Development of an Ex Vivo 3D Functional Assay for Novel Drug Discoveries

Abstract Cancer drug discovery is in crisis since only 5.1% of the total drugs entering Phase 1 clinical trials succeed to approval. A reason for the low success rate in clinical trials lies in the initial steps of drug screening using 2D cell cultures and use of immunocompromised mice. Cancer is no...

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Bibliographic Details
Published inThe Journal of immunology (1950) Vol. 198; no. 1_Supplement; pp. 205 - 205.8
Main Authors Murali, Vasanth Siruvallur, Danuser, Gaudenz, Cobanoglu, Murat Can
Format Journal Article
LanguageEnglish
Published 01.05.2017
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Summary:Abstract Cancer drug discovery is in crisis since only 5.1% of the total drugs entering Phase 1 clinical trials succeed to approval. A reason for the low success rate in clinical trials lies in the initial steps of drug screening using 2D cell cultures and use of immunocompromised mice. Cancer is not just a malignant outgrowth of cells, rather a complex malignancy where the clinical outcome is determined through crosstalk between the malignant, stromal, and immune cells. Furthermore the extracellular matrix is an integral and dynamic part of this crosstalk; examples include TAMs remodeling the ECM and the ECM in turn determining the invasiveness of the malignant cells. since ECM has been shown to be affected by these cells. Thus, a system needs to be designed that introduces all these factors into a drug discovery platform. My aim is to develop an ex vivo assay with humanized synthetic ECM embedded with patient tumor tissue and co-cultured with stromal cells, fibroblast and immune cells isolated from patient. To validate clinical relevance of this complex environment functional assays would be performed to compare drugs response between patients and patient derived tumor. Drug responses on malignant cells will be assessed by 3D high-resolution imaging and computational image morphometrics. My long term goal is to develop a medium throughput novel drug discovery platform that would be guided by machine learning to identify novel chemical compounds.
ISSN:0022-1767
1550-6606
DOI:10.4049/jimmunol.198.Supp.205.8