Fragmenting natural products – sometimes PAINfully

Many drugs have their origins in natural products. But as any synthetic organic chemist will tell you, natural products often have complex architectures that can take years of effort and dozens of chemists to make in the lab. Thus, many of the compounds made in industry look quite different from natural products, particularly in the past few decades. High failure rates in drug discovery have led folks to return to natural products or similar compounds, such as those from diversity oriented synthesis (DOS). In a recent issue of Nature Chemistry, Herbert Waldmann and colleagues at the Max-Planck Institute in Dortmund examine whether natural products can serve as starting points for new fragments.

The researchers started by computationally deconstructing 183,769 natural products into 751,577 component fragments. After various filters (size, lipophilicity, reactivity, etc.) they arrived at 110,485 fragments sorted by similarity into 2000 clusters. The resulting fragments differ in their overall calculated properties from commercial fragments. This is all highly reminiscent of the Emerald (nee deCODE) “fragments of life”, though surprisingly that work is not referenced.

One challenge of designing new fragments is that you may not be able to buy them. In this case, nearly half of the clusters did have a compound that could be purchased – though perhaps this somewhat defeats the purpose of trying to explore novel chemical space. At any rate, 193 fragments were either bought or synthesized. These were tested in functional assays against p38a MAP kinase and several protein phosphatases. A number of hits were identified, and in the case of p38a, nine kinase-fragment co-crystal structures were solved. Some of these were similar to previously reported fragments, but others were more unusual. Together with the crystal structures, these fragments provide new ideas for a well-studied target.

Looking at the structures of some of the phosphatase inhibitors, however, I started to worry. One strong point of the paper is that it is very complete: the chemical structures of all 193 tested fragments are provided in the supplementary information. Unfortunately, the list contains some truly dreadful members; 17 of the worst are shown here, with the nasty bits shown in red. All of these are PAINS that will nonspecifically interfere with many different assays.

Compounds 15, 44, 49, 159, 166, 173, 174, and 175 are catechols; compounds 89 and 151 (yes, they are the same molecule – guess they really liked this one), 165, 166, 167, and 168 are quinones; compounds 55, 89/151, and 166 are hydroquinones; compound 20 is a Michael acceptor; compound 76 is an epoxide; and compound 184 is a redox cycler. In other words, these fragments are a depressing example of life imitating art (or at least satire).

To be blunt: none of these molecules should appear in a screening library today.

I don’t want to pick on these researchers; it is after all laudable that they fully disclosed the structures of their molecules.

However, I am concerned that other people may build libraries containing some of these fragments, or worse, that opportunistic vendors will start selling “natural-product derived fragments.” Indeed, most of these molecules are commercially available. It is disappointing that so many nuisance compounds would find their way into research published in a Nature family journal, and I think it is important to call it out. Only by publicizing the problems that can arise will people be made aware of the dangers.

FAK This

FAK, also known as PTK2, is a well known oncology target.  Current known inhibitors can be broken into three different binding classes:   Cpds I-IV are hinge binders, the chloropyramine targets the FAK-VEGF interface, and Y15 targets the Y397 site. Cpds V and VI were recently reported as novel allosteric inhibitors of FAK.

In this paper, a group led by researchers at Merck Serono, report their discovery of a new core from an "accelerated knowledge-based fragment growing approach".  

They used a commercially available fragment library (defined as:  MW, <200 solubility="">1 μg/mL; number of hydrogen bond donors and acceptors, ≤3) was screened against the immobilized kinase domain of FAK by SPR, which allowed them to determine kinetics for most of the fragments.  Compound I (Magenta) was found to be a 43 μm inhibitor.  The X-ray structure showed it to make excellent contacts with the protein.  Addition of the spinach shown in green, afforded an order of magnitude increase in potency.  In order to facilitate better elaboration, they chose to use 7-azaindole as the scaffold.

Then, going through traditional SAR and medchem, they end up with this table.  The best compound is a single digit nanomolar inhibitor with cell-based activity. One important aspect of this work is that the lead series can induce a rare helical loop DFG conformation.  In their conclusion, they state 

it was easier to improve kinase inhibition than kinase selectivity.

The first thing that stands out here is the solubility limit.  For a 250 Da fragment, 1μg/mL corresponds to 4 μM.  To me that sounds incredibly low; is it a typo?  They don't mention who those fragments are from.  I would love to know out of sheer curiousity.   Secondly, although they talk about their accelerated fragment growing approach, they don't actually explain what they mean by that?  To the best of my reading, I don't think they have introduced anything novel here.  

Fragment linking for LDHA: Ariad’s turn

Last year we highlighted a paper from AstraZeneca in which researchers there used a fragment-linking approach to tackle an enzyme important for cancer metabolism, lactate dehydrogenase A (LDHA). Turns out they weren’t alone – researchers at Ariad had also been working on the same target, as Stephan Zech reported at FBLD 2012. They have now published some of this work in J. Med. Chem.

Anna Kohlmann and colleagues at Ariad started with a fairly small library, just 735 fragments from Maybridge. These were screened using STD-NMR at 2-3 mM per fragment, resulting in 38 hits, about half of which contained carboxylic acids – not surprising given that the substrate and cofactor are both negatively charged. Most of the fragments could be competed by the cofactor NADH, and although they bound too weakly to show any inhibition in an enzymatic assay, they did show binding by SPR. Crystal soaking led to a co-crystal structure of compound 1, which binds in the substrate and part of the cofactor site (where the nictotinamide moiety of NADH normally binds).

Fragment growing led to compounds 2 and 5, both with enhanced affinity. Interestingly, crystallography revealed that compound 5 binds in a distant part of the cofactor binding site, where the adenosine moiety of NADH normally binds. Elaboration of this molecule didn’t do much for affinity but did suggest a linking strategy, resulting in molecules such as compound 9, with nanomolar potency and detectable cell-based activity.

Apropos to Darwin Day, this is an interesting example of convergent evolution: two companies applying fragment-linking to discover molecules that bear some similarity to one another (Ariad compound 8 in blue, AstraZeneca compound 26 in red).

Near the end of the paper, the researchers also carefully investigated some of the other previously reported “inhibitors” of LDHA and found that they are in fact aggregators. This is not surprising given their structures, which look like something that might appear in an April Fool’s post. Unfortunately these molecules were reported in prominent journals such as Chem. Biol. and Proc. Nat. Acad. Sci. USA; the later, published in 2010, has already been cited at least 100 times. Publicly revealing them to be artifacts is a beautiful example of the self-correcting nature of science. I hope we’ll see more of it.

Beware correlation inflation

Drug discovery today is replete with rules and metrics: the Rule of 5, the Rule of 3, (though perhaps not 1), not to mention ligand efficiency and friends. The hope is that these encapsulate physical trends that will guide drug hunters towards better compounds. However, there is a danger that rules will become strait-jackets; plenty of drugs, after all, lie well outside the Rule of 5 (Ro5). In a paper recently published in J. Comput. Aided Mol. Des., Peter Kenny (of FBDD-Lit fame) and Carlos Montanari argue that the correlations underlying many rules may not be as robust as they appear. The article is full of the trenchant prose we’ve come to expect of Kenny, so I’ll quote liberally.

The background:

Those who have followed the drug discovery literature over the last decade or so will have become aware of a publication genre that can be described as ‘retrospective data analysis of large proprietary data sets’ or, more succinctly, as ‘Ro5 envy’.

The problem:

Although data analysts frequently tout the statistical significance of the trends that their analysis has revealed, weak trends can be statistically significant without being remotely interesting.

This is especially likely to occur when data are “binned” into a smaller number of categories before being analyzed, thereby hiding variation and making correlations appear stronger than they really are. Since many published analyses use proprietary, unavailable data, Kenny and Montanari constructed model “noisy” data sets and looked for correlations in the primary data and the binned data. They found that correlations in the binned data were inflated. Perhaps counter-intuitively, the effect actually gets more pronounced the larger the data set.

Having described the problem, Kenny and Montanari go on to question some recent high-profile papers correlating, for example, lipophilicity with pharmacological promiscuity, or the percentage of sp3-hybridized carbons (Fsp3) with solubility (see also here). In the latter case, all the data were publicly available, and a reanalysis with the primary data as opposed to binned data caused the correlation coefficient (r) to drop from 0.972 to 0.247!

Graphical representation of data comes under heavy scrutiny too. In particular, the common practice of subdividing data points into small numbers of categories (often red, yellow, and green) can make these categories appear discrete when the underlying data are better described as a continuum.

The overall message is that weak correlations may lead to misguided strategies:

To restrict values of properties such as lipophilicity more stringently than is justified by trends in the data is to deny one’s own drug-hunting teams room to maneuver while yielding the initiative to hungrier, more agile competitors.

There is something to this, though acting on it is not without risk. As the old saying goes, nobody gets fired for buying IBM. Most drug discovery efforts fail, but if you fail making conventional compounds, you’re less likely to come under fire than if you fail by doing something outside the accepted norm.

But whatever you do, it’s worth remembering:

The human liver remains an effective antidote to the hubris of the drug designer.

Fragment merging for Mcl-1

One of the most heroic examples of fragment-based drug discovery is navitoclax (ABT-263), which blocks the anti-apoptotic proteins Bcl-xL and Bcl-2 from binding to their partner proteins. This Abbott (AbbVie?) compound is in Phase 1 and 2 clinical trials for a variety of cancers. Abbott has also reported inhibitors of Bcl-2 that don’t inhibit Bcl-xL. However, many cancer cells are unfazed by inhibitors of Bcl-2 and Bcl-xL because they can instead rely on another protein, Mcl-1. Thus, ABT-263 can be overcome when cancer cells overexpress Mcl-1. Previously, Mcl-1 had been considered by many to be a “Teflon target.” Happily, it has now been successfully tackled with fragments.

The work, published recently in J. Med. Chem., was led by Stephen Fesik, now at Vanderbilt University. Fesik was one of the inventors of the SAR by NMR technique that led to navitoclax, and in this case the team used a similar approach, screening a fairly large fragment library (> 13,800 compounds) in pools of 12 using ¹H-¹⁵N HMQC NMR. This produced 132 hits, of which two chemical classes were pursued.

One chemical class, exemplified by compound 2, consisted of 6,5-fused heterocyclic carboxylic acids, while another class, exemplified by compound 17, consisted of hydrophobic aromatic groups separated by a linker from a (usually) anionic substituent. NOE-guided fragment docking indicated that these compounds bind in similar but non-overlapping regions of Mcl-1, suggesting a fragment-merging approach.

Indeed, merging the compounds led to nanomolar binders such as compounds 60 and 53, which were also completely selective against Bcl-xL and more than 15-fold selective against Bcl-2. Crystal structures of these molecules bound to Mcl-1 confirmed the binding hypothesis. A number of additional analogs were synthesized; pleasingly, the SAR of the isolated fragments generally translated to the merged compounds.

This is a beautiful example of FBLD in academia. Of course, there is still a long way to go: there is a large and disconcerting disconnect between biochemical and cell-based potency for many reported Bcl-family inhibitors, and the lack of cell data here suggests that the same may hold true for Mcl-1. Still, it is nice to see that a venerable technique can succeed against this challenging protein.

News and Updates

I am not sure how many of you follow the discussion in the LinkedIn FBDD group (Dan and I try to cross post as much as possible), but Ben Davis started a discussion based on a status update I had (how meta and 21st century of us).  How many commercially available fragment libraries come with the associated 1H spectrum (for NMR screening).  I only know of Maybridge's collection having 1H spectra.  However, I would think most companies would have the spectrum as part of their QC (or I hope they would).  

That leads into the second point of discussion: how do you QC your collection?  I would think LC-MS and NMR are a minimum.  But, what do people do for solubility?  The old stick-it-in-solution-and-see-if-it-craps-out or something more "science-y"?  

Lastly, I just received word this morning in my Inbox that Infarmatik has closed up shop.  I had heard it as a rumor, but now its real. 

STD-SPR smackdown

Once you’ve established a library and chosen a target, the first step in FBLD is performing a fragment screen. There are lots of ways to do this, and since each method has its pros and cons it is best to use more than one. A good illustration of why this is important has just been published in J. Biomol. Screen. Results were also discussed last November at FBDD Down Under.

Two separate research groups were both interested in the core domain of HIV-1 integrase (IN). They both purchased 500-compound fragment libraries from Maybridge, though since they were purchased about six months apart they contained only 455 compounds in common. One group screened pools of 10 fragments by STD-NMR to identify 84 hits, of which 62 confirmed as single compounds both by STD-NMR and ¹⁵N-HSQC NMR. All of these were soaked into crystals of IN, resulting in 15 co-complexes.

The second group used SPR to screen each compound individually; compounds that showed a significantly stronger signal binding to IN than to a reference protein were confirmed by doing full dose-response curves. 16 hits were taken into crystallography, resulting in 6 co-structures, and another 3 gave ambiguous electron density.

The problem, as shown in the figure, is that there was no overlap between the confirmed NMR hits and the SPR hits, or between the crystallographically confirmed fragments!

To try to understand this discrepancy, the researchers re-tested the SPR hits by NMR, and the crystallographically confirmed NMR hits by SPR. The two assays were originally run under slightly different buffer and pH conditions, but these seemed not to be a significant factor. Eight of the 15 crystallographically-confirmed NMR hits did show activity in the SPR assay, but also hit the reference protein, so had not been taken forward. Another five had technical issues in the SPR screen (DMSO mismatches); only two showed no binding by SPR.

Five of the crystallographically confirmed SPR hits were retested by STD-NMR, though at a lower concentration (0.3 mM) than the original screen (1 mM) due to solubility issues. Four of these gave good signals, while the fifth produced a weaker signal that could only be detected at the pH of the original SPR screen. The reason the others may not have been detected initially could be because of competition in the original pooled NMR screen: with a 17% hit-rate, many pools probably contained multiple binders.

The title of the paper is “Parallel screening of low molecular weight fragment libraries: Do differences in methodology affect hit identification?” Clearly the answer is yes. Nonetheless, it is important to note that, at the end of the day, this may not matter so much. As the researchers observe:

We find that despite using different approaches with little overlap of initial hits, both approaches identified binding sites… that provided a basis for fragment-based lead discovery and further lead development.

In other words, no matter what technique you use, as long as you have a tractable target and you’re careful (and a little bit lucky) you’ll be able to find useful fragments.

Poll Results - Hurray for Diversity

In our latest poll, we asked what kind of libraries people like, giving three options:

• I like a maximal diverse library (SAR comes from follow up)    
• I like diversity, but not at the expense of SAR (follow up is easier with some SAR)              
• My target is teflon so any active fragment is welcome news.   

 60% of respondents like a maximally diverse library, 31% like diversity with some SAR, and 8% work of teflon targets, so any hit matter is welcome.  

The way I read this is that 60% of people don't consider the screen done when the first results come in.  In my eyes, the screen is over when there are actives identified with testable SAR hypotheses.  This is probably just my bias of having lived in a very resource constrained environment where follow up to a screen was a second serving of resources.  To me, this is great news; companies that are doing fragment screening are invested and not giving short shrift to these efforts. 

I would be curious to hear in the comments how people develop SAR with a maximally diverse library.  Do you just pick every available fragment that has the same central core and evaluate all possible side chains?  Would you apply a similarity cutoff of 0.9 or something?  How many compounds do you follow up with per active fragment?

Fragment events in 2013

2013

As far as we know there are only a few fragment-heavy events this year, all in the first half, but please leave a comment if you know of anything else.

March 4-5: Fragments 2013, the 4th RSC-BMCS Fragment-based Drug Discovery meeting, will be held at the Harwell Science and Innovation Campus near Oxford, UK. There is also a pre-conference training course on Sunday, March 3. Abstracts for posters are being accepted through January 31, with a special invitation to graduate students and postdocs.

March 19-20: Select Biosciences is holding its Discovery Chemistry Congress in Munich, Germany, with a full two days devoted to fragment-based lead discovery.

April 16-18: Cambridge Healthtech Institute’s Eighth Annual Fragment-Based Drug Discovery will be held in San Diego. You can read impressions of last year's meeting here, the 2011 meeting here, and 2010 here. Also, on April 15, Teddy and I will teach a short course on FBDD. Rumor has it this meeting will be moving to Boston in 2014, so if you're looking for an excuse to visit San Diego don't wait!

June 19-21: Cambridge Healthtech Institute’s Thirteenth Annual Structure-Based Drug Design will be held in Boston, with several talks on FBLD.

Looking for trouble

Anyone who runs a fragment screen, especially for the first time, is likely to encounter problems. Large companies with sophisticated screening groups generally have a wealth of experience and procedures for dealing with false positives, but smaller organizations and academic labs can all too easily get lured into blind alleys. To help folks avoid these, Ben Davis and I are putting together a mini-review that summarizes the problems that can arise. While there are plenty of examples in the literature, we are also interested in hearing from you about artifacts and other problems that you’ve encountered but either not gotten around to publishing or decided against doing so. Feel free to leave comments here, anonymously if you wish, or email fbldproblems@gmail.com. Thanks – and may all your hits confirm!

Fragments in the clinic: 2013 edition

It’s been more than two years since Practical Fragments updated its list of fragment-derived compounds in the clinic, and a lot has changed since then – mostly for the better. The latest list is inspired by a fantastic news article in Nature Review Drug Discovery that quotes a wide range of fragment-practitioners and outside experts. It’s a fun, fast read, so definitely check it out. It also includes a handy table of late-stage fragment-derived clinical compounds, their ClogPs, and their molecular weights, along with those of the initial fragment hits.

The list below borrows from this table and also includes molecules from other sources, whether or not they are still in development (indeed, some of the originator companies no longer exist). Those listed as still active in clinicaltrials.gov or company websites are in bold, and those that have been covered in Practical Fragments are hyperlinked to the relevant post.

Approved

Vemurafenib (PLX4032)        Plexxikon         B-Raf(V600E) inhibitor

Phase 2/3

MK-8931                                Merck              BACE1 inhibitor

Phase 2

AT13387                                 Astex              HSP90 inhibitor

AT7519                                   Astex              CDK1,2,4,5 inhibitor

AT9283                                   Astex              Aurora, Janus kinase 2 inhibitor

AUY922                         Vernalis/Novartis      HSP90 inhibitor

Indeglitazar                             Plexxikon         pan-PPAR agonist

Linifanib (ABT 869)                Abbott             VEGF & PDGFR inhibitor

LY2886721                             Lilly                 BACE1 inhibitor

LY517717                        Lilly/Protherics          FXa inhibitor

Navitoclax (ABT 263)              Abbott             Bcl-2/Bcl-xL inhibitor

PLX3397                                 Plexxikon        FMS, KIT, and FLT-3-ITD inhibitor

Phase 1

ABT-518                                 Abbott             MMP-2 & 9 inhibitor

ABT-737                                 Abbott             Bcl-2/Bcl-xL inhibitor

AZD3839                                AstraZeneca     BACE1 inhibitor

AZD5363                        AstraZeneca/Astex  AKT inhibitor

DG-051                                  deCODE            LTA4H inhibitor

IC-776                                   Lilly/ICOS         LFA-1 inhibitor

JNJ-42756493                     J&J/Astex         FGFr inhibitor

LEE011                             Novartis/Astex      CDK4 inhibitor

LP-261                                   Locus               Tubulin binder

LY2811376                              Lilly                 BACE1 inhibitor

PLX5568                                 Plexxikon         kinase inhibitor

SGX-393                                 SGX                 Bcr-Abl inhibitor

SGX-523                                 SGX                 Met inhibitor

SNS-314                                 Sunesis            Aurora inhibitor

There are some interesting trends, such as the number of BACE1 inhibitors – a fact the Nat Rev Drug Disc piece also notes. This has been an immensely difficult target, so it’s nice to see fragment-based approaches deliver compounds to the clinic. Whether BACE1 inhibitors will ultimately prove useful for treating Alzheimer’s disease remains to be seen, but at least FBLD has provided the tools to test this hypothesis.

The current list contains 26 clinical-stage drugs but is certainly incomplete, particularly in Phase I. If you know of any others (and can mention them!) please leave a comment.

Review of 2012 reviews

2012 has been a bumper year for fragment conferences and reviews. Starting with the Molecular Medicine Tri-Con in San Francisco, moving south to the CHI FBDD meeting in San Diego, east to the ACS Fall Meeting in Philadelphia, back to FBLD 2012 in San Francisco, and ending with FBDD Down Under in Melbourne, there have been plenty of opportunities to learn about the latest work in the field. Two new books were also published, one focused particularly on crystallography and the other focused heavily on computational methods.

Practical Fragments has highlighted one review paper, and I thought I’d mention a few others that came out over the past year.

Chris Abell and colleagues at the University of Cambridge published “Fragment-based approaches in drug discovery and chemical biology” in Biochemistry. This is an excellent and wide-ranging general review, covering theory, library design, screening methods, fragment advancement, applications, limitations, and future trends. If you’re new to the field or want a good refresher, this is the place to go.

Tom Blundell and coworkers, also at the University of Cambridge, published “Biophysical and computational fragment-based approaches to targeting protein-protein interactions: applications in structure-guided drug discovery” in Quarterly Review of Biophysics. As the title suggests, the focus is on protein-protein interactions, but there is plenty of general interest, including lots of unpublished data and practical suggestions.

Aaron Oakley and colleagues at the University of Wollongong, Australia, published “Fragment-based screening by protein crystallography: successes and pitfalls” in Int. J. Mol. Sci. This covers the entire process of crystallographic screening, from library assembly through model building, with a nice table of recent examples and several in-depth case studies. It also touches on potential pitfalls and complementary fragment-finding methods.

Finally, Chungquan Sheng and Wannian Zhang of the Second Military Medical University in Shanghai published “Fragment informatics and computational fragment-based drug design: an overview and update” in Medicinal Research Reviews. With 267 references, this is a great compilation of computational methods that touch on numerous aspects of fragment-based lead discovery.

And with that, Practical Fragments thanks all our readers and says goodbye to 2012. Please keep your comments coming, and may 2013 be a splendid year!

GDB-17: 166 billion fragments and counting

How many possible fragments are there? Jean-Louis Reymond and colleagues at the University of Berne have been trying to answer this question computationally by enumerating all stable molecules from first principles. In their previous effort they found nearly a billion molecules with up to 13 atoms. In a new paper published in J. Chem. Inf. Model. they have now extended this analysis to molecules containing up to 17 carbon, oxygen, nitrogen, sulfur, and halogen atoms. There are 166,443,860,262 of them.

What do they look like? Before addressing that question, it is worth noting that this set of molecules—dubbed the GDB-17—is not exhaustive. The researchers intentionally excluded many potentially unstable moieties. Most of these are probably best ignored, though doing so does leave out functionalities found in some drugs, such as hemiaminal ethers (acyclovir), sulfoxides (omeprazole), and some non-aromatic double bonds (cyclosporine). In fact, more than 40% of similarly-sized molecules in PubChem (ie, they’ve actually been synthesized) are not represented in GDB-17.

But even looking at the PubChem molecules that do show up in GDB-17, there are dramatic differences between existing molecules and enumerated possibilities. For example, a huge fraction of the GDB-17 set contains 3- or 4-membered rings. Aromatic rings are surprisingly rare, at only 0.8%, compared with roughly a third of similar-sized molecules in PubChem. On the other hand, 57% of the GDB-17 molecules contain nonaromatic heterocycles, compared with just 12% in PubChem; these may be particularly attractive in terms of drug-like properties.

Consistent with their reduced aromaticity, the GDB-17 molecules also contain many more stereocenters: on average more than 6 per molecule compared with just 2 for PubChem. With all these stereocenters, it’s inevitable that the molecules are more three-dimensional than those in PubChem. By the same token, though, synthetic accessibility is likely to be a challenge.

In general the GDB-17 molecules are also considerably more polar than known molecules of similar size, with more than half of them having ClogP ≤ 0. This could in part be due to the fact that it’s often tough to purify molecules that are too soluble in water.

There’s a lot of other fun stuff here: for example, almost half a billion isomers of procaine! And for the synthetic chemists in the audience, this work illuminates vast fields of uncharted chemical territory, just waiting to be explored.

Drugs Against Bugs

This paper, from a consortium  of academics in Holland, Belgium, and Switzerland and a UK pharmaceutical company, reports on inhibitors for parasite specific phosphodiesterases (PDE). Trypanosomal disease is a major health obstacle in Africa; if the diseases enters into stage 2 (CNS penetration by the parasite) the patient dies. Trypanosoma brucei has two subspecies: T.b. gambiense and T.b. rhodesiense which makes the search for a pan-anti-trypanosomal agent even harder.  The current treatment options: 

...are limited and suffer from suboptimal dosage regimens and/or severe toxicity. During the second stage of the disease, the only choices are the arsenic-containing drug melarsoprol and the ornithine decarboxylase inhibitor eflornithine, used as monotherapy or in combination with nifurtimox (NECT).  However, eflornithine is not effective against T. b. rhodesiense.

Mapping of the T. brucei genome lead to the identification of trypanosomal PDEs.  Two of these PDEB1 and B2 play a pivotal role in proliferation.  Knocking down one or both TbrPDEB1 andTbrPDEB2 simultaneously leads to an arrest of parasite cell division, lysis of the parasites, and elimination of the infection in vivo (in an infected mouse model). Of supreme interest, the "P-pocket" (found in the crystal structure of  Leishmania major crystal structure) was conserved in their homology model. 

The HTS found compound 1 and in their follow up to chemotypes related to this, the came up with compound 6b.  Rolipram itself was inactive (>100uM) despite fitting nicely in the pocket (according to modeling).  However, the catechol moiety was shown to be the key for activity.  Replacing it with a less "worrisome" moiety led to a significant decrease in potency.  Compound 8d (R1, Obenzyl, R2=cycloheptyl) had 500 nM potency that maintained the same ligand efficiency, which indicates the atoms added were reliably efficient, but not super-efficient.  It also had anti-proliferative effects without cytotoxicity; the authors guess that this is due differences in cell-permeability compared to the cyclopentyl compounds. 

The benzylcatechol chemotype was then chosen for further optimization.  The docking shows that the benzyl moiety is at the entrance of the P-pocket (figure 3a) which should afford access to it through growing.  Table 2 shows the results of their efforts here with both the cycloheptyl and isopropyl (which were more ligand efficient).  The docking suggested that they should be able to grow into the p-pocket from the 4 position (Figure 3a).  Compound 20b (49nM) was the best inhibitor and as shown in the docking (Figure 3b) this is most likely due to interactions in the P-pocket.  Interestingly, they also propose that it may be displacing water from this pocket causing some sort of increase in affinity.  

The cLogP of the cycloheptyl compounds was a real issue affecting solubility and may have affected the antitrypanosomal activity.  20b, with a solubilizing tetrazole group, concentration-dependently worked in the anti-trypanosomal assay with a IC50 of 520nM.  It was also found to be very potent against PDE4A-D enzymes.  It should also be noted that 20b is 40 heavy atoms, which is 0.18LEAN. However, 20b was a good inhibitor of  T. b. rhodesiense (60nM).  It was tolerated by a human fibroblast cell line (250 fold sensitivity index)  Lastly, they were able to show that cAMP accumulated in the cells, indicating the target specific effects.

Often, I am very harsh on academic drug discovery, but this paper is an excellent example of what can be accomplished when it is done right.