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Data-Driven Development of Predictive Models for Sustained Drug Release.

Stijn H S Koshari1, Debby P Chang2, Nathan B Wang2

  • 1Center for Molecular and Engineering Thermodynamics, Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716.

Journal of Pharmaceutical Sciences
|July 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a stochastic optimization algorithm for robust parameter estimation in complex drug release models. The method refines models and aids in designing effective sustained drug delivery systems.

Keywords:
biopharmaceutical releaseparallel tempering algorithmparameter correlationpoly(lactic-co-glycolic acid)solvent depotzeroth-order release profile

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Area of Science:

  • Pharmacokinetics and Drug Delivery
  • Computational Modeling and Simulation
  • Biomaterials Science

Background:

  • Mathematical modeling of drug release is crucial for sustained delivery systems.
  • Parameter estimation in these models is complex due to nonlinearities and interdependencies.
  • Overly parameterized models can lead to overfitting and inaccurate predictions.

Purpose of the Study:

  • To demonstrate an efficient stochastic optimization algorithm for robust parameter estimation in complex drug release models.
  • To generate metadata for evaluating parameter sensitivity and correlation.
  • To refine model development and aid in formulation design.

Main Methods:

  • Utilized a stochastic optimization algorithm for parameter estimation.
  • Analyzed a predictive drug release model using published experimental data.
  • Applied the refined model to design a zeroth-order release profile.
  • Experimentally validated the designed drug release profile.

Main Results:

  • The stochastic optimization algorithm successfully identified robust parameter estimates.
  • Generated metadata enabled quantitative evaluation of parameter sensitivity and correlation.
  • The developed model accurately predicted and designed a zeroth-order release profile.
  • Experimental validation confirmed the model's predictive capability.

Conclusions:

  • Stochastic optimization offers a robust approach to parameter estimation in complex drug release models.
  • This methodology facilitates model refinement, rational decision-making, and formulation design.
  • The approach enhances the development of effective sustained drug delivery systems.