A Better Predictive Model for Oncology Drug Development Beyond Traditional Platforms

Delineation of the intra-tumor microenvironment in a dynamic, spatiotemporal setting is critical for investigating the activity and efficacy of candidate oncology drugs. The majority of solid cancers contain unorganized, highly-complex microenvironments wherein a dysregulated phenotype impacts treatment outcomes at a personalized level.

Join this webinar to learn about a clinically-validated and fully human ex vivo platform technology which uses fresh patient material (tumor, autologous ligands and immune cells) to explore the mechanism of, and predict efficacy for, clinically-directed compounds across several drug classes.¹

The platform affords many drug development and discovery applications, including:

• Optimal combination therapies – the prediction outcome system provides a standardized approach to the prioritization of the combinatorial strategies tested
• Mechanism of action – integrated phenotypic outcome and subsequent omic data can shed light on the potential mechanisms of action that underlie a response of interest
• Drug impact – the platform’s preservation of the tumor microenvironment, including the tumor immune compartment, can help hone immunomodulatory strategies
• Biomarker discovery – the tool’s predictive power can be leveraged to identify biomarkers that distinguish populations of responders from non-responders
• Indication prioritization – prioritization algorithms can be used to determine which tumor types will be best addressed by a lead candidate
• Parallel clinical trials – a flexible platform can be harnessed to rapidly assess differential response and resistance profiles within a patient population
  

Drugs that modulate the immune system have been shown to be very effective in some patients. In general, tumor infiltrating lymphocytes are required for the function of these drugs. A case study on the use of this ex vivo platform technology to understand the role of functional immune phenotypes when using a checkpoint inhibitor on patient tumor samples will be presented.

This webinar will explore how this platform can better enable your translational efforts (including mechanism of action and efficacy) and aid in advancing your highest potential candidate into successful clinical trials with high confidence.

1. Majumder B et al. Nature Comm., 6:6169:1-14 2015

Speaker

Stefan Jellbauer, Ph.D., Technical Liaison at Mitra Biotech

Dr. Stefan Jellbauer is the Technical Liaison for Translational Applications and works with clients to craft custom solutions for their drug development needs. He supports the Biopharma Business Development side of Mitra Biotech. After 10 years in academic research, he joined Affymetrix/eBioscience, where he worked in the roles of Field Application Scientist and Technical Specialist with Affymetrix and Thermo Fisher Scientific. He joined Mitra Biotech in May 2018.

Dr. Jellbauer received his Ph.D. in Biology from Ludwig-Maximilians University in Munich (Germany), focusing on tumor vaccination. He completed his post-doctoral work at the University of California, Irvine studying mucosal immunology and host-pathogen interactions.

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