Ontologies in medicinal chemistry: current status and future challenges

[Abstract] Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.Instituto de Salud Carlos III; FIS-PI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/21

A 2013 workshop: vaccine and drug ontology studies (VDOS 2013)

The 2013 “Vaccine and Drug Ontology Studies” (VDOS 2013) international workshop series focuses on vaccine- and drug-related ontology modeling and applications. Drugs and vaccines have contributed to dramatic improvements in public health worldwide. Over the last decade, tremendous efforts have been made in the biomedical ontology community to ontologically represent various areas associated with vaccines and drugs – extending existing clinical terminology systems such as SNOMED, RxNorm, NDF-RT, and MedDRA, as well as developing new models such as Vaccine Ontology. The VDOS workshop series provides a platform for discussing innovative solutions as well as the challenges in the development and applications of biomedical ontologies for representing and analyzing drugs and vaccines, their administration, host immune responses, adverse events, and other related topics. The six full-length papers included in this thematic issue focuses on three main areas: (i) ontology development and representation, (ii) ontology mapping, maintaining and auditing, and (iii) ontology applications

Medication concepts, records, and lists in electronic medical record systems

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references.A well-designed implementation of medication concepts, records, and lists in an electronic medical record (EMR) system allows it to successfully perform many functions vital for the provision of quality health care. A controlled medication terminology provides the foundation for decision support services, such as duplication checking, allergy checking, and drug-drug interaction alerts. Clever modeling of medication records makes it easy to provide a history of any medication the patient is on and to generate the patient's medication list for any arbitrary point in time. Medication lists that distinguish between description and prescription and that are exportable in a standard format can play an essential role in medication reconciliation and contribute to the reduction of medication errors. At present, there is no general agreement on how to best implement medication concepts, records, and lists. The underlying implementation in an EMR often reflects the needs, culture, and history of both the developers and the local users. survey of a sample of medication terminologies (COSTAR Directory, the MDD, NDDF Plus, and RxNorm) and EMR implementations of medication records (OnCall, LMR, and the Benedum EMR) reveals the advantages and disadvantages of each. There is no medication system that would fit perfectly in every single context, but some features should strongly be considered in the development of any new system.(cont.) A survey of a sample of medication terminologies (COSTAR Directory, the MDD, NDDF Plus, and RxNorm) and EMR implementations of medication records (OnCall, LMR, and the Benedum EMR) reveals the advantages and disadvantages of each. There is no medication system that would fit perfectly in every single context, but some features should strongly be considered in the development of any new system.by Jaime Chang.S.M

Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

Ontology-based collection, representation and analysis of drug-associated neuropathy adverse events

Abstract Background Neuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-saving chemotherapy treatment. Results To support data representation and analysis of drug-associated neuropathy adverse events (AEs), we developed the Ontology of Drug Neuropathy Adverse Events (ODNAE). ODNAE extends the Ontology of Adverse Events (OAE). Our combinatorial approach identified 215 US FDA-licensed small molecule drugs that induce signs and symptoms of various types of neuropathy. ODNAE imports related drugs from the Drug Ontology (DrON) with their chemical ingredients defined in ChEBI. ODNAE includes 139 drug mechanisms of action from NDF-RT and 186 biological processes represented in the Gene Ontology (GO). In total ODNAE contains 1579 terms. Our analysis of the ODNAE knowledge base shows neuropathy-inducing drugs classified under specific molecular entity groups, especially carbon, pnictogen, chalcogen, and heterocyclic compounds. The carbon drug group includes 127 organic chemical drugs. Thirty nine receptor agonist and antagonist terms were identified, including 4 pairs (31 drugs) of agonists and antagonists that share targets (e.g., adrenergic receptor, dopamine, serotonin, and sex hormone receptor). Many drugs regulate neurological system processes (e.g., negative regulation of dopamine or serotonin uptake). SPARQL scripts were used to query the ODNAE ontology knowledge base. Conclusions ODNAE is an effective platform for building a drug-induced neuropathy knowledge base and for analyzing the underlying mechanisms of drug-induced neuropathy. The ODNAE-based methods used in this study can also be extended to the representation and study of other categories of adverse events.http://deepblue.lib.umich.edu/bitstream/2027.42/134596/1/13326_2016_Article_69.pd