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

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)

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

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

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

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

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 (N0000146334)

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

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

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

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

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

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

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)

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

Medical Ontology Research Contact: Web: olivier@nlm.nih.gov 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