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Drug Discovery


Drug discovery in the age of big data


How RNA interference and CRISPR/Cas9 technologies are helping to build better mouse models and push drug discovery into a new era.


By Dr Prem Premsrirut


M


ost, if not all, consumers are feeling the weight of rising drug costs. But while much of the controversy surrounding rising drug prices has focused on a handful of phar- maceutical companies that have snapped up old drugs and then aggressively raised prices, the fact is that drug prices are far more likely to further increase due to the volume of candidates that falter in late-stage efficacy studies and the fact that there is no more low-hanging fruit.


This may seem incongruous but it is not. A wide- ly circulated study published last year by a trio of economists found that the costs of compounds abandoned during testing were linked to the costs of compounds that obtained FDA approval1. Built into the jaw-dropping US$2.5 billion plus that the analysts estimated it now costs to bring a new drug to market – a figure derived from an analysis of 106 randomly selected drugs from 10 companies – were the cost of unsuccessful projects that faltered in the clinic.


A study published last year illustrates the sub- stantial financial risks that can occur when candi- date drugs flop in the clinic. From 2013-15, 24% of candidates in Phase II and Phase III trials failed to meet safety endpoints, while 54% of the candi- dates got shelved because they did not work2. A separate analysis reported last year by PAREXEL, a life science consulting company, identified 38 trials enrolling more than 145,000 patients that failed to show efficacy3. Not only is this impact- ing drug prices, it is also impacting productivity and patient care. One study found that for every billion US dollars spent on R&D, the number of


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new drugs approved has decreased by approxi- mately 50% every nine years since 1950 (infla- tion-adjusted)4.


One way to improve the outcomes of clinical tri- als is to rigorously validate each new drug in numerous preclinical assays and animal models; however, the difficulty is that there are so many targets being discovered that we cannot possibly build compounds to all targets and test each one in multiple disease states. Big data genomics, expres- sion profiling and screening platforms are continu- ously identifying new causes for genetic disorders which has dramatically increased the number of potential novel therapeutic targets, but we do not know enough about each newly-identified target to readily pick winners. At least not fast enough to make a dent on raging drug prices.


This overload of data has created a bottleneck in the target validation process5.What we need is to take advantage of new technologies in pre-clinical research to help us validate novel targets quickly and tell us what the potential toxicity profile is before we begin to develop a drug and spend that $100 million to get to a Phase II trial. While we can use bioinformatics and in vitro culture systems to help understand gene function, there is no substi- tute for animal models. Disease states do not exist outside a whole organism, which contain an intact immune system, microenvironment and 3-D struc- tures that play a role in not only disease pathogen- esis, but also therapeutic responses. So while ani- mal models remain the gold standard for target validation and toxicity assessment, the long lead times and high cost associated with genetically-


Drug Discovery World Fall 2017


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