1. Pharmaceutical and biotech companies that apply GNS' compound simulation models to improve the quality of drug candidates; and
2. Cancer clinics that use GNS capabilities to assess which emerging cancer therapies will work best for patient subpopulations.

In both types of alliances, GNS offers advanced capabilities in data integration, in silico cell simulation modeling, supercomputing, molecular biology and mathematics. Clients provide their data, and GNS then produces proprietary models that simulate how and why a given compound or set of compounds affect cells and tissues, and produce certain clinical outcomes.

Pharmaceutical/Biotech Alliances

GNS has successfully engaged in drug development alliances with major pharmaceutical companies at the early target identification and validation phase, at the late pre-clinical phases, and in the clinical development stage. GNS technology can also provide solutions in post-approval trials, label enhancement clinical trials, and drug rescue efforts.

GNS capabilities are being used most extensively in oncology drug development, cardiac risk assessment, and analysis of potential drug toxicity in the following situations:

• In the transition from late pre-clinical to Phase 1 development, to improve the quality of pre-clinical compounds and the probability of a compound's Phase I and subsequent success;
• In the clinical setting, to guide the design of clinical trials and to maximize the response rate of patients in clinical trials; and
• Throughout the drug development process, to enable early evaluation of arrhythmia-related cardiovascular safety issues.

During the first phase of an alliance, the partner provides compound-specific data to GNS. Next, using its propriety Network Inference Engine to explore millions of network hypotheses, GNS produces customized in silico models of the relevant cell type and/or animal system. During the inference and simulation process, GNS discovers new information about known and unknown genes and pathways, which link the compound's mechanism of action to clinical outcomes of efficacy (tumor shrinkage, patient survival, etc.) and toxicity(nausea and emesis, anemia, neutropenia, death, etc.). Conventional investigational methodologies are not capable of delivering the depth or breadth of results available from GNS.

Subsequent research phases utilize the model to discover and test additional hypotheses, suggest new experimental protocols, incorporate new data, and carry out validation experiments. This iterative process increases the predictive power of the model, which addresses additional client questions about the compound as it moves through development. GNS carries out projects on a per compound/per development step basis or on a panel of compounds/per development step basis.

Cancer Patient Clinic Alliances

Partnerships with cancer patient clinics help determine which therapies work best for which patients. GNS takes molecular profile data from patient tumor biopsies and normal tissue along with clinical endpoints of tumor shrinkage and circulating tumor cell count to group patients into biologically homogeneous subsets. GNS simulations test which therapies will have the best clinical outcomes in different patient subpopulations.

GNS uses its Network Inference Engine to analyze patients' clinical data, discovering biomarkers and helping distinguish patients that will respond to a given therapy. Data comes from patients' tissue biopsy, proteomic and genomic analysis, and circulatory tumor cell count assessments. GNS utilizes its computational platform to integrate this data into a model of compound response.

GNS receives compensation from clinics on a per-patient basis.

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