Printer-friendly versionTranslational research can accelerate basic research into medical and healthcare practice, as long as the many challenges are proficiently addressed with a robust integrated informatics solution.
Some of the biggest informatics challenges related to translational research are:
• Increasing numbers and scale of studies
• Sourcing well-characterized biospecimens for research
• Provisioning clinical information for research purposes
• Managing large scale projects across a research enterprise
• Increasing robustness for translational research
• Growing data generation with many new and diverse technologies
• Understanding biology, pathology and therapies as a system
• Collaborating across institutions and traditional boundaries
Increasing numbers and scale of studies
Many translational research efforts are focused at biomarker validation, precipitating an increase in translational research studies. These studies require robust protocol management similar to a clinical trial, but unlike clinical trials, are often flexible and adaptive and do not need to follow stringent FDA regulations.
Sourcing well-characterized biospecimens for research
Institutions often house multiple biorepositories spread across their enterprises. These biorepositories are often poorly annotated. Translational research requires increased specimen counts to ensure strong statistical support and rich specimen annotations to diminish confounding factors.
Provisioning clinical information for research purposes
Most clinical (phenotype) information is not structured for research purposes, whether in pathology reports, physician notes, detailed ICD9/10 codes, or hard to access hospital systems. Translational researchers require HIPAA compliant clinical information for cohort and specimen selection, and to infuse this information into their analysis. This remains a major hurdle for even the most advanced translational research enterprises.
Managing large scale projects across a research enterprise
Research has transcended any one principal investigator’s laboratory and often involves multiple collection sites, translational research studies with patient consents and IRB approvals, specimen counts into the tens of thousands, biorepositories and core technology labs, instrumentation, and disparate systems biology data sets. All this information and activity requires tracking, management, and traceability through the research enterprise.
Increasing robustness for translational research
For years, the basic sciences were often managed with limited infrastructure due to its diverse and adhoc nature and low sample counts. Translational research changes this, with its increased specimen counts and rigorous statistical requirements. This means a single point of failure can have devastating effects, can be very costly, and impede the realization of translational research benefits to patients and society.
Growing data generation with many new and diverse technologies
New technologies continue to flourish with the next generation of sequencing, imaging instrumentation, mass spectrometers, and robotics systems. With increasing specimen counts and larger and more complex data sets, many core technology facilities are or will soon be overwhelmed with data generation and data processing requirements.
Understanding biology, pathology, and therapies as a system
Much of the real understanding lies in the systems context of how genes, proteins, and metabolites interact, but fusing these complex data sets together for analytical purposes holds back investigators from realizing the true value of their research and experimentation.
Collaborating across institutions and traditional boundaries
Interdisciplinary by nature, translational research often prescribes multiple institutions working together to run the full range of clinical to research efforts. A host of policy, IT infrastructure, security, coordination, and communications issues can play into larger-scale translational research initiatives.
As research enterprises actively initiate and engage in large scale translational research and life sciences research efforts, they require biomedical informatics data management systems such as GenoLogics’ solutions for translational research, to address the above challenges and more.
