The development of a PK model identifying a PK interaction between pregabalin and sildenafil in rats was investigated. Furthermore, these results served to inform the design of a future quantitative PD study examining the interaction of these drugs. This PK study design also serves as the first step toward identifying and quantifying a synergistic PD interaction between pregabalin and sildenafil.
Fundamental to identifying the source for an observed interaction between two compounds is to take the influence of a potential PK interaction into account (14
). The results of this analysis served that purpose by quantifying the PK of pregabalin with and without the presence of sildenafil in rats, noting a 30.2% decrease in the clearance of pregabalin when co-administered with sildenafil. This decrease in clearance was unexpected, and the mechanism for the PK interaction remains unclear. One potential hypothesis to explain this interaction may be an effect of sildenafil on renal transporters affecting pregabalin, although there is no information to enlighten this theory. The decrease in pregabalin clearance did produce a slight increase in the observed plasma concentrations of pregabalin over the observation period of interest.
The effect of pregabalin on the PK of sildenafil was not investigated. N-methyl sildenafil attained a Cmax
of 2,100 ng/mL at 4–7 h, compared to sildenafil with a Cmax
of 2,400 ng/mL at 2–4 h. While the metabolite Cmax
was slightly lower than sildenafil, the ratio between metabolite/parent (0.90) was much higher than that observed for the metabolite and parent in humans (0.4) (48
). While the higher ratio may influence model development and subsequent human scale-up for non-saturating doses of sildenafil, the bolus and maintenance dose selected within this study attained a saturable plasma concentration of sildenafil for the PDE-5 receptor (>>3.5 nM) at all time points (49
). As concentrations of sildenafil that would saturate the PDE-5 enzyme were attained, any minor impact of pregabalin on the PK of sildenafil would be negligible in this study design.
An additional objective was to determine a PK sampling strategy sufficient for representing actual rat PK profiles during future planned PD experiments. Capturing individual PK would not be straight-forward, as blood sampling during the PD experiment could lead to an introduction of PD measurement errors. For this reason, simulations provide an ideal platform for evaluating multiple limited sampling strategies for describing individual PK (17
). Furthermore, this type of preclinical trial simulation allows efficient examinations of multiple experimental scenarios before any animals are utilized, ensuring that experimental animals are utilized in the most effective manner possible.
A total of eight sampling scenarios were examined by combining PK experimental data with simulated rat pregabalin plasma concentrations based on the above developed pregabalin PK model. Sampling strategy success was then assessed by comparing the PE of the predicted clearance to the actual (combined experimental and simulated data set) population clearance. Sampling the same rats on another occasion was determined to be of no added value compared to taking no blood samples at all due to the large degree of intra-individual and inter-occasion variability identified in this PK model for the volume (0.015(9.0%)) and clearance (0.098 (27.1)%). PK predictions using no blood samples from any of the rats produced no bias in the estimates of clearance, but it introduced a lack of precision in estimating individual PK. Optimally, taking eight blood samples spaced during and after the experiment produced the lowest bias and highest precision in the prediction errors.
Sampling in this optimal fashion described above served only as a benchmark, since collecting blood samples during the PD experiment would confound the PD experimental results. For this reason, additional sampling strategies involving a varying number of post-PD samples were also examined. Collecting one post-PD blood sample offered a small improvement in precision over no blood sampling, but it also introduced bias into the prediction errors. Increasing the number of post-PD samples reduced this bias and improved the precision; however, the degree of improvement in both bias and precision diminished with increasing blood samples. Increasing from three to eight post-PD samples slightly improved prediction error precision, but it was decided that these improvements in precision were not sufficient to warrant the collection of five additional samples due to the resulting blood volume alterations, animal stress, and research costs. Thus, collecting three post-PD blood samples was deemed sufficient for removing bias in the prediction error and drastically improving the precision.
Static allodynia PD simulations supported this conclusion by demonstrating that the three-sample schedule provided increased precision in estimating EC50s compared to taking no blood samples, and was only marginally worse than the unrealistic best-case scenario of eight samples taken during and after the PD experiment. This increased precision in identifying individual EC50s will allow a greater ability to identify changes in potency when examining the interaction between these two compounds. Furthermore, these types of simulations may also serve as an example for other preclinical studies to help refine experimental design.
Overall, this study developed a two-compartment model capable of accurately describing pregabalin PK in the presence or absence of sildenafil. These results also served to inform the sampling schedule design for future quantitative PK-PD study examining the interaction of these two drugs. Finally, this PK study also served as an initial step towards identifying and quantifying potential PK and PD interactions between pregabalin and sildenafil.