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

Biomarkers Reshape Drug Development

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    Imanova takes a structured approach to the development of imaging biomarkers, or i-biomarkers.

    Biomarkers defining specific phenotypes are becoming increasingly important for developing new drugs for specific patient subpopulations. The value of a new biomarker is measured by its ability to reduce risk.

    Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.
    For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.
    “Imaging biomarkers have been Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.

    Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.

    For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.

    “Imaging biomarkers have been largely underutilized in drug development,” says Kevin Cox, Ph.D., CEO of London-based Imanova. “But we believe that molecular imaging has the power to assist in successful translation of molecules by reducing the risk of several specific causes of failure in Phase II clinical studies. Imaging biomarkers, or i-biomarkers, are especially valuable in giving confidence of tissue delivery, determination of target engagement, and the evaluation of a drug’s pharmacodynamic effects.”

    While imaging is routinely used in clinical diagnostics for cancer, its acceptance in drug development has been slow. “This is a highly specialized area of knowledge,” Dr. Cox observes. “Designing imaging experiments to answer the right questions is not trivial. Combined with the perceived high costs and dearth of well-equipped facilities, this has slowed down the adoption of imaging as an integral step in drug development.”

    Imanova presents an innovative and highly integrated solution in reducing the barriers for use of molecular imaging. Located in the former GlaxoSmithKline imaging center, Imanova’s staff applies the knowledge needed for translational application of imaging science.

    “Another historical barrier for use of molecular imaging has been the lack of versatile PET tracers for key therapeutic targets,” remarks Dr. Cox. Together with its pharmaceutical clients, Imanova develops proprietary tracers that can answer critical questions about target engagement directly after drug administration. A structured approach for i-biomarker development takes the novel tracer from the candidate pool to clinical validation.

    Uniquely, Imanova utilizes in silico biomathematical modeling to predict a candidate with ideal physicohemical characteristics. “The i-biomarker development pipeline adheres to a strict quality system,” continues Dr. Cox. “We not only provide candidate selection and labeling, but also rigorous preclinical evaluation in several species, combined with blood chemistry or other physiological measurements.”

    The resulting biomarker provides quantitative information to make informed go/no-go decisions. Imanova hopes to develop an open innovation approach to i-biomarker research, and to encourage pharmaceutical companies to collaborate on tracer development.

    “By collaborating in this pre-competitive space, a pharma-academic consortium can de-risk i-biomarker development programs and generate new tools to eliminate costs associated with futile activities downstream,” concludes Dr. Cox. “Most tracers need to be utilized early in the drug development process. Used at the right time, imaging biomarkers are able to inform the design of Phase II studies, including dose ranging and possibly patient selection, saving many months in development and millions of dollars in costs.”

  • Answers from Big Data

    “Clinical bioinformatics is the application of a data-driven, high-tech approach in clinical setting,” says Jerome Wojcik, Ph.D., CEO of Quartz Bio, a clinical bioinformatics service provider located in Plan-Les-Ouates, Switzerland. “We use clinical bioinformatics to adapt treatment to patients, that is, to identify cohorts that respond to the drug in a predictable manner,” says Dr. Wojcik.

    Pharmaceutical partners supply Quartz Bio with data collected in a course of clinical trials. The data (which may include information from protein and RNA expression, genotyping, molecular diagnostics, and flow cytometry studies) often exists in silos within a pharma company. To make sense of the data, Quartz Bio integrates heterogeneously formatted data, analyzes it for consistency, and identifies gaps and outliers.

    Dr. Wojcik’s team dedicates over 40% of the overall analysis time to the biomarker data management. This key step is crucial for the quality of the overall analysis. According to Quartz Bio, all the data-management processes are documented, auditable, and reproducible.

    Once the “Big Data” horde is adequately cleaned up, the team applies adaptive statistical methods to generate multiple hypotheses linking the drug action with subpopulations of patients. “Our challenge is to generate reliable hypotheses on a fairly small statistical patient sample, for example, a thousand patients, but using millions of biomarker datapoints,” continues Dr. Wojcik. “We do not rely on statistics alone. Graphical visualization adapted to the objectives of the study is necessary for interpretation of results.”

    In a recent project, Quartz Bio analyzed multiple oncology biomarkers, such as gene expression, circulating tumor cells, and immunohistochemistry, to identify patient cohorts that would most likely benefit from a novel treatment. Biomarker analysis revealed a subpopulation whose survival rate increased significantly over the population average, bringing a potential application of personalized medicine closer to reality.



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