Drug Discovery
Figure 1
R&D spend by disease area. Data courtesy of AT Kearney (2013)6
by all sources. In this way, implementing standard methodologies could result in higher quality and more reliable data for drug discovery. Improvements in the early phases of drug discov-
ery are imperative across all disease areas but are especially needed in cancer research. Cancer is one of the most funded research areas for pharmaceu- tical companies, but there are challenges associated with bringing cancer drugs to market.
The challenges with preclinical data in cancer With an estimated 14 million new cancer diag- noses worldwide in 2012 and high mortality rates attributed to the disease5, it is not surprising that cancer continues to be a prevalent research area for academia, pharma and biotech. According to a report by AT Kearney, cancer remains the most funded disease in R&D over neurology, infectious disease, and cardiology (Figure 1)6. Target dis- covery and target validation in cancer drug dis- covery stand out as obvious areas for improve- ment, primarily because these phases are impor- tant to discovering targets which feed into late- stage success. If the results from these early phas- es are inaccurate or unreliable, then the entire pipeline could fail. An issue that has been more recently highlighted
within the industry is the need to improve preclin- ical models in cancer. A fitting move towards tack-
26
ling this challenge would be to reduce or eliminate the use of data from mischaracterised cell lines within the early target discovery and target valida- tion phase. Interestingly, a recent study indicates that oncology, by far, has the highest share of liter- ature based on contaminated cell lines (Figure 2)7. This published literature typically uses data obtained from widely used and shared cell lines that have historically become the standard tool for use in cancer research. Unfortunately, until recent- ly, not much emphasis has been put on authentica- tion or characterisation of these cell lines or even questioning their biological relevance. As a result, mischaracterised, contaminated cell lines have made their way into publications over the years as a standard tool for cancer studies. Not only does this lead to inaccurate data being published, but ultimately becomes the basis for more incorrect data in subsequent research. There are implications with using data from such
misidentified or contaminated cell lines, ranging from costly drug compound testing in the discovery phase based on faulty data, or missed drug targets never tested or reported on relevant cells. Such issues are a key indicator for the need to improve method- ologies in the early phases of cancer drug discovery. If the industry acknowledges the criticality of sound preclinical research feeding into the success of a clin- ical phase, it is equally important to accept and be cognisant of the use of reliable building blocks (ie
Drug Discovery World Spring 2018
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