Gene-drug Associations Research Articles

PHARMACOGENOMIC DETERMINANTS OF RESPONSE TO CARDIOVASCULAR DRUGS.

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Despite considerable advances in cardiovascular pharmacology, significant inter-individual variability in response to drugs affects both their efficacy and safety profile. Drug-gene associations have emerged as important factors determining a spectrum of response to therapy. Pharmacogenomic interactions in cardiovascular medicine are also involved in etiology of adverse effects that may be life-threatening, such as statin-induced myopathy or a hemorrhage/thrombosis event during anticoagulant therapy. Introduction of genetic tests prior to the initiation of therapy and implementation of genetically-guided therapy represent a step forward to achieving a goal of individualized medicine in cardiology, already present in recommendations for warfarin and clopidogrel. However, further investigations addressing genomic predictors of variability in response to drugs are still needed and translating these findings into routine clinical practice remains a substantial challenge.

Pharmacogenetic approaches in the treatment of alcohol use disorders: addressing clinical utility and implementation thresholds.

Despite advances in characterizing genetic influences on addiction liability and treatment response, clinical applications of these efforts have been slow to evolve. Although challenges to clinical translation remain, stakeholders already face decisions about evidentiary thresholds for the uptake of pharmacogenetic tests in practice. There is optimism about potential pharmacogenetic applications for the treatment of alcohol use disorders, with particular interest in the OPRM1 A118G polymorphism as a moderator of naltrexone response. Findings from human and animal studies suggest preliminary evidence for the clinical validity of this association; on this basis, arguments for clinical implementation can be made in accordance with existing frameworks for the uptake of genomic applications. However, generating evidence-based guidelines requires evaluating the clinical utility of pharmacogenetic tests. This goal will remain challenging, largely due to minimal data to inform clinical utility estimates. The pace of genomic discovery highlights the need for clinical utility and implementation research to inform future translation efforts. Near-term implementation of promising pharmacogenetic tests can help expedite this goal, generating an evidence base to enable efficient translation as additional gene-drug associations are discovered.

Data Science Approaches to Pharmacogenetics

Pharmacogenetic studies rely on applied statistics to evaluate genetic data describing natural variation in response to pharmacotherapeutics such as drugs and vaccines. In the beginning, these studies were based on candidate gene approaches that specifically focused on efficacy or adverse events correlated with variants of single genes. This hypothesis driven method required the researcher to have a priori knowledge of which genes or gene sets to investigate. According to rational design, the focus of these studies has been on drug metabolizing enzymes, drug transporters, and drug targets. As technology has progressed, these studies have transitioned to hypothesis-free explorations where markers across the entire genome can be measured in large scale, population based, genome-wide association studies (GWAS). This enables identification of novel genetic biomarkers, therapeutic targets, and analysis of gene-gene interactions, which may reveal molecular mechanisms of drug activities. Ultimately, the challenge is to utilize gene-drug associations to create dosing algorithms based individual genotypes, which will guide physicians and ensure they prescribe the correct dose of the correct drug the first time eliminating trial-and-error and adverse events. We review here basic concepts and applications of data science to the genetic analysis of pharmacologic outcomes.

Congress of Clinical Chemistry and Laboratory Medicine

• Objectives: To give an up-to-date overview of the research in pharmacogenetics of cardiovascular disease, and the clinical implications of this research. • Methods: In this lecture I will focus on these groups cardiovascular drugs where many pharmacogenetics studies have been performed (including statins, coumarins, clopidogrel). • Results: Many studies have been performed in this area of research. These pharmacogenetic studies have been dealing with many methodological issues, such as power issues, issues with exposure and outcome definition etcetera. This is also at least part of the reason that many gene-drug associations that were initially reported were not replicated. For some gene-drug interactions trials have been performed to study the clinical utility of implementing genetic testing. • Conclusion: Even though many studies have been performed for many different drugs currently there are hardly any clinical implications in cardiovascular pharmacogenetics.

Inconsistency in large pharmacogenomic studies

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.

Clinical implementation of germ line cancer pharmacogenetic variants during the next-generation sequencing era.

More than 100 medications approved by the US Food and Drug Administration include pharmacogenetic biomarkers in the drug label, many with cancer indications referencing germ line DNA variations. With the advent of next-generation sequencing (NGS) and its rapidly increasing uptake into cancer research and clinical practice, an enormous amount of data to inform documented gene-drug associations will be collected that must be exploited to optimize patient benefit. This review focuses on the implementation of germ line cancer pharmacogenetics in clinical practice. Specifically, it discusses the importance of germ line variation in cancer and the role of NGS in pharmacogenetic discovery and implementation. In the context of a scenario in which massive amounts of NGS-based genetic information will be increasingly available to health stakeholders, this review explores the ongoing debate regarding the threshold of evidence necessary for implementation, provides an overview of recommendations in cancer by professional organizations and regulatory bodies, and discusses limitations of current guidelines and strategies to improve third-party coverage.

Clinically relevant cancer biomarkers and pharmacogenetic assays

The number of pharmacogenetic assays available is continuously expanding as more molecularly targeted anticancer drugs are under clinical development. While the literature regarding drug-gene associations and therapeutic implications is often robust, reviews regarding clinical assay availability and profiling methodologies of commonly used cancer biomarkers are often lacking. To concisely identify and describe cancer biomarkers and their respective pharmacogenetic assays currently available in clinical practice. Analysis of germ-line DNA mutations can often help to predict pharmacokinetic and pharmacodynamic responses, whereas somatic DNA mutations are particularly useful in predicting tumor response. Molecular profiling and pre-emptive identification of cancer biomarkers can help to predict disease prognosis as well as response to anticancer therapy. Dozens of pharmacogenetic assays, utilizing several common methodologies, are currently available in clinical practice. It is essential for clinicians to understand the molecular pathways for anticancer drugs, the therapeutic implications of mutations within these pathways, the clinical assay(s) available to test for pharmacogenetic differences, and the common profiling methodology employed. As research continues to unveil more drug-gene and disease-gene associations, it is critical that clinicians understand which pharmacogenetic assays are available to identify inter-individual differences that predict safety and efficacy of anticancer drugs as we move toward the concept of personalized medicine.

Pharmacogenomics Knowledge for Personalized Medicine

The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene-drug associations and genotype-phenotype relationships. Curators assign levels of evidence to variant-drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine-implementation projects.

DTome: a web-based tool for drug-target interactome construction

BackgroundUnderstanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process.ResultsWe designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome.ConclusionsAs demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions.

A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions

MotivationGenetic factors determine differences in pharmacokinetics, drug efficacy, and drug responses between individuals and sub-populations. Wrong dosages of drugs can lead to severe adverse drug reactions in individuals whose drug metabolism drastically differs from the “assumed average”. Databases such as PharmGKB are excellent sources of pharmacogenetic information on enzymes, genetic variants, and drug response affected by changes in enzymatic activity. Here, we seek to aid researchers, database curators, and clinicians in their search for relevant information by automatically extracting these data from literature. ApproachWe automatically populate a repository of information on genetic variants, relations to drugs, occurrence in sub-populations, and associations with disease. We mine textual data from PubMed abstracts to discover such genotype–phenotype associations, focusing on SNPs that can be associated with variations in drug response. The overall repository covers relations found between genes, variants, alleles, drugs, diseases, adverse drug reactions, populations, and allele frequencies. We cross-reference these data to EntrezGene, PharmGKB, PubChem, and others. ResultsThe performance regarding entity recognition and relation extraction yields a precision of 90–92% for the major entity types (gene, drug, disease), and 76–84% for relations involving these types. Comparison of our repository to PharmGKB reveals a coverage of 93% of gene–drug associations in PharmGKB and 97% of the gene–variant mappings based on 180,000 PubMed abstracts. Availabilityhttp://bioai4core.fulton.asu.edu/snpshot.

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework--Similarity-based Inference of drug-TARgets (SITAR)--for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline.com/cmb.

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy.

A rational approach to personalized anticancer therapy: chemoinformatic analysis reveals mechanistic gene-drug associations.

To predict the response of cells to chemotherapeutic agents based on gene expression profiles, we performed a chemoinformatic study of a set of standard anticancer agents assayed for activity against a panel of 60 human tumor-derived cell lines from the Developmental Therapeutics Program (DTP) at the National Cancer Institute (NCI). Mechanistically-relevant gene:drug activity associations were identified in the scientific literature. The correlations between expression levels of drug target genes and the activity of the drugs against the NCI's 60 cell line panel were calculated across and within each tumor tissue type, using published drug activity and gene expression data. Compared to other mechanistically-relevant gene-drug associations, that of triciribine phosphate (TCN-P) and adenosine kinase (ADK) was exceptionally strong--overall and within tumor tissue types-across the 60 cell lines profiled for chemosensitivity (1) and gene expression (2). The results suggest ADK expression may be useful for stratifying TCN-P-responsive vs. non-responsive tumors. Based on TCN-P's mechanism of action and the observed TCN-P:ADK association, we contend that catalytic drug activation provides a rational, mechanistic basis for personalizing cancer treatment based on tumor-specific differences in the expression of drug-activating enzymes.