Disease model generation: 5 steps to a 3D cancer spheroid model

Presented By:
Edwin Golez

Speaker Biography:
Edwin has been working in San Diego biotech/pharma for the last 20 years, primarily from the bench scientist role. The laboratories he has worked in spanned into various research areas such as vascular biology, tumor immunology, diabetes, obesity, and regenerative medicine. His 3D experience primarily stems from working with a San Diego biotech company that utilized a 3D printer to bioprint human tissues for model development and drug toxicity screening. Edwin worked on various models such as bone, skin, breast cancer, and liver. There he was able to understand the challenges associated with model development but also understood what impact the end product had on drug discovery. Edwin is now leveraging is 3D experience as the Field Application Scientist for 3D and Advanced Cell Models for Thermo Fisher Scientific.

Webinar:
Disease model generation: 5 steps to a 3D cancer spheroid model

Webinar Abstract:
This presentation highlights a 5-step outline for the development of a 3D spheroid model using breast cancer as an example. These guidelines can be extrapolated to cells of other cancer types as well.

Since R&D initiatives provide a lot of novel concepts to drug development and growth of the cancer therapeutics market, it is important to have relevant model systems to assess drug response. As such, three-dimensional (3D) cell cultures of cancer have increasingly become the model of choice for basic as well as preclinical research in cancer biology.

The generation of organoids and spheroids for cancer, includes:

Choosing the appropriate cell and tissue types
Optimizing growth and maintenance conditions for the 3D cultures
Characterizing the models
Assessing drug responses on such models to better understand the physiological effects of such drugs
This presentation will highlight a 5-step outline for the development of a 3D spheroid model using breast cancer as an example, however these guidelines can be extrapolated to cells of other cancer types as well.

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