Virtual Twin-PBPK Modelling: A Step Toward Precision Dosing in Patients with Obesity

Obesity significantly alters drug disposition and contributes to large inter-individual variability in pharmacokinetics (PK). The virtual-twin concept is increasingly used to support model-informed precision dosing in specific populations. In this study, physiologically-based pharmacokinetic models linked with virtual twins (VT-PBPK) have been developed and applied to predict the PK of midazolam and digoxin in patients with obesity (n = 15) and severe obesity (n = 22). The first step of the individualization included basic demographic data with lean liver volume. In the second step, individual serum creatinine, albumin, and hepatic CYP3A4/5, UGT1A4 and P-gp abundance quantified from liver biopsies in the same individuals, were integrated within models. Substrate specific improvements were presented via the stepwise individualization. The final (Step 2) VT-PBPK models predicted midazolam AUC_0-inf,iv within 2-fold for 86% of the individuals (geometric mean fold error, GMFE = 1.5; 95% confidence interval (CI95) = 1.36-1.78), with 36% within the 0.8 to 1.25-fold of the observed values. For digoxin, 97% of C_max and AUC_0-24 values were predicted within 2-fold of the observed data (GMFE = 1.25; CI95 = 1.19-1.33), with 59% of predicted values within the 0.8-1.25-fold range. In the case of digoxin, the prediction accuracy was higher for patients with severe obesity (60% of C_max and AUC_0-24 values within the 1.25-fold range); no clear trends were evident for midazolam. This is the first study that applied the VT-PBPK modelling approach in patients with obesity. It highlights the potential of this approach to predict the PK of other CYP3A and P-gp substrates to support individual dose optimization in this population.