Targeting the spliceosome to treat MYC-driven cancers

MYC overexpression or hyperactivation is a well known driver of human cancer. Despite being the subject of intense study for many years, attempts to therapeutically inhibit MYC directly have been unsuccessful, making synthetic lethal strategies attractive. MYC is a transcription factor, and indeed its oncogenic activity is attributed to its prominent role in gene expression. However, whilst increased production of RNA, and thereby proteins, may enable cancer cell growth, there is a resultant burden on these cells to process all this RNA. Last month, Hsu et al. reported that the spliceosome, which is required for processing of precursor mRNA to mature mRNA, is a target of oncogenic stress in MYC-driven cancers.1 They identified components of the core spliceosome which are synthetically lethal with MYC activation and showed that genetic or pharmacological inhibition of these spliceosomal factors impaired survival of MYC-dependent breast cancers.

In a genome-wide MYC-synthetic lethal screen performed previously, BUD31 was identified as a MYC-synthetic lethal gene.2 While in yeast BUD31 had been linked to the spliceosome, its function in mammalian systems had yet to be determined. Using co-immunoprecipitation and bimolecular fluorescence complementation experiments it was shown that BUD31 associates with 79 of the 134 core spliceosomal components, which are involved in a variety of the major spliceosomal subcomplexes, inferring that it is present at several stages of spliceosomal assembly. Furthermore, knockdown experiments indicated that loss of BUD31 significantly inhibited pre-mRNA splicing and led to defects in early spliceosome assembly. It appears therefore that mammalian BUD31 functions as a core spliceosomal protein.

Accordingly the authors suggest that cells with oncogenic MYC require BUD31 for survival due to this role in the spliceosome. Indeed it was shown that if BUD31 is unable to associate with the spliceosome, the proliferation of MYC-driven cancer cells is significantly inhibited. Other components of the spliceosome assembly were consequently examined. It was found that partial depletion of all the components studied led to loss of cell viability and increased apoptosis of MYC-hyperactivated cells. One of these components was SF3B1 (splicing factor 3b, subunit 1). To test whether pharmacological inhibition of the spliceosome is synthetically lethal with MYC, SD6 was developed as a bioavailable small molecule inhibitor of SF3B1.3 Low (10-20 nM) concentrations of SD6 were able to selectively suppress colony formation and induce apoptosis of MYC-hyperactivated cells.

By comparing intron retention (IR) after BUD31 knockdown in MYC-normal or MYC-hyperactivated cells it was shown that in combination MYC activation and partial spliceosome inhibition result in increased IR. This indicates that the MYC-induced increase in mRNA synthesis increases cellular dependency on the spliceosome. Moreover, depletion of BUD31 caused a considerably larger decrease in cellular polyadenylated mRNA following inhibition of transcription in MYC-hyperactivated cells than in control cells, indicative of defects in pre-mRNA maturation and stability.

Finally the authors queried whether MYC-driven breast cancers exhibit increased sensitivity to knockdown of spliceosomal genes. A pronounced correlation was observed between MYC-dependency and spliceosome-dependency in basal breast cancer lines. In one example the effects of genetic and pharmacological inhibition of the spliceosome were tested on MYC-dependent metastatic triple-negative breast cancer (TNBC) models. Use of BUD31 or SF3B1 shRNA reduced cell viability and the TNBC cells were significantly more sensitive (IC50 ≈ 4nM) to treatment with inhibitor SD6 than were MYC-normal cell lines (IC50 ≈ 53nM). Overall the results suggested that MYC-driven breast cancers are more highly dependent on the spliceosome.

This study clearly highlights targeting of the core spliceosome as a promising strategy for treatment of MYC-driven cancers and explains the basis for the synthetic lethality of BUD31 and MYC.

  1. Hsu, T. Y.-T. et al. The spliceosome is a therapeutic vulnerability in MYC-driven cancer. Nature 525, 384–388 (2015).
  2. Kessler, J. D. et al. A sumoylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis. Science 335, 348–353 (2012).
  3. Lagisetti, C. et al. Optimization of antitumor modulators of pre-mRNA splicing. J. Med. Chem. 56, 10033–10044 (2013).

Blog written by: Katie Duffell

Organs on a Chip – The next step in 3D culture

Methods of modelling complex diseases have developed dramatically in recent years. Co-cultures as well as 3D cultures are now widely used in the drug discovery process, but with advances in microfluidics more complex co-culture systems are being developed that permit the investigation of complex biological processes. In these models biomimetic devices are used that are engineered to represent the structural and functional units found in organs such as bone, heart, liver, lung, intestine, brain and kidney. Also engineered within these devices is the ability to assay the result, therefore these devices are transparent enabling visualisation of the cells and immunological staining as well as being able to sample the media for assaying secreted factors (2).

Device for studying interaction of neuronal and astrocytes by metabolic communication (3)

Device for studying interaction of neuronal and astrocytes by metabolic communication (3)

One example of this is a model from Kunze et al., (3) where they used such a device for an astrocyte neuronal co-culture. Astrocyte neuronal co-cultures are not new, but these typically use an astrocyte feeder layer and therefore measure physical interactions between neuronal cells and astrocytes. In this model however, the group wanted to measure the effect of non-physical interactions between these cell types, i.e. secreted factors, or metabolic communication as described by the group. This microfluidic device is designed with two side channel in which astrocytes and neurons are seeded, separated by a central ‘contact’ area. The contact area in the middle enables fluid movement between the two cell types and neurite outgrowth, but the distance of 0.9mm means there is no physical contact between the cell types. Using this set-up the team was able to observe that astrocytes stably expressing mutant SOD (super-oxide dismutase) reduce neuronal survival in the ‘contact area’ compared to when the astrocytes express wild-type SOD, with no physical interaction between the astrocytes and neurons. Although this was a case-study for the device it demonstrates that this system can clearly be used for compounds targeting non-physical cross-talk between astrocytes and neurons as well as other cell-types. Additionally read-outs using both immunological staining of neurons or astrocytes as well looking at factors secreted can be used demonstrating the flexibility of the system.

Another example is a model that enables endothelial function to be observed in a 3D microenvironment (1). Here, a microfluidic device has an inner channel that in the study was lined with HUVECs (human umbilical vein endothelial cells). Inside the channel was a matrix gel where the group cultured A549 lung cancer spheroids. The device itself was clear and therefore enabled direct visualisation of the spheroids in the channel, therefore the group was able to measure EMT (Epithelial-mesenchymal transition)-induced spheroid dispersal due to the interaction of the HUVECs and the A549s. A number of FDA approved compounds known to inhibit EMT were applied to the inner matrix and their ability to inhibit EMT measured. Interestingly these compounds were as much as three fold more potent in the 3D assay than the conventional 2D equivalent. Interestingly the efficacious concentrations identified in the 3D micro-device were more similar to those measured in human trials than those in the 2D model.

To study epithelial-mesenchymal transition A549 spheroids are cultured in a gel-filled inner channel lined with HUVECs (1)

To study epithelial-mesenchymal transition A549 spheroids are cultured in a gel-filled inner channel lined with HUVECs (1)

These are simply two examples of sophisticated devices capable of modelling diseases or cell interactions that normal 2D or even 3D culture is unable to do. As the accessibility of microfluidics is expanding, so are the number and complexity of these devices. The limitations are clear; current throughput is low, labour intensive, and cost is high in comparison to traditionally screening. They do, however, provide possibilities for use in drug discovery such as late stage screening as well as target validation.


  1. Aref AR, Huang RY-J, Yu W, Chua K-N, Sun W, Tu T-Y, Bai J, Sim W-J, Zervantonakis IK, Thiery JP, Kamm RD. Screening therapeutic EMT blocking agents in a three-dimensional microenvironment. Integr. Biol. (Camb). 5: 381–9, 2013.
  2. Esch EW, Bahinski A, Huh D. Organs-on-chips at the frontiers of drug discovery. Nat. Rev. Drug Discov. 14: 248–60, 2015.
  3. Kunze A, Lengacher S, Dirren E, Aebischer P, Magistretti PJ, Renaud P. Astrocyte-neuron co-culture on microchips based on the model of SOD mutation to mimic ALS. Integr. Biol. (Camb). 5: 964–75, 2013.

Blog written by Trevor Askwith

Reducing Data: Ligand Efficiency and Other Fallacies

Ben 21-10-2015 Picture 1This article refers to an interesting yet controversial work of Michael Schultz1 and the debate on the topic of Ligand Efficiency and other psuedometrics.2

This article represents the view of the author and not the SDDC as a whole, but hopes to open the discussion on this important topic.

As a computational chemist part of my time is spent in the realms of data theory (often debating the healthcare of cats in boxes, and how much it cares if we observe it), however today, I intend to take the cat from Schrodinger’s Thunderdome (two states enter, one state leaves) and set it firmly amongst the pigeons in an attempt to discuss the controversial topic of Ligand Efficiency.

Data reduction is, as its name suggests, the process of reducing the amount of data for any dataset, whilst trying to retain the information – that is, effectively reducing the noise and keeping the signal. Some may argue that the essence of this thing we call Science is in fact the way in which we conduct this data reduction.

All members of a drug discovery team use data reduction in slightly different ways according to their ilk, but realistically it normally boils down to analysing which components of a data set cause the greatest variance in the output metric (e.g. which chemical properties affect the largest change in activity). Lipinski’s seminal works on the Rule of Five (despite technically being a Texas sharpshooter fallacy) selected items such as Mass, LogP and number of acceptor and donors as key components in whether a material would be an orally available drug-like material. His work effectively forced data reduction of countless criteria down to a handful (despite them being selected ex post facto rather than letting the model decide – in recent pipelines the average MW is increasing, but failure rate due to PK/bioavailability is decreasing, so this clearly needs redressing, as there is little correlation between them.3 Modified subsets of these rules emerged (e.g. Rule of three), based, unfortunately also on the same fallacy – mistaking plausibility for probability. There is no doubt however, that Lipinski’s work made a significant change in the paradigm of compound development which was decidedly for the better. The problem comes in the application: calling it a Rule or a Law when realistically they are guidelines which are easily and successfully sidestepped.

Ligand Efficiency – data reduction for small molecules

Ligand efficiency is a term used a lot by those involved in fragment based drug discovery – though it has as much application in larger molecules as it does in FBDD. Ligand Efficiency (LE) attempts to provide a useful measure of the effective contribution of each atom in the material’s activity – it is not a normalisation as the authors suggested, but is in fact a simple average of atomic contribution. It works somewhat oversimply by taking the number of heavy (non-hydrogen) atoms as the size metric. The Ligand Efficiency equation is shown in Eqn 1.

Ben 21-10-2015 Picture 2

This gives a simple number where higher is better which determines how “efficient” a ligand is at binding. There are issues in this application which are discussed below:

All non-hydrogen atoms are the same: This equation treats all heavy atoms the same. Apart from the obvious reductio ad absurdum case that for any ligand, if you swap a carbon for a Uranium atom, the LE does not change, it has a more serious implication: atoms that are capable of being donors or acceptors (e.g. which contribute large parts of a binding energy) are treated the same as a carbon or any other atom). This over simplification tends towards “smaller is better”, not “functional is better”.

Group Contributions disallowed: π-stacking interactions are penalised in the sense that all atoms are counted as singular, and thusly effects on binding are considered as singular too. e.g. in order to keep LE high, we can reduce atom count. However slicing an aromatic ring in half will destroy its π-stacking capability and will thusly reduce the activity too.

Non-Linearity: LE exhibits non-linearity across heavy atom count: in very small materials (such as fragments) a single atom addition can have the same effect as a large number of additional atoms in a larger complex, as shown in the graph below.

Fig 1. For a given pIC50, Ligand Efficiency does not exhibit linearity – This applies a very heavy penalty in small compound size (and in a more mathematically real sense, tends to infinity as heavy atom count tends to zero).

Fig 1. For a given pIC50, Ligand Efficiency does not exhibit linearity – This applies a very heavy penalty in small compound size (and in a more mathematically real sense, tends to infinity as heavy atom count tends to zero).

Built for Failure: The premise of this metric is to demonstrate that a higher number is a more efficient ligand, however it is recognised that LE will typically degrade as a material goes through development. Final materials rarely have the better LE than the original hit due to the heavy small change penalty taken on from a fragment hit – so LE will go down in the case of any development. As a result, LE should only be used at immediate hit stages and loses relevance rapidly as the material is developed.

What IC50?: Ligand efficiency attempts to include an activity metric as a way of taking it out of a theoretical domain and linking it to real data. This is problematic however as the IC50 will of course vary in vivo, in vitro, by assay type and even by handling. This makes LE a very singularly applicable metric: They are ONLY comparable when the assay type is the same across the data range. Take any material from your development pipeline, it will have variable IC50s as it goes along your cascade. Which do you use for this metric – and, more importantly, is it the same IC50 that the rest of your team are using? Data from external sources are thusly equally non-comparable (as there is often variability from lab to lab). So given this information, when can you really use it in a meaningful way?

So far the analysis looks pretty poor in using LE as a metric for making decisions on development, however there is a real debate to be had over the importance of Useful vs. Accurate. Much like the aforementioned Rule of Five, whilst LE may be flawed in conception and in the very way it is banded about in project meetings, it does serve a purpose as a common language that allows medicinal chemists to engage in discussions comparing materials for development – as long as care is taken in understanding exactly what they are referring to when they cite a ligand efficiency.

As a computational chemist, data reduction is a normal part of many processes, from QSAR to MPOs, and in my opinion LE fails to capture a fair share of the information due it’s over simplified nature. A question that is rarely asked in a world of easily coined metrics is “what is this measure really showing us”. In the case of LE, I think the answer is “not a lot”.

1 (a) Improving the Plausibility of Success with Inefficient Metrics; Shultz, Med. Chem. Lett. 2014 5, 2-5

(b) Improving the Plausibility of Success in Drug Discovery with the Use of Inefficient Metrics; Shultz Proceedings of Guiding Optimal Compound Design Symposium, Cambridge, MA, March 19th 2015

2 (a) Myriad Metrics, but which are useful:

(b)Too Many Metrics:

(c)More Thoughts on Compound Metrics:

3 Relating Molecular Properties and in vitro assay results to in vivo drug disposition and toxicity outcomes; Sutherland et al. J.Med.Chem. 2012, 55, 6455-6466

Blog written by Ben Wahab

New drugs for cystic fibrosis: what’s next?

In the last 15 years, to its enormous credit the Cystic Fibrosis Foundation (CFF) has profoundly altered the drug discovery landscape in cystic fibrosis (CF). The CFF drive to both seed and subsequently fund drug discovery projects that would focus on repairing the faulty CF gene, CFTR (Cystic Fibrosis Transmembrane Conductance Regulator), has ultimately lead to the registration of ivacaftor and lumacaftor (Vertex Pharmaceuticals). These are the first drugs that have been specifically designed as CFTR repair therapies. These drugs represent a significant milestone in the development of a cure for CF, but this is a complex disease characterised by numerous different mutations in the CFTR gene and approximately 50% of CF patients have mutations that are insensitive to these first generation therapies. So what is next in terms of therapies for CF patients? The North American Cystic Fibrosis Conference (NACFC) that was held in Phoenix, AZ last week (October 8-10 2015) highlighted many of the areas that hold promise for both near term and future delivery of medicines for these patients, but these do not come without new challenges and problems that will need solving.

The CFTR gene encodes a cAMP regulated anion channel that primarily conducts chloride and bicarbonate ions across mucous membranes including the lung and gut. Mutations in the CFTR gene can result in different protein phenotypes that range from no protein synthesis, through to reduced numbers of the channel at the plasma membrane or impaired opening of the channel. The net result of this is abnormal anion and therefore water secretion into these compartments which results in thick, sticky mucus which can block airways or the GI tract. In the airways this leads to recurrent infections and a slow, unrelenting destruction of the lungs.

The first of Vertex’s brace of CF drugs, ivacaftor, is termed a ‘potentiator’ as it improves the functioning of the protein by at least partially repairing its defective channel gating. This drug has shown remarkable efficacy in a subset of CF patients who have ‘gating mutations’ such as G551D and R117H but these patients represent only about 5-7% of the total CF patient population1. The majority of CF patients carry at least one copy of the F508del mutation which results in a misfolded protein that shows very low levels of expression at the plasma membrane and these patients need an additional drug called a ‘corrector’, a drug that will improve delivery of the mutated protein to the cell surface. Lumacaftor is the first CFTR corrector to be registered, and when combined with ivacaftor the pair do show clinical efficacy, albeit rather modest and only in patients carrying 2 copies of the F508del mutation2.

The mechanistic basis for CFTR potentiators (A) and correctors (B). From

The mechanistic basis for CFTR potentiators (A) and correctors (B). From

So there are at least 2 distinct challenges here: 1) the current first generation CFTR repair therapies need some improvements in clinical efficacy, and 2) what about the rest of the CF population (~50%) who still have nothing?

Improving clinical efficacy will need to be achieved through the development of new ‘stand-alone’ drugs as well as the rational use of combinations of CFTR repair agents. The CFF strategy to catalyse drug discovery has also had the effect of attracting a number of pharma and biotech into the area, buoyed by the early success of ivacaftor in the gating mutation patients. New companies equals new drugs with potentially better profiles when used as a single therapy, but perhaps more importantly when used in combination with drugs with additive or synergistic modes of action. The challenge however is recruiting the patients. CF patients are a well-motivated group who are willing to participate in trials, but they are relatively small in number. Furthermore, ivacaftor and lumacaftor represent a new ‘standard of care’ for the largest single population of CF patients (F508del homozygous) and testing new, potentially improved CFTR repair therapies on this background represents a challenge particularly as these 2 drugs will be formulated in combination (Orkambi) making withdrawal from one or the other very difficult. Questions have also been raised as to the suitability of ivacaftor as a potentiator of the F508del channel as chronic exposure of this form of the channel to ivacaftor can attenuate function3,4. The relevance of these preclinical observations can only be tested in the clinic with alternative potentiator molecules which do not share this putative negative pharmacology. But again, with ivacaftor and lumacaftor co-formulated as a single pill, this will be extremely difficult and may preclude F508del CF patients getting access to better, more effective treatments.

What about those CF patients not treated with current potentiators and correctors? Approximately 50% of CF patients have mutations that are insensitive to ivacaftor and lumacaftor, and in fact most of the pipeline of CFTR repair candidates which have been largely designed to repair F508del CFTR. Many of these patients have premature stop codon mutations which result in either no protein translation or truncated and inactive protein. Clinical trials with ataluren (PTC124), a candidate drug that was predicted to improve protein translation beyond the stop codons have been largely negative5 leaving an obvious gap in the CF portfolio. Early discovery work describing new premature stop codon mutation screens were reported at the NACFC 2015 last week and this will be an area of focus for years to come. The UK Gene Therapy Consortium reported back on the liposome formulated CFTR cDNA trial, re-emphasising the belief that there was a positive, albeit modest signal6. This group plans to study a higher dose of the liposome formulation and also have a viral-based delivery system as a backup program.

Of note at the NACFC 2015 was the relative absence of non-CFTR based discovery programs. The ivacaftor data in particular have instructed us that by improving mucosal hydration it is possible to enhance mucus clearance and reduce the frequency of exacerbations of CF lung disease. So why are there not more efforts to utilise alternative mechanisms to hydrate the airways? Do companies really not want to take the risk in a non-CFTR based mechanism? Is the allure of the now ‘clinically validated’ CFTR paradigm too strong? Or are those funding the research focused by the success of CFTR? The most advanced ‘CFTR-independent’ approach is that of Parion Sciences in collaboration with Vertex Pharmaceuticals who have P-1037, and inhaled ENaC blocker in Phase 1/2. Blocking ENaC activity in the airway will prevent sodium absorption out of the mucosa thereby maintaining hydration and mucus clearance. The key challenge with this approach is a target based side effect in the kidney but if this can be avoided, then ENaC blockers will represent a new target class that will be agnostic to the individuals CF mutation. Perhaps the most forward looking approaches in CF treatment relate to gene editing and stem cell therapies. The CFF are beginning to fund a bold initiative in these areas that if eventually successful will provide treatments for all CF patients. These are exciting technologies, but technologies that it will take years or even decades to translate from the lab into therapies.

Recent progress in drug discovery for CF highlights the tremendous potential for personalised medicine, whereby an individual’s particular mutation status will govern the selection of any given therapy or combination. However to get to this point solutions to some of the challenges highlighted above will need to be found. The financial implications for this path will also require further consideration. There was sharp criticism from many camps regarding the pricing point for ivacaftor and there will likely be a similar backlash regarding Orkambi. Competition in the field will force down prices, but to get to that point some of the problems with patient numbers and standard of care must be addressed. Concert Pharmaceuticals recent disclosure of a deuterated form of ivacaftor (CTP-656)7 will be an early test of how aggressively those already playing on the CF field will attempt to defend their turf…

Blog written by Henry Danahay

  1. Ramsey, BW et al. (2011) NEJM 365(18):1663-72
  2. Wainwright, CE et al. (2015) NEJM 373(3):220-31
  3. Cholon, DM et al. (2014) Sci Tansl Med 6(246):246
  4. Veit, G et al. (2014) Sci Transl Med 6(246):246
  5. Kerem, E et al. (2014) Lancet Respir Med 2(7):539-47
  6. Alton, EW et al. (2015) Lancet Respir Med 3(9):684-91
  7. Harbeson, S et al. (2015) Ped Pulmon 41(Supp):29

Selective Targeting Protein Degradation as a Novel Therapeutic Strategy

A classical approach used in drug discovery is to use a small molecule to bind to a protein and thereby inhibit its function which is in some way implicated in a disease process. This approach frequently requires high levels of occupancy of the target protein for a prolonged period. To ensure the required occupancy, high levels of circulating drug are needed which can potentially cause undesirable effects and frequently toxicity. The consequence of this is that frequently molecules with narrow safety windows re progressed into the clinic and mechanisms are not fully tested.

A novel and very different strategy which is gaining momentum is to selectively degrade the disease related protein by selectively promoting its destruction via a cells endogenous   biochemical pathway. An excellent example was recently published by Crews et al (Nature Chemical Biology 2015, 11, 611–617).

In this publication the authors describe the proteolysis targeting chimeras (PROTACs) method, whereby bidentate molecules are utilised which selectively bind to target (disease relevant) proteins and relevant ubiquitin ligases at the same time and thus leading to the selective ubiquitination of the undesired protein promotion of its degradation by the proteasome.

Figure 1

Two examples of PROTACs are described (figure 1), both contain a common fragment (shown in blue) which is a ligand of a protein (VHL) which selectively recruits E3 ligases. The common ligand is then linked via different flexible chains to compounds which potently and selectively bind to two different proteins involved in disease pathways. (b) contains a potent ligand for the oestrogen receptor ERalpha (in orange) and (c) a compound which potently binds the kinase RIPK2.

Looking at the compounds from a medicinal chemistry perspective, at first sight they appear to be very unattractive, being of very high molecular weight, highly polar and containing multiple rotatable bonds, yet they are active in cellular assays. Figure 2 clearly demonstrate a concentration dependent degradation of ERRa in MCF7 cells. The paper describes further experiments with both compounds in different cellular systems demonstrating both efficacy and specificity for the mechanism under investigation.

Figure 2

The data presented in this publication and those cited within clearly demonstrate the potential of this novel approach albeit at a very early stage and it will be interesting to see how far the methodology is able to go towards becoming a drug over the coming years. Of particular interest will be the developability profiles of such unexplored chemotypes and also the chronic safety implications of targeting the proteasome.

Blog written by Paul Beswick

Gli1 – a brand new drug target for demyelinating disorders?

The elucidation of a pathway involved in remyelination has yielded a novel potential target in the treatment of neurodegenerative diseases such as multiple sclerosis (MS).

MS is an autoimmune neurological condition affecting around 100,000 people in the UK alone and is characterized by demyelination – damage to the insulating myelin sheath of neurons. MS causes a wide range of symptoms depending on the location of the damage in the CNS and can include fatigue, blurred vision, mobility problems and muscle weakness, often drastically decreasing quality of life. Many current treatments target the immune system in an attempt to slow the disease, but drugs that have the ability to combat demyelination itself have so far remained elusive. Now, a series of elegant experiments by researchers at NYU Neuroscience Institute ( appears to have shed some light on the process of endogenous remyelination, which has important implications for the treatment of demyelinating disorders.

In adult human and mouse brains, remyelination is performed by two cell types: oligodendrocyte progenitor cells (OPCs) and neural stem cells (NSCs). Little is known about how NSCs in particular are recruited to lesion sites for their ultimate differentiation into oligodendrocytes, but a likely candidate involved in their regulation is sonic hedgehog (Shh), a morphogen with important roles in CNS development and NSC maintenance. Specifically it is Gli1, a transcription factor downstream in the Shh signaling pathway that has been the focus of this research.

Studying mice that express green fluorescent protein (GFP) in all Gli1-expressing cells, the researchers were first able to identify that Gli1+ NSCs have a prominent role in remyelination, and continue to generate glial cells for a prolonged period of time after demyelination. Using cuprizone to stimulate selective demyelination in the corpus callosum (CC), it was observed that GFP-expressing cells were recruited to damaged areas after six weeks, which then differentiated exclusively into glial cells – primarily oligodendroglia – two weeks after cuprizone was removed. Further, the numbers of GFP+ cells – comprising of OPCs, oligodendrocytes and astrocytes – continued to increase ten weeks after cuprizone removal.

Gli1-expressing cells are recruited to sites of demyelination. Markers of oligodendrocytes (CC1), oligodendrocyte progenitors (PDGFR-alpha) and astrocytes (GFAP) are observed in increasing amounts after cessation of cuprizone.

The relationship between Shh and Gli1 was then probed by further fate-mapping experiments of Gli1+ cells in mice, and it was found that Shh-responsive NSCs are recruited to demyelinated lesions in the CC where they downregulate Gli1 and differentiate into mature oligodendrocytes. Based on this finding, the question was raised as to whether inhibition of Shh signaling might enhance remyelination. To this end, the researchers fate-mapped NSCs in Gli1-null mice and indeed found an increase in GFP+ cells in the CC and an overall increase in myelin, but interestingly targeted ablation of Shh signaling did not increase GFP+ cells in the CC, indicating Gli1 has a specialized role in Shh signaling and myelination.

The next logical step, then, was to attempt targeted inhibition of Gli1 as a novel means to promote remyelination. This was done using the experimental drug GANT61, a small molecule inhibitor of Gli1, originally identified from a National Cancer Institute of chemical compounds as a potential therapy for brain and basal cell cancers.

Excitingly, it was found that GANT61 strongly promoted remyelination by enhancing the recruitment and differentiation of Shh-responsive NSCs into oligodendrocytes at demyelinated lesions. Specifically, mice receiving GANT61 during and after cuprizone treatment had significantly greater GFP+ cells, a significant amount of which were oligodendrocytes, resulting in more myelin present in the CC of treated mice compared to control-treated mice. In addition, when GANT61 was tested in a relapsing-remitting model of experimental autoimmune encephalitis (RR-EAE), a model of inflammatory demyelination and remyelination, they observed enhanced levels of remyelination and neuroprotection.

Mice given GANT61 had an approx. sevenfold increase in GFP-labeled cells in the CC (a) and a significant increase in the number of mature oligodendrocytes (b). Mice fed cuprizone then treated with either vehicle or GANT61 showed significantly higher fluorescence levels in GANT61-treated mice (c).

The implications of these findings are twofold: we now have an improved understanding of the mechanisms underlying myelination, and a starting point for the next generation of demyelination-targeting MS drugs. With an increasingly unmet need for well-tolerated MS therapies that do not target the immune system, it would not be surprising if this paves the way for numerous novel drug discovery avenues.
Blog written by Chloe Koulouris

Copper-Catalyzed Synthesis of Trifluoroethylarenes

Trifluoromethyl groups have found a use in medicinal chemistry for many reasons which include lowering the basicity of a molecule and increase the metabolic stability of alkyl groups.

Lewis 08-10-15 Picture 1A recent paper by Altman et al. expanded on the previous work of Chen in which he published a method for the decarboxylative trifluoromethylation of benzyl bromodifluoroacetates.

Altman noticed that there are currently methods for accessing trifluoroethylarenes via nucleophilic trifluoromethylation of benzyl electrophiles but there were no catalytic methods for this transformation on electron-deficient or heterocyclic substrates.

The current systems for benzylic trifluoromethylation require either stoichiometric copper (1), exclusive transformation of electron-neutral (2) or electron-rich substrates (3)(scheme 1)

Lewis 08-10-15 Picture 2The benefits of Chen’s work was the easy access to substrates derived from benzylic alcohols and the benign and easily separable by-products (CO2 and KBr). This method was not shown to convert a wide variety of substrates which Altman believes is related to the reaction mechanism described by Chen (scheme 2).

Lewis 08-10-15 Picture 3

Chen’s mechanism postulates a free CF3 anion, generated by an outer-sphere decarboxylation, which could react with sensitive carbonyl groups or deprotonate acidic sites. Altman hoped that he could refine the reaction so that an inner-sphere decarboxylation took place which generated Cu-CF3 and therefore making the reaction more tolerant of functional groups.

In this paper Altman investigated the reaction solvent, the catalytic copper source, additives and total quantity of iodide present on the reaction yield and by-product profile (table 1).

Lewis 08-10-15 Picture 4The optimised reaction conditions, 0.2 eq CuI, 0.25 eq KI, 0.4 eq MeO2CCF2Br and 4 eq KF in 1:1 DMF/MeCN, were then used to test if substrates containing a broad range sensitive groups and heterocycles could by trifluoromethylated in reasonable yields (table 2).

Lewis 08-10-15 Picture 5As can be seen from table 2 these reaction conditions tolerated a range of functionalities. The reaction was also conducted on a gram scale (2b) without a decrease in yield. Due to this functional group compatibility with amide (2c) and carbonyls Altman suggested that free CF3 anion (pKa = 27 in water) was not in existence in solution. To reflect the observations from this series of experiments, and previous work, including the absence of the CF3 anion and the importance of iodide in the reaction mixture Altman suggested the reaction sequence in figure 1.

Lewis 08-10-15 Picture 6To demonstrate the utility of this reaction an intermediate of fluorinated tebufenpyrad was synthesised in fewer steps and higher yield compared with a published route and also without the use of manganese or tin (scheme 3).

Lewis 08-10-15 Picture 7This paper has taken a previously published useful reaction for the transformation of benzylic alcohols into trifluoroethylarenes and further optimised it. The reaction can now be used to form trifluoroethylarenes from reagents that contain unactivated electrophiles, reactive carbonyls, acidic protons and heterocycles.

Blog written by Lewis Pennicott

Single cell analysis of human metastatic breast cancer cells

Metastasis is still the cause of most deaths from cancer, despite major advances in the fields of molecular and genetic characterisation of tumours. Developing an understanding of how metastatic cells arise and go on to form tumours may uncover more information to aid in the development of new treatments. Current thinking is that tumour cells with stem cell like properties may initiate the formation of metastatic tumours.

Lawson et al., (Nature, 2015) have published an elegant study in which they have succeeded in   isolating early stage metastatic cells from patient-derived xenograft (PDX) models of triple negative breast cancer. The authors then examined the gene expression profiles of metastatic cell populations.

Normal human breast epithelium were used to establish a 49 gene differentiation signature as a reference against which to analyse gene differentiation in metastatic cells. They examined these differentiation gene signatures in populations of both basal lineage cells, containing stem cells, and luminal lineage cells containing progenitor and mature cells.

PDX models of triple negative breast cancer were used due to the aggressive, metastasis forming nature of the tumours formed in these mice. Metastatic cells were isolated from mouse lung, lymph node, bone marrow, liver, brain and peripheral blood using PDX breast cancer specific cell surface marker genes (human CD298). The authors found that expression of this marker correlated with the tumour burden observed in the animal and so could then investigate the gene expression signatures of low-burden (early-stage metastatic) vs high-burden (advanced-stage metastatic) disease.

Sarah's 5-10-15 Pic 1

Identification of human metastatic cells in PDX mice

They found that in low-burden tissues, the metastatic cells found were different from the main tumours that they were derived from and that the differentiation gene signature in these cells was of a basal, stem-cell nature. This stem cell differentiation gene signature was conserved in low-burden metastatic cells across all animals and models tested. These metastatic cells expressed very high levels of the pluripotency genes OCT4 and SOX2 that are found in embryonic stem cells. The authors also observed that genes involved in cellular processes such as DNA damage response, chromatin modification, differentiation, apoptosis and cell cycle control were differentially expressed in low-burden metastatic cells. When these low-burden cells were transplanted into mammary glands they found that an unusually high amount of tumour formation was observed, suggesting that these early-stage metastatic cells can initiate tumour formation. The authors concluded that primary tumours contain a rare sub-population of stem-like cells and that a higher percentage of these cells within the tumour correlates with a higher metastatic potential.

Higher-burden metastatic cells expressed lower levels of quiescence and dormancy genes compared to the low-burden metastatic cells, but higher levels of cell cycle genes such as MYC and CDK2, suggesting that there is a shift to a more proliferative gene expression signature with an increasing ability to metastasise. By using a CDK inhibitor (Dinaciclib) the authors were able to demonstrate a reduction in the number of animals presenting with metastatic cells.

Treatment with Dinaciclib dramatically reduced metastasis in mice

Treatment with Dinaciclib dramatically reduced metastasis in mice

What relevance does this study have for drug discovery? The identification and isolation of metastatic cells to study their particular characteristics may enable the identification of potential new targets against metastatic disease. The methods used here to assess metastatic cells could also be used in the development of novel compounds and potentially translate into the clinic to assess patient response to new therapies.

Blog written by Sarah Walker