Background and Purpose: Pharmacokinetics have traditionally been assessed using concentration measurements from methods with low temporal resolution, such as blood draws, leading to profiles being estimated from sparse or blended data. Recent advances in in vivo sensors, however, now enable the collection of hundreds of observations over a few-hours for each individual drug administration. Previous analyses of such data for the antibiotic tobramycin have identified significant (several-fold), hours-scale changes in the efficiency with which this renally cleared drug is eliminated. Here we apply similar analyses to study the pharmacokinetics of another renally cleared drug, the antibiotic vancomycin. Experimental Approach: We estimate vancomycin pharmacokinetic profiles using previously collected time-dense plasma concentration measurements within six anesthetized rats. Specifically, we fit standard one- and two-compartment models, as well as time-varying one-compartment models (in which the proportionality relating concentration to elimination rate is time-varying), to these data to investigate if the time-varying models are statistically preferred for describing individual-level vancomycin pharmacokinetics, over standard one- and two-compartment models. Key Results: One-compartment models incorporating time-varying elimination proportionalities are statistically preferred over standard one- and two- compartment models for 5 of our 6 vancomycin time courses. When the initial impact of the distribution phase is removed from these data, a reciprocally time-varying one-compartment model is preferred over the standard-one compartment model in 4 of 5 considered datasets. Conclusion and Implications: These results provide further animal-model evidence that the pharmacokinetics of renally cleared drugs can vary significantly over timescales as short as a few hours.

Aim Pharmacokinetics 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 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 is to assess the ability of these time-dense datasets to describe time-varying pharmacokinetic models with good statistical significance. Methods Here we use 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. Results We find 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 reduce the impact of the drug’s rapid distribution phase on the model, this one-compartment, time-varying model is statistically preferred over or tied with the standard two-compartment model for 80% of our datasets. Conclusions Our 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.