Drug Discovery

RNA expression profiling can provide a genomic signature which is correlated with a particular physiological or disease state of interest to the discovery researcher. Genomic Signature Sequencing (GSS)™ will enable discovery researchers to accurately and rapidly apply genomic signature monitoring to preclinical animal models to assess compound specific changes to specific RNA signatures. Monitoring these genomic signatures through the use of the GSS application allows the researcher to measure RNA expression changes to identify those compound classes which shift the expression to more closely mimic the normal tissue or cellular state.

The GSS application becomes an important component of the discovery researchers tool kit. Examining multiple compounds, either of structurally similar or distinct classes, in a high through put manner provides the opportunity to maximize the information obtained from each experiment. Similarly, in toxicology testing, a comprehensive GSS survey will allow the assessment of selected candidate genes in cellular toxicology assays or in evaluation of target organ toxicity to allow quantitative measures of changes indicating off target action or effects on undesirable pathway activities.

Drug Development

There is a critical need to translate a genomic signature profile which describes a physiological or disease model state to the clinical characterization of both healthy subjects and patients. Only then will we achieve the full use of this genomic information to better inform the process of new drug development. Our ability to fully translate genomic signatures to the clinic has been limited by the lack of a cost effective, high throughput methodology to investigate quantitative RNA expression changes across multiple patients and in multiple samples. Consider the application of genomic measures to Phase I studies aimed at the careful examination of safety and pharmacokinetic factors for a new investigative drug - your genomic signature of interest requires the interrogation of tens to hundreds of RNAs in hundreds of healthy volunteer subjects across multiple time points over to drug dosing regimen. These data points quickly saturate existing RNA measurement platforms.

Through application of GSS clinical researchers will possess the ability to interrogate gene expression signature of interest. This will allow the integration of quantitative gene expression profiling into clinical trials at the requisite throughput beginning with single dose studies through large, multicenter Phase II and Phase III programs.

The study of new oncology medicines will similarly be dramatically enhanced by the application of genomic signature sequencing. The underlying genomic heterogeneity of phenotypically similar tumors has been well documented and clinical studies have been published. GSS will provide the application to apply these analyses across broader sample sets to allow extensive panels of genes to be examined with minimal RNA sample requirements and allow molecularly similar tumors to be studied for successful treatment characteristics.

Finally, the examination of RNA profiles and corresponding clinical characterization of patients will provide direct feedback to discovery scientists about the perturbation of disease pathways by various molecular interventions. This will provide new insight into the relevance of various animal models and cell lines currently used in the discovery and selection of the next new molecules destined for the clinic. The potential opportunity to improve the translatability of these models is a key business need to address the high rates of attrition seen in the pharmaceutical industry.

Thus the use of Genomic Signature Sequencing in drug discovery and development provides significant benefits which include:

• Genomic signatures developed using a multitude of methodologies can be converted to the GSS assay without compromising the number of RNAs of interest as well as the sample volumes anticipated
• Providing the ability to monitor preclinical genomic signatures across multiple models and compounds
• Allowing the seamless translation of preclinical signatures to the clinic and the important feedback from clinical researchers back to the discovery bench
• Providing the throughput and quantitative measurements needed for analysis of clinical trial samples throughout the development continuum