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February 21, 2026CPT Pharmacometrics & Systems Pharmacology0 citationsOpen Access

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

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HCHannah ClearyNFNikoletta FotakiTPTim Persoons

Key Points

  • The aim is to quantify the impact of critical bioavailability attributes on long-acting injectables using PBPK models.
  • Developed PBPK models for methylprednisolone acetate
  • Analyzed the effects of five critical bioavailability attributes
  • Compared simulated in vivo dissolution profiles with in vitro tests from FDA database
  • Identified the need for larger effective particle sizes in dissolution testing
  • Simulated in vivo dissolution profiles showed 80-100% dissolution after 1200 hours
  • In vitro tests indicated 90% dissolution within 90 minutes
  • Best models displayed good predictive ability despite varying attribute values
  • Exploration of low fluid velocities in dissolution testing is suggested

Abstract

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.

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Cite This Study

Cleary et al. (2026) studied this question.

synapsesocial.com/papers/69994bef873532290d01ffe3https://doi.org/10.1002/psp4.70212
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