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Feb 1, 2014 (Vol. 34, No. 3)

Biomarkers Reshape Drug Development

  • In another example, Quartz Bio correlated the genetic and pharmacokinetic data to categorize the patients into fast and slow metabolizers of an investigational drug. In slow metabolizers, the compound persisted in the bloodstream long enough to create a toxic effect; in fast responders, the drug was excreted before it could reach the target.

    Such stratification analysis assists in adapting therapies to the biological characteristics of a patient, as measured by biomarkers. “Precision medicine is a fast-growing field that in the near future will have tremendous impact on drug development, insurance reimbursement, and disease management,” concludes Dr. Wojcik.

  • miRNA as a Blood-Based Biomarker

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    This microRNA biomarker development pipeline was developed by the Comprehensive Biomarker Center (CBC). Unlike traditional approaches, which isolate miRNAs from solid tissues, the CBC approach discovers microRNA biomakers in blood cells.

    The Heidelberg-based Comprehensive Biomarker Center (CBC) specializes in innovative biomarker discovery and validation, with a specific focus on microRNA (miRNA). miRNAs regulate genes post-transcriptionally in what seems to be virtually all cellular pathways.

    “Traditionally, miRNAs were isolated from solid tissues, such as tumor samples,” says Markus Beier, Ph.D., vice president of strategy and IP. “CBC developed a unique pipeline for discovery of miRNA biomarkers in blood cells.” Because microRNAs are remarkably stable in blood and because their blood levels do not fluctuate under normal physiological conditions, these molecules hold great promise as biomarkers of pathogenic processes.

    When answering clinical questions, CBC narrows a set of 2,000 currently known miRNA biomarkers to a biomarker signature consisting of approximately 2–50 molecules, with the goal of further reducing the number of miRNAs in assay and clinical validation. The company, which holds several patent applications covering biomarker signatures for disease diagnostics, focuses its current clinical validation efforts on acute myocardial infarction (AMI), heart failure, prostate cancer, and multiple sclerosis.

    “AMI is a very challenging indication, because time is of the essence,” states Dr. Beier. “Cardiac troponins are the gold standard for AMI diagnostics, but due to their high sensitivity, they also pick up other cardiovascular diseases. Our miRNA signature is designed to complement this test and seems to reflect processes that precede myocardial necrosis.”

    CBC and its clinical partner, the department of cardiology at the University of Heidelberg, compared 20 patients who had AMI with 20 patients who reported chest pain but did not have AMI. Out of 121 differentially expressed miRNAs, a signature of 20 allows for diagnosing AMI with 93% accuracy. In addition, a first-ever kinetic study showed that changes in the miRNA are the most evident during early stages of AMI, and the differences with the control diminish 24 hours post AMI.

    To underscore the companion value of miRNA profiling in blood cells, the company notes that its biomarkers can provide information about disease processes originating from the immune system. For example, CBC developed a set of 48 miRNAs that allow for the discrimination of multiple sclerosis (MS) patients from normal controls with an accuracy of 96%. The company also refined a set of miRNA candidates for monitoring MS therapy. “Finding reliable blood-based biomarkers means improving diagnosis of the disease, and rapid selection of personalized treatments,” concludes Dr. Beier.

  • Mining Published Works

    “Could drug research be supported by mining the texts of knowledge repositories, such as PubMed? We believe that semantic technologies bring new e-biomaker discovery opportunities, in a manner which is 50% more cost-efficient than traditional molecular biomarker discovery process,” asserts Paul Walti, Ph.D., CEO of InfoCodex, a software provider based in Buchs, Switzerland.

    InfoCodex software distinguishes itself from more traditional software, which is based on natural language processing, by recognizing and “comprehending” the actual content of a large number of unstructured documents. By analyzing seemingly unrelated documents, publications, and reports, InfoCodex can categorize unstructured information and correlate small, seemingly unrelated facts.

    The software combines a very large thesaurus organized in a complex taxonomy of about 10,000 concepts. It applies information theory to transform documents into mathematical models, conduct unsupervised semantic clustering, and match multilingual documents according to meaning.

    “Our software is able to determine the meaning of unknown words and correlate them with words in the InfoCodex Linguistic Database, providing a cross-language content recognition,” says Dr. Walti.

    As opposed to natural language processing, which recognizes relationships between facts only if they are already explicitly stated in the document, the InfoCodex semantic engine can find hidden correlations distributed over groups of documents. “Our engine is almost like a supreme human super-reader, except that no human team, however specialized, is able to create and maintain the overview of all publications, simply because of their sheer number and the rate of accumulation,” continues Dr. Walti.

    In a pilot experiment with Merck, InfoCodex took on the task of discovering new e-biomarkers from over 120,000 PubMed abstracts, clinical trial summaries, and internal Merck documents. Without any involvement of subject matter experts, InfoCodex was able to identify over 10,000 potential biomarker/phenotype candidates, further narrowed down to just 1,000 of specific genes or proteins.

    Next, the Merck and Thomson Reuter scientists further parsed the cohort to about 20 novel high-quality candidates prioritized by confidence scores. Some of these have since been validated in biological experiments.

    It should be noted that none of the 20 abstracts contained the term “biomarker.” Despite the constraints of this first pilot experiment, the ability of automated data mining to identify new e-biomarker candidates has the potential to impact pharmaceutical research.

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