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\begin{document}

\title{Using seconds-resolved pharmacokinetic datasets to assess
pharmacokinetic models encompassing time-varying physiology}



\author[1]{Matthew McDonough}%
\author[2]{Sophie Stocker}%
\author[1]{Tod Kippin}%
\author[1]{Wendy Meiring}%
\author[1]{Kevin Plaxco}%
\affil[1]{University of California Santa Barbara}%
\affil[2]{The University of Sydney Faculty of Medicine and Health}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Main Text - clean - Using seconds-resolved pharmacokinetic datasets to assess pharmacokinetic models encompassing time-varying physiology.docx`  available at \url{https://authorea.com/users/494469/articles/576507-using-seconds-resolved-pharmacokinetic-datasets-to-assess-pharmacokinetic-models-encompassing-time-varying-physiology}

\textbf{Hosted file}

\verb`Tables - Using seconds-resolved pharmacokinetic datasets to assess pharmacokinetic models encompassing time-varying physiology.docx`  available at \url{https://authorea.com/users/494469/articles/576507-using-seconds-resolved-pharmacokinetic-datasets-to-assess-pharmacokinetic-models-encompassing-time-varying-physiology}

\textbf{Hosted file}

\verb`Figures - Using seconds-resolved pharmacokinetic datasets to assess pharmacokinetic models encompassing time-varying physiology.docx`  available at \url{https://authorea.com/users/494469/articles/576507-using-seconds-resolved-pharmacokinetic-datasets-to-assess-pharmacokinetic-models-encompassing-time-varying-physiology}

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