The goal of this project is to develop a comprehensive, open-source software platform for bioequivalence (BE) study design. Components of the platform will implement a framework for population modeling based on physiological- ly-based pharmacokinetic (PBPK) models integrated with a trial simulator. The framework will consist of: 1) Model structures appropriate for a wide range of compounds and routes of administration; 2) Diverse and robust parametric and nonparametric methods for population modeling to update prior PBPK model parameter value distributions with individual data; 3) Simulation of a variety of BE trial designs with standard and novel optimal sampling strategies; 4) Software implementation of an integrated workflow. To build the framework, the project will focus on the following aims that align with the workflow.
Aim 1 : Add inhalational and dermal penetration routes to existing intravenous and oral routes in the Open Systems Pharmacology (OSP) Suite and an in vitro-in vivo formulation/dissolution correlation module.
Aim 2 : Integrate a variety of parametric and nonparametric population algorithms based in part upon the Pmet- rics package for R to update PBPK models when individual data are available.
Aim 3 : Develop a BE trial simulator by re- purposing existing capabilities within OSP and Pmetrics to simulate output for a wide range of BE trial designs, incorpo- rating standard and novel optimal sampling algorithms and existing R modules for sample size calculations and analysis of predicted trial outcomes.
Aim 4 : Implement a seamlessly integrated software workflow of model building, updating and simulating, hosted on the OSP platform, and using state of the art engineering to create a freely available, open- source product.
The goal of this project is to develop a comprehensive, open?source software platform for bioequivalence (BE) study design for generic compounds. We will include a variety of physiologically based pharmacokinetic (PBPK) models, including intravenous, oral, dermal, and inhalational routes of administration, all coupled to a variety of population modeling techniques to update specific variables in the PBPK models when individual data are available. Finally, a soft- ware simulator will use the updated models to predict outcomes from a variety of clinical trial designs, sample sizes, and optimized blood sample number and timing, all to increase probability of a successful BE study.