Category Archives: Pharmacogenomics

There are several reasons why some patients may or may not respond to a drug, or may exhibit a certain side effect that other patients do not. Some of those reasons are genetic, as pharmacogenomics researchers have shown certain alleles can predict response to a drug or the likelihood of an adverse reaction. But pharmacogenomics has been unable to explain all the variability in drug response, so metabolomics researchers have stepped in to see whether their discipline can help explain why some patients respond to drugs the way they do.

While metabolomics researchers look at metabolic profiles in plasma, serum, or urine to determine the differences between people with a certain disease and healthy people, pharmacometabolomics- is an extension of that, says Imperial College London's John Lindon. "Once you've got the biomarkers of the disease these are the metabolites you can go back and look for the mechanism by looking at the enzyme pathways, to see which pathways are involved in using up those metabolites," Lindon says. "We look at a group of people's urine and we look for metabolic differences in the pre-dose, which would then be predictive of what happened post-dose."

Like pharmacogenomic researchers, pharmacometabolomic researchers look for signals in a person's biology that may indicate why a drug affects a person the way it does. But instead of looking at genetic differences, these researchers look at differences in enzymes, metabolites, and small molecules. Using nuclear magnetic resonance spectroscopy and different kinds of mass spectrometry, "we look for the metabolic fingerprint that says this person would process this drug differently it might be more toxic in that person or more beneficial in that person. The idea would be to go towards personalized medicine," Lindon adds. His group published the first pharmacometabo-lomic study on pharmacometabolomic phenotyping and its potential use as a personalized medicine tool, in Nature in April 2006.

The benefit of looking at drug response on a metabolic level rather than a genomic level, Lindon says, is that while genomics reveals everything about a person's DNA, it says nothing at all about a person's environment. "Epigenetics tells you about your environment, but genetics and genomics people are largely blind to the environmental influences," he adds. "Metabolism is the endpoint of all the processes of the body, and is exquisitely sensitive to environment."

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A New 'Omics Emerges

A brain surgeon and a scientist doing basic research on genes were honored with the city's top award for science innovation yesterday.

The prizes went to Zhou Liangfu, 70, a neurosurgeon at Huashan Hospital, and He Lin, 58, a geneticist at Jiao Tong University, who detected many genes related to diseases.

Each will receive 500,000 yuan (US$79,365).

Projects and research involving life science, pharmaceutical development and food safety covered 42 percent of the awards this year.

The two foreign experts who received this year's international scientific and technological cooperation award were also in the medical field. Michael Phillips, a Canadian mental health expert, and Issei Komuro, a Japanese cardiovascular expert, were honored for their efforts to boost China's health development and improve China's position on the international stage.

"Different from previous year's science and technology awards focusing mainly on scientific innovation, this year's awards focused more on the introduction of scientific research into practice and industrialization and its economic result," said Yin Bangqi of the Shanghai Science and Technology Commission. "This encourages scientists to focus on civil needs and translate their research into practical use and create more profits."

Zhou Liangfu said he will put all of his 500,000 yuan prize into his research, which leads neurosurgery development in China. At age 70, he has a full schedule - doing surgery two days a week, serving at an outpatient clinic on Wednesdays and checking his patients on the remaining two days.

"I have been working in clinical practice for almost 50 years and have done over 10,000 surgeries, witnessing the growth of China's neurosurgery from blankness to the current status," said Zhou, who created surgical methods to treat tumors at the bottom of the brain.

He Lin completed the accurate localization, cloning and mutation detection of a gene, IHH, which causes the disease brachydactyly type A-1, which results in babies born with shortened toes or fingers. It is a common but not serious prenatal disease, with a global incidence of about 2 percent.

He also set up the world's largest psychosis sample library and made important progress in studies of psychosis nutrigenomics and pharmacogenomics - the effects of nutrition and drugs on genes. He also confirmed that prenatal nutritional deficiencies seriously increase the risk of schizophrenia.

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Eastday-Neurosurgeon, geneticist take top prizes in science

GE Healthcare has taken initial steps to integrate 'omics data into its Centricity electronic medical record system through an exploratory research project that is developing a genomics data analysis infrastructure.

Mark Dente, GE healthcare's chief medical officer for healthcare information technology, discussed the project last week during a panel discussion at the American Medical Informatics Association's Translational Bioinformatics conference in San Francisco.

The panel discussed several projects that are looking to integrate genomics data into EMRs. In addition to Dente, panel participants included representatives from the Electronic Medical Records and Genomics Network, the Pharmacogenomics Research Network, and the HL7 clinical genomics workgroup.

BioInform spoke to Dente after the conference to get additional details about GE Healthcare's genomics infrastructure development plans. The following is an edited version of that conversation.

During your presentation at AMIA, you mentioned that GE Healthcare is developing a genomics analysis infrastructure. Could you provide some more details about what you hope to develop and where those efforts currently stand?

What we presented at AMIA was a mix of our technologies that we have today like our EMR and our ability to have large datasets to do research against. [T]he genomics effort is where we are headed, [but] it is not a product [now and] it may never become a product.

What we are talking about here is the driving of personalized medicine and translational medicine. I am a biomedical, clinical informaticist ... and my claim to fame is to think about knowledge management and clinical decision support and how we can shorten the ... bench-to-bedside timeline, [which] is about 17 years for something to go from research to full adoption in clinical practice. Now you compound that with a part of medicine that most clinicians in clinical practice are [unfamiliar with]. They learn a little bit of genomics in undergraduate school [and] in medical school but how do you educate folks as to ... where the research is going? Finally, how do we deal with new knowledge repositories in medicine?

A lot of our industry is run on old technology. We've made a large investment on the technology side looking at services-oriented-architecture. We can put legacy systems ... and new technology into this new infrastructure and because is platform we can aggregate data across the institution and even across the community and do analytics on this data in our data warehouse.... this SOA architecture is a modern way of doing that. [We have a] joint venture [with] Microsoft [called Caradigm that is] focused around that and advanced clinical decision support.

The final leg is [the] genomics platform itself One thing around genomics is that there needs to be a higher expectation on the technology's ability to handle large datasets. An SOA infrastructure allows us to be more flexible on the technical side of dealing with genomic information. [Also,] you really want to think about a genomic repository external to the EMR. That is my personal approach and how I will strongly suggest that we as GE will approach this. You do not want to clog up your operational EMR database with genetic data because it's just too large. [Also, because] its genetic data, we need to have a higher expectation of security. We have rigorous HIPAA and other internal standards of how we manage and keep private patient information and that will get ratcheted up in the future.

As you start to put data into a genomics database, we need to marry up the genomic data with the phenotypical data coming off an EMR. The real exciting part [is] we can start looking at the genomic data coupled with the phenotypical data in and a genomic analytic engine concept. With a analytics engine and the creation of algorisms to look for signal how do we start to think about running very targeted studies and [looking] for signals that suggest that these four hypothetical genes, [for example,] could be predictive of a [disease] state?

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Q&A: GE Healthcare's Mark Dente on the Challenges of Integrating Genomics Data with EMRs

By Turna Ray

CHICAGO A recent analysis of genotype and clinical data from more than 700 heart failure patients found that individuals who are homozygous for the wild-type allele Ser49 in the beta 1-adrenergic receptor gene experienced prolonged survival when they received treatment with beta-blockers compared to Ser49 homozygotes who didn't receive such treatment.

The study, led by researchers from the University of North Carolina, Chapel Hill, also found that carriers of one or two copies of the Gly49 allele in the beta1-AR gene did not experience a survival advantage when treated with beta-blockers compared to Gly49 carriers who didn't receive such drugs.

Beta-blockers inhibit beta-adrenergic substances that regulate the nervous system of the heart and are often prescribed to control high blood pressure, to manage irregular heart rhythms, and to treat heart failure patients. There are several beta-blockers on the market approved by the US Food and Drug Administration, including AstraZeneca's Toprol XL and GlaxoSmithKline's Coreg.

Based on data from past studies, UNC researchers Jasmine Talameh, Kirkwood Adams, and others hypothesized that Ser49 allele status "will be associated with beta-blocker survival benefit in a large, heterogeneous, US heart failure population," said Talameh, who presented data from this study at the American College of Cardiology meeting here this week.

Study investigators reviewed beta-blocker use and survival for heart failure patients enrolled in the United Investigators to Evaluate Heart Failure Biomarker Registry from 2000 to 2002. The current analysis involves more than 700 patients in the registry for whom there was information on beta-blocker use, survival, and genotype.

"The UNITE-HF DNA Registry was started by Kirkwood in 1999 as a way to promote clinical registry, biomarker, and genomic research in heart failure," Talameh said during her presentation. "These prospective, multicenter, observational registries were designed to study medication use, long-term outcomes, and the genomics of heart failure patients seen in US heart failure specialty clinics."

Patients in the registry had a history of heart failure and could have been asymptomatic at the time they were enrolled. According to Talameh, the majority of patients in the registry had received Coreg and Toprol.

Study investigators used mass spectrometry to genotype patients for the Gly49 and Ser49 alleles in the lab of Howard McCleod, director of UNC's Institute for Pharmacogenomics and Individualized Therapy. During the follow-up period, researchers collected patients' clinical information at the study sites and then periodically with the Social Security Death Index.

Among the study participants, 68 percent were Ser49 homozygous and 32 percent were Gly49 carriers. Using the Social Security Death Index, researchers found that more than 340 patients died after an average follow up of seven years, of which 52 percent were Gly49 carriers and 47 percent were Ser49 homozygous.

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UNC Analysis Finds Beta1-AR Alleles Impact Survival in Patients Treated with Beta-Blockers

By Turna Ray

CHICAGO Data from a prospectively designed, randomized study involving patients who have undergone a percutaneous coronary intervention suggests that individuals who are carriers of the CYP2C19*2 allele experience lower platelet aggregation, and therefore greater response, to standard-dose Effient than they do to high-dose Plavix.

In the study, called RESET, University of Rome's Gennaro Sardella and colleagues also identified a possible platelet aggregation cutoff above which patients may be more likely to harbor genotypic variations in CYP2C19 that compromise their ability to respond to Plavix.

Although the US Food and Drug Administration has placed a "black box" warning on Plavix to note that patients with certain CYP2C19 genotypes may not respond to the drug, physicians have been reluctant to adopt testing without more specific guidance on how genotypic information can guide dosing. Preliminary data from the RESET trial, presented here this week at the American College of Cardiology's annual meeting by Sardella, may further inform such a genotype-driven dosing strategy.

The data confirms results from other trials suggesting that patients who have undergone PCI and harbor certain CYP2C19 alleles respond better to Daiichi Sankyo/Eli Lilly's Effient (prasugrel) than they do to Plavix (clopidogrel), marketed by Bristol-Myers Squibb and Sanofi-Aventis. Specifically, the findings in RESET corroborate results from a retrospective gene substudy of the GRAVITAS trial, in which Matthew Price and colleagues from the Scripps Clinic found that CYP2C19*2 carriers compared to those with the normal allele experienced increased platelet reactivity despite a double dose of Plavix (150 mg/day).

Meanwhile, a prospective study published by researchers at Brigham and Women's Hospital last November in the Journal of the American Medical Association genotyped more than 300 patients with cardiovascular disease and reported the most detailed genotype-guided dosing data for Plavix to date. In that study, called ELEVATE-TIMI 56, the researchers found that patients with CYP2C19*2 genotypes given triple the maintenance dose of clopidogrel (225 mg/day) experienced the same level of platelet reactivity as patients without the CYP2C19*2 allele who received a 75 mg/day dose of the drug. However, the researchers, led by Jessica Mega, found that in patients who carried two copies of the *2 allele, "doses as high as 300 mg daily did not result in comparable degrees of platelet inhibition."

While the Mega study investigated the influence of genotype on response to increasing doses of Plavix, the Sardella study compared the influence of genotype on response to high-dose Plavix and standard-dose Effient. Also, the Meta study broke down Plavix response by whether patients had one or two copies of the *2 allele, whereas Sardella's study only considered *2 carriers versus non-carriers. The retrospective GRAVITAS genetic substudy, meanwhile, also found that *2 homozygous patients fared worse on Plavix than did heterozygous *2 patients.

It is currently controversial in medical practice to use genetic testing to determine whether patients should be treated with Plavix, since a number of studies have come to divergent conclusions about the association between CYP2C19 genotypes and Plavix response, depending on whether researchers focused on surrogate markers of response, such as platelet reactivity, or patient outcomes in terms of cardiovascular events. Many of these studies have been retrospective in design, involved heterogenous disease populations, or been too small to provide definitive answers. Most doctors are waiting for the FDA to provide more definitive dosing recommendations by genotype before deciding whether to adopt genetic testing in this setting.

RESET

In RESET, the study investigators used a crossover, randomized design to compare the antiplatelet effect of standard-dose Effient (10 mg/day) versus high-dose Plavix (150 mg/day) in patients who were stable after a PCI, but had high on-treatment antiplatelet activity upon receiving moderate- to low-dose Plavix. Researchers looked at the relationship between platelet reactivity and CYP2C19*2 genotype when patients were on Effient and then switched to Plavix, or were first on Plavix and then given Effient.

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Study: Higher Plavix Dose Doesn't Improve Response for CYP2C19*2 Carriers; Effient May Be Best Option

The Broad Institute and Sanger Institute announced yesterday (March 28) details from their separate cancer cell line databases, the largest such repositories of genomic and drug profiling data to date. With preliminary results published in two Nature papers, the databases should help researchers identify which drugs to use against which cancers to streamline drug development efforts.

This continues to move us towards cancer being understood as a molecular disease instead of an anatomical disease, said Eileen Dolan, who studies pharmacogenomics at the University of Chicago and was not involved in either study. It will help us understand our existing drugs, as well as new drugs, to make more informed decisions in phase I and phase II trials.

In recent years, researchers have become increasingly aware that whether a tumor will respond to a given drug treatment depends on its genomic profile. But because of the vast number of cell lines and variety of drug options, researchers in smaller labs often dont have the resources to identify the best fit for the cancer type or drug theyre studying.

For any variety of cancer drugs that are being developed, we cant necessarily know in advance which cancers are going to be vulnerable, said Levi Garraway, a cancer biologist at the Dana Farber Cancer Institute who spearheaded the Broad project. If you have a large collection of cell lines that are deeply annotated genetically and molecularly, you can probe the biology linked to many types of genetic alterations of interest.

Four years ago, Garraway and his colleagues began a massive screen of 947 cancer cell lines, sequencing cancer-associated genes, profiling drugs, collecting RNA expression data using microarrays, and combing the cancer genomes for repeated regions. And they werent too far along when they learned of a parallel project at the Sanger Institute, led by genomicist Mathew Garnett.

The projects arent identical; they screen different genes and different drugs using slightly different methods. For this reason, Garnett views the two databases as complementary. There was sufficient non-overlap that it was possible to make different observations, agreed Garraway. (See table for details.)

Plus, having two separate databases rather than pooling the data, as previous databases have done, could lend more weight to certain findings. I think having two independent resources is a good thing, said Jian Ma, a computational genomicist at the University of Illinois, who did not participate in the research. If two different groups have the same result for one cell line, it would be more reliable.

The two Nature papers, submitted as a pair, describe how the data for each project were collected, and include confirmatory experiments to demonstrate how the databases could enhance cancer drug development. Garnetts project, called the Cancer Cell Line Encyclopedia, identified a mutation in Ewings sarcoma cells that is highly sensitive to PARP inhibitors, for example. Meanwhile, Garraways database, the Genomics of Drug Sensitivity in Cancer project, includes data suggesting that MEK inhibitors, a class of cancer drugs that target the RAS oncogene, may have increased efficacy in cancers with a mutation in another gene, AHR.

The ultimate hope is that the databases will be used to help people with cancer by better matching a cancer type to a drug, and identifying which patients to enroll in clinical trials based on their genetic flavor of cancer. Often, drugs fail [in clinical trails] simply because theyre not tested in the right people, said Garnett. A better understanding of how drugs respond to genetic mutations, helped by the databases, could help clinicians single out what populations are most likely to respond.

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Collecting Cancer Data