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FormulationLAI: A physiology-based machine learning framework for accelerated development of long-acting injectable

Ping Xiong1, Jinying Zhu1, Hao Zhong1

  • 1The State Key Laboratory of Mechanism and Quality of Chinese Medicine (MQCM), Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, 999078, China.

Journal of Controlled Release : Official Journal of the Controlled Release Society
|November 19, 2025
PubMed
Summary

Developing long-acting injectables (LAIs) for chronic diseases is slow. A new computational framework using machine learning and modeling accelerates LAI design, reducing development time from years to months.

Keywords:
In situ forming gelsLong-acting injectablesMachine learningMolecular dynamicsPhysiologically based pharmacokinetic modeling

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Area of Science:

  • Drug delivery and formulation science
  • Computational modeling and simulation
  • Pharmacokinetics and pharmacodynamics

Background:

  • Long-acting injectables (LAIs) offer potential for chronic disease management but face challenges in development time, mechanistic understanding, and in vitro-in vivo correlation.
  • Current LAI development relies heavily on lengthy trial-and-error processes, hindering efficient translation to clinical practice.

Purpose of the Study:

  • To develop and validate a closed-loop computational framework integrating machine learning (ML), physiologically based pharmacokinetic (PBPK) modeling, and molecular dynamics (MD) simulations to accelerate LAI formulation design.
  • To demonstrate the framework's ability to predict optimal in vitro release profiles and guide the development of efficient LAI formulations.

Main Methods:

  • Integration of ML, PBPK/PD modeling, and MD simulations to create a predictive computational framework.
  • Utilized Zilretta® (PLGA-based injectable) as a benchmark for PBPK/PD modeling to define target in vitro release profiles.
  • Developed an optimized in situ forming gel (ISFG) formulation based on ML predictions for intra-articular delivery of triamcinolone acetonide.

Main Results:

  • The computational framework successfully guided the development of an optimized ISFG formulation.
  • The developed ISFG formulation demonstrated a 34% increase in drug exposure compared to a commercial suspension in rat pharmacokinetic studies.
  • MD simulations provided insights into drug-polymer interactions crucial for achieving sustained release.

Conclusions:

  • The proposed computational framework significantly reduces LAI preclinical development time from 5-7 years to approximately 1-2 years.
  • This predictive "compute-and-validate" approach redefines LAI formulation development, moving away from empirical methods.
  • The framework offers a scalable strategy for rational and efficient development of long-acting injectable formulations.