Background/Objectives: Poly(lactic-co-glycolic acid) (PLGA) microspheres are widely used in long-acting injectable (LAI) formulations because PLGA exhibits well-established biocompatibility and undergoes controlled hydrolytic degradation into metabolizable byproducts. However, optimization of microspheres typically requires time-consuming in vitro testing. Therefore, we developed a predictive machine learning model for early-stage and full time-dependent release profiles of drug-loaded PLGA microspheres. Methods: Using a published dataset comprising 321 release profiles from 89 drugs, we first developed a classification model to identify slow-release behavior (≤20% release within 3 days) and subsequently integrated the predicted early-release probability into a regression model to estimate cumulative release over time. Results: Among tree-based ensemble models, XGBoost achieved the lowest mean absolute error (MAE = 0.126) and highest Pearson correlation coefficient (r = 0.831). SHapley Additive exPlanations (SHAP) analysis revealed that drug and polymer molecular weight, predictive slow-release probability, and polymer concentration substantially influence release behavior. We also assessed this framework with external datasets. Drug release data for olaparib-loaded PLGA microspheres were obtained in-house, whereas those for semaglutide-based microspheres were obtained from the published literature. In both datasets, this framework demonstrated low MAE values (0.096 and 0.068, respectively). Conclusions: This suggests that the proposed framework can predict in vitro drug release and support efficient optimization of PLGA-based LAI formulations.

Viruses manipulate host cellular machinery to propagate their life cycle, with one key strategy being the mimicry of short linear motifs (SLiMs) found in host proteins. While databases continue to expand with virus–host protein–protein interaction (vhPPI) data, accurately predicting viral mimicry remains challenging due to the inherent degeneracy of SLiMs. In this study, we investigate how viral genomic composition influences motif mimicry and the mechanisms through which viruses hijack host cellular functions. We assessed domain–motif interaction (DMI) enrichment differences, and also predicted new DMIs based on known viral motifs with varying stringency levels, using SLiMEnrich v.1.5.1. Our findings reveal that dsDNA viruses capture significantly more known DMIs compared to other viral groups, with dsRNA viruses also exhibiting higher DMI enrichment than ssRNA viruses. Additionally, we identified new vhPPIs mediated via SLiMs, particularly within different viral genomic contexts. Understanding these interactions is vital for elucidating viral strategies to hijack host functions, which could inform the development of targeted antiviral therapies.

Small-molecule immunomodulators have become important components of modern immunotherapy by targeting immune checkpoints, cytokine signaling pathways, metabolic enzymes, and intracellular kinases. Despite pharmacological rationale, many of these agents underperform clinically due to unfavorable physicochemical properties, rapid systemic clearance, limited target accumulation, and dose-limiting toxicities, reflecting inadequate exposure control rather than a lack of target validity. Polymeric micelles, formed through the self-assembly of amphiphilic block copolymers, offer a versatile delivery platform to address these challenges by enhancing solubility, modulating pharmacokinetics, enabling stimuli-responsive release, and facilitating targeted or synchronized co-delivery. In this review, we classify representative small-molecule immunomodulators according to their immunological targets and examine the delivery constraints that shape their therapeutic performance. We then discuss design principles of polymeric micelle systems, including solubilization-driven formulations, microenvironment-responsive architectures, spatial targeting strategies, and co-delivery approaches that align cytotoxic and immunomodulatory mechanisms. Attention is given to the distinction between direct immunomodulators and cytotoxic agents that induce immunogenic cell death, highlighting how micelle-based delivery can enhance efficacy through improved exposure control. By integrating immunopharmacology with formulation science, this review outlines how polymeric micelles may advance the efficacy and safety of small-molecule immunomodulators and identifies key considerations for future translational development.