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The Good and Bad of Generic Drugs

April 30, 2014 – 5:00 pm | Edit Post

The definition of a generic drug, from the U.S. Food and Drug Administration (FDA), is a product that is identical, or bioequivalent, to a brand name drug in dosage form, safety, strength, route of administration, quality, performance characteristics and intended use. Generic drugs are chemically identical to their branded counterparts, but they are typically sold at substantially lower prices. According to the Congressional Budget Office, generic drugs save consumers an estimated $8 to $10 billion a year at retail pharmacies. Billions more are saved when hospitals use generics.

Generic drugs must pass rigorous controls, just like the innovative drug originally created. There is no loss in quality, strength, purity and stability of the generic drug. However, patients may have the perception that a generic drug is not as effective as the original brand name.

Multiplicity, Columbia Pictures

So, let’s take a look at some facts and evaluate what is known about generic drugs as well as brand name drugs.

The development of a new drug is a very complex process, which involves not just the research and development of the chemical, but also clinical trials and all of the regulatory processes to get the drug approved for human use. The cost of this process has been estimated to be as much as $800 million. Such an investment needs to be recovered and there needs to be sufficient margin for pharmaceutical companies to re-invest in more research and development, and keep the wheels rolling in biomedical applied research.

However, there is no doubt that alternatives to these high cost therapies are needed, regardless of how much effort or investment goes into developing a drug. Some countries or individuals simply cannot afford to pay such a high price. Thus, since the approval of the Drug Price Competition and Patent Term Restoration Act in 1984 (USA), generics have opened the doors to those with less resources. It is also helping to reduce health care costs in developed countries.

Now from the FDA perspective, here are some important facts that support the use of generics:
FDA requires generic drugs to have the same quality and performance as brand name drugs.
Research shows that generics work just as well as brand name drugs.
When it comes to price, there is a big difference between generic and brand name drugs. On average, the cost of a generic drug is 80% to 85% lower than the brand name product.
Cheaper does not mean lower quality.
FDA closely monitors adverse events reports for generic drugs.
FDA is actively engaged in making all regulated products – including generic drugs – safer.

However, it is a challenging task for watchdogs like the FDA to guarantee the performance of the generic drugs. So let’s look at the other side. In other words, are generics actually identical to the brand drugs?
The FDA requires that you get between 80% to 125% of the brand name drug activity from a generic medication, compared to the original drug. There is more variability added if you get your generic from different vendors, as one may pack 80% of “effective” drug and another one 120%, for example.
Components other than active ingredients are not required to be identical and the formulation could potentially affect how the drug is delivered to your blood stream.
Regarding the controls, the FDA admitted that some generics had not been tested, and even pulled one out from the market.

So what can you do if you feel your body is not responding in the same way to the brand name drug and the generic ones?
Find out if an “authorized” generic exists for your drug. These are generics typically made by the same manufacturer of the brand name medication but sold under a generic brand name. They are not similar, but identical. Ask your pharmacist if one exists for your medication.
When switching to a generic, monitor your condition carefully. When switching from a brand name to a generic drug, or from one generic to another, note any changes you feel and tell your doctor immediately.

Finally, and more importantly, always consult your doctor regarding any questions you have regarding generic and brand name drugs. Always make an informed decision with the help of your health care provider. The points expressed in this post are just factual and for the only purpose of promoting a healthy debate (with the emphasis on healthy when talking about drugs).
Doctor Who, BBC

If you want to know more, you can take a look at these websites:
Generic Drugs: Questions and Answers from the FDA
Generic Drugs, World Health Organization (WHO)
Generic Drugs on Wikipedia
You can give us your comments too: mtam@biolegend.comContributed by Miguel Tam, Ph.D.

Next-Generation Sequencing: Challenges and Clinical Translation

April 30, 2014 – 1:10 pm | Edit Post

As the research world advances the field of genomics, the clinical counterpart attempts to translate these technologies for patients. The intersection of these two worlds and a candid discussion of genomic application to cancer were on display at the recently concluded 105th annual meeting of the American Association of Cancer Research (AACR) in San Diego. Promises and Challenges of Cancer Genomics The first human genome that was sequenced cost a staggering $3 billion just over a decade ago1. Today, we have come close to getting an entire genome sequenced for $1000. This tremendous fall in the price was made possible by unprecedented technological advances and the subsequent availability of increasingly efficient platforms. However, in spite of increased access and the plummeting costs of sequencing, considerable challenges remain, primarily related to data analysis and interpretation. Understanding the relevance of genomic variation in the context of cancer will require the sequencing analysis of a substantial number of samples to get statistical correlation and validation by functional genomic approaches. Sequencing the genome (whole-genome sequencing, WGS), exome (whole-exome sequencing, WES), and transcriptome (RNA sequencing, RNA-Seq) are three approaches that help researchers detect somatic cancer genome alterations such as nucleotide substitutions, insertions, deletions, copy number variations, and chromosomal rearrangements. Targeted sequencing can be used for regions of interest at significantly lower costs compared to the whole-genome approach. These investigations not only help in understanding the pathogenesis of cancers but also provide biomarkers that can identify novel targets for drug development. Importantly, these data can help guide cancer therapy using existing drugs against actionable molecular targets. Cancer genome sequencing involves significant challenges – such as the quality (paraffin-embedded, variably degraded, heterogeneous) and quantity of samples available. There are ways to overcome most of these challenges and get meaningful data. For instance, increasing sequence depth can counter low sample purity and increased ploidy. Sequencing the ends of DNA library molecules can identify discordant pairs representing deletions, amplifications, inversions, or translocations. Paired-end reads have become a valuable strategy for cancer genomics. Finally, since most genetic abnormalities in cancer are somatic and not germ line, a comparison of a patient’s matched “normal” genome is crucial to interpret the alterations identified through deep sequencing. Christos Hatzis, Ph.D. from Yale Cancer Center expounded on additional challenges associated with sequencing, in an AACR symposium “NGS: From Bench To Bedside”. Most of the data obtained with state-of-the-art sequencers is in the form of short reads. Hence, analysis and interpretation of these data encounters several challenges, including those associated with base calling, sequence alignment and assembly, and variant calling. These challenges have led to the development of innovative computational tools and bioinformatics approaches to facilitate data analysis and clinical translation. Bioinformatics and Multiple ‘Omics Approaches Nearly 600 bioinformatics tools were developed over the past two years, and are being used to enable data analysis and interpretation. Some of these tools include those that assess the quality of short reads such as FastQC and htSeqTools, or a tool like MuTect that can be used for sequence alignment to detect somatic mutations with low allele fractions. One such tool that Hatzis discussed at length was a mutational analysis pipeline that uses sequencing data in addition to clinical information in order to develop correlations among mutations, genes, and pathways. This pipeline – the Mutational Significance in Cancer (MuSiC) can help differentiate passenger mutations from the so-called driver mutations. Similarly, MutSig is an interpretation tool that can detect significantly mutated genes. Multiple computational tools are used by cancer researchers, many of which have specific requirements because cancer genome data: Needs to be analyzed in conjunction with normal matched genome Involves highly rearranged genomes, and Have immense heterogeneity A few examples include the ELAND aligner tool and CASAVA for mutation calling from Illumina2, BFAST alignment tool3, and PINDEL to detect indels4. A comprehensive database of tools for analysis and interpretation tools for NGS can be found on SEQwiki5. Analyzing the protein-encoding regions of the DNA by WES represents a powerful tool in the sequencing armamentarium. Hatzis quoting from an article in Scientific American, pointed out, “Analyzing an exome to understand disease is, in some cases like reading Cliff Notes to understand a classical textbook”6.This is because the exome represents less than 2% of the total human DNA; hence WES only examines this small part while missing the majority of DNA. However, WES is a valuable tool because exons contain >85% of disease-causing mutations in all Mendelian disorders, in addition to majority of single-nucleotide variations in the genome. A recent study has demonstrated use of WES for characterizing circulating tumor cells7. In addition to WGS and WES, transcriptome analysis through RNA-Seq can identify alternative splice variants and gene fusion events. RNA-Seq is also a powerful technique for expression profiling of therapeutically relevant transcripts. In spite of the advantages that sequencing technologies offer, the rapidly dwindling costs are resulting in an ever-increasing amount of data generation. This threatens to overwhelm the analytical capacity, thereby creating a bottleneck. However, owing to the multitude of research groups working on the bioinformatics tools for sequencing, this may not be as big a problem as it is perceived. Optimism, Collaboration, and the Way Forward For one, Elaine Mardis, Ph.D. from The Genome Institute of Washington University is optimistic. In her talk at the same NGS symposium, she expressed her conviction that in spite of hurdles, sequencing data analysis “will get easier” as instrument manufacturers provide highly tuned pipelines. In addition, cloud-based or locally installable analytic pipelines are becoming commercially available. Moreover, inclusion of RNA and protein information in conjunction with sequencing data is important; this combination of data from different sources provides an orthogonality that often increases precision. Finally, the availability of long read platforms would obviate the need for alignment. Last year, Illumina’s MiSeqDx became the first next generation sequencer to be approved by the FDA for IVD use. Furthermore, Illumina recently introduced a new sequencing platform for research – the NextSeq 500 that will likely fuel future advances in analytical and interpretation tools. But the technology will only take us only so far. Ultimately, a truly successful bench-to-bedside translation requires a multidisciplinary approach where basic scientists, bioinformaticians, pathologists, genetic counselors, nurses, and physicians collaborate on genomic data. Discoveries driven by sequencing are set to revolutionize clinical practice, leading to the development of novel diagnostic and prognostic tools in addition to realizing the goal of truly personalized medicine. Author Information Sandeep Pingle is a physician-scientist with a background in pharmacology and neuroscience. He is currently working in the field of translational oncology. In addition, he is passionate about science communication, and blogs at sciberomics. References National Human Genome Research Institute (NHGRI), National Institutes of Health (NIH): The Human Genome Project Accurate whole human genome sequencing using reversible terminator chemistry. Bentley DR et al., 2008. doi:10.1038/nature07517 BFAST: An Alignment Tool for Large Scale Genome Resequencing. Homer N et al., 2009. DOI: 10.1371/journal.pone.0007767 Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Ye K et al., 2009. doi: 10.1093/bioinformatics/btp394 The SEQanswers wiki: a wiki database of tools for high-throughput sequencing analysis. Li J-W et al., 2011. doi: 10.1093/nar/gkr1058 10 Things Exome Sequencing Can’t Do – but Why It’s Still Powerful. By Ricki Lewis. Scientific American. May 16, 2012. Whole-exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer. Lohr JG et al., 2014. Nature Biotechnology. doi:10.1038/nbt.2892 Related Pages Highlights from the Illumina AACR Discovery Symposium Building Bridges to Conquer Cancer Genomics Mutiplatform Tumor Profiling by The Cancer Genome Atlas (TCGA) Transforming the Future of Oncology with Genomics Image Source Credit: Jonathan Bailey, NHGRI NIH (genome.gov)

Scientist and/or Associate Scientist at Trinity Biotech (Carlsbad, CA)

April 30, 2014 – 5:37 am | Edit Post
Scientist and/or Associate Scientist at Trinity Biotech (Carlsbad, CA)

Trinity Biotech is a leading player in the international diagnostics industry. Trinity Biotech specializes in the … manufacture and marketing of diagnostic test kits. Trinity Biotech’s continued success is based on the fact that as a…

D Fence: My PhD Finale

April 28, 2014 – 10:44 am | Edit Post

28 April 2014 Dear Reader, The primary focus of the last five years of my life has been the pursuit of a PhD, culminating with my PhD dissertation and defense. A week ago I defended my PhD and was awarded a Doctor of Philosophy in Biomedical Sciences, more colloquially known as a “PhD” or “doctor […]

The Legacy of Leo Lefrançois

April 23, 2014 – 5:00 pm | Edit Post

Leo Lefrançois was the Chairman of the Department of Immunology and Director of the Center for Integrated Immunology and Vaccine Research at the University of Connecticut Health Center. He passed away on July 20th, 2013 while hiking a path in the Dolomites, Northern Italy.But, he was not just the holder of these titles. He was a remarkable scientist, committed not just to producing cutting edge science, but also to supporting young scientists and the future of research in the U.S. In his own words:

“NIH funding right now is in a serious crisis. (…) Layoffs of personnel are happening across the country due to loss of NIH grant funding. Interim paylines at NIAID are at 6% and some other institutes are no better or worse! The system will soon implode at these funding levels and we will lose many investigators particularly at the junior faculty level. As a seasoned investigator, I have seen budgets rise and fall, but the future for funding looks poor for the foreseeable future, regardless of the outcome of the election. As scientists, we need to be advocates for funding, but we need the NIH to back us, not tell us that everything is rosy. We need the NIH leadership to step up and tell Congress that a disaster is in progress that will further distance us from the rest of the world in science and lead to further job losses and reduced domestic spending. Perhaps those in power do not realize that the grant money we receive is spent primarily on people and supplies, the latter purchased from many American companies. There are long-term consequences that will result from the ongoing crisis that soon will be irreversible.”

Leo left this statement as a comment in an NIH page, in October 2012. You can read it here.

Like many other young immunologists, I read countless papers from Lefrançois’s group during my Ph.D. studies. They are all great papers, and I’m still reading through them. His lab was tremendously productive and with 183 high quality papers in PubMed, it is guaranteed that you will find interesting reading.

Leo had a long and fruitful collaboration with BioLegend, and this will be the inaugural year of the Lefrançois-BioLegend Memorial Award, at IMMUNOLOGY 2014™, The American Association of Immunologists Annual Meeting. We wish to celebrate his career and discoveries, and also contribute to passing along his legacy to young scientists.You can read further about Leo’s life and career in these sites:Leo Lefrançois in memoriam, from his friends and family Obituary in Nature immunology, by Michael J. Bevan: http://www.nature.com/ni/journal/v14/n10/full/ni.2706.htmlYou can give us your comments too: mtam@biolegend.comContributed by Miguel Tam, Ph.D.

RNA-Seq Apps Now Available on BaseSpace

April 23, 2014 – 12:26 pm | Edit Post

We are very excited to announce that the expert-preferred suite of RNA-Seq data analysis software is now available on BaseSpace for any researcher, irrespective of bioinformatics experience. The B aseSpace Core Apps for RNA are b ased on the Tuxedo suite of RNA analysis tools:  ● The TopHat Alignment App can be used to align RNA reads as well as detect gene fusions using the industry-standard method. Illumina’s Isaac method further enables the calling of SNVs and small indels. ● Cufflinks enables gene expression profiling and detection of novel transcript isoforms. These applications are packaged in a n intuitive, click-and-go user interface that is designed to enable any bench biologist to process their own data, from start to finish.  The pipeline generates easily-interpretable, publication-ready data in clear tables and graphs, and a lso provides output files that can be submitted as input into any number of third-party secondary analysis tools. As a result, BaseSpace Core Apps for RNA can be applied to a broad range of research projects, including: Gene expression profiling mRNA expression profiling, including transcript-level abundance, and discovery of novel features including alternate transcripts Total RNA expression profiling, including detection of gene fusions and cSNPs Importantly, RNA-Seq apps work with data from all Illumina instruments, NextSeq, HiSeq, and MiSeq – and are compatible with the complete portfolio of TruSeq RNA sample preparation solutions. This includes the newly announced TruSeq RNA Access Kit – a highly robust, low sequencing output-requiring solution for FFPE samples.  And because the BaseSpace Core Apps for RNA leverage the inherent parallelism of the cloud, the time-to-answer can be as little as four minutes per sample for gene-expression profiling experiments. The RNA-Seq apps are uniquely suited to perform comprehensive cancer research studies. As the TCGA and other consortia have repeatedly shown, adding RNA-Seq analysis to a DNA sequencing project is critical  for identifying the biological significance of somatic mutations in cancer. Without the RNA-Seq results, it is difficult to assess whether the gene(s) harboring the somatic mutations are expressed at all in the cancer sample. Moreover, the patterns of expression among related genes, and the detection of gene fusions, so critical to cancer research, can only be elucidated with an RNA-Seq experiment. Finally, robust sample preparation solutions for FFPE-stored cancer samples are now unlocked so that researchers can carry out comprehensive, multi-assay studies to advance their research.
The RNA-Seq apps have been designed with an obsessive focus on accessibility. The traditional command-line versions of these apps require expert bioinformaticians to maintain and run the tools, in-house IT staff to maintain the hardware, and a fairly massive compute infrastructure. But with the RNA Apps, all that is required is a web browser and a connection to the internet. Moreover, it takes only a few clicks of the mouse to get the analysis started, and the user interface is as easy as navigating any Web 2.0 (or are we at Web 3.0 already?) website. During our early-access period, we have received enthusiastic responses specifically regarding usability. We were happiest with the following feedback, which comes from Ganesh K. Boora, a clinician at the Beutler Oncology lab at Mayo Clinic (Ganesh has never run data analysis tools before): “I am really impressed with the two applications I worked with so far. You have made working with next-gen sequencing data as easy as launching an app on iPhone! The results showed all the relevant and important data in publication ready visuals.” As Ganesh mentions, the output includes rich, graphical charts and interactive plots that summarize the biologically significant results in a very intuitive manner. Of course, if you are familiar with the standard command-line output of the TopHat/Cufflinks suite, these are faithfully preserved for expert users.
While the RNA-Seq apps have been optimized for use by non-bioinformaticians, labs with deep in-house expertise in informatics have also seen value in the apps. James Hadfield, Director of the Core Lab at Cancer Research UK, had this to say about the new Apps: “I anticipate using the RNA-seq apps to QC our RNA-seq library preparations before HiSeq sequencing. This should save us time when samples or experiments have gone wrong by quickly pointing to the sample or library prep as the issue.” Another important use case for expert labs is the routing of samples/ projects to the BaseSpace apps when an unexpected high volume of samples/projects arrive at the lab. Regardless of the specific use case, our early access users have shown that a wide variety of customers can benefit from the RNA-Seq apps.
Fig 1: graphical output of the TopHat Alignment app indicating reads aligned to different regions of the transcriptome.
Fig 2: An interactive scatter plot of gene expression levels. The filters on the left can be used to filter out genes with low control:comparison expression rations, or to perform a Google-style search for specific genes or gene families Finally, we will soon be releasing the RNAExpress App , which encapsulates the STAR aligner and DESeq in a single, efficient App. This will be the method of choice for rapid expression profiling at the gene level, and is an ideal transition to customers performing microarray based gene expression experiments who want to transition to the digital resolution and efficiency delivered by RNA-Seq. If you’d like to learn more about BaseSpace Core Apps for RNA,  we invite you to visit www.illumina.com/BaseSpaceRNA where you can find a video tour of a typical run-through, a data sheet describing the features and benefits of the App, as well as the user guide for in-depth technical information. The analysis of RNA-seq data has never been easier, and we look forward to showing you why! 

SDBN’s #BIO2014 Snapshot 4/21/2014

April 21, 2014 – 7:34 pm | Edit Post

Here is our latest snapshot of activities leading up to the Biotechnology Industry Organization (BIO) 2014 convention which will take place in San Diego June 23-26.

Buzz of BIO. Voting for the Buzz of BIO …

The Cure at Salk Cancer Day Symposium

April 21, 2014 – 8:42 am | Edit Post

Karthik G. and I will be presenting a poster tomorrow at the Salk Institute’s Cancer Day Symposium.  We will be presenting data from a year with the scientific discovery game The Cure.  You can read more about those results on the arXiv.If you are coming, please stop by for a chat!  We would especially love the chance to discuss the new, collaborative decision tree-building interface that Karthik has created.  Who knows if the conference wifi will work, so please try it now! The Cure: Making a game of gene selection for breast cancer survival prediction from goodb

Mass Spec Cytometry -April SDSC meeting

April 17, 2014 – 11:14 am | Edit Post

SDSC meeting                             sponsored by DVS (a Fluidigm Company)                                             4/09/14 Carina Torres and Cheryl Kim organized a great group of…

Cool Places To Do Science: Life In The Lab 2

April 16, 2014 – 5:00 pm | Edit Post

We have talked about the physical space where you do your science stuff before. And we illustrated how a nice environment can have a strong impact overall. So talking again about cool places to do science…what if your lab was a boat? Let’s get even more crazy: what if you wanted to do a trip round the globe, stopping at places like the Caribbean and the Mediterranean Sea, while collecting your samples? Crazy? Not really. If you love sailing and science, you should have convinced J. Craig Venter to take you on his team.

Would you like this to be the view from your window in the lab? http://blogs.jcvi.org/2011/01/a-look-back-at-2010-at-the-jcvi%E2%80%A6/

Back in 2003, J. Craig Venter started the Global Ocean Sampling Expedition (GOS) as a pilot sampling project in the Sargasso Sea. Later on, the full expedition project was announced, which used Craig Venter’s personal yacht named Sorcerer II. It started in Halifax, Canada and circumnavigated the globe, returning to the U.S.A. in January 2006. According to their website, the expedition was inspired in part by the journeys of the HMS Beagle and the HMS Challenger. However, more aligned with the 21st century, the Sorcerer II expedition’s aim was to collect and classify microorganisms according to their genetic material, compiling a database of their genome sequence.

Sampling sites of The Sorcerer II Global Ocean Sampling Expedition

But, who is J. Craig Venter and why is this expedition important? Dr. Venter was a major force behind the sequencing of the human genome during the late ’90s. His team contributed a lot to this major endeavor and since then, he has diversified his interests to include the aforementioned project on genetic diversity in marine microbial communities and the novel field of synthetic genomics. Ultimately, in his own words, his goal is “…to find an alternative to taking carbon out of the ground, burning it, and putting it into the atmosphere. That is the single biggest contribution I could make.”

Now coming back to the place of work, sampling the ocean around the globe is quite cool. However, no matter how motivated you are, the Sorcerer II crew is a very skillful, small group of people, and it would have been very difficult to be included in the team. But, this might at least motivate you to start your own scientific expedition, or start dreaming about where you want that lab of yours sitting when you are doing your research.

The Sorcerer II yacht. http://www.jcvi.org/cms/research/projects/gos/photos/

For your inspiration, you can find out more about this here: Global Ocean Sampling Expedition (GOS) J. Craig Venter on the web J. Craig Venter Institute Or watch the first episode of BBC’s series “Bang Goes the Theory” where the research of Dr. Venter is introduced. If you have any stories that you want to share, or know a cool place where scientists are working every day please let me know: mtam@biolegend.com Contributed by Miguel Tam, Ph.D.