The One Health Promise and the Data Reality

The One Health approach is not new, but its formalisation has accelerated over the last five years. The Quadripartite Alliance, formed when the United Nations Environment Programme joined FAO, WHO, and WOAH under a Memorandum of Understanding, published joint guidance on integrated surveillance that explicitly treats human, animal, and environmental data as inputs to the same analytic question.1 Approximately 65% of major disease outbreaks worldwide have a zoonotic origin, which alone would justify the framework.2

Yet when a pharmaceutical or animal health organisation tries to operationalise One Health inside its own walls, the promise collides with a stubborn reality: the data standards used to capture, code, and submit information about human medicines look almost nothing like the ones used for veterinary medicines. This is not a philosophical problem. It is a plumbing problem, and plumbing problems compound.

65% Major disease outbreaks worldwide with a zoonotic origin, per Quadripartite guidance
4 Agencies in the Quadripartite Alliance: WHO, WOAH, FAO, UNEP
Rev 16 Current VeDDRA revision effective 1 October 2025, next comments due February 2026

The Association for Veterinary Informatics and the CTSA One Health Alliance, writing jointly, have been direct about this: there is a lack of formal support for informatics training and methods development in veterinary medicine, which limits the ability to set basic standards for data organisation. FAIR principles (Findable, Accessible, Interoperable, Reusable) have not become an integral part of veterinary data management practice, despite being routine in human clinical research.3

None of this is a criticism of the veterinary community. It is a structural observation. Human pharma has spent thirty years and enormous capital building an interoperability layer under CDISC. Veterinary pharma is a smaller, more fragmented market, and its regulators have taken a different path. The consequence is that the interoperability layer for cross-species questions barely exists yet.

Regulatory Frameworks That Shape the Divide

To understand why the data standards diverged, it helps to look at the regulatory bodies that shaped them. The frameworks are parallel but not connected.

Human Medicines Oversight

Human pharmaceutical development is governed by a tightly coupled international system. The International Council for Harmonisation (ICH) brings regulators and industry together to align expectations. The US Food and Drug Administration, the European Medicines Agency, Japan’s Pharmaceuticals and Medical Devices Agency, and the UK’s Medicines and Healthcare products Regulatory Agency all accept a common submission format. Organisations must use the CDISC SDTM standard when submitting clinical data to the FDA, MHRA, and PMDA, which is what makes SDTM the de facto lingua franca of clinical data.4

Veterinary Medicines Oversight

Veterinary medicine has its own parallel constellation. In the United States, the FDA’s Center for Veterinary Medicine (CVM) approves new animal drugs and manages post-approval reporting, while the USDA’s Animal and Plant Health Inspection Service (APHIS) Center for Veterinary Biologics (CVB) regulates veterinary biologics like vaccines. In the European Union, the Committee for Veterinary Medicinal Products (CVMP), part of the EMA, oversees veterinary medicines under Regulation (EU) 2019/6, which became applicable on 28 January 2022.5 Sponsors must submit adverse drug event reports, product defects, and periodic drug reports through separate channels using separate templates.6

HUMAN

ICH / FDA / EMA / PMDA

Harmonised standards, common technical document, CDISC-based submissions, MedDRA-coded adverse events, EudraVigilance and FAERS pharmacovigilance systems.

VETERINARY (US)

FDA CVM & USDA APHIS CVB

Split oversight: FDA CVM for drugs, USDA CVB for biologics. NCAH Portal and eSubmitter for electronic submissions. No CDISC mandate. Adverse event reporting on FDA Form 1932.

VETERINARY (EU)

EMA CVMP & Regulation (EU) 2019/6

Union Product Database launched January 2022 as single source for authorised veterinary medicines. Mandatory antimicrobial sales and use reporting to EMA under the Veterinary Medicinal Products Regulation.

CROSS-DOMAIN

WHO / WOAH / FAO / UNEP

Quadripartite guidance on integrated One Health surveillance. GLASS for AMR data. Voluntary standards, not enforceable at the marketing authorisation level.

The critical observation is that each regulator’s data standards evolved to fit its own scope. Human regulators harmonised aggressively under ICH because pharmaceutical companies operate globally and needed one submission package. Veterinary regulators harmonised less because the veterinary market is smaller, more regional, and structurally different (food-producing animals, companion animals, and aquaculture each have their own realities). This is a rational history, but it has created a data standards environment where a molecule tested in humans and then repurposed for dogs (or vice versa) travels through entirely different pipes.

Adverse Event Terminology: MedDRA, VeDDRA, and the Translation Gap

Nowhere is the divergence more visible than in adverse event coding.

MedDRA in Human Medicine

The Medical Dictionary for Regulatory Activities (MedDRA) was developed in 1994 by the pharmaceutical industry and regulators, and it is now the required terminology for adverse event reporting across ICH regions. MedDRA terms are organised into a five-level hierarchy: System Organ Class (SOC), High Level Group Term (HLGT), High Level Term (HLT), Preferred Term (PT), and Low Level Term (LLT).7 The dictionary is updated twice a year and licensed globally. Every FDA MedWatch report, every EudraVigilance human case, and every clinical trial safety database uses this vocabulary.

VeDDRA in Veterinary Medicine

The Veterinary Dictionary for Drug Regulatory Activities (VeDDRA) is the veterinary counterpart, but it is not a subset or extension of MedDRA. It was developed by the EMA and is maintained by the CVMP Pharmacovigilance Working Party. The current version is Revision 16, effective 1 October 2025, with the next comment period closing 14 February 2026.8 VeDDRA uses a four-level hierarchy (SOC, HLT, PT, LLT) and includes designations for animal (A), common (C), and human (H) terms, since VeDDRA is used to report suspected adverse events not only in animals but in humans exposed to veterinary medicinal products.9

Where the Translation Breaks Down

The two dictionaries share conceptual DNA but not codes. A “vomiting” event in a dog and a “vomiting” event in a human live in different SOCs, use different preferred terms, and cannot be joined without a mapping table that does not officially exist. Frontiers researchers examining musculoskeletal adverse events in dogs receiving a novel monoclonal antibody noted that VeDDRA offers standardised veterinary-specific terminology that is especially useful for categorising musculoskeletal and neurologic events, and observed that veterinary medicine lacks a comparable unified system to MedDRA. International harmonisation efforts through coordinated VeDDRA use and transparent reporting infrastructure could support advancements, but such harmonisation is still nascent.10

What this means in practice. When a pharmacovigilance signal appears in both human and animal data (for example, an antimicrobial associated with a specific toxicity pattern), analysts today cannot query one warehouse and get a coherent answer. They must query FAERS or EudraVigilance for humans, EudraVigilance Veterinary or the FDA CVM adverse event database for animals, and then build the crosswalk themselves. That crosswalk is where errors, delays, and defensible-decision documentation problems live.

Clinical Data Models: SDTM, ADaM, SEND, and Their Veterinary Counterparts

The CDISC family of standards is the foundation of the modern human clinical data pipeline. Three of its foundational standards are especially relevant to a One Health discussion.

SDTM (Study Data Tabulation Model)

SDTM is a framework for organising data collected in human clinical trials. Data are assigned to domains (DM for demographics, LB for laboratory data, AE for adverse events, VS for vital signs, and so on), each with a defined structure. Studies conducted under the SDTM Implementation Guide are portable across sponsors, contract research organisations, and regulators. The standards have been shown to decrease resource requirements by 60% overall and 70 to 90% during study start-up when implemented at the beginning of a research programme.11

ADaM (Analysis Data Model)

ADaM sits on top of SDTM and provides analysis-ready datasets designed to support statistical analysis and reproducibility. It is the layer between the raw tabulation dataset and the tables, listings, and figures that appear in a clinical study report.

SEND (Standard for Exchange of Nonclinical Data)

SEND is the nonclinical implementation of SDTM. It specifies how to present data from animal toxicology studies (single-dose, repeat-dose, carcinogenicity, respiratory and cardiovascular safety pharmacology) in a consistent format for FDA submission. Raw data of toxicology animal studies started after 18 December 2016 to support submission of new drugs to the US FDA must be submitted using SEND.12

This last point is where One Health leaders often stumble. SEND is a “veterinary” standard in the sense that it describes animal studies, but it is a human-pharma standard in the sense that its purpose is to support a human drug submission. The animals in a SEND dataset are laboratory models. Data captured in a SEND study rarely flows into a VeDDRA-coded veterinary pharmacovigilance system, and it is not intended to. SEND, VeDDRA, and the FDA CVM adverse event submission requirements are three separate universes describing animals for three separate regulatory purposes.

The Veterinary Clinical Data Landscape

On the veterinary side, there is no analogue to SDTM. Veterinary clinical trials are conducted, but their data structures are largely bespoke or CRO-specific. The FDA CVM accepts electronic submissions through eSubmitter but does not mandate a CDISC-equivalent tabulation model.13 USDA APHIS CVB accepts biologics submissions through the NCAH Portal using APHIS Forms 2007, 2008, 2020, and others, none of which are structured for cross-study analytic reuse.14 Some sponsors voluntarily adopt CDISC-like structures internally for consistency, but there is no regulatory pressure driving convergence.

The SD perspective. This is not an argument that veterinary regulators should copy the human framework. Veterinary submissions have their own structural realities (multiple target species, food safety endpoints, environmental risk assessment for excretion) that CDISC was not designed for. The argument is that when a pharma organisation wants to combine data across the two domains, someone has to build the translation layer, and that translation layer needs to be a strategic capability, not a spreadsheet on someone’s desktop.

Product, Substance, and Organisation Master Data

Product identification is another domain where the standards have parted ways.

On the Human Side

The EU has spent the last decade building the SPOR services (Substances, Products, Organisations, Referentials) as the master data backbone for human medicines regulation. SPOR provides standardised terminology and identifiers that flow into EudraVigilance, the electronic Application Form, and product information management. RxNorm, maintained by the US National Library of Medicine, provides normalised names for clinical drugs and is widely used in electronic health records.

On the Veterinary Side

The Union Product Database (UPD), established under Regulation (EU) 2019/6 and launched in January 2022, serves as a single source of information on all authorised veterinary medicines and their availability in EU and EEA Member States. The UPD uses standardised terminology from the four SPOR data management services for managing master data.15 This is quiet good news: the UPD is one of the few veterinary systems that explicitly leverages the human-medicines master data backbone. A veterinary substance recorded in the UPD uses the same SMS (Substances Management Service) identifier as a human substance, which means a One Health substance-level join is at least technically possible.

Data Domain Human Pharma Standard Veterinary Pharma Standard Cross-Domain Bridge
Clinical trial tabulation CDISC SDTM (required by FDA, MHRA, PMDA) None mandated; sponsor-specific structures Voluntary adoption of SDTM concepts
Analysis datasets CDISC ADaM None mandated Manual harmonisation
Nonclinical / animal tox CDISC SEND (FDA required post-2016) Not applicable (different regulatory purpose) SEND describes lab animals for human submissions
Adverse event terminology MedDRA (5-level hierarchy) VeDDRA (4-level hierarchy, includes human-exposure terms) No official crosswalk; case-by-case mapping
Pharmacovigilance system FAERS (US), EudraVigilance (EU), VigiBase (WHO) EudraVigilance Veterinary (EU), FDA CVM (US) Separate databases, separate access, separate schemas
Product master data SPOR (EU), RxNorm (US) Union Product Database (EU, uses SPOR terminology) Shared SMS substance identifiers where UPD applies
Antimicrobial use data Voluntary NHSN AUR module (US), various EU registries Mandatory reporting under Reg (EU) 2019/6 Article 57 WHO GLASS integrates at the aggregate level
Diagnostic terminology SNOMED CT, ICD-10-CM, LOINC SNOMED CT Veterinary Extension (partial coverage) SNOMED CT is the closest shared vocabulary

Where the Divergence Actually Hurts: Four Cross-Domain Use Cases

An abstract discussion of data standards does not motivate change. Specific business scenarios do. Here are four places where the divergence between human and veterinary standards imposes real cost.

Use Case 1: Zoonotic Disease Surveillance

Approximately two-thirds of major disease outbreaks have a zoonotic origin. Cross-sectoral data interpretation is assumed to improve prevention, prediction, and control, and integrated One Health surveillance has been shown to improve risk classification accuracy and the efficacy of detection. Knowledge of spatial and temporal patterns of animal host distribution can be used to raise awareness of human risk and enhance early prediction accuracy of human incidence.16 Yet when researchers attempt to build integrated systems, they consistently find that data from human public health, animal health, and environmental sectors resides in silos with incompatible schemas.17 Zoonoses surveillance could be improved by integrating and exchanging data across databases, but interoperability at the schema level is the persistent bottleneck.

Use Case 2: Antimicrobial Resistance Monitoring

AMR is the flagship One Health case. Under Regulation (EU) 2019/6, collection of data on sales of veterinary antimicrobials and on use in animals became a mandatory activity for Member States, who must report to EMA. WHO’s Global Antimicrobial Resistance Surveillance System (GLASS) integrates data across countries. The Quadripartite has issued dedicated guidance on integrated AMR surveillance.18 But the data feeding these systems arrives from separate national systems designed with separate assumptions. The Quadripartite guidance itself acknowledges that harmonising surveillance across sectors requires standardised approaches to collecting, analysing, interpreting, and sharing data by country, region, and area, which is precisely the harmonisation that does not yet exist end-to-end.

Use Case 3: Environmental Toxin and Residue Tracking

Veterinary medicines used in food-producing animals must meet residue limits, and their environmental fate must be assessed. Human drugs excreted into wastewater eventually reach the environment as well. Both create signals that a well-instrumented One Health system would connect. In practice, veterinary residue monitoring uses one set of analytic methods and reporting formats, human pharmaceutical environmental risk assessment uses another, and wastewater surveillance (which grew rapidly during the pandemic) uses a third. Cross-references between the datasets require post-hoc reconciliation.19

Use Case 4: Comparative Pharmacology and Reverse Translation

Comparative oncology has been recognised as a method to accelerate development of therapies by studying naturally occurring tumours in companion animals. Model-based reverse translation between veterinary and human medicine has been proposed as a One Health initiative that could benefit both fields.20 A canine osteosarcoma patient and a paediatric osteosarcoma patient share more biology than their data systems admit. But the veterinary case sits in a veterinary EHR using veterinary vocabularies, while the human case sits in a hospital EHR using SNOMED CT and MedDRA, and the two cannot be cross-queried without heroic effort. Standardising drug exposures using RxNorm can help resolve the current limited ability to evaluate comparative drug exposures, and integrating cross-species-enabled ontologies such as those from the Monarch Initiative into clinical records would allow more accurate capture of phenotypic features and facilitate interoperability between humans and veterinary species.21

The hidden cost. Each of these use cases is a place where an organisation might spend a quarter of an FTE (or more) per project just doing manual data reconciliation. Multiply that across the portfolio and the divergence becomes a meaningful operating expense, plus a source of decision latency that leaders rarely see reflected in dashboards.

Where Harmonization Is Quietly Happening

The picture is not entirely bleak. Several harmonisation efforts are progressing, though most are still upstream of production impact.

WHO, WOAH, FAO, UNEP Quadripartite

The 2025 Quadripartite guidance on integrated One Health surveillance of antimicrobial use and resistance is the most concrete artefact of cross-sector alignment to date. It provides recommended data elements, minimum reporting standards, and case study patterns that member states can adopt.22 The guidance stops short of mandating a common data model, but it establishes a shared vocabulary for the AMR conversation.

EMA’s Veterinary Big Data Initiative

The Veterinary Big Data initiative orchestrated by EMA and the Heads of Medicines Agencies aims to transition the European medicines regulatory network towards a data-driven culture, with digital systems collecting and processing data for regulatory frameworks under the Veterinary Medicinal Products Regulation.3 The Union Product Database, which reuses SPOR master data infrastructure, is the first concrete deliverable.

Cross-Species Ontologies

Community-driven, cross-species ontologies from groups such as the Monarch Initiative are being used in research settings to standardise phenotype and disease terms across species. SNOMED CT, which has a veterinary extension, offers another partial bridge. Neither is yet routine in commercial pharmacovigilance workflows.

The American Veterinary Medical Association Position

The AVMA supports standardised health information systems that contribute to interoperability and One Health information exchange through consensus-based standards such as SNOMED CT, HL7, LOINC, and DICOM.23 These are all human-medicine-origin standards being extended into veterinary practice, which is one legitimate path to convergence.

A modest reason for optimism. The Union Product Database’s decision to reuse SPOR master data is exactly the kind of quiet, unglamorous interoperability decision that pays off over decades. It does not solve the adverse-event terminology gap, but it demonstrates that regulators can choose reuse over reinvention when the incentives align.

A Maturity Model for Cross-Domain Data Interoperability

Leaders asking “where do we stand?” benefit from a maturity framework. The following model draws on the general shape of enterprise data maturity models but is specific to the One Health cross-domain interoperability question. Most organisations we work with sit at Level 1 or 2. A few have reached Level 3 for specific use cases. Level 4 and Level 5 are aspirational at scale.

1

Level 1: Siloed

Human pharma data lives in CDISC-compliant systems. Veterinary data lives in sponsor-specific or CRO-specific systems. No cross-domain queries are attempted. Cross-domain questions are answered anecdotally or with manual literature review. This is the default state for most pharmaceutical and animal health organisations.

2

Level 2: Ad Hoc Bridged

Individual analysts have built spreadsheet-based crosswalks between MedDRA and VeDDRA, or between human clinical outcome domains and veterinary equivalents. These crosswalks live on desktops. They are not versioned, not governed, and not reusable. When a question is asked, someone rebuilds the crosswalk. Answers arrive but are hard to defend.

3

Level 3: Governed Crosswalks

The organisation has published, versioned mapping tables between key human and veterinary vocabularies for at least one strategic use case (typically AMR, safety, or comparative oncology). Mappings are reviewed by subject matter experts and tied to a data governance council. Cross-domain queries are possible for the covered use cases but require manual assembly for anything else.

4

Level 4: Common Semantic Layer

The organisation has built a semantic layer (typically an ontology or knowledge graph) that abstracts human and veterinary data behind a common query interface. Analysts can ask a question in domain terms and the layer resolves it to the underlying systems. Reference data (substances, species, indications) is centrally governed. This requires meaningful investment and a real data platform strategy.

5

Level 5: Federated One Health Analytics

The organisation participates in industry-level or regulator-level federated analytics. Standards are contributed back to the community rather than kept internal. Real-time surveillance signals flow across sectors. This level exists in aspiration and in specific research consortia, but no commercial organisation is fully at Level 5 across the enterprise.

A Practical Path Forward for Human and Veterinary Data Teams

Leaders reading this article are unlikely to be in a position to fix VeDDRA, or to persuade FDA CVM to adopt SDTM. Those decisions belong to regulators and standards bodies. What leaders can do is manage the internal cost of the divergence more deliberately.

Start with One Strategic Use Case

Rather than attempt an enterprise-wide harmonisation, pick the cross-domain question that recurs most often in your organisation. For most pharma companies with veterinary exposure, that is AMR surveillance or safety signal detection. For animal health companies exploring comparative oncology or dermatology, it is efficacy read-across. Build a governed crosswalk for that use case first. Prove value before generalising.

Invest in Reference Data Governance

Species, substances, and indications are the joining keys for any cross-domain query. If these are inconsistently coded across your human and veterinary systems, no downstream architecture will save you. Central reference data governance is unglamorous but is the highest-leverage investment for most organisations.

Use SPOR and RxNorm Where You Can

The substance-level identifiers in SPOR (for organisations operating in the EU) and RxNorm (in the US) already bridge parts of the human and veterinary domains. Where your systems can natively store these identifiers rather than proprietary product codes, cross-domain reconciliation gets easier for essentially free.

Treat the Crosswalk as a Living Asset

MedDRA is updated twice a year. VeDDRA is on Revision 16 and will be updated again. A crosswalk built once and forgotten is worse than no crosswalk at all, because analysts will trust it. Assign an owner, put it under change control, and review it at least annually.

Contribute to Standards Where You Can

EMA’s VeDDRA comment periods and the Quadripartite consultation processes are open to industry input. Contributing to standards shapes them, and even small contributions can build institutional relationships that pay back later.

Conclusion

The One Health framework is not a slogan. It is a serious position on how disease, medicine, and environment interact, and it is increasingly reflected in regulator-level policy. But the data standards inherited from thirty years of human pharma development and the parallel set inherited from veterinary pharma do not currently make One Health analytics easy. The divergence is real, it is expensive, and it is not going away quickly. Leaders who understand where the seams are, and who invest deliberately in the interoperability layer rather than pretending the seams do not exist, will move faster on the questions that matter most: safety signal detection, antimicrobial resistance, environmental exposure, and comparative therapeutic development.

Sakara Digital works with pharma and biotech organisations building the data governance, interoperability, and cross-domain analytics foundations that One Health questions require. If you are exploring where your human and veterinary data pipelines intersect (or should intersect) and want an independent perspective on how to sequence the investment, we are happy to have that conversation.