Within the eTRANSAFE project [1], the focus has been on integrating preclinical and clinical databases with the purpose of translational drug safety assessment and research. As preclinical databases, the eTOXsys database resulting from the eTOX project [14] and a SEND database with preclinical studies from the pharmaceutical companies participating in eTRANSAFE were used. The clinical databases contain adverse drug events data from ClinicalTrials.gov [15], the FDA Adverse Event Reporting System (FAERS) [16], drug label texts (DailyMed) [17], and published literature (Medline) [18].

The preclinical data are expressed as concepts from the histopathology terminology (HPATH) [19], the Mouse Adult Gross Anatomy Ontology (MA) [20], and the Clinical Data Interchange Standards Consortium (CDISC) Standard for Exchange of Nonclinical Data (SEND) controlled terminology [21]. The HPATH terminology was developed in the eTOX project [14, 19] and contains 1,047 concepts. The MA terminology is a publicly available resource that covers about 3,100 concepts [20]. The SEND terminology consists of various code lists. The data in eTRANSAFE predominantly use the Specimen, Laboratory test name, Neoplasm type, and Non-neoplastic finding type code lists, which contain about 3,700 concepts. The clinical data are expressed as concepts from the Medical Dictionary for Regulatory Activities (MedDRA) terminology, which contains about 80,000 concepts [22]. MedDRA concepts are organized in a five-level hierarchy: system organ classes, high-level group terms, high-level terms, preferred terms (PTs), and lowest-level terms (LLTs), where the LLTs mostly are synonyms of the PTs. The roughly 25,000 MedDRA PTs are used as mapping targets.

Observations in the preclinical domain are often a combination of a finding and a specific organ (e.g., “Necrosis” and “Liver”). In the clinical domain, these observations are pre-coordinated findings and represent the combination of a morphologic abnormality and a location (e.g., “Hepatic necrosis”). To bridge the gap between preclinical data and clinical data and to be able to suggest alternative mappings, our approach was to map each terminology to an intermediate terminology, SNOMED CT, and use the rich semantic concept model of SNOMED CT to handle differences in coordination between preclinical and clinical concepts. SNOMED CT is a large terminology that arranges over 450,000 medical concepts in a hierarchical structure. An overview of the mapping approach is show in Fig. 1. In addition, each concept has a semantic class label, e.g., disorder, body structure, morphologic abnormality, etc. Semantic relations such as finding site and associated morphology link concepts that have different semantic classes. We developed a coordination template to specify which SNOMED CT semantic classes can be combined in the mapping process and what hierarchical traversals can be made.

A schematic overview of the mapping approach. The preclinical database has fields that refer to different SEND terminologies (specimen and non-neoplastic finding). Combinations of concepts of these terminologies have been manually mapped to SNOMED CT concepts. Within SNOMED CT the relations defined in the coordination template and hierarchical relations are explored to find a SNOMED CT concept that has been mapped to a MedDRA concept. Relations are associated with different penalties that add up to a penalty score for a mapping. The clinical database refers to MedDRA pre-coordinated concepts. The path from a pair of SEND codes via SNOMED CT to a MedDRA code yields a mapping between preclinical and clinical records

For mapping the preclinical MA, HPATH, and SEND terminologies to SNOMED CT we developed a tool called CodeMapper. CodeMapper can read the various preclinical terminologies and use the preferred term of a preclinical concept to lexically match against all terms in SNOMED CT. The tool shows for each preclinical concept a list of SNOMED CT terms ordered by lexical similarity, with the code, semantic class, and synonyms for each suggested SNOMED CT concept. Combinations of preclinical concepts can be expressed by coordinating a group of concepts with the AND operator. Multiple closely related inexact mappings can be specified using an OR operator. As an example, the HPATH concept Adenoma, leydig cell can be mapped to the SNOMED CT concepts Leydig cell hyperplasia OR Leydig cell tumor, benign. Each of these mappings is seen as a potential mapping and can be exploited to construct a mapping between a preclinical and clinical term. If an exact mapping does not exist, one can define an inexact mapping with a more general term or more specific term and indicate that the mapping is broader or narrower. For the clinical mappings from MedDRA to SNOMED CT, we used existing mappings available from OHDSI [23], the WEB-RADR 2 project [7], and the UMLS [3]. Both OHDSI and WEB-RADR 2 provide a manually curated concept mapping. Most of the available mappings are to MedDRA PTs, which are used in clinical databases, but some mappings are to MedDRA LLTs. Since each LLT is uniquely related to a single PT it was trivial to convert these mappings from SNOMED CT to MedDRA PTs.

The search for a mapping from preclinical concepts to clinical concepts starts with a pair of finding and organ concepts from preclinical terminologies. If this pair is mapped to SNOMED CT, the search continues to find a traversal path from the SNOMED CT concepts to a clinical target concept. The coordination template defines what selection of semantic relations can be explored within SNOMED CT and specifies the preferred path for mapping. The path consisting of an exact mapping from a pair of pre-clinical concepts to an initial pair of SNOMED CT concepts, subsequently following only the relations defined in the coordination template to a common final SNOMED CT concept that has been mapped to a MedDRA preferred concept (PT) is called the preferred path and has a zero penalty. The coordination template associates each deviation from this preferred path with a cost. The penalty score of a path consists of the sum of the costs of all deviations from the preferred path. Paths can be sorted on penalty score and a maximum penalty score can be specified as a parameter of the mapping algorithm. In an application this maximum penalty could be set by the user, affecting the precision and recall of returned mappings. Similarly, the coordination template can be used to search for reverse mappings from clinical terminology to preclinical terminology.

As an example, Fig. 2 shows how a SEND preclinical pair of concepts from the classes finding and organ is mapped to SNOMED CT concepts of the classes body structure and morphologic abnormality, and then are coordinated into a disorder concept and subsequently mapped to a MedDRA finding concept. The coordination template specifies that the semantic classes finding site and associated morphology can be explored to coordinate a set of preclinical concepts into a clinical concept. The coordination template limits the set of semantic relations that can be followed in SNOMED CT and thus reduces the computational search effort. If not all semantic relations as specified in the coordination template are satisfied, this may result in inexact mappings. For example, if the coordination with a body structure is missing, one or more mappings based only on the morphologic abnormality may be generated as inexact mappings. Inexact mappings can also originate from the mapping function following the is-a or subsumes semantic relations of a concept as obtained from the hierarchy of SNOMED CT.

Mappings with a penalty of zero are exact mappings, and mappings with a non-zero score are inexact mappings. The mapping shown in Fig. 2 is an exact mapping: the path between the preclinical concepts Liver and Necrosis and the clinical concept Hepatic necrosis has a penalty score of 0.

The coordination template distinguishes between larger semantic inexactness (i.e., missing the organ or making a hierarchical step) and smaller inexactness with a penalty score of 1.0 and 0.1, respectively. SNOMED CT contains three types of body structure concepts: the entire body structure, part, or structure (e.g. Entire liver, Liver part, or Liver structure). A preclinical organ concept is always mapped to the SNOMED CT structure concept ). To find paths from a combination of preclinical concepts to a clinical concept, traversals between the three body structure concepts are allowed, incurring the small penalty score. Different types of inexactness are accumulated in an overall penalty score. The exact value of the penalties is somewhat arbitrary, their main purpose is to rank the mappings in terms of exactness. The penalties for different types of inexactness are listed in Table 1.

An example of how an organ and finding concept from CDISC SEND can be mapped via SNOMED CT to a MedDRA finding concept. The red lines indicate the mappings provided to allow for CDISC SEND mapping to SNOMED CT and from SNOMED CT to MedDRA. The blue lines denote the semantic relations provided by SNOMED CT. The path shown from SEND via SNOMED CT to MedDRA is the preferred path as defined in the coordination template

The MedDRA-SNOMED CT mappings provided by WEB-RADR 2 are not congruent: both directions have to be explicitly mentioned since the reverse relation is not always automatically implied. An example of this is that the MedDRA concept Emotional lability (LLT) (Affect lability as the MedDRA PT term used in our mapping) maps to the SNOMED CT concept Mood swings (finding), but that the SNOMED CT concept Mood swings (finding) maps to the MedDRA concept Mood swings (LLT) (Mood swings as the MedDRA PT term in our mapping). Apart from incongruent mappings, also only one direction can be provided. The coordination template can allow for automatic inference of the reverse relation with a penalty of 0.1 points.

The clinical sources within eTRANSAFE are based on MedDRA PTs, but some of the existing mappings map SNOMED CT concepts to MedDRA LLTs. Therefore, an additional step has been implemented to go from an LLT to a PT with a penalty score of 0.1. SNOMED CT contains three types of body structure concepts (entire, part, and structure), which are hierarchically related, e.g., Liver part and Entire liver are subsumed by Liver structure. Therefore, if, for a given organ, a hierarchical traversal is needed between the body structure concepts, a small penalty of 0.1 is added. The total penalty score is an indication of the exactness of the mapping. The mapping function will return all mappings that have a penalty score lower than a user-specified threshold.

In Fig. 3, three examples of concept mappings with their associated penalty scores are shown. The first example shows how a SEND finding Necrosis without specification of an organ is mapped to the SNOMED CT Necrosis of anatomical site concept. The latter concept is mapped to the MedDRA term Necrosis and follows the preferred coordination path. The second example shows how the mapping follows an additional is-a relation in the SNOMED CT hierarchy. The total penalty for this mapping is 1.0. In the third example, an additional is-a relation from SNOMED CT is followed (+ 1.0 penalty) and the organ is ignored (+ 1.0 penalty), followed by a traversal in MedDRA from an LLT to a PT (+ 0.1 penalty), leading to a total penalty of 2.1. Penalty scores for mappings that do not include the organ are given as negative scores, to make it easy to distinguish the mappings that include an organ from those that do not. In this example, the mapping function therefore returns − 2.1.

Three examples of concept mappings with their penalty scores: (a) for a SEND finding without specification of an organ (score 0.0), (b) for a SEND finding and organ with a SNOMED CT hierarchy traversal (score 1.0), and (c) for same finding and organ with unsuccessful mapping of organ (score 1.0), hierarchy traversal (score 1.0) and traversal from MedDRA LLT to PT (score 0.1), for a total score of 2.1 (negative because of absent organ). The red lines indicate the mappings provided to allow for CDISC SEND mapping to SNOMED CT and from SNOMED CT to MedDRA. The blue lines denote the semantic relations provided by SNOMED CT

The Rosetta Stone approach to mapping between pre-coordinated clinical concepts and combinations of preclinical concepts has been implemented as a web service that is used in eTRANSAFE applications [24]. The service takes as input the concepts to be mapped, the source and target terminologies, and a maximum penalty score, and returns all mappings that have a penalty score less than or equal to the maximum. The web service generally returns results for a request within a second.

The terminology service uses a database schema based on the terminology section of the OMOP Common Data Model (CDM) maintained by the Observational Health Data Sciences and Informatics network (OHDSI) [25]. MedDRA and SNOMED CT were already available in the CDM schema. For the preclinical terminologies, a conversion script has been developed that converts and stores these terminologies and their mappings to SNOMED CT according to the CDM schema. The mappings obtained from WEB-RADR 2 and UMLS were imported into this database as well as the manual mappings created with CodeMapper.

For each of the two preclinical terminologies SEND and HPATH/MA the Rosetta Stone terminology service was used to compile for each concept all the best mappings to MedDRA and saved these mappings with its minimum penalty score. From these list we selected per preclinical terminology a random set of 10 mappings with a minimum score of 0.0, of 0.1 and of 1.0 (6 sets with a total of 60 mappings).

Each mapping was reviewed independently by the two authors JK and EvM. With the SNOMED CT browser and the MedDRA browser the mapping was analyzed and, if present, annotated with a better match from MedDRA. Only in a few cases the two reviewers had different annotations and in an interactive session a consensus was reached for the whole set. Additionally, a lexical search based on the search endpoint of the UMLS Knowledge Source Server was used to find MedDRA concepts for each mapping [26]. The organ with the finding were used as input for the search. If the results yielded by the services contained one of the MedDRA concepts from the best mapping it was considered a match.