Are We Encouraging the Best Experimental Design?

Undertaking a PhD project is a great way to start a scientific career, and in many cases a prerequisite for research employment. The number and scope of PhD projects in all scientific disciplines is vast; however the projects themselves are pre-determined by supervisors, and often occur in labs where particular experimental techniques are established and frequently used. Considering time constraints, this is advantageous to the student as there research has direction from the start, and they rapidly acquire the expertise to carry out their research in a particular way and generate data quickly. Success for both student and supervisor. Win-win, surely?

Projects where a student has had to set up research techniques from scratch are more challenging. There can be a great deal more ‘down-time’ while the bugs and inconsistencies are ironed out, but this can provide an opportunity to really consider the research hypothesis and ensure that the particular approach really is the best way to address it. More scientifically satisfying perhaps, especially when the experiments finally start to work, but there’s more of a danger of running out of time, or not generating sufficient data.

Either way, lab work uses increasingly complicated techniques that in many cases have been simplified by technology. There is a danger that lab work can become an exercise in learning to set up and use a particular machine or experimental model, rather than learning underlying principles, adapting the tech to hand, and possibly innovating with alternative approaches to those already established in the lab.

In reality, PhD projects can contain elements of all of the above, and discerning students and supportive supervisors will be aware of the issues. So in order to produce the best scientists who are equipped to approach any research question effectively, there is real value in encouraging PhD students to go back to first principles when addressing the key questions of their research. How does my experimental approach work? Is it the best way to obtain the data needed to test the hypothesis? Are there alternatives? Can I bring something new to the lab? How can I manage the project effectively?

Just one way to support PhD students is by providing or promoting specialized training courses. In the field of electrophysiology for example, there is a superb microelectrode techniques training course which has run annually in September for the last 35 years:

http://www.mba.ac.uk/microelectrode-techniques-cell-physiology

I was able to attend as a PhD student a few (ahem!) years ago, and have had the privilege to help out on the course on seven of the intervening years. This year I was given the opportunity to introduce a new element to the course – high-throughput patch clamp, which gave me the chance to showcase some of the new technologies which are no longer just the preserve of the pharmaceutical industry. The students, as ever, were enthusiastic and keen to experience a variety of electrophysiological recording techniques, right from the first principles of being able to set up a microscope correctly, to fundamental electronics and building their own amplifiers, making sure their solutions have the correct properties for their experiments, and most importantly, knowing that they are using the most appropriate recording technique to capture the signal they want to measure. And as grant-writers and group leaders of the future, they now have an insight into just some of the new technology that is or will be available to them, and may help shape their research direction and ideas about project management.

Courses like this not only provide teaching on research fundamentals and best practice, they are also an invaluable opportunity for knowledge exchange. Every year I learn something new, and it is particularly rewarding to share my own experience with others. So if you are a PhD student or early career researcher, I would encourage you to look for these opportunities (for which it is possible obtain bursaries from organisations to which you may belong as a student member such as the British Pharmacological Society or Physiological Society). And if you are a supervisor or experienced post-doc, please consider supporting these teaching courses by offering your time and experience – everyone will have something to offer, and we are never too old to learn!

Above : The laboratories of the Marine Biological Association in Plymouth (home of the microelectrode techniques course), where Hodgkin and Huxley conducted their Nobel prize-winning research into nerve conduction using the squid giant axon.

Below: main teaching lab.

Blog written by Sarah Lilley.

Want to disrupt a protein:peptide interaction using HTS? 5 steps to successful FP assay development

The Fluorescence Polarisation (FP) assay described here works through the binding of two partners of differing size, a small fluorescent peptide (< 1500 Da) to a larger target protein (> 10,000 Da). When the target protein binds to the peptide, a large complex forms, rotating slowly in solution. The rotation is not fast enough to disrupt the light, so it remains polarised (Figure 1). When a small molecule (or hit compound) displaces the fluorescent peptide, it falls into solution and begins to spin rapidly disrupting the light and forcing it to become depolarised (Figure 1).

Step 1. Titrate the fluorescent peptide alone

Titrate the fluorescent peptide to determine the concentration at which the polarisation signal remains stable and has low variability. At low concentration, there may be higher variability as the fluorescent signal nears the limits of sensitivity of the plate reader. Choose the lowest concentration of peptide at which the signal remains stable, shows low variability and gives FI at least 10-fold higher than buffer only control wells.

Note: it is prudent to label your peptide with a Red (TAMRA, BODIPY) rather than a Green (Fluorescein) dye to reduce the number of compounds that will cause interference within the 405 nm range.

Graph 1. Example of a fluorescent peptide titration

 Step 2. Titrate the target protein at the chosen fixed concentration of the fluorescent peptide

This step measures the binding affinity between the fluorescent peptide and target protein. A plot of milli-polarisations (mP, see glossary for the equation) versus protein concentration will produce a hyperbola curve that begins at the baseline polarisation for the free probe (approx. 30 – 50 mP) and rises to plateau at a maximal polarisation value that corresponds to 100% bound peptide (approx. 100 – 800 mP). If the Kd of the peptide is unknown several binding curves over a range of protein concentrations (1nM to 1µM) should be measured and evaluated.

Graph 2. Example of Kd calculated for fluorescent peptide binding to target protein.

For the next steps in development choose a protein concentration that yields an acceptable assay window (approx. 100 – 800 mP) at which 50% (Kd) to 80% of the peptide is bound.

 Step 3. Displacement of the fluorescent peptide using an identical unlabelled peptide

This experiment will demonstrate that the unlabelled peptide can fully displace the fluorescent peptide from the target protein and whether labelling has dramatically increased or decreased the affinity of the peptide. Following addition of the unlabelled peptide the mP values should decrease to the value observed with the free fluorescent probe alone (Graph 3). Use a fixed concentration of the target protein and fluorescent peptide, chosen in steps 1 & 2. Then mix with your unlabelled peptide over a range of concentrations. The top concentration of unlabelled peptide should be at least 100X the estimated Kd and is usually approx. 100 µM. Equilibrium is usually reached within the first 5 minutes if not within the first few minutes for most binding assays so long incubation times are not normally required. Perform this experiment according to the protocol you will use for the final screen. A pre-incubation (~ 30 – 60 min) with your compound/peptide and target protein followed by incubation with the fluorescent peptide (~ 30 – 60 min) is standard.

Graph 3. Example of IC50 calculated for unlabelled peptide competitively displacing the labelled peptide from the target protein.

Step 4. DMSO tolerance & Z Prime Assessment

You have successfully shown your assay is capable to detecting compounds that bind to the same site as your fluorescent peptide, now it is time to assess the robustness and reproducibility.

As with all HTS assays it is essential to assess tolerance to DMSO and ensure a robust S:B and low variability across your plate. In general, FP binding assays are very insensitive to higher than average concentrations of DMSO. Titrate a range of concentrations of DMSO from 0 – 10 % at your chosen concentrations of target protein and fluorescent peptide to determine any impact on Z’ (Graph 4).

Graph 4. Z’ remains stable up to 10% DMSO.

Next perform a full plate assessment of S:B using the method you will use to dispense reagents during your screen, either using manual or automated additions. Alternate columns of a 384 well plate contain either max (target protein plus fluorescent peptide) or min (fluorescent peptide only) controls.

Figure 2. Example Plate Map for a full 384 well plate assessment 

Table 1. Example of Max & Min mP, SD, S:B and Z’ calculated from a full 384 well plate assessment for an FP assay

Step 5. Data Analysis

You have successfully developed an FP binding assay! Time to go to the pub to celebrate. However, look closely at your data, there may be some further questions that need answering.

Is your assay practical to run? Is it sensitive enough to detect hit compounds?

An interesting paper by Huang, X. 2003, contrary to popular belief, explains that the higher the affinity (lower Kd) between your binding partners the greater the range of resolvable potency. They argue that using tight binding peptides has no impact on the ability to detect inhibitors of low or intermediate affinity but instead allows for improved detection of very potent inhibitors. In short, the lowest resolvable inhibitor potency (lowest Kd achievable) is the Kd value of the fluorescent peptide.

Figure 3. IC50 and Ki decreases with increases in peptide affinity. Taken from Huang, X. 2003. Square = 10 nM Kd, Triangles = 100 nM Kd, Inverted Triangle = 1000 nM Kd, Diamonds = 10,000 nM Kd.

If the affinity between your protein and peptide is low (µM range) it may not be practical to use your assay for a HTS. A large amount of target protein is required and the assay would be insensitive for the reasons discussed above.

To determine the IC₅₀ use a sigmoidal dose-response (4-parameter, variable slope equation). To determine the Kd and Ki you can use Cheng-Prusoff equation (Figure 5), but is this the most appropriate equation?

The use of Cheng-Prusoff results in an overestimation of the Ki from the IC50 due to the fact FP assays are set up with a high amount of fluorescent peptide bound (50-80%). As a large amount of the fluorescent peptide is bound, you cannot substitute the [L_f] (free ligand/peptide concentration) for total ligand/peptide concentration. The Kenakin equation (Figure 6) takes into account the proportion of peptide bound allowing for accurate determination of Kd. Another possibility is the use of biophysical techniques to determine Kd following primary hit identification.

Can you remove any false positives? If you have used a green fluorophore, such as FITC, fluorescent compounds can interfere at these wavelengths resulting in false positives. Luckily, there is a quick and easy way to eliminate these. Calculate the total fluorescence (Figure 7) by taking into account the fluorescent intensity observed from both the Perpendicular and Parallel channels. If the total fluorescence is > 3 fold that observed in the control wells (protein & peptide plus vehicle only) this is indicative of compound fluorescence.

Congratulations, you are now the proud owner of an FP binding assay for HTS! Check out the references for further information on this topic.

Glossary

HTS = High Throughput Screening

FP = Fluorescence Polarisation

Ligand = peptide or small compound

FI = Fluorescence Intensity

S:B = Signal (Target Protein plus Peptide) to Background (Peptide only)

mP = Millipolarisation

Figure 4. Equation to calculate milli-polarization. S = Parallel emission P = Perpendicular emission G = G Factor. Taken from Assay Guidance Manual, Eli Lilly & Company and the National Center for Advancing Translational Sciences.

Figure 5. Cheng Prusoff Equation.

Figure 6. Kenakin Equation.

Total Fluorescence Intensity = Parallel FI + 2*Perpendicular FI

Figure 7. Equation for total fluorescence intensity

Blog written by Jess Booth

References

Auld, R et al., 2012. Practical Use of Fluorescence Polarization in Competitive Receptor Binding Assays. Assay Guidance Manual. Eli Lilly & Company and the National Center for Advancing Translational Sciences https://www.ncbi.nlm.nih.gov/books/NBK91992/

Matthew D Hall et al., Fluorescence polarization assays in high-throughput screening and drug discovery: a review. Methods Appl. Fluoresc. 2016, 4.

Huang X. Fluorescence polarization competition assay: The range of resolvable inhibitor potency is limited by the affinity of the fluorescent ligand. J. Biomol. Screening 2003; 8:34–38.

Kenakin, TP (1993) in Pharmacologic analysis of drug/receptor interaction, 2nd ed., New York:Raven p. 483.

Roehrl, M et al., Discovery of Small-Molecule Inhibitors of the NFAT-Calcineurin Interaction by Competitive High-Throughput Fluorescence Polarization Screening. 2004. Biochemistry 2004; 43 (51):16067–16075.

Turconi S, Shea K, Ashman S, Fantom K, Earnshaw DL, Bingham RP, Haupts UM, Brown MJB, Pope A. Real experiences of uHTS: A prototypic 1536-well fluorescence anisotropy-based uHTS screen and application of well-level quality control procedures. J. Biolmol. Screening 2001;6:275–290.

 

 

 

Interactions of drugs and gut microbiota syndrome

The well known interaction between antibiotics and gut microbiota have been reported in recent years, for example, some studies have shown that the antibiotic use (oral or intravenous) reduces and influences significantly the gut microbiota (Bartosch et al. 2004; Palmer et al. 2007; Iizumi et al. 2017). The change in gut microbiota composition and function depends on the type of antibiotic, the dose concentration and the period of time of the treatment. Antibiotics treatment not only affects microbiota imbalance but can also cause the gene expression, protein activity and metabolism of the gut microbiota that can lead to chronic human disorders such as asthma and irritable bowel syndrome (Figure 1; Francino 2016).

(Francino 2016)

In 2015, a study found that the treatment of metformin in type 2 diabetes patients affects the composition of gut microbiota, in which reduction of butyrate-producing bacteria and an increase of Escherichia species was observed (Forslund et al. 2015). Some of the adverse effects of metformin treatment in 30% of diabetes patients are diarrhoea, nauseas and bloating and this might be related to the change in gut microbiota but the mechanism is not fully understood. This evidence suggests that not only antibiotic drugs can cause a microbiota imbalance in the gut but other drugs can cause this variation as well. For example, the consumption of proton pump inhibitors (PPIs) drugs have been found to be associated with changes in gut microbiota, causing a higher risk of enteric infections, more specifically with a 65% higher incidence of Clostridium difficile infections (Imhann et al. 2016; Jackson et al. 2016).  In addition, the use of nonsteroidal anti-inflammatory drugs (NSAIDs) have been associated with changes in the gut microbiota and this modification can be related to the bleeding, inflammation and ulceration in the stomach and intestine caused by NSAIDS treatment.  A recent study investigated the relative abundance of operational taxonomic units (OTUs) by types of different classes of medication. They found for example that the OTU of Enterobacteriaceae family was 32% higher with the medication of antidepressant citalopram (Fig. 2), as well as 20% higher with Naproxen treatment compared with patients not taking any mediation (0.7%), Figure 2 (Rogers & Aronoff 2016).

(Rogers & Aronoff 2016).

A new study argues that most of the drugs commercially available will influence the gut microbiota; the screen of 1,197 compounds monitoring the growth of 40 representative bacteria in the human gut showed that 24% of the drugs tested inhibited the bacteria growth of at least one species in vitro (Maier et al. 2018). All these results are suggesting that drugs are affecting the normal microbiota in the gut, however these changes might not be affecting human health. In addition, there is increasing interest in the interactions of drug-microbiome, in which bacteria can interact and modify the efficacy of drugs and their therapeutic effect (Willyard 2018). Future research needs to focus on detecting the drug-microbiota interactions for human disease, health and drug efficacy. We should not be surprised that these type of studies are included as a clinical trial before a drug is commercially available.

Blog written by Thalia Carreno Velazquez

References

Bartosch, S. et al., 2004. Characterization of bacterial communities in feces from healthy elderly volunteers and hospitalized elderly patients by using real-time PCR and effects of antibiotic treatment on the fecal microbiota. Applied and environmental microbiology, 70(6), pp.3575–81.

Forslund, K. et al., 2015. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature, 528(7581), pp.262–266.

Francino, M.P., 2016. Antibiotics and the human gut microbiome: Dysbioses and accumulation of resistances. Frontiers in Microbiology, 6(JAN), pp.1–11.

Iizumi, T. et al., 2017. Gut Microbiome and Antibiotics. Archives of Medical Research, 48(8), pp.727–734.

Imhann, F. et al., 2016. Proton pump inhibitors affect the gut microbiome. Gut, 65(5), pp.740–748.

Jackson, M.A. et al., 2016. Proton pump inhibitors alter the composition of the gut microbiota. Gut, 65(5), pp.749–756.

Maier, L. et al., 2018. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature, 555(7698), pp.623–628.

Palmer, C. et al., 2007. Development of the Human Infant Intestinal Microbiota Y. Ruan, ed. PLoS Biology, 5(7), p.e177.

Rogers, M.A.M. & Aronoff, D.M., 2016. The influence of non-steroidal anti-inflammatory drugs on the gut microbiome. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases, 22(2), p.178.e1-178.e9.

Willyard, C., 2018. When drugs unintentionally affect gut bugs. Nature Publishing Group, 17(6), pp.383–384.

 

 

 

 

Is your child getting enough sleep?

If your child wakes up fairly happy in the morning and remains engaged throughout the day, you can be almost certain he or she has probably had a good night’s sleep. Higher sleep quality enhances neural connections, enabling better memory consolidation and information processing1. Hence poor sleep is thought to negatively impact children’s learning and early academic achievement (AA2).Children of the age bracket 6-13 could easily minimise behavioural problems and cognitive deficits with 9-11 hours of sleep however in reality only 23% of school children are getting less than 8 hours of sleep where 2% sleep 7 hours or less3. The effect of good sleep on academic achievement among children with lower effortful control (EC4, is a dimension of temperament related to the self-regulation of emotional reactivity and behaviour) has been established recently5.

There are many reasons for the lack of sleep among children and the side effects of poor sleep pattern. Interestingly, parental insomnia symptoms have an impact on children’s sleep as evident by in-home sleep- electoencephalaograpgy6. Another major cause of sleep deprivation among children includes anxiety disorder7 where many children are being referred to mental health services. Most common sleep problems associated with anxiety include difficulty in initiating/maintaining sleep, refusal to sleep independently and nightmares8. Research studies also link children’s sleep problems, parental sleep and daily functioning9. Several reports have demonstrated a link between children’s sleep problems and parental sleep difficulties in healthy population. ADHD and autism also have big influence on the quality of sleep in children and their parents as well10, 11.

Many studies also indicate the relationship between sleep duration and childhood obesity12, which is one of the hugest healthcare burdens for the NHS. Lack of sleep can influence the development of obesity by certain biological pathways13, 14, 15 like increased energy intake (unhealthy dietary patterns); decreased energy expenditure (diminished physical activities, increased screen time).

Fig 1. Potential mechanism12 by which sleep deprivation predisposing obesity. ↑ – short sleep duration leads to the increase of the affected factors; ↓-short sleep duration leads to the affected factors; (?) sufficient evidence not available.

Other potential mediators are hormonal (leptin, insulin, ghrelin) changes evoked by sleep deprivation16, 17, and 18. Many reviews focused on paediatric populations have established a clear link between short sleep and obesity12 and some studies have investigated the association between sleep duration and dietary habits where a 5-day duration sleep restriction resulted in the consumption of higher glycaemic index foods leading to obesity19. Decreased sleep duration also increase the tendency of external eating (Eating in response to the sight or smell of food20. Studies also revealed that short sleepers tend to consume higher energy foods, more added sugar and high sugar beverages compared to long sleepers21.

Large multicentre study (Australia, Brazil, Canada China, India, Colombia, Finland, Kenya, Portugal, South Africa, UK, and USA) concluded low MVPA (medium to vigorous physical activity), short sleep duration and high TV viewing are the important correlates of childhood obesity22.

Here are some of the recommended tips23 – mostly a focus for children but many of the strategies apply to all age groups. The easiest thing parents can do is to make sleep a priority in their children’s lives. At times, avoid scheduling activities (music lessons, sports, social events, etc.) that would delay bedtime. This can be more challenging with older children dealing with heavy homework loads and relatively early school start times but with sufficient organisation and prioritisation of activities, it can be done.

It is easier for kids to wind down if there is a predictable bedtime routine to follow. For example, the four B’s of bedtime: bathing, brushing teeth, books, and bedtime. It can also be helpful for kids maintain sleep and wake times roughly the same (within an hour or so) each day, even on weekends.

It is highly recommended for children to get at least an hour of exercise every day. While daytime exercise can contribute to sleeping better at night, vigorous exercise too close to bedtime may keep kids awake, so try to plan your child’s physical activity accordingly.

Avoid keeping computers or TVs in your child’s bedroom, and keep portable devices (phones, tablets, handheld games) out of reach. A helpful tip is to have a central charging station where all of the family’s electronics spend the night.

Exciting or violent programming in particular can make it difficult for children to fall asleep and bright screens (or brightly lit rooms) can consequently delay melatonin release and, as a result, the onset of sleep.

Setting the stage for good sleep is an important habit that can make a difference in your child’s future health. If you have concerns, talk to a paediatrician, or check out health guide to better sleep by mental health foundation24 and NHS choices25

Blog by Srini Natarajan

 

The butterfly effect: Combinations of immunotherapy and radiotherapy to amplify the abscopal effect in metastatic disease.

Combination therapies are known to be effective in treating cancer, and there have been many advances with the use of two or more therapies that have either an additive or a synergistic effect upon treating disease. Often, the use of a combination therapeutic will reduce the overall toxic effects of chemotherapeutics on healthy tissue, which has a huge clinical benefit. A lot of combination therapies are used in conjunction with chemotherapy, but there are also combination therapies that are being researched that can positively impact the “abscopal effect” of radiotherapy.

A recent article in Nature Reviews Cancer has outlined the developments in research that have provided evidence that the abscopal effect can be boosted with a combination of radiotherapy and immunotherapy¹.

The abscopal effect (from the Latin for “away from target”, ab scopus), was first described in a paper in 1953 in which metastatic cancer sites were found to regress away from the site where radiotherapy was administered².  This beneficial off-target impact is known to be driven by the immune system, but is a rare event because in the tumour microenvironment, weakened immune responses may restrict the development of an abscopal response. However, there is growing evidence to support the idea that using a combination of radiotherapy and immunotherapy may boost the abscopal effect.

Whilst the exact mechanism of the abscopal response is not completely understood, there are multiple studies that have highlighted how combining an immunotherapeutic with radiotherapy can boost its effect. Upon injury through radiation, a tumour can release tumour-specific antigens, which are presented by antigen-presenting cells to CD8⁺ T cells. These T-cells can then identify both the tumour that has been irradiated and also areas of metastatic disease to be targeted for attack by the adaptive immune system³ (Fig. 1). Damage-associated molecular patterns (DAMPs) and cytokines may also be expressed by the irradiated tumour cells to further feed into the inflammatory cascade that leads to the ultimate cellular elimination by CD8⁺ T-cells ⁴.

Fig.1 The inflammatory cascade involved in the abscopal effect¹

However, the rarity of the abscopal effect suggests that the immunosuppressive microenvironment of tumours inhibits the ability of primed CD8⁺ T-cells to identify tumour cells and target them for elimination. This is possibly due to immunosuppressive cytokines such as TGF-β and surface receptors such as CTLA4, which can inhibit T-cell function⁴. Therefore, methods to overcome the immunosuppressive microenvironment of tumours have been explored to assess whether overcoming immunosuppression by inhibiting T_reg cells using anti-CTLA4 and PDL-1  (programmed cell death protein 1) blockade can lead to increased CD8⁺ T-cells:T_reg cell ratios. In theory, this would make the tumours more sensitive to CD8⁺ T-cells and lead to adaptive immune responses aimed at the tumour and metastases, which would boost abscopal responses.

Other methods of overcoming immunotherapy include direct injection of IL-2 (a proinflammatory cytokine) or dendritic cells (DC) into the irradiated tumour. Boosting DC numbers in the irradiated tumour will lead to an increase in tumour-specific antigen presentation and therefore a larger adaptive immune response. Injection with IL-2 leads to inflammation and therefore a boost in the abscopal effect. All of these methods are aimed at overcoming immunosuppression and therefore better targeting of the tumour and metastases by the immune system.

There are many examples in which combinations of radiotherapy and immunotherapy could be used to boost abscopal effects and therefore treat metastatic disease, which is known to account for most of the fatality associated with cancer. It is therefore an important area of research and an interesting approach to combination therapy in cancer.

Blog written by Will Pearce

1. Ngwa, Wilfred et al., Nat. Rev. Cancer., 2018, Vol.18., 313-322
2. Mole, R.H., BR J Radiol., 1953, Vol.26, 234-241
3. Grass, G.D. et al., Curr. Probl. Cancer., 2016, Vol. 40., 10-24
4. Vatner, R.E., et al., Front Oncol., 2014, Vol. 4., 325

 

Mibefradil, a new class of compound to study TRPM7 channel function

Transient receptor potential (TRPM) is a family of non-selective cation channels that are widely expressed in mammalian cells. TRP channels are composed of six transmembrane domains and the family consists of eight different channels, TRPM1–TRPM8. TRPM7 is compromised of an ion channel moiety essential for the ion channel function, which serves to increase intracellular calcium levels and to help regulate magnesium ion homeostasis. The current hypothesis is that decreased cytosolic Mg²⁺ ion concentration activates the divalent cation-selective TRPM7 current ².

Xiong and colleagues⁴ were the first to demonstrate TRPM7 mediated sensing of Ca²⁺ concentration. The permeability of TRPM7 channels to monovalent cations is decreased In the presence of divalent ions, promoting outward rectification in whole cell recording with a reversal potential of 0mV. When divalent ions were removed from the external buffer solution a large TRMP7 current was activated. The order of permeability of TRPM7 to divalent cations follows the order Zn²⁺> Ni²⁺> Ba²⁺> Co²⁺> Mg²⁺> Mn²⁺> Sr²⁺> Cd²⁺> Ca²⁺. Mutation of the mouse TRPM7 gene in vivo demonstrates that it is essential for early embryogenesis, organ development, cardiac automaticity, and systemic Mg²⁺ homeostasis. Deletion of the TRPM7 gene in cells in vitro showed that this bi-functional channel, not only regulates divalent cation homeostasis, but also cell motility, proliferation, mechanosensitivity and exocytosis. TRPM7 may also play a role in anoxic neuronal death, immune responses, hypertension, neurodegenerative disorders, tissue fibrosis and in tumour growth^{2,3, 4,5,}. TPRM7 is clearly an important drug target.

Figure showing; Regulation of cellular Mg²⁺ homeostasis

Andrea M.P. Romani Arch Biochem Biophys. 2011 Aug 1; 512(1): 1–23.

Schafer & colleagues¹ have recently reported on mibefradil and naltriben, two new classes of compound (fig 1a) that act on 5-HT7 receptors. In the calcium imaging assay, mibefradil potentiated TRPM7 current (fig 1b) in the absence of extracellular Mg²⁺ and blocked the TRPM7 current in it is presence (1C). Mibefradil also induced TRPM7 mediated Ca²⁺ entry in a concentration dependent manner with an EC₅₀ of 53mM (fig 1d). The Influence of intracellular Mg²⁺ concentration was also investigated in a whole cell e-phys study which shown that in the absence of intracellular Mg²⁺ the addition of mibefradil failed to increase the current further (fig 2a). When cells were perfused with the physiological concentration of free Mg²⁺ (0.9mM) there was a pronounced increase in the whole cell current (fig 2b). These results suggest that mibefradil preferentially activates TRPM7 responses at physiological or low intracellular Mg²⁺. Extracellular divalent cations are known to block the TRPM7 channel, resulting in a small divalent cation selective inward current at the physiological membrane potential. Mibefradil elicited a moderate increase of the inward current for the TMRP7 (E1047Q) mutant compared to WT channel at physiological Mg²⁺ concentration. Application of mibefradil, stimulate both inward as well as outward monovalent currents in this mutant (fig 3a). The authors conclude that mibefradil acts as a specific agonist of the TRPM7 channel, and proposed that mibefradil specifically affects TRPM7 channel gating in a Mg²⁺ – dependent manner.

Sebastian Schafer et.al. (2016) Eur J Physiol 2016; 468; 623-634

Mibefradil is a calcium channel antagonist of both L and T type voltage-dependent calcium channels and has been used clinically for the treatment of angina and hypertension⁷. Unfortunately, side effects arising from inhibition of cytochrome p450 enzymes led to the withdrawal of mibefradil withdrawn from clinical use. Currently, mibefradil is only used experimentally as a tool compound to identify voltage gated calcium channels. Mibefradil shows moderate potency for TRPM7 activation when compared to P/L type voltage gated calcium channel, the EC₅₀ 53mM, compared to 2.7mM for T-type, and 18.6mM for L-type Ca²⁺ channels. Published data are consistent with mibefradil action at the TRPM7 channel (reference?). Specifically, mibefradil induces the TRPM7 –mediated influx of divalent cations such Ca²⁺, Zn²⁺ and Mg²⁺ which may alter cellular process.

Schafer & colleagues¹ also identified naltriben as an activator of the TRPM7 channel at high-level intracellular Mg²⁺ levels. The authors have suggest that that two types of TRPM7 agonist act in different ways: type 1 naltribune, induces TRPM7 activity independently of Mg²⁺ concentration whereas type 2 Mibefradil acts in a Mg²⁺ -dependent manner². Mg²⁺ is the most abundant intracellular cation (~15-18mM) and is mostly complexed with phosphormetabolites such that physiological levels of free Mg²⁺ are only in the range of 0.5-1mM. Under certain conditions, the free intracellular concentration may reduce further e.g. as a result of increasing phosphormetabolite synthesis during cell division. The authors propose that the two different types of TRPM7 activator could be directed to two different types of target; Type 1 to stimulate TRPM7 irrespective of Mg²⁺ concentration and Type 2 to act preferentially on cells with reduced Mg²⁺ levels.

Blog written by Shamim Choudhury

References

1. 1. Sebastian Schafer et.al. (2016) Mibefradil represents a new class of benzoimidazole TRPM7 channel agonists. Eur J Physiol 2016;468;623-634
   2. Andrea M.P. Romani (2011) Cellular Magnesium Homeostasis. Arch Biochem Biophys. 2011 august 1; 512(1): 1–23. Doi:10.1016/j.abb.2011.05.010.
   3. Valdimir Chubnav, silvia Ferioli, Thomas Gudermann (2017) Assessments of TRPM7 functions by drug like molecules. Call Calcium 2017;67;166-173
   4. Xiong Z, Zu W and Macdonald JF (1997) Extracellular calcium sensed by a novel cation channel in hippocampal neurons. Proc Natl Acad Sci USA 1997;94;7012-7017
   5. Yugang Sun et.al. (2015) TRPM7 and its role in neurodegenerative diseases Channels 2015; 9:5,253-261
   6. Yosuke Kaneko and Arpad Szallasi (2014) Transient receptor potential (TRP) channels : a clinical perspective British J Pharmacology 2014; 171 2474-2507
   7. S. Aczel, B. kurka, & S. Hering (1998) Mechanism of voltage and use dependent block of class Ca²⁺ channels by mibefradil. British J of Pharmacology 1998;125;447-45

 

Conference Report: RSC BMCS Mastering MedChem IV, 14th March 2018

On Wednesday 14^th March 2018 I attended the RSC Biomedical and Medicinal Chemistry Sector’s 4th Mastering MedChem Symposium. The opening session chair, Professor Joe Sweeney (University of Lancaster) described the event as the sector’s “most successful new conference”. This year it was being held at my almer mater, University of Strathclyde in Glasgow.

The day was split into three sessions where a series of talks from a mixture of speakers across academia and industry shared stories of good practice in drug discovery. A variety of disease areas were covered. Prof. Stuart Conway (University of Oxford) gave a useful overview of epigenetics and bromodomain ligands. Malaria was covered by Prof. Ian Gilbert of the University of Dundee’s Drug Discovery Unit, although I was surprised to learn the project had progressed as far as it had given no mode of action for their compounds had been elucidated due to the complex nature of the malaria disease pathway.

During the lunchbreak and afternoon coffee session I was able to chat to attendees about the poster I had brought which summarised some of my PhD project involving the development of kinase inhibitors to target cancer via a synthetic lethality strategy. It was interesting discussing with other students attending with posters how they had prepared similar benzimidazole compounds to mine but for entirely different disease targets.

While I was a tad concerned there would be no conversation around kinases given the general nature of a number of the talk titles, Dr. Iain Simpson of AstraZeneca shared the story of the optimisation of a compound for a kinase project that, serendipitously, had the same core as one of my own series. It was very useful to speak to him afterwards to compare notes on dialling in selectivity for our respective targets.

There were also more generic talks about the industry and techniques for improving the efficiency of the laborious drug discovery process. Prof. Adam Nelson (University of Leeds) commented on the small pool of reactions most medicinal chemists use for preparing libraries (amide formation, Pd-catalysed couplings, alkylations etc.). He also shared a way of finding bioactive compounds faster without having to purify every single reaction run in the lab by assaying crude reaction mixtures and then only scaling up and purifying reactions with bioactive components, which he termed “activity-directed synthesis”, a rather intriguing method but must have limitations given the toxicity of many chemical reagents!

Dr. Craig Johnstone (Evotec) gave examples of case studies where focussing on multiple drug parameters at once had increased the productivity of a number of his company’s projects, instead of the scenic route most projects take to optimise individual physiochemical properties. He spoke about the potential of artificial intelligence to predict what reactions would work before heading into the lab and he also took an opportunity to justify his theory that every good drug candidate has a logD of around 2.

To round up the day, the memoriam McGuigan Lecture was given by Prof. Chris Schofield (University of Oxford) who took us on a whistle-stop tour of the development of inhibitors of serine and Zn(II) dependent beta-lactamases and their involvement in combating bacterial resistance. Due to the unfortunate absence of Dr. Nicole Hamblin from Charles River laboratories (the only female speaker in the programme I might add), an alternative Q&A was held with a panel of the speakers from the day. Attendees asked a number of questions around the feasibility of artificial intelligence in drug discovery, the importance of specialisation for career progression and a particularly useful discussion point in their careers often have to keep moving around the country, jumping from project to project, before a rare long-term lectureship or industry post arises, if at all. The panel sympathised with this view and mainly came to the conclusion that the way research is funded needs to change to give scientists more sustainable livelihoods.

I found the symposium to be a very useful experience, particularly as it was aimed at early career researchers such as myself. It was nice to be back in Scotland for a bit and I would hope to attend future Mastering MedChem meetings.

Blog written by Fiona Scott, PhD researcher, Sussex Drug Discovery Centre

 

Homocysteine

This publication written by Dong-Mei Zhang et al ¹ describes the investigation of serum homocysteine and its role in cognitive impairment.

The authors investigate hyperhomocysteinemia (increased levels of serum homocysteine) and its association with an increased risk of cognitive impairment. According to Sudha Seshadri et al ², an increased plasma homocysteine level is a strong independent risk factor for the development of dementia and Alzheimer’s disease, where they say that the risk of developing Alzheimer disease doubles with a plasma homocysteine level greater than 14 μmol/L, as well as being at major risk of suffering from coronary artery disease. Homocysteine levels increase with age, with common adult levels in Western populations being 10 to 12 μmol/L.

The graph below is taken from Axis-Shield ⁵ who carry out in-vitro diagnostic testing. The graph shows that the higher the homocysteine level, the greater the risk of developing coronary artery disease/Alzheimer’s disease.

Homocysteine is a homologue of the amino acid cysteine, with an extra methylene bridge. It is biosynthesised in the body through metabolism of methionine (S-demethylation). Metabolism of homocysteine is aided by vitamin B12, folic acid and vitamin B6. As well as other factors, deficiencies in these vitamins may increase serum homocysteine levels. Hyperhomocysteinemia can contribute to a greater risk of developing diseases such as cardiovascular diseases/retinal vascular disease as well as neuropsychiatric diseases. B vitamins have therefore been considered as a possible option to reduce the risk of Alzheimer’s and dementia.

The authors wanted to address the discrepancy in the existing literature on the therapeutic effect of vitamin B and folates in patients with significant cognitive deficits, secondary to Alzheimer’s disease or dementia. They carried out a meta-analysis of randomized controlled trials in elderly patients with poor cognitive ability secondary to Alzheimers or dementia, who received homocysteine lowering B vitamins supplements and had serum homocysteine levels reported.

Existing evidence on vitamin B supplement induced reduction of cognitive decline by lowering homocysteine levels is conflicting. The authors mention other studies where daily folic acid supplements were taken by people with folate deficency (Durga et al ³, 800μg/d for 3 years and Fioravanta et al ⁴ 15mg/d for 60 days), showing an improved cognitive performance. These results were not included in the meta-analysis because the serum homocysteine levels were not reported.

The overall results suggested that folate in combination with vitamin B12 and/or B6 supplements failed to offer any significant advantage in slowing down or preventing the progression of cognitive decline, although Vitamin B supplements were shown to significantly reduce homocysteine levels.

Blog written by Kamlesh Bala

1 Dong-Mei Zhang et al, Journal of Geriatric Psychiatry and Neurology 2017, Vol 30 (1) 50-59

2 Seshadri S, Beiser A, Selhub J et al, N Engl J Med 2002; 346:476-483

3. Durga J et al, Lancet. 2007; 369 (9557): 208-216

4. Fioravanti M et al, Arch Gerontol Geriatr. 1998; 26 (1): 1-13

5. http://www.homocysteine.co.uk/measuring-homocysteine/

Keywords: Hyperhomocysteinemia, homocysteine, cognitive decline

Modular C(sp2)-C(sp3) radical cross-coupling with PT-sulfones

The construction of sp²-sp³ carbon-carbon bonds is sometimes not trivial with standard two-electron coupling reactions such as Suzuki, Heck and Negishi. To offer chemists alternative options to form these types of carbon-carbon bonds the Baran Lab have been working on metal-catalysed radical cross-coupling (RCC) reactions. I have previously written about some of their earlier work in this area. In their latest paper (Science 360, 75 –80 (2018)) they write about their discovery that a redox-active phenyl-tetrazole (PT) sulfone could be used in these RCC reactions (figure 1).

Figure 1

Some PT-sulfone reagents are commercially available but others such as (3f) can be easily made from phenyl-tetrazole (6) by sulfur-carbon bond formation (alkyl halide displacement or Mitsunobu) and then oxidation of the resulting sulphide (mCPBA or ammonium molybdate/hydrogen peroxide). These sulfones are useful building blocks and can be used in RCC reactions as they are or further functionalised such as α-alkylation or α-fluorination (figure 2).

Figure 2

A small set of fluorinated sulfone building blocks (8-11) were used to introduce mono/di-fluoromethyl and mono/di-fluoroethyl moieties onto a selection of aromatics (figure 3). Unfortunately, these reaction conditions are not able to install a trifluoromethyl group. Sulfones (7-11) are not currently commercially available but 100-500 mg quantities can be requested directly from the Baran lab via this link.

Figure 3

Baran ran a series a competition experiments and under these reaction conditions observed the following reactivity trend Cl/Br < SO₂PT < NHPI/TCNHPI (figure 4). This observation was tested with sequential chemoselective RCC reactions. Firstly, a decarboxylative cross-coupling (DCC) was performed followed by a desulfonylative cross-coupling (SCC) (figure 5).

Figure 4

Figure 5

These new reagents and chemoselective reaction conditions offer a simple and general method to add to the tool box of sp²-sp³ carbon-carbon bond forming reactions. The ability to diversify and fluorinate a common building block will increase the interest from medicinal chemists as will the ability to introduce simple alkyl fluorides without the use of harsh reaction conditions or toxic reagents.

Blog written by Lewis Pennicott

 

A Brief Comparison of Microscale Thermophoresis (MST) and Isothermal Titration Calorimetry (ITC)

1.  MST

MST assay is based on thermophoresis, the directed movement of molecules in a temperature gradient induced by an infrared laser. Thermophoresis is highly sensitive to all types of binding-induced changes, such as size, charge, hydration shell or conformation, which allows for a precise quantification of molecular events. (Jerabek-Willemsen, André et al. 2014) Initial, the molecules are distributed homogenously with an initial fluorescence signal. When the IR laser is activated, the fluorescent signal is decreased as a ‘T-Jump’ form. With the turnoff of IR-laser, the molecule diffusion is back, solely driven by mass diffusion. The trace difference between a fluorescent molecule binding with or without non-fluorescent ligands indicates a binding signal. (Figure 1)

MST can handle weak conformation change on binding of two molecules in different buffer system, such as biological liquids. Another advantage is low sample consumption and MST can measure dissociation constants from pM to mM. (Wienken, Baaske et al. 2010, Jerabek-Willemsen, André et al. 2014) However, in most of cases, the sample should be labeled with hydrophobic fluorophores which would probably cause non-specific binding effects. (Table 1)

 

Figure 1. MST setup and experiments. A. The machine Monilith NT. 115 from NanoTemper Technologies GmbH. B. Schematic representation of MST optics. C. Typical signal of a MST experiment. D. Typical binding experiment.(Jerabek-Willemsen, André et al. 2014)

2. ITC

ITC is a biophysical technique to measure the heat exchange associated with molecular interactions at a constant temperature. (Duff Jr, Grubbs et al. 2011, Milev 2013) It directly determines the binding affinity (Ka), enthalpy changes (ΔH), and binding stoichiometry (n) of the interaction between two or more molecules in solution. The experimental methodology involves performing several titrant injections from a syringe (usually the ligand) into the solution (usually the macromolecule) in the cell, while maintaining the system at isobaric, quasi-isothermal conditions. When the ligands are injected to the cells, the ligands bind to macromolecules and the machine detects the heat upon binding. With several injections, ligands bound to protein continually. However, when the target protein becomes saturated with the ligand, less binding occurs and the heat change starts to decrease. If the macromolecule is saturated with ligand, no more binding occurs, and only heat of dilution is observed.

ITC has been widely applied as a major tool in drug discovery fields, validating and optimizing the hits (Leavitt and Freire 2001, Peters, Frasca et al. 2009) and also in binding studies, such as protein-protein, protein-DNA, small molecule-protein interactions(de Azevedo, Walter et al. 2008, Liang 2008). ITC is a fast and straight way to detect binding affinity of two molecules by the change of binding enthalpy. However, some complexes may exhibit rather small binding enthalpies that are not suitable for the ITC measurement. (Table 1)

	MST	ITC
Advantages	²   Small sample size ²   Immobilization free ²   Minimal contamination of the sample ²   Ability to measure complex mixtures ²   Wide size range for interactants (ions to MDa complexes)	²   Ability to determine thermodynamic binding parameters in a single experiment ²   Modification of binding partners are not required
Disadvantages	²   Hydrophobic fluorescent labelling required, may cause non-specific binding ²   No kinetic information ²   Highly sensitive to any change in molecular properties	²   Large sample quantity needed ²   Kinetics cannot be determined ²   Limited range for consistently measured binding affinities ²   Non-covalent complexes may exhibit rather small binding enthalpies since signal is proportional to the binding enthalpy ²   Not suitable for HTS

Table 1. Advantages and disadvantages of ITC and MST.

3. Ligand-protein binding affinities detected by ITC and MST

One example is about comparing the biophysical data of small molecules with Protein kinase CK2 using both MST and ITC assays.(Winiewska, Bugajska et al. 2017) In this paper, the interactions of four halogenated benzotriazoles with the catalytic subunit of human protein kinase CK2 had been investigated. Among the four compounds, only one compound (5-BrBt) had a consistent binding affinity data in both MST and ITC assays, the solubility of which substantially exceeded the ligand concentration. For another three compounds, when the compounds titrated to the protein solution for ITC measurement, the binding affinities determined by ITC were around 10-folded weaker than by MST. The main problem was the limited titrant solubility that resulted in the formation of nano-aggregates. The issue was ignored by titrating the protein to the compound solution as the protein was soluble enough. (Figure 2) The protein-ligand affinities that derived from ITC may be underestimated because of the compound solubility problem, while the problem can be avoided by MST. 

Figure 2. Correlation between MST- and ITC-derived binding affinities determined for complexes of halogenated benzotriazoles with hCK2α. K_d(ITC), were obtained with ITC experiment, in which either inhibitor (red) or protein (blue) was used as a titrant. Vertical and horizontal bars represent standard deviation (MST) and 67% confidence intervals (ITC), respectively.(Winiewska, Bugajska et al. 2017)

Blog written by Xiangrong (Tina) Chen

de Azevedo, J., F. Walter and R. Dias (2008). “Experimental approaches to evaluate the thermodynamics of protein-drug interactions.” Current drug targets 9(12): 1071-1076.

Duff Jr, M. R., J. Grubbs and E. E. Howell (2011). “Isothermal titration calorimetry for measuring macromolecule-ligand affinity.” J Vis Exp 55: e2796.

Jerabek-Willemsen, M., T. André, R. Wanner, H. M. Roth, S. Duhr, P. Baaske and D. Breitsprecher (2014). “MicroScale Thermophoresis: Interaction analysis and beyond.” Journal of Molecular Structure 1077: 101-113.

Leavitt, S. and E. Freire (2001). “Direct measurement of protein binding energetics by isothermal titration calorimetry.” Current opinion in structural biology 11(5): 560-566.

Liang, Y. (2008). “Applications of isothermal titration calorimetry in protein science.” Acta biochimica et biophysica Sinica 40(7): 565-576.

Milev, S. (2013). “Isothermal titration calorimetry: Principles and experimental design.” General Electric 9.

Peters, W. B., V. Frasca and R. K. Brown (2009). “Recent developments in isothermal titration calorimetry label free screening.” Combinatorial chemistry & high throughput screening 12(8): 772-790.

Wienken, C. J., P. Baaske, U. Rothbauer, D. Braun and S. Duhr (2010). “Protein-binding assays in biological liquids using microscale thermophoresis.” Nature communications 1: ncomms1093.

Winiewska, M., E. Bugajska and J. Poznański (2017). “ITC-derived binding affinity may be biased due to titrant (nano)-aggregation. Binding of halogenated benzotriazoles to the catalytic domain of human protein kinase CK2.” PloS one 12(3): e0173260.