Translational medicine: the discovery of bridging biomarkers using pharmacogenomics

Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources

BACKGROUND

Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically drug-indication relations, are a prime candidate for representation in ontologies. There is a wealth of available drug-indication information, but structuring and integrating it is challenging.

RESULTS

We created a drug-indication database (DID) of data from 12 openly available, commercially available, and proprietary information sources, integrated by terminological normalization to UMLS and other authorities. Across sources, there are 29,964 unique raw drug/chemical names, 10,938 unique raw indication "target" terms, and 192,008 unique raw drug-indication pairs. Drug/chemical name normalization to CAS numbers or UMLS concepts reduced the unique name count to 91 or 85% of the raw count, respectively, 84% if combined. Indication "target" normalization to UMLS "phenotypic-type" concepts reduced the unique term count to 57% of the raw count. The 12 sources of raw data varied widely in coverage (numbers of unique drug/chemical and indication concepts and relations) generally consistent with the idiosyncrasies of each source, but had strikingly little overlap, suggesting that we successfully achieved source/raw data diversity.

CONCLUSIONS

The DID is a database of structured drug-indication relations intended to facilitate building practical, comprehensive, integrated drug ontologies. The DID itself is not an ontology, but could be converted to one more easily than the contributing raw data. Our methodology could be adapted to the creation of other structured drug-disease databases such as for contraindications, precautions, warnings, and side effects.

Translational researches require effective protocols for knowledge and technology transfer and integration

Integration of several disciplines (nonclinical, preclinical and clinical researches) during drug discovery and development through learning and confirmation process needs a dynamic process; "translational medicine" (TM) to give a holistic understanding of the entire process. To achieve the highest impacts, however, effective standard protocols need to be performed.

Genetic polymorphism of metabolic enzymes P450 (CYP) as a susceptibility factor for drug response, toxicity, and cancer risk

The polymorphic P450 (CYP) enzyme superfamily is the most important system involved in the biotransformation of many endogenous and exogenous substances including drugs, toxins, and carcinogens. Genotyping for CYP polymorphisms provides important genetic information that help to understand the effects of xenobiotics on human body. For drug metabolism, the most important polymorphisms are those of the genes coding for CYP2C9, CYP2C19, CYP2D6, and CYP3A4/5, which can result in therapeutic failure or severe adverse reactions. Genes coding for CYP1A1, CYP1A2, CYP1B1, and CYP2E1 are among the most responsible for the biotransformation of chemicals, especially for the metabolic activation of pre-carcinogens. There is evidence of association between gene polymorphism and cancer susceptibility. Pathways of carcinogen metabolism are complex, and are mediated by activities of multiple genes, while single genes have a limited impact on cancer risk. Multigenic approach in addition to environmental determinants in large sample studies is crucial for a reliable evaluation of any moderate gene effect. This article brings a review of current knowledge on the relations between the polymorphisms of some CYPs and drug activity/toxicity and cancer risk.

Translational science: past, present, and future

The concept of translational science is at least 15 years old. However, in its most recent incarnation, it represents the identification of a funding category designed to encourage academic participation in a critical stage of the drug discovery and product development process. It is hoped that this will make the process both shorter and more efficient. In this review, the author first considers the historical development of the pharmaceutical R&D process. The place of translational science in the process, the scientific techniques involved, and aspects of the business environment necessary for its success are then considered. Translational science does not displace preclinical development. Both concepts are relevant to the paramount importance of successfully and expeditiously bridging the gap between preclinical science and clinical testing, “from bench to bedside.” Translational science is particularly likely to stimulate biomarker research in the universities and related business community and will probably give a modest boost to early clinical testing and commercialization of discoveries within the academic setting. Whether there will be a consequent improvement in the quality and efficiency of the overall process remains to be seen.