Using Software to Standardize New Methods for Testing Cancer Drugs on Fission Yeast

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Fission yeast cells are easier for scientists to work with than human cells, and their applications to cancer research are growing. Image Credit: Flickr user Col Ford and Natasha de Vere

Time and money are two of the most significant barriers to new drug discovery in cancer research. Because human cells are slow-growing and demand costly inputs, scientists are always looking for opportunities to avoid time-consuming, resource-intensive processes. This past year, researchers at Okinawa Institute of Science and Technology Graduate University in Japan identified a new way to identify and test certain kinds of cancer drugs using fission yeast.1

In their July 2016 study, the researchers were looking at ICRF-193, a topoisomerase inhibitor that induces polyploidy by allowing for nuclear division without proper chromosome segregation. As a result, the compound has the potential to prevent cell growth and proliferation in a wide variety of cancers. Previous studies had already demonstrated an increase in ploidy in HeLa cells, but these researchers were able to observe a change in spindle dynamics in fission yeast after ICRF-193 treatment.2  The “arched and snapped” appearance of the treated cells had not previously been observed, and it may be able to serve as a marker of the efficacy of other potential cancer drugs that are intended to disrupt chromosome segregation.3  

As the number of ways to test cancer drugs using fission yeast rather than HeLa or CHO cells grows, life science companies can start incorporating yeast-based screening and testing methods into their labs. Modern software can streamline integration in ways that allow researchers to make the most of these novel methods and possibly bring about the next cancer breakthrough even quicker.

Developing Testing Protocols for Fission Yeast

As yet, fission yeast cannot be used to test  every cancer drug, so some researchers stick to human and rodent cells for cancer drug testing. However, as more ways to test with fission yeast are discovered, companies can save money by dedicating less time and money to the culturing of HeLa cells and opt for fission yeast tests whenever possible. In order to make the most of the potential time and cost savings, it will be important to develop standard protocols for stock maintenance and cancer-specific tests. Software can be used to optimize processes in ways that improve resource efficiency and ensure high quality, consistent results.

Another way that software supports process improvements is by facilitating the automation of redundant methods and data analysis. Since one of the main reasons to use fission yeast instead of human cells is to save time, it doesn’t make sense to adopt new testing methods if process time is simply reallocated rather than reduced. Automation of basic methods and analysis gives researchers the opportunity to focus their efforts elsewhere, which can ultimately reduce the time it takes to get a particular drug to market.

Collaboration on Fission Yeast Research

For researchers working with fission yeast, collaborative efforts with colleagues are essential during protocol development and in future research. There are several ways that software can make it easier for researchers across the company work together as they use fission yeast to explore different cancer treatments:

  • Process Development Insights

 

As scientists become more familiar with new protocols using fission yeast to test cancer drugs, it will be important for them to communicate with each other about their successes and the challenges they face in order to optimize methods. Protocols should be tweaked and easily distributed across the lab so that researchers can share tips and tricks that improve data quality and cut down on wasted time.   

 

  • Sharing Screening Results

 

Once standard protocols are in place, cancer researchers can start using fission yeast for a wide variety of tests, from explorations of single drugs to high throughput screens. Especially when their tests generate large amounts of data, it must be easy to disseminate data and results to other research team members. Also, when hundreds of possible drugs are identified in a screen, the information should be quickly transferred to another group within the lab or the company so that work can begin on multiple potential cancer drugs at the same time.

  • Interdepartmental Strategizing

 

Choosing the ideal drug candidates for development and marketing requires well-informed business decision-making. Scientific researchers need to provide higher level decision-makers with the data they have collected from fission yeast tests, in order to help them make strategic choices about which drugs to try to bring to market.

Overall, the identification of a new way use fission yeast in cancer research opens the door to more tests with this model organism. To make the most of the benefits of replacing traditional studies on human and animal cells with yeast cell tests, life science companies can use advanced software that improves process development and facilitates collaboration within the company.

BIOVIA ONE Lab is a high-level software solution that can streamline research and development within labs and across life science companies. ONE Research Lab can improve efficiency and ensure experiment quality, while ONE Development Lab facilitates process improvements and can help companies convert promising drug candidates into marketable products. Contact us today to learn more about how BIOVIA One Lab can transform R&D at your life science company.

  1.  “Fission yeast could help discover novel cancer drugs,” October 24, 2016, http://www.news-medical.net/news/20161024/Fission-yeast-could-help-discover-novel-cancer-drugs.aspx
  2. ICRF-193, an anticancer topoisomerase II inhibitor, induces arched telophase spindles that snap, leading to a ploidy increase in fission yeast,” 26 July 2016, http://onlinelibrary.wiley.com/doi/10.1111/gtc.12397/full
  3. “Paving the road to drug discovery,” October 21, 2016, https://www.sciencedaily.com/releases/2016/10/161021084509.htm