible PK sampling strategies were simulated in NONMEM and examined through trial simulation techniques. RESULTS: A two-compartment population PK model best described pregabalin pharmacokinetics. Significant model covariates included either a binary effect of sildenafil administration (30.2% decrease in clearance) or a concentration-dependent effect due to sildenafil's active metabolite. CONCLUSIONS: Analysis of simulations indicated that three post-PD samples had the best cost/benefit ratio by providing a significant increase in the precision (and minor improvement in bias) of both PK and PD parameters compared with no PK sampling.

Population pharmacokinetics of CCI-779: correlations to safety and pharmacogenomic responses in patients with advanced renal cancer.

OBJECTIVE: Our objective was to estimate the pharmacokinetic parameters of CCI-779 and its metabolite, sirolimus, and evaluate associations of exposure parameters with safety and clinical activity. Exposure parameters were also correlated with pharmacogenomic responses in peripheral blood mononuclear cells (PBMCs). METHODS: In this randomized, double-blind, multicenter trial, once-weekly intravenous doses of 25, 75, or 250 mg CCI-779 were administered to patients with advanced renal cancer. Whole blood for CCI-779 and sirolimus concentrations was drawn. Population pharmacokinetic analyses yielded Bayesian-predicted exposure metrics that were correlated with severity and duration of adverse events and survival. PBMC samples taken before and after treatment were examined for pharmacogenomic responses. Ribonucleic acid samples were converted to labeled probes and hybridized to oligonucleotide arrays containing more than 12,600 human sequences. RESULTS: The final population pharmacokinetic models of CCI-779 and sirolimus included 235 and 305 observations, respectively, from 50 patients. For CCI-779, dose, single versus multiple dose, and body surface area were significant pharmacokinetic covariates. For sirolimus, dose and hematocrit were significant covariates. Age, sex, or race did not influence drug disposition. CCI-779 area under the curve correlated with adverse event severity for thrombocytopenia (P = .007), pruritus (P = .011), and hyperlipemia (P = .040). Exposure (CCI-779 cumulative area under the curve) correlated with a specific subset of gene transcripts in PBMCs following 16 weeks after therapy (P < .001, Spearman correlation). CONCLUSIONS: Concentrations of CCI-779 and sirolimus were adequately described with a population model incorporating factors for dose, attenuated exposure of multiple doses, body surface area, and hematocrit. Correlations with adverse event severity and duration profiles were provided to aid in the detection of treatment-emergent effects. Pharmacogenomic profiling of PBMCs identified altered ribonucleic acid transcript expression levels that correlate with exposure. These transcripts represent potential biomarkers of CCI-779 exposure in peripheral blood.