{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [
    "College of Pharmacy, Keimyung University, Daegu 42601, Republic of Korea",
    "Department of Pharmaceutical Engineering, Daegu Catholic University, Gyeongsan 38430, Republic of Korea",
    "College of Pharmacy, Yeungnam University, Gyeongsan 38541, Republic of Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Younghun Song"
    },
    {
      "affs": [
        0
      ],
      "name": "Saroj Bashyal"
    },
    {
      "affs": [
        0
      ],
      "name": "Hyuk Jun Cho",
      "orcid": "0000-0001-8982-1259"
    },
    {
      "affs": [
        1
      ],
      "name": "Mi Ran Woo",
      "orcid": "0000-0002-5913-3663"
    },
    {
      "affs": [
        2
      ],
      "name": "Jong Oh Kim",
      "orcid": "0000-0002-4929-851X"
    },
    {
      "affs": [
        0
      ],
      "name": "Duhyeong Hwang",
      "orcid": "0000-0002-5650-1643"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dtdyjsn0dammm.bib",
  "cited_by": 7,
  "doi": "10.3390/ph19050767",
  "fulltext_url": null,
  "id": "dtdyjsn0dammm",
  "issns": [
    "1424-8247"
  ],
  "landing_url": "https://doi.org/10.3390/ph19050767",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3003|Advanced Drug Delivery Systems"
    ]
  ],
  "openalex": "W7162105806",
  "pdf_url": null,
  "pmcid": "PMC13209779",
  "pmid": "42198441",
  "published": "2026-05-14",
  "publisher": "Multidisciplinary Digital Publishing Institute",
  "ris": "https://research.draconitic.com/paper/dtdyjsn0dammm.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:15Z",
      "native_id": "W7162105806",
      "source": "openalex",
      "url": "https://doi.org/10.3390/ph19050767"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:3003|Advanced Drug Delivery Systems",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "Interpretable Two-Stage Machine Learning for Early and Full Drug Release Prediction in PLGA Microspheres",
  "title_text": "Interpretable Two-Stage Machine Learning for Early and Full Drug Release Prediction in PLGA Microspheres",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T13:21:00Z",
  "url": "https://research.draconitic.com/paper/dtdyjsn0dammm",
  "venue": "Pharmaceuticals",
  "versions": []
}