As a builder asks which tools are most beneficial, so do scientists as they work to create compounds to send into the clinic. Finding the protein and pathway targets of interest are only part of the puzzle; the means to find these are only as good as the tools scientists can employ.
“Companies don’t often invest in biomarkers until it is too late,” noted Mark Parrish, senior manager of assay development at Covance. “Scientists need to think broadly—cast a wide net, understand your sample, and understand your platform.”
“Biomarker research has seen an exponential increase in use over the last decade and this will continue,” added Richard Houghton, principal scientist, bioanalytical science at Quotient Bioresearch. “Biomarkers will be used earlier in the drug discovery process to demonstrate proof of mechanism mitigating some of the development risks and ensuring that only those demonstrating potential efficacy are taken into Phase I trials.”
At CHI’s “Biomarker World” conference, speakers addressed the changing role of biomarkers in research, how technology is accommodating that role, and the implications both have for drug discovery.
New Engines for Data Analysis
Data analysis remains a perpetual pain point in biomarker research. Ilya Mazo, Ph.D., president at Ariadne, noted that high-throughput data-generation methodologies such as microarray gene expression, require new approaches for gathering information.
“Ariadne’s focus is on proprietary linguistic algorithms that help people access more information about biological pathways and systems. Our technology helps scientists make informed decisions about what biomarker to pursue.” The company believes that the computational approaches used for high-throughput data analysis require that the biological information from literature is a coherent and integrated part of the analysis software itself.
To that end, Ariadne applies its MedScan technology to produce the ResNet (mammalian and plant) and ChemEffect (drug-centric) knowledge databases by harvesting knowledge from literature. The databases can be supplemented by users with any other type of information including in-house documents and third-party data sources. Once captured, this information is transformed into biological relationships and stored for use in hypothesis testing and verification, mechanistic modeling, and drug and patient stratification strategies.
At the meeting, Dr. Mazo presented a case study demonstrating the use of MedScan in biomarker research. “There is a strong case in the data to boost identification of molecularly defined markers in classes of patients. Using MedScan, we compiled a knowledge database from scientific literature to hypothesize a mechanism behind fibromyalgia (FM).
“Researchers performed genotyping by using Algynomics’ pain research panel, a chip-based platform that assays 3,295 SNPs representing 350 candidate genes for pain sensitivity, inflammation, and effect. The results from the association tests were then analyzed with Pathway Studio, which identified cellular pathways both common to FM as well as distinct between clusters.”
According to Dr. Mazo, the data generated provides evidence that clinical phenotypic subgroups may be underlined by separate but specific cellular pathways, providing rationale for individualized treatment of patients with FM.
“What we observed requires further study, but we made some promising discoveries. There is still a lot of opportunity out there. I believe we can help people by leveraging this knowledge with informatics to provide answers.”