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<p>The latest release of the BioAssay Ontology (BAO), version 2.8.16, introduces major updates that expand its ability to describe and categorize assays related to pharmacokinetics, pharmacodynamics, and safety pharmacology. These refinements, driven by collaboration with the Semantic Enrichment of Electronic Laboratory Notebook Data project, an industry initiative led by the Pistoia Alliance, address previously identified gaps in ontology coverage. New terms were added for disease models, preclinical study parameters, toxicological measurements, and detailed classifications of pharmacokinetics and pharmacodynamics assays. The update also incorporates biologically relevant target classes, including cytochrome P450 enzymes, solute carrier transporters, and uridine diphosphate-glucuronosyltransferase enzymes. Beyond content expansion, structural improvements include reassignment of terms to more specific and semantically appropriate parent classes, refinement of class dependencies, and enhanced alignment with external ontologies. Anatomical terms were reorganized to follow the Uber Anatomy Ontology hierarchy, and new chemical classes were incorporated to improve compatibility with the Chemical Entities of Biological Interest Ontology. Hundreds of additional axioms were added using existing object properties to capture assay formats, endpoints, detection methods, substrates, design strategies, and biological context. These refinements improve BAO’s semantic precision, interoperability, and reasoning capabilities. As a demonstration of these capabilities, we present a reasoning-based use case in which BAO’s equivalent class axioms enable automated classification of passive cell permeability and active efflux substrate assays. The ontology infers broader mechanistic categories from shared modeled characteristics, grouping transporter-specific and cell-based permeability assays under their mechanistic parents, while excluding assays that do not meet all restrictions. This example demonstrates its ability to support precise, inference-driven retrieval and integration of assay classes. By extending its scope and improving the clarity, consistency, and semantic depth of its classifications, BAO continues to serve as a vital resource for organizing pharmacological data and advancing research in both academic and industrial settings.</p>

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Advancing the bioassay ontology through integrated PK/PD and safety pharmacology representation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

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  • Joan Glenny-Pescov,
  • Caty Chung,
  • Nicolette Ross,
  • Jiaming Hu,
  • Michael Sinclair,
  • Rabia Khurshid,
  • Anneli Karlsson,
  • Stephan C. Schürer

摘要

The latest release of the BioAssay Ontology (BAO), version 2.8.16, introduces major updates that expand its ability to describe and categorize assays related to pharmacokinetics, pharmacodynamics, and safety pharmacology. These refinements, driven by collaboration with the Semantic Enrichment of Electronic Laboratory Notebook Data project, an industry initiative led by the Pistoia Alliance, address previously identified gaps in ontology coverage. New terms were added for disease models, preclinical study parameters, toxicological measurements, and detailed classifications of pharmacokinetics and pharmacodynamics assays. The update also incorporates biologically relevant target classes, including cytochrome P450 enzymes, solute carrier transporters, and uridine diphosphate-glucuronosyltransferase enzymes. Beyond content expansion, structural improvements include reassignment of terms to more specific and semantically appropriate parent classes, refinement of class dependencies, and enhanced alignment with external ontologies. Anatomical terms were reorganized to follow the Uber Anatomy Ontology hierarchy, and new chemical classes were incorporated to improve compatibility with the Chemical Entities of Biological Interest Ontology. Hundreds of additional axioms were added using existing object properties to capture assay formats, endpoints, detection methods, substrates, design strategies, and biological context. These refinements improve BAO’s semantic precision, interoperability, and reasoning capabilities. As a demonstration of these capabilities, we present a reasoning-based use case in which BAO’s equivalent class axioms enable automated classification of passive cell permeability and active efflux substrate assays. The ontology infers broader mechanistic categories from shared modeled characteristics, grouping transporter-specific and cell-based permeability assays under their mechanistic parents, while excluding assays that do not meet all restrictions. This example demonstrates its ability to support precise, inference-driven retrieval and integration of assay classes. By extending its scope and improving the clarity, consistency, and semantic depth of its classifications, BAO continues to serve as a vital resource for organizing pharmacological data and advancing research in both academic and industrial settings.