# A Physiologically Based Pharmacokinetic and Pharmacodynamic (PBPK/PD) Model of Dapagliflozin in Type 2 Diabetes Mellitus: The Effect of Dosing, Hepatorenal Impairment, and Food

## TL;DR

This paper develops a model to understand how dapagliflozin, a diabetes drug, behaves in the body under different conditions like food and organ impairment.

## Contribution

A novel PBPK/PD model for dapagliflozin that integrates absorption, metabolism, and variability due to renal/hepatic function and food.

## Key findings

- The model accurately predicted dapagliflozin's dose-dependent pharmacokinetics within 10–15% of observed data.
- Renal impairment reduced urinary glucose excretion by 40–60%, while hepatic impairment had minimal impact on pharmacokinetics and pharmacodynamics.
- The model confirmed that food intake reduces peak drug concentrations by 30–50%, matching clinical observations.

## Abstract

Background/Objectives: Dapagliflozin is an SGLT2 inhibitor prescribed for the management of type 2 diabetes mellitus. The drug lowers blood glucose levels by increasing urinary glucose excretion (UGE). Despite established efficacy, dapagliflozin demonstrates significant inter-individual variability in pharmacokinetics (PK) and pharmacodynamics (PD), with potential impact on treatment outcomes. Methods: To evaluate the sources of variability and to support patient stratification and model-informed individualized therapy, we developed a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of dapagliflozin using curated data from 28 clinical studies. This framework integrates absorption, distribution, metabolism, excretion, and pharmacodynamics, and accounts for key determinants of variability including renal and hepatic function, and food effects. Results: The simulations reproduced dose-dependent pharmacokinetics with predicted Cmax and AUC values typically within 10–15% of observed data. Renal impairment reduced UGE by 40–60% despite modest changes in plasma exposure, while hepatic impairment produced only small shifts in PK and PD. The model also reproduced the fed-state reduction of peak concentrations, consistent with the 30–50% decrease reported clinically. Conclusions: All model files, code, and curated datasets are openly available in line with FAIR standards and Open Science practices, enabling transparent and reproducible analyses and providing a mechanistic basis for individualized therapy in type 2 diabetes.