Using seconds‐resolved pharmacokinetic datasets to assess pharmacokinetic models encompassing time‐varying physiology

Abstract

AimPharmacokinetics have historically been assessed using drug concentration data obtained via blood draws and bench‐top analysis. The cumbersome nature of these typically constrains studies to at most a dozen concentration measurements per dosing event. This, in turn, limits our statistical power in the detection of hours‐scale, time‐varying physiological processes. Given the recent advent of in vivo electrochemical aptamer‐based (EAB) sensors, however, we can now obtain hundreds of concentration measurements per administration. Our aim in this paper was to assess the ability of these time‐dense datasets to describe time‐varying pharmacokinetic models with good statistical significance.MethodsWe used seconds‐resolved measurements of plasma tobramycin concentrations in rats to statistically compare traditional one‐ and two‐compartmental pharmacokinetic models to new models in which the proportional relationship between a drug's plasma concentration and its elimination rate varies in response to changing kidney function.ResultsWe found that a modified one‐compartment model in which the proportionality between the plasma concentration of tobramycin and its elimination rate falls reciprocally with time either meets or is preferred over the standard two‐compartment pharmacokinetic model for half of the datasets characterized. When we reduced the impact of the drug's rapid distribution phase on the model, this one‐compartment, time‐varying model was statistically preferred over the standard one‐compartment model for 80% of our datasets.ConclusionsOur results highlight both the impact that simple physiological changes (such as varying kidney function) can have on drug pharmacokinetics and the ability of high‐time resolution EAB sensor measurements to identify such impacts.