> For the complete documentation index, see [llms.txt](https://docs.snomed.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snomed.org/implementation-guides/drug-model-implementation-guide/content-development/drug-extension-development-principles/drug-extension-authoring.md).

# Drug Extension Authoring

## **Model**-Driven and Scalable Authoring

Drug extension content is not typically authored manually on a concept-by-concept basis. Instead, it is derived from existing national drug dictionaries using a structured and scalable process.

This approach is comparable to the development of terminologies such as LOINC, where content is generated based on combinations of well-defined attributes.

## Process

{% stepper %}
{% step %}

### Mapping Atomic Components

The process begins with mapping the atomic elements of the local drug dictionary to SNOMED CT and related standards. These include:

* substances (active ingredients)
* dose forms
* units of measure
* strength representations

SNOMED CT aligns with EDQM (European Directorate for the Quality of Medicines) standards for dose forms. Where local dictionaries also use EDQM, this significantly facilitates mapping and interoperability.
{% endstep %}

{% step %}

### Deriving Drug Concepts

Once atomic elements are mapped, higher-level drug concepts are generated based on combinations identified in the local dictionary.

These include:

* Medicinal Products (MP)
* Medicinal Product Forms (MPF)
* Clinical Drugs (CD)
* Real Clinical Drugs (RCD)
* Packaged Clinical Drugs (PCD)
* Real Packaged Clinical Drugs (RPCD)

Concepts are typically created algorithmically, using rules derived from the SNOMED CT concept model.
{% endstep %}

{% step %}

### Reuse and Gap Filling

During concept generation:

* Existing concepts in the International Edition (MP, MPF, CD) should be reused wherever available
* Missing concepts are created in the national extension
* These missing concepts may later be submitted for promotion to the International Edition

This ensures consistency while allowing extensions to meet immediate national requirements.
{% endstep %}

{% step %}

### Validation and Refinement

The generation process is typically implemented as:

* an automated bulk creation process
* followed by manual review and quality assurance

This hybrid approach balances scalability with clinical and terminological accuracy.
{% endstep %}
{% endstepper %}

## Typical Authoring Challenges

Drug extension development involves several common challenges, including:

* Variations in ingredient representation across local dictionaries
* Limited granularity in source data (e.g. missing strength or form details)
* Rounding rules for strength normalization
* Non-normalized strength expressions, including numerator/denominator inconsistencies
* Differences in units of measure and representation conventions

Addressing these challenges requires careful normalization and alignment with the SNOMED CT concept model.

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