Post Doc Scientist Gilead Sciences, Inc. foster city, California
Owing to the complexity in release kinetics of long-acting injectables (LAI), developing in vitro-in vivo (IVIVC) for such formulations remains a conundrum. Moreover, LAI formulations are prone to exhibit higher inter-patient variability compared to conventional formulations. Therefore, it is critical to use a statistical tool to represent highly variable in vivo data and more accurately evaluate IVIVC. In this study, correlations were established between two datasets (in vitro and in vivo profiles) of LAI aqueous suspensions using a random sampling with replacement technique known as bootstrapping, which resulted in multiple linear correlations, each characterized by parameters specific to a given in vivo-in vitro pairing. The mean and 95% confidence intervals of the resulting parameter distribution were used to establish the IVIVC. This work demonstrates the feasibility of establishing IVIVCs via bootstrapping for complex LAI formulations. More importantly, the IVIVC can capture the high inter-subject variability within a reasonable prediction range.
Learning Objectives:
Upon completion, participant will be able to understand the impact of high inter-subject variability on the IVIVC development.
Upon completion, participant will be able to describe how inter-subject variabilities are incorporated into IVIVC.
Upon completion, participant will be able to utilize such strategy for model development.
