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PHARMA/TECHNOLOGY: Picking your statin to match your genotype

It’s for long been a somewhat dirty little secret of the pharma world that not all drugs work for not all patients. That goes for statins too, despite the best medical advice saying that we should all be on them. Forbes has an interesting little article showing some developments in the combination of genomic diagnosis with therapy to figure out which statin works best for which genome. Some farsighted pharma professionals (notably Kim Slocum at Astra Zeneca) have been preaching for years that the combination of genomics, information systems and better targeted pharmaceuticals will not only improve health, but also improve the financial health of big pharma.

Of course the corresponding fear of many within big pharma is that as a result of this trend there will be no such thing as a blockbuster, because genomic testing will show that most drugs should be restricted to a smaller segment of their target population. So instead of 3-4 leading statins, we’ll need 20-30 — each with their own need for clinical trials, $800m development costs and expensive outreach programs.

Whether Kim’s right or the nay-sayers are right, it looks like we’re going to a world where genomic testing, drug delivery and outcomes information will be better linked. And that will be a different world for pharma and doctors, and hopefully a better one for patients.

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  1. In regard to cancer medicine, the introduction of new “targeted” drugs has not been accompanied by specific predictive tests allowing for a rational and economical use of the drugs. Given the technical and conceptual advantages of Cell Culture Drug Resistance Tests (CCDRTs) together with their performance and the modest efficicay of therapy prediction on analysis of genome expression, there is reason for a renewal in the interest for CCDRTs for optimized use of medical treatment of malignant disease.
    Clinical study results published at the annual meeting of the American Society of Clinical Oncology (ASCO) show that a new laboratory test, called EGFRx (TM), has accurately identified patients who would benefit from treatment with the molecularly-targeted anti-cancer therapies. The finding is important because the EGFRx (TM) test, which can also be applied to many emerging targeted cancer drugs, could help solve the growing problem of knowing which patients should receive costly, new treatments that can have harmful side-effects and which work for some but not all cancer patients who receive them. The test can discriminate between the activity of different targeted drugs and identify situations in which it is advantageous to combine the targeted drugs with other types of cancer drugs.
    The new test relies upon what is called “Whole Cell Profiling” in which living tumor cells are removed from an individual cancer patient and exposed in the laboratory to the new drugs. A variety of metabolic and apoptotic measurements are then used to determine if a specific drug was successful at killing the patient’s cancer cells. The whole cell profiling method differs from other tests in that it assesses the activity of a drug upon combined effect of all cellular processes, using combined metabolic and morphologic endpoints. Other tests, such as those which identify DNA or RNA sequences or expression of individual proteins often examine only one component of a much larger, interactive process.
    The whole cell profiling method makes the statistically significant association between prospectively reported test results and patient survival. Using the EGFRx (TM) assay and the whole cell profiling method, can correlate test results which are obtained in the lab and reported to physicians prior to patient treatment, with significantly longer or shorter overall patient survival depending upon whether the drug was found to be effective or ineffective at killing the patient’s tumor cells in the laboratory.
    Over the past few years, researchers have put enormous efforts into genetic profiling as a way of predicting patient response to targeted therapies. However, no gene-based test has been described that can discriminate differing levels of anti-tumor activity occurring among different targeted therapy drugs. Nor can an available gene-based test identify situations in which it is advantageous to combine a targeted drug with other types of cancer drugs. So far, only whole cell profiling has demonstrated this critical ability.
    Not only is this an important predictive test that is available “today,” but it is also a unique tool that can help to identify newer and better drugs, evaluate promising drug combinations, and serve as a “gold standard” correlative model with which to develop new DNA, RNA, and protein-based tests that better predict for drug activity.
    These “targeting” drugs are expensive, costing patients and insurance carriers $5,000 to $7,000 or more per month of treatment. Patients, physicians, insurance carriers, and the FDA are all calling for the discovery of predictive tests that allow for rational and cost-effective use of these drugs.
    The whole cell profiling approach, holds the key to solving some of the problems confronting a healthcare system that is seeking ways to best allocate available resources while accomplishing the critical task of matching individual patients with the treatments most likely to benefit them.
    Genomic testing is not the answer, without cell culture analysis. In developing a program to discover gene expression microarrays, which predict for responsiveness to drug therapy, the way to identify informative gene expression patterns is to have a gold standard and that cell culture assays are by far the most powerful, efficient, useful gold standard to have.
    The assay is the only assay that involves direct visualization of the cancer cells at endpoint. This allows for accurate assessment of drug activity, discriminates tumor from non-tumor cells, and provides a permanent archival record, which improves quality, serves as control, and assesses dose response in vitro (includes newly-emergent drug combinations).

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