1. Medinfo 2013
Copenhagen, Denmark
Session: Data models and representations - II
August 21, 2013
Exploring pharmacoepidemiologic groupings of drugs from a clinical perspective
Rainer Winnenburg, Olivier Bodenreider
Lister Hill National Center for Biomedical Communications Bethesda, Maryland - USA

2. Motivation Anatomical Therapeutic Chemical (ATC) classification
Used in pharmacoepidemiologic studies
Recently also used in applications from a clinical perspective
Assumes homogeneity of drug groups (in terms of therapeutic use, mechanism of action, physiologic effect)
BUT: “Substances classified in the same ATC fourth level cannot be considered pharmacotherapeutically equivalent since their mode of action, therapeutic effect, drug interactions and adverse drug reaction profile may differ.” (ATC documentation)

3. Objectives
To investigate the extent to which pharmacoepidemiologic groupings are homogeneous in terms of clinical properties
Pharmacoepidemiologic groupings
ATC classification WHO
For comparison: Micromedex
Clinical properties
National Drug File-Reference Terminology (NDF-RT)
Homogeneity
Distribution of clinical properties of drugs within a grouping

4. Anatomical Therapeutic Chemical (ATC) drug classification
Hierarchical classification
Levels 1-4: drug groups (~ pharmacologic classes)
1,255 drug group codes
Level 5: individual drugs
4,464 drug codes
(as of 2012)
Code
Label
Level C
Cardiovascular system
1 - anatomical
C01
Cardiac therapy
2 - therapeutic
C01A
Cardiac glycosides
3 - pharmacological
C01AA
Digitalis glycosides
4 - pharmaceutic
C01AA05
Digoxin
5 - drug

5. National Drug File-Reference Terminology (NDF-RT)
Organized into several hierarchies
7,162 active moieties (level = ingredient)
Relations to entities from other hierarchies
e.g., has_MoA relationships to mechanism of action hierarchy
We used NDF-RT API for
Mapping drug names from ATC to NDF-RT
Querying drug properties
Drug
Property
Value
atorvastatin
has_MoA
Hydroxymethylglutaryl-CoA Reductase Inhibitors
has_PE
Decreased Cholesterol Synthesis
may_treat
Hypercholesterolemia
may_prevent
Coronary Artery Disease

6. Methods Overview
Mapping ATC drugs to ingredients in NDF-RT and acquiring clinical properties for ingredients
Computing homogeneity scores for each drug class based on properties of drugs in class
Comparison to the clinical reference Micromedex

7. Mapping ATC drugs to clinical properties
Step 1
Mapping ATC drugs to clinical properties
Eligibility
ATC
Drugs
5th level
ATC Drugs
(single ingredients
within scope)
Exclude:
Multi-ingredient drugs
Radiopharmaceuticals
Unspecific, collective terms
Prednisolone (R01AD02)
Prednisolone (R01AD02)
Lexical mapping
and via RxNorm
Clinical Properties
(any of the
3 categories)
Relationships
NDF-RT
Ingredients
may_treat
has_MoA
has_PE
has_MoA
5-Lipoxygenase Inhibitors
Glucocorticoid Receptor Agonists
Lipoxygenase Inhibitors
PREDNISOLONE (N )

8. Computing homogeneity scores (classes)
Step 2
Computing homogeneity scores (classes)
Grouping
A10BG
Thiazolidinediones
Drugs in grouping +
Clinical
Properties
A10BG01
troglitazone 
Peroxisome Proliferator-activated Receptor alpha Agonists
Peroxisome Proliferator-activated Receptor gamma Agonists
A10BG012
rosiglitazone
Insulin Receptor Agonists
A10BG013
pioglitazone 
3 distinct properties
Homogeneity Score
How many distinct properties (or sets of properties) are necessary to account for at least 90% of the drugs in a given subgroup?
1 property accounts for 2 drugs (66% of the drugs)
2 properties account for all 3 drugs (>90% of the drugs)
=> Homogeneity score = 2

9. Homogeneity scores Examples
Step 2
Homogeneity scores Examples
Example 1: homogeneous group
ATC 4th level group Corticosteroids (R01AD)
10 drugs
All 10 drugs have the same mechanism of action: Glucocorticoid Receptor Antagonists
One single property accounts for 100% of the drugs in this group
=> homogeneity score =1

10. Homogeneity scores Examples
Step 2
Homogeneity scores Examples
Example 2: heterogeneous group
ATC 4th level group Antidotes (V03AB)
12 drugs
8 mechanism of action properties are needed to account for > 90% of the drugs in this group
Siderophore Iron Chelating Activity, Cholinesterase Inhibitors, GABA B Antagonists, Free Radical Scavenging Activity, Alcohol Dehydrogenase Inhibitors, {Noncompetitive Opioid Antagonists, Competitive Opioid Antagonists}, {Adrenergic alpha1-Antagonists, Adrenergic alpha2-Antagonists}, and Cholinesterase Reactivators.
=> homogeneity score =8

11. Comparison to a clinical reference
Step 3
Comparison to a clinical reference
Clinical reference: drug groupings in Micromedex
Extracted from a drug-drug interaction system
Distribution of homogeneity scores (“profiles”) in ATC and Micromedex
3 profiles
Therapeutic group
Mechanism of action
Physiologic effect
Hypothesis: if ATC is less homogeneous than Micromedex, it should have greater scores

12. Therapeutic group profile
53% of 2nd level groups in ATC have homogeneity score of 1 or 2
vs. 75% for Micromedex
ATC therapeutic (2nd level) groups less homogeneous than groups in Micromedex

13. Profiles for mechanism of action (MoA) and physiologic effect (PE)
> 60 % of 4th level groups in ATC have homogeneity score of 1
Homogeneity of ATC groups comparable to that of groups in Micromedex

14. Limitations and future work
Only 50% of single drugs in ATC could be mapped to NDF-RT properties
Some drugs are out of scope, not marketed in the U.S.
Incompleteness of NDF-RT in terms of drug properties
No statistical methodology used for the comparison of homogeneity distributions
We plan to explore alternative drug information sources for evaluation of ATC
Only few reliable and publicly available (e.g., DrugBank)
Most are commercial products (e.g., First Databank)

15. Summary
Investigated homogeneity of pharmacoepidemiologic groupings in terms of clinical properties
Mapped ATC 5th level drugs to NDF-RT properties
Based on these properties we computed homogeneity scores for all ATC groups and contrasted their distribution against the Micromedex reference
ATC classes are generally homogeneous in terms of clinical properties, especially mechanism of action and physiologic effect, less so for therapeutic use
Incomplete drug description in NDF-RT is a major issue

16. Medical Ontology Research
Contact:
Web:
mor.nlm.nih.gov
My presentation is entitled: “Beyond synonymy: Exploiting the UMLS semantics in mapping vocabularies”.
First, I would like to acknowledge my co-authors from the National Library of Medicine: Stuart Nelson, Bill Hole and Florence Chang.
This research has been conducted at NLM in 1997 and early this year, as part of the Indexing Initiative.
Olivier Bodenreider
Lister Hill National Center for Biomedical Communications Bethesda, Maryland - USA