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Despite the clinical benefit of statin therapy and the numerous strategies used to improve adherence, no strategy has used direct communication of genetic test results to the patient as an adherence and persistence motivator. We investigated in a real-world setting the effect of a process of providing KIF6 test results and risk information directly to 647 tested patients on 6-month statin adherence (proportion of days covered (PDC)) and persistence compared with concurrent non-tested matched controls. Adjusted 6-month statin PDC was significantly greater in tested patients: 0.77 (95% confidence interval (CI) 0.72-0.82) vs controls 0.68 (95% CI 0.63-0.73), P<0.0001. Significantly more tested patients were adherent (PDC⩾0.80) (63.4% (59.6-67.1%) vs 45.0% (41.1-48.8%), P<0.0001) and persisted on therapy (69.1% (65.4-72.5%) vs 53.3% (49.4-57.1%), P<0.0001). Similar results were observed in a secondary comparison with 779 unmatched patients who declined testing. The Additional KIF6 Risk Offers Better Adherence to Statins trial provides the first evidence that pharmacogenetic testing may modify patient adherence.

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To develop a benchmark measure of US physicians' level of knowledge and extent of use of pharmacogenomic testing, we conducted an anonymous, cross-sectional, fax-based, national survey. Of 397,832 physicians receiving the survey questionnaire, 10,303 (3%) completed and returned it; the respondents were representative of the overall US physician population. The factors associated with the decision to test were evaluated using χ(2) and multivariate logistic regression. Overall, 97.6% of responding physicians agreed that genetic variations may influence drug response, but only 10.3% felt adequately informed about pharmacogenomic testing. Only 12.9% of physicians had ordered a test in the previous 6 months, and 26.4% anticipated ordering a test in the next 6 months. Early and future adopters of testing were more likely to have received training in pharmacogenomics, but only 29.0% of physicians overall had received any education in the field. Our findings highlight the need for more effective physician education on the clinical value, availability, and interpretation of pharmacogenomic tests.

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Biologics can be seen as "designer" drugs whose mode of action in a specific disease mechanism is frequently well understood, making it often possible to predict better efficacy and safety profiles for biologics when compared with small molecule drugs. Biologics have been approved for the treatment of major disease classes, such as inflammatory disease, cardiovascular disease, and cancer. However, as it is true for small molecule drugs, often only a fraction of the treated population responds to biologics, and clinical markers for prediction of efficacy are seldom available. It is reasonable to expect that the use of genetic or genomic markers will contribute to improving the prediction of safety and efficacy of both biologics and small molecule drugs. In this paper, we will review the differences between biologics and small molecule drugs, focusing on studies highlighting the relevance of genetic and genomic information on safety and efficacy issues in therapies with biologics. The potential impact of these studies on the promotion of personalized medicine and on regulatory decisions will also be discussed.

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Drug developers have been using genomic information in drug development strategies for a number of years, but it was unclear how this information would be reviewed by the Food and Drug Administration (FDA). In order to evaluate the regulatory impact of genomic data in current drug development, a workshop was held in May 2002 to discuss aspects surrounding genomic data submission to the FDA (Figure 1).

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This work describes the in situ synthesis of oligonucleotide arrays on glass surfaces. These arrays are composed of features defined and separated by differential surface tension (surface tension arrays). Specifically, photolithographic methods were used to create a series of spatially addressable, circular features containing an amino-terminated organosilane coupled to the glass through a siloxane linkage. Each feature is bounded by a perfluorosilanated surface. The differences in surface energies between the features and surrounding zones allow for chemical reactions to be readily localized within a defined site. The aminosilanation process was analyzed using contact angle, X-ray photoelectron spectroscopy (XPS), and time-of-flight/secondary ion mass spectroscopy (TOF-SIMS). The efficiency of phosphoramidite-based oligonucleotide synthesis on these surface tension arrays was measured by two methods. One method, termed step-yields-by-hybridization, indicates an average synthesis efficiency for all four (A,G,C,T) bases of 99.9 +/- 1.1%. Step yields measured for the individual amidite bases showed efficiencies of 98.8% (dT), 98.0% (dA), 97.0% (dC), and 97.6% (dG). The second method for determining the amidite coupling efficiencies was by capillary electrophoresis (CE) analysis. Homopolymers of dT (40- and 60mer), dA (40mer), and dC (40mer) were synthesized on an NH(4)OH labile linkage. After cleavage, the products were analyzed by CE. Synthesis efficiencies were calculated by comparison of the full-length product peak with the failure peaks. The calculated coupling efficiencies were 98.8% (dT), 96.8% (dA), and 96.7% (dC).

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One mechanism by which cells adapt to environmental changes is by altering gene expression. Here, we have used cDNA microarrays to identify genes whose expression is altered by exposure to the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). The goal of our study was to enhance our understanding of toxicity mediated through the pathway by which TCDD stimulates gene expression. To model this toxicity response, we exposed human hepatoma (HepG2) cells to TCDD (10 nM for 18 h) and analyzed mRNA by two-color fluorescent hybridization to cDNA sequences immobilized on glass microscope slides (2.5 x 7.5 cm) covering a surface area of 2.25 cm(2). We analyzed approximately one-third of the genes expressed in HepG2 cells and found that TCDD up- or down-regulates 112 genes two-fold or more. Most changes are relatively subtle (two- to four-fold). We verified the regulation of protooncogene cot, XMP, and human enhancer of filamentation-1 (HEF1), genes involved in cellular proliferation, as well as metallothionein, plasminogen activator inhibitor (PAI1), and HM74, genes involved in cellular signaling and regeneration. To characterize the response in more detail, we performed time-course, dose-dependence studies, and cycloheximide experiments. We observed direct and indirect responses to TCDD implying that adaptation to TCDD (and other related environmental stimuli) is substantially more complex than we previously realized.

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Phenobarbital elicits pleiotropic effects in the liver, including induction of enzymes involved in xenobiotic metabolism. The spectrum of this response was analyzed by differential display of a large population (approximately 7500) of mRNAs in chicken embryo liver treated in vivo with phenobarbital. We identified 29 cDNA fragments that reproducibly and significantly changed in intensity after a 48-hr in ovo treatment. Eighteen of these (62%) were increased, whereas 11 (38%) were decreased. Twenty strongly regulated cDNA fragments were subcloned and further analyzed. Nucleotide sequence analysis revealed three types of genes: (a) those previously described to be regulated by phenobarbital, including CYP2H1, glutathione S-transferase, and uridine diphosphate-glucuronosyltransferase; (b) genes reported herein for the first time to be regulated by phenobarbital, including fibrinogen beta-chain and gamma-chain, retinal glutamine synthetase, apolipoprotein B, two gene products with homologies to elongation factor 1delta and complement factor H, respectively, and (c) several novel genes with hitherto unknown functions. If these data are extrapolated to the entire population of mRNAs of a liver cell, phenobarbital seems to significantly modulate the expression of more than 50 different genes. Our results also demonstrate that a large fraction of genes is negatively regulated by drug treatment.

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