WHO Drug Dictionary - Pharmacovigilance-Material

WHO Drug Dictionary (WHO – DD)

The WHO Drug Dictionary (WHO-DD) is administered and licensed by the World Health Organization’s (WHO) Uppsala Monitoring Center (UMC). The UMC collaborates globally with regulators, researchers and other professionals from the health care and pharmaceutical industries in the practice of pharmacovigilence, which WHO defines as “the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problems.” 1

A drug dictionary proves useful when tabulating medication usage because it classifies the same medication, often known by different names, into a single name. For example, Tylenol®, acetaminophen and paracetamol all refer to the same active ingredient, and WHO-DD uses the ingredient name paracetamol.

This paper will describe the structure of the dictionary, including PROC SQL example code to illustrate relationships among the dictionary tables. Next the paper will provide example data summaries using different components of the tables. Finally, I will discuss SAS® coding strategies for implementation.

In 2005 the UMC released the WHO-DD Enhanced, which follows the same structure as WHO-DD, but incorporates a more timely system for including newly launched pharmaceutical products. While this paper refers to the WHO-DD, the same lessons would apply to WHO-DD Enhanced. Examples in this paper follow “Format B” of the dictionary.

DICTIONARY STRUCTURE

The WHO-DD includes tables that describe the manufacturer of the pharmaceutical products and a published source (e.g., Physician’s Desk Reference). These tables are omitted from the discussion below.

DD TABLE

The DD table contains the drug names that are used for coding source data records (e.g., case report form entries). Drug names can be generic or trade names, and many do refer to drug products that contain multiple active ingredients (e.g., Excedrin® contains aspirin and caffeine). There are different drug names for different salts or esters of the same active ingredient (e.g., morphine sulfate vs. morphine tartrate), but often one would want to collapse these together in order to tabulate on the active ingredient (morphine). For this purpose the WHO-DD provides what this paper refers to as the “preferred” drug name, which is typically a generic name omitting the salt/ester specification. Each drug name is identified by a unique combination of the Drug Record Number, Sequence Number 1 (Seq1) and Sequence Number 2 (Seq2), with the “preferred” drug name identified by Seq1=01 and Seq2=001. Different salt/ester formulations of the same drug are identified by different Seq1 values, and different names for the drug – whether trade names or generic names – are identified with different Seq2 values.

INGREDIENTS TABLES

Drug products are composed of one or more active ingredients, and the ingredients included in each drug product are listed in the ING table, by linking Drug Record Numbers with Chemical Abstract Service Registry Numbers (CAS Numbers). The names of the ingredients are listed in the BNA table, linked by the CAS Number. Non-“preferred” drug names often are not included in the ING table; therefore it is recommended that the DD and ING tables be joined on the Drug Record Number alone, after subsetting on Seq1=01 and Seq2=001.

ATC TABLES

With thousands of drug products on the market, there is an obvious need to group these into meaningful categories. The Anatomic Therapeutic Chemical (ATC) classification system does this, and it is part of WHO-DD. The ATC system originated in the early 1970s in Norway, and a search engine for it is available today in the public domain at the WHO website (http://www.whocc.no/atcddd). However, the linkage between Drug Record Numbers and ATC codes is only available in the WHO-DD.

The ATC system is hierarchical and includes four levels of granularity. An example of this is listed below, with the four levels shown from most general (level 1) to most specific (level 4):

TABLE 1 – ATC LEVELS AND EXAMPLE Level	Type	ATC Example
1	Anatomical main group	A	ALIMENTARY TRACT AND METABOLISM
2	Therapeutic subgroup	A02	DRUGS FOR ACID RELATED DISORDERS
3	Pharmacological subgroup	A02A	ANTACIDS
4	Chemical subgroup	A02AA	MAGNESIUM COMPOUNDS

The level 1 codes are always a single letter; the level 2 codes are always a two-digit number appended to the corresponding level 1 code; etc.

Each drug product in the DD table is associated with one or more ATC codes. (Some drugs operate on multiple anatomic systems, and thus are associated with multiple ATC codes). The ATC code(s) associated with each drug product are listed in the DDA table, by the highest ATC level for each association. (For example, a drug that is associated with chemical subgroup A02AA would be listed at only the A02AA level, not A02A, etc.). The names of the ATC categories are listed in the INA table by the ATC code. As with the association between drug names and ingredients, non-“preferred” drug names often are not included in the INA table; therefore it is recommended that the DD and INA tables be joined on the Drug Record Number alone, after subsetting on Seq1=01 and Seq2=001.

FLOWCHART DESCRIBING STRUCTURE OF SELECTED WHO-DD TABLES

SUGGESTIONS FOR SUMMARIZING MEDICATION USAGE

ATC codes are an excellent tool for grouping drug names in a summary, but levels 3 and 4 tend to be so granular that there are very few medications in each category. Thus in the example below only levels 1 and 2 are used.

TABLE 2 – EXAMPLE SUMMARIZING BY ATC LEVELS 1 AND 2 Anatomic Group	Treatment A
Therapeutic Subgoup	(N=xxx)
Preferred Drug Name	n pct
ALIMENTARY TRACT AND METABOLISM
STOMATOLOGICAL PREPARATIONS
ACETYLSALICYLIC ACID	xx xx%
DOXYCYCLINE	xx xx%
IRON	xx xx%
METRONIDAZOLE	xx xx%
POTASSIUM	xx xx%
DRUGS FOR ACID RELATED DISORDERS
ALMINOX	xx xx%
ESOMEPRAZOLE	xx xx%
OMEPRAZOLE	xx xx%
RABEPRAZOLE	xx xx%
RANITIDINE	xx xx%
ANTIDIARR., INTEST. ANTIINFL./ANTIINFECT. AGENTS
BETAMETHASONE	xx xx%
BUDESONIDE	xx xx%
IMODIUM	xx xx%
MESALAZINE	xx xx%

TABLE 3 – EXAMPLE OF THE SAME ACTIVE INGREDIENT USED IN DISPARATE DRUG PRODUCTS Active Ingredient	Preferred Drug Name		ATC Codes (Level 1 􀃆 Level 2)	What is it?
Paracetamol	Tylenol®	Nervous System 􀃆 Analgesics
Paracetamol	Sudafed®	Respiratory System 􀃆 Cold and Flu Preparations
Timolol	Betacentyl®		Cardiovascular 􀃆 Beta Blocking Agents	Oral antihypertensive
Timolol	Xalcom®		Sensory Organs 􀃆 Ophtamological	Eye drops for glaucoma

IMPLEMENTATION STRATEGIES

WHO-DD is now available as two different data formats: B Format and C Format. The examples shown above are from the B Format. The C Format includes a more specific definition related to the drug name, the “medicinal product ID”. The C Format uses this additional level of detail to classify the drugs into ATC codes a bit differently than the B Format.2

When analyzing concomitant medication usage data from clinical trials, it can be desirable to store the data in three different data structures:

As captured from the source documents, with one row in the dataset for each entry in the source documents.

                         

Joined with ATC codes. Since there is often more than one ATC code corresponding to each drug name, this join increases the number of rows in the dataset.

Joined with generic ingredients. Since many drugs include multiple active ingredients, this join also increases the number of rows in the dataset.

                         

The redundant data in the three data structures described above suggests that constructing these as SAS® PROC SQL views is a more efficient alternative to permanent data sets.

                         

CONCLUSION

TheWHO-DD dictionary is used to code medications, classify these into ATC (anatomic, therapeutic and chemical) categories, and identify the active ingredients associated with each medication.

For the purposes of analyzing clinical trial data, it is useful to use only the “preferred” drug names (those with Seq1=01 and Seq1=001).

One can group drug names by ATC code, but drug ingredients cannot be grouped in this manner because a single ingredient can be used in disparate types of drugs.

Given the conflicting desires to: preserve the number of medication records; expand the number of records corresponding to the ATC code(s) associated with each drug name; and expand the number of records corresponding to the active ingredient(s) associated with each drug name; it makes sense to create SAS® views to provide these different data structures.