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DBL%20Hendrix%20small.png College chemistry, 1983

Derek Lowe The 2002 Model

Dbl%20new%20portrait%20B%26W.png After 10 years of blogging. . .

Derek Lowe, an Arkansan by birth, got his BA from Hendrix College and his PhD in organic chemistry from Duke before spending time in Germany on a Humboldt Fellowship on his post-doc. He's worked for several major pharmaceutical companies since 1989 on drug discovery projects against schizophrenia, Alzheimer's, diabetes, osteoporosis and other diseases. To contact Derek email him directly: Twitter: Dereklowe

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January 14, 2011

Fishing Around for Biomarkers

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Posted by Derek

Everyone in this industry wants to have good, predictive biomarkers for human diseases. We've wanted that for a very long time, though, and in most cases, we're still waiting. [For those outside the field, a biomarker is some sort of easy-to-run test that for a factor that correlates with the course of the real disease. Viral titer for an infection or cholesterol levels for atherosclerosis are two examples. The hope is to find a simple blood test that will give you advance news of how a slow-progressing disease is responding to treatment]. Sometimes the problem is that we have markers, but that no one can quite agree on how relevant they are (and for which patients), and other times we have nothing to work with at all.

A patient's antibodies might, in theory, be a good place to look for markers in many disease states, but that's some haystack to go rooting around in. Any given person is estimated, very roughly, to produce maybe ten billion different antibodies. And in many cases, we have no idea of what ones to look for since we don't really know what abnormal molecules they've been raised to recognize. (It's a chicken-and-egg problem: if we knew what those antigens were, we'd probably just look for them directly with reagents of our own).

So if you don't have a good starting point, what to do? One approach has been to go straight into tissue samples from patients and look for unusual molecules, in the belief that these might well be associated with the disease. (You can then do just as above to try to use them as a biomarker - look for the molecules themselves, if they're easy to assay, or look for circulating antibodies that bind to them). This direct route has only become feasible in recent years, with advanced mass spec and data handling techniques, but it's still a pretty formidable challenge. (Here's a review of the field).

A new paper in Cell takes another approach. The authors figured that antigen molecules would probably look like rather weirdly modified peptides, so they generated a library of several thousand weirdo "peptoids". (These are basically poly-glycines with anomalous N-substituents). They put these together as a microarray and used them as probes against serum from animal models of disease.

Rather surprisingly, the idea seems to have worked. In a rodent model of multiple sclerosis (the EAE, or experimental autoimmune encephalitis model), they found several peptoids that pulled down antibodies from the model animals and not from the controls. A time course showed that these antibodies came on at just the speed expected for an immune response in the animal model. As a control, another set of mice were immunized with a different (non-disease-causing) protein, and a different set of peptoids pulled down those resulting antibodies, with little or no cross-reactivity.

Finally, the authors turned to a real-world case: Alzheimer's disease. They tried out their array on serum from six Alzheimer's patients, versus six age-matched controls, and six Parkinson's patients as another control, and found three peptoids that seems to have about a 3-fold window for antibodies in the AD group. Further experimentation (passing serum repeated over these peptoids before assaying) showed that two of them seem to react with the same antibody, while one of them has a completely different partner. These experiments also showed that they are indeed pulling down the same antibodies in each of the patients, which is an important thing to make sure of.

Using those three peptoids by themselves, they tried a further 16 AD patient samples, 16 negative controls, and 6 samples from patients with lupus, all blinded, and did pretty well: the lupus patients were clearly distinguished as weak binders, the AD patients all showed strong binding, and 14 out of the 16 control patients showed weak binding. Two of the controls, though, showed raised levels of antibody detection, up to the lowest of the AD patients.

So while this isn't good enough for a diagnostic yet, for a blind shot into the wild blue immunological yonder, it's pretty impressive. Although. . .there's always the possibility that this is already good enough, and that the test picked up presymptomatic Alzheimer's in those two control patients. I suppose we're going to have to wait to find that out. As you'd imagine, the authors are extending these studies to wider patient populations, trying to make the assay easier to run, and trying to find out what native antigens these antibodies might be recognizing. I wish them luck, and I hope that it turns out that the technique can be applied to other diseases as well. This should keep a lot of people usefully occupied for quite some time!

Comments (18) + TrackBacks (0) | Category: Analytical Chemistry | Biological News | The Central Nervous System


1. Lester Freamon on January 14, 2011 11:37 AM writes...

Kodadek has been messing around with peptoids for years now, and i've always wondered why...I imagine this is what he had in mind the whole time. Brilliant.

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2. Mark on January 14, 2011 11:45 AM writes...

That is some bloody impressive work.

This is what quality research is really about. Creativity and good design.


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3. Hasufin on January 14, 2011 12:21 PM writes...

I bet that building up that library of "peptoids" will yield a great deal of value beyond immunological markers. Assuming it's made fully available for other researchers, of course.

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4. BigSky on January 14, 2011 3:17 PM writes...

The potential in the peptoid array is unreal. You've got diagnostics/biomarkers, as they show in the Cell paper. Then therapeutics with perhaps the same binding ligands. Purification reagents to grab a defined protein from a mixture. Really, really cool.

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5. Nflo on January 14, 2011 6:43 PM writes...

There also seems to be a lot of hype in dynamic proteomics, and using stable isotopes to trace protein kinetics for biomarker discovery.

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6. Nonplussed on January 14, 2011 7:01 PM writes...

All fine, but measuring antibodies in plasma that bind to peptoids on an array, many of which have never been encountered by the body, seems a bit of a stretch. All the approach is measuring is a difference between antibodies in the cases that bind to the peptoids and comparing those to the controls. Fine. But there's no mechanistic link to pathology to give you any kind of confidence that this is specific for the disease of interest and if you extrapolated this out to hundreds (or thousands) of patients whether the peptoid panel would still segregate cases from controls or whether it would fail. Its cool, but perhaps a bit too cool.

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7. peptoids? on January 14, 2011 7:50 PM writes...

Why not just use random peptides? That way you have shot at identifying the target protein.

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8. C-drug on January 15, 2011 2:20 AM writes...

I wonder if it would be possible to raise antibodies to a peptoid that was picking up the cases, and then see what it pulls down out of homogenized brain tissue? Seems like finding the target would be pretty awesome

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9. Konstantinos Spigos on January 15, 2011 11:00 AM writes...

Decoding of biomarkers in Alzheimer's is really exciting news. But a biomarker is not a treatment target. Through the antibody-biomarker pathway, I can certainly see joy for researchers, but not for patients and families.

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10. Morten G on January 16, 2011 9:41 AM writes...

Comments seem to ignore that AD is physiologically quite progressed by the time you can make a diagnosis based on symptoms. Basically, the brain is dead by the time we would know to start treating.

Does this mean that the researchers think there's an immunological component to AD?

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11. imatter on January 16, 2011 2:30 PM writes...

Isolate and purify the IgG and screen it against a phage display. BLAST the phage display hits to find epitope (presumably the drug target that everyone is interested).

The peptoids used the paper uses unnatural, therefore "unbiased". And, I am assuming it should be more stable during the assay.

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12. jekbradbury on January 16, 2011 4:31 PM writes...

Yep. I hope that's exactly what the team is doing as we speak...

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13. Donnarsf on January 16, 2011 4:32 PM writes...

"The authors figured that antigen molecules would probably look like rather weirdly modified peptides, so they generated a library of several thousand weirdo "peptoids". (These are basically poly-glycines with anomalous N-substituents)."

Derek, can you explain why they thought that might be the case?

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14. imatter on January 16, 2011 9:17 PM writes...

I am assuming that the panel of peptoids were already made and the paper's reference to a similar study using "natural" peptide array was already in progress and decided to apply their technology to a similar study, except that it's a peptoid. And a reasoning for this is that it's an "unbiased" system (some sequence that was never seen by the IgG) was something that made it more novel.

Looking at the peptoid structures, it's difficult to make sense of any SARs (structure/affinity).

What is fascinating about these poly-N-alkyl peptoids is the limited range in structure based on what I know about the Ramachandran plot. So, there might be more structural bias than they claim.

So maybe the epitope for the IgG is simply just a poly-proline protein--the simplified version of poly-N-alklyl peptide. Have they tested their IgG against poly proline. Does AD cause an overexpression of a protein with long sequences of proline?

Let's do some BLAST search for proline rich proteins.

I just did a Bing search on "proline rich proteins Alzheimer's". There's a lot of hits.

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15. RB Woodweird on January 17, 2011 10:39 AM writes...

This is very impressive, but in the real world, if you were offered a screen for presymptomatic Alzheimer's, should you take it? What are the chances that you would have to change health insurance plans before you retire? Is your predisposition to Alzheimer's going to be an existing condition and disqualify you from benefits you would be eligible for if you had refused the test and lived in ignorance? Why would you want to know that you might eventually get an incurable ailment?

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16. Tok on January 17, 2011 8:13 PM writes...

RB Woodweird #15:
Wasn't that taken care of in the new health-care legislation? No insurance disqualification based on genetic possibilities or pre-existing conditions?

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17. Anonymous on January 18, 2011 1:29 PM writes...

This is just a wonderful piece of science... so simple and yet so elegant!

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18. JMB on January 18, 2011 2:32 PM writes...

A few notes on quirks of peptoids that may answer some questions above:

The N-substitution on the peptide nitrogen to form a peptoid destroys the amide hydrogen. The consequence is that the backbone becomes achirial and significantly more flexible than peptides. A frequent concern regarding peptoids is they may be TOO flexible (and therefore have high entropic cost to bind to things). Peptoids with bulky chiral sidechains have been shown to adopt poly-proline helical formations, and several groups try to take advantage of this to make peptoid mimics of alpha-helical proteins (e.g. antimicrobial peptides or lung surfactant proteins).
A good study on folding / structure of peptoids:

The substitution on the amide nitrogen also makes peptoids resistant to protease degradation, making them a potential improvement over peptide-based drugs which can be rapidly chewed up in vivo.

Almost any primary amine can be incorporated as a peptoid sidechain (via SN2), which is how you can easily get all the non-biological looking sidechains. It's a 2-step synthesis similar to solid-phase peptide synthesis, so it readily lends itself to automated synthesis of large parallel or combinatorial libraries with a wide array of chemical diversity, like those in Kodadek's paper.

Ron Zuckermann (and others) spent significant time at Chiron working to develop peptoids for therapeutics in the 90's. I believe the (very broad) patents are now held by Novartis. Other (academic) peptoid contributors to read besides Zuckermann and Kodadek include K. Kirshenbaum (NYU), A. Barron (Stanford), and H. Blackwell (Wisc).

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