This project will continue to advance drug discovery by enabling researchers to target pathways and cell subpopulations in primary tissues with greater specificity. Selective targeting of pathways and cell subsets is a critical issue in human disease in fields including cancer and autoimmunity, as increased selectivity can mean the difference between a broad therapy with significant side effects and a focused therapy that selectively targets diseased cells. To meet the growing software needs in the fields of personalized medicine, drug screening, and translational biology, we created Cytobank, a web-based platform for software development, data sharing, and publication of flow cytometry experiment results. We are serving the growing flow cytometry community in a way that no other software can. With Cytobank, a community of users manages experiments like email and shares scientific illustrations like photos in a web 2.0 cloud computing environment. However, tools for single cell drug discovery, including IC50 calculation and display, pathway visualization and compound screening have historically been missing from this framework. In Phase I we developed new plate and pathway visualization tools. Going forward, we will use the Cytobank web framework for flow cytometry data management and analysis to create innovative new tools that connect 3 new drug discovery tools, large dataset network visualization tools, and single cell data driven modeling tools. For all of these tools, big picture views will be related to underlying raw single cell data files. The Cytobank Inc. team developed key aspects of the flow cytometry technology, including single cell signaling profiles, fluorescent cell barcoding, and a rapid flow cytometry signaling diagnostic for a human cancer. This SBIR brings together these flow cytometry experts and a team of software engineers to create a tool that will incorporate cell type and pathway information into biochemical assays for drug discovery. This will enable screening for drugs that target cancer cells and not tumor infiltrating T cells and drugs that specifically kill cancer stem cells. This project has significant commercial potential, as the Cytobank flow cytometry data analysis platform is already employed daily by hundreds of users (www.cytobank.org). It has the support of large vendors in the flow cytometry space, biotechnology and pharmaceutical companies, and flow cytometry thought leaders in three clinical research universities. Cytobank's advisory board includes a seasoned technology entrepreneur with over twenty-five years of experience in Silicon Valley and a professor from Stanford University driving both the technology and techniques of applying phosphoflow cytometry!""" Longer term, the potential of this project is that thousands of scientists around the world will be able to undertake drug discovery in primary samples, comparing the effect of targeted inhibitors of key populations, such as bulk cancer cells, cancer stem cells, and immune cell developmental subsets. In addition to the immediate application in biotechnology industry, this project will also create tools that are useful for basic scientific research."
This project addresses the collaboration and analysis software needs for phospho flow cytometry - a molecular technology that is revolutionizing how we understand disease mechanism with broad implications for treatment of human diseases, including HIV, autoimmune disorders, and cancer. By combining knowledge and expertise from wide- ranging disciplines - such as statistics, bioinformatics, biochemistry, immunology and medicine -- this technology has the potential to guide analysis from the basic research phase into disease mechanism, drug discovery, and in vivo monitoring during clinical trials. Cytobank is a web-based platform that enables simple, rapid and efficient communication around phospho-flow cytometry within these disciplines. In this grant, we propose to extend the Cytobank platform by develop web-based analysis suites that will guide drug screening in primary cells by identifying cell type selective compounds and displaying summary-level views that are linked through to rich sources of primary data. Long term, this project will accelerate translational drug discovery by significantly lowering the barrier for researchers to do biochemical studies in tissue samples taken directly from patients. !