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

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±0.005 and an R2 of 0.854±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±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.