PBPK Modeling to Predict Transporter-Mediated Clearance and DDI
It is well established that transporters can play a significant role in drug efficacy and safety. Transporters can affect drug absorption, metabolism, distribution, and excretion. Naturally, any of these processes can also be affected by transporter-mediated drug-drug interactions (tDDIs). Therefore, it is of high interest to scientists in academia, industry and regulatory bodies to be able to predict transporters effects on drugs clearance and tDDI. In Vitro-In Vivo Extrapolation (IVIVE) techniques linked to Physiologically based Pharmacokinetic (PBPK) model are used to predict clearance and tDDI to different degree of success.
Model-Based Drug Development
This presentation will focus on the progress made in improving the quality of transporter mediated clearance prediction/modeling and simulation using transporter in vitro data, in vivo data, and PBPK modeling.
Hot Topic: Atorvastatin PBPK Model Incorporating Acid/Lactone Conversion and Delayed Gastric Emptying
The drug–drug interaction profile of atorvastatin confirms that disposition is determined by CYP3A4 and OATPs. Drugs that affect gastric emptying also affect atorvastatin pharmacokinetics. Assessment of atorvastatin acid stability in simulated gastric fluid indicated it is rapidly converted to its lactone form at gastric pH. A PBPK model incorporating gastric acid-to-lactone conversion and all major atorvastatin metabolites was constructed and verified for prediction of interactions involving CYP3A4, OATPs and gastric emptying. The resulting model indicated that alteration of the gastric acid-lactone equilibrium can reproduce the effect of gastric emptying delay on atorvastatin PK. Furthermore, incorporation of gastric acid-lactone conversion elucidated the mechanistic role of this process in the dose-dependent PK of atorvastatin and the role of atorvastatin lactone in observed enzyme- and transporter-mediated DDIs. This modeling emphasizes the need to consider gastric acid-lactone equilibrium when assessing/modeling statin pharmacokinetics.
It is well established that transporters can play a significant role in drug efficacy and safety. Transporters can affect drug absorption, metabolism, distribution, and excretion. Naturally, any of these processes can also be affected by transporter-mediated drug-drug interactions (tDDIs). Therefore, it is of high interest to scientists in academia, industry and regulatory bodies to be able to predict transporters effects on drugs clearance and tDDI. In Vitro-In Vivo Extrapolation (IVIVE) techniques linked to Physiologically based Pharmacokinetic (PBPK) model are used to predict clearance and tDDI to different degree of success.
Model-Based Drug Development
This presentation will focus on the progress made in improving the quality of transporter mediated clearance prediction/modeling and simulation using transporter in vitro data, in vivo data, and PBPK modeling.
Hot Topic: Atorvastatin PBPK Model Incorporating Acid/Lactone Conversion and Delayed Gastric Emptying
The drug–drug interaction profile of atorvastatin confirms that disposition is determined by CYP3A4 and OATPs. Drugs that affect gastric emptying also affect atorvastatin pharmacokinetics. Assessment of atorvastatin acid stability in simulated gastric fluid indicated it is rapidly converted to its lactone form at gastric pH. A PBPK model incorporating gastric acid-to-lactone conversion and all major atorvastatin metabolites was constructed and verified for prediction of interactions involving CYP3A4, OATPs and gastric emptying. The resulting model indicated that alteration of the gastric acid-lactone equilibrium can reproduce the effect of gastric emptying delay on atorvastatin PK. Furthermore, incorporation of gastric acid-lactone conversion elucidated the mechanistic role of this process in the dose-dependent PK of atorvastatin and the role of atorvastatin lactone in observed enzyme- and transporter-mediated DDIs. This modeling emphasizes the need to consider gastric acid-lactone equilibrium when assessing/modeling statin pharmacokinetics.