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Design and Development of PLGA and Polysaccharide Microparticles

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Dear Colleagues,

The design and development of PLGA (poly(lactic-co-glycolic acid)) and polysaccharide-based microparticles represent a promising strategy in the field of controlled drug delivery systems. Due to its biocompatibility and biodegradability, PLGA allows for the formation of stable particles capable of protecting and gradually releasing active ingredients. Polysaccharides, such as chitosan, alginate, and dextran, can be used alone or in combination with PLGA to modulate physicochemical properties such as hydrophilicity, mucoadhesion, and permeability. Combining these polymers allows for the adjustment of the release profile, the greater stability of the encapsulated drug, and specific targeting, expanding therapeutic applications in different biomedical areas. Recognizing the importance of these drug delivery systems in the pharmaceutical field, we invite you to contribute a short communication, research article, or review article to this Special Issue, entitled “Design and Development of PLGA and Polysaccharide Microparticles.” This Special Issue will publish articles describing the design and development of ligand-targeted microparticles for the active targeting of therapeutic agents.

Dr. Suzana Gonçalves Carvalho
Dr. Marlus Chorilli
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35 pages, 4011 KB

Open AccessArticle

MC-NODE: A Mechanism-Decomposed Neural Differential Model for PLGA Microsphere Drug Release Prediction and Attribution

by Zi’an Tang, Hui Li, Tianfu Li and Feng Xue

Pharmaceuticals 2026, 19(8), 1227; https://doi.org/10.3390/ph19081227 - 4 Aug 2026

Viewed by 251

Abstract

Background/Objectives: Poly(lactide-co-glycolide) (PLGA) microspheres support long-acting drug delivery, but their release profiles are difficult to predict because burst release, diffusion, polymer degradation, and formulation-dependent effects interact across multiple time scales. This study aimed to develop a continuous-time model that combines accurate release prediction with physically admissible trajectories and release-component attribution. Methods: MC-NODE encodes ten drug, polymer, and formulation descriptors, decomposes the non-negative release rate into burst, diffusion, degradation-associated late-stage, and neural-residual components, and applies a formulation-dependent plateau through a semi-analytical state map. The model was evaluated on a literature-curated dataset containing 321 in vitro release curves, 4913 observations, 89 drugs, and 113 publications using DOI-grouped five-fold cross-validation, complementary extrapolation and sparse-sampling protocols, synthetic mechanism-recovery experiments, and retrospective orthogonal consistency analysis. Results: MC-NODE achieved an RMSE of

0.094 \pm 0.005

and an

R^{2}

0.854 \pm 0.018

, with all 321 out-of-fold trajectories satisfying monotonicity and range criteria. It recovered synthetic contribution labels more accurately than the ablated variants. The degradation-associated late-stage contribution showed positive associations with experimental degradation, molecular-weight loss, pore-evolution, and mass-loss indicators, while the diffusion contribution was positively associated with an experimental diffusion indicator. Dominant-process agreement was 83.3%, and matched external or out-of-fold trajectories achieved an RMSE of

0.108 \pm 0.020

. Under drug-grouped, chemical-cluster, and alternative sparse-sampling evaluations, MC-NODE retained the lowest absolute trajectory-level errors among the compared models. Conclusions: MC-NODE improves formulation-level PLGA release prediction while preserving continuous, monotonic, and bounded trajectories. Its component outputs provide experimentally supported, model-attributed summaries for comparative formulation analysis. Full article

(This article belongs to the Special Issue Design and Development of PLGA and Polysaccharide Microparticles)

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