Abstract

Clinical drug dosing would, ideally, be informed by high-precision, patient-specific data on drug metabolism. The direct determination of patient-specific drug pharmacokinetics (“peaks and troughs”), however, currently relies on cumbersome, laboratory-based approaches that require hours to days to return pharmacokinetic estimates based on only one or two plasma drug measurements. In response clinicians often base dosing on age, body mass, pharmacogenetic markers, or other indirect estimators of pharmacokinetics despite the relatively low accuracy of these approaches. Here, in contrast, we explore the use of indwelling electrochemical aptamer-based (E-AB) sensors as a means of measuring pharmacokinetics rapidly and with high precision using a rat animal model. Specifically, measuring the disposition kinetics of the drug tobramycin in Sprague-Dawley rats we demonstrate the seconds resolved, real-time measurement of plasma drug levels accompanied by measurement validation via HPLC-MS on ex vivo samples. The resultant data illustrate the significant pharmacokinetic variability of this drug even when dosing is adjusted using body weight or body surface area, two widely used pharmacokinetic predictors for this important class of antibiotics, highlighting the need for improved methods of determining its pharmacokinetics.

Highlights

  • A drug’s pharmacokinetics can vary widely from patient to patient (Kiang et al, 2014; Reichel and Lienau, 2016) due to, for example, genetic differences (Hayashi, 2013)

  • electrochemical aptamer-based (E-AB) sensors produced plasma levels and pharmacokinetic parameters closely comparable to those obtained via HPLC-MS measurements (Figures 1D,E)

  • While the cumbersome nature of blood draws and the limited amount of blood we can remove from a rat without causing undue harm limited our ex-vivo measurements to just 7 time points, the 7 s measurement frequency of E-AB measurements leads to many hundreds of data points collected per pharmacokinetic profile (Figures 1D,E)

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Summary

Introduction

A drug’s pharmacokinetics can vary widely from patient to patient (Kiang et al, 2014; Reichel and Lienau, 2016) due to, for example, genetic differences (Hayashi, 2013). As the approach requires blood draws and laboratory measurements it is slow and cumbersome, returning an answer only hours to days after sample collection (Gross, 1998) Because of these limitations dose determination is instead often performed using indirect predictors of pharmacokinetics (Poulin et al, 2011). BSA-based dose determination remains common, beginning in Phase I preclinical trials (FDA), where the BSA-pharmacokinetic relationships of animals are based on body mass and scaled non-linearly to infer the appropriate BSA-based dosing for humans (Reagan-Shaw et al, 2008; Nair and Jacob, 2016)

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