What do leaders of disease-specific advocacy organizations know about pharmacogenomics and biomarkers, anyway?

Sharon F Terry
2009 Personalized Medicine  
The future is a world where we understand the pathomechanisms of disease, use precise biomarkers to measure the efficacy of a multitude of safe and effective treatments in order to tailor them to the individual in a specific environment, and monitor all events, adverse and otherwise, through ubiquitous and easily accessible surveillance systems that ensure person-centered and specific privacy and confidentiality controls. Of course, all of this is paid for by a comprehensive health and wellness
more » ... care system. We are not so naive as to believe this scenario will be realized any time soon, but we do think this future is achievable. We are not even too troubled when we do not understand the disease pathway. We are certainly satisfied with safe and effective treatments, even if we do not understand their mechanism of action. To accelerate the discovery of tests and therapeutics, Disease Advocacy Organizations (DAOs) are engaged in all aspects of therapeutic development and in some cases are leading the way, for example, in Marfan syndrome [1] and cystic fibrosis [2]. We understand the enormous complexity of the major scientific hurdles in both common and rare diseases. Nevertheless, the greatest impact DAOs appear to be ready to make is in the realm of cohort development, while exploring new models and systems to encourage and aid translational science. At the core of personalized, or individualized, medicine is the translation of genetic and/or genomic information to relevant personal health information. This translation requires that tests and therapies be measured against appropriate clinical end points. These clinical end points are not easy to determine and quantify, and their validation, and that of companion drug targets, is a complex and difficult process. Though basic research has thrived in the last few decades, these steps in the therapeutic pipeline have not been so successful (Figure 1) . A recent US Government Accounting Office report stated: "On average, drug sponsors can spend over 13 years studying the benefits and risks of a new compound, and several hundred millions of dollars completing these studies before seeking US FDA's approval. Approximately one out of every 10,000 chemical compounds initially tested for their potential as new medicines is found to be safe and effect ive and eventually approved by the FDA, making the drug discovery and development process complex, time consuming, and costly" [3]. The drug development industry is being further challenged as personalized medicine begins to shift success away from blockbuster drugs and towards drugs for stratified populations with no obvious pathway to profit. This stratification requires accurate assays. At the same time, many DAOs have transformed themselves to facilitate test and therapeutic development leading to health outcomes. Disease Advocacy Organizations (DAOs) make meaningful contributions to the development of tests and therapeutics across the development pipeline, from cohort development to actual drug discovery. The process of developing biomarkers and validating them is fraught with a high failure rate and enormous expense. DAOs can harness new information technologies to increase effectiveness, including systems to dynamically consent individuals to participate in registries and trials. These new technologies can alleviate some of the expense in biomarker development. Information aggregation with consumer control of information at its core will eventually permit a national surveillance system for pre-and post-treatment analysis. A stronger and more scientific basis on which to build quality control and assurance of biomarker determination is needed. Validation must be supported in the future, in the same manner discovery was in the past, including through federal funding and philanthropic giving. DAOs can accelerate the process of biomarker development by building robust, well-characterized cohorts.
doi:10.2217/17410541.6.2.171 pmid:29788608 fatcat:6qjlicfhsrczdfq7lfielslbvy