Teaching computers to read the pharmacogenomics literature ... so you don't have to

Development of the pharmacogenomics and genomics literacy framework for pharmacists

BACKGROUND

Pharmacists play a unique role in integrating genomic medicine and pharmacogenomics into the clinical practice and to translate pharmacogenomics from bench to bedside. However, the literature suggests that the knowledge gap in pharmacogenomics is a major challenge; therefore, developing pharmacists' skills and literacy to achieve this anticipated role is highly important. We aim to conceptualize a personalized literacy framework for the adoption of genomic medicine and pharmacogenomics by pharmacists in the United Arab Emirates with possible regional and global relevance.

RESULTS

A qualitative approach using focus groups was used to design and to guide the development of a pharmacogenomics literacy framework. The Health Literacy Skills framework was used as a guide to conceptualize the pharmacogenomics literacy for pharmacists. The framework included six major components with specific suggested factors to improve pharmacists' pharmacogenomics literacy. Major components include individual inputs, demand, skills, knowledge, attitude and sociocultural factors.

CONCLUSION

This framework confirms a holistic bottom-up approach toward the implementation of pharmacogenomics. Personalized medicine entails personalized efforts and frameworks. Similar framework can be created for other healthcare providers, patients and stakeholders.

A mutation-centric approach to identifying pharmacogenomic relations in text

OBJECTIVES

To explore the notion of mutation-centric pharmacogenomic relation extraction and to evaluate our approach against reference pharmacogenomic relations.

METHODS

From a corpus of MEDLINE abstracts relevant to genetic variation, we identify co-occurrences between drug mentions extracted using MetaMap and RxNorm, and genetic variants extracted by EMU. The recall of our approach is evaluated against reference relations curated manually in PharmGKB. We also reviewed a random sample of 180 relations in order to evaluate its precision.

RESULTS

One crucial aspect of our strategy is the use of biological knowledge for identifying specific genetic variants in text, not simply gene mentions. On the 104 reference abstracts from PharmGKB, the recall of our mutation-centric approach is 33-46%. Applied to 282,000 abstracts from MEDLINE, our approach identifies pharmacogenomic relations in 4534 abstracts, with a precision of 65%.

CONCLUSIONS

Compared to a relation-centric approach, our mutation-centric approach shows similar recall, but slightly lower precision. We show that both approaches have limited overlap in their results, but are complementary and can be used in combination. Rather than a solution for the automatic curation of pharmacogenomic knowledge, we see these high-throughput approaches as tools to assist biocurators in the identification of pharmacogenomic relations of interest from the published literature. This investigation also identified three challenging aspects of the extraction of pharmacogenomic relations, namely processing full-text articles, sequence validation of DNA variants and resolution of genetic variants to reference databases, such as dbSNP.

Recent progress in automatically extracting information from the pharmacogenomic literature

The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals. In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature. By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports. We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies. Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature. These methods enable extraction of specific types of information and can also provide answers to general, systemic queries. In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications.

Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications

Providing prior knowledge about biological properties of chemicals, such as kinetic values, protein targets, or toxic effects, can facilitate many aspects of drug development. Chemical information is rapidly accumulating in all sorts of free text documents like patents, industry reports, or scientific articles, which has motivated the development of specifically tailored text mining applications. Despite the potential gains, chemical text mining still faces significant challenges. One of the most salient is the recognition of chemical entities mentioned in text. To help practitioners contribute to this area, a good portion of this review is devoted to this issue, and presents the basic concepts and principles underlying the main strategies. The technical details are introduced and accompanied by relevant bibliographic references. Other tasks discussed are retrieving relevant articles, identifying relationships between chemicals and other entities, or determining the chemical structures of chemicals mentioned in text. This review also introduces a number of published applications that can be used to build pipelines in topics like drug side effects, toxicity, and protein-disease-compound network analysis. We conclude the review with an outlook on how we expect the field to evolve, discussing its possibilities and its current limitations.

Databases in the area of pharmacogenetics

In the area of pharmacogenetics and personalized health care it is obvious that databases, providing important information of the occurrence and consequences of variant genes encoding drug metabolizing enzymes, drug transporters, drug targets, and other proteins of importance for drug response or toxicity, are of critical value for scientists, physicians, and industry. The primary outcome of the pharmacogenomic field is the identification of biomarkers that can predict drug toxicity and drug response, thereby individualizing and improving drug treatment of patients. The drug in question and the polymorphic gene exerting the impact are the main issues to be searched for in the databases. Here, we review the databases that provide useful information in this respect, of benefit for the development of the pharmacogenomic field.