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May 3, 2016

Are Current Cancer Drug Discovery Methods Flawed?

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    Researchers utilized a systems biology approach to develop new methods to assess drug sensitivity in cells. [The Institute for Systems Biology]

    Understanding how cells respond and proliferate in the presence of anticancer compounds has been the foundation of drug discovery ideology for decades. Now, a new study from scientists at Vanderbilt University casts significant suspicion on the primary method used to test compounds for anticancer activity in cells—instilling doubt on methods employed by the entire scientific enterprise and pharmaceutical industry to discover new cancer drugs.  

    "More than 90% of candidate cancer drugs fail in late-stage clinical trials, costing hundreds of millions of dollars," explained co-senior author Vito Quaranta, M.D., director of the Quantitative Systems Biology Center at Vanderbilt. "The flawed in vitro drug discovery metric may not be the only responsible factor, but it may be worth pursuing an estimate of its impact."

    The Vanderbilt investigators have developed what they believe to be a new metric for evaluating a compound's effect on cell proliferation—called the DIP (drug-induced proliferation) rate—that overcomes the flawed bias in the traditional method.

    The findings from this study were published recently in Nature Methods in an article entitled “An Unbiased Metric of Antiproliferative Drug Effect In Vitro.”

    For more than three decades, researchers have evaluated the ability of a compound to kill cells by adding the compound in vitro and counting how many cells are alive after 72 hours. Yet, proliferation assays that measure cell number at a single time point don't take into account the bias introduced by exponential cell proliferation, even in the presence of the drug.

    "Cells are not uniform, they all proliferate exponentially, but at different rates," Dr. Quaranta noted. "At 72 hours, some cells will have doubled three times and others will not have doubled at all."

    Dr. Quaranta added that drugs don't all behave the same way on every cell line—for example, a drug might have an immediate effect on one cell line and a delayed effect on another.

    The research team decided to take a systems biology approach, a mixture of experimentation and mathematical modeling, to demonstrate the time-dependent bias in static proliferation assays and to develop the time-independent DIP rate metric.

    "Systems biology is what really makes the difference here," Dr. Quaranta remarked. "It's about understanding cells—and life—as dynamic systems."This new study is of particular importance in light of recent international efforts to generate data sets that include the responses of thousands of cell lines to hundreds of compounds. Using the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases will allow drug discovery scientists to include drug response data along with genomic and proteomic data that detail each cell line's molecular makeup.

    "The idea is to look for statistical correlations—these particular cell lines with this particular makeup are sensitive to these types of compounds—to use these large databases as discovery tools for new therapeutic targets in cancer," Dr. Quaranta stated. "If the metric by which you've evaluated the drug sensitivity of the cells is wrong, your statistical correlations are basically no good."

    The Vanderbilt team evaluated the responses from four different melanoma cell lines to the drug vemurafenib, currently used to treat melanoma, with the standard metric—used for the CCLE and GDSC databases—and with the DIP rate. In one cell line, they found a glaring disagreement between the two metrics.

    "The static metric says that the cell line is very sensitive to vemurafenib. However, our analysis shows this is not the case," said co-lead study author Leonard Harris, Ph.D., a systems biology postdoctoral fellow at Vanderbilt. "A brief period of drug sensitivity, quickly followed by rebound, fools the static metric, but not the DIP rate."

    Dr. Quaranta added that the findings "suggest we should expect melanoma tumors treated with this drug to come back, and that's what has happened, puzzling investigators. DIP rate analyses may help solve this conundrum, leading to better treatment strategies."

    The researchers noted that using the DIP rate is possible because of advances in automation, robotics, microscopy, and image processing. Moreover, the DIP rate metric offers another advantage—it can reveal which drugs are truly cytotoxic (cell killing), rather than merely cytostatic (cell growth inhibiting). Although cytostatic drugs may initially have promising therapeutic effects, they may leave tumor cells alive that then have the potential to cause the cancer to recur.

    The Vanderbilt team is currently in the process of identifying commercial entities that can further refine the software and make it widely available to the research community to inform drug discovery.

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