Scientists have long relied on mouse models for human disease exploration and drug candidate evaluation. Despite large investments into drug development, the overall success rate of drugs during clinical development remains low. This has been identified as an over dependence on the use of animal models to bridge the translational gap to the clinic.

“We have moved away from studying human disease in humans,” said the former director of the US National Institutes of Health, Elias Zerhouni.

Research moving in the wrong direction

Associate professor Joseph Garner from Stanford University is more straightforward in his diagnosis: “I think we’ve got ourselves into a mess right now, with lab mice in particular,” said Garner, who runs Stanford’s Technique Refinement and Innovation Lab, which focuses on maximising the efficiency of scientific research and the well-being of the animals involved.

“The benefits to humans, in certain diseases and mouse models, have shrunk to such low levels that it’s time we found better ways to work with animals in medical research,” he adds.

The ability to knock out or insert genes into mice has lead researchers to shift their focus away from people.

“The problem is that it hasn’t worked, and it’s time we stopped dancing around the problem… We need to refocus and adapt new methodologies for use in humans to understand disease biology in humans,” Zerhouni added.

Garner has been studying the low success rate of drugs progressing from clinical trials to the market. He says that an important contributor is the wide availability of genetically modified animal models that display the characteristics of human diseases.

However, an outstanding question that has yet to be addressed is, “Does what ails animals also ail humans?”

There is compounding data that shows that this is increasingly not true.

Re-evaluate current wisdom or face higher medicine prices

For instance, most drug research on neurological diseases relies heavily on animals that have been genetically manipulated into displaying characteristics that are Parkinson’s-like, OCD-like, anxiety-like, autism-like – you name it, there’s probably a mouse, or a primate, for it.

The conventional thinking in science is to find a way to treat an animal disease model and it might equate to a treatment that is applicable to humans, too. However, of the drugs that do make it past animal testing and into human trials, only about one in nine makes it to market.

Currently about US$ 2 billion is spent on bringing a single drug to market and that is largely due to failed human trials, which usually fail because the drug does not work or not as well as the animal tests had predicted. This adds pressure to make up for losses, which causes drug investment to double every decade because animals have been failing to properly model human diseases.

Additionally, animal models are standardised in order to control any confounding factors that may affect the study. This narrows the information gathered to fall within a range of specific conditions that further removes animals from representing human populations as humans are not all the same, yet the models that portray them are.

New gene-editing technology has also made creating genetically modified animals easier and cheaper. If left unchecked, research funds may be squandered investigating unique quirks in these animal models that have little to no relevance to humans.

Scientific pressure leading to less innovation

Scientific inquiry has many checks and balances, with research publications being reviewed by other scientist not affiliated with the study. However as negative studies are never published, fewer than one in five cancer clinical trials find their way into scientific journals and animal studies tend overestimate the likelihood that a treatment will work by about 30%. In addition, the pressure to publish may lead more scientists to depend on established traditional methods rather than innovate.

A study by Jacob B. Foster, professor of sociology at the University of California at Los Angeles, states that “Pursuing innovation is a gamble, without enough payoff, on average, to justify the risk. Nevertheless, science benefits when individuals overcome the dispositions that orient them toward established islands of knowledge … in the expanding ocean of possible topics.”

The authors suggest that colleges and universities can promote more innovation simply by not linking job security to productivity, in terms of easy metrics. They state that such a strategy has proved successful at Bell Labs, where scientists could work on project for a year without being evaluated. MIMS

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