Researchers from Vanderbilt University (Nashville, TN, USA) have determined in a recent report that the primary method used to test compounds for anti-cancer activity in cells is flawed, casting doubt on methods near-ubiquitous in academic and industry drug discovery, which may be partially responsible for the high rate of anti-cancer drugs failing in late-stage clinical trials.
“More than 90 percent of candidate cancer drugs fail in late stage clinical trials, costing hundreds of millions of dollars,” explained co-author Vito Quaranta. “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.”
Quaranta and colleagues have developed a novel metric to determine the effect a compound has on cell proliferation, known as the drug-induced proliferation (DIP) rate, overcoming a flawed bias in the standard method. Traditionally, scientists have added the compound to be tested to cells, and counted how many cells were alive after 72 hours, but these proliferation assays fail to take into account the bias introduced by exponential cell proliferation.
“Cells are not uniform; they all proliferate exponentially, but at different rates,” continued Quaranta. “At 72 hours, some cells will have doubled three times and others will not have doubled at all.” He added that drugs do not affect every cell line in a uniform manner; the tested drug may have an immediate effect on some cells, and a delayed impact on others.
Quaranta’s team, in collaboration with computational biologist Carlos Lopez, used a mixture of experimentation and mathematical modelling to demonstrate the time-dependent bias in standard proliferation assays. They then used the system to develop the time-independent DIP rate metric.
“Systems biology is what really makes the difference here,” Quaranta elaborated. “It’s about understanding cells — and life — as dynamic systems.”
The findings are particularly timely, as international databases have recently been set up to include the responses of thousands of cell lines to hundreds of different compounds. Databases such as the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer include drug response data, alongside genomic and proteomic data.
The team evaluated the responses of four different melanoma cell lines to the drug vemurafenib, with the standard metric, used for both the databases mentioned above, and with their novel DIP rate metric. In one cell line, there was a significant disparity between the two metrics.
Quaranta’s team concluded that: “we should expect melanoma tumors treated with this drug to come back, and that’s what has happened, puzzling investigators.” He added, “DIP rate analyses may help solve this conundrum, leading to better treatment strategies.”
The DIP rate also offers an advantage in that it allows researcher to distinguish between cytotoxic and cytostatic drugs. The team has developed a software package for other researchers, available through their article, and hopes to make this more widely available in future.