# Using PBPK to Simulate Target Biopredictive Dissolution Profiles for Long‐Acting Injectables ‐ Where to Begin With Critical Bioavailability Attributes?

## TL;DR

This paper explores how to simulate biopredictive dissolution tests for long-acting injectables using PBPK models to improve drug formulation and testing.

## Contribution

The study introduces a novel approach using PBPK models to simulate dissolution profiles for LAIs, identifying critical bioavailability attributes.

## Key findings

- Simulated dissolution profiles showed much slower rates in vivo compared to in vitro tests.
- Critical bioavailability attributes like particle size and diffusion layer thickness significantly impact dissolution.
- The approach provides a design space for developing biopredictive in vitro dissolution tests.

## Abstract

Long‐acting injectables (LAI) are of increasing interest as they facilitate improved medication adherence and exposure, with target plasma concentration levels maintained over weeks/months. Biopredictive in vitro dissolution tests can aid formulation development of LAIs and guide quality control dissolution testing by facilitating accelerated test development. However, it is not easy to develop such tests when mechanisms underlying in vivo dissolution are not fully understood. The question of interest (QOI) and context of use (COU) of this study involve quantifying the impact of in vivo parameters which are critical bioavailability attributes (CBAs), using physiologically based pharmacokinetic (PBPK) models generated for LAI methylprednisolone acetate. Simulated dissolution profiles from the PBPK models can provide a design space for biopredictive in vitro dissolution testing methods. The five CBAs explored in this study were particle size, solubility, diffusion layer thickness, diffusion coefficient, and depot volume. Although the best performing models displayed good predictive ability, they used different (literature/prediction derived) attribute values. Simulated in vivo dissolution profiles generated suggested much slower dissolution rates, with 80–100% dissolved after 1200 h, than in vitro dissolution tests from FDA ‘Dissolution Methods Database,’ where almost 90% was dissolved in 90 min. To conclude, in vitro dissolution conditions resulting in larger effective particle sizes and diffusion layer thickness, suggesting low fluid velocities, need to be explored to generate biopredictive dissolution profiles. The current approach illustrates how using models with plausible CBA value ranges can be used to simulate a target dissolution profile design space, assisting in vitro LAI dissolution test development.

○Long‐acting injectables (LAIs), once injected into either the muscle/adipose tissue, display an initial burst release, then a drug pocket known as a depot is formed. From this depot, drug is released into the systemic circulation over a period of time depending on physiological and formulation‐related critical bioavailability attributes.

Long‐acting injectables (LAIs), once injected into either the muscle/adipose tissue, display an initial burst release, then a drug pocket known as a depot is formed. From this depot, drug is released into the systemic circulation over a period of time depending on physiological and formulation‐related critical bioavailability attributes.

○It is unclear to what extent/which critical bioavailability attributes affect absorption of drug from the depot. The evolving nature of dissolution methods for LAIs is reflected in the FDA ‘Dissolution Methods Database,’ highlighting the need to investigate dissolution rates which would align with observed in vivo data.

It is unclear to what extent/which critical bioavailability attributes affect absorption of drug from the depot. The evolving nature of dissolution methods for LAIs is reflected in the FDA ‘Dissolution Methods Database,’ highlighting the need to investigate dissolution rates which would align with observed in vivo data.

○This study presents simulated dissolution profiles of an LAI product correlated to an in vivo dataset providing a design space for more biorelevant dissolution tests. It also suggests parameters that have a notable impact on the biopharmaceutics aspects of the LAI PBPK model.

This study presents simulated dissolution profiles of an LAI product correlated to an in vivo dataset providing a design space for more biorelevant dissolution tests. It also suggests parameters that have a notable impact on the biopharmaceutics aspects of the LAI PBPK model.

○This research will provide a starting point for developing more biopredictive LAI dissolution tests.

This research will provide a starting point for developing more biopredictive LAI dissolution tests.