**5. Conclusions**

While there is widespread recognition of the value of biomarkers, scientific progress is slow. Over the years, biomarkers have sometimes been the center of excessive "hype", prompting excessive or unreasonable expectations. In addition, the use of biomarkers as surrogate endpoints have led to some public failures when they were felt to be falsely reassuring, creating general skepticism amongst scientists and clinicians alike (e.g. Petricoin et al., 2002). In addition to limited validation, resistance still hindering biomarker acceptance includes:


Tools to support development, medical care, health policy such as the FDA's critical path, and BioPharma investment decisions. The biomarker development and validation process is necessary but costly for one company. Innovation takes place in many organizations and, as such, stakeholders work redundantly on the same effort. Many collaborative forums exist but these usually involve sharing "safe" information that really does not hasten overall progress. Consequently, most existing biomarkers have taken decades to become part of medical practice.

applications have received little attention. It is very often difficult to receive funding from traditional grant programs to validate markers: funding agencies balk at the prospect of funding a 're-measurement' of the same entity in larger independent cohorts. Additionally, the continuum from discovery through to validation is tedious and extends well beyond the time-frame of a typical research grant. In fact, the time from initial discovery to routine use can take up to a decade (Anderson, 2010; Wilson et al., 2007). A recent example illustrates the seven year journey from discovery to FDA approval for the multivariate diagnostic test

Similarly, when validation fails it is difficult for academic investigators to publish these 'negative' results; when validation succeeds, the emphasis frequently shifts to

While there is widespread recognition of the value of biomarkers, scientific progress is slow. Over the years, biomarkers have sometimes been the center of excessive "hype", prompting excessive or unreasonable expectations. In addition, the use of biomarkers as surrogate endpoints have led to some public failures when they were felt to be falsely reassuring, creating general skepticism amongst scientists and clinicians alike (e.g. Petricoin et al., 2002). In addition to limited validation, resistance still hindering biomarker acceptance includes: Resistance to sharing data across independent efforts – Organizations may work on similar research or discover keystone advances yet resist sharing knowledge because they feel that doing so will jeopardize their competitive advantage. However, sharing information could help companies achieve greater overall progress and reduce costs. Need for new R&D models with greater precision and flexibility – The industry needs an R&D model with greater precision to improve pipelines, leveraging active clinical knowledge to offset the declining success in new drug development. Some research and development leaders are concerned that using an approach that targets treatment for only a subset of patients decreases profits and increases research costs. Others recognize that this direction has already created value beyond costs and are building these capabilities into their business strategy. For example, Herceptin® is considered an effective targeted treatment for breast cancer. Targeted treatments could actually

increase both the medical and economic success of a therapeutic.

different sources to find common meaning.

 Insufficient interoperability – Traditional data resides in disparate places that often do not easily connect. Factor in imaging biomarkers constituted by terabytes of data and you have a complex mix of data from which it is difficult to extract new insights (Poste, 2011). The path forward – interoperability – is a design and intent to have systems share information that relies on data standards, and more importantly, semantics. Semantics use common vocabularies and business rules to relate clinical terms reported across

Tools to support development, medical care, health policy such as the FDA's critical path, and BioPharma investment decisions. The biomarker development and validation process is necessary but costly for one company. Innovation takes place in many organizations and, as such, stakeholders work redundantly on the same effort. Many collaborative forums exist but these usually involve sharing "safe" information that really does not hasten overall progress. Consequently, most existing biomarkers have taken decades to become part of

OVA1, used to screen ovarian cancer patients (Fung, 2010).

commercialization rather than publication.

**5. Conclusions** 

medical practice.

Currently there are few FDA-approved proteomic tests for autoimmune disease. Although there is little doubt that such tests could help the diagnosis and treatment of arthritis, it is a major clinical and financial challenge to develop, validate and market them. Robust validation data including evidence of sensitivity, specificity and correlation to the existing limited set of clinical or laboratory criteria are necessary to support clinical utility. Disease activity scores (DAS-CRP and DAS28), for example, combine inflamed joint count and ESR/CRP to document levels of disease activity at a static time point. The measurement of specific proteins that flag a particular patient's status add objectivity in circumstances where the clinician currently relies on clinical judgment alone.

From a clinician's perspective, it is important to address several questions in a timely fashion for a given patient presenting with autoimmune disease. In each instance, the clinician is attempting to minimize underlying disease and adverse outcomes, such as joint damage in arthritis. Key questions that can currently only be partially answered by clinical observation and patient history include: (a) is this true autoimmune-driven arthritis (i.e., diagnosis), (b) how severe or at what stage is the disease process, (c) what is this patient's likely outcome (i.e., prognosis) and (d) which drugs could abrogate that outcome (i.e., prediction)? Decision-making also extends to selection of therapy: (e) what is the patientspecific titer, (f) which disease subgroups will benefit from a specific therapeutic strategy and (g) when should treatment be terminated?

This chapter has addressed and discussed three key areas for consideration, which if addressed after initial discovery work could provide solid evidence of their clinical utility and commercial viability: (i) limiting bias in study design, (ii) thorough protein isoform verification and (iii) modes of orthogonal and targeted validation.
