{
  "abstract": "Reliable prediction of long-term drug release remains challenging for formulations developed under experimental conditions that differ from those represented in literature-derived training data. Standard formulation descriptors capture important physicochemical and formulation characteristics, but they do not fully represent study-specific factors that shape the realized release trajectory. Here, we develop a machine-learning-based few-shot adaptation strategy that combines formulation descriptors with sparse early release measurements to forecast the remaining release trajectory under previously unseen experimental settings. Using a public dataset comprising 321 formulations, 4,913 release measurements, and 113 source studies, we evaluate the framework using strict study-grouped validation. Zero-shot prediction shows limited generalization to new experimental domains, whereas incorporating only a few early observations produces a marked and reproducible improvement in predictive performance. Across repeated held-out study splits, two early measurements increase mean pooled from −0.084 to 0.333 and reduce formulation-level MAE by approximately 30%, with further improvements as additional observations are incorporated. Although absolute predictive accuracy remains moderate, the improvement is consistent across repeated splits and demonstrates that limited target-specific information can substantially reduce the generalization gap. These findings show that sparse early release measurements can serve as an effective calibration signal for machine-learning models, providing a practical strategy for improving long-term drug-release forecasting under previously unrepresented experimental conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Engineering",
    "Pennsylvania State University",
    "Harold & Inge Marcus Department of Industrial & Manufacturing Engineering",
    "Department of Neurosurgery",
    "Penn State Health Milton S. Hershey Medical Center",
    "Penn State College of Medicine, Pennsylvania State University",
    "Materials Research Institute (MRI",
    "State University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Tengxiao Liu"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Juchen Zhang"
    },
    {
      "affs": [
        3,
        4,
        5
      ],
      "name": "Alireza Mansouri"
    },
    {
      "affs": [
        2,
        1,
        6,
        7
      ],
      "name": "Hongtao Sun",
      "orcid": "0000-0003-3259-6091"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/01rymaear9csb.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010043/v1",
  "fulltext_url": null,
  "id": "01rymaear9csb",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010043/v1",
  "language": null,
  "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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220863794",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010043/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/01rymaear9csb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010043",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010043/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Few-Shot Early-Release Adaptation Enables Generalizable Prediction of PLGA Drug Release",
  "title_text": "Few-Shot Early-Release Adaptation Enables Generalizable Prediction of PLGA Drug Release",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/01rymaear9csb",
  "venue": "ChemRxiv",
  "versions": []
}