{
  "abstract": "Introduction: This study explored pharmacokinetic and pharmacodynamic factors associated with circulating vascular endothelial growth factor (VEGF) dynamics during bevacizumab treatment.\n\nMethods: A secondary analysis was conducted using data from 74 patients with breast, ovarian, cervical, non-small cell lung, or colorectal cancer. Paired pre- and post-infusion serum concentrations of bevacizumab and VEGF were measured by ELISA. Exploratory dimensionality-reduction methods and supervised tree-based models were applied. Artificial neural networks were used only as a complementary exploratory analysis on the same dataset.\n\nResults: The main outcome of the analysis was VEGF_D, the absolute difference between the paired VEGF concentrations. The supervised models did not perform above the naive three-class benchmark; therefore, their variable-importance outputs were treated as secondary, model-specific, and hypothesis-generating observations. The results show exploratory model patterns of bevacizumab exposure and circulating VEGF dynamics. We did not assess clinical efficacy, safety results, and other dosing strategies in this study.\n\nDiscussion: The results do not support clinical implementation, personalized dosing, or treatment optimization and need to be confirmed in large independent cohorts.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pharmacy, National and Kapodistrian University of Athens , ,",
    "Department of Medicine, University of Patras , ,",
    "Department of Pharmacy, University of Patras , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ileana Theofili"
    },
    {
      "affs": [
        1
      ],
      "name": "Elias Liolis"
    },
    {
      "affs": [
        1
      ],
      "name": "Angelos Koutras"
    },
    {
      "affs": [
        0
      ],
      "name": "Vangelis D. Karalis"
    },
    {
      "affs": [
        2
      ],
      "name": "Gregory Sivolapenko"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9n0f2xq79f817.bib",
  "cited_by": 0,
  "doi": "10.3389/fphar.2026.1899527",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690702/?tool=EBI",
  "id": "9n0f2xq79f817",
  "issns": [
    "1663-9812"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13690702",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Angiogenesis and VEGF in Cancer"
    ]
  ],
  "openalex": "W7221117261",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690702/pdf/?tool=EBI",
  "pmcid": "PMC13690702",
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9n0f2xq79f817.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:00Z",
      "native_id": "PMC:PMC13690702",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13690702"
    },
    {
      "first_seen": "2026-10-30T12:20:06Z",
      "native_id": "10.3389/fphar.2026.1899527",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1899527/full"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Angiogenesis and VEGF in Cancer",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Exploratory machine-learning analysis of bevacizumab exposure and circulating VEGF dynamics in patients with cancer",
  "title_text": "Exploratory machine-learning analysis of bevacizumab exposure and circulating VEGF dynamics in patients with cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:06Z",
  "url": "https://research.draconitic.com/paper/9n0f2xq79f817",
  "venue": "Frontiers in pharmacology",
  "versions": []
}