Anticancer drugs form colloidal aggregates and lose activity

Over the last few years, one of the most interesting findings in drug screening and testing at a preclinical level has been the observation that many drugs form colloidal aggregates under standard testing conditions and nonspecifically inhibit target proteins which they otherwise would not affect. This are large aggregates, a hundred nanometers or more in diameter, and they cause proteins to stick and partially unfold, creating the illusion of inhibition. This leads to false positives, especially in high-throughput screening protocols. And these false positives can be absolutely rampant.

What's striking is the sheer ubiquity of this phenomenon which has been observed with all kinds of drugs under all kinds of conditions; while the initial observation was limited to isolated protein-based assays, the phenomenon has also been seen in simulated gastric fluids and in the presence of many different kinds of proteins like serum albumin which are found inside the body. The colloid spirit seems to emphatically favor a shotgun approach.

Now a team led by the brother-sister duo Brian and Molly Shoichet (UCSF and Toronto) has found something that should give drug testers further pause for thought; they see some bestselling anticancer drugs forming colloids (shown above) in cell-based assays to an extent that actually diminishes their activity, leading not to false positives but to false negatives. They test seven known anticancer drugs in cell assays both under known colloid forming conditions along with conditions that break the colloids up. This is not as easy as it sounds since it involves adding a detergent which would usually be too toxic to cells; fortunately in this case they find the right one. Another interesting finding is the re-evaluation of a popular dye used to study "leaky" cancer blood vessels; unlike the previously proposed mechanism, the current study seems to suggest that the dye too forms large aggregates and nonspecifically inhibits the protein serum albumin.

The testing essentially reveals that the drugs when they form colloids basically show activity that's so low as to be negligible and equivalent to the controls. That's a self-(un)proclaimed false negative. Now anybody who deals with error analysis knows that false negatives are fundamentally worse than false positives since by definition they cannot even be detected. The present study raises the pertinent question; how many promising drugs might we be missing because they form aggregates and lower the observed response in cells? And since the colloid forming phenomenon has been shown to be so ubiquitous, could it possibly be influencing the mechanism of action of all kinds of drugs inside the body? And in what ways? It's a fascinating question, and one of those that continues to make basic research in drug discovery still so interesting.
Image source and credit: ACS

Steering library bias toward A2A adenosine receptor ligand discovery

The A2A adenosine receptor is an important GPCR, well-known for binding caffeine. Adenosine receptors are emerging as relevant drug targets for a variety of disorders including Parkinson's disease, and there is interest in discovering new ligands that bind to them. Among adenosine receptor subtypes, the A2A receptor is one of the few GPCRs whose crystal structure is available. Thus the A2A is amenable to structure-based design efforts, and virtual screening is an especially attractive endeavor in this regard.

In the present report, a team of researchers from NIH and UCSF led by Brian Shoichet, John Irwin and Kenneth Jacobson use virtual screening to discover new ligands for the A2A. There are several points to note here. The authors use the ZINC library of drug like molecules to dock about a million and a half compounds into the binding pocket of the A2A crystal structure. They pick the best-scored 500 (0.035% of the total) ligands and investigate their fit in the binding site. Using criteria like electrostatic and VdW complementarity and novelty of chemotype, they finally select 20 of these 500 hits and test them in assays. Out of these 20, 7 inhibited binding by more than 40% at 20 μM concentration, thus constituting a hit rate of 35%. While the compounds formed the same kinds of interactions as some other A2A ligands, they were also relatively diverse in structure. The ligands were also tested in aggregation-based screens to determine that their activity was not a spurious artifact of aggregation-based inhibition.

This is a pretty good hit rate. Generally virtual screening campaigns are lucky to have a hit rate of a few percent. Curiously, the authors also found a similarly high hit rate during a past VS campaign against the well-known β2 adrenergic receptor. What could be responsible for this high hit rate against GPCRs? The reasons are interesting. One reason could be that GPCRs are very well adapted to bind small molecules in compact pockets, enclosing them and forming many kinds of productive interactions. But more intriguingly, as the authors have noted earlier, there is "biogenic bias" in favor of certain target-specific chemotypes in commercial libraries that are screened, both during VS as well as HTS. This in turn reflects the biases of medicinal chemists in picking and synthesizing certain kinds of chemotypes based on the importance of drug targets and past successes in hitting these targets. GPCRs clearly are enormously important, and GPCR-friendly ligand chemotypes thus constitute a large part of screening libraries. These chemotypes are much more prevalent than those for kinases or ion channels for instance.

This observation has both positive and negative implications. The positive implication is that one is likely to keep finding high hit rates for GPCRs using VS. However, the negative implication is that one is also going to be constrained by biogenic bias, and this might preclude finding more diverse and entirely novel subtypes. Thus, while VS campaigns for GPCRs might find a good number of hits, the novelty of these hits might not always be satisfying. One other quite intriguing point emerging in this study is that the kind of hits found (agonist, inverse agonist, antagonist etc.) reflects the ligand which the target structure used for VS is co-crystallized with. Thus the A2A houses an antagonist in the binding site, leading to a preponderance of antagonists in the top docking hits. Indeed, agonists ranked abysmally low in the list.

GPCR ligand discovery is one of the most important goals in drug discovery. This and other similar studies demonstrate that, with all its caveats, VS can be productively used to mine for new GPCR drugs.

Carlsson, J., Yoo, L., Gao, Z., Irwin, J., Shoichet, B., & Jacobson, K. (2010). Structure-Based Discovery of A2A Adenosine Receptor Ligands Journal of Medicinal Chemistry DOI: 10.1021/jm100240h

The same and not the same: more aggregates in HTS

High-throughput screening (HTS) is now a mainstay of drug discovery and usually the starting point for most drug discovery projects. Industry usually has a lot of resources invested in HTS and therefore needs to be aware of false positives and false negatives that could hamper useful results and lead one down an erroneous path.

Among the many factors responsible for false positives in HTS, one of the most startling and important factors recently unearthed is the non-specific and potent inhibition of enzymes by aggregates of molecules occurring under typical assay conditions. These aggregates are large enough to be observed under a microscope and to be detected by dynamic light scattering. The aggregates adsorb enzyme molecules on their surface, and one of the best tests for detecting their presence is to re-run the enzyme assay under high detergent concentration. High detergent concentrations usually break up the aggregates and lead to a loss of potent inhibition. The phenomenon of aggregation-based inhibition was accidentally discovered by Brian Shoichet's group at UCSF and has been comprehensively explored by him and his students in a series of papers throughout the last decade, although much is still to be known about the exact physical nature of these aggregates. The reason why this has become a big deal is because it has been observed in an unusual number of cases, which leads to the suspicion that much effort might have been already expended in drug discovery campaigns in pursuing such false leads.

In a recent paper, Shoichet and Craik's groups at UCSF accidentally discovered aggregate-based inhibition in discovering inhibitors for the enzyme cruzain which is a part of the metabolic machinery of the parasite responsible for Chagas disease. The authors had started with an initial hit from a virtual screening campaign and were engaged in the usual process of modifying the hit based on SAR. The initial tinkering led to a series of oxadiazole inhibitors which exhibited potent inhibition of cruzain.

However, many of these molecules failed to show activity in cell-based assays. Such a discrepancy between enzyme and cell-based assays can be traced back to many reasons including poor permeability. But in this particular case, kinetic measurements hinted at aggregates of the oxadiazoles that were inhibiting the enzyme. At this stage it was also discovered that unlike the initial hits series, the oxadiazole series had been accidentally assayed under low detergent conditions. The molecules also inhibited another intensely studied enzyme in the Shoichet group- AmpC beta-lactamase. The quintessential test for aggregate-based inhibition, namely increasing the concentration of detergent (Triton in this case), also proved positive confirming the phenomenon. Interestingly the initial set of hit molecules also seemed to exhibit this phenomenon but only in case of AmpC lactamase and not in case of cruzain. In case of cruzain, experiments with differing detergent concentrations proved that the initial set of molecules were equally potent under both conditions, while the oxadiazoles lost activity under high detergent conditions, indicating divergent modes of inhibition between the two sets of molecules.

Finally, note that the aggregation-based inhibition would likely have not been discovered if the oxadiazole series had been assayed under the same low detergent condition as the initial hit series. What seemed like similar molecules turned out to behave very differently under different assay conditions. Sometimes mistakes can reward you with unexpected treasures, and similarity needs to be pried out from the eye of the experimenter. Never underestimate the importance of going wrong (of course revealed only in retrospect).

As the authors narrate, the moral of such studies should not be lost on medicinal chemists, who usually interpret high and low potency of related molecules based on local structural features like hydrogen bonding, electrostatics and hydrophobicity. Aggregation-based enzyme inhibition proves that chemists have to look beyond single molecule structural features toward supramolecular features of several molecules that are interacting with each other. Chemists regularly engaged in HTS campaigns might well keep this valuable piece of advice in mind. Scientific enumeration, it seems, has to always be done at several different levels.

Note: Apologies to Prof. Roald Hoffmann for appropriating the title

Ferreira, R., Bryant, C., Ang, K., McKerrow, J., Shoichet, B., & Renslo, A. (2009). Divergent Modes of Enzyme Inhibition in a Homologous Structure−Activity Series Journal of Medicinal Chemistry DOI: 10.1021/jm9009229

The anti-question, or when bias can be a good thing

A recent publication indicates that more bias in the form of natural product scaffolds not yet synthesized could improve hit rates in screening



Most drug discovery projects are inaugurated with some kind of screening campaign where millions of molecules are screened against a biological target. Even though the hit rate from High-Throughout Screening (HTS) can be quite low, HTS still provides one of the best starting points to discover interesting new structures that display biological activity. In spite of this, there is frequent disappointment at the low rates from HTS which could be as low as 0.05%.

But instead of focusing on the low hit rate from HTS, what if we express surprise that this hit rate is actually high? This thought takes me into a slight digression. In his remarkable book The Black Swan, the author Nassim Nicholas Taleb talks about an "anti-library", the set of all books you have not read. The anti-library is in some ways more important than your library because it really tells you what you are ignorant about.

Similarly we can define an "anti-question". The anti-question is a question opposite to one which we might usually ask. So instead of asking; "Why is this drug specific for this protein?", we could ask "Why is this drug not hitting other proteins?". The value of the anti-question is that it forces us to analyze and evaluate things that we otherwise may not and enables us to think outside the box. As the wise doctor constantly exhorts detective Sponer in "I Robot" to get to the all-important right question, so it could be important to get to the right anti-question.

In the context of HTS, the anti-question actually turns out to be logical. Instead of asking, "Why is the hit rate from HTS so low"?, one should ask "Given the number of small molecules in small-molecule space (~10*60) compared to the extremely low number typically screened in HTS campaigns (10*6), why should we get any hits from HTS at all?". Even narrowing down the unimaginably large small-molecule universe to more drug-like or lead-like entities, we still run into a numbers paradox since even this number is orders of magnitude greater than what is usually screened.

In their most recent paper, Brian Shoichet and his team ask this important anti-question, and it leads them down an interesting road. Most campaigns that screen libraries focus on readily available commercial compounds and fragments that can be synthesized by organic chemists. This bias in turn reflects what has been more or less synthetically accessible through more than a hundred years of synthesis. Compared to this, the Kyoto Encyclopedia of Genes and Genomes (KEGG) contains metabolites whose structures are untainted by the minds of organic chemists. These are scaffolds among secondary metabolites and natural products that have simply been found.

There is another set of structures; the Generated Database (GDB), a theoretical set which contains all possible molecules containing less than 11 heavy atoms consisting of first-row elements (C, O, N, F). This number is not as large as may be imagined and amounts to about 26 million. In the study the authors essentially compare the set of purchasable or commercial KEBB compounds found in their own annotated library called ZINC with the GDB. They use a similarity measure called a Tanimoto coefficient derived from 2D fingerprint comparison to accomplish this. 2D fingerprints use different kinds of protocols for breaking up a molecule into bit strings and then compare bit strings by distances and atom types.

The comparison indicates something interesting; the compounds in the purchasable set are much more similar to the KEBB compounds than are the compounds from the rest of the GDB. In other words, purchasable compounds contain scaffolds that are biased towards those in the KEBB. This is a good thing, since metabolites are usually primed by nature to show at least some biological activity. Another noteworthy finding was that the bias also increased with molecular size, as compounds became more drug-like or lead-like in terms of size.

However, the more surprising and useful observation was that there are hundreds of scaffolds in the KEBB that are notpresent in the commercial library. The authors also do this comparison for other popular commercial libraries designed specifically for screening and find a similar result. The bottom line; while synthesized commercial libraries of molecules show a bias toward natural products and metabolites, there are also several natural product scaffolds that are not found in these libraries.

So what is the prescription? Introduce further bias! The compounds in the KEGG are more or less optimized for biological activity. If their scaffolds are not yet present in the commercial libraries, organic chemists should go ahead and focus on synthesizing these scaffolds and adding them to screening libraries. More such scaffolds could increase the hit rate in HTS by enriching libraries in biologically relevant scaffolds. Of course the usual caveats of false positives and promiscuous compounds should be kept in mind, and it's also not clear that proteins like kinases which are optimized to bind certain core scaffold structures would greatly benefit from these diverse scaffolds. But in terms of unmined drug space, introducing such further bias would be beneficial.

This study again goes to show the possibilities for finding new stars in the constellations and galaxies of the drug universe. Hopefully the universe will keep on expanding.

Hert, J., Irwin, J., Laggner, C., Keiser, M., & Shoichet, B. (2009). Quantifying biogenic bias in screening libraries Nature Chemical Biology DOI: 10.1038/nchembio.180

New ligands for everyone's favorite protein

A landmark event in structural biology and pharmacology occurred in 2007 when the structure of the ß2-adrenergic receptor was solved using xray crystallography by Brian Kobilka's and Raymond Stevens's groups at Stanford and Scripps respectively. The structure was co-crystallized with the inverse agonist carazolol. Until then the only GPCR structure available was that of rhodopsin and all homology models of GPCR were based on this structure. The availability of this new high resolution structure opened new avenues for structure-based GPCR ligand discovery.

The ß2 binding pocket is especially suited for drug design since it is tight, narrow and lined with mostly hydrophobic residues with polar residues well-separated. Two crucial residues, an Asp and a Ser bind to the ubiquitous charged amino nitrogen present in most catecholamines and the aromatic section of the molecule docks deep into the hydrophobic pocket. These particular features also make computational docking more facile; a mix of polar and non-polar features with bridging waters can make docking and scoring more challenging.

Since the ß2 structure has been published, attempts are being made to use it as a template to build homology models of other GPCRs. A couple of months back I described an interesting proof-of-principle paper by Stefano Costanzi that sought to investigate how well a homology model based on the ß2 would perform. In that study carazolol itself was used as a ligand for docking into the homology model. Comparison with the original crystal structure revealed that while the ligand docked more or less satisfactorily, an important deviation in its orientation could be explained by a counterintuitive orientation of a Phe residue in the binding site. The study indicated that the devil is in the details when one is considering homology models.

However, finding ligands for the ß2 itself is also an important and interesting endeavor. Virtual screening could help in such studies. To this end Brian Shoichet, Brian Kobilka and their group have used the DOCK program to virtually screen one million lead-like ligands from their ZINC database against the ß2. Out of the 1 million ranked poses, they chose and clustered the top 500 compounds (0.05% of the database) into 25 unique chemotypes, a choice also guided by visual inspection of the protein-ligand interactions and commercial availability. They then tested these 25 compounds against the ß2 and found 6 compounds with IC50s better than 4 µM. One of these compounds with an IC50 of 9 nM is perhaps the most potent inverse agonist of the ß2 known. The binding poses revealed substantial overlap of similar functional groups with the carazolol structure. Two compounds turned out to have novel chemotypes and bore very little similarity with known ß2 ligands. A negative test was also run where a known predicted binder was chemical modified so that it would not bind.

Interestingly all the compounds found were inverse agonists. The ZINC library is somewhat biased against aminergic ligands as is most of chemical space. The catecholamine scaffold is one of the favourite scaffolds in medicinal chemistry. However, subtle difference in protein structure can sometimes turn an inverse agonist into an agonist. In this case, small changes in the orientation of the crucial Ser residue near the mouth of the binding pocket. In a past study for instance, slightly changing the rotameric features of the Ser residue thus resulting in a different orientation of the hydroxyl was sufficient to retrieve agonists.

The study thus shows the value of virtual screening in the discovery of new ß2 ligands and indicates the effect of library bias and protein structure on such ligand discovery. Many factors can contribute to the success or failure of such a search; nature is a multi-armed demon.

Reference:
Kolb, P., Rosenbaum, D., Irwin, J., Fung, J., Kobilka, B., & Shoichet, B. (2009). Structure-based discovery of ß2-adrenergic receptor ligands Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0812657106

Post-docking as a post-doc, and some fragment docking

I am now ready to post-doc. I am also now ready to post-dock, that is, engage in activities beyond docking. Sorry, I could not resist cracking that terrible joke. It's been a long journey and I have enjoyed every most moments of it. Thanks to everyone in the chemistry blogworld who regaled, informed, provoked and entertained on this blog. I am now ready to move on to the freakingly chilly Northeast. Location not disclosed for now, but maybe later.



Speaking of docking, here is a nice paper from the Shoichet group in which they use fragment docking to divine hits from a large library for a beta-lactamase. Fragment docking can often be tricky compared to "normal" docking since fragments being small usually demonstrate promiscuity, low-affinity and non-selectivity in binding. Fragment docking thus is not yet a completely validated technique.

In their study, the present authors screen their ZINC library for fragments binding to the ß lactamase CTX-M by docking using the program DOCK. They also screen a lead-like library for larger molecules. The top hits from the fragment docking results were assayed and showed micromolar inhibition against the lactamase. These included tetrazole scaffolds not seen before. Importantly, five of these hits could be crystallized and the high-res crystal structures validated the docking modes.

What was interesting was that the same tetrazole scaffolds in the larger lead-like library were ranked very low (>900) and would not have ever been selected had their tetrazole fragments not showed up at the top in the fragment docking results. These compounds, when assayed showed sub-milimolar to micromolar activity against the lactamase. Thus, the protocol essentially demonstrated that fragment docking can reveal hits that can be missed by docking larger lead-like molecules. One of the reasons DOCK succeeds in this capacity is because of its use of a physics-based scoring function that has no bias against fragments. It also helps that the active site of CTM-X is relatively rigid with little protein motion.

The fragments were also assayed against another lactamase for Amp C. Usually, hits for CTM-X and Amp C are mutually exclusive. What was seen was that the higher the potency of the fragments for CTX-M, the higher the specificity for CTX-M, not surprising considering that increased potency translates to a much better complementary fit of the fragments for CTX-M.

Fragment docking can be messy since fragments can bind non-selectively and haphazardly to many different parts of many different proteins. But this study indicates that fragment docking is not an uninteresting strategy to possibly find hits from other lead-like libraries that may be otherwise concealed.

The potencies of the compounds found may look pretty weak, but because there are extremely few molecules inhibiting these medicinally important lactamases, such advances are welcome. Lactamases are of course an important target for overcoming resistance in antibiotic treatment.

Reference:
Chen, Y., & Shoichet, B. (2009). Molecular docking and ligand specificity in fragment-based inhibitor discovery Nature Chemical Biology DOI: 10.1038/nchembio.155

Water-Inclusive Docking with Remarkable Approximations

The role of water in mediating protein-ligand interactions has now been well-recognized by both experimentalists and modelers. However it's been relatively recently that modelers have actually started taking the unique roles that water plays into account. While the role of water in bridging ligand and protein atoms is obvious, a more subtle but crucial role of water is to fill up hydrophobic pockets in proteins. Such waters can be very unhappy in such pockets because of both unfavourable entropy (not much movement) and enthalpy (inability to form a full complement of 4 hydrogen bonds). If one can design a ligand that will displace such waters, significant gains in affinity would be obtained. One docking approach that does take such properties of waters into consideration is Schrodinger's Glide, with a recent paper attesting to the importance of such a method for Factor Xa inhibitors.

Clearly the exclusion of water molecules during docking and virtual screening (VS) will hamper enrichment factors, namely how well you can rank actives above inactives. Now a series of experiments from Brian Shoichet's group illustrates the benefits of including waters in active sites when doing virtual screening. These experiments seem to work in spite of two approximations that should have posed significant problems, but surprisingly did not.

To initiate the experiments, the authors chose a set of 24 targets and their corresponding ligands from their well-known DUD ligand set. This is a VS data set in which ligands are distinguished by topology but not by physical properties such as size and lipophilicity. This feature makes sure that ligands aren't trivially distinguished by VS methods on the basis of such properties alone. Importantly, the complexes were chosen so that the waters in them are bridging waters with at least two hydrogen bonds to the protein, and not waters which simply occupy hydrophobic pockets. Note that this would exclude a lot of important cases where affinity comes from displacement of such waters.

Now for the approximations. Firstly, the authors treated each water molecule separately in multiple configurations. They then scored the docked ligands against each such configuration as well as the rest of the protein. The waters were treated as either "on" or "off", that is, either displaced or not displaced. Whether to keep a water or not depended on whether the score improved or not when it was displaced by a ligand. The best scored ligands were then selected and figured high on the enrichment curve. This is a significant approximation because the assumption here is that every water contributes to ligand binding affinity independently of the other waters. While this would be true in certain cases, there is no reason to assume that it would generally hold.

The second approximation was even more important and startling. All the waters were regarded as energetically equivalent. From our knowledge of protein-ligand interactions, we know that the reason why evaluating waters in protein active sites is such a tricky business is precisely because each water has a different energetic profile. In fact the Factor Xa study cited above takes this profile into consideration. Without such an analysis it would be difficult to tell the medicinal chemist which part of the molecule to modify to get the best binding affinity from water displacement.

The most important benefit of this approximate approach was a linear increase in computational time instead of an exponential one. This was clearly because of the separate-water configuration approximation. The calculation of individual water free energies would also have added to this time.

In spite of these crucial approximations, the results indicate that the ability to distinguish actives from inactives was considerably improved for 12 out of 24 targets. This is not saying much, but even 50% sounds like a lot in the face of such approximations. Clearly an examination of the protein active site will also help to evaluate which cases will benefit, but it will also naturally depend on the structure of the ligand.

For now, this is an encouraging result and indicates that this approach could be implemented in virtual screening. There are probably very few cases where docking accuracy decreases when waters are included. With the sparse increases in computational time, this would be a quick and dirty but viable approach for virtual screening.

Reference:
Niu Huang, Brian K. Shoichet (2008). Exploiting Ordered Waters in Molecular Docking Journal of Medicinal Chemistry, 51 (16), 4862-4865 DOI: 10.1021/jm8006239

A rash of molecular personalities

Just like human beings, molecules have personalities. And just like human beings, they display those personalities best when they react to a stimulus. For a medicinal chemist, one such stimulus is HTS where one can identify different flavors of molecules through their interaction with protein targets. But this is not always done, and quantitative analysis of molecules in HT screens is lacking. Clearly such analyses will help to identify compositions of such screens and give insight into future screens.

In his latest offering, Brian Shoichet does just that. He and his group set out to identify essentially every molecular character from a colorful screen of about 70000 molecular personalities applied to ampicillin resistant beta-lactamase. Their results are surprising.

Out of 70000, about 1274 showed activity. Shoichet has already extensively documented the alarming frequency of aggregate-forming molecules in common HTS screens. It's a very substantial contribution from his laboratory. In this case, 1204 (95%) of the 1274 turned out to be inhibiting the enzyme through non-specific aggregation. This can be found out by adding detergent, which breaks up the aggregates and gets rid of the spurious activity.

So now there were 70 detergent-insensitive compounds. How many of these were true, reversible binders? 25 of these were beta-lactams, and since they are covalent modifiers of the enzyme and known chemical scaffolds, they were not considered further. So out of the remaining ones, 25 were re-synthesized and were found to be false positive through lack of reproducible activity. There were now 20 non beta-lactams. Out of these 9 were again found to be aggregators- the earlier screen had skipped them because of low detergent concentration.

That left 12 molecules. After some more scrutiny, these were all found to be covalent, irreversible modifiers of the enzyme. A neat and simple trick can be used to identify covalent modification; mass spectra of the modified enzyme are clearly different from the apo enzyme.

So how many non-covalent, reversible inhibitors of beta-lactmase were found? Zero.

To shed some more light on this strange phenomenon, the authors turned to docking with DOCK. To make sure the program can identify reversible binders, some known binders were seeded among the unknown binders. After docking and observing that the first 500 hits contained the known binders, 16 out of these 500 compounds were selected based on structural diversity and then assayed. Interestingly, two among these compounds were found to inhibit the enzyme at IC50 values of >100 µM. No wonder the initial screen had missed these phthalimide culprits- the highest concentration in the screen was 30 µM.

In other studies, they also did some SAR on the hits and verified the docking poses by obtaining crystal structures. There are other interesting details in the paper.

But even if the study did not unearth reversible, potent, novel binders, it is of course still very instructive. It tells us about the variety of beasts existing in HTS. It also again sheds light on docking as a valuable complement to HTS. In this case, 70000 compounds may been too less for assaying, and 30 µM must have been two low a threshold for finding hits. In any case, higher thresholds for testing are limited by practical difficulties, including material availability and solubility. But what is valuable is that given due effort, we can identify compounds that give false positive results in screens through novel mechanisms- in this case by aggregation (detected by detergent addition) and by covalent modification (detected by mass spec)

There are clearly some notorious and dirty candidates in HTS screens- more than everyone would be comfortable with- and this study provides a good model for being on one's guard and seeking to identify them as thoroughly as possible. When we lay down the red carpet, we want only the cream of the crop, not asses disguised as lions.

Babaoglu, K., Simeonov, A., Irwin, J.J., Nelson, M.E., Feng, B., Thomas, C.J., Cancian, L., Costi, M.P., Maltby, D.A., Jadhav, A., Inglese, J., Austin, C.P., Shoichet, B.K. (2008). Comprehensive Mechanistic Analysis of Hits from High-Throughput and Docking Screens against β-Lactamase. Journal of Medicinal Chemistry DOI: 10.1021/jm701500e

Interview with Brian Shoichet: aggregation-induced inhibition

Ok, now that we have gotten past the Nobel mania (or maybe not; go Somorjai), we can hopefully come back to real life. I was reading an interview with Brian Shoichet, who is one of the most promising stars in the areas of screening, docking, and structure-based design. He has gotten his fingers in many pies, both computational and experimental.

However, it was somebody's comment about the pharmaceutical industry thinking that "Shoichet deserves a heroes prize" that got me looking at his work, and I quickly learnt the reasons for that quote. As we all know, one of the biggest or perhaps the biggest problem facing HTS in industry is false positives. A lot of times, molecules that are found to be active in an assay fail to be active later. If industry could weed out such nuisances ahead of time, a lot of time, money and energy could be saved.

Shoichet, after a lot of interesting initiation and investigation, came up with one simple reason for why molecules may be showing false colours; because they form colloidal aggregates that somehow inhibit the proteins in the assay. If these are broken up say with detergent, the individual molecules no longer show activity. Thus, a relatively simple physical phenomenon is responsible for these molecules showing false activities. Such molecules were detected in earlier assays by some characteristics, mainly very steep dose-response curves and flat SAR; changes in structure usually causing very small changes in activity. They are also often promiscuous inhibitors. But nobody knew what was exactly happening and all the analysis was post-"mortem".

The first step in Shoichet's lab was the elucidation of this aggregation-induced inhibition. The aggregation can be detected with dynamic light scattering (DLS). The more challenging and useful step is to be able to come up with a list of chemical scaffolds that are likely to show this phenomenon, so that one can watch out for them beforehand. Before that, one would also need to know the exact mechanism of aggregation-based inhibition. In case of some molecules, there is some structural correlation, flat aromatic dye-like molecules being prone to aggregation for example by stacking. But many other scaffolds seem more diverse and at first glance show no common functionalities. Ths phenomenon is linked by common physical forces, not chemical ones. The details are not known but continue to be worked out.

Shoichet's lab continues to make progress, and he has recently come up with a screen for detecting such aggregation-based inhibitors (DOI: 10.1021/jm061317y). There are two major conclusions from the study; first, that breaking up aggregates with detergents can be a good way of identifying them, and secondly that aggregation may be a much more common phenomenon for false positives in screens than was thought before. This fact may be extremely significant for industry and could potentially save a lot of time, money and labour beforehand.

In other quite different work (DOI: 10.1038/nature05981), Shoichet also made the cover of Nature, when he used docking and structure-based design to predict the function for an enzyme whose function was unknown, based on substrate docking and analysis. The strategy used was quite clever; docking thousands of high-energy forms of metabolites rather than the metabolites themselves to know which ones would optimally interact with the active site. In this particular case, the "optimum interaction" pointed to a deamination, and the protein of unknown function indeed experimentally turned out to be a good deaminase.

All in all, a very promising chemist and I believe one to watch out for. Unfortunately, the interview itself is published in the journal Assay and Drug Development Technologies, not one which libraries usually subscribe to (I got it through ILL). But here's the DOI anyway (DOI: 10.1089/adt.2007.9996)

Also, again, check out his Colbert-style interview on youtube.

"I run from reality"

Yes, that's why I prefer to do computational chemistry and virtual screening too. Presenting UCSF's Brian Shoichet