Time-dependent inactivation (TDI) of CYPs is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to over-predict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human liver microsomes. Inhibitors evaluated include troleandomycin (TAO), erythromycin (ERY), verapamil (VER), Paroxetine (PAR), itraconazole (ITZ) and diltiazem (DTZ) along with primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (MA). Complexities incorporated in the models included multiple binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The different factors affecting TDI kinetics were evaluated such as lipid partitioning, inhibitor depletion, presence of transporters. The inactivation parameters obtained from numerical method were incorporated into static in-vitro – in-vivo correlation (IVIVC) models to predict clinical DDIs. For 123 clinically observed DDIs, using a hepatic CYP3A synthesis rate constant of 0.000146 min-1, the average fold difference between observed and predicted DDIs was 2.97 for the standard replot method and 1.66 for the numerical method. Similar results were obtained using a synthesis rate constant of 0.00032 min-1. These results suggest that numerical methods can successfully model complex in-vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach. Chapter one presents the detailed introduction along with the hypothesis and significance of the project. Chapter 2 includes the development of the bioanalytical method for quantitation of various compounds which includes inactivators and their primary metabolites. Chapter 3 entails the discussion on in-vivo studies in rats involving TDI mediated DDI studies. Chapter 4 discusses the in-vitro studies and use of the numerical method for evaluation of TDI kinetics. Chapter 5 and chapter 6 provides discussion on the impact of inhibitor depletion and partitioning of TDI kinetics and how these two could lead to misinterpretation of TDI results. Chapter 6 also provides a discussion on how transporters could affect TDI results mainly from hepatocyte studies. Chapter 7 involves prediction of TDI mediated DDI using static modeling. Chapter 8 is a case study on bosentan involving induction mediated DDI. / Pharmaceutical Sciences
| Identifer | oai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/3873 |
| Date | January 2018 |
| Creators | Yadav, Jaydeep |
| Contributors | Nagar, Swati, Korzekwa, Kenneth, Childers, Wayne E., Polli, Joseph |
| Publisher | Temple University. Libraries |
| Source Sets | Temple University |
| Language | English |
| Detected Language | English |
| Type | Thesis/Dissertation, Text |
| Format | 453 pages |
| Rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | http://dx.doi.org/10.34944/dspace/3855, Theses and Dissertations |
Page generated in 5.5422 seconds