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The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation effects drug response. The PharmGKB web site, www.pharmgkb.org, displays genotype, molecular, and clinical primary data integrated with literature, pathway representations, protocol information, and links to additional external resources. Users can search and browse the knowledge base by genes, drugs, diseases, and pathways. Registration is free to the entire research community but subject to an agreement to respect the rights and privacy of the individuals whose information is contained within the database. Registered users can access and download primary data to aid in the design of future pharmacogenetics and pharmacogenomics studies.

Category: Genotype PharmGKB Submission Numbers: PS113737, PS115406, PS114503, PS200420, PS201057, PS115245 Date of Submission: May 19, 2003 Project: Pharmacogenetics of Anticancer Agents, St. Jude Children's Research Hospital HGNC Symbol

Category: Genotype PharmGKB Submission Number: PS203001 Date of Submission: June 16, 2003 Project: Pharmacogenetics of Membrane Transporters HGNC Symbol: SLC22A1 HGNC Name: solute carrier family 22 (organic cation transporter), member 1

The development of high throughput techniques and large-scale studies in the biological sciences has given rise to an explosive growth in both the volume and types of data available to researchers. A surveillance system that monitors data repositories and reports changes helps manage the data overload. We developed a dbSNP surveillance system (URL: http://www.pharmgkb.org/do/serve?id=tools.surveillance.dbsnp) that performs surveillance on the dbSNP database and alerts users to new information. The system is notable because it is personalized and fully automated. Each registered user has a list of genes to follow and receives notification of new entries concerning these genes. The system integrates data from dbSNP, LocusLink, PharmGKB, and Genbank to position SNPs on reference sequences and classify SNPs into categories such as synonymous and nonsynonymous SNPs. The system uses data warehousing, object model-based data integration, object-oriented programming, and a platform-neutral data access mechanism.

The Pharmacogenetics Research Network, which has the long-term goal of genotype-phenotype correlation related to pharmacotherapy, mandates timely electronic publication of results by participating research groups through submission to PharmGKB, the consortium's repository database. Because informatics expertise across groups varies, many groups need help in managing their own data and in generating electronic submissions. To assist these operations, we perform a needs assessment to determine an optimum database implementation strategy, which varies from standalone microcomputer database application to Web-based solutions, depending on the group and problem scope. Solution implementation is coupled with transfer of expertise through hands-on training, so as to reduce the groups' long-term dependence on us. Where multiple groups face common problems, such as managing genotyping data or clinical study support, we have devised generic software that can be reused in its entirety by individual groups, or customized with modest effort.

With the recent publication of the human genome project there has been an explosion of data available for pharmacogenetic research. Web-based databases containing information on single nucleotide polymorphisms (SNPs) are readily accessible to researchers, but there has been little comment on their utility. We used seven major international databases to identify SNPs in 74 genes involved in drug pathways. Very little overlap was seen among the databases, with only eight out of a putative 893 SNPs ( approximately 1%) common to the most commonly used databases. Problems with false positives, secondary to a high degree of homology in gene families, were also observed. These studies suggest researchers limiting their studies to one database would miss a great deal of information. Effort to update compilation databases, such as HGVbase, GeneSNP, PharmGKB, and HOWDY, and the aggressive removal of false positives from all databases is required if these resources are to facilitate the intended growth in pharmacogenetics research.

Background: Missense pharmacogenomic (PGx) variants refer to amino acid substitutions that potentially affect the pharmacokinetic (PK) or pharmacodynamic (PD) response to drug therapies. The PGx variants, as compared to disease-associated variants, have not been investigated as deeply. The ability to computationally predict future PGx variants is desirable; however, it is not clear what data sets should be used or what features are beneficial to this end. Hence we carried out a comparative characterization of PGx variants with annotated neutral and disease variants from UniProt, to test the predictive power of sequence conservation and structural information in discriminating these three groups. Results: 126 PGx variants of high quality from PharmGKB were selected and two data sets were created: one set contained 416 variants with structural and sequence information, and, the other set contained 1,265 variants with sequence information only. In terms of sequence conservation, PGx variants are more conserved than neutral variants and much less conserved than disease variants. A weighted random forest was used to strike a more balanced classification for PGx variants. Generally structural features are helpful in discriminating PGx variant from the other two groups, but still classification of PGx from neutral polymorphisms is much less effective than between disease and neutral variants. Conclusions: We found that PGx variants are much more similar to neutral variants than to disease variants in the feature space consisting of residue conservation, neighboring residue conservation, number of neighbors, and protein solvent accessibility. Such similarity poses great difficulty in the classification of PGx variants and polymorphisms.

Donald Berry in his recent editorial (1) regarding the role of CYP2D6 and tamoxifen highlights several controversies that exist in the clinical and biomarker field for women with breast cancer. The International Tamoxifen Pharmacogenomics Consortium (ITPC) was formed with the intent to aggregate, curate, and analyze the data available in published breast cancer studies with the hope of answering the question, “Should CYP2D6 genotyping guide the use of tamoxifen in breast cancer?” The ITPC analyzed nearly 5000 patients regarding the association between CYP2D6 and clinical outcomes (2). These data demonstrated that CYP2D6 genotype was associated with the risk of recurrence or death in those patients who received tamoxifen monotherapy for 5 years but not in patients who received different doses or duration of tamoxifen or those who received chemotherapy along with tamoxifen. We are in agreement with Berry that further research is required to understand these complex relationships, and our intent has always been that others will be able to use the ITPC dataset to understand the data as fully as possible in conjunction with their own research efforts. The complete dataset of genotypes and clinical variables, the analysis code, and the full analyses are transparently available at PharmGKB (http://www.pharmgkb.org). Although we disagree with Berry about his conclusion in answering the all-important question of whether CYP2D6-guided genotype therapy is appropriate, we invite other researchers and practitioners to examine the data from their own perspectives and engage in ongoing efforts to use these data to the benefit of patients with breast cancer.

Research directed toward discovering how genetic factors influence a patient’s response to drugs requires coordination of data produced from laboratory experiments, computational methods, and clinical studies. A public repository of pharmacogenetic data should accelerate progress in the field of pharmacogenetics by organizing and disseminating public datasets. We are developing a pharmacogenetics knowledge base (PharmGKB) to support the storage and retrieval of both experimental data and conceptual knowledge. PharmGKB is an Internetbased resource that integrates complex biological, pharmacological, and clinical data in such a way that researchers can submit their data and users can retrieve information to investigate genotype–phenotype correlations. Successful management of the names, meaning, and organization of concepts used within the system is crucial. We have selected a frame-based knowledge-representation system for development of an ontology of concepts and relationships that represent the domain and that permit storage of experimental data. Preliminary experience shows that the ontology we have developed for gene-sequence data allows us to accept, store, and query data submissions.

Background: For any biorepository, it is necessary to develop measures that determine sample quality and ensure each sample can be correctly identified. One method of devising this type of quality control measure is to rely on DNA polymorphism panels developed for forensic applications. Although valid for identification, these panels are not useful for other purposes, such as medical research. Aims: We developed an identification panel for the Personalized Medicine Research Project (PMRP) that uses medically relevant polymorphisms. This panel not only uniquely identifies samples and tests for sample quality but can also be used by investigators for candidate gene studies. Methods: Polymorphism candidates were taken from investigator requests, PharmGKB (www.pharmgkb.org) and the disease association database (http://geneticassociationdb.nih.gov/). To be considered, a polymorphism had to be associated with disease in two independent populations and it had to have a reported minor allele frequency in a Caucasian population of at least 0.20. The final assay was developed on the Sequenom platform and all individuals within the PMRP cohort were analyzed. Probability of identity for the panel was calculated and each polymorphism was tested for Hardy Weinberg equilibrium. The allele frequencies found in our population of 20,000 were compared to reported allele frequencies. The allele frequencies in the PMRP population were compared between males and females and between different age categories. Results: From a list of 116 potential polymorphisms we developed a single assay of 36 medically relevant somatic polymorphisms with at least one polymorphism on each chromosome and a single sex marker. The probability of identity for the assay was 6.132x10−15 and the sibling probability of identity was 3.077x10−8. Three polymorphisms differed from previously reported allele frequencies by more than 10%. In our population, four polymorphisms were different in males and females and four polymorphisms varied in allele frequencies between age groups. Furthermore, the chosen polymorphisms have been used in two studies and as preliminary data in several other studies. Conclusions: We were able to create a DNA panel of 36 medically relevant polymorphisms that successfully tested DNA quality, increased the knowledge of population wide allele frequencies, and is useful to investigators for candidate gene studies.

Category: genotype Project: Pharmacogenetics of Arrhythmia Therapy [Table 1][1] provides the HUGO Gene Nomenclature Committee (HGNC) symbol, PharmGKB submission URLs, submission dates, and release dates. [Table 2][2] provides the HGNC symbol, HGNC names, synonyms, GenBank accession number, and

Category: genotype Project: Pharmacogenetics, Biomarkers, and Antithrombotic Therapy [Table 1][1] provides the HUGO Gene Nomenclature Committee (HGNC) symbol, PharmGKB submission URL, submission date, and release date. [Table 2][2] provides the HGNC symbol, HGNC name, synonym, GenBank accession

Category: genotype Project: Pharmacogenetics of Membrane Transporters [Table 1][1] provides HUGO Gene Nomenclature Committee (HGNC) symbols, PharmGKB submission URLs, submission dates, and release dates. [Table 2][2] provides HGNC symbols, HGNC names, synonyms, GenBank accession numbers, and locus

The need for efficient text-mining tools that support curation of the biomedical literature is ever increasing. In this article, we describe an experiment aimed at verifying whether a text-mining tool capable of extracting meaningful relationships among domain entities can be successfully integrated into the curation workflow of a major biological database. We evaluate in particular (i) the usability of the system's interface, as perceived by users, and (ii) the correlation of the ranking of interactions, as provided by the text-mining system, with the choices of the curators.

Adverse drug reactions, side effects and the wide range of efficacy in patient responses to drugs have driven the need to identify the genetic variation that affects this. The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is a unique online resource that contains genotype and phenotype data derived from individual members of the public who have agreed to allow their deidentified medical information to be used by the scientific community for pharmacogenetics and pharmacogenomics research (Klein et al, 2001). PharmGKB aims to promote shared information, providing data and analytical tools that will drive focused studies, with the ultimate goal of defining genedrug-disease interactions. This knowledge will help to reduce drug toxicity and improve treatments, eventually impacting prescribing practice and leading to personalized medicine.

Graphical abstractDisplay Omitted Highlights? Clinical outcome is gaining more attention in pharmacogenomics (PGx) studies. ? ClinicalTrials.gov is rich in clinical outcome information for PGx studies. ? ClinicalTrials.gov provides more timely PGx data than the biomedical literature. ? We developed a method for identifying PGx information from ClinicalTrials.gov. ? This research has potentials in enriching the existing knowledge on PGx relations. Recent progress in high-throughput genomic technologies has shifted pharmacogenomic research from candidate gene pharmacogenetics to clinical pharmacogenomics (PGx). Many clinical related questions may be asked such as 'what drug should be prescribed for a patient with mutant alleles?' Typically, answers to such questions can be found in publications mentioning the relationships of the gene-drug-disease of interest. In this work, we hypothesize that ClinicalTrials.gov is a comparable source rich in PGx related information. In this regard, we developed a systematic approach to automatically identify PGx relationships between genes, drugs and diseases from trial records in ClinicalTrials.gov. In our evaluation, we found that our extracted relationships overlap significantly with the curated factual knowledge through the literature in a PGx database and that most relationships appear on average 5years earlier in clinical trials than in their corresponding publications, suggesting that clinical trials may be valuable for both validating known and capturing new PGx related information in a more timely manner. Furthermore, two human reviewers judged a portion of computer-generated relationships and found an overall accuracy of 74% for our text-mining approach. This work has practical implications in enriching our existing knowledge on PGx gene-drug-disease relationships as well as suggesting crosslinks between ClinicalTrials.gov and other PGx knowledge bases.

BackgroundKinase inhibition is an increasingly popular strategy for pharmacotherapy of human diseases. Although many of these agents have been described as “targeted therapy”, they will typically inhibit multiple kinases with varying potency. Pre-clinical model testing has not predicted the numerous significant toxicities identified during clinical development. The purpose of this study was to develop a bioinformatics-based method to predict specific adverse events (AEs) in humans associated with the inhibition of particular kinase targets (KTs).MethodsThe AE frequencies of protein kinase inhibitors (PKIs) were curated from three sources (PubMed, Thompson Physician Desk Reference and PharmGKB), and affinities of 38 PKIs for 317 kinases, representing >50% of the predicted human kinome, were collected from published in vitro assay results. A novel quantitative computational method was developed to predict associations between KTs and AEs that included a whole panel of 71 AEs and 20 PKIs targeting 266 distinct kinases with Kd

We disambiguate PharmGKB drug and disease associations by NDF-RT and SPL.Detailed clinical associations are clearly represented in PharmGKB.The work helps to understand drug and disease relations in details from PharmGKB.Reveals standardized drug information will accelerate clinical drug integration. PharmGKB is a leading resource of high quality pharmacogenomics data that provides information about how genetic variations modulate an individual's response to drugs. PharmGKB contains information about genetic variations, pharmacokinetic and pharmacodynamic pathways, and the effect of variations on drug-related phenotypes. These relationships are represented using very general terms, however, and the precise semantic relationships among drugs, and diseases are not often captured. In this paper we develop a protocol to detect and disambiguate general clinical associations between drugs and diseases using more precise annotation terms from other data sources. PharmGKB provides very detailed clinical associations between genetic variants and drug response, including genotype-specific drug dosing guidelines, and this procedure will armGKB. The availability of more detailed data will help investigators to conduct more precise queries, such as finding particular diseases caused or treated by a specific drug.We first mapped drugs extracted from PharmGKB drug-disease relationships to those in the National Drug File Reference Terminology (NDF-RT) and to Structured Product Labels (SPLs). Specifically, we retrieved drug and disease role relationships describing and defining concepts according to their relationships with other concepts from NDF-RT. We also used the NCBO (National Center for Biomedical Ontology) annotator to annotate disease terms from the free text extracted from five SPL sections (indication, contraindication, ADE, precaution, and warning). Finally, we used the detailed drug and disease relationship information from NDF-RT and the SPLs to annotate and disambiguate the more general PharmGKB drug and disease associations.

Background: Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response. Results: We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DRneutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly. Conclusions: The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.

Background Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant – drug – adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks “clinical counterpart” or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources. Results In this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins. Conclusions The proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources.

There is a growing number of evidence-based indications for pharmacogenetic (PGx) testing. We aimed to evaluate clinical relevance of a 16-gene panel test for PGx-guided pharmacotherapy. In an observational cohort study, we included subjects tested with a PGx panel for variants of ABCB1, COMT, CYP1A2, CYP2B6, CYP3A4, CYP3A5, CYP2C9, CYP2C19, CYP2D6, CYP4F2, DPYD, OPRM1, POR, SLCO1B1, TPMT and VKORC1. PGx-guided pharmacotherapy management was supported by the PGx expert system SONOGEN XP. The primary study outcome was PGx-based changes and recommendations regarding current and potential future medication. PGx-testing was triggered by specific drug–gene pairs in 102 subjects, and by screening in 33. Based on PharmGKB expert guidelines we identified at least one “actionable” variant in all 135 (100%) tested patients. Drugs that triggered PGx-testing were clopidogrel in 60, tamoxifen in 15, polypsychopharmacotherapy in 9, opioids in 7, and other in 11 patients. Among those, PGx variants resulted in clinical recommendations to change PGx-triggering drugs in 33 (32.4%), and other current pharmacotherapy in 23 (22.5%). Additional costs of panel vs. single gene tests are moderate, and the efficiency of PGx panel testing challenges traditional cost-benefit calculations for single drug–gene pairs. However, PGx-guided pharmacotherapy requires specialized expert consultations with interdisciplinary collaborations.

The early prediction of drug adverse effects is of great interest to pharmaceutical research, as toxicity is one of the leading reasons for drug attrition. Understanding the cell signaling and regulatory pathways affected by a drug candidate is crucial to the study of drug toxicity. In this study, we present a computational technique that employs the propagation of drug-protein interactions to connect compounds to biological pathways. Target profiles for drugs were built by retrieving drug target proteins from public repositories such as ChEMBL, DrugBank, IUPHAR, PharmGKB, and TTD. Subsequent enrichment test of the protein pool using Reactome revealed potential pathways affected by the drugs. Furthermore, an optional tissue filter utilizing the Human Protein Atlas was applied to identify tissue-specific pathways. The analysis pipeline was implemented in an open-source KNIME workflow called Path4Drug to allow automated data retrieval and reconstruction for any given drug present in ChEMBL. The pipeline was applied to withdrawn drugs and cardio- and hepatotoxic drugs with black box warnings to identify biochemical pathways they affect and to find pathways that can be potentially connected to the toxic events. To complement this approach, drugs used in cardiac therapy without any record of toxicity were also analyzed. The results provide already known associations as well as a large amount of additional potential connections. Consequently, our approach can link drugs to biological pathways by leveraging big data available in public resources. The developed tool is openly available and modifiable to support other systems biology analyses.

Background: One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Several large resources of curated relations between biomedical entities are currently available, such as the Pharmacogenomics Knowledge Base (PharmGKB) or the Comparative Toxicogenomics Database (CTD). Biomedical text mining systems, and in particular those which deal with the extraction of relationships among entities, could make better use of the wealth of already curated material. Results: We propose a simple and effective method based on logistic regression (also known as maximum entropy modeling) for an optimized ranking of relation candidates utilizing curated abstracts. Furthermore, we examine the effects and difficulties of using widely available metadata (i.e. MeSH terms and chemical substance index terms) for relation extraction. Cross-validation experiments result in an improvement of the ranking quality in terms of AUCiP/R by 39% (PharmGKB) and 116% (CTD) against a frequency-based baseline of 0.39 (PharmGKB) and 0.21 (CTD). For the TAP-10 metrics, we achieve an improvement of 53% (PharmGKB) and 134% (CTD) against the same baseline system (0.21 PharmGKB and 0.15 CTD). Conclusions: Our experiments with the PharmGKB and the CTD database show a strong positive effect for the ranking of relation candidates utilizing the vast amount of curated relations covered by currently available knowledge databases. The tasks of concept identification and candidate relation generation profit from the adaptation to previously curated material. This presents an effective and practical method suitable for conservative extension and re-validation of biomedical relations from texts that has been successfully used for curation experiments with the PharmGKB and CTD database.

BackgroundIn the past decade, RNA sequencing and mass spectrometry based quantitative approaches are being used commonly to identify the differentially expressed biomarkers in different biological conditions. Data generated from these approaches come in different sizes (e.g., count matrix, normalized list of differentially expressed biomarkers, etc.) and shapes (e.g., sequences, spectral data, etc.). The list of differentially expressed biomarkers is used for functional interpretation and retrieve biological meaning, however, it requires moderate computational skills. Thus, researchers with no programming expertise find difficulty in data interpretation. Several bioinformatics tools are available to analyze such data; however, they are less flexible for performing the multiple steps of visualization and functional interpretation.ImplementationWe developed an easy-to-use Shiny based web application (named as OMnalysis) that provides users with a single platform to analyze and visualize the differentially expressed data. The OMnalysis accepts the data in tabular form from edgeR, DESeq2, MaxQuant Perseus, R packages, and other similar software, which typically contains the list of differentially expressed genes or proteins, log of the fold change, log of the count per million, thePvalue,q-value, etc. The key features of the OMnalysis are multiple image type visualization and their dimension customization options, seven multiple hypothesis testing correction methods to get more significant gene ontology, network topology-based pathway analysis, and multiple databases support (KEGG, Reactome, PANTHER, biocarta, NCI-Nature Pathway Interaction Database PharmGKB and STRINGdb) for extensive pathway enrichment analysis. OMnalysis also fetches the literature information from PubMed to provide supportive evidence to the biomarkers identified in the analysis. In a nutshell, we present the OMnalysis as a well-organized user interface, supported by peer-reviewed R packages with updated databases for quick interpretation of the differential transcriptomics and proteomics data to biological meaning.AvailabilityThe OMnalysis codes are entirely written in R language and freely available athttps://github.com/Punit201016/OMnalysis. OMnalysis can also be accessed from -http://lbmi.uvlf.sk/omnalysis.html. OMnalysis is hosted on a Shiny server athttps://omnalysis.shinyapps.io/OMnalysis/. The minimum system requirements are: 4 gigabytes of RAM, i3 processor (or equivalent). It is compatible with any operating system (windows, Linux or Mac). The OMnalysis is heavily tested on Chrome web browsers; thus, Chrome is the preferred browser. OMnalysis works on Firefox and Safari.

In pursuing personalized medicine, pharmacogenomic (PGx) knowledge may help guide prescribing drugs based on a person’s genotype. Here we evaluate the feasibility of incorporating PGx knowledge, combined with clinical data, to support clinical decision-making by: 1) analyzing clinically relevant knowledge contained in PGx knowledge resources; 2) evaluating the feasibility of a rule-based framework to support formal representation of clinically relevant knowledge contained in PGx knowledge resources; and, 3) evaluating the ability of an electronic medical record/electronic health record (EMR/EHR) to provide computable forms of clinical data needed for PGx clinical decision support. Findings suggest that the PharmGKB is a good source for PGx knowledge to supplement information contained in FDA approved drug labels. Furthermore, we found that with supporting knowledge (e.g. IF age

The Clinical Pharmacogenomics Service (CPS) at Boston Children's Hospital (BCH) was established to use genomic information to make pediatric medications safer. Nearly one-quarter of outpatients are prescribed one or more drugs with genetic information in the FDA label [1]. However, there are still important barriers that must be overcome for routine pharmacogenomic (PGx) clinical use: (1) identification of clinically significant variants, (2) knowledge of variant genotype prior to prescribing medication, and (3) integration with current electronic health record (EHR) systems. To tackle these challenges at BCH, the CPS decided to standardize thiopurine S-methyltransferase (TPMT) testing hospital-wide. TPMT is best known for its role in the catalyzing the S-methylation of the thiopurine drugs such as azathioprine, 6-mercaptopurine and 6-thioguanine. Approximately 13% of Caucasians and African Americans are heterozygous and have reduced TPMT activity, while approximately 0.3% are completely deficient. Defects in the TPMT gene can lead to decreased methylation and excessive levels of the toxic thioguanine nucleotides, particularly with azathioprine and 6-mercaptopurine, and are at risk for bone marrow suppression. Although the FDA drug label recommends testing for TPMT deficiency prior to dosing and the PharmGKB CPIC group published a guideline [2] with a recommended dosing strategy and interpretation, testing is not universal because these guidelines are difficult to translate into a clinical decision support (CDS) system and integrate with the EHRs. We developed models and specifications to execute PGx CDS rules based on a patient's genotype. Rules are modeled at four levels of abstraction: (1) unstructured (narrative), (2) semi-structured, (3) structured, and (4) executable. As genomic sequencing becomes routine, standardized methods to interpret the data and make clinical decisions are paramount. In conjunction with the BCH DNA Diagnostic Laboratory, we streamlined the TPMT testing process to fit into the usual clinical routine (including ordering, testing in-house and return of results to the clinician). We consolidated all genetic sequencing testing into a single clinical workflow (blood to report) that is run, analyzed and interpreted in a Clinical Laboratory Improvement Amendments (CLIA) certified laboratory using the codified CDS rules. The interpretation reports are generated automatically directly from the genotype calls and then manually reviewed for accuracy. Once cleared by the laboratory director, the reports are uploaded into the EHR (Cerner). Specialty flow sheets enable providers to easily view the allele status and interpretation report. We intend to expand the PGx platform to include additional drug/gene pairs.