{
  "abstract": "Purpose Semi-mechanistic pharmacodynamic (PD) models quantify bacterial dynamics from time-kill (TK) assays, often showing multi-phasic kill and regrowth dynamics. Regrowth is typically captured with two subpopulations with distinct concentration-effect relationships. Since only total bacterial counts are observed, subpopulation dynamics must be inferred, causing identifiability issues, sensitivity to initial estimates, and model-testing burden. We aimed to establish an efficient and robust approach for developing semi-mechanistic PD models, demonstrated with 24-hour TK data for colistin against Pseudomonas aeruginosa. Methods A three-step workflow was developed: (1) log-transformed TK data were segmented into kill and regrowth phases, with linear regression estimating phase-specific slopes per concentration and replicate; (2) empirical slopes were pooled to derive concentration-effect relationships; (3) selected relationships and parameter estimates were incorporated into a semi-mechanistic PD model. Results Segmented regression captured kill and regrowth slopes at all concentrations. Slope saturation with increasing colistin and consistently weaker drug effect during regrowth indicated a less susceptible subpopulation, motivating a two-subpopulation model. The semi-mechanistic model adequately described bacterial dynamics at all concentrations, with parameters comparable to Step 2. Conclusion This three-step workflow enables efficient and robust identification of concentration-effect relationships, reducing model testing and computational burden for streamlined semi-mechanistic antibiotic PD model development.",
  "abstract_shown": true,
  "affiliations": [
    "Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "A. D. Pham"
    },
    {
      "name": "W. van Os"
    },
    {
      "name": "J. Brink"
    },
    {
      "name": "J. G. C. van Hasselt"
    },
    {
      "affs": [
        0
      ],
      "name": "L. B. S. Aulin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eak1bbk63ns0x.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.03.763865",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763865v1.full",
  "id": "eak1bbk63ns0x",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763865v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "pharmacology and toxicology"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763865v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/eak1bbk63ns0x.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:21:36Z",
      "native_id": "10.64898/2026.11.03.763865",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763865v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "biorxiv:pharmacology and toxicology",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "Segmented regression to streamline semi-mechanistic pharmacodynamic modelling of antibiotic effects",
  "title_text": "Segmented regression to streamline semi-mechanistic pharmacodynamic modelling of antibiotic effects",
  "type": "preprint",
  "updated_at": "2026-11-06T00:21:25Z",
  "url": "https://research.draconitic.com/paper/eak1bbk63ns0x",
  "venue": "bioRxiv",
  "versions": []
}