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Effect of polymer/basic drug interactions on the two-stage diffusion-controlled release from a poly(L-lactic acid)
M Miyajima1, A Koshika, J Okada
1Product Development Laboratories, Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa-ku, Tokyo, Japan. miyaji@shina.sankyo.co.jp
Summary
Drug release from poly(L-lactic acid) (P(L)LA) matrices depends on drug-polymer interactions and matrix transformations. Stronger interactions and polymer crystallization significantly influence drug diffusion rates and release profiles.
Area of Science:
- Materials Science
- Pharmaceutical Sciences
- Polymer Chemistry
Background:
- Poly(L-lactic acid) (P(L)LA) is a biodegradable polymer used in drug delivery systems.
- Understanding drug release mechanisms from polymer matrices is crucial for effective drug formulation.
Purpose of the Study:
- To investigate how drug physicochemical properties affect the release of basic drugs from P(L)LA matrices.
- To elucidate the role of polymer-drug interactions and P(L)LA matrix transformations in controlling drug release kinetics.
Main Methods:
- Fabrication of P(L)LA cylindrical matrices loaded with basic drugs.
- In vitro drug release studies in aqueous medium.
- Evaluation of polymer-drug interactions through drug partitioning.
- Analysis of P(L)LA matrix structural changes (amorphous to semicrystalline transformation).
Main Results:
- P(L)LA matrices exhibited a two-stage, diffusion-controlled drug release profile.
- Drug release was influenced by the transformation of P(L)LA from amorphous to semicrystalline states.
- Polymer-drug interactions and matrix porosity modulated drug diffusion rates.
- Increased drug content enhanced release rates at specific pH conditions due to altered partitioning.
Conclusions:
- Drug release from P(L)LA matrices is governed by a complex interplay of drug physicochemical properties, polymer-drug interactions, and matrix structural evolution.
- The findings provide insights into designing controlled-release drug delivery systems using P(L)LA.
- Further research can optimize P(L)LA-based formulations by tuning drug loading and considering pH-dependent interactions.