Technology

Newly developed Technology makes it possible to count tissue stem cells

Technology

11:50, September 22 2016

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James Sherley, Director, Asymmetrex, talks to CTA to discuss a disruptive innovation that could change stem cell research

Previously in scientific research, there has been no way to count tissue stem cells, due its difficulty. Experts over the years have lamented the lack of available technology, resigning themselves to not being able to resolve that problem.

But is that about to change?

James Sherley is the founder and director of Asymmetrex,an adult stem cell biotechnology start-up company. As the developer of AlphaSTEM Technology – a product that counts tissue stem cells – Sherley has been able to achieve a method that hasn’t been possible for 50 years.

In this compelling interview, Sherley sits down with CTA to explain how AlphaSTEM could mark a paradigmatic shift in stem cell and clinical research.

Clinical Trials Arena: How did you come about developing AlphaSTEM technology and what was the motivation?

James Sherley: It’s based on my 20-plus year research on tissue stem cell kinetics. Essentially, AlphaSTEM involves taking any primary human tissue cells and placing them in what’s known as serial culture. By passaging them over several days in culture, you keep track of the overall cell number following how it changes over time. The underlying principal is that stem cells in the culture are responsible for that output; they are the rate limiting cells that are responsible for all the cell output that one sees in the culture.

More specifically, their division is called asymmetric self-renewal, which produces cells that differentiate. Those are the cells that are counted. If you do a careful job of counting the total cell number over time, based on that understanding of how cells come about in culture, you can adapt mathematical approaches and computer simulation approaches to determine the number of stem cells that are responsible. The AlphaSTEM technology is a cell culture, computer-simulated method for inferring the number of stem cells at any time.

CTA: So how would a sponsor company implement AlphaSTEM in a clinical trial?

JS: For any clinical trial, whether for an approved therapy like a hematopoietic stem cell transplant, a sponsor company using this technology can learn the dose of active stem cells they’re transplanting. Currently, in cases like hematopoietic stem cell transplants of a bone marrow, for instance, one could argue you don’t really need a counting mechanism. That's because the transplants are so effective there’s hardly any problem due to not having enough tissue stem cells. However, in the case of cord blood transplants, where there aren’t as many stem cells in the preparation, they often fail 10 to 20 percent of the time.

So, having a way of counting the stem cells before the transplant will allow investigators and physicians to avoid having patients receive ineffective transplants. Similarly, in most cases of experimental trials where people are asking for the stem cell preparation to help the heart for example, those trials are being conducted without knowing the number of stem cells present. That means it’s very difficult to actually compare what happens from one patient to the next because the stem cell numbers are unknown, and that’s the most important principle in a study. By having the ability of gathering information about the dose of active stem cells, you can make trials much more effective and much more interpretable than they are currently.

CTA: Since it hasn’t been implemented in any clinical trials as of yet, how are you sure AlphaSTEM will work as you expect?

JS: The reason I know it will work is because we’ve done four validation studies of our own. In the past, studies have been conducted where human blood stem cells were transferred into animals – called SCID mouse repopulation studies – where the best available estimates are based on the activity of hematopoietic stem cells to restore bone marrow function. We compared our numbers to those and we’re bang on those numbers – we get the same magnitude of stem cell numbers as those complicated animal models, yet we get it simply by culturing cells.

The only thing we’ve done, so far, in terms of demonstrating the ability of our technology (for applications in drug development) is to identify drug candidates that are known to cause chronic organ failure. Through testing them – in what we refer to as the AlphaSTEMTest – we’ve been able to identify compounds that will cause chronic organ failure in patients. Now that we’ve done some early validation of our own; we’d like to find resources that will enable us to do more validation, but we think we’ve done enough to be able to provide a valuable resource to pharma companies.

CTA: Could you give a sense of the challenges you faced in developing this technology?

JS: The challenges that we face is that we’re trying to introduce a disruptive technology that I think will be quite beneficial to the industry, but we’re looking for our first early adopters of this service.

From the perspective of stem cell treatments and stem cell clinical trials, not having dose information for stem cells is currently an accepted practice. We have to educate physicians to the importance and advantages of knowing stem cell dose for both their patients and the advancement of stem cell medicine.

When you think about drug development, the key piece of information is how much drug you’re giving to the patient. If, for instance, you’re in a clinical trial testing a high risk stem cell source to see if they can change the disease course in individuals and you compare individuals who received the treatment and those who didn’t – already there’s an assumption they’re getting sufficient stem cells. If they’re not getting sufficient stem cells and you don’t know the number, you can’t really interpret what the outcome is soundly.

If you compare one population with another, well you have no denominator for comparing the outcome in terms of stem cells. This is known to be a problem with nothing in the past to do about it; and so now we can give that information, and we hope it will change the effectiveness and the course of these types of experimental trials and treatments.