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David Hammond on How the Red Cross Sifts Through Liters of Plasma

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At A Glance

Name: David Hammond

Position: Executive director of Plasma Derivatives Department, American Red Cross, Holland Laboratory, since 2000.

Co-founder, Pathogen Removal and Diagnostic Technologies.

Background: Director, Plasma Proteins, V.I.Technologies, 1997-2000.

Director, Biological Research, Bayer, 1996-97.

Director, Technology, Bayer, 1991-96.

Senior scientist/associate director of cell and molecular biology, cancer research, Bristol-Meyers Squibb, 1986-91.

Visiting scientist, School of Pharmacy, University of California, San Francisco, 1984-86.

Scientist, Wellcome Research Labs, UK, 1982-84.

Post-doc in biological science, University of Kent at Canterbury, 1979-82.

PhD in biochemistry, University of Edinburgh.

BS in biochemistry, University of Bristol.

 

How did you become involved in proteomics?

My background even as a PhD and post-doc student was to look for proteins for therapeutic intervention. My background was fairly heavily influenced by industry — I have about 19 years of industrial experience, more recently with Pathogen Removal and Diagnostic Technologies, a joint venture between our investors at the American Red Cross, and Prometic Biosciences [in] Montreal, Canada.

I joined the Red Cross in December 2000. I currently head a department of about 50 people. Included within that department is a state-of-the-art analytical facility, [where we use] the classical proteomic techniques of 2D-LC, MS/MS, MudPIT analysis and other techniques, primarily as a tool for identifying active compounds.

So you started up the proteomics program at the Red Cross?

Yes. There wasn’t anything really existing within the area that we now call proteomics [when I arrived], although there was biochemistry and discovery — but nothing to the degree that we have currently.

Tell me about the FIoNA technique that you have developed.

FIoNA stands for functional identification of novel activities. [It is also the name of my wife.] Proteomics seeks to catalogue essentially all of the proteins present in plasma or other source material. We’re not interested in trying to catalogue everything — what we’re really interested in doing is finding the one in a million proteins or antibodies that have the desired activity. So we say that this is the ultimate in functional proteomics, since we just go straight for function.

How we achieve that is, we take advantage of combinatorial libraries. We mix the libraries with libraries of proteins. Of course at the American Red Cross we have rather a lot of plasma — we’re talking about millions of liters, not liters. So we have a lot of plasma, a lot of different proteins, a lot of antibodies. We mix those antibodies or proteins with combinatorial libraries. Providing the diversity of the libraries is large enough, our experience tells us that most, if not all proteins in a highly complex mixture will find and bind to a ligand on a bead from a combinatorial library. This is classical affinity chromatography, although performed simultaneously with tens of millions of different affinity resins.

When a protein present in trace amounts binds to specific ligands on the beads, it becomes concentrated, and is removed from other proteins. So this is a way of doing mass separation of proteins simultaneously in millions of different ligands such that we concentrate the protein, we isolate it, and then we’re interested in understanding what proteins are active in a specific assay — for example it could be an assay for cell death in cancer. In this case we are interested in identifying what proteins kill a cancer cell — we now have a number of different assays. Importantly, we can functionally evaluate every protein simultaneously for a desired activity.

Do you pool a lot of plasma and use that as your starting material?

We can pool plasma, we have used plasma, we can use tiny complex immunoglobulin preparations, and those preparations might be from tens of thousands of donors all pooled together. We can have diversity — I think that is the bottom line.

We certainly have a special resource [at the American Red Cross].

So you’re looking for proteins that have functions in various disease?

Yeah, we go directly for function. And when we find a bead with an attached protein that has the desired function, it should be remembered that we have no idea of either the protein or the ligand that it binds to — we only know that there’s a desired function. The first thing we want to do is determine the structure of the ligands and the proteins. So for the ligands, we can select the bead bearing the protein with the desired activity; we can decode the structure of the ligand. As for the protein, we can either identify directly the protein on the bead through MudPIT-type analysis, or we can use the structure of the ligand to produce a lot more resin. [We can] scale the synthesis of the resin up so instead of having a library, we just have one particular resin with one specific ligand on it. And then we can use that for affinity purification of the target protein in large scale. Large scale for us means thousands of liters. And we believe that that’s incredibly useful that we can just scale it up so quickly. Because the real issue in proteomics and drug discovery today is target validation — how do you know the target is right, how do you know your protein has the desired activity. Then, even if you know those, how do you scale it up so now you have multigrams of the target protein. We can do that if it’s a plasma source very quickly because we’ve got an affinity resin — that’s how we selected the protein in the beginning — and we have access to facilities that would allow us to really produce that enlarged set.

What findings have you made so far using the FIoNA technique?

We’re currently searching for activities for cancer, and we’ve out-licensed that aspect of the technology — the field of use for cancer — to a company called Automated Cell. They’re based in Pittsburgh. We’ve also out-licensed most of the fields of use to a new company called FIoNA Discovery Systems. Specifically, we’ve also used this technology for identification and removal of prions. That [is what] we’re commercializing right now with Prometic through PRDT. So those leads are being commercialized to that company.

Also, we have a lot of ligands for which we’re interested in the proteins which are also being currently commercialized, again through Prometic. Now, proteins [currently] in discovery — slightly earlier in development — are ones for cancer and also we’re very interested in organophosphatases [and] growth factors. So where we have relevant assays, we’re applying our technology to it, and we’re doing this primarily in partnership with people who have the capabilities of developing the products.

Is your work funded entirely by the Red Cross?

We also have other sources of income [from government groups and others], but our primary source is internal American Red Cross funds.

What else is significant about FIoNA?

A couple of things. The way we operate is, we know neither the active protein nor the target that we’re trying to identify as our lead for therapeutic intervention. So it’s a very unbiased system. We use proteomic techniques as a tremendous tool, but we use it differently from other people. Our focus primarily is going straight to function. Personally, I think that’s the way that proteomics will be going in the future. One of the benefits we have with this affinity separation is, we don’t have to remove hyper-abundant proteins. The technique that we have developed avoids the necessity of removing the abundant proteins. Also, our goal as much as anything [is to study] the antibodies. Again, most people in proteomics conveniently ignore the antibody, whereas we don’t conveniently ignore them: We actually want to go out and find the functions of those antibodies, and the trace proteins. So they’re amenable to us, and they’re available to discover their function.

What do you mean when you say FIoNA is an unbiased system?

A biased system would be, say, I have a receptor that I know I want to target an agonist and antagonist to, and I am going to select purely those that are active in that system. And then they want to use those in a way analogous to, say, cancer research for targeting to the receptor. But when we screen for activity, we’re not biasing ourselves for a known epitopic pathway. We say, let’s measure the desired endpoint, and then let’s find the activity that does that, and then let’s say, well what is the target of that activity? So we don’t come in with a preconceived idea that such a pathway — a signal transduction pathway — should exist. Maybe we rediscover it and yes we’ve rediscovered some, but we don’t go into the selection process with that bias. Because we don’t think we know every signal transduction pathway and we happen to know we don’t.

Is FIoNA Discovery Systems already an established company?

It’s being established. It exists as a Delaware incorporation. Final licensing agreements are in process.

What do you see coming up in the near future for proteomics?

In the next five to seven years we expect to see enhancements of the changes that have occurred in our power and our ability to implement the technology ,as proteomics methods become far more powerful by orders of magnitude. So where we are today and where we’re going to be in five years time — there are going to be pretty dramatic changes.