- Genome databases
- Open Access
e-PKGene: A knowledge-based research tool for analysing the impact of genetics on drug exposure
© Henry Stewart Publications 2011
- Received: 20 December 2010
- Accepted: 20 December 2010
- Published: 1 July 2011
e-PKGene (http://www.pharmacogeneticsinfo.org) is a manually curated knowledge product developed in the Department of Pharmaceutics at the University of Washington, USA. The tool integrates information from the literature, public repositories, reference textbooks, product prescribing labels and clinical review sections of new drug approval packages. The database's easy-to-use web portal offers tools for visualisation, reporting and filtering of information. The database helps scientists to mine pharmacokinetic and pharmacodynamic information for drug-metabolising enzymes and transporters, and provides access to available quantitative information on drug exposure contained in the literature. It allows in-depth analysis of the impact of genetic variants of enzymes and transporters on pharmacokinetic responses to drugs and metabolites. This review gives a brief description of the database organisation, its search functionalities and examples of use.
- cytochrome P450 enzymes
Differences in drug response among patients are common, often leading to challenges in optimising a dosage regimen for an individual patient. Genetic factors have long been known to cause interindividual differences in the pharmacokinetics, efficacy and adverse events of a number of drugs, and drug metabolising enzymes have been shown to be the greatest source of pharmacogenetic (PGx) variability identified to date. It is estimated that over half of the ~170 genes with products affecting drug disposition are polymorphic, and clinically important polymorphisms have been identified for most major enzymes involved in both phase I and phase II drug metabolism . More recently, the polymorphic variability of several transporter proteins, such as the hepatic uptake transporter, organic anion transporter polypeptide 1B1 (OATP1B1), has been shown to have an impact on the exposure to, and safety of, widely prescribed drugs . Thus, incorporating the knowledge gained from PGx research to make decisions in drug development and clinical care has the potential to increase the safety and efficacy of drug treatment, and is central to the strategies of personalised medicine . In spite of current efforts to incorporate the use of PGx information in drug development, clinical practice and in making cost-effective healthcare decisions, however, information uptake remains low. Translational research is required to move PGx discoveries effectively to evidence-based application in these areas. Translational research has been described as having four iterative phases with feedback loops, to allow integration of new knowledge . Phase 1 (T1) and Phase 2 (T2) translational research informs the development of clinical interventions and evidence-based guidelines; Phase 3 (T3) research assesses the implementation of guidelines in health practice; and Phase 4 (T4) research evaluates the health outcomes of changes in practice following the implementation of guidelines . All phases have become data intensive, with studies in PGx discovery increasing rapidly in both number and throughput. For example, in 2009 there were at least 12 PGx genome-wide association studies conducted . In a review of 100,000 PubMed-listed publications on pharmacogenomics, less than 2 per cent were identified as original research manuscripts, illustrating the difficulty that exists in locating reliable information for translational research pursuits.
e-PKGene (http://www.pharmacogeneticsinfo.org) is a manually curated knowledge-based product which facilitates easy access to, and search for, quantitative information contained in the PGx literature base. It provides in-depth analysis of the impact of genetic variants of metabolising enzymes and transporters on pharmacokinetics. The tool's focal point is the identification of the genetic variants that are best correlated with drug exposure. e-PKGene is designed directly to support drug development or Phase 1 translational research (T1). This tool also has the capacity to support other phases of translational research, however, including Phase 2 (T2) evidence-based evaluations, which can better predict patient responses by providing clinical recommendations. Details about e-PKGene design and content, examples of use and types of support provided to various types of users are described in the following sections.
The application has a typical multi-tier architecture in a Microsoft®. NET environment. The web part of the database, which is accessed by the user over the internet, is hosted on a Microsoft Windows 2003 server running IIS and version 2.0 of the ASP.NET framework. All data are stored on a Microsoft SQL Server 2005 database. The use of the web facilitates worldwide access, as well as upgrades and updates. e-PKGene is being developed by scientists from the Department of Pharmaceutics, University of Washington, USA.
e-PKGene allows for a high level of development flexibility through incorporating structured data and standardised representations of PGx knowledge with use of controlled terminologies. The tool uses hierarchical categorisation to characterise data sources and evidence.
e-PKGene integrates information from the literature, public repositories, reference textbooks, product prescribing labels and clinical review sections of new drug approval packages. The current pilot version focuses on drug metabolising enzymes and transporters that are routinely assessed in the context of drug development, and which are of interest to clinical practice and healthcare economics. The core content of e-PKGene is represented by published pharmacokinetics studies, performed in human subjects (healthy volunteers or patients). Each research article (citation) may contain one or more pharmacokinetics studies in which one or more genes have been investigated. A study is defined as a set of assessments (pharmacokinetics, pharmacodynamics and safety) following the administration of a target compound to a well-defined population. The population is usually divided into a 'reference' group and an 'impaired' group. The reference group (the one to which the other groups are compared) consists either of individuals who are 'extensive metabolisers' or carriers of two copies of the wild-type allele of the gene of interest.
Available datasets in e-PKGene
Curated facts from publications
Natural language descriptions/comments
Quantitative data (eg AUC, Cmax, half-life, Tmax)
Population details (eg ethnicity, gender)
External links to PubMed
Design and methods details
Design and drug administration
Alleles and genes tested
Pharmacology and interactions
Metabolising enzymes and transporters involved
Classification (therapeutic uses, therapeutic range)
PubChem record, CAS number
Gene (common name, synonyms)
Genetic variations (DNA sequence variations [SNP], rs numbers)
AUC, area under the curve; Cmax, maximum (peak) observed drug concentration; Tmax, time to reach maximum concentration following drug administration; CAS, Chemical Abstracts Service
Where PK variant = PK parameter of the variant group, PK ref = PK parameter of the reference group (control).
Gene and allelic variants
Examples of queries and output
The citation listing contains complete reference information, as well as comments entered by the database editorial team when applicable. The full abstract from PubMed can be retrieved by clicking on the PubMed icon (circled in red). The initial 'effects' window shows summarised information for all studies contained within each citation (single citations often involve multiple studies if different doses or populations, or multiple enzyme systems are evaluated separately). The database 'impact assignment' is shown for each population examined.
Allowing searches by compound, gene or population enables the user quickly to focus on the desired parameters. Pharmaceutical researchers may find the information useful in designing studies on human subjects by highlighting populations (ethnic and genotypic) that will require more in-depth examination. Similarly, clinicians may find this platform valuable for identifying drugs that may require dosing adjustment in subjects with a known ethnic background, genotype or phenotype.
Domains of use
In spite of the wealth of information relating variants of metabolising enzymes and transporters to pharmacokinetic parameters, it is rare for a genotype to be a critical determinant in selecting a drug dose . While there are several instances of PGx and personalised medical applications, few have undergone the rigorous evaluations required for regulatory approval . Even in cases where there is adequate knowledge on genotype-drug response-phenotype correlations to appear in drug labels, PGx language is often for informational purposes only. This is because of what is described as the 'evidence dilemma', since there is often a lack of sufficient evidence to weigh the benefits and risks of population screening or routine use of PGx applications and decision making in clinical practice . The web-based application e-PKGene emphasises the quantitative analysis of PGx data related to pharmacokinetics, pharmacodynamics and the safety of drugs in various populations. It provides access to information that is important to consider when evaluating genetic factors affecting drug exposure, populations affected and potential clinical relevance. The tool has the capacity to support all phases of translational research, but is of direct use to Phase 1 (T1) research, which seeks to move PGx discoveries into candidate health applications. Additionally, the tool will provide a portal for better understanding the scientific steps involved in applying PGx information and integrating genomics into drug development programmes: for example, validating drug targets, choosing and validating lead compounds, identifying optimal early patient populations, determining surrogate endpoints (biomarkers) for the design of clinical trials and predicting likely variability in clinical trials. As the field of pharmacogenetics continues to expand and mature, the tool will shape into a large repository that will become invaluable in guiding both research and clinical practice decisions by providing primary literature findings necessary for establishing genotype-phenotype relationships.
- Lesko LJ, Woodcock J: 'Translation of pharmacogenomics and pharmacogenetics: A regulatory perspective'. Nat Rev Drug Discov. 2004, 3: 763-769. 10.1038/nrd1499.View ArticlePubMedGoogle Scholar
- Katz DA, Murray B, Bhathena A, Sahelijo L: 'Defining drug disposition determinants: A pharmacogenetic-pharmacokinetic strategy'. Nat Rev Drug Discov. 2008, 7: 293-305. 10.1038/nrd2486.View ArticlePubMedGoogle Scholar
- Wilkinson GR: 'Drug metabolism and variability among patients in drug response'. N Engl J Med. 2005, 352: 2211-2221. 10.1056/NEJMra032424.View ArticlePubMedGoogle Scholar
- Aoki M, Terada T, Ogasawara K, Katsura T, et al: Impact of regulatory polymorphisms in organic anion transporter genes in the human liver. Pharmacogenet Genomics. 2009, 19: 647-656. 10.1097/FPC.0b013e32832fabd3.View ArticlePubMedGoogle Scholar
- Woodcock J, Lesko LJ: 'Pharmacogenetics -- Tailoring treatment for the outliers'. N Engl J Med. 2009, 360: 811-813. 10.1056/NEJMe0810630.View ArticlePubMedGoogle Scholar
- Khoury MJ, Gwinn M, Yoon PW, Dowling N, et al: 'The continuum of translation research in genomic medicine: How can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention?'. Genet Med. 2007, 9: 665-674. 10.1097/GIM.0b013e31815699d0.View ArticlePubMedGoogle Scholar
- Daly AK: 'Genome-wide association studies in pharmacogenomics'. Nat Rev Genet. 2010, 11: 241-246. 10.1038/nrg2751.View ArticlePubMedGoogle Scholar
- Holmes MV, Shah T, Vickery C, Smeeth L, et al: Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies. PLoS One. 2009, 4: e7960-10.1371/journal.pone.0007960.PubMed CentralView ArticlePubMedGoogle Scholar
- Single Nucleotide Polymorphism Database (dbSNP): (Accessed 1st November 2010), [http://www.ncbi.nlm.nih.gov/projects/SNP/]
- Human Cytochrome P450 (CYP) Allele Nomenclature Committee: (Accessed 1st November 2010), [http://www.cypalleles.ki.se/]
- Bhathena A, Spear BB: 'Pharmacogenetics: Improving drug and dose selection'. Curr Opin Pharmacol. 2008, 8: 639-646. 10.1016/j.coph.2008.07.013.View ArticlePubMedGoogle Scholar
- Deverka PA: Pharmacogenomics, evidence, and the role of payers. Public Health Genomics. 2009, 3: 149-157.View ArticleGoogle Scholar
- US Food and Drug Administration: Table of valid genomic biomarkers in the context of approved drug labels, 09/21. 2010, US Food and Drug Administration, (Accessed 1st November 2010), [http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm]Google Scholar
- Khoury MJ: 'Dealing with the evidence dilemma in genomics and personalized medicine'. Clin Pharmacol Ther. 2010, 87: 635-638. 10.1038/clpt.2010.4.View ArticlePubMedGoogle Scholar