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<p>Over the past three years, we have published a series of nine manuscripts demonstrating that all relevant pharmacokinetic relationships may be simply derived independent of differential equations, offering an alternative to traditional pharmacokinetic analyses. These derivations are based on an understanding of parallel and in series rate-defining processes, and account for all relevant drivers, including organ blood flow and drug delivery clearance kinetics, across both linear and nonlinear scenarios. In this tutorial, we present the simple derivation of renal clearance and hepatic clearance directly relevant to clinical pharmacokinetics, as applied to making drug dosing decisions based on measures of systemic exposure. We further advocate for a more streamlined and practical approach to teaching and applying clinical pharmacokinetics, noting that compartmental modeling, protein binding in hypothetical compartments, trapezoidal AUC calculations, and alternative volume of distribution parameters, aside from (the unfortunately misnamed) volume of distribution steady-state, often overcomplicate pharmacokinetics in practice. The key advantage of this simplified methodology is the ability to directly incorporate clearance from the drug delivery site into systemic pharmacokinetic relationships. This enables a clear understanding of how entering clearance can influence systemic AUC, helping explain: enhanced pharmacodynamic outcomes of slow drug delivery <i>versus</i> immediate-release formulations; systemic bioavailability measures exceeding unity, statistically significant discrepancies between urinary and systemic bioavailability measures; and changes in renal clearance as a function of drug clearance from the delivery site. These key concepts are illustrated by applying the proposed methodology to an example drug, analyzing all relevant clinical pharmacokinetic relationships required for dosing decisions.</p> Graphical Abstract <p></p>

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Simplifying Pharmacokinetics, Applying it to Drug and Dosage Form Development, and Making Drug Dosage Decisions in Clinical Medicine: The Adaptation of Kirchhoff’s Laws from Physics

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  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • Leslie Z. Benet,
  • Jasleen K. Sodhi

摘要

Over the past three years, we have published a series of nine manuscripts demonstrating that all relevant pharmacokinetic relationships may be simply derived independent of differential equations, offering an alternative to traditional pharmacokinetic analyses. These derivations are based on an understanding of parallel and in series rate-defining processes, and account for all relevant drivers, including organ blood flow and drug delivery clearance kinetics, across both linear and nonlinear scenarios. In this tutorial, we present the simple derivation of renal clearance and hepatic clearance directly relevant to clinical pharmacokinetics, as applied to making drug dosing decisions based on measures of systemic exposure. We further advocate for a more streamlined and practical approach to teaching and applying clinical pharmacokinetics, noting that compartmental modeling, protein binding in hypothetical compartments, trapezoidal AUC calculations, and alternative volume of distribution parameters, aside from (the unfortunately misnamed) volume of distribution steady-state, often overcomplicate pharmacokinetics in practice. The key advantage of this simplified methodology is the ability to directly incorporate clearance from the drug delivery site into systemic pharmacokinetic relationships. This enables a clear understanding of how entering clearance can influence systemic AUC, helping explain: enhanced pharmacodynamic outcomes of slow drug delivery versus immediate-release formulations; systemic bioavailability measures exceeding unity, statistically significant discrepancies between urinary and systemic bioavailability measures; and changes in renal clearance as a function of drug clearance from the delivery site. These key concepts are illustrated by applying the proposed methodology to an example drug, analyzing all relevant clinical pharmacokinetic relationships required for dosing decisions.

Graphical Abstract