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Using seconds-resolved pharmacokinetic datasets to assess pharmacokinetic models encompassing time-varying physiology
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  • Matthew McDonough,
  • Sophie Stocker,
  • Tod Kippin,
  • Wendy Meiring,
  • Kevin Plaxco
Matthew McDonough
University of California Santa Barbara

Corresponding Author:[email protected]

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Sophie Stocker
The University of Sydney Faculty of Medicine and Health
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Tod Kippin
University of California Santa Barbara
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Wendy Meiring
University of California Santa Barbara
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Kevin Plaxco
University of California Santa Barbara
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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.
08 Jul 2022Submitted to British Journal of Clinical Pharmacology
11 Jul 2022Submission Checks Completed
11 Jul 2022Assigned to Editor
17 Aug 2022Reviewer(s) Assigned
23 Sep 2022Review(s) Completed, Editorial Evaluation Pending
23 Sep 2022Editorial Decision: Revise Major
24 Jan 20231st Revision Received
25 Jan 2023Assigned to Editor
25 Jan 2023Submission Checks Completed
25 Jan 2023Review(s) Completed, Editorial Evaluation Pending
09 Feb 2023Reviewer(s) Assigned
01 Apr 2023Editorial Decision: Revise Major
05 Apr 20232nd Revision Received
06 Apr 2023Assigned to Editor
06 Apr 2023Submission Checks Completed
06 Apr 2023Review(s) Completed, Editorial Evaluation Pending
10 Apr 2023Editorial Decision: Accept