Pharmaceuticals, in vitro diagnostics, and medical devices have historically used different marketing techniques. For the most part, pharmaceutical marketers have targeted office-based physicians, both primary care and specialty; pharmacists, both retail and hospital-based; and hospital and managed care formulary committees. Diagnostic marketers have targeted hospital-based physicians—particularly pathologists, laboratory managers, laboratory department supervisors, and point-of-care coordinators. Device marketing has an extremely broad range of targets, from physician specialties such as radiologists, cardiologists, orthopedic surgeons, and other surgical disciplines to nursing and materials management representatives.
One of the major differences between marketing in these three segments has been the size of the promotional budget: large for pharmaceuticals, small for diagnostics, and somewhere in between for devices, depending on the type. Personalized medicine blurs those boundaries, and will require interdisciplinary marketing of drugs and diagnostics by marketers that historically have been siloed. Except in those situations where therapeutic drug monitoring is necessary, drugs and diagnostics have been historically separated. Now, however, drugs and diagnostics will not only be co-developed, but also co-marketed, requiring marketers to possess both pharmaceutical and diagnostic expertise.A Glimpse into the Future
What do marketers consider when trying to sell a diagnostic test? Sample type and ease of sample preparation, specificity, sensitivity, reproducibility, accuracy, precision, false positives and false negatives, turnaround time, hands-on time, compatibility with automated platforms, and cost/test. When personalized medicine arrives (as it has in the case of breast cancer and Herceptin, leukemia (CML) and Gleevec, metastatic colorectal cancer and Erbitux), the key attribute for a genetic-based diagnostic test will be all of the above plus its clinical utility.
Within the proposed FDA drug and diagnostic co-development guidelines will be a definition of clinical utility. The definition previously developed by the National Institutes of Health Secretary's Advisory Committee on Genetics, Health, and Society (SACGHS), reads as follows: "Clinical utility refers to the usefulness of the test and the value of information to the person being tested. If a test has utility, it means that the results—positive or negative—provide information that is of value to the person being tested because he or she can use that information to seek an effective treatment or preventative strategy. Even if no interventions are available to treat or prevent disease, there may be benefits associated with knowledge of a result."
One of the first examples of clinical utility in a molecular diagnostic test was Roche Molecular Systems' quantitative PCR assay (Amplicor HIV Monitor), which allowed clinicians to measure extremely low levels of HIV viral load in order to monitor a patient's response to a Roche antiretroviral drug. While not an example of a genetic-based companion diagnostic, this serves as a good example of using a diagnostic assay based in molecular medicine to demonstrate clinical utility.
When marketing diagnostic products, it is very tempting to sell the technology rather than the result, particularly when the input is coming from the point of view of the assay designer. That may be fine for the laboratory performing the test—they'll want to know how the assay is performed to help determine if they have confidence in the test platform. The same will not be true, however, for physicians.
When discussing a diagnostic test, a physician will invariably ask, "If I know the result, will it change the way I treat the patient?" In the case of many personalized medicine examples, the answer is a resounding "yes."
The basic strength of any diagnostic test is the assay's ability to get the same result on the same sample day after day (reproducibility); the ability to detect the target even when it is present in extremely low quantities (sensitivity); and the ability to measure the correct concentration (accuracy) on a single sample every time it is tested (precision). If the assay is robust, its reproducibility, precision, sensitivity, and specificity will result in few, if any, false readings so that physicians can rely on the results to help direct treatment.