… The occurrence of drug–drug interactions (DDIs) can … based pharmacokinetic (PBPK)
modeling has been increasingly used to predict DDI potential. Here, we present a PBPK modeling …

The development of in vitro–in vivo extrapolation (IVIVE), a ‘bottom-up’ approach, to predict
pharmacokinetic parameters and drug–drug interactions (DDIs) has accelerated mainly due …

… drug–drug interactions by the PBPK model using in vivo K i values and drug–drug interaction
data of drugs … The predictability of the PBPK model was compared with the results obtained …

… Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to
quantitatively translate in vitro data and evaluate temporal effects from drug–drug interactions (…

… could reverse suppression, manifesting as TP-drug–drug interactions (TP-DDIs). A
physiologically based pharmacokinetic model was used to quantitatively predict the impact of …

… were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models
… In summary, the PBPK models reasonably estimated interactions with various CYP3A …

… for the drug–drug interaction simulations used for the filing of macitentan (Opsumit). Of
note, … an independent modeling effort, using compartmental population pharmacokinetic (popPK) …

… Drug–drug interaction (DDI) studies were only … drug associations can be safely coadministered
with bictegravir. We used physiologically-based pharmacokinetic (PBPK) modeling to …

… This work demonstrates the value of utilizing PBPK modeling to predict ADC drug
interactions. It demonstrates the power of using a mixed ‘bottom-up’ and ‘top-down’ approach to …

… endogenous biomarker of OATP1B-mediated drug–drug interactions (DDIs) relative to
clinical probe rosuvastatin using nonlinear mixed-effect modeling. Plasma and urine CPI data in …