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Biomarker-based discovery

Effective biomarker research is a data problem

Biomarker-based clinical trials and biomarker-based discovery are commonplace as pharma, biotech and healthcare organizations strive to realize the vision of personalized medicine. Effective biomarker-based research depends on uniting clinical and research information, which poses significant challenges to existing informatics infrastructures. In addition biomarker-based research depends very heavily on having highly annotated biospecimens, so managing specimens and their associated data also needs to be considered.


The challenges of biomarker-based research

Like translational research, biomarker research faces many of the same challenges in bridging the divide between research and clinical domains. As a result achieving frictionless information exchange is also similarly important and the barriers to this need to be overcome for effective biomarker research.

Multiple, disparate data sources – Biomarker studies require simultaneous analysis of multiple types of specimen assay data such as pharmacogenomic, genotyping, proteomic or other molecular analytical or cell-based data. These datasets all too often reside in different applications or file directories with little integration. Furthermore patient information linked to this specimen data is also often not integrated, or even easily accessible,  creating a bottleneck for extraction of higher scientific insights and downstream decision making.

Drive the science - the expectation is that new knowledge can be obtained from the biological and clinical information that will lead to the discovery of new biomarkers. Hence, tools to explore the data and develop new hypotheses are  critical to generating scientific insights.

Support collaboration – biomarker research is multidisciplinary and collaborative studies are commonplace. Informatics systems must be able to support this collaborative model while maintaining regulatory compliance.

Ensure compliance and security – on the clinical side of the divide is data that is sensitive patient health information (PHI) that must be managed in accordance with regulatory guidelines.


Enabling biomarker discovery through translational informatics

Effective biomarker discovery can be achieved by adopting BioFortis’ transaltional informatics platform, Labmatrix, which is designed bridge the informatics gap between research and clinical domains. Labmatrix provides;


Why choose Labmatrix for  pre-clinical and clinical biomarker discovery?

Labmatrix was designed from the ground up  to support translational research and so is ideal for your next pre-clinical or clinical biomarker project.

Collection and harmonization of research and clinical data – support for data integration and harmonization with specialized informatics systems (such as ELN, EMR, and LIMS) provides a holistic, unified view of information across the research and clinical divide.

Sample management – key sample management workflows for day-to-day next generation biobanking operations needed for specimen management for biomarker studies.

Support for collaboration- Providing the critical infrastructure to maximize the value of large integrated data sets, while ensuring appropriate access controls.

Generation of scientific insights – puts powerful tools for data exploration, reporting and ad hoc query in the hands of researchers so that they can mine the richly annotated information to drive biomarker discovery.

Enhanced security and compliance – Fine-grained access controls and security that extend across the research-clinical divide and ensure that all collaborators have the access they need, while maintaining compliance.