A recent article in Drug Discovery World describes how two tools are working together to build better mouse models.
It can’t be stated enough how much the gene-editing tool known as CRISPR/Cas9 is changing how we create mouse models. It used to take 12-18 months to make a transgenic mouse using traditional techniques. CRISPR/Cas9 does it anywhere from 3-9 months. And there are other naturally occurring Cas proteins that could potentially be incorporated into other systems once they become better characterized.
But CRISPR/Cas9 is by no means the only tool transforming how we can study diseases in mouse models. In a recent article in Drug Discovery World, Prem Premsrirut, the Founder and Chief Executive Officer of Mirimus and a partner of Charles River, talks about the natural synergy between CRISPR and RNAi a naturally occurring process that regulates gene expression in many organisms. Used effectively RNAI technology allows us to turn genes off and on.
In a recent article in Drug Discovery World, Premsrirut, whose company uses RNAi and CRISPR/Cas9 technologies to engineer mouse models, talks about how both these technologies are allowing us to get much more mileage out of animal models, particularly rodent models, which continue to be the gold standard for discovery and testing drugs in animals.
Powerful new algorithms and expression vectors give us the ability to generate reliable RNAi tools, which can be exploited experimentally to effectively and reversibly silence nearly any gene or gene combinations not only in vitro but also in live mice and soon rats and higher organisms, “ says Premsrirut. “In addition, continued progress in the implementation of CRISPR/Cas9 as a gene editing tool allows us to introduce specific genetic alterations in animals and create “designer” models.”
Recognizing the potential efficiencies this presents for drug discovery researchers, CROs like Mirimus and Charles River have invested in sophisticated genome editing platforms to enable their partners to combine RNAi and CRISPR technologies to streamline the discovery process for target identification, validation, and disease modeling both in vitro and in vivo, the article points out.
By synergizing these technologies we will be able to model clinical disorders better, be in a better position to evaluate genetic and environmental stimuli in animal models and ultimately increase our confidence in predicting drug responses in humans, notes Premsrirut.
Which gets us to the overarching message of this article. At a time when vastly more drugs fail than make it to market, finding new ways to improve the predictive responses in preclinical studies is sorely needed if we can ever hope to improve these odds and reduce the price of drugs.
A widely circulated study published last year by a trio of economics from Tufts University, Duke University and University of Rochester found that the costs of compounds abandoned during testing were linked to the costs of compounds that obtained FDA approval. 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.
So the more we broaden our understanding about the disease mechanisms early on in the drug discovery process, the better our chances are of finding drugs that succeed in patients, too.