The term ‘biomarker’ is also used broadly and may encompass work spanning pharmacodynamic measures in early clinical trials to personalized medicine theranostics for commercial products. Given this large span it is not surprising that expectations are high while clear deliverables and specific impact may be difficult to define.

Further complicating evaluation of biomarker impact in therapeutic development is a history of excessive expectations. This may be due to an intriguing combination of optimism, desire for better decision making tools and passion for the power of genome sciences. The typical biomarker research team, while having strong roots and achievements in the genomics and technology communities are often significantly less well informed about drug development, operational and regulatory processes. This knowledge gap presents an important barrier to successful biomarker implementation.

We must also consider institutional barriers. Although biomarkers are considered to be essential, business as usual still predominates and this perspective resists change and delays impactful biomarker implementation. Representative barriers include: (i) hesitation for development teams to make difficult go/no-go decisions based on measurements other than a traditional validated clinical endpoint or regulatory accepted surrogates; (ii) failure to implement biomarker planning early enough in the development process; (iii) hesitation to include robust biomarker sampling in clinical trials, typically for reasons of complication, concerns of delayed enrollment and increased cost; and (iv) not including biomarker impact in the metrics utilized for assessing study quality and developmental team performance. An additional barrier to impactful biomarker implementation is governance. Given the time value of money, there is a continual desire to go faster and this leads to decisions being made without full data sets. Given the nature of the double blinded clinical study, it is often not possible to deliver biomarker data quickly enough to impact the next decision and to effectively alter decision making. While rational, these barriers all combine to reduce the impact of biomarker strategies.

The optimist suggests that knowledge gaps will be filled and barriers eliminated as biomarker sciences evolve and mature. Thus, when evaluating a field such as biomarkers, which is still very much in its infancy, an apparent short term temporal disconnect is no reason to lose sight of the opportunities presented by well-executed biomarker strategies within the context of well-executed clinical development programs. Further, organizations that marry these two objectives are expected to find competitive advantages.

Biomarkers in immunology – Johnson & Johnson Pharmaceuticals R&D

We are focused on delivering transformational therapeutic solutions to address unmet clinical need in inflammatory and respiratory disease. Remarkable progress in treating these diseases has been made in the last two decades, particularly therapeutics targeting soluble cytokines, T and B-cell activation. Having addressed primary needs in many disorders we turn our attention to the higher hurdle of providing patients with therapeutic options capable of inducing remission and perhaps cure. In order to effectuate, we will turn to biomarker characterizations to better define phenotypes and to direct new therapies to patients who will respond with higher levels of efficacy. To realize the above objectives, we have implemented an Immunology Therapeutic Area biomarker strategy in personalized medicine. This strategy focuses on five key objectives:

Biomarkers for improved decision making
As the costs of therapeutic development continue to escalate, development teams must provide governance with information which provides for crisp decision making. In this environment, each clinical trial would incorporate robust PD and pathway modulation biomarkers. It is essential for decision making that teams can clearly define target engagement and pathway modulation. Early trials therefore evolve from safety and tolerability to safety, tolerability, target engagement and pathway modulation experiments. The latter two objectives are almost always dependent on the quality of biomarker measurements and maximize opportunity to learn about the drug candidate being tested.

Improved learnings from clinical trials
While performing clinical trials to understand the therapeutic, it is also necessary to maximize the information regarding the diseases being studied. Clinical trials offer a unique opportunity to study human disease in a controlled environment and this opportunity is increasingly costly and difficult. While incorporation of biosample collection increases costs and complexity, this is easily justified. To maximize the value of this expense we have implemented information technology solutions such that disease data learned in clinical trials can be shared broadly across discovery and development to foster learning, innovation and discovery opportunities.

Improved differentiation of therapeutics
While clinical response against regulatory endpoints will remain the primary objective in most trials, we also need improved understanding of how different drugs work to combat disease and how they are differentiated. Biomarker strategies have a critical role in defining differentiation and producing datasets to better inform upon mechanism of action of efficacious molecules.

Consistent and intelligent application of technology
To execute against our biomarker strategy we employ a buy vs. build approach to data delivery. Given the rapid evolution of technology, it is often more efficient to partner strategically. Rather than focus on technology, we focus on experimental design, data acquisition and interpretation. By focusing on key questions rather than platform building we gain nimbleness and flexibility.

Co-Diagnostic Center of Excellence
There is clear need to increase response rates and stratified medicine provides a critical opportunity toward this objective. While early attempts at response prediction, mostly through investigation of genetic polymorphisms, have not been overly successful, application of other approaches is showing promise. Realizing the objectives of stratified medicine is a compound problem. First, the science must be proven and robust marker sets and assays developed. Second, assays must be commercialized under evolving regulatory rules for diagnostics. Finally commercial solutions for reimbursement and enhanced value propositions applicable across multiple therapeutic areas must be developed. By forming a central resource we can leverage learnings in multiple areas while simultaneously specifying dedicated resource whose sole function is to understand the issues around co-diagnostic implementation.

Conclusions

Biomarker measurements are a component of the data totality which is evaluated at each step in the development process. Biomarker considerations should be fully integrated into the program decision trees and commercialization objectives. Importantly, to recognize the promise of biomarkers, the development team and governance committees must be aligned on goals, objectives and decision making criteria. Biomarker work does not stand alone, off to one side of a development project waiting to be consulted if something goes amiss. This is a recipe for biomarker failure. Rather, when executed well, incorporation of robust biomarker planning coupled to a well considered developmental decision tree leads to increased confidence in decision making, improved probability of success and product differentiation based on data and science. The goals of personalized medicine and targeting of drugs to patients who will recognize the most benefit remains a significant component of biomarker research which is beginning to show return on investment. In summary, the future of biomarker research is broad and it is bright. This work is a fundamental component of development, and as teams learn how to better incorporate and interpret the vast amounts of genome and pathway data being generated, organizations that incorporate robust biomarker strategies will reap benefits over competitors who take a shorter term view and fail to maximize the potential in increasingly costly and difficult clinical trials.

Biography

Mark Curran is Senior Director of Immunology Biomarkers at Centocor Research & Development, Inc. His team is responsible for biomarker strategy, implementation, analysis and interpretation of results for all molecules in the immunology portfolio.

Mark has spent more than 25 years in genetic and genomics research and has experience in academia, biotechnology and pharmaceuticals. He has authored over 40 scientific publications and holds multiple patents. Mark has contributed seminal findings and gene discoveries in the field of heritable cardiac arrhythmias, heritable vascular disease, ion channel therapeutic targets, and genetics of autoimmune disease. Prior to joining the pharmaceutical industry he led development of the Familion™ diagnostic test for long QT syndrome and was involved in identification of novel ion channel drug targets. In addition to biomarker research, Mark has led teams responsible for early clinical development, experimental medicine, phase I and phase II proof of concept strategies and clinical trials.

When not pursuing robust biomarkers he enjoys competitive bicycle racing and amateur radio communications.