Objective: To align pharmacologic classes in ATC and MeSH with lexical and instance-based techniques. Methods: Lexical alignment: we map the names of ATC classes to MeSH through the UMLS, leveraging normalization and additional synonymy. Instance-based alignment: we associate ATC and MeSH classes through the drugs they share, using the Jaccard coefficient to measure class-class similarity. We use a metric to distinguish between equivalence and inclusion mappings. Results: We found 221 lexical mappings, as well as 343 instance-based mappings, with a limited overlap (61). From the 343 instance-based mappings we classify 113 as equivalence mappings and 230 as inclusion mappings. A limited failure analysis is presented. Conclusion: Our instance-based approach to aligning pharmacologic classes has the prospect of effectively supporting the creation of a mapping of pharmacologic classes between ATC and MeSH. This exploratory investigation needs to be evaluated in order to adapt the thresholds for similarity. The National Library of Medicine (NLM) and the Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) are collaborating on a research project to extract adverse drug reactions from the biomedical literature. More specifically, this investigation leverages the indexing of MEDLINE citations to extract associations between co-occurring drug entities and clinical manifestations in the context of adverse events.
The biomedical literature is indexed with the Medical
Subject Headings (MeSH) vocabulary. For data mining
purposes, however, adverse drug reactions are usually analyzed in
reference to other standard vocabularies, namely the
Anatomical Therapeutic Chemical (ATC) drug classification
system for drug entities, and the Medical Dictionary for
Regulatory Activities (MedDRA) for clinical
manifestations. Toward this end, drug entities have to be mapped
from MeSH to ATC, and manifestations from MeSH to
MedDRA. This paper focuses only on the drug entities.
Drug entities include not only individual drugs (e.g.,
atorvastatin), but also drug classes (e.g., statins). In
previous work, we have mapped individual drugs between
RxNorm (which includes MeSH drugs) and ATC
The objective of this study is to investigate various ontology matching techniques for aligning pharmacologic classes between MeSH and ATC. Such methods are expected to facilitate the curation of a mapping by experts. To our knowledge, this work represents the first effort to map pharmacologic classes between MeSH and ATC using a sophisticated instance-based alignment technique. 2
The general framework of this study is that of ontology
alignment (or ontology matching). Various techniques have
been proposed for aligning concepts across ontologies,
including lexical techniques (based on the similarity of
concept names), structural techniques (based on the similarity
of hierarchical relations), semantic techniques (based on
semantic similarity between concepts), and instance-based
techniques (based on the similarity of the set of instances of
two concepts). An overview of ontology alignment is
provided in
The main contribution of this paper is not to propose a novel technique, but rather to apply existing techniques to a novel objective, namely aligning pharmacologic classes between MeSH and ATC. To this end, we use lexical and instancebased techniques, because the names of pharmacologic classes and the list of drugs that are members of these classes are the main two features available in these resources. 2.1
Lexical techniques for ontology matching compare concept names across ontologies. When synonyms are available, they can be used to identify additional matches. Matching techniques beyond exact match utilize edit distance or normalization to account for minor differences between concept names.
As part of the Unified Medical Language System (UMLS),
linguistically-motivated normalization techniques have been
developed specifically for biomedical terms
Also called extensional techniques, instance-based
techniques compare classes based on the sets of individuals (i.e.,
instances) of each class. Many biomedical ontologies
consist of class hierarchies, but do not contain information
about instances. Here, however, individual drugs (e.g.,
atorvastatin) are the members – not subclasses – of
pharmacologic classes (e.g., statins). In other words, pharmacologic
classes have individual drugs as instances, not subclasses.
Several methods have been proposed to implement
instancebased matching.
As part of the EU-ADR project,
Lexical techniques are a component of most ontology
alignment systems
The ATC is a clinical drug classification system developed and maintained by the World Health Organization (WHO) as a tool for drug utilization research to improve quality of drug use (ATC, 2013). The system is organized as a hierarchy that classifies clinical drug entities at five different levels: 1st level anatomical (e.g., A: Alimentary tract and metabolism), 2nd level therapeutic (e.g., A10: Drugs used in diabetes), 3rd level pharmacological (e.g., A10B: Blood glucose lowering drugs, excluding insulins), 4th level chemical (e.g., A10BA: Biguanides), and 5th level chemical substance or ingredient (e.g., A10BA02: metformin). The 2013 version of ATC integrates 4,516 5th-level drugs and 1,255 drug groups (levels 1-4). 3.2
The Medical Subject Headings (MeSH) is a controlled vocabulary produced and maintained by the NLM (NLM, 2013). It is used for indexing, cataloging, and searching the biomedical literature in the MEDLINE/PubMed database, and other documents. The MeSH thesaurus includes 26,853 descriptors (or “main headings”) organized in 16 hierarchies (e.g., Chemical and Drugs). Additionally, MeSH provides about 210,000 supplementary concept records (SCRs), of which many represent chemicals and drugs. Each SCR is linked to at least one descriptor. While most chemical descriptors provide a structural perspective on drugs, some descriptors play a special role as they can be used to denote pharmacological actions in drug descriptors and SCRs. MeSH 2013 is used in this study. 3.3
RxNorm is a standardized nomenclature for medications produced and maintained by the U.S. National Library of Medicine (NLM) (NLM, 2013). RxNorm concepts are linked by NLM to multiple drug identifiers for commercially available drug databases and standard terminologies, including MeSH. RxNorm serves as a reference terminology for drugs in the US. The March 2013 version of RxNorm used in this study integrates about 10,500 base and salt ingredients. NLM also provides an application programming interface (API) for accessing RxNorm data programmatically (NLM, 2013). 3.4
The UMLS is a terminology integration system created and maintained by the National Library of Medicine (NLM) (NLM, 2013). The UMLS Metathesaurus integrates over 150 terminologies, including MeSH, but not ATC. Synonymous terms across terminologies are grouped into concepts and assigned the same concept unique identifier. The Metathesaurus provides a comprehensive set of synonyms for biomedical concepts and is often used for integrating terminologies beyond its own. NLM provides an application programming interface (API) for accessing UMLS data programmatically. Version 2012AB of the UMLS is used in this study. 4
Our approach to aligning pharmacologic classes between MeSH and ATC based on their instances is depicted in Figure 1 and can be summarized as follows. First, we established a lexical alignment of MeSH and ATC classes based on the class names and their synonyms (Figure 1, right). We then constructed an instance-based alignment of MeSH and ATC classes considering the individual drugs shared by the classes (Figure 1, left). We mapped individual drugs from MeSH and ATC via their ingredients (IN) or precise ingredients (PIN) in RxNorm. We used a similarity measure and thresholds to identify class mappings and compared them with the mappings retrieved by the lexical approach. In our alignment work, we excluded the 14 ATC groups of level 1 (anatomical classification), because they are too broad classes. We also excluded 164 of the 1,241 ATC groups (2nd – 4th level) corresponding to drug combinations, because combination drugs are often underspecified in ATC.
Similarly, in MeSH, we excluded the top-level descriptors of the Chemicals and Drugs hierarchy (i.e., D01 - D27), as well as the top-level of the pharmacological action descriptors (Pharmacologic Actions, Molecular Mechanisms of Pharmacological Action, Physiological Effects of Drugs, and Therapeutic Uses). 4.1
We mapped all 1,077 eligible ATC classes (2nd – 4th level) to MeSH descriptors in the Chemicals and Drugs [D] tree using the UMLS Terminology Services (UTS). More precisely, we used the ExactString and NormalizedString search function of the UTS API 2.0 to establish mappings from the names of the ATC classes to UMLS concepts. We used normalization only when the exact technique did not result in a mapping. We then mapped the UMLS concepts to MeSH descriptor IDs. 4.2
Mapping ATC drugs to RxNorm ingredients. In previous work we have mapped ATC single-ingredient drugs to Ingredients (IN) and Precise Ingredients (PIN) in RxNorm using a lexical approach with additional normalization steps (Winnenburg and Bodenreider, 2012). We used these mappings to establish the alignment of ATC and RxNorm drugs in this study.
Mapping MeSH drugs to RxNorm ingredients. Since MeSH drugs are integrated in RxNorm, mappings to equivalent drug concepts from MeSH can be obtained via the getProprietaryInformation function from the RxNorm API. We systematically exploited this information for all Ingredients (IN) and Precise Ingredients (PIN) in RxNorm and created a mapping table between RxNorm CUIs and MeSH Main Headings (MH) and Supplementary Concept Records (SCR).
Inferring class membership in ATC. We considered the hierarchical relations from 5th level drugs to their 4th level chemical groups as asserted drug class membership. We inferred membership between 5th level drugs and groups of level 3 and 2 through transitive closure. For example, temafloxacin (J01MA05) is a member of the chemical group Fluoroquinolones (J01MA - asserted), the pharmacological group QUINOLONE ANTIBACTERIALS (J01M - inferred), and the therapeutic group ANTIBACTERIALS FOR SYSTEMIC USE (J01 - inferred). Inferring class membership in MeSH. We associated each RxNorm ingredient (IN or PIN) with its corresponding MeSH supplementary concept record (SCR) or main heading (MH). In turn, we associated these drugs with their asserted classes. For an SCR, we considered its pharmacological actions, as well as the MeSH heading(s) mapped to. For a MH, we considered its pharmacological actions, as well as its direct ancestors. These constitute the asserted classes. We inferred membership between the drugs and higher-level descriptors in the MeSH hierarchy. For example, as shown in Table 1, the SCR temafloxacin has Anti-Bacterial Agents as pharmacological action and Fluoroquinolones as main heading mapped to. Form these asserted classes, we infer membership to Anti-Infective Agents (from Anti-Bacterial Agents) and to Quinolones, Quinolines, and Heterocyclic Compounds, 2-Ring (from Fluoroquinolones).
Measure for aligning ATC and MeSH classes. Based on
the asserted and inferred class membership of drugs in ATC
and MeSH we conducted a pairwise comparison of all ATC
against all MeSH classes. For each pair of ATC class (A)
and MeSH class (M), we computed the Jaccard coefficient.
In order to reduce the similarity of pairs of classes with a
small number of shared members, we used a modified
version of the Jaccard coefficient, JCmod, as suggested in
The Jaccard coefficient measures the similarity between the two classes, but does not reflect whether one class is included in the other. Because of the difference in granularity between classes in ATC and MeSH, we introduce a simple metric for detecting whether the drugs that are not shared by both classes are primarily in one of the two classes. This “one-sidedness” coefficient is calculated as follows: 0,
for a = 0 and m = 0 |a-m| / a+m,
otherwise. where a and m are the number of drugs specific to the ATC class and the MeSH class, respectively. Thus, a “onesidedness” coefficient close to 0 indicates that the drugs that are not shared by the two classes are evenly distributed between the ATC and MeSH class. In contrast, a coefficient close to 1 indicates that only one of the classes contains most of the drugs that are not shared by the other. Thresholds. In order to select the best equivalent or inclusion mappings between ATC and MeSH, we characterize each pair of ATC and MeSH classes with respect to Jaccard similarity and one-sidedness. Low one-sidedness indicates equivalence and high one-sidedness indicates inclusion. High Jaccard similarity indicates strong overlap between the two classes. Based on preliminary analysis, we selected of a threshold of 0.5 for the one-sidedness metric. Similarly, we selected of a threshold of 0.5 and 0.25 for Jaccard similarity for equivalence (low one-sidedness) and inclusion (high one-sidedness), respectively. The lower threshold for Jaccard similarity for inclusion was determined empirically. As shown in Table 2, each pair of ATC and MeSH classes is characterized as an equivalence mapping (EQ+), an inclusion mapping (IN+), or not a mapping (EQ- and IN-). 5 5.1
For the 1,077 eligible ATC groups of level 2-4, we were able to retrieve 226 mappings to descriptors from the Chemicals and Drugs [D] tree in MeSH. We have 18 mappings for therapeutic classes (2nd level), 42 for pharmacological classes (3rd level), and 161 for chemical classes (4th level). We ignored mappings for the broad anatomical classes (1st level). Of the 221 mappings, 96 are to pharmacological actions (functional perspective) in MeSH, whereas 125 are to other descriptors at various levels of the MeSH hierarchy (structural perspective). 5.2
Of the 1,077 eligible ATC groups, 874 (81%) could be associated with at least one descriptor or pharmacological action in MeSH. We identified a total of 933 associations for the 874 ATC groups (multiple associations per ATC group possible). As shown in Table 2, based on the onesidedness metric, we characterized 323 associations as equivalence and 610 as inclusion. Of the 323 equivalence associations, 113 (35%) exhibit high Jaccard similarity and are selected as equivalence mappings (EQ+). Of the 610 inclusion associations, 230 (38%) exhibit high Jaccard similarity and are selected as inclusion mappings (IN+). The other associations (EQ- and IN-) are not deemed strong enough to denote mappings. In summary, we were able to characterize as a mapping (EQ+ and IN+) 343 (37%) of the associations between ATC and MeSH classes. It should be mentioned that we were not able to obtain mappings to MeSH classes for 203 ATC classes, because they only contain drug instances that could not be mapped to drugs in MeSH.
As illustrated in Table 3, from the 221 lexical mappings between ATC and MeSH classes, we could confirm 61 with our instance-based approach (30 as equivalence mappings, 31 as inclusion mappings). For 19 of the lexical mappings we found an association with low Jaccard similarity (IN- / EQ -), and for 141 of the lexical mappings we did not find any association through the instance-based alignment (mainly due to the lack of any mapping for the drug instances in these classes). Finally, the instance-based approach produced 282 additional drug class mappings that were not detected by the lexical approach, whereas 633 (571 + 62) ATC classes could neither be mapped by the lexical nor the instance-based approach. True positive for equivalent instance-based mappings. We identify an equivalence mapping between the 4th-level ATC group Fluoroquinolones (J01MA) and the MeSH descriptor Fluoroquinolones (D024841). The two classes share 14 drugs. The ATC group has one extra drug (moxifloxacin), and the MeSH descriptor has 2 (flumequine and besifloxacin). Jaccard similarity is high (0.82) and the one-sidedness score is low (0.33), because the 3 drugs that are not in common are not all on the same side. This mapping is also identified by the lexical technique (exact match). True positive for inclusion instance-based mappings. We identify an inclusion mapping between the 4th-level ATC group Fluoroquinolones (S01AE) and the MeSH descriptor Fluoroquinolones (D024841). Although the two classes are seemingly identical, our mapping is identified as an inclusion, with 7 drugs in common, 1 drug specific to the ATC class and 9 drugs specific to the MeSH class. In fact, the ATC class is not the same general class for anti-infective agents as in the example above (J01MA), but rather the specific class of fluoroquinolones for ophthalmic use (S01AE). The fluoroquinolones used for eye disorders are a subset of all fluoroquinolones and the ATC class S01AE is appropriately characterized as being included in the MeSH class for fluoroquinolones. This example also illustrates a false positive for the lexical mapping, since it is generally assumed that lexical mappings are equivalence mappings.
False negative for equivalent instance-based mappings.
Many ATC and MeSH classes share only one or very few
drugs, making it difficult to assess equivalence or inclusion.
For example, the 4th-level ATC group Silver compounds
(D08AL) and the MeSH descriptor Silver Compounds
(D018030) share only one drug (silver). The modified
version of the Jaccard coefficient has a score of 0.45 in this
case, which is below our threshold of 0.5 for equivalence.
During this failure analysis, we discovered that some MeSH
drugs did not have a pharmacological action assigned to
them as we expected. For example, while pyrantel is listed
as Antinematodal Agents, oxantel is not. We are
investigating whether the pharmacological action for this SCR should
be inferred from the descriptor to which it is mapped
(Pyrantel in this case). Because of these missing pharmacologic
actions, the 3rd-level ATC group ANTINEMATODAL
AGENTS (P02C) fails to be mapped to the MeSH
pharmacological action Antinematodal Agents (D000969), the
Jaccard similarity being just below the threshold (0.49).
Discrepancy between lexical and instance-based alignment
(missed lexical mapping). Despite the use of UMLS
synonymy and normalization, the lexical alignment fails to
identify a mapping between the 3rd-level ATC group
POTASSIUM-SPARING AGENTS (C03D) and the MeSH
pharmacological action Diuretics, Potassium Sparing
(D062865). In contrast, the instance-based alignment
identifies an equivalence mapping with very high Jaccard
similarity (0.99). This finding is consistent with the conclusions of
Discrepancy between lexical and instance-based alignment (missed instance-based mapping). We have identified several causes for discrepancies between lexical and instancebased alignments. As mentioned earlier, some ATC classes only contain drugs that cannot be mapped to MeSH through RxNorm, which we used to bridge between the two. Sometimes, the best instance-based mapping is to another class than the class found by the lexical technique. Finally, some drugs entities and biologicals (e.g., vaccines) are less well standardized than common drugs. For this reason, the instance-based alignment is unable to map these classes, when simple lexical techniques can.
This exploratory investigation has several limitations, which we plan to address in future work.
Evaluation. This exploratory investigation focuses primarily on the methodology and feasibility of the alignment, and does not include a formal evaluation. Since ATC and MeSH pharmacological actions are being integrated into RxNorm, we will use the alignment created by RxNorm experts as the gold standard to evaluate our methods.
Perspective. Our perspective in this investigation is ATCcentric, because we consider the best MeSH mapping for each ATC class, but not the best ATC mapping for each MeSH class. One future goal is to explore both directions using the same methodology.
Bias towards equivalence mappings. Because we restrict our exploration to the MeSH class with the best Jaccard similarity for each ATC class (which we subsequently categorize as equivalence or inclusion), and because of the differential threshold for Jaccard similarity between equivalence (0.5) and inclusion mappings (0.25), we potentially fail to consider a good inclusion mapping (e.g., with a similarity score of 0.39 [> 0.25]), when the best MeSH class is a bad equivalent mapping (e.g., with a similarity score of 0.41 [< 0.5]).
To our knowledge, our work is the first attempt to align pharmacologic classes with instance-based techniques, distinguishing between equivalence and inclusion relations, as well as the first application of alignment between pharmacologic classes in ATC and MeSH. Our instance-based approach to aligning pharmacologic classes has yielded 343 mappings, and has the prospect of effectively supporting the creation of a mapping of pharmacologic classes between ATC and MeSH. This exploratory investigation needs to be evaluated in order to adapt the thresholds for similarity.
This work was supported by the Intramural Research Program of the NIH, National Library of Medicine and by the Center for Drug Evaluation and Research of the Food and Drug Administration. The authors want to thank Rave Harpaz and Anna Ripple for useful discussions.
The findings and conclusions expressed in this report are those of the authors and do not necessarily represent the views of the FDA.