Personalized medicine—the use of genetic and genomic information to predict, prevent, and treat disease—is receiving widespread attention from both the public and the scientific community. It has the potential to transform the practice of medicine by improving understanding of the mechanisms of disease and permitting safer and more effective care for patients.

Another driver is the observation that drugs that work successfully in some individuals can be ineffective or cause serious adverse events in others. If the genetic determinants of efficacy or adverse events for a drug can be identified, a drug that might have ordinarily failed in clinical testing in the general population can be safely and effectively targeted to treat disease in a more narrowly-defined and genetically-identified sub-population (in effect, “rescuing” the drug from failure). Driven by an understanding of the molecular genetic basis of disease, personalized drugs and treatments vary based on an individual’s genotype. Many targeted drugs are already on the market or in the process of clinical development today.

Although it holds tremendous promise in the long run, truly realizing the potential of personalized medicine presents many challenges for life sciences companies. In the last five years, the breadth and depth of data available electronically from healthcare providers, pathology labs, genetic diagnostic labs, and other research institutions has exploded. The challenge now is to organize the data and to make sense of it to guide research. Valuable insights typically emerge when phenotypic clinical data can be combined with pathology and genetic data in the context of a specific disease or category of diseases. Personalized medicine will rely heavily on clinical informatics to identify and exploit such insights into the genetic variability of diseases.

Within clinical trials, the mandate to incorporate genetic information requires patient recruitment that provides genetic diversity and statistical significance of major genotypes in a trial sample. Hence, in order for the personalized medicine model to be both economical and scalable, pharmaceutical R&D will have to be more closely tied to clinical care delivery. In turn, information that is trapped in silos either on the R&D side or on the care delivery side will have to be more effectively combined and cross-referenced.

Once approved by the US Food and Drug Administration (FDA), targeting appropriate patients for safe and effective use of a drug will also require expanded informatics support. Recent FDA mandates for increased post-market surveillance will drive further integration between clinical trial software and healthcare delivery systems.

Accomplishing the integration of applications and data required to support informatics for personalized medicine is a challenge for most life sciences companies. Mergers and acquisitions further complicate the IT landscape by initially introducing more heterogeneity in applications and data models before systems can be rationalized across the merging companies. Over the next decade, the drive to implement informatics capabilities will surely lead to a near complete overhaul of the way R&D information is managed and utilized within an enterprise. Those leading the charge must be careful to ensure that incremental investments in applications and infrastructure also provide direct business value along the way, while adhering to a long-term road map. From that standpoint, the most significant near-term benefits of informatics will be realized in three areas:
• Streamlining clinical trial design, operations, and decision making
• Accelerating trial recruitment through closer collaboration with academic medical centers
• Expanding the research network and managing content effectively

click to enlarge   Personalized medicine is characterized by increasing interdependence between sponsors of clinical trials and academic research sites. Research networks will allow care delivery organizations, clinical research institutions, diagnostic labs, and bio-banks to collaborate flexibly. Clinical trials will become more adaptive, supporting a “learn and confirm” approach toward drug development, with higher standards for drug safety and pharmacovigilance. (Source: Oracle)

Streamlining trials
Personalized medicine, due to its data-driven investigational approach, will require clinical trials to become more adaptive. As information pours in during the course of a trial, it must be quickly assimilated to support effective decision making and course correction. This can vary from minor changes in protocol or dosage to substantial decisions, such as terminating a trial or refocusing it on a different market segment. Bottlenecks in any stage of the clinical trial process can contribute significantly to the overall risk of cost overruns and delays in the development cycle.

Traditionally, protocol design, recruitment, trial operations, and safety monitoring were not integrated functions within pharmaceutical companies. Each function served a distinct purpose in the development process, which played out more-or-less sequentially. This is reflected in the fact that most pharmaceutical companies use different, non-integrated applications for each of these functions. Already, point solutions for these functions have demonstrated considerable value. For example, pharmacokinetic and pharmacodynamic (PK/PD) modeling and simulation have evolved to become powerful scientific tools for trial design, and are aimed at improving predictability, and mitigating some of the risks in recruitment and execution. Similarly, adverse event monitoring has been a mainstay of the drug development process and application capabilities in this area continue to improve. However, in an adaptive environment, there will be significant interplay between these functions. A comprehensive informatics platform must allow scientists and business executives to look across these key functions to generate insights and drive decisions.

Furthermore, the number of data sources feeding into the development process will continue to grow. For example, the FDA and WellPoint Inc., Indianapolis, Ind., recently announced a new initiative to launch a real-time drug tracking and surveillance system to improve safety monitoring using health insurance claims data to reveal drug usage patterns and side effects. It is clear that in the world of personalized medicine, the evidence base for the safety and efficacy of new drugs will be more diverse and cover a longer time frame than current drugs. A standards-based development platform can help incorporate such new external inputs securely alongside internal data to provide a comprehensive view.

Geographically, cost pressures continue to drive clinical trials to ever expanding corners of the world. Portfolio management is getting increasingly complex as niche markets become more important and reimbursement models catch up with personalized treatments. For senior management, an integrated development platform can provide “vertical” informatics around specific drugs (from discovery, through clinical trials, and beyond to post-market surveillance) and also “horizontal” informatics required for R&D portfolio management across the drug pipeline.

Accelerating trial recruitment
Until recently, much of the data required to drive collaboration between healthcare providers and life sciences companies was locked up in siloed provider systems. Data for research had to be painstakingly duplicated between clinical information systems for care delivery and research systems. Recently, academic medical centers (AMCs) and large hospital networks have been investing substantially in electronic medical records (EMR) systems and research platforms that make deep, normalized, clinical information accessible for secondary uses like research and clinical trial recruitment.

Regulatory constraints—such as the Health Insurance Portability and Accountability Act (HIPAA)—which prevent healthcare information from being shared externally are alleviated using de-identification capabilities. These functions, commonly available in clinical IT applications and middleware products, strip personally-identifiable information before it is shared with external entities.

To encourage further integration of clinical healthcare and R&D efforts in the near term, the National Institutes of Health (NIH) has spurred investment at major AMCs through its Clinical and Translational Science Awards (CTSA). These incentives encourage AMCs to invest in the infrastructure necessary to bridge the gap between R&D silos and clinical practice, and to collaborate across multiple institutions through disease-specific “research grids.” Such integration efforts will go a long way in accelerating trial recruitment and will provide new flexibility for AMCs and trial sponsors in designing and executing trials.

To truly complete the integration, however, diagnostic labs must be brought into the framework. The number of genetic tests offered through diagnostic labs is expanding rapidly. In addition, bio-banks are building up repositories of genetically-identified tissue samples. These two sources yield mountains of new data on genotype-phenotype correlations, which can and must be leveraged in personalized medicine R&D. These new data often do not fit within the designs of older laboratory information management systems (LIMS) and EMRs. While many leading AMCs are developing their own data models to store and utilize genetic data, the industry, as a whole, requires open standards-based solutions in order to truly leverage all available data.

Diagnostic images are another important component of clinical data that has long been difficult to manage and, hence, remains trapped within specialized imaging systems such as picture archiving and communications systems (PACS). New database technology now makes it possible to work with images directly within databases, thereby making image repositories more robust, scalable, and accessible to informatics applications.

Life sciences companies must take full advantage of these opportunities to leverage the infrastructure investments being made on the healthcare side to accelerate trial recruitment and deepen research relationships with AMCs.

Managing expanding networks
As pharmaceutical development attempts to address an ever-increasing variety of targeted patient populations and conditions, trial recruitment will increasingly become a bottleneck—unless a much wider net for participants is cast. While AMCs have the greatest access to patients with critical and complex conditions, a significant patient population with chronic diseases receives treatment over long periods in primary care and other similar settings.

As pressures on trial recruitment grow, secure access to these patients and their anonymized medical records will become increasingly important. Unlike larger hospitals and AMCs that are are creating normalized EMR repositories for secondary uses such as trial recruitment, EMRs in primary care and other physician practices are not as easily accessible.

Even without direct access to patient data, trial sponsors can do a lot to educate physicians in these settings about current research trends and opportunities to enroll their patients in trials. Doing so will help physicians prepare for the more dynamic future of personalized medicine in which care delivery decisions will be increasingly based on rapidly-evolving research findings. Participation in trials will also provide physicians with an incentive to leverage their clinical patient data for secondary uses.

Thus far, life sciences companies have focused primarily on detailing and other marketing communications around approved drugs in their messaging to physicians. Going forward, it will be much wiser to engage with physicians earlier in the development cycle by providing them with relevant, personalized and differentiated content, thereby effectively expanding the R&D network.

Leveraging data to generate insights
Finally, the most ambitious frontier in R&D informatics is in the systematic capture, retention, and use of scientific knowledge and insights within an enterprise. While companies routinely consolidate drug pipelines through mergers and acquisitions, the knowledge and acquired insights within the merging companies have typically been very difficult to consolidate and leverage.

New “Web 2.0” tools promise to simplify the integration and sharing of semantic information in an enterprise. Ontology-based search engines—combined with natural language processing capabilities—can help researchers find and correlate relevant scientific insights buried across multiple data sources, taking content management and data mining to a whole new level. While these tools are still in the early stages of development and adoption, they provide hope that the informatics revolution will continue to power the transformation of pharmaceutical R&D and prevent the deluge of data from overwhelming scientists.

Even in the best of scenarios, broad informatics capabilities as described above will take many years to implement in any life sciences organization. As research boundaries between traditional silos begin to blur, key stakeholders must first act individually to implement the internal capabilities that serve their needs, while participating externally in collaborative initiatives to move toward common industry architectures. With a proper planning framework, adherence to open standards, and some forward-looking investments, organizations can ensure that their incremental efforts over time will yield a valuable and flexible informatics platform.

About the Authors
Kris Joshi helps to drive Oracle’s strategy and business development for the health sciences. Previously, he crafted healthcare payer and provider solutions strategies at IBM, where he wrote on personalized healthcare. He also was a strategy consultant with McKinsey and Company.

This article was published in Drug Discovery & Development magazine: Vol. 11, No. 7, July, 2008, pp. 14-18.