The missing link to effective research collaboration

Jay Campbell discusses how inefficiencies in data collection during the pre-clinical phase can disrupt long term collaborations and insights



Pharma IQ
03/05/2019

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Between 50 to 60 million rodents are used every year in pre-clinical research. Despite being a vital data source, they have yet to be managed in a robust way.

In this interview, Jay Campbell, Chief Commercial Officer at Somark, shares how companies fall short when they try to increase productivity, the long term impact of poor data accuracy and why accessible data is essential to driving global collaboration.

Deep Dive: Join Jay Campbell and Paul Donohoe for their webinar at Smart Labs Digital. Secure your place here

 

Pharma IQ: Where do you think companies are falling short when trying to increase productivity in the lab?

Jay: Many companies are falling short when it comes to the basic identification of animals at the pre-clinical stage. Most, if not all, companies still use a very archaic process, whether it’s physically notching out the ear, using ear tags or adding a tattoos on the tail or hind flanks.

We’ve not seen the identification of animals move progressively in a number of years.

The problem is that traditional methods are open to too many human errors in identification. We’ve completed studies with customers where we have seen anywhere from 30% to higher of the animals on study have experienced a misinterpretation of their identification number. When you use a method of identification that isn’t robust or automated, you have a far greater propensity for error in the study. You can no longer guarantee that the right animal is getting the right dose or that the right animal is even on the study.

The irreproducibility of pre-clinical research exceeds 50% so approximately $28 billion per year is lost on research that is not reproducible

We actually created the Labstamp that automates the tattoo process to address this. From a welfare standpoint, an automated solution ensures that the needle isn’t entering too deep into the animal’s tail. You just load the animal into a restraint device and the machine automatically creates a tattoo in your choice of alpha and numeric codes or combined. This is then easy to read and use and can be viewed at the cage level, to limit human interaction which might confound results. Using this type of solution then significantly limits the propensity for error and for re-work.

Anytime you can identify an animal quickly and make sure you’ve got the correct animal for the study, you increase overall productivity and the resulting success rate of the study.

Find out more: How to increase productivity through data capture and integrity

 

Pharma IQ: What is the long term impact of being able to collect data accurately and improve productivity in the pre-clinical phase?

Jay: Well, there have been many studies where they find out all too late that the wrong animal was used or the wrong animal was dosed. By having a proper identification system for the animals, you create a more robust chain of custody. This means you can guarantee the right animal is chosen for the right experiment and maintain data integrity throughout the study.

There’s a famous example from Vanderbilt, where the researcher started with a specific animal, then over the life of the experiment had only come to find out in the end that the final animal they were doing the research one was not the original one they started with. So all that time, money and experimentation is wasted.

When you don't find out until the end of the study that the wrong animal has been used or dose, all the time and resource is wasted

We actually started using our RFID technology to assist with this by removing human interpretation from the identification of animals and data logging during procedures. For example, if a researcher wants a technician to go and weigh all of the animals on a research study, they would traditionally have to collect all cages, pick up each animal, weigh them, interpret the number and record that against the correct animal. Naturally, this leaves a lot of fields open to error.

By utilizing technology like the RFID tag, inserted in the tail of an animal, you can; grab the cage, put the animal in a beaker, use a reader to identify the animal and using a connected weight scale can automatically log the weight against their ID. You are then given an exact time stamp of when the animal was weighed and by whom, giving you full contextual data on the result.

The animal itself becomes a living storage unit for data. This significantly limits the effort needed from the technician, removes the need for human interpretation and increases data accessibility by storing the information in the cloud.

The animal itself can become a living storage unit for data. This can change the way data is managed and communicated

Where this becomes really important is when we think about one of the biggest areas of research where a lot of money is being spent; oncology. Let’s imagine that you’re a research company and you have thousands of animals with tumors growing in them. Part of the research study protocol is measuring the growth of these tumors, as you are dosing the animals with a compound to halt the growth. If you were to leave that up to human interpretation, you will have multiple technicians come on different days or weeks all interpreting the results in their own way. Whereas with a digital tag, connected to the calipers, then you can remove that risk of human error by electronically recording and logging tumor growth. Long term, having the correct information can increase the effectiveness of your study and allow you to gather more from the data you collect.

 

Pharma IQ: What value does having cloud storage for pre-clinical data have?

Jay: When you have the cloud environment for your data, you can have a technician in a certain environment conducting the experiment but you can also have a researcher in the lab pulling up real time data for their office. So it opens up the capability of sharing data.

We know in science that there are so many global collaborations, so would you rather send a spreadsheet to someone with limited information or have a protected environment where you can see all of the data with ease.

There are opportunities to increase the shareability of data so that researchers across the globe can collaborate

You can have a researcher in France conducting an experiment and a counterpart in San Francisco can log in, see the results in real time and assign tasks to add to the study. If you have a global set up, it allows for the capability to truly act globally.

 

Pharma IQ: Why is it crucial to focus on the pre-clinical phase?

Jay: There are 50-60 million rodents used a year for research. That’s a huge number of animals not to be professionally managed as an important data source.

From a cost standpoint, if Pharma companies could increase the amount of studies done correctly, they could improve their speed to market and bring about cures at a much quicker pace. If you’re a drug company and you’re first to market then the likelihood is that you will receive 80-90% of the revenue for that drug.

With 50-60 million rodents used every year for research, it's a huge number of data points to not be professionally managed

While speed to market is one thing, the most important benefit is robust data that can help bring a cure to those that need it most.

You constantly have articles, interview and news about the cost of drug, why studies aren’t reproducible and why only one drug out of some many potentials is ever coming to market. Developments in the pre-clinical stage can allow for a true chain of custody of the data so that scientists can fully explain and contextualize their experiments and learn from their successes and failures.

The president of PhRMA, Billy Tauzin, actually said a great quote on this. He said “there is one great problem that seriously challenges the ability of America’s research based pharmaceutical companies to continue… to research and develop new cures and treatments. In a word, it is trust”.

What we’re trying to do is not only provide better research and data management, but also put that trust back into science.

 

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