Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68–99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping. EHRs contain a myriad of systematically collected, longitudinal, patient-level information and are a valuable resource for population-level research6. The cornerstone of medicine, diagnosis or clinical phenotyping, aims to identify empirically observable traits exhibited by patients  known to be characteristic of a specific disease7. Computational phenotyping is the process of converting clinical phenotypes into computer-executable algorithms in order to identify relevant patients from large sources of clinical data, usually EHRs8. One promise of EHR-based computational phenotyping is the ability to perform population-level investigations of mechanistic drivers of disease in diverse patient populations10. Despite significant progress, this objective remains largely aspirational14.Electronic health record (EHR) adoption, which is nearly universal within the US healthcare system18. Deep phenotyping, or “the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described”7, is a fundamental component of precision medicine that requires timely synthesis of multiple types of patient data20. Deep phenotyping has been successfully applied to rare disease and genetic disorders33, cancer40, and pregnancy43 using a variety of clinical and -omic data. Despite large-scale biobanking efforts and resources like the UK Biobank (https://www.ukbiobank.ac.uk) and the All of Us (AoU) Research Program , most EHRs do not systematically integrate nor have the infrastructure to integrate patient-level genomic data or other forms of external knowledge  with clinical data46.Traditionally, computational phenotypes have been imprecise due to their exclusive reliance on EHR data, which has been shown to be insufficient at capturing the phenotypic heterogeneity present in most complex diseases49. Hundreds of clinical vocabularies have been developed and their use differs by hospital and country. Examples include the International Classification of Diseases (ICD)50, the Logical Observation Identifiers, Names and Codes (LOINC)51, the Systematized Nomenclature of Medicine—Clinical Terms , and RxNorm52. Most clinical vocabularies were not designed to be integrated or interoperable with other vocabularies, which is one of the long standing barriers preventing the secondary use of EHR data for research46. Common data models (CDMs) like the Observational Medical Outcomes Partnership (OMOP)53 have solved many of the challenges of standardizing, representing, and utilizing clinical EHR data. Unfortunately, most CDMs and associated terminology management systems are not yet able to integrate and interpret genomic data or other sources of external knowledge or publicly available data54.Within an EHR, most data used for research  are stored using clinical terminologies or vocabularies. A clinical vocabulary is a standard representation of preferred terms which may or may not be hierarchical or have formally defined relationships and is designed to facilitate meaningful and unambiguous information exchange within the medical domain49. Ontologies, like those in the Open Biological and Biomedical Ontology (OBO) Foundry, exist for nearly all scales of biological organization and when combined, can provide a semantically rich and biologically accurate representation of molecular entities and mechanisms57. Unlike clinical vocabularies, ontologies are semantically computable and interoperable with formally defined relationships, which means they can be logically verified and integrated with data from basic science and clinical research49. Mapping clinical vocabularies to ontologies has been recognized as a fundamental requirement for use in deep phenotyping58. An example of how aligning these resources improves deep phenotyping was recently demonstrated by Zhang et al. who mapped LOINC to the Human Phenotype Ontology (HPO)59, which enabled the harmonization of laboratory tests with different clinical codes to common HPO concepts60.Similar to clinical vocabularies, ontologies are classification systems that provide detailed representations of our knowledge of a specific domain61. For example, when mapping the concept “Peptic Ulcer without Hemorrhage AND without Perforation but with Obstruction” (SNOMED-CT:54157007) to the HPO, most automated approaches would return a single best mapping, most likely “Peptic Ulcer” (HP:0004398). This HPO concept is much broader in meaning than the clinical concept. A more precise mapping would explicitly capture the presence and absence of all relevant phenotypic features: “Peptic Ulcer” (HP:0004398) and “Gastrointestinal Obstruction” (HP:0004796) and NOT “Gastrointestinal Hemorrhage” (HP:0002239) or “Intestinal Perforation” (HP:0031368). To the best of our knowledge no existing mappings or mapping algorithms are capable of capturing this type of complex semantics.Due to the time-consuming manual effort required to map clinical vocabularies to OBO Foundry ontologies, no comprehensive mapping across commonly used ontologies currently exists. While automated mapping approaches exist, they largely remain unable to correctly capture the complex semantics underlying clinical data and the knowledge encoded by clinical vocabulary concepts56 spanning diseases, phenotypes, anatomical entities, organisms, chemicals, vaccines, and proteins. The mappings were evaluated on: (1) accuracy, examined by a team of domain experts; (2) generalizability, examined through comparison to a large set of mapped concepts used at least once in clinical practice from 24 hospital systems; and (3) clinical utility, examined through the identification of patients with an undiagnosed rare disease. OMOP2OBO is open source  and includes a custom built interactive dashboard .Building on LOINC2HPO, the goal of this work is to develop OMOP2OBO, an algorithm that enables semantically interoperable mappings between clinical vocabularies in the OMOP CDM to OBO Foundry ontologies Fig. . The resSupplementary Table 62. Standardized vocabularies are a fundamental component of the OMOP CDM, which serve as primary vocabularies within each OMOP domain; all other vocabularies within a specific domain are aligned to a standard vocabulary using mappings provided by the CDM63. The standard vocabularies used in this work included: SNOMED-CT , RxNorm (the OMOP Drug domain for drug products and vaccines), and LOINC . Concepts from these three vocabularies, including labels, synonyms, source codes , and ancestor concepts obtained from the OMOP CDM, were extracted and used as input to the OMOP2OBO mapping algorithm. Using the CHCO OMOP Database, concepts were organized into two data waves according to whether or not they had been used at least once in clinical practice  or not . Only “Concepts Used in Practice” were manually mapped.The OMOP2OBO mappings were created using a de-identified pediatric dataset from the Children’s Hospital of Colorado (CHCO) normalized to the OMOP CDM  and 11,803 drug ingredient concepts  available to map. For measurements, there were 4083 concepts, representing 11,269 measurement results  available to map. With respect to the Concepts Used in Practice, the 29,129 conditions had a median frequency of 25 , the 1693 drug ingredients had a median frequency of 251 , and the 1606 measurement concepts had a median frequency of 313.5 .The counts of concepts eligible for mapping by OMOP domain and data wave are shown in Table 64; Cell Ontology [CL]65), organisms , chemicals , vaccines (the Vaccine Ontology [VO]68), and proteins (the Protein Ontology [PRO]69). Each set of ontology concepts also included metadata, which was obtained by querying each ontology for labels, definitions, synonyms, and database cross-references . The amount of metadata available for mapping is shown in Table p < 0.0001). Post hoc tests with Bonferroni adjustment confirmed the ontologies provided significantly different amounts of metadata .Under the guidance of domain experts, eight OBO Foundry ontologies were selected to represent the following domains: diseases (Mondo), phenotypes (HPO), anatomical entities  were found for 96.6% of condition concepts  representing 69 unique Semantic Types71. The mapping results for each OBO Foundry ontology are displayed in Fig. n = 73,417) mapped to 5654 unique HPO concepts  and 57.8%  mapped to 9637 unique Mondo concepts . Only 50 concepts we attempted to map (excluding purposefully unmapped concepts) were unable to be mapped to at least one OBO Foundry ontology concept.Unified Medical Language System (UMLS)ts n = 10,976 reprn = 3601; Mondo: n = 4836) and Concepts Not Used in Practice . The majority of the manual mappings were one-to-many . Cosine similarity-scored concept embeddings enabled 1374 HPO  and 667 Mondo  mappings  representing 23 unique Semantic Types. The mapping results for each OBO Foundry ontology are displayed in Fig. n = 411) mapped to 4072 unique ChEBI concepts , 21.5% (n = 4661) mapped to 2535 unique NCBITaxon concepts , 2.1% (n = 4249) mapped to 142 unique PRO concepts , and 1.3% (n = 154) mapped to 132 unique VO concepts . All of the OMOP concepts were mapped to at least one ChEBI concept.UMLS CUIs were found for 99.3% of drug ingredient concepts n = 11,71 represenn = 959; NCBITaxon: n = 20; PRO: n = 1; VO: n = 90) and Concepts Not Used in Practice . The majority of the manual mappings were one-to-one . Cosine similarity-scored concept embeddings enabled 109 ChEBI , 4241 NCBITaxon , 18 PRO , and 17 VO  mappings  representing a single Semantic Type. The mapping results for each OBO Foundry ontology are displayed in Fig. n = 10,888) mapped to 1115 unique HPO concepts  and 45 unique Uberon concepts , 76.8% (n = 8657) mapped to 425 unique NCBITaxon concepts , 42.6% (n = 4804) mapped to 172 unique PRO concepts , 87.9% (n = 9904) mapped to 443 unique ChEBI concepts , and 9.9% (n = 1114) mapped to 38 unique CL concepts . Only 13 concepts we attempted to map (excluding purposefully unmapped concepts) were unable to be mapped to at least one OBO Foundry ontology concept.UMLS CUIs were found for 94.8% of measurement concepts n = 869 repren = 17; Uberon: n = 1793; NCBITaxon: n = 444; PRO: n = 44; ChEBI: n = 264; CL: n = 182) and Concepts Not Used in Practice . The majority of the manual mappings were one-to-one . Cosine similarity-scored concept embeddings enabled 113 HPO , 142 Uberon , 150 NCBITaxon , 132 PRO , 476 ChEBI , and 102 CL  mappings . Of the 116 reviewed drug ingredient mappings, 70.7% (n = 82) were correct. Upon review, it was found that 165 (31.6%) of the incorrect condition and 14 (41.2%) of the incorrect drug ingredient mappings could be improved by creating more specific mappings through adding new concepts to the OBO Foundry ontologies or by replacing multiple mappings to broad ancestor concepts with a single best representative ancestor concept. Measurement concepts were reviewed at the result-level using a survey and manual domain expert review. On the survey, 92.9% (n = 251) of the mappings were found to be correct. Of the 1350 measurement results, 97.3% (n = 1314) were correct.The goal of this task was to verify the accuracy of randomly selected sets of manual one-to-one and one-to-many OMOP2OBO mappings from each OMOP domain through domain expert review. Of the 2000 condition mappings, 73.9% were correct . If we assume that the automatic one-to-one mappings created using resources provided by the UMLS, OMOP CDM, and OBO Foundry ontologies are correct and exclude mappings that occur at the ancestor level (assuming those are too broad) and unmapped concepts, then the following concepts received at least one form of review: (1) Conditions: 18.4% of Mondo and 9.9% of HPO; (2) Drug Ingredients: 95.3% of NCBITaxon, 90.3% of VO, 85.3% of ChEBI, and 33.3% of PRO; and (3) Measurements: 79.2% of HPO, 50.8% of Uberon, 48.5% of CL, 12.7% of ChEBI, 10.6% of NCBITaxon, and 3.9% of PRO.75. For this evaluation, we leveraged data  from 24 independent study sites, which included hospitals, academic medical centers, and claims databases. For this analysis, the OMOP2OBO mappings were filtered to identify all concepts with at least one valid mapping  across all of the ontologies mapped within each OMOP domain.The goal of this evaluation was to characterize the generalizability or coverage of concepts in the OMOP2OBO mapping set to a set of OMOP standard concepts that are commonly utilized in clinical practice. The Observational Health Data Sciences and Informatics (OHDSI) Concept Prevalence Study contains OMOP standard concepts that are commonly utilized in practice from several independent study sites across the OHDSI network . Of the remaining condition concepts, 34,704 were only found in OMOP2OBO  and 4672 were only found in the OHDSI Concept Prevalence Data . These findings are visualized in Fig. p < 0.0001). Post hoc tests using Bonferroni adjustment confirmed that 32% of the pairwise OHDSI Concept Prevalence Data site comparisons had significantly different OMOP2OBO coverage . The results of this analysis are visualized as a heatmap in Supplementary Fig. The OHDSI Concept Prevalence Data contained 62,335 condition concepts from 24 independent sites. The filtered OMOP2OBO mapping set contained 92,367 eligible condition concepts, which covered 92.5% (99.5% weighted coverage) of the OHDSI Concept Prevalence Data condition concepts (n = 367) of condition concepts were accounted for using a newer version of the OMOP CDM and occurred in an average of 2.6 sites with a mean frequency of 27,412.3 . In total, 90.6% (n = 4231) of condition concepts purposefully excluded from the OMOP2OBO mapping set  occurred in an average of 1.7 sites with a mean frequency of 6139.3 . The remaining condition concepts  were truly missing and occurred in an average of 2.7 sites with a mean frequency of 5320.1 . The frequency of distributions of the covered condition concepts from OMOP2OBO and Concept Prevalence condition concepts missing from OMOP2OBO in the OHDSI Concept Prevalence Data by site are visualized in Supplementary Fig. Results for the 4672 (7.5%) OHDSI Concept Prevalence Data condition concepts missing from OMOP2OBO are visualized in Fig. n = 4037 concepts; median 7299, range 100–1,308,580,305). Of the remaining drug ingredient concepts, 4574 were only found in OMOP2OBO  and 551 were only found in the OHDSI Concept Prevalence Data . These findings are visualized in Fig. p < 0.0001). Post hoc tests using Bonferroni adjustment confirmed that 22% of the pairwise OHDSI Concept Prevalence Data site comparisons had significantly different OMOP2OBO coverage . The results of this analysis are visualized as a heatmap in Supplementary Fig. The OHDSI Concept Prevalence Data contained 4588 drug ingredient concepts from 18 independent sites. The OMOP2OBO mapping set contained 8611 eligible drug ingredient concepts, which covered 87.9% (99.9% weighted coverage) of the OHDSI Concept Prevalence Data concepts (n = 5) of drug ingredient concepts were accounted for using a newer version of the OMOP CDM and occurred in an average of 8.4 sites with a mean frequency of 51,732 . In total, 82.8% (n = 456) of drug ingredient concepts purposefully excluded from the OMOP2OBO mapping set  occurred in an average of 3.9 sites with a mean frequency of 18,847.3 . The remaining drug ingredient concepts  were truly missing and occurred in an average of 2.7 sites with a mean frequency of 3361.2 . The frequency of distributions of the drug ingredient concepts covered by OMOP2OBO and Concept Prevalence drug ingredient concepts missing from OMOP2OBO in the OHDSI Concept Prevalence Data by site are visualized in Supplementary Fig. Results for the 551 (12%) OHDSI Concept Prevalence Data drug ingredient concepts missing from OMOP2OBO are visualized in Fig. n = 10,676 results), which covered 11.1% (67.7% weighted coverage) of the OHDSI Concept Prevalence Data measurement concepts . Of the remaining measurement concepts, 1208 were only found in OMOP2OBO  and 20,893 were only found in the OHDSI Concept Prevalence Data . These findings are visualized in Fig. p < 0.0001). Post hoc tests using Bonferroni adjustment confirmed that 56% of the pairwise OHDSI Concept Prevalence Data site comparisons had significantly different OMOP2OBO coverage . The results of this analysis are visualized as a heatmap in Supplementary Fig. The OHDSI Concept Prevalence Data contained 25,513 measurement concepts from 18 independent sites. The resulting OMOP2OBO mapping set contained 3828 eligible measurement concepts n = 10,66 resultsn = 13) of measurement concepts were accounted for using a newer version of the OMOP CDM and occurred in an average of 3.2 sites with a mean frequency of 9836.3 . In total, 0.8% (n = 158) of measurement concepts purposefully excluded from the OMOP2OBO mapping set  occurred in an average of 5.2 sites with a mean frequency of 282,115.3 . The remaining measurement concepts  were truly missing and occurred in an average of 2.8 sites with a mean frequency of 218,874 . The frequency of distributions of the measurement concepts covered by OMOP2OBO and Concept Prevalence measurement concepts missing from OMOP2OBO in the OHDSI Concept Prevalence Data by site are visualized in Supplementary Fig. 76. The domain experts identified comparable measurement concepts in the OMOP2OBO mapping set.Results for the 20,893 (81.9%) OHDSI Concept Prevalence Data measurement concepts missing from OMOP2OBO are visualized in Fig. 80. Longitudinal EHR data has been used to identify patients with genetic disorders84. Inspired by the fact that most genetic diseases manifest as a recurring pattern of multiple symptoms or phenotypes affecting multiple organ systems82, the phenotype risk score (PheRS), which measures the similarity between an individual’s diagnosis codes and phenotypic features of known genetic disorders, was developed81. While the PheRS has shown great promise for identifying patients with undiagnosed Mendelian disease from EHR data58, it requires mappings that link ICD codes to HPO concepts, which most EHRs do not contain. The existing mappings58 developed to support PheRS were manually constructed, which may limit scalability when applied to new data.Many patients with a genetic disease never receive a specific diagnosis, even after genetic sequencing58 using data from the AoU Research Program. The AoU Data were selected for this task because it provides access to a large sample of EHR data and genetic testing results , multiple endocrine neoplasia (MEN1 and RET), neurofibromatosis (NF2), paragangliomas , and tuberous sclerosis . These diseases were associated with 2257 unique phenotypic features (HPO codes). When querying AoU data to identify patients who had at least one of these phenotypic features, the ICD-HPO mappings (n = 7815 ICD codes) took ~30 min to complete and returned 210,718 patients and the OMOP2OBO mappings (n = 3783 OMOP concepts) took ~10 min to complete and returned 209,342 patients. Of the 208,831 patients found in common, 1887 were only identified by the ICD-HPO mappings, and 601 patients were only identified by the OMOP2OBO mappings. When the PheRS was applied to patients from both mappings they were found to be highly correlated . This suggests that the patients returned by both mappings were similar.The goal of this evaluation was to determine if the OMOP2OBO mappings could be used to facilitate the application of the PheRS to EHR data and to compare their performance to an existing set of validated manual mappings. For this analysis, the OMOP2OBO HPO mappings were compared to the ICD-HPO mappingsp < 0.001 for all diseases). Collectively, these results support the use of OMOP2OBO mappings as a scalable alternative to an existing set of validated manual mappings for use with PheRS to aid in the systematic identification of patients who might benefit from genetic testing.For additional validation, case-control studies using only the OMOP2OBO mappings were performed: Marfan syndrome , multiple endocrine neoplasia , neurofibromatosis , paraganglioma , and tuberous sclerosis . The results of these studies are shown in Supplementary Table In this paper we present OMOP2OBO, an algorithm that semantically aligns conditions, drug ingredients, and measurement results from standard vocabularies in the OMOP CDM to OBO Foundry ontologies. Using OMOP2OBO, we built mappings for 92,367 condition, 8615 drug ingredient, and 10,673 measurement result concepts to ontology concepts representing 9636 diseases, 6309 phenotypes, 83 anatomical entities, 2704 organisms, 4261 chemicals, 132 vaccines, and 272 proteins. The mappings were evaluated on accuracy, generalizability, and clinical utility. For the first task, a panel of 10 domain experts reviewed subsets of the manually-derived mappings from each of the OMOP domains and found that 73.9% of the condition, 70.7% of the drug ingredient, and 92.9% of the measurement result mappings were correct. For the second task, we examined the generalizability of the concepts and found that 99.5% of conditions, 99.9% of drug ingredients, and 68% of measurement results overlapped with concepts used in clinical practice from 24 independent hospitals and claims databases. For the final task, we compared OMOP2OBO HPO mappings to an existing set of validated manual mappings when used to identify patients with five rare genetic diseases using data from the AoU Research Program. Queries using the OMOP2OBO mappings identified 99.3% of the patients returned by the validated manual mappings using fewer codes and one-third of the time. To the best of our knowledge, the OMOP2OBO mappings are the largest set of publicly available mappings between clinical vocabularies and OBO Foundry ontologies. The OMOP2OBO algorithm can easily be incorporated into existing clinical workflows and presents new opportunities to advance EHR-based deep phenotyping (recently published examples are described below).85, rare diseases87, and cancer88) and for the investigation of specific biological  and clinical domains . Our work is most similar to LOINC2HPO60, which we have expanded in our current mapping set . OMOP2OBO complements existing phenotyping efforts like the Electronic Medical Records and Genomics or eMERGE Network (https://emerge-network.org) and the AoU Research Program, by providing access to resources not currently available in EHRs and opportunities to improve the semantic interoperability of definitions through alignment to the OBO Foundry ontologies.Existing work to develop mapping sets and mapping algorithms has largely focused on using ontologies to improve the phenotyping of specific diseases , HPO , and NCBITaxon . Of these mappings, only the HPO and NCBITaxon are relevant to our work. Of the 1,776,212 CUIs aligned to NCBITaxon, 1128 were mapped to LOINC and 138 were mapped to RxNorm covering 0% of the measurement and 1.1% of the drug ingredient concepts in the CHCO OMOP Database, respectively. Of the 16,154 CUIs aligned to HPO, 993 were mapped to LOINC and 18,212 were mapped to SNOMED-CT covering 0% of the measurement and 4.2% of the condition concepts in the CHCO OMOP Database, respectively. Similar to the OMOP CDM and the UMLS, some of the OBO Foundry ontologies provide mappings to vocabularies in these resources. Collectively, the eight OBO Foundry ontologies used in this work provided 489,794 unique database cross-references from 179 unique data sources. Of these, only the HPO , Mondo , CL (949 ontology concepts to 1376 codes from 29 data sources), and Uberon  mappings were relevant to our work. Of the 19,569 HPO and 159,918 Mondo database cross-references only 3.6% and 15.6% mapped to a condition concept in the CHCO OMOP Database, respectively. These findings highlight that while there are some existing mappings between the resources that OMOP2OBO aligns, at best, they covered only ~15% of the OMOP concepts that we aimed to map supporting the need for its development. Further, it should be noted that the vast majority of the mappings provided by the OMOP CDM, UMLS, and OBO Foundry ontologies are simple one-to-one mappings. While OMOP2OBO contributes one-to-one mappings, it also provides more complex one-to-many mappings.A portion of the mappings that are automatically derived by OMOP2OBO overlap with existing mappings provided by the OMOP CDM, the UMLS, and the OBO Foundry ontologies. The UMLS and OMOP CDM each align more than 200 vocabularies. At the time of our analysis (and determined using the same data), only the UMLS provided mappings to an OBO Foundry ontology, which included: the Gene Ontology92, generate long COVID phenotypes94, and improve the categorization and prediction of psychiatric diseases among patients with long COVID95. Additionally, our recent work in pediatric rare disease subphenotyping demonstrated that patient representations constructed from the OMOP2OBO mappings produced more clinically meaningful clusters than representations built using OMOP concepts alone96. We further demonstrated the value of the mappings by leveraging them to successfully integrate external gene expression data from an independent sample of pediatric patients resulting in more clinically-meaningful and biologically-actionable phenotypes than those generated using only clinical data. One potential use of OMOP2OBO is to aid in the alignment of patient data to ontologies in the Global Alliance for Genomics and Health’s Phenopacket schema97, which was designed to support the global exchange of computable patient-level phenotypic information.The OMOP2OBO mappings have been used to characterize differences in definitions of long COVID98. We only evaluated the accuracy of a small subset of the manual mappings. It is important to evaluate the remaining manually-derived mappings as well as to provide citations from the resources from which they were derived. The Accuracy evaluation revealed limitations of our expert review procedures; some of the experts experienced challenges when trying to use the OBO ontologies, which may have negatively impacted the results. Providing better training and offering outcomes other than correct/incorrect should be considered. Finally, OMOP standard clinical vocabularies are also dependent upon a large set of CDM-specific mappings and may be subject to similar errors as our mappings.OMOP2OBO has not been optimized for performance; all possible ancestors are mapped when unable to generate a mapping at the concept-level. A prioritization strategy would significantly improve performance. OMOP2OBO does not take advantage of all of the knowledge available in the UMLS. Leveraging information in the mapping and hierarchy tables could improve the automatically mapped concepts and would enable use of other UMLS-aligned resources like the SemMedDBhttps://bioregistry.io)99. The Bioregistry provides extensive governance policies and templates, which make it easy to incorporate new and modify existing identifiers. They also developed a robust, semi-automated infrastructure that facilitates review by the maintainers and triggers rebuilds of the registry anytime changes are made. To improve the shareability of the mappings, we would also like to extend the mapping output formats to include Semantic Web standards like RDF/XML and the Simple Standard for Sharing Ontological Mappings or SSSOM100. In addition to creating a system like the Bioregistry, future work may include adoption and adaptation of OBO Foundry protocols for ontology development and maintenance101.There are two primary challenges that remain given the initial development of the OMOP2OBO algorithm and mapping set. The first challenge is to establish procedures and build infrastructure to enable community sharing, monitoring, and updating of the mappings. While the GitHub repository for the OMOP2OBO currently contains policies for contributing to the mapping algorithm, we have yet to establish an infrastructure or policies for the mappings. Future opportunities include the adoption of a system like the one utilized by the Bioregistry  to verify the database cross-references provided by the OBO Foundry ontologies, UMLS, and OMOP CDM database prior to running OMOP2OBO.The second challenge is to improve and expand the evaluation of the algorithm and the mapping set. The UMLS, OMOP CDM, and the OBO Foundry ontologies provide mappings between clinical vocabularies and ontologies, which are automatically- or manually-derived . While the OMOP2OBO algorithm leverages these mappings , verifying the quality of existing mappings was not within the scope of the current work. Currently, no modules in the OMOP2OBO algorithm verify the quality of existing mappings used by OMOP2OBO or mappings generated by it. This should include resources to validate automatic mappings as their accuracy depends upon the quality of the resources from which they were built, and ontologies are subject to a variety of errorshttps://github.com/callahantiff/OMOP2OBO), available on PyPI (https://pypi.org/project/omop2obo), and includes an interactive dashboard that summarizes the current mapping set . We also created a dedicated Zenodo Community, which provides access to data, mappings, and presentations (https://zenodo.org/communities/omop2obo). A list of the acronyms used in this paper are provided in Supplementary Table OMOP2OBO is open source (105])70. These data are used to annotate each OMOP concept with a UMLS CUI and a Semantic Type71. Additionally, the mappings provided by the MRCONSO table are used to enhance existing database cross-reference mappings provided by OMOP and the OBO Foundry ontologies (both described in detail in the “Input data used to create OMOP2OBO mappings” section).Although it is possible to apply the OMOP2OBO algorithm to any clinical vocabulary, the OMOP CDM was selected because of its rich data representation, standard vocabularies (and hierarchies) and the mappings it provides to more than 200 commonly used clinical vocabularies. To increase the coverage of the resources and the potential of an automatic mapping, OMOP2OBO leverages the National Library of Medicine’s UMLS , it automatically downloads the OBO Foundry ontologies using OWLTools .The second component is Mapping OMOP Vocabulary Concepts to OBO Foundry Ontology Concepts. This component is designed to automatically map or align OMOP concepts to OBO Foundry ontology concepts. The algorithm includes several different approaches, prioritizing those that result in high-confidence mappings. This component includes concept alignment and concept embedding.Concept alignment: exact-string matches between OMOP and OBO Foundry ontology concept labels, definitions, and synonyms are obtained. Prior to alignment, the label and synonym fields are both made lowercase. This step also obtains exact matches between OMOP standard concepts and source codes to OBO Foundry ontology database cross-references. To increase the likelihood of finding a match, the OMOP standard concepts and source codes are first merged with terminologies in the UMLS using core functionality from OHDSI Ananke106, a program developed to align OMOP concepts to UMLS CUIs. Prior to performing this alignment, the OMOP standard concepts and source codes and the OBO Foundry ontology database cross-references are normalized using a custom dictionary (source_code_vocab_map.csv107). This resource ensures that concepts referenced by the same code using different prefixes or symbols can be aligned .Concept embedding: using scikit-learn108, a bag-of-words (BoW)109 vector space model with term-frequency inverse-document frequency (TF-IDF)110 and L2 normalization is used to learn concept embeddings for all OMOP and OBO Foundry ontology concepts and concept ancestors label and synonym text strings. While the BoW model was used because it is easy to understand and has shown great success when applied to EHR data and when used to align biomedical ontologies112, any language or embedding model could be utilized. The BoW model is implemented as an N × M document-term matrix with one row per input string and one column for each tokenized word appearing in the universe of all input strings. The value of each cell in the matrix is the normalized frequency each word occurred in each input string . Prior to building the model, all text fields are made lowercase, stop words are removed using the wordnet list from Python’s NLTK library113, white spaces are removed, and word-level tokenization and lemmatization are applied. After learning the model, a final embedding is constructed for each input string by aggregating the constituent concept embeddings. Cosine similarity is used to compute scores between all pairwise combinations of OMOP and OBO Foundry concept embeddings. Given that each OMOP and OBO concept can have a label and one or more synonyms, only the single highest-scoring pairwise comparison is selected for the final mapping. Cosine similarity scores range from 0–1, where a score of one indicates a greater match between the embedding pairs. To improve the efficiency of this process, only the top 75% of pairs with scores ≥0.25 are output, which was decided after visualizing the score distribution using a histogram. All thresholds and cut-offs are customizable. Concept embeddings are created for all OMOP concepts, regardless of whether or not they were automatically mapped by a prior Component. All remaining unmapped concepts require manual curation.The third component is Synthesizing and Outputting Mapping Results. The mapping results from the prior component are post-processed to include a mapping category and human-readable evidence. The mapping category is constructed by combining the following elements: (1) one or more OBO Foundry ontology identifiers and labels; (2) mapping logic applied to specify semantics when there are multiple ontology concepts  or to denote negation ; (3) a mapping category derived from the mapping approach , cardinality , and level ; and (4) mapping evidence represented as a pipe-delimited string that denotes all resources that support the mapping . Supplementary Table rs); and  mapping The OMOP2OBO mappings were constructed from two data sources: (1) the CHCO OMOP Database and (2) OBO Foundry ontologies (both are described in detail below). Figure https://www.healthdatacompass.org). The data conformed to the structure defined by the National Pediatric Learning Health System (PEDSnet) OMOP CDM v3.0, which is an adaptation of the OMOP CDM version 5.062. Data were obtained from a de-identified database that was determined by the Colorado Multiple Institutional Review Board to be non-human subjects (#15-0445). Due to the broad scope of the projects approved to be performed on this database, a Waiver of consent was obtained as it was not practical to obtain consent from all patients.The OMOP2OBO mappings were constructed using data from the CHCO OMOP Database, a de-identified database that contained data from more than six million pediatric patients. The CHCO OMOP Database is stored within University of Colorado Anschutz Medical Campus Health Data Compass’ Health Insurance Portability and Accountability Act compliant Google Cloud-based infrastructure  from the Condition Occurrence, Drug Exposure (at the drug ingredient level), and Measurement tables, respectively. For each concept set, metadata were extracted from the OMOP CDM including concept codes , labels, synonyms, and ancestor concepts . Concept lists were organized into two data waves according to whether or not they had been used in clinical practice . As manual annotation requires significant resources, only concepts from the first data wave  were manually mapped. Prior to constructing the concept lists, Condition and Measurement data were preprocessed to ensure the mapping process was robust and reproducible.Concept lists were derived from standard OMOP vocabularies  were marked as unmapped and the reason for exclusion was provided in the evidence field. The Semantic Types were also used to group OMOP concepts such that those typed as “Findings” or “Signs and Symptoms” were treated as phenotypes and only mapped to HPO and concepts typed as “Disease or Syndrome” were only mapped to Mondo. For Concepts Not Used in Clinical Practice, all possible automatic mappings were obtained and concepts which were unable to be mapped automatically were marked as unmapped and “NOT YET MAPPED” was provided as the mapping evidence. This same approach was applied to drug ingredients.60; results were annotated with respect to their result type:Concepts with result type “Normal/Low/High”: for example, “Corticotropin [Mass/volume] in Plasma—4th specimen post XXX challenge” (LOINC:12460-2). Results above the reference range are mapped to “Increased Circulating ACTH Level” (HP:0003154). Results below the reference range are mapped to “Decreased Circulating ACTH Level” (HP:0002920). Results within the reference are mapped to “Abnormality of Circulating Adrenocorticotropin Level” and logically negated (NOT HP:0011043).Concepts with result type “Positive/Negative”: for example, “Amphetamine [Presence] in Urine by Screen Method” (LOINC:19343-3). Positive results are mapped to “Positive Urine Amphetamine Test” (HP:0500112). Negative results are mapped to “Positive Urine Amphetamine Test” and logically negated (NOT HP:0500112).For all measurement concepts, a scale and result type were created. The scale  of each measurement was identified from the OMOP CDM or by parsing the concept synonym field. For all Concepts Used in Practice, reference ranges were used to determine the result type; concepts with numeric reference ranges were typed as “Normal/Low/High” and concepts with reference ranges that included “positive” or “negative” were typed as “Positive/Negative”. Concepts Not Used in Practice with an ordinal scale or with synonyms that contained the words “presence” or “screen” were typed as “Positive/Negative”. Concepts with a quantitative scale were typed as “Normal/Low/High”. All other scale types were typed as “Unknown Result Type”. While it is possible to infer the result type from the scale type , our approach aimed to maximize the inclusion of concepts from all scale types. Mappings were created for each result type using the procedures defined by LOINC2HPOAlso consistent with the procedures adopted by LOINC2HPO, all concepts lacking sufficient detail  were marked as unmapped and “Unspecified Sample” was provided as the mapping evidence.60, which were downloaded on August 2, 2020 from the LOINC2HPO annotation Github repository114. OMOP2OBO expands the LOINC2HPO mappings by including the measurement substance , the entity being measured , and the species of the measured entity . All modifications to the original LOINC2HPO annotations were recorded in the mapping evidence field, enabling users to easily identify when an original LOINC2HPO annotation had been updated. All LOINC concepts in the LOINC2HPO mappings that were not used at least once in clinical practice in the CHCO pediatric OMOP Database were categorized as a Concept Not Used in Practice.The initial set of measurement concepts was supplemented with LOINC2HPO annotations115 [v2020-09-14]), phenotypes (HPO59 [v2020-08-11]), anatomical entities , organisms (NCBITaxon66 [v2020-04-18]), chemicals (ChEBI67 [v191]), vaccines (VO68 [v1.1.102]), and proteins (PRO69 [v61.0]). Similar to the clinical concepts, each ontology was queried to obtain labels, definitions, synonyms (including synonym type), and database cross-references. All OBO Foundry ontologies were downloaded in September 2020 using OWLTools .OBO Foundry ontologies were selected under the advice of several clinicians, molecular biologists, and professional OBO Foundry biocurators to cover the following domains: diseases  by a practicing resident physician (J.M.W.) and clinical pharmacist (J.S.), respectively.Automatic mappings are created from exact alignments between resources available in the OMOP CDM and the OBO Foundry ontologies and thus are assumed to be accurate and high-confidence mappings. The goal of this evaluation was to examine the accuracy of a portion of the manually-derived mappings. For conditions and drug ingredients, of all manual mappings (including one-to-one and one-to-many), 20% were randomly selected for manual review (Survey. A subset of the mappings (n = 270) were independently validated by five domain experts including three practicing pediatric clinicians , a PhD-level molecular biologist (A.L.S.), and a Computational Biology PhD candidate with Masters-level training in epidemiology and biostatistics (T.J.C.) using a Qualtrics Survey116. Any mapping that did not meet agreement by at least one clinician and both the biologist and the epidemiologist were re-evaluated by the most senior clinician. These mappings were also vetted on the LOINC2HPO GitHub tracker (https://github.com/TheJacksonLaboratory/loinc2hpoAnnotation/issues) by members of the biocuration team. (2) Biocurator validation. A random subset of 1350 measurement results were manually verified by an OBO Foundry biocurator.Measurement mappings are significantly more complex as they require interpreting lab test results and annotating the source of the sample , entity being measured , and organism of the measured entity. While annotating the samples and entities is straightforward, interpreting lab tests results and aligning them to HPO concepts can be challenging. As a result, only the HPO mappings were evaluated by domain experts. These mappings were evaluated in two ways: (1) https://github.com/callahantiff/OMOP2OBO/wiki/Accuracy).All of the manual mappings were derived by a member of the research team who at the time of the analysis was a Computational Biology PhD candidate with Masters-level training in epidemiology and biostatistics (T.J.C.). As this individual does not have specialized medical or pharmacological training, it is assumed that these mappings may contain errors. Additional details are provided on GitHub  concepts that existed in the OMOP2OBO set and in at least one Concept Prevalence Study site ; (2) concepts only present in the OMOP2OBO set ; and (3) concepts only present in the Concept Prevalence Study set . Institutional review board approval was not required to use these data as the dataset was completely de-identified and contained no patient-level information.The generalizability of the OMOP2OBO mappings were examined using the OHDSI Concept Prevalence Study data“Recovered in Newer Version of CDM”: concepts that could be recovered using a newer version of the OMOP CDM ; (2) “Purposefully Excluded”: concepts without clear pathological or biological origin that were purposefully excluded from the OMOP2OBO mapping set; and (3) “Truly Missing”: concepts that could not be accounted for using the prior two scenarios. For all scenarios, concept frequency within the Concept Prevalence Study sites was used as a measure of concept importance. Findings from each scenario were reviewed by a practicing resident physician and a clinical pharmacist. See GitHub for additional details .An error analysis was performed to examine the Concept Prevalence Only concept set. Three scenarios were examined: (1) 58) when used to identify undiagnosed rare disease patients. For this analysis, AoU Data  was selected because it provides access to a large sample of EHR data with genetic testing results. For this evaluation, the version 6 build was used, which contained data from ~630 sites on more than 528,000 patients. Five genetic diseases for which diagnosis codes have been found to be of high positive predictive value in EHRs58 were selected, which included: Marfan syndrome, multiple endocrine neoplasia, neurofibromatosis, paraganglioma, and tuberous sclerosis. These diseases are associated with 11 of the 73 American College of Medical Genetics and Genomics (ACMG) secondary finding genes , which have specific mutations known to cause disorders, have well-defined phenotypes, and are clinically actionable117. The diseases and associated genes included: FBN1 and TGFBR1 (Marfan syndrome); MEN1 and RET (multiple endocrine neoplasia); NF2 (neurofibromatosis); SDHAF2, SDHB, and SDHC (paragangliomas); and TSC1, TSC2 (tuberous sclerosis). Using the Online Mendelian Inheritance in Man  database and the HPO gene annotation table118, each gene and its corresponding set of phenotypic features were aligned to the HPO. To calculate the phenotypic burden of each genetic disease, HPO mappings to OMOP condition concepts from OMOP2OBO (v2.0.0 beta) and ICD concepts from a validated set of ICD-HPO mappings58 were queried against the AoU data. PheRS for each gene were then calculated for patients from each the OMOP2OBO and Phecode mapping sets. The PheRS81 is an algorithm used to identify patients with phenotypic features that are clinically similar to OMIM Mendelian profiles but who lack formal diagnosis and has demonstrated utility for identifying underdiagnosed rare disease patients using only EHR data81. The standardized version of the PheRS was used because it is easier to interpret and reduces noise when it is suspected that a large number of phenotypes will overlap between cases and controls81. The OMOP2OBO and ICD-HPO mappings were compared and evaluated on time to complete the query against the AoU Data and differences in the returned patient cohorts. As validation, case-control studies were performed for each of the five diseases using the patients returned from the OMOP2OBO mappings. Cases were defined as patients with at least two occurrences of a relevant diagnosis code and control patients had no instances of these codes. Cases and controls were matched on age, sex, and length of EHR record. For each disease, a one-sided Wilcoxon rank sum test was performed in order to determine if PheRS were significantly higher for cases than controls. Results were verified by a PhD-level Epidemiologist specializing in genetics (C.Z.).The clinical utility of the OMOP2OBO mappings was compared to an existing set of validated manual mappings  by an authorized researcher (C.Z.). Informed consent is obtained from all participants who enroll in the AoU Research program119. Because the authors were not directly involved with the participants and all data were de-identified, the use of these data was exempt from institutional review. For additional details, see “Do I need my project reviewed by the AoU Institutional Review Board (IRB) in order to access this data using the Researcher Workbench?” for more information .All analyses were performed in the AoU Researcher Workbench . The OMOP2OBO (v1.0) mappings are publicly available from Zenodo122. The OMOP2OBO Mapping Dashboard was built with R (v4.2.1) using Rmarkdown (v2.14) and flexdashboard (v0.5.2).OMOP2OBO was developed using Python 3.6.2 on a single machine with 8 cores and 16GB of RAM. All code and project information are publicly available and detailed on GitHub  using R (v4.1.2) and Python (v3.7). Analyses were performed on a machine with 16 CPUs and 60GB of memory.Descriptive and inferential statistics were performed to evaluate the data available for mapping and the OMOP2OBO mapping set. Chi-square tests of independence with Yate’s correction were used to: (1) assess differences in the proportions of metadata available from each OBO Foundry ontology; and (2) assess differences in the proportions of mapped concepts between OHDSI Concept Prevalence sites. Post hoc tests using Bonferroni adjustment to correct for multiple comparisons were performed for significant omnibus tests. Analyses were performed in Jupyter Notebooks (v6.1.6) using the scipy (v1.4.1), statsmodels (v0.12.1), statistics (v1.0.3.5), and numpy (v.1.18.1) libraries. Visualizations were created using matplotlib (v.3.3.2). The “Clinical Utility” evaluation was performed in the AoU Researcher Workbench (Further information on research design is available in the Supplementary MaterialReporting Summary


Tg, Young’s modulus, shear modulus, fractionalfree volume, and diffusion for PVC–plasticizer systems. Wheredata was available, the results from this study are in good agreementwith the experiment; we conclude that DBS and DHS are most promisinggreen plasticizers for PVC, since they have properties comparableto DEHP but not the environmental and toxicity concerns.Environmental and toxicity concerns dictate replacementof di(2-ethylhexyl)phthalate (DEHP) plasticizer used to impart flexibility and thermalstability to polyvinyl chloride (PVC). Potential alternatives to DEHPin PVC include diheptyl succinate (DHS), diethyl adipate (DEA), 1,4-butanedioldibenzoate , and dibutyl sebacate (DBS). To examine whetherthat these bio-based plasticizers can compete with DEHP, we need tocompare their tensile, mechanical, and diffusional properties. Thiswork focuses on predicting the effect these plasticizers have on  To combat this issue, plasticizersare commonly added to the resin to impart flexibility and thermalstability.3Polyvinyl chloride (PVC)plays a key role in the production ofa large variety of products including building materials, furniture,toys, medical devices, electrical insulation, hygiene products, andfood wrappers.1 In particular,PVC tubes using di(2-ethylhexyl)phthalate (DEHP) are used for external processing of blood and otherbio-substances, presenting the concern that DEHP might leach intothe blood to cause toxicity. Moreover, phthalates need to be eliminatedbecause of serious environmental and public health concerns. In particular,DEHP has been found to induce a wide range of developmental and reproductivetoxicities in mammals, and it is a suspected endocrine disruptor inhumans.7 Directive 2005/84/EC of the European Parliament and Council addressedthese alarming findings by restricting the use of DEHP from all toysand childcare products in the EU.8 Despitegrowing awareness of the harm that phthalates present, DEHP continuesto be the most frequently used plasticizer in the world and can befound in commercial PVC in concentrations up to 30–40 wt %.9 In order to reduce the usage of DEHP and similarphthalate plasticizers, recent studies have focused on developingbio-based plasticizers without adverse effects that do not compromisethe properties of the final product.11Traditionally, phthalates are themost widely used PVC plasticizer.In orderto accelerate this development of new plasticizers, weuse in silico techniques to predict the effect of new candidate plasticizerson the tensile and migration properties of PVC. We consider here fourpotential new plasticizers and compare their properties to DEHP. Thisin silico approach, in principle, could also be applied to a widerange of new candidates much faster than experimental synthesis andcharacterization, which would allow experiments to focus on the bestpredicted plasticizers.In this study, we use a general computationalapproach that couldbe applied to a wide range of plasticizers, however, we focus on systemsfor which there is already some experimental data available. Indeed,we found that our predictions agree well with the available experiments,setting the stage for a broader sampling of possible plasticizersthat have not yet been studied experimentally. Our studies indicatethat dibutyl sebacate (DBS) and diheptyl succinate (DHS) plasticizershave properties comparable to DEHP but none of the toxic attributesof DEHP. Thus, we recommend the gradual switch from DEHP to DBS orDHS.1.1Tg).2Plasticizers for PVC are used to enhance the flexibility and processability,which is achieved by lowering the glass transition temperature . They showed that diethyl adipate (DEA), which has a linearstructure and a relatively small molecular weight, had the best plasticizationeffects on PLA.26 1,4-BDB was also shown to bebiodegradable by soil microorganisms.28 Furthermore, an in vivorat study showed that 1,4-BDB does not significantly alter adult malereproductive function.29 The 1,4-BDB structurehas two aromatic rings which can act as compatibilizer bocks to impartflexibility and two ester groups that can act as cohesive blocks toprevent leaching.The diol dibenzoatefamily of compounds is of interest in the searchfor viable alternative plasticizers because diethylene glycol dibenzoate(DEGDB) is already in wide commercial use. However, there is concernthat its ether linkages may lead to the formation of metabolites thatare difficult to degrade microbially. These metabolites, similar tothe formation of MEHP in DEHP, could be more toxic than its parentcompound. 1,4-Butanediol dibenzoate  is an interesting alternativeto DEGDB because it eliminates the ether functional group that posesa risk.30 studied theireffectiveness in ethyl cellulose polymer. They found that dibutylsebacate was the most successful at lowering Tg among thesebacate-based plasticizers studied. DBS also has the longest aliphaticchains compared to the aforementioned plasticizers, suggesting thatthese spacer components will generate more free volume and increaseplasticization.Another class of chemicals of interest isthe sebacates. Whilethe tensile properties of sebacate plasticizers in PVC have not beenstudied, Mahnaj et al.33The above results show the promise of diheptylsuccinate, diethyladipate, 1,4-butanediol dibenzoate, and dibutyl sebacate as potentialalternatives to DEHP in PVC 1. In addTg, Young’s modulus, shearmodulus, fractional free volume, and diffusion for PVC systems.In order to ensure our chosen bio-based plasticizerscan competewith DEHP, we need to examine their tensile, mechanical, and migrationproperties. This work focuses on predicting the effect these plasticizershave on These blended polymer properties can be determined experimentally,however, such experimental tests are resource and time expensive andmost have not yet been reported for our 4 candidates. Instead, weuse classical molecular dynamics (MD) simulations as a practical approachto predict such properties, enabling in silico predictions to identifythe most promising candidates for future experimental validation.MD also allows investigation into the mechanisms connecting atomisticstructure with diffusion and tensile properties. These propertiesdepend heavily on a diverse set of factors including free volume,density, pressure, temperature, and the interaction of the plasticizerwith the PVC backbone, all of which can be monitored systematicallythroughout the simulation. Thus, our use of MD is two-pronged: tomaximize efficiency and to exact more control and monitoring withinthe simulation. Our MD simulations predict the dynamics of atomicmovements based on force fields fitted to quantum mechanics (QM).This enables practical calculations of the atomistic behavior andvarious-mechanical properties of novel polymer blends. In order tovalidate these in silico methods, we compared our predicted resultsto experimental findings where available. Indeed our results are withinthe same order of magnitude as what was reported, indicating a goodagreement. It would be valuable to obtain experimental data for themechanical and transport properties for these novel PVC-plasticizerblends.234 We used the universal force field (UFF), whichincludes parameters for every atom of the periodic table (up to atomicnumber 103).35 This allows for MD calculationsof organic, biological, and inorganic molecules and solids. UFF describesthe potential energy of a system as in eq ER), bond bending (Eθ), dihedral torsion (EΦ), and inversion terms (Eω). The nonbonded interactions consists of van der Waals(Evdw) and electrostatic interactions(Eel).All MD calculationswere carried out using the LAMMPS software.We used an MD time step of1 fs. All simulations were conductedwith a temperature damping constant of 100 fs while the NPT MD usedan isotropic barometer with a pressure damping constant of 1000 fs.2.136 We first built a system of pure PVC using the“Amorphous Cell Builder” in Maestro. Considering commercialPVC varies greatly but generally has an atactic stereochemistry, wechose a system consisting of 16 atactic-PVC chains with 20 monomerseach. This system, along with the plasticizer of choice, was loadedinto the “Disordered System Builder”. All systems werecarried out with a plasticizer composition of ∼40 wt % as thisis the rough upper limit for the amount of DEHP used in commercialPVC. Exaggerating the amount of plasticizer present in the systemallows for more obvious observation of plasticization properties.9We built the molecularstructures using Materials Studio with the OPLS2005 forcefield parameters.37 to ensure fullinterspersion of the plasticizer molecules within the system. Thisprotocol included the following steps:NVT) to heat the systemto 600 K and equilibrate.Energy minimization at a low density (0.5 g/cc) followedby an 8 ns MD with the volume fixed but with the temperature controlledby a thermostat  at 1 atm.Once the target density was reached, the temperaturewas reduced to 300 K and equilibrated for 3 ns with the temperaturecontrolled by a thermostat and the pressure controlled by a barostat, respectively. The predicteddensities are compared with experiment and shown in After convergence, the pressure fluctuated around zeroindictingequilibration. The time evolution of the potential energy, pressure,and temperature are found in 2.2E, is the slope of theinitial linear part of the stress–strain curve.37 As the amounts of plasticizer are increased,the polymer system has greater chain mobility, leading to less resistanceto deformation. This results in a Young’s modulus that decreaseswith increasing plasticizer concentration, which is desired in commercialplastics. The Young’s modulus also generally decreases withincreasing temperature.40The Young’smodulus or elastic modulus, E(T). To do this, we calculated elastic moduli bydeforming each system in the PVC chain direction (z) at a constant engineering strain rate of 0.0001 ns–1 up to a total strain of 1% for various temperatures over the rangeof 150–500 K. We heated (or cooled) the system to the desiredtemperature using NVT MD. The system was then equilibratedat the new temperature at 0 atm pressure for 0.5 nanoseconds using NPT. The final step before deformation was a brief relaxationperiod using the NPT ensemble, where the x and y directions were allowed to relaxcompletely using anisotropic barometer conditions.We examinedthe Young’s modulus as a function of temperature, xx, Σyy, Σzz, Σxy, Σyz, Σxz) were fittedto the Von Mises Criterion .The shear modulus, E(T). Since the PVCchains are oriented in the z direction, we shearedeach system in the yz and xz planesat a constant engineering shear strain rate of 0.0001 ns–1 from 150 to 500 K.We used a protocol similarto that for E(T), we heated (or cooled)the system to the desired temperature using the NVT and then equilibrated at the new temperature for 0.5 nanosecondsat 0 atm pressure using NPT. Prior to deformationwe equilibrated using NPT, allowing the xx, yy, zz, and xy planes to relax completely.As with xz and yz planes were shearedindependently. The Von Mises Criterion equation was again fitted tothe pressure components. The resulting shear stress value was evaluatedas a function of the applied shear strain. Then, the two stress valuescalculated from shearing in the xz and yz planes were averaged and plotted against the shear strain, to findthe overall shear modulus of the system.The 2.445Vs is thespecific volume of the polymer and V0 isthe occupied volume, which is equivalent to 1.3Vw. Vw is the van der Wall’ssurface of the polymer, which can be calculated by46NB is thenumber of bonds, RA is the number of aromaticrings, and RNR is the number of nonaromaticrings. FFV for the polymer blends were calculated for temperaturesbetween 250 and 425 in 25 intervals. These temperatures cover thebroad range of glass transition temperatures expected for PVC blends.In order tovalidate the glass transition temperatures obtained in 2.5D, from the Fickian relationPlasticizers migrationis critically important to potential commercial uses of the PVC polymerand to public health. We calculated the mean square displacement (MSD)as a function of time to get the diffusion coefficient, 47NVT simulations at 20 K for 10ps.To generate initial velocities for the atoms in simulations,we carried out our NVT. MD simulations werealso carried out at 400, 500, 600, 700, and 800 k, each for ∼15–20ns, allowing us to predict the activation energy for diffusion ofthe additive. This range of temperatures was chosen to allow sufficientlylong simulation times at the higher temperatures to attain the Fickiandiffusion limit (The system was then heated from 20K to room temperature(300 k) over 0.5 ns using on limit 5.NPT simulationsat the target temperature for 1 ns to release any residual stressfrom heating the structure.Then we carried out NVT simulations were carriedout for ∼15–20 ns at each temperatures, while applyingthe Berendsen thermostat (damping time = 0.1 ps).Finally, Figures S1–S18 ofthe Supporting Information.The pressure and density fluctuations during the simulationsare reported in Spatial coordinatesand time step data for all the atomswere saved every ps from the 15 to 20 ns trajectories.t) over all t increments along the trajectory:A python code was used to calculate the MSD vs timestep (τ) as in The MD procedure to obtain MSD is asfollows:T wereused to obtain the activation barrier, Ea , we compare in In order toensure that our calculational protocol leads to an accurate We found no literature regarding PVC blends withDBS, DEA, and1,4-BDB.E(T) for 40 wt% DEHP is higher than the values measured by Kendall and Siviour48 which have 40 wt % DINP and those calculatedby Zhou and Milner49 which have 40 wt %DEHP  of allplasticized systemsconverged as the temperature reached 500 K. DBS was the only plasticizerthat consistently decreased the Young’s modulus of PVC as wellas or better than DEHP .E(x) atsome temperatures was observed in all systems. We used this changein slope as an indicator of glass transition temperature, since theglass transition region is the point at which increased movement ofcarbon chains is observed, causing the polymer to shift from a rigidto rubbery state. Since the purpose of a plasticizer is to increasethe chain mobility, Tg has been demonstratedto be a good indicator of the polymer structure and efficacy. We showedthat DEHP lowers the Tg of PVC by almost100 K, from between 350 and 400 K to between 250 and 300 K. DBS andDHS were the only other plasticizers able to lower the Tg to such an extent. DEA, despite its lowmolecular weight,was able to reduce the Tg by only 50 Kto between 300 and 350 K. Elsiwi et al. determined that DHS has a Tg of 248.15 K at a 26.8 wt % This substantiallylower percent weight may explain the deviation of our predicted resultsfrom experiment.24A dramatic decreasein Tg of PVC orthe E(x) at temperatures above 250K. This apparent lack of plasticization was also observed in an experimentalstudy conducted by Erythropel et al.26 One proposed hypothesis to explain this anomaly is thatthe stacking of the two benzyl groups along with 1,4-BDB spacer componentscaused rapid partial crystallization within the blend, therefore losingits ability to plasticize PVC.The evident outlieramong our results is 1,4-BDB that we foundto not decrease either the 3.2G(T), we compare our MDresults to experimental data for pure PVC in xz plane is similar to that when shearingin the yz plane for each system, so we quote theaverage value here.In order to ensurethat the protocol used in this work would accurately predict the shearmodulus, G(T) of the variousplasticizer systems demonstrate that DEHP, DBS, and DHS all successfullylower the glass transition temperature of PVC to between 250–300K (G(T) of PVC as well as or better than DEHP. 1-4-BDB continuedto exhibit anomalous behavior that suggested an inability to plasticizePVC (at least at this concentration).The results obtained from shear simulations corroboratethe findingsfor the Young’s modulus. The glass transition temperaturesshown in the 50–300K 6. DBS wa3.3Tg predicted from the Young’sand shear modulus, we calculated the fractional free volume, FFV.FFV is a measure of the volume not occupied by the polymer, makingit an indicator of plasticization efficiency.45 It has been shown that thermal expansion increased free volume andat a certain temperature, the mobility PVC backbone increased transformingit into rubber. This rubber transition boundary can be interpretedas the glass transition temperature.53 Wecalculated the FFV in the PVC–plasticizer systems as a functionof temperature to estimate Tg.In order tovalidate the Tg values based on the shearbehavior. A sudden change in slope was observed between 275 and 300K for the PVC/DEHP and PVC/DBS blends while a similar phenomenon wasobserved between 250 and 275 K for DHS. DEA exhibited a glass transitiontemperature between 300 and 325 K and 1,4-BDB once again demonstratedthe highest Tg. However, Vw. This approach takes into account “dead volume”in the repeat units of the chain which can depend on the conformationso that its use to estimate V0 cannotreliably describe polymers at room temperatures.54Weobserved that the predicted FFV values correspond well with the previouslypredicted Nonetheless, the prediction based on FFV valuessupport the previousconclusion that DBS and DHS blends perform at similar or even betterlevels than DEHP at increasing chain mobility and plasticization.3.4t) (and with the pressure fluctuating aroundzero). The MSD vs Time plots of all the systems investigated reachedthe Fickian diffusive regime, however the time required depended onthe system and the temperature . TheArrhenius plots for all five systems showed a change in slope between600 and 700 K. This temperature range has been associated with thede-hydrochlorination of the PVC backbone, which may explain the increasein D observed in the plots.56 The MSD vs time, pressure vs time, and Arrhenius plots for eachcalculation are in Figures S4–S22 of the Supporting Information.We established the validityof our diffusion results by ensuring that the MD was sufficientlylong to reach the Fickian Diffusion regime, where log MSD is tangentto log(perature 8. The sy15 We found that onlyDEA has a larger diffusion coefficient than DEHP at room temperature,while DBS, DHS, and 1,4-BDB all have high coefficients within a similarrange (55 Increasedplasticizer polarity further leads to an increase in the probabilityof dehydrochlorination, which can lead to the degradation of the PVCsystem. As the system degrades, the migration of DEA molecules increasessubstantially.58 The tendency of plasticizer to remain in the plasticized materialis also dependent on molecular size, the larger the plasticizer moleculethe greater its permanence.55 This proclivity canbe explained by the free-volume theory: larger pockets of free spaceare required for larger molecules. Moreover, there is an inverse relationshipbetween molecular size and the diffusion rate.60 Thus, low diffusivitycan be achieved by high molecular weight and highly branched isomericstructures, as exhibited by DBS, DHS, and 1,4-BDB. They contain estergroups, whose van der Waals forces, hydrogen bonds, and electrostaticinteractions dominate the interactions with the PVC backbone, leadingto lower diffusion rates for DBS, DHS, and 1,4-BDB.The diffusivity of the additives involves manyvariables, includinginteractions between the additive molecules and the polymer, the movementof the within the polymer, and the molecular size/structure of theplasticizer itself.arrange 3. DEAis61 experimental study found the diffusion coefficient of PVCplasticized with 40 wt % DEHP at 40 °C to be 4.77 × 10–10 or 5.75 × 10–10 m2/s depending on the solvent in which the PVC was submerged. Whilethis value is within the same order of magnitude as our predictedvalue, the Kim study and the vast majority of experiments calculatethe diffusion of DEHP from a sheet of PVC into a mixture of water,ethanol, and/or acetonitrile, which cannot be compared directly toour predicted diffusion coefficient.The experimental data on diffusion are based on thediffusion ofDEHP from a sheet of PVC into a mixture of water, ethanol, and/oracetonitrile, which cannot be compared directly to our predicted diffusioncoefficient. In contrast, our study examines directly the diffusionof a plasticizer within the PVC. The Kim et al.4identify promising plasticizers without the excess resourcesand time required to carry out experiments andto identify the important atomistic factors affectingthe properties of interest.After extensiveliterature search we considered four bio-basedplasticizers as potential alternatives to using DEHP in PVC: DBS,DHS, DEA, and 1,4-BDB. For all four systems and for pure PVC and DEHP,we carried out MD simulations to predict their mechanical and diffusionalproperties. There are two advantages to using atomistic simulations:Our simulations used a PVC backbone consisting of 16chains with20 monomers each. However commercial PVC has a variety of compositions.Discrepancies found between our predicted results and experimentalstudies may arise from this difference in frameworks. Future in silicoinvestigations could consider using single bi-populated chains ofPVC; since studies have shown that this minimizes the impact of chainend attractions. Our studies used 40 wt % plasticizers, which is atthe upper boundary of the amounts used commercially. This was chosento exaggerate the effects of the plasticizers for more obvious comparisons.The amount of experimental data to validate our predicted values waslimited, however where available we found good agreement. This workserves as a baseline for comprehensive atomistic investigations intoplasticizers. However, future experimental studies would be usefulto corroborate our findings.In order to quantify the effectof the polymer blends on mechanicalproperties, we predicted Young’s modulus, shear modulus, fractionalfree volume, and diffusion coefficients. The consensus from this seriesof simulations on bulk moduli, diffusion, and thermal stability isthat the plasticizer performance of DBS and DHS in PVC are both isat the same level or better than DEHP. Furthermore, both DBS and DHShave been shown to be nontoxic while DHS is biodegradable and canbe sourced from renewable feedstock. This leads us to conclude thatDHS and DBS are excellent candidates for replacing DEHP as a plasticizer.In practice, other knowledge of other properties is required to ensuresuitability for industrial production. Using the aptitude dibutylsebacate and diheptyl succinate demonstrated in this work, propertieslike viscosity, bulk moduli, thermal stability, and impact on colorcan be explored. In summary, this work sets the stage for future experimentationinto increasing the sustainability of commercial PVC plastics.


Muscle ultrasound is a fast, non-invasive and cost-effective examination that can identify structural muscular changes by assessing muscle thickness and echointensity (EI) with a quantitative analysis (QMUS). To assess applicability and repeatability of QMUS, we evaluated patients with genetically confirmed facioscapulohumeral muscular dystrophy type 1 (FSHD1), comparing their muscle ultrasound characteristics with healthy controls and with those detected by MRI. We also evaluated relationships between QMUS and demographic and clinical characteristics.pectoralis major, deltoid, rectus femoris, tibialis anterior and semimembranosus muscles in patients and healthy subjects. For each muscle, we acquired three images, which were analysed calculating muscle EI by computer-assisted grey-scale analysis. QMUS analysis was compared with semiquantitative 1.5 T muscle MRI scale.Thirteen patients were included in the study. Clinical assessment included MRC sum score, FSHD score and The Comprehensive Clinical Evaluation Form (CCEF). QMUS was performed with a linear transducer scanning bilaterally Tibialis anterior MRC showed a significant inverse correlation with EI. Higher median EI was found in muscles with more severe MRI fat replacement.All muscles in FSHD patients showed a significant increased echogenicity compared to the homologous muscles in healthy subjects. Older subjects and patients with higher FSHD score presented increased muscle EI. QMUS allows quantitative evaluation of muscle echogenicity, displaying a tight correlation with muscular alterations, clinical and MRI data. Although a confirmation on larger sample is needed, our research suggests a possible future application of QMUS in diagnosis and management of muscular disorders. Muscular imaging represents an important diagnostic tool for the identification and quantification of muscular alterations in patients affected by neuromuscular disorders (NMDs) . It alsoTo date, muscle MRI is the most used imaging technique in muscle disorders, thanks to its ability to detect fat infiltration and muscle edema and quantify tissue changes over time , 3. MoreMuscle ultrasound (US) is a fast, non-invasive, patient friendly and cost-effective examination, which can also be performed at bedside . It is aTo assess applicability and repeatability of QMUS, we compared the muscle US characteristics of a group of patients with genetically confirmed facioscapulohumeral muscular dystrophy type 1 (FSHD1) with those of a matching healthy control group. Muscle US findings of FSHD1 patients were also compared to their muscle MRI characteristics. In addition, we evaluated the correlation between QMUS and demographic and clinical characteristics.We conducted our study on FSHD1 patients as this is a common muscular dystrophy  characteWe included in this study 13 genetically confirmed FSHD type 1 (FSHD1) adult patients, aged 18 years or over, attending the outpatient service of the neuromuscular clinic at Sant’Andrea Hospital, and 8 healthy controls, recruited from our work environment, matched by sex, age and BMI. Obese subjects were excluded from the study due to excessive attenuation of the ultrasound beam caused by the great thickness of the subcutaneous fat layer. Each patient underwent a neurological examination and QMUS study, which were performed by two different evaluators blinded to each other and to MRI data. The study was conducted according to the principles of the Declaration of Helsinki (version October 2013) and in accordance with the Medical Research Involving Human Subjects Act (WMO). Our institutional ethics committee approved the study and all patients and healthy controls provided written informed consent.tibialis anterior, as its function is scarcely influenced by the activity of additional muscles, unlike the other muscles that were included in the study. The Comprehensive Clinical Evaluation Form (CCEF) proposed by the Italian Clinical Network for FSHD [Each patient underwent a neurological examination, comprehensive of muscle strength evaluation with the Medical Research Council (MRC) scale . For stafor FSHD  and the for FSHD  were usepectoralis major, rectus femoris, tibialis anterior and semimembranosus) or spared (deltoid) in FSHD1 and suited for US measurements. Each muscle was scanned in the transverse plane in a specific standard transducer location, which corresponded to the largest muscle diameter at the following sites: deltoid about three centimetres above the origin from the acromion process; pectoralis major at his humeral head just medially to the axilla; rectus femoris halfway along the line from the anterior–superior iliac spine to the superior aspect of the rotula; tibialis anterior at one-quarter of the distance from the inferior aspect of the rotula to the lateral malleolus; semimembranosus halfway between ischial tuberosity and popliteal fossa. For each muscle, three consecutive screen images were taken and analysed to reduce intra-observer variation in determination of EI. During examination, particular care was taken to keep the transducer in the same exact location and the same standardized position of the patient. Every screen image was then stored offline in DICOM format and analysed using Adobe Photoshop . In each stored image, we manually selected a region of interest (ROI) using the calliper function, to include as much muscle mass as possible, excluding the bone and possibly the muscular fascia, which have high EI. The standard histogram function was used to translate the median EI of the ROI into a numeric value between 0 (= black) and 255 (= white) [Muscle US was performed using a General Electric Voluson E6 imaging system  with a broadband linear transducer (frequency band 10–18 MHz), in the same date ± 1 day of clinical assessment and ± 1 month by MRI study by a trained investigator. All muscles were scanned keeping gain, focus, depth and compression constant, to ensure compatibility between each measurement. A generous amount of contact gel was used to reduce the required pressure of the transducer on the muscle. Patients were analysed in supine and prone position, with arms and legs extended and relaxed muscles. The transducer was placed perpendicularly to the muscle and oblique scanning was avoided by changing the angle of the transducer to achieve the best bone EI. QMUS measurements were performed bilaterally on five different muscles, selected because frequently affected (= white) . QuantitThe whole protocol required about 30 min to acquire the measurements and another 20 min for EI analysis.Muscle MRIs were obtained in all patients using a 1.5-T MRI following previously described protocols in accordance with the international consensus recommendations . PatientTwo independent neurologists with experience in the analysis of MRIs  blinded to clinical and US features analysed all MRIs. In case of discordance in MRI evaluation, examiners reviewed together the MRI to find an agreement on the final score.deltoid, pectoralis major, rectus femoris, tibialis anterior and semimembranosus on both sides) between patients and controls. Median EI in homologous muscles in patients and controls was compared with the Mann–Whitney test. Test–retest reliability was checked to evaluate intraclass correlation coefficient (ICC). Differences in the distribution of median EI in tibialis anterior of patients with respect to its strength as evaluated in 3 MRC subcategories  were evaluated with Kruskal–Wallis test.For the statistical analysis, we compared the US quantitative characteristics of each scanned muscle  and marked replacement (Mercuri 3–4).The difference in the distribution of median EI score between muscles with and without MRI changes (fat replacement and/or edema) was evaluated with Mann–Whitney SD: 3.47) and 8 healthy subjects , with median age of 49.5 years (IQR: 31.5–58.75) and mean BMI of 24.50 (SD: 2.77), were included in this study. Age difference between patients and controls ranged between 0 and 3 years . BMI difference ranged between 0 and 2.85 kg/mq . A total of 210 muscles were scanned both in patients (130 scanned muscles) and healthy controls (80 scanned muscles).Thirteen patients  with genetically confirmed FSHD1 with median age 51 years (IQR: 33–59) and mean body mass index (BMI) 24.50 .All muscles in FSHD patients showed a significant increase in EI compared to the homologous muscles in healthy subjects (ICC = 0.977 and ICC = 0.963 respectively). The muscle with the highest median EI was pectoralis major, followed by tibialis anterior, rectus femoris, deltoid and semimembranosus. Values of single muscle median EI in patients and controls are reported in Table Test/retest analysis, both in patients and controls, showed a high reliability in the three determinations (p < 0.001). There were no correlations between EI and genetic characteristic in terms of allele length (p = 0.27). Multiple regression analysis showed that, keeping all other independent variables constant, median EI in FSHD muscles increased overall of 3.41 points for every 10-year increase of age  and of 3.47 points for every point of increase in the FSHD score . Gender did not significantly influence the model.Both in patients and controls, EI was linearly correlated with age. EI was higher in female patients and in male healthy controls. EI was directly correlated with FSHD score  Out of the total number of 130 muscles scanned in FSHD patients with US, 111 were evaluable at MRI. Seventy-one (64%) muscles showed MRI changes, which consisted in fat replacement of grade 1 in 20 (18%), grade 2 in 13 (11.7%), grade 3 in 15 (13.5%) and grade 4 in 23 (20.7%) as per Mercuri grading scale.p < 0.001). EI was similar in muscles with and without STIR hyperintensity. Nineteen muscles (17.1%) showed STIR hypertintensity and all muscles with STIR hyperintensity also showed T1 hyperintensity (Table Median EI was significantly higher in muscles with MRI changes than in muscles without MRI changes (ty Table .Table 3Cp < 0.001) .Based on the grade of fat replacement, we considered three different MRI categories: no replacement, mild replacement (Mercuri 1–2) and marked replacement (Mercuri 3–4), and found a higher median EI in muscles with more severe fat replacement  and with disease severity, as evaluated by FSHD score, showing that QMUS can reflect muscle changes due to ageing, disease progression and disease severity. Accordingly, EI of measures , 31.deltoid muscle, which is generally spared in FSHD imaging. In our study, scanned muscles were chosen based on both the general knowledge of muscle involvement in FSHD [In our study, EI was increased in all FSHD muscles, while only 64% of them showed qualitative MRI changes (Mercuri 1–4). In particular, QMUS was able to identify muscle alterations also in the presence of mild or no MRI changes Fig. , demonst in FSHD  and the QMUS and MRI data in the single muscles showed that the most T1-replaced muscles were the most hyperechoic Fig. . HoweverAccordingly, QMUS and semiquantitative MRI should be considered complementary techniques with different advantages: MRI can better analyse different types of morphological alterations (e.g. edema vs. fat replacement), is able to evaluate deeper muscles and is not affected by the presence of a thick adipose panniculus; on the other hand, QMUS is a dynamic assessment of muscular echogenicity, which allows to study different muscles in their whole length within a reasonable time and lower costs; it has a good sensitivity for fat replacement, with an excellent reliability.Some limitations in our study deserve to be highlighted. The main ones are represented by the small size of our cohort and the heterogeneity of the sample in terms of disease progression and muscle impairment. Additional studies with wider samples are needed to determine the accuracy of QMUS and its sensitivity also in longitudinal studies. To date, in neuroimaging studies in NMDs, quantitative MRI methods are increasingly used to assess muscle alterations, as they are more objective and sensitive tools , 34. HowIn our study, we demonstrated that QMUS has a good sensitivity in detecting structural muscular alterations in FSHD, supporting its potential use as a screening and diagnostic tool in NMDs in general. Furthermore, its good repeatability confirms the potential applicability of this technique in follow-up studies and in the context of clinical trials as well , 28. In