Springs, Balls and Crystal Structures:

Drug Discovery Implications of Molecular Dynamics for the Algebraically Despondent


This blog article refers to the J. Med. Chem. Perspective: Role of Molecular Dynamics and Related Methods in Drug Discovery De Vivo, Masetti, Bottegoni and Cavalli.1 Not only is it a fair review of current flavours of Molecular Dynamics (MD), within a drug discovery context, it also does the impressive task of making it reasonably accessible to those without a double first in maths and Greek. This blog article will comment on some of the basic concepts within the paper (and in the field of MD in general) but if the reader is interested, they should definitely pick up this paper – whilst there are key equations for MD contained within, they are well explained, and held within the context of what they give the modern drug discovery scientist as observables.


Classical MD was originally fathomed in the late 1950’s with landmark papers within the biochemical domain slightly later,2 but for a long time was prohibitively computationally expensive. Modern technology (namely, parallelisation via clusters and the GPU, and coding architecture utilising this such as NVidia’s CUDA)3, as well as new thinking in drug discovery has moved MD to the forefront of many drug discovery strategies. More recently, Free Energy Perturbation (FEP) with MD sampling has become a rapidly growing field with many of the big pharma picking up on this too. A colleague’s SBDD blog article4 a few weeks ago detailed an FEP keystone paper from Sherman and collaborators.5


Molecular Dynamics for the Non-Computational Mind



If we consider atoms to be balls, and the bonds to be springs, then MD is really all about how these balls and springs move around in relation to each other under Newton’s Laws of Motion over a given timespan. A force field is applied for this comprising of 5 elements (equation 1), which seeks to calculate potential energies for a predetermined time step. Knowing the starting point of each of our balls on their springs (usually from properly prepared crystallographic data), and having determined the movement energies they have, we can assess where they will be at the end of the time step using Newton’s Second Law (f(t) = ma(t)). A collection of these time steps is called a trajectory – often shown as videos or overlays such as the image atop this article.

V = ∑bond + ∑angles +∑torsions +∑van der Waals + ∑electrostatics (eqn 1)

Where V is the empirical potential energy of a system, and each of the components (e.g. bond, angles, etc) are sums of contributions across a system. This is a much simplified version of Equation 2 from De Vivo et al.’s paper.


The simulation of this movement, using various control parameters, allows us to explore the energy landscape of a protein. For example: If we know the energy of the system (and parts of the system), and we know the energy barriers for certain movements (e.g. side chain rotations), we can see which parts of the protein can move for a given energy (e.g. the energy it may have at physiological temperature). We allow the protein to move around these energetically allowed configurations. Likewise, for any given configuration, we can assess various energy terms.  Figure 1 demonstrates some basic biological motions within the time domain, and what kind of things normal MD can be used to probe.


Figure 1: Timescale of various biological processes, and the techniques used to investigate them

Simulations of biological systems are particularly useful when considering the movement of a protein pockets or a protein-ligand complex – especially since understanding of drug discovery has moved rapidly (and rightly) away from Emil Fischer’s classical lock-and-key binding paradigm towards the idea of induced fit and conformational selection.6 Understanding how a pocket moves in relation to solvent and ligand, which residues are more mobile compared with others and which interactions they are open to, and how a ligand may approach and behave in the pocket becomes a very powerful collection of insights. Take a kinase for instance (Figure 2)- understanding how a DFG loop (for example) moves in response to ATP binding would be a key piece of information for drug design teams. Gosu and Choi attempted to model such movement early last year, demonstrating a change in the dynamics of the kinase in response to its ATP-bound or unbound state.7

IRAK4 image

Figure 2: An example of the scope of movement of IRAK kinase (where uIRAK4 is apo unphosphorylated, pIRAK4 is apo phosphorylated, and p-IRAK4-ATP is ATP-bound phosphorylated IRAK4.7

MD is as good as the force field it’s built upon, and the authors bring to light some limitations of this, as well as common methods to increase sampling, such as Umbrella Sampling, Replica Exchange MD (REMD), and Metadynamics approaches to getting out of landscape minima. The authors also briefly discuss FEP, which in the last few years has become a matter of significant interest. FEP is an alchemical method that can effectively allow the comparison of free binding energies between ligands in a design series. It is beyond the scope of this blog, and even this perspective article to discuss FEP to the level that it deserves, but the authors do set out a useful overview.

The authors do a pretty good job of making the rather dry subject of MD and allied techniques suitable for the med chemist or team leader who wants to understand what tools his CADD support can offer his project in terms of understanding the movement of his target, and is written in way that explains what exactly the techniques can (and cant) provide, and where the limitations are, with relevant illustrations and references to document real-world projects. As well as describing more classical usage such as conformational sampling for virtual screens and ligand design, they also detail some interesting methods such as steered MD to assess binding or unbinding events, and the use of MD in seeking out allosteric sites in proteins.8 They detail a recent study which demonstrated the ability to define binders from non-binders within a similar chemical series using a modified ligand pulling experiment (where a force is applied to pull the ligand from the binding pocket in silico. The required force is related to its binding energy, and thus peaks in force required to remove the ligand, reveal the tightness of binding.­9


Fig 3: Plot of force vs time for “pulling” or “undocking” a ligand from its pocket. Two clear profiles are shown here which represent those that bind well versus those which do not. Those with the higher force profile have a proton donor which the low force do not, suggesting that the donor is important to binding.

The perspective concludes that various flavours of MD can demonstrate significant insight into binding / unbinding events, long-residency waters as well as understanding target movement, but warn that more prospective studies are required to bring this into the truly into the light, as well as some well-placed concerns about force field limitations. Whether you agree with them that “FEP is ready for prime time” or not, this is a well written perspective, surely worthy of a read.


Notes from blogger (whose opinions are his own)

MD is definitely a useful tool, and with hardware advances it is becoming more common – especially as we see more and more complex drug targets going forward, where conformational changes in proteins are better understood, and we target more flexible or non-classical pockets. It is just another tool, however, and care needs to be taken to really balance its value in modern drug discovery.

Hardware changes such as GPUs (Graphical Processing Units – effectively the graphics cards in your computer), which have hundreds or thousands of processing units (albeit slower and more restricted in their calculations that your CPU), can accelerate MD calculations significantly. Many common MD packages such as NAMD, Desmond and AMBER can be parallelised on GPUs. We routinely use NVidia K40 GPUs which tend to accelerate our MD significantly (3-10 fold on Desmond/Schrodinger 2016-1, OPLS3.0). You can technically use consumer level GPU’s but beware they are not designed to fun flat out 24/7 and your normal desktop is not likely set up for the power and cooling requirements. If you are in academia, you can apply to NVIDIA for Academic Hardware Grant.10

Free Energy Perturbation has risen in popularity over the last few years, breaking out from purely academic interest to major pharmaceutical companies on-boarding this technology11 (it even made the Pipeline).12 At the time of writing, I can name at least half a dozen colleagues in different big pharma organisations investing significantly (as far as computational drug discovery goes), in setting up FEP as part of their CADD support offering. FEP is currently being invested in heavily by Schrodinger Inc., using the Desmond MD platform for it – on speaking with their development team they see it as a “game changer”. Over at CCG, when I spoke to their CEO, Paul Labute early last year,  he said that they had no current plan to follow Schrodinger’s move with their platform, MOE – focussing more on the ease of use and accessibility of their offering, though this may change.

Whether you see FEP as the gateway to a new paradigm in drug discovery, or whether you’re on the fence about this “game changing” tech (I mean, we’ve all been there in presentations about “the next big thing”), it has brought significant interest into MD for Drug Discovery.


Blog written by Ben Wahab


  1. De Vivo et al. Role of Molecular Dynamics and Related methods in Drug Discovery. J. Med. Chem. 2016 in press. DOI: 10.1021/acs.jmedchem.5b01684 
  2. (a) McCammon, J. A.; Gelin, B. R.; Karplus, M. Dynamics of folded proteins. Nature 1977, 267, 585−590. DOI: 10.1038/267585a0 ; (b) Levitt, M.; Warshel, A. Computer simulation of protein folding. Nature 1975, 253, 694−698. DOI: 10.1038/253694a0 
  3. https://developer.nvidia.com/about-cuda
  4. https://sussexdrugdiscovery.wordpress.com/2016/01/20/structure-based-drug-discovery/
  5. Sherman et al. Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field J. Am. Chem. Soc., 2015, 137 (7), pp 2695–270. DOI: 10.1021/ja512751q 
  6. (a) Changeux, J.-P.; Edelstein, S. Conformational selection or induced fit? 50 years of debate resolved. F1000 Biol. Rep. 2011, 3, 19. DOI: 10.3410/b3-19 ; (b) Vogt, A. D.; Di Cera, E. Conformational selection or induced fit? A critical appraisal of the kinetic mechanism. Biochemistry 2012, 51, 5894−5902. DOI: 10.1021/bi3006913 ; (c) Vogt, A. D.; Di Cera, E. Conformational Selection Is a Dominant Mechanism of Ligand Binding. Biochemistry, 2013, 52 (34), pp 5723–5729. DOI: 10.1021/bi400929b 
  7.   Choi, S and Gosu, V. Structural dynamic analysis of apo and ATP-bound IRAK4 kinase.  Nature Scientific Reports, 4, No.5748, 2014 DOI: 10.1038/srep05748 
  8. (a) Desdouits, N.; Nilges, M.; Blondel, A. Principal Component Analysis reveals correlation of cavities evolution and functional motions in proteins. J. Mol. Graphics Modell. 2015, 55, 13−24.  DOI: 10.1016/j.jmgm.2014.10.011 ; (b) Kokh, D. B.; Richter, S.; Henrich, S.; Czodrowski, P.; Rippmann, F.; Wade, R. C. TRAPP: a tool for analysis of transient binding pockets in proteins. J. Chem. Inf. Model. 2013, 53, 1235−1252. DOI:  10.1021/ci4000294 
  9.  Colizzi, F.; Perozzo, R.; Scapozza, L.; Recanatini, M.; Cavalli, A. Single-molecule pulling simulations can discern active from inactive enzyme inhibitors. J. Am. Chem. Soc. 2010, 132, 7361−7371. DOI: 10.1021/ja100259r 
  10. https://developer.nvidia.com/academic_hw_seeding
  11. http://www.outsourcing-pharma.com/Preclinical-Research/Sanofi-Schroedinger-forge-up-to-120m-drug-discovery-deal
  12. http://blogs.sciencemag.org/pipeline/archives/2015/04/03/sanofi_bets_on_schrodinger



The Alzheimer’s Research UK (ARUK) conference 2016 took place in Manchester on the 8th and 9th of March. Researchers mostly from the UK, but also guest speakers from Germany and the US, presented their research that covered different fields of study related to Alzheimer’s disease (AD). However, the conference for the author of this blog already started the day before with the PhD day. This day was just for PhD students working on AD or AD related topics and gave them the opportunity to present their work in a more informal environment. The students presented their results in the form of posters or presentations and to the blog author’s delight also negative (or less “good looking”) results were presented which promoted vibrant discussion. Tea and lunch breaks were used to browse posters which pushed PhD students to get in touch with each other. The day was completed by presentations of academic and industrial representatives that gave insight into different career paths.


Figure 1 | Sussex PhD students who participated in the conference. From left to right: Mahmoud, Karen, Saskia, Devkee, Lucas, Rebecca, Joanne and Luca.

With the PhD day already being a great success, the following days were sure to turn out to be as good. While more basic AD-related concepts and research were covered during the first day, the second day provided talks about new treatment approaches for dementia, as well as AD drug discovery and development. Two talks were in particular interesting:

Evidence is pointing towards inflammation processes that may trigger and influence AD pathology. One inflammation factor that seems involved and is activated in AD is the NLRP3 inflammasome (1). The NLRP3 inflammasome is a multiprotein complex which is formed inside macrophages and microglial cells and that catalyses the activation of caspase-1. Caspase-1 in return converts interleukin-1β (IL-1β) into its active form which is secreted and triggers an immune response. Most commonly, nonsteroidal anti-inflammatory drugs (NSAIDs) are used in the treatment of inflammatory conditions that act through inhibition of cyclooxygenase 1 and/or 2. Dr David Brough and colleagues at the University of Manchester hypothesized that NSAIDs may supress inflammation through a mechanism dependent on NLRP3 inflammasome inhibition and thus could potentially be repurposed as inflammasome inhibitors. Screening identified fenamates (fenamic acid, mefenamic acid) to be able to block NLRP3 formation by inhibition of the volume-regulated anion channel (VRAC). Other NSAIDs such as ibuprofen or diclofenac did not show any effect on NLRP3 mediated inflammation. Nevertheless, the other NSAIDs may still exert a positive effect via alternative pathways. Prof Michael Heneka from the University of Bonn (Germany), who gave a talk on targeting innate immunity in AD, demonstrated that these NSAIDs are great activators of peroxisome proliferator-activated receptor gamma (PPAR-γ). Activation of PPAR-γ was shown in transgenic APP/PS1 mice to increase Aβ removal by microglial cells (2). The activating effect of NSAIDs on PPAR- γ may also explain their efficacy in reducing the risk of AD (3). Conclusively, NSAIDs may be an interesting class of anti-inflammatory drugs that could be repurposed in the treatment and/or prevention of AD.


Figure 2 | Activation of the NLRP3 inflammasome and production of active IL-1β. Activation of microglial cells via the Toll-like receptor (TLR) or cytokine receptors induces the production of components of the NLRP3 inflammasome, as well as pro-IL-1β. Lysosomal damage by Aβ leads to assembly and activation of the inflammasome that in turn activates caspase-1. Caspase-1 processes pro-IL-1β to its bioactive form which is released. Picture from (3)

The second talk that strongly caught the interest of the author of this blog was the introduction of the Alzheimer’s Research UK Drug Discovery Alliance – a coordinated initiative between the ARUK, Oxford University, Cambridge University and University College London that aims to accelerate the identification for new treatment for AD and other forms of dementia. The Drug Discovery Alliance is especially interested in new and unexplored biological targets and in doing so is keen to hear from researchers across the research community about potential proteins, enzymes or pathways that play a role in AD. By combining the individual strengths of all the three university institutes, the alliance hopes to drive innovation in dementia drug discovery.


Blog writted by: Lucas Kraft



  1.            Heneka, M. T., Kummer, M. P., Stutz, A., Delekate, A., Schwartz, S., Vieira-Saecker, A., Griep, A., Axt, D., Remus, A., Tzeng, T.-C., Gelpi, E., Halle, A., Korte, M., Latz, E., and Golenbock, D. T. (2012) NLRP3 is activated in Alzheimer’s disease and contributes to pathology in APP/PS1 mice. Nature. 493, 674–678
  2.            Mandrekar-Colucci, S., Karlo, J. C., and Landreth, G. E. (2012) Mechanisms underlying the rapid peroxisome proliferator-activated receptor-γ-mediated amyloid clearance and reversal of cognitive deficits in a murine model of Alzheimer’s disease. J. Neurosci. 32, 10117–28
  3.            Heneka, M. T., Golenbock, D. T., and Latz, E. (2015) Innate immunity in Alzheimer’s disease. Nat. Immunol. 16, 229–236

Screening cascade targeting PPIs

Screening cascades need to be designed specifically for the target chosen and for the biological activity for the target. Projects targeting the interactions between two proteins (protein-protein interaction [PPI]) targets frequently have no enzyme activity that can be measured and therefore the screening cascade needs to employ different non-enzymatic methods to measure compound efficacy. Whilst there are many methods around a recent paper published exemplifies an excellent screening cascade for targeting a PPI (1).

In this paper the group were targeting a DNA-damage repair complex involved in the Fanconi anaemia pathway, a mechanism involved in tumour resistance. Specifically the interaction of FA complementation M group (FANCM) and the RecQ-mediated genome instability protein (RMI) complex.

The screening cascade used a fluorescence polarisation (FP) assay as the primary screen, followed by hit confirmation in an alpha screen-based proximity assay to demonstrate inhibition of the PPI. Surface Plasmon Resonance (SPR) and Isothermal titration calorimetry (ITC) were then used to demonstrate compound binding to the RMI core complex.

The group was specifically targeting the interaction within the FANCM complex, between RMI and MM2 and the FP assay was designed to measure this interaction. Therefore a standard format (figure 1) with the labelled MM2 protein having a Kd <5 nM. The assay was miniaturised to 384 format and the screen produced Z` of 0.53. The alpha screen assay (figure 2) was a proximity-based assay with MM2 bound to the streptavidin donor bead and the RMI complex bound to the acceptor bead. The Z` for the alpha screen assay (0.75) was better than the FP assay. Despite this the FP assay was chosen for the initial single point screen rather than the alpha screen, presumably on cost grounds.


Figure 1 Schematic of the fluorescence polarisation (FP)


Figure 2 Schematic of the alpha screen proximity assay

The alpha screen was therefore used as the counter screen and interestingly on the hit published there was a 10 fold discrepancy in the IC50 from the FP assay (450 uM) vs. alpha screen (36 uM). Direct interaction of this hit to the RMI complex was confirmed by both SPR and ITC that produced a consistent Kd of 7.8 and 3.4 uM respectively.

What is particularly nice about this cascade is that three of the screens, the FP assay, alpha screen and SPR can all be run in high throughput therefore although the publication only talks about a pilot screen of 74,000 compounds it could easily be scaled up to many more compounds. The only lower-throughput assay is the ITC, which is at the end of the cascade.


Blog writted by Trevor Askwith


  1. Voter AF, Manthei K a, Keck JL. A High-Throughput Screening Strategy to Identify Protein-Protein Interaction Inhibitors That Block the Fanconi Anemia DNA Repair Pathway. J. Biomol. Screen. (March 8, 2016). doi: 10.1177/1087057116635503.

More tales from the cystic fibrosis pig

The cystic fibrosis (CF) pig generated by the Welsh Group in Iowa continues to deliver insight into the mechanistic basis of CF lung disease. Perhaps the most salient of the lessons that this model has provided to date is the importance of airway pH and its critical influence on the early bacterial colonisation of the lungs. The latest instalment of the story highlights the role of the non-gastric proton pump, ATP12A, in the airway and how in the absence of normal CFTR function, it drives acidification and the compromised ability of the lung to defend itself (Science. 2016 351(6272): 503-507).

Prior to the generation of the CFTR-/- pig it had been appreciated that the airway mucosa of CF patients was relatively acidic when compared to healthy controls although the putative implications for the disease were unknown. Early studies with the pig highlighted that within hours of birth, the bacterial load in the airways was higher in the CF animals (Sci Transl Med. 2010 2(29):29ra31). Follow-up studies demonstrated that the airway surface liquid (ASL) of the CF pigs was lacking a bactericidal capacity that was evident in the littermate controls. It transpired that the acidic pH of the ASL attenuated the activity of a number of innate defence molecules including lysozyme and lactoferrin and this likely conferred the susceptibility to early bacterial colonisation (Nature. 2012 487(7405):109-13).

Studies in the pig and also primary human airway epithelial cells are now demonstrating that in the absence of CFTR-mediated bicarbonate secretion, it is H+ secretion via the non-gastric H+/K+ ATPase (ATP12A) that leads to an unchecked acidification of the mucosa. Inhibition of apical ATP12A with oubain partially alkalinised the ASL and restored the anti-bacterial activity of mucosal innate defence proteins in cultured human and porcine airway epithelial cells. Furthermore, adenoviral-mediated expression of Atp12a in mouse airway epithelium induced a ‘CF-like’ acidic / immunocompromised phenotype. Of note, endogenous expression of Atp12a in mouse airways was significantly lower than observed in human and pig, a finding that may contribute to the protection of the various CF mouse models of cystic fibrosis, all of which fail to develop any signs of lung disease.

ATP12A is a cAMP activated H+ pump. To this end, Welsh and colleagues raise the intriguing question as to the risk-benefit balance of using agents that can elevate cAMP in the lung epithelium. Inhaled β2-receptor agonists will unquestionably relax airway smooth muscle and offer the potential to bronchodilate the airways. However, could there also be a downside to this elevation in cAMP in terms of an acidification of the ASL and increased susceptibility to infection? Of note, similar theoretical risks regarding the use of cAMP-elevating agents in CF have been previously raised in respect to their potential to elevate ENaC function and therefore further dehydrate the airway mucosa (Science. 1983 221(4615):1067-70).

ATP12A does therefore offer the potential to be a new drug target whereby inhibitors would be predicted to boost innate defence in the CF lung. In view of the low airway pH in additional respiratory diseases that are associated with increased infection risk, could this mechanism and therapeutic benefit be extended beyond cystic fibrosis?


Blog written by Henrey Danahay

Filtering promiscuous compounds in early drug discovery: is it a good idea?

This is the question posed by the authors of a recent Drug Discovery Today article (Senger et al, Drug Discovery Today, article in press ). On first reading the question the instant answer from most practising medicinal chemists is surely yes, however the authors make some very good points that eliminating certain groups such as indoles from screening collection is somewhat short sighted or just simply wrong. On the other hand the authors do make some rather surprising and possibly naïve statement such as the recommendation to include quinone structures in phenotypic screening collections, an experienced medicinal chemist would argue there is simply no point testing them as they will be active anyway!

The article highlights the difference between classical biochemical screening with the objective of identifying hits which are active against a single target and phenotypic screening where the objective is to identify functionally active compounds irrespective of their mechanism of action. The authors propose that the now popular PAINS filters (Nature 2014, 513, 481-483) are appropriate for biochemical screening but should not be applied to compound collections used for phenotypic screening. They highlight a number of examples of structural types which are present in known drugs but would be removed by applying PAINS filters for example quinones (table 1). It is true that there are many quinone containing drugs on the market, but it is difficult to agree with the recommendation that even in phenotypic screens such compounds should be included. The compounds simply work by scavenging electrons and given the number of marketed drugs that contain quinones there is little need for more. Perhaps the only justification is to include marketed drugs in phenotypic screening collections with a view to future repositioning.

.pault Table1

A second set of problematic compounds highlighted as potentially useful are acyl hydrazones. Most medicinal chemists would not agree with the recommendation to include these in  screening collections as whilst there are examples of drugs (fig 1) which contain this group such functionality brings with it so many potential issues during development (genetoxicity, chemical instability, metabolism to a reactive hydrazine and many more) that they are best avoided in the first place.



Despite the recommendation to consider unattractive groups such as quinones and acyl hydrazones in phenotypic assays the third set of compounds highlighted by the authors is very sensible and prompts the question are filters in some cases too strict? The authors highlight indoles are privileged scaffolds. Indoles occur widely in marketed drugs they are chemically inert and generally safe. There are reports that certain indoles can form reactive metabolites – but this is true of any scaffold and is certainly not a reason to remove them from screening collections.

This is an interesting and thought provoking article and whilst it makes some interesting suggestions rightly challenges current thinking on the composition of screening collections. Drug discovery organisations are very risk averse these days some would say too risk averse. Whilst there are certain functionalities that should never appear, removing too many structural types can limit diversity and ultimately lead to a reduced chance of success in all modes of screening.

Blog wiritten by Paul Beswick

Tackling cancer: How new updates for old techniques could be the future of drug screening

An overwhelming proportion of drug development in both academia and industry is focused on identifying novel drug targets for the treatment of cancer. The Sussex Drug Discovery Centre is no exception; our oncology portfolio is constantly expanding and changing in order to be at the cutting edge of research for a disease that has an economic cost of £15.8bn a year in the UK alone, and is responsible for almost 15% of human deaths worldwide.

Typically, the drug development process begins with identifying inhibitors of a therapeutic target, which involves screening small molecules against a biological component involved in the disease pathway – often a specific enzyme or receptor. Because cancers are caused by mutations to genes that regulate cell growth and differentiation, resulting in unregulated cell division, most targets in cancer research are transcription factors or their corresponding gene products. While many drugs have entered clinical development this way – such as those targeting the bromodomain reader BRD4, histone deacetylase (HDAC) enzymes, and DNA methylation – challenges remain in identifying drugs that do more than simply indiscriminately kill cells, and that are able to target key transcription factors with molecular features that are considered ‘undruggable’.

In a revolutionary new study, researchers at the University of North Carolina developed and tested a new high-throughput screening technique that makes it possible to test potential drug compounds against an altered transcription factor previously thought to be ‘undruggable’ that is present in most Ewing sarcomas – a highly malignant bone and soft tissue tumour that affects children and young adults. Ewing sarcoma is characterized by a chromosomal translocation that produces the chimeric transcription factor EWSR1-FLI1; its corresponding gene product then localizes to specific regions of DNA, causing the chromatin to unwind. This creates a unique, disease-specific chromatin signature resulting from nucleosome depletion, making EWSR1-FLI1 an attractive drug target despite the fact that the mechanism for its action remains unknown. In this study, the researchers adapted formaldehyde-assisted isolation of regulatory elements (FAIRE), a biochemical assay for the identification of nucleosome-depleted regions of the genome, to a miniaturized and automated form that allowed high-throughput testing of the effects of compounds on proteins that regulate chromatin.

FAIRE in its standard format is dependent on organic extraction with a mixture of phenol and chloroform; in high-throughput (HT)-FAIRE this phase was substituted with a column-based approach to allow for the switch to automation, and performed similarly when compared side-by-side with standard FAIRE. Then, using a custom library of 640 small molecules, a screen was performed against a cell model of Ewing sarcoma to see if any of the molecules could restore normal chromatin structure. This was defined by a reduction in the FAIRE signal, indicative of a decrease in chromatin accessibility as the EWSR1-FLI1-mediated effects are reversed and the chromatin re-folds. Through the screen it was found that HDAC inhibitors were particularly effective at restoring chromatin structure, and while this class of compounds had been previously identified at a potential treatment for Ewing sarcoma, the screen also elicited novel compounds that were active in the cell model.

These results are undoubtedly remarkable, and highlight the advantages of a technique such as HT-FAIRE over the target-based screening approach that is the staple of modern drug discovery. By targeting a specific characteristic of Ewing sarcoma, HT-FAIRE removes the need for lengthy enzyme assay optimization or a comprehensive understanding of the molecular mechanisms underpinning the disease. If this method can identify potential drugs in one specific type of cancer, it may be possible to apply this technique to other cancers, and potentially reform drug discovery in the process.

Blog written by Chloe Koulouris 

One-Pot oxidative Conversion of Alcohols into Nitriles

When looking for a mild synthetic method which could interconvert an alcohol functional group into a nitrile I came across this 2014 paper by Jean-Michel Vatèle published in Synlett. Vatèle has previously published some interesting papers using 2,2,6,6-tetramethylpiperidine-1-oxyl  (TEMPO) with a co-oxidant such as a practical one-pot procedure for the oxidation/olefination of primary alcohols using a TEMPO – Bis(acetoxy)iodobenzene (BIAB) system and stabilized phosphouous ylides.

There have been many publications describing the sequential oxidation-imination-aldimine oxidation of an alcohol to yield a nitrile. However, Vatèle’s metal free method uses cheap reagents and a simple reaction procedure which made it the most appealing of the methods that I looked into.

Vatèle had previously seen that a TEMPO/BIAB system can oxidise aldimines to nitriles and so wanted to investigate, if with the addition of an ammonium salt, the process could be extended to sequentially oxidise an alcohol to a nitrile. A small selection of solvents and ammonium salts were screened (table 1) using TEMPO (5 mol%) and BIAB (2.2 eq) as a co-oxidant. The conditions in entry 1 were chosen to investigate the scope of this reaction.lewis1


There were 22 alcohols subjected to these optimised reaction conditions and they all gave the desired product in >80% yields (summary in table 2). It was observed that in general alkyl alcohols were oxidised faster than benzyl alcohols (entries 1,2,3 vs 4,5). Acid sensitive groups such as TBS, Boc (entry 3), trityl and acetyl groups were stable under the reaction conditions. No racemisation was seen where chiral centres were present (entry 3) and electron withdrawing/donating groups on the aromatic ring had little effect on the oxidation (entry 4 and 5). There was no scrambling of cis or trans (entry 7) double bond geometry and chemoselectivity for a primary over a secondary alcohol was achieved (entry 8).

Table 2 Summary of products (table from Organic Chemistry Portal)


In addition to all of these examples we at the SDDC have used Vatèle’s conditions on a variety of 5 and 6-membered heterocyclic alcohols in which we generally isolated the corresponding nitriles in >80% yields. The only exception was when a quinone like motif was present and in this case a large volume of gas was produced and no starting material or product could be isolated.

In summary this is a very simple, cheap and general method for the oxidation of alcohols to nitriles. The reactions are relatively quick and high yielding. There is a simple work up and as there are minimal by-products the final product isolation is fairly simple. I would fully recommend giving these condition a try the next time you need to synthesise a nitrile.

Blog written by Lewis Pennicott

A Human Blood Vessel Microphysiological System for in vitro Drug Screening

A huge proportion of drugs (over 80%) that enter clinical trials fail because of problems with toxicity or because they are not shown to be effective. Pre-clinical studies in animals are useful but do not always accurately predict toxicities in humans. Microphysiological systems (MPS), small scale models of human tissues or organs, may act as a bridge between two-dimensional cell culture studies and in vivo animal studies, in order to better predict toxicity issues earlier in the drug development process.

A particular problem known as drug induced vascular injury (DIVI) is evident in pre-clinical animal studies as inflammation and changes in the level of blood vessel constriction, and can prevent on-going development of drug candidates. A human tissue-engineered blood vessel that is able to respond to stimuli would therefore be a useful model system in which to analyse toxicity and efficacy of potential drug candidates.

Biomedical engineers at Duke University have succeeded in developing a new technique in which to produce artificial arteries that do just that. These miniaturised blood vessels contain both the endothelial layer, which is the internal lining of the vessels and a media layer containing smooth muscle cells helping to control the diameter of the blood vessels.

One of the advances has been that the lab have reduced the time it takes to produce these blood vessels from six to eight weeks to just a few hours.  They based their method on published techniques used to create trachea. In this case, human neonatal dermal fibroblasts (hNDFs) were suspended in collagen and then compressed to reduce the water volume and increase the collagen fibre density.sarah

A suspension of hNDFs in collagen is poured into the mold containing an 810-μm diameter mandrel and allowed to gel for 30 minutes (a). Collagen fiber density increases through plastic compression and removal of water (b). Compressed TEBVs are immediately mounted in custom chambers (c). CAD EPCs are seeded into the lumen of the TEBV (d) and the chamber is rotated at 10 rph for 30 minutes (e). After endothelialization, TEBVs are mounted into the perfusion circuit and cultured for at least 1 week at a flow rate of 2 mL/min (f). TEBVs before (g) and after compression (h). Live-dead assay performed 24 hours after compression (i). H&E cross-section of TEBV after 1 week of perfusion culture (j). Scale bars indicate 200 μm unless otherwise noted.

The blood vessels were shown to respond normally to stimuli such as treatment with drugs to induce vasodilation or vasoconstriction. The blood vessels also responded to drugs that are known to cause drug-induced vascular injury, confirming that this is a suitable model for the study of this effect in vitro.


Fernandez, C. E. et al. Human Vascular Microphysiological System for in vitro Drug Screening. Sci. Rep. 6, 21579; doi: 10.1038/srep21579 (2016)

Duke University. “Rapidly building arteries that produce biochemical signals: New technique speeds tissue engineering of functional arteries.” ScienceDaily. ScienceDaily, 18 February 2016.

Blog written by Sarah Walker