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Label and Label-free Technologies in Synergy Creating a Powerful Approach to Drug Discovery

[0:00:00] Sean Sanders: Hello and welcome to this Science/AAAS webinar. I'm Sean Sanders

commercial editor and webinar editor at Science. Slide 1 Today's presentations and discussion concern the use of label-free

technologies, an area that has been gaining wider acceptance in both academic research and drug discovery laboratories. With the advent of faster and more accurate label-free technologies, and their pairing with traditional systems for label detection, the use of highly sensitive cellular and biochemical assays in microplate format is now routinely possible. This advance promises to provide scientists with a fuller picture of their system under study and speed the screening of compounds for drug discovery. In our webinar presentation today, we will explore in depth the pros and cons of combining traditional label technologies with label-free assays, and discuss how label-free technologies can be applied particularly in drug discovery research.

It gives me great pleasure to introduce our speakers today. To my left, I

have Mr. William Janzen from the University of North Carolina in Chapel Hill. Next to him is Dr. Charles Lunn from Merck Research Laboratories in Kenilworth, New Jersey. And finally, we have Dr. Brian Shoichet from the University of California San Francisco. Welcome to you all and many thanks for being here.

Dr. Brian Shoichet: Thank you. Dr. Charles Lunn: Thank you. Sean Sanders: Each of our speakers is going to give a short presentation after which, we

will have a Q&A session during which the panel will address the questions submitted by you, the live audience.

But before we get going, some instructions for our viewers. You can

resize or hide any of the windows in your viewing console. The widgets at the bottom control what you see. Click on these to see the speaker bios or additional information about products related to today's discussion or to download a PDF of the slides. If you're joining us live, you can submit questions to the panel at any time by typing them into the box at the bottom left of your viewing console and clicking the submit button. If you

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cannot see this box, click the red Q&A widget at the bottom of the screen. Please remember to keep your questions short and to the point as this will give them the best chance of being put to our panel. You can also log in to your LinkedIn accounts during the webinar to post updates about the event just click the blue and white LinkedIn logo at the bottom of the screen.

Finally, thank you to PerkinElmer for their sponsorship of today’s

webinar. Slide 2 Now, I’d like to introduce our first speaker for this webinar, Mr. William

Janzen. Mr. Janzen received his educational training in physics at the University of North Carolina. He went on to manage a comparative endocrinology laboratory there, after which he joined the startup biotechnology company, Sphinx Pharmaceuticals. Following Eli Lilly and Company’s acquisition of Sphinx in 1994, Mr. Janzen became director of Lead Generation Technologies and Operations for Lilly’s Sphinx Laboratories site. In 2001, Mr. Janzen left Lilly to help found Amphora Discovery as Vice President of Operations and later as president. He joined the Center for Integrative Chemical Biology and Drug Discovery in August of 2008 as a professor of the Practice and director of Assay Development and Compound Profiling. Mr. Janzen was instrumental in founding the Society for Biomolecular Screening, now the Society for Laboratory Automation and Screening serving as president of this organization in 1999 and 2000.

Welcome Dr. Janzen. Dr. William Janzen: Thank you. Well, since I've drawn the short straw and will be presenting

first, what I would like to do first is talk a little bit about label-free technologies in general and then about a few specific label-free technologies that we're applying at UNC.

Slide 3 The first question of course is why use label-free technologies. This really

comes down to the observer principle and that can be paraphrased as the act of observation can perturb the phenomenon being observed. It's often confused with the Heisenberg Uncertainty Principle, which deals with a very specific area in Quantum Mechanics.

Slide 4 But the probably more important aspect of this is that in biological

sciences, we often have to make changes in the system we're observing

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in order to make observations. Some examples of this are the use of isolated cellular systems to mimic complex in vivo biology, the expression of receptors within those cellular systems in order to interrogate receptor biology. We often must add fluorescent or radioactive tags to both large and small molecules in isolated biochemical assays and we use truncated enzymes, receptors, and other proteins in order to mimic activity. The list of this goes on and on, but the obvious advantage of label-free technologies is that we can get a more direct readout of these phenomena.

Slide 5 So, I'd like to provide you with a few examples of the label-free

technologies. The first one is Isothermal Titration Calorimetry or ITC. Slide 6 This is a concept that's very simple but difficult in practice. Two reference

cells containing the macromolecule of interest are isolated within an Aidabatic shield and the analyte of interest is slowly introduced into one of the reference cells, and changes in temperature between the two are noted. This allows one to look at thermodynamic shifts. The plot on the right side of the screen shows the actual output from an ITC instrument and then the calculation of a Kd. This was done for an epigenetic binding target within our lab.

Slide 7 [0:05:06] Another example and maybe the best well known of label-free

techniques is SPR or Surface Plasmon Resonance. Slide 8 In this technology, analytes are flowed over a thin film surface to which

molecules have been adhered. So, your molecule of interest is actually bound to the thin film, usually gold. This is illuminated with a polarized light source and the refractive index of the surface of the gold film will change as binding events occur. One can then measure the shift in the angle of refraction from the gold film. This is usually measured in terms of pixels of deflection to a CCD camera. And some example plots of that are shown immediately below the diagram here. With this, the advantage is one can very precisely measure association and dissociation constants as well as binding.

Slide 9 A third example of label-free technologies is the Resonant Waveguide

Grating.

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Slide 10 An example here from the Corning Epic system is shown where again,

analytes are bound to the surface of this grating. When this is illuminated with a broadband source, the changes in the refraction index near the surface will lead to a shift in the wavelength of refracted light. So again, changes within near proximity, hundreds of angstroms to the surface of the grating can be detected without any label.

Slide 11 This can also be used to measure cellular processes. This is known as

Dynamic Mass Redistribution. In this case, cells are adhered to the surface and changes in these cells including morphological changes, spreading or rounding, shift of mass in terms of receptor internalization, or signaling events can be measured. An example plot of this is shown on the right side of the screen where the changes in the response are shown over a period of time. Different responses within the cell will give very characteristic curves in this, which can be mapped. And then the effect of novel pharmacological agents can be used to reproduce those plots.

Slide 12 So there are others, there are many other examples of label-free

techniques. Some of the ones that are very common and usually aren't thought of in this aspect are HPLC, LC/MS, and the list goes on and on.

Slide 13 But I'd like now to show you a little bit of work we've done in this area at

UNC. UNC0638 is a highly selective probe for the G9a methylase, and we've been using this in our lab to compare a variety of label-free technologies. This is still a work in progress, but I'll provide you with some of the data that we've generated.

Slide 14 We first measured the binding of UNC0638 to G9a using the Biorad

ProteOn XPR36 Protein System. Here G9a was immobilized to the surface of the chip and 638 was flowed over the surface. With this, we were able to get an association constant of 2.12, a dissociation of 5.7 x 10-2, and a Kd of 27 nanomolar.

Slide 15 We then repeated this in another SPR system, the ICx Nomadics and

found very similar constants. The Ka, although the exponential is slightly different here, this is only a two-fold difference. Kd also was very close. The dissociation constant also comes out to be very comparable.

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Slide 16 We then checked this on the PE EnSpire Multimode Plate Reader to

measure the refraction index of these using an example of the Epic technology. And what we found was that when we measured this in the absence of peptide, we are able to detect the binding of 638 to G9a. This did not saturate though so we were unable to measure Kds. This work will continue to go on. We'll do a bit more on this and see if we can learn more of it. The interesting thing here is that we are able to detect small molecule binding to G9a.

Slide 17 We also wanted to measure a cellular system in this same system using

the Dynamic Mass Redistribution. For this, we chose Gi Coupled Receptor because of the difficulty in measuring these signals. Normally, to measure the signal of these, we need to boost cyclic AMP concentrations within the cell in order to be able to measure the effect of adenylyl cyclase in reducing cyclic AMP.

Historically, we have used the Promega GloSensor for this and the plot is

shown in the lower right-hand corner for our control, CCPA, with an EC50 of 52 nanomolar.

Slide 18 We then used the label-free response to measure the same response as

well as two additional A1 ligands. For a strong agonist, we received an EC50, very similar, 67 nanomolar. For a weak A1 agonist, this is a UNC synthesized compound, we were able to detect a weaker binding 297 nanomolar EC50. In this middle plot, one of the things you can see is that there is an effect on endogenous receptors in this cellular system. We measured a third compound, which is known to not be an A1 agonist and saw very little response only in the endogenous receptors within the cell.

[0:10:21] Slide 19 So with that, I'd like to wrap up my portion of the talk. I do want to thank

some people who did most of the work that I showed you here. Victoria and Emily did the work with NCICB in generating these label-free curves. Jian Jin and his chemistry team are the originators of G9a and Martin Herold provided the ITC. The collaborators both at PerkinElmer and ICX as well as Tim Wigle, a former post doc from our lab who did the initial G9a work. And of course our collaborators at the Structural Genomics Consortium and the director of the center, Stephen Frye.

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Thank you. Slide 20 Sean Sanders: Great. Thanks very much Mr. Janzen. Our second speaker today is Dr. Charles Lunn. Dr. Lunn received his B.A.

and Ph.D. degrees from Johns Hopkins University in Baltimore, Maryland in 1985. Following postdoctoral training at the State University of New York, Stony Brook campus, he joined the Schering-Plough Department of Immunology, where he characterized the biology of a new class of cannabinoid CB2 receptor-specific inverse agonists.

Slide 21 He joined the New Lead Discovery group at Schering-Plough in 2004 and

retained this position when they merged with Merck in 2009. Dr. Lunn has published on the screening of drug potential targets using high throughput mass spectrometry and also edited the book, "Membrane Proteins as Drug Targets". He is currently a research fellow with the Department of In Vitro Pharmacology at Merck Research Laboratories in Kenilworth, New Jersey.

Dr. Lunn, thanks for being here. Dr. Charles Lunn: Thank you, Sean. What I'd like to do is talk a little bit about the drug industry and what we

think of label-free technologies. We feel that it does have an important role to play and I hope to be able to convince you of that in the course of the next couple of minutes.

Slide 22 And first, this all comes down to a discussion that has been going on in

the literature and at pharma as to why it has been so hard for us to improve the number of compounds that actually get out of the laboratory into the clinic. A number of people have speculated about this, several references are shown on this slide.

And so, the question is why with all of the advances in technology have

we not been able to be better at getting drugs to the clinic. Slide 23 Well, one possibility could be that there is something about the way that

we do drug discovery. Here's a quick view of how that occurs. Normally, a target area would be identified and we would carry out a high

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throughput screen using an assay system that is most appropriate or easiest for us to get a signal. We would then select from those hits, compounds with potency and selectivity and chemistry of interest and then use them to an iterative process using that same assay system to develop better and better compounds.

Now, the decisions that we make at these two stages, the ultra high

throughput screening stage and the drug validation stage, are critical to the success of the program, and could be impacted by some of the decisions that are made. For biochemical targets, the use of a non-native substrate or a non-native protein domain may doom a project from its start. For cell biology, selecting an appropriate receptor and the type of cell, host cell that is used to carry out that screen could be critical. So the question is, how can we improve upon this?

Slide 24 Particularly in regards to the fact that there are many underappreciated

characteristics of targets that could lead us in directions of generating a false lead. The sort of the unknown unknowns associated with every particular target that we try to address in drug discovery.

Slide 25 Well, label-free technologies could offer us an advantage. For

biochemical type assays, here's an example of a way that high throughput mass spectrometry can offer an advantage. As you can see for this antibacterial target LpxC, we have been able to follow a very complicated native substrate conversion to a still pretty complicated product by mass spectrometry and to do all the type of enzymology that one would want to do in order to categorize this enzymology and to find modulators. We have done that and we have reported on this result.

[0:15:08] Slide 26 This technology has proven itself to be able to handle a number of types

of targets using the so-called RapidFire system developed by BIOCIUS Biosystems, now part of Agilent Technologies as of yesterday as I understand it. Note that some of these targets would be very difficult to follow using normal biochemical methods.

Slide 27 For cell-based assays, there also are some issues. Most of the assays that

have been developed use engineered cell systems and surrogate endpoints, which are very good to ensure target selectivity for your screen, but may not be as biologically relevant as we would prefer.

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Slide 28 Again, cell based or label-free technologies could help in this and at least

one example of this I show in this slide. We've been looking to try to find compounds capable of blocking HIV-induced cell fusion. And a member of the lab, Elizabeth Smith, has come up with a strategy using as is shown on the left-hand part of this slide, the result of which is upon cell fusion, β-lactamase becomes activated and can be quantitated. It's a fairly complicated system, but it seems to work pretty well.

On the right-hand side of the slide, Azra Maliksay in our lab has done a

similar type of thing using a label-free system, in this case, the cell key technology. What you can see is you see exactly the same type of signal and exactly the same kinetics as is seen in the reporter based system. And potentially this could be used for non-engineered cells, which we would find of great interest.

Slide 29 For 7-transmembrane receptors, there's a similar sort of story. Usually,

what people would do is to pick the receptor of interest, find some downstream reporters, cyclic AMP, calcium mobilization, etc and then go into your screen. But as you can see on this slide, what I've tried to do is pull together all the varying different ways in which you can get signaling through GPCRs, multiple G proteins, signaling through β-arrestin, the potential of forming heterodimers with other receptors. Plus, you add to that the possibility of receptor associated proteins that could be modulating these activities. It's very hard to pick exactly what pharmacology would be of greatest interest to you.

Slide 30 My favorite receptor and the one that really brought this home to me is

looking at the cannabinoid CB2 receptor. To try to pick a pharmacology to pursue is very difficult because the literature is filled with controversial pharmacologies both pro and con using an agonist for chemotaxis, bone growth, or EAE. We ended up developing this inverse agonist as shown on the lower right-hand side and showed that it had all of the activities that we would want for a generalized anti-inflammatory modulator, the activities of which in some cases were consistent with the literature and some went against. But the bottom line is, it seemed to work in our cell based systems.

Slide 31 Now, if we had available to us a label-free system when we were doing

this, perhaps it would have been easier. What you see here is our initial

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attempt using this EnSpire label-free system being developed. And what you could see is that in the same tube, we were able to generate those response curves for our inverse agonist, in this case, Sch. 319, a couple of known agonists as listed there. And at least for two of them gave very good hill slopes on round one and very good Z factors potentially could lead us directly into screening by simply using these cells at this early stage. And this has very little development that we've done in order to generate this data.

Slide 32 Another aspect of this or perhaps an advantage of this technology is

shown in this slide where on evaluation of a number of different technologies, we found that fenofibrate, a PPARα receptor agonist, would activate or showed some activity toward the CB2 receptor, but only in selective assay cases. For cyclic AMP, it had no effect. Using β-arrestin, we had an EC50 of 55 nanomolar.

[0:20:22] In the lower right looking at a label-free system, it clearly seems to be

behaving as an agonist and this is something we will continue to pursue. But this is one of those serendipitous findings that would have been of interest when we're trying to pursue this type of chemo type.

Slide 33 And so label-free does have a role to play in high throughput screening.

People are starting to use it. Here is one example in the literature in which a comparative screen of a hundred thousand compounds was shown using a either a FLIPR based assay or in this case an Epic system, and there were a number of compounds that were shown to be selective for the label-free technology, in the little blue-green circle in the insert there. Those compounds could be off-target false positives, but they could also be unique opportunities for enhancing the success of the particular 7-transmembrane target that was evaluated here.

Slide 34 So how do we determine the difference? And it really comes down back

to one of the first slides, the strategy and where you put the label-free technology in your system. There are two possibilities I list here. There certainly are others. In the upper right, the B side, you could put the label-free system after a standard high throughput screen to identify those to increase the information content within your multiple lead series and perhaps find a unique biology of a particular compound early in your discovery effort.

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Secondarily, you could have it -- you could elevate the ultra high throughput high in your screening tree. So, you elevate the significance of the unique of the unique biology that you wish to find in your system and then later determine through different target engagement assays exactly how these compounds are working later in the process.

Slide 35 So, in conclusion, I basically have listed here that one of the solutions to

try to do a better job finding good compounds is basically to find better compounds. You can do that by making better choices in your screening assays and better choices of the compounds that you choose for development. And that label-free technologies may offer a strategy for unbiased interrogation of compound libraries that allow us to get to those better compounds.

Thanks a lot. Slide 36 Sean Sanders: Great. Thank you very much, Dr. Lunn. I appreciate that. Our final speaker today is Dr. Brian Shoichet. Dr. Shoichet received his

B.Sc. in chemistry and a B.Sc. in history from MIT in 1985. He completed his Ph.D. on molecular docking in 1991 at the University of California, San Francisco. Dr. Shoichet's postdoctoral research was largely experimental, focusing on protein structure and stability and was done at the Institute of Molecular Biology in Eugene, Oregon, as a Damon Runyon Fellow. He later joined the faculty at Northwestern University as an assistant professor in the Department of Molecular Pharmacology & Biological Chemistry in 1996. He was promoted to a tenured-associate professor in 2002 and shortly thereafter was recruited back to UCSF, where he is now a professor in the Department of Pharmaceutical Chemistry. Currently, research in the Shoichet Lab uses computational and experimental techniques to investigate enzyme structure, function, stability and inhibition, and the links amongst them.

Welcome Dr. Shoichet. Dr. Brian Shoichet: Thanks, Sean. Thanks for having me. Slide 37 So, I'll begin by thanking the people who did the work I'll describe. The

work in our own lab was done by two graduate students, Brian Feng and Rafaela Ferreirra and a talented post doc Kerim Babaoglu. And none of it would have been possible without a terrific collaboration with the NIH

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Chemical Genomics Center; Anton Simeonov, Jim Inglese, Chris Austin, and Ajit Jadhav.

Slide 38 So, I think the focus of my talk will be the importance of understanding

mechanism for the actives that are coming out of high throughput and other screens that are conducted. I think our experience, and maybe it's true of the whole field, is that in undertaking discovery projects for really new molecules, one is threatened to be overwhelmed by false positives, by artifactual actives. And the first most important task is to separate the few really interesting molecules from the vast majority of false leads that emerge from those studies.

[0:25:22] And those can come from a number of different mechanisms especially

for soluble proteins. What you're really looking for is a molecule that has a classic stoichiometry of binding and binds with -- typically, you're looking for reversible inhibitors, sometimes covalent but typically reversible inhibitors that bind with a 1:1 stoichiometry, at least a classical, well understood stoichiometry. Those can be masked by molecules that look like they have activity in your screen but are really just interfering with the spectroscopic readout for instance if it's a label technology that are forming covalent adducts, which are frequently not wanted, but will lead to inhibition for instance.

And for molecules that form colloids in solution, it turns out that a

substantial number of organic small molecules at micromolar and even submicromolar concentrations will form colloids. These are on the order of 200 nanometers in size. They adsorb proteins and denature them and they can look like inhibitors for instance of soluble proteins. So, these and other problems are mechanisms that you want to identify and discard as rapidly as possible.

Slide 39 And I'll illustrate that with two different stories with two different targets

that we've prosecuted in collaboration with the NIH Chemical Genomics Center. The first is β-lactamase, which is an antibiotic resistance target. And we were looking for novel molecules not resembling the known β-lactam ligands for that target using both a structure based screen and a high throughput screen. The latter of which was conducted at the NCGC here in DC.

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At the NCGC, they actually conducted two screens, one in the presence of a detergent and one in the absence of a detergent. And the reason why that was done is to get rid of the colloidal aggregators. Because what we had observed is that these colloids will disappear on the addition of a small amount of non-ionic detergent.

Slide 40 The results are illustrated on this slide. One on the top, you see a β-

lactam inhibitor. It's a classic inhibitor. Not what we were looking for but it's well behaved in the assay classic dose-response curves both in the presence and the absence of a detergent. And then on the bottom, a molecule that emerged, an active molecule that emerged from this screen looked very interesting, but when you add a detergent activity, completely went away and that was because of it was forming these colloidal aggregates.

And it turns out that we got 1274 hits, it was very exciting. But it turns

out that 95% of them had this detergent response where activity would essentially largely or completely disappear on the addition of a small amount of detergent. And so 1204 of the initial actives were colloidal aggregators leaving only 5% of the molecules as interesting to follow up.

Slide 41 And then began almost a year-long campaign to understand the

mechanism of action of each one of those 70 molecules. The first 25 were actually easy to get rid of. They were all β-lactams. They formed covalent adducts with the enzyme, with β-lactamase. And we weren't interested in them. We knew that β-lactamase recognized β-lactams. That was not new to the field. And they formed covalent adducts as we well and we were only interested in reversible inhibitors. So those first 25, we got rid of.

Of the 45 that remained, on detailed, careful, one-at-a-time testing also

on re-synthesis of those compounds, activity could not be confirmed and that in our experience is very common. It's very common to get basically a well-based artifact from a high throughput screen. In our experience, all label-free high throughput screens where the actual material has degraded or there's a product from synthesis that's still left in the well and, you know, on re-synthesis or retesting or repurchasing the compounds from the original, you can't show activity. So that was our experience. That left 21 compounds from 1274. Nine of them also turned out to be colloidal aggregators and were not interesting. These were discarded leaving 14, as it turns out, molecules.

Slide 42

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So now, we're down to 14. Of those 14, 12 were nonspecific. They

inhibited chymotrypsin and cruzain as well as β-lactamase. They turned out to be irreversible. When we did the mass spec of the compound with the protein, it turned out that they were covalently modifying the protein. We showed that both by mass spec with Al Burlingame's lab at UCSF. And also, Kerim Babaoglu had the dubious pleasure of being, I think, one of the first people to publish a covalent artifact from high throughput screening by atomic resolution protein crystallography and that's shown at the top. And you can see the molecules from the covalent adduct with a catalytic serine. That was true of all of those molecules.

[0:30:31] Two of the molecules were actually well behaved, simple 1:1

stoichiometry, reversible competitive inhibitors of β-lactamase that were specific of that molecule. Kerim also determined those structures and they're shown at the bottom. So, out of 1274 careful analysis of mechanism of action, winnowed that down to 2 molecules that were progressible for affinity and selectivity.

Slide 43 So, we found that label-free technologies offer us a good opportunity to

do that, to get an orthogonal view of mechanism. We've used several of them. Bill mentioned this one in his talk. It's the Corning Epic technology for looking at surface effects on wavelength and refractive index.

In this experiment, this is actually done at Corning by David Randall and

Todd Upton. What they looked at are known colloidal aggregators that we had identified in the lab previously and put them, you know, in solution and looked at the deposition of those colloids just by gravity on to the solid support. And they were able, actually with a lot of sensitivity, to show at which point the aggregates formed in solution, the so-called critical aggregation concentration and they were able to do this in relatively high throughput. What I'm showing here are results they found for known aggregators like sulconazole and known non-aggregators, which don't show this effect like compounds in the same chemical series for instance like ketoconazole.

Slide 44 We, in our own hands, have used dynamic light scattering to the same

effect. We were looking at the overlap between dynamic light scattering, which also detects colloid formation and enzyme inhibition where we can compare the two directly, and this is a work that Brian Feng did several years ago.

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You know, one of the things that comes up is that they are meant to be

orthogonal views and it's the overlap between them that usually gives you the best indication of mechanism. We found for instance that in salt molecules that were insoluble will also scatter light in dynamic light scattering of course. Those tend to not inhibit proteins. They're not going to be problems. They're not interesting. They're not in solution, but they're not going to be false positives and that was illuminated by the cross between, the overlap between molecules that scattered and inhibited. So we found it's often useful to use both techniques, both sorts of techniques.

Slide 45 The last story I'll relate is very similar to the β-lactamase story. This is a

campaign against the cruzain enzyme, which is a target for Chagas disease. And this was also done with the NIH Chemical Genomics Center. It's with a bigger library about 200,000 MLSMR molecules. We went to the same process and we sort of got the same overall results. Nine-five percent of the hits turned out to be colloidal aggregators. And then in winnowing down what remained there were actually more covalent molecules. Cruzain is a thiol-active protease so it's more prone to electrophilic inhibitors. There were more molecules that interfered with, in this case, fluorescent assay.

Slide 46 But I think the real illumination that came out of this story was that

there's no place to hide from these artifacts. You can't really get around them by choosing the exact perfect library to screen. We found, you know, pretty much the same hit rates for covalents, colloid formers, fluorescent inhibitors among drugs and bioactive molecules that you would get from for instance Tocris or Prestwick are these, you know, terrific sources of compounds as you'd get from just the normal MLSMR, which had been bought from standard organic chemistry vendors.

And so, I think the message we've learned is that organic molecules just

by nature have these problems and, you know, of course they're wonderful sources of new leads and that's what we're all looking for. But to get to those real new lead molecules, it's been our experience that you have to invest in understanding the mechanism of actions of each. Without that, you're very likely to spend months and years of time progressing molecules that in fact are artifacts and that can be a great tragedy.

[0:35:09]

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Sean Sanders: Great. Thank you so much, Dr. Shoichet. Slide 47 So many thanks to all of our speakers for the very informative

presentations and we're going to move on to the questions submitted by our online viewers.

A quick reminder to those watching us live that you can still submit your

questions by typing them into the textbox and clicking the submit button. If you don’t see the box on your screen, click the red Q&A icon and it should appear. We've been getting some great questions in so please keep them coming.

The first question I'm going to put to the panel, and I guess we'll start

with Dr. Janzen and we'll work our way down the table, is what are some of the hurdles that you have found or imagined prevent the broader adoption of label-free technologies?

Dr. William Janzen: Okay. Thanks, Sean. The adoption of any technology really comes down

to two main factors. Once the benefit of a technology is seen, it really comes down to a question of understanding and cost. Once a technology is well understood, the way it fits into the laboratory and into the experimental scheme can be easily seen. Adoption is always going to be a factor of the cost it takes to adopt that technology, the uptake cost.

The problem we have is that most labs have an installed capital base,

there is equipment in the lab. To bring in new equipment is always going to provide a barrier. You know, where that new technology can be integrated with existing equipment or integrated with existing technologies, it always lowers that barrier.

Sean Sanders: Dr. Lunn? Dr. Charles Lunn: I think in the pharmaceutical industry, one of the big issues is making sure

that the signals that you see as part of your assay come from a known target. We are very chemistry-centric and chemists love to know exactly what target they're going after because it's much easier for them to develop a very specific modulator of whatever that target is -- if indeed it is a single target.

The possibility with label-free is sometimes it's a little harder to make

sure that you are modulating one specific target. That can be an advantage because you get unexpected results that could lead to superior compounds. But it also has this drawback that you need to know

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exactly what targets you're pursuing at some level to do the chemistry. So I think as we try to understand exactly how to balance those two factors, I think we will see more adoption within pharma.

Sean Sanders: Dr. Shoichet? Dr. Brian Shoichet: I don't have anything to add. I think it was well answered by my

colleagues. Sean Sanders: Great. So to come to -- I know Dr. Shoichet, you discussed as part of your

talk some of the issues with false positives. Maybe I can get an idea from the panel of the prevalence of false positives and negatives in label-free screening. Is what you showed the norm?

Dr. Brian Shoichet: I don't know. It's, certainly, what we've seen over and over again. I think

what people do as part of their workflow is from the start, they'll reduce the number of -- they'll adjust their hit rate to an arbitrary point. Like for instance you hear this figure of 0.1%, that's very common. So they'll get rid of a lot of their initial hits by just ensuring that no more than so many come through and that's actually one way to get rid of the weaker and maybe more promiscuous molecules. But it's relatively rare where people have the opportunity to run everything to the ground so you'd get really good statistics.

Sean Sanders: Uh-hum. Dr. Brian Shoichet: But what I guess I can say, I mean putting the actual numbers aside for a

second is that there is a predominance of false positives, I think, that are seen widely. And they can be very misleading.

Sean Sanders: Uh-hum. Anything else to add from…? Dr. Charles Lunn: I think that there are challenges in doing random library screening. I think

you can find false positives a lot. I think we are trying to build on databases and on work like Brian has been generating here to see if we can identify those as early in the process as possible. And it is important to be able to have multiple systems in order to look at the compounds to confirm that you really understand what's going on with every compound before you put a lot of effort into compound development, but it is a very big problem.

Sean Sanders: Uh-hum.

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Dr. William Janzen: I think any technology is going to have a false positive and false negative rate. The key with many of the label-free technologies is that they're actually being used to determine what that false positive and negative rate are.

[0:40:07] Sean Sanders Uh-hum. Dr. William Janzen: You know, the example would be flow systems with SPR or injection

systems with resonant gratings where you can actually watch on and off rates. This will tell you fairly specifically if compounds are false positives or negatives. But I don't think any of these are a magic bullet to remove high throughput false positives and negatives.

Sean Sanders: Uh-hum. Great. I'm going to pass on a question, actually, we'll stay with you Dr. Janzen,

that asks about the throughput of label-free technologies compared to label technologies. Is there a large difference? You know, how do you use it in your work?

Dr. William Janzen: Well, we're just beginning to use the label-free technologies and

historically people have used these in low throughput. They've been individual compound injections into SPR. The resonant grating has been a higher throughput but hasn't been broadly adopted. I think, you know, again the issues we talked about with technology adoption are going to be issues here in terms of scaling these up into high throughput. Devices are being manufactured in all of these areas, which allow them to be used in a higher throughput.

Sean Sanders: Uh-hum. Dr. William Janzen: The definition of high throughput is always a questionable one and I've

found as I've moved from industry into biotech and academia, high throughput changes from a hundred thousand compounds a day to a total library of five to ten thousand compounds many times. In this lower definition of high throughput, these technologies are clearly very applicable.

Sean Sanders: Uh-hum. Great. Dr. Shoichet? Dr. Brian Shoichet: No, I think that's dead on. Right. Dr. Charles Lunn: Uh-hum.

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Dr. Brian Shoichet: That it's relatively rare to see the label-free technologies that we've used

in initial ultra high throughput, but in smaller more focused sets that emerged, they're really powerful.

Sean Sanders: Great. So, our next question is related to data analysis. If you're doing a

high throughput, you're producing a lot of data, how is this handled? Is this an issue with these technologies? Maybe, we'll stay with Dr. Shoichet for the moment.

Dr. Brian Shoichet: Actually, I would defer to my colleagues on that one. Sean Sanders: Okay. Dr. Janzen? [chuckles] Dr. William Janzen: I get to go first again. The key here I think is again looking at where the

technology is being applied. Initially during the development of your assay, you'll be using a very data-rich environment. And one of the advantages of the label-free technologies are that they do provide you with a very rich set of data, potentially giving you association, dissociation as well as binding constants. When moving into high throughput, you have to take just a narrow slice of that data.

Sean Sanders: Uh-hum. Dr. William Janzen: You have to look at a specific example, a specific time point and then use

the instruments, onboard software to export only that specific bit of data so to speak. The key will be that then using the same technology, you can then go back and gather the data-rich package as you move in to a lower throughput follow-up.

Sean Sanders: Uh-hum. Dr. Lunn? Dr. Charles Lunn: Yeah. I do think that as we go forward, you know, strategies for mining

this rich dataset will improve. You know, I was always impressed with how well the data mining of data coming off of FLIPR curves could be used in high throughput, and we really could get a lot of information about the characteristics of compounds in fairly high throughput using fairly simplistic algorithm used within that instrument. And I suspect that before too long, that type of algorithm could be used thinking mainly now about cell-based assays with the label-free and GPCRs to be able to categorize or at least bin compounds into areas that might be of most interest that could then proceed farther ahead. But it is going to be an issue if these things are going to find wider use and really be as valuable as I think they could be.

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Sean Sanders: Uh-hum. Dr. Brian Shoichet: I would just add then when -- you know, sort of a different take on that

question that there's a… With a lot of these observations from direct high throughput screens coming into the public domain, you know, being deposited for instance in pub chem, there's a real draw to analyzing the output of primary screens to get structure activity relationships and understand chemo informatically what's going on. And that I think has to be approached with a lot of caution because not all of the observations are trustworthy, not in the primary screen. I think that would be my cautionary take on that question.

Dr. Charles Lunn: Yeah. I would agree with that. I think that particularly if you're trying to

dive very deeply into your datasets to try to analyze compounds that really don't have really good potency, you can get some real complicated datasets that it's really hard to understand.

[0:45:11] Sean Sanders: Uh-hum. Dr. Charles Lunn: If you can bin them into a separate pile and that they can be analyzed

separately then I think there's a value to that. Because those are going to be the compounds that have all of the problems associated with aggregation, covalent and all of those things that you talk about. So, you know -- but I think that collecting that data or figuring out how to collect that data might be something worth trying.

Dr. William Janzen: One other comment on that to build on what Brian just said. We found

with some early studies we did at Amphora that just random activity within the library will generate structure activity relationships. If you have a sufficient hit rate, you will find SAR even in random activity.

Sean Sanders: Uh-hum. Okay. So, Dr. Shoichet, one for you. This came in referring

specifically to your talk. What is the best strategy to counter screen detergent-like false positives, which denature your target protein?

Dr. Brian Shoichet: Well, I think, biophysical reads will address that point. You can look for

direct -- ITC for instance will show that up pretty clearly if you can afford the protein and I think the hit on ITC is it's a real pig for protein. So anything biophysical will get at protein -- well not anything, many biophysical techniques will get at protein denaturation detergent-like properties. So that would be my -- but the trick is you don't know that

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you've got them actually, right. So you don’t know, oh well, this is a detergent-like effect so I'm going into ITC so…

Sean Sanders: Okay. Dr. Brian Shoichet: I think you have to have several different ways, different techniques to

look at your hits to convince yourself that they're real. And the more tools you have at your disposal or the more collaborations you have to look at a compound, the better.

Dr. Charles Lunn: Good. Sean Sanders: Great. Dr. Charles Lunn: That's absolutely true. I think you have to be able to… You know, there is

no such thing as a perfect technology and you will never be able to use one assay system in one way and prove conclusively that you have anything.

Sean Sanders: Uh-hum. Dr. Charles Lunn: I think you have to be able to show a consistent set of data that lead to

the development of the model that you're trying to put forward. And I think what we're trying to do here is to talk about some of the ways in which you can make this process a little less onerous. But there's no way that a single assay system is going to be important for everything you could possibly want to do in drug discovery. It just won't work.

Sean Sanders: Well that actually brings me nicely to another question asking if you can

discuss a little bit more about the synergy that's been created between label-based technologies and label-free technologies and how… There's questions that have come in, is one going to replace the other to which it seems like the other is clearly no. That they can work together. So maybe if one of you wants to take that question, you know talk a little bit about how you see this synergy happening.

Dr. Charles Lunn: Again, I think it's a -- if you are knowledgeable about the assay system

that you were using and you use it smartly, then you can get a lot of information about it. But you then have to look at those same set of compounds using these other strategies and again be smart about it. And be cognizant of the fact that if you use a soluble kinase domain with a peptide, you may not hit every compound that is of greatest interest to you. And that you run risks when you run these label technologies as

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opposed to the label-free and when you run biochemical assays in isolation as opposed to cell-based assays in a primary cell.

Sean Sanders: Uh-hum. Dr. Charles Lunn: And, you know, we make as good a guess as we can when we go into

these screening campaigns and sometimes we guess right and sometimes we don't. And what we're trying to do here is to guess better I guess.

Sean Sanders: Uh-hum. Dr. Janzen, maybe you could answer this question about

comparison of some of the different label-free technologies that are out there 'cause I know you've used a number of them. Are there different applications that some are better than others?

[0:50:01] Dr. William Janzen: Oh, I think there clearly are. You know, I could spend the rest of our time

talking about where different technologies can be applied. The key really comes down to the individual target. It's almost impossible to answer that question without looking at an individual application. SPR has been used for many years. It's been around for years and has been used for direct binding of proteins and smaller analytes.

The resonant waveguide has been available for a number of years and I

think it's just beginning to be broadly applied. I think it is a higher throughput technique so far than SPR. But again, we need to get it out into the public domain and a little more broadly used. So I guess that's a nice of way of not completely answering the question but saying it's so target specific that you can't --

Sean Sanders: Right. Dr. William Janzen: -- you can't specifically really pick an individual technology. Sean Sanders: Right. Great. Dr. Charles Lunn: Certainly, if you have a target that does not tolerate being attached to a

substrate that's going to impact the choices that you make. It maybe that your only choice is to analyze a particular target in solution in which case, label-free may not be a way to go forward. You will have to do with cell-based. Certainly, there are a lot of cell-based assays, which there's -- you know, for GPCRs, it's very difficult to figure how you would be able to use anything -- or there are significant advantages for GPCRs using a cell-based assay I think.

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Sean Sanders: Uh-hum. Dr. Charles Lunn: But again, it is very target specific. Sean Sanders: Right. Here's a question that I guess is applicable to just about any library,

but this person is asking for advice on using on natural product libraries for cancer compound screening. So, Dr. Lunn, you might be the best person to answer that.

Dr. Charles Lunn: There are people who love natural product libraries. There are people

who feel that they add a level of complexity for doing your day-to-day screening. Because most natural product libraries are mixtures of compounds that gives a level of complexity to trying to tease out exactly what the active component is that I don't think it's any secret that a number of pharma have moved away from natural product screening.

Sean Sanders: Uh-hum. Dr. Charles Lunn: Having said that, it is clear that whatever the cell makes is by its design

has a biological relevance to it. So, it's a very hard question to answer. Sean Sanders: Okay. So, a question that just came in actually was asking I guess do you

look at the step beyond this work, which is evaluating leads found from label-free screening. What do you see as the next step you'd take? They ask is it x-ray, XMR, NMR? Dr. Shoichet?

Dr. Brian Shoichet: Well, if you can get a crystal structure or an NMR structure, if you can get

an atomic resolution structure, I think that's terrific because there's nothing more convincing. Those techniques give you so much more data, so many more observations than any other and the resolution is so much better. So if you want to be sure, hey is my compound really hitting my target the way I think it is with the stoichiometry, is it making the interactions that I can progress, those are great.

Those options are not available to every target and every program. And

often, what I've heard in pharma is that it's a trailing indicator, right. The crystal structure happens by the time the compound is in the clinic so I think which is… So, it really depends on the target. If it's an option, I think it's terrific. There are other ways, you know, and this gets back to this issue of just multiple angles of looking at it.

And I think -- so for instance, you know, you've got a label-free hit and

then you want to know is it active in the cell, you know, or if it is active in the cell is it active "on my target" and not on some other target as well.

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You know, what's the polypharmacology, what is its penetration properties, all those things start to become -- they're critical because you're developing molecules not to treat a biosensor, but to treat an animal or to use as a probe. So, those become the next questions I think.

[0:54:53] Dr. Charles Lunn: It is on one of the problems. And when you get compounds that are assay

specific as the ones that I talked about, how do you prove that those compounds have biologic efficacy? And I think that's one of the things that's critical. So, you can show that a compound binds to your target, does it affect the activity, does it affect the activity in the cell, does it affect the activity in tissue, and does it affect the activity in an organism? It's very complicated and there aren't real shortcuts to making those decisions. And again, what we try to do is to make the best guesses that we can at each step of the way so that the number of compounds that fall off as being just crap are minimized and that's basically what we're talking about here.

Sean Sanders: Uh-hum. Dr. William Janzen: It really comes down to an escalating physiological relevance that you

begin with. An example of an enzyme with an interaction with an isolated enzyme, if it's a binding event, as Chuck mentioned, you have to show that it's actually active. If it's an activity event, you have to show that it's true binding, which was Brian's presentation. But what you find is that the levels of confirmation as your system becomes closer and closer to native physiology go higher. So isolated cell assays have a higher confirmation rate moving forward. Phenotypic cellular assays have an even higher one. If you can start with animal pharmacology, that's obviously your best step, almost impossible but your best step.

Sean Sanders: Great. So, we're running out of time so I'm going to come to a final

question for all of you. Out of the label-free -- the questions that are being asked now, what do you think can be answered by label-free technologies that couldn't previously be addressed? You know, either things that you're working on or things that you see out there that you think this technology is specifically suited to address. So let's start with Dr. Shoichet and --

Dr. Brian Shoichet: Direct binding, thermodynamics of binding. So, for instance in the SPR,

Surface Plasmon Resonance with the right controls, you can get kons and koff kinetics of binding. Those are very hard to get from other techniques without a lot of work. Titration calorimetry promises to give you direct thermodynamic readouts whether it really can other than free energy

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binding, it's not clear. But it's clearly measuring reversible equilibrium binding events and that's often not the case with the label technologies and so I think that's a real important contribution.

Sean Sanders: Uh-hum. Dr. Lunn? Dr. Charles Lunn: I've been very interested looking at GPCRs in particular. The number of

ancillary proteins that associate with GPCRs that can modulate their activity that we really don’t target very well. And it's because we normally work in, as I said, highly engineered cells. It's usually a Chinese hamster cell, which has nothing to do with human biology and usually a very isolated, just looking simply singly at the receptor.

Sean Sanders: Uh-hum. Dr. Charles Lunn: I think potentially, you could start to use the cell based label-free assays

to perhaps be able to interrogate some of these other interactions that you can't predict would be useful a priori, but may ultimately have some effect. Now it's going to be a down-the-road piece and it's going to be tough to convince yourself that basically an allosteric modulator of receptor activity functioning through one of these ancillary proteins is really a good drug target. But certainly, there's a lot more interest in looking for these allosteric modulators of receptor activity and perhaps label-free could get us there a little more quickly than the standard ways in which we look at GPCRs now.

Sean Sanders: Uh-hum. Great. Dr. Janzen? Dr. William Janzen: I think those were very comprehensive. The only thing I would add is the

ability to look at enzymatic activity where either the turnover rates are too slow to measure by conventional means or substrates are unknown. So, you can look in an iterative fashion at binders and then put those into more physiological settings.

Sean Sanders: Uh-hum. Excellent. Great. Well, thank you all very much. Unfortunately, we are out of time so I'd

like to thank our speakers for being with us today and for sharing their personal experiences in this field; Mr. William Janzen from the University of North Carolina, Dr. Charles Lunn from Merck Research Laboratories, and Dr. Brian Shoichet from the University of California San Francisco. Thank you all again for being here.

Dr. William Janzen: Thank you.

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Dr. Brian Shoichet: Thank you. Dr. Charles Lunn: Thank you. Sean Sanders: Many thanks to all of our viewers for the excellent questions that you

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