NAMO: a unified ontology for New Approach Methodology metadata

The problem NAMO is solving

New Approach Methodologies — the collective term for non-animal alternatives in biomedical research — include cellular systems, microphysiological systems, and computational models [1]. Brain organoids, liver-on-chip devices, and QSAR toxicity predictors generate different evidence types and therefore different metadata needs [2]. The NAMO manuscript argues that current NAM resources remain fragmented across incompatible terminology, reporting conventions, and databases [2].

OrganoidDB catalogs transcriptomics for 16,000+ organoid samples [2]. MPS-DB covers pharmacokinetic endpoints from microphysiological systems [2]. Cell Model Passports focuses on cancer model genomics [2]. The NAMO manuscript identifies these resources as difficult to query together because they were not designed around a shared cross-NAM schema [2]. The practical implication is familiar from traditional animal research: detailed data can remain locked in formats that limit cross-study comparison and systematic regulatory evaluation.

The passage of the FDA Modernization Act 2.0 in 2022 eliminated mandatory animal testing requirements for drug development [2]. That policy change makes NAM data fragmentation more consequential because regulatory acceptance depends on structured, comparable evidence rather than isolated assay outputs [2]. NAMO is one proposed data-infrastructure response: a unified ontology and schema for describing NAM metadata across model types [1].

What NAMO is and how it is structured

NAMO (New Approach Methodology Ontology) is developed by the Monarch Initiative and built on LinkML [1]. LinkML can generate Python dataclasses, OWL ontology files, JSON Schema, and GraphQL schemas from a single YAML source definition [5]. The key practical advantage of this approach is consistency across formats without manual synchronisation [5].

The ontology organises all model systems under a single abstract root class (ModelSystem), which branches into AnimalModel and NAMModel [2]. The NAMModel branch then divides into three major categories reflecting different experimental logics [2]:

• Cellular systems — 2D cultures, 3D cultures, spheroids, organoids, and co-cultures [2]. The Organoid class adds properties specific to self-organising structures, including differentiation methods, maturation timelines, and branching morphogenesis indicators [2].
• Microphysiological systems — organ-on-chip and tissue-on-chip devices [2]. This category captures microfluidic channel architecture, membrane specifications, flow parameters such as perfusion rate and shear stress, and mechanical stimulation parameters [2].
• In silico models — QSAR, physiologically-based pharmacokinetic (PBPK) models, machine learning classifiers, digital twins, and metabolic models [2]. Algorithm specifications, training data characteristics, and performance metrics are formalised as structured slots [2].

Current v0.1.0 statistics include 48 classes, 156 properties, 12 enumerations, and 6 integrated external ontologies [2]. The schema is published in the GitHub repository and the ontology export is browsable on BioPortal [3][4].

Ontology integration and interoperability

Rather than redefining terms already established elsewhere, NAMO uses LinkML's reachable_from mechanism to reference branches of external ontologies and validate that annotations stay within those branches [2]. A hepatocyte is CL:0000182; liver tissue is UBERON:0002107; cystic fibrosis is MONDO:0009563 [2]. These annotations reuse the semantic structure of their source ontologies — including hierarchical relationships and cross-references — without NAMO having to maintain those definitions itself [2].

The six integrated ontologies are UBERON, Cell Ontology, ChEBI, NCBITaxon, OBI, and ECO [2]. The practical implication is that a NAMO-annotated organoid record can, in principle, be queried alongside records from other systems that use the same ontology terms, which is the foundation of semantic interoperability [1][2].

The concordance framework: structured validation evidence

One of the more interesting design decisions in NAMO is how it handles validation [2]. Most NAM reporting treats validation as a binary or as unstructured text; the NAMO manuscript instead introduces a StructuredConcordanceResult class that decomposes validation into five dimensions, each with its own quantitative metrics and methodological metadata [2]:

• Molecular similarity — gene expression correlation coefficients, protein abundance patterns, metabolite profiles, specific genes or metabolites, and confidence intervals [2]
• Pathway concordance — pathway activation scores such as GSEA or PROGENy and directional consistency of regulation [2]
• Phenotype overlap — quantified using standardised phenotype ontologies, with severity and penetrance metrics [2]
• Functional parity — physiological readouts such as TEER for barrier integrity or beat frequency for cardiac models [2]
• Statistical measures — confidence intervals, p-values, and effect sizes for the validation dimensions [2]

This is a meaningful step beyond current practice. The manuscript notes that published organoid validations typically report concordance for only 10-50 marker genes, while comprehensive transcriptomic comparisons remain rare [2]. The practical implication is that the schema provides a place to record both what was assessed and what was not, making absence of evidence visible rather than hidden in narrative prose.

Practical relevance for preclinical researchers

NAMO is at an early stage: the public documentation describes v0.1.0, the manuscript is hosted as a preprint-style document, and the source schema is available on GitHub [1][2][3]. It is not yet a deployed database standard [1][3]. But it is worth tracking for several reasons.

First, the Monarch Initiative is publishing NAMO through established ontology infrastructure, including GitHub documentation and BioPortal export [1][3][4]. Second, the FDA Modernization Act 2.0 context creates institutional pressure for structured validation evidence [2]. Third, the LinkML-based multi-format generation approach solves a practical implementation problem: the same schema can produce OWL for semantic reasoning, JSON Schema for API validation, and Python classes for programmatic access [5].

For teams working with organoids or organ-on-chip alongside traditional animal or HCM data, NAMO is worth watching as a potential bridge. In Neuronautix's view, its main value is a common metadata layer that could make multi-modal, cross-platform comparisons tractable without starting from scratch, provided adoption and tooling mature. The ontology is available at github.com/monarch-initiative/namo and documentation at monarch-initiative.github.io/namo [1][3].