Comparison of the modes of action of Apremilast and Roflumilast

The phosphodiesterase 4 (PDE4) family are cAMP specific phosphodiesterases that play an important role in the inflammatory response and is the major PDE type found in inflammatory cells. A significant number of PDE4 specific inhibitors have been developed and are currently being investigated for use as therapeutic agents. Apremilast, a small molecule inhibitor of PDE 4 is in development for chronic inflammatory disorders and has shown promise for the treatment of psoriasis, psoriatic arthritis as well as other inflammatory diseases. It has been found to be safe and well tolerated in humans and in March 2014 it was approved by the US food and drug administration for the treatment of adult patients with active psoriatic arthritis. The only other PDE4 inhibitor on the market is Roflumilast and it is used for treatment of respiratory disease. Roflumilast is approved in the EU for the treatment of COPD and was recently approved in the US for treatment to reduce the risk of COPD exacerbations. Roflumilast is also a selective PDE4 inhibitor, administered as an oral tablet once daily, and is thought to act by increasing cAMP within lung cells. As both (Apremilast and Roflumilast) compounds selectively inhibit PDE4 but are targeted at different diseases, there is a need for a clear understanding of their mechanism of action (MOA). Differences and similarity of MOA should be defined for the purposes of labelling, for communication to the scientific community, physicians, and patients, and for an extension of utility to other diseases and therapeutic areas. In order to obtain a complete comparative picture of the MOA of both inhibitors, additional molecular and cellular biology studies are required to more fully elucidate the signalling mediators downstream of PDE4 inhibition which result in alterations in pro- and anti-inflammatory gene expression. My studies were conducted to directly compare Apremilast with Roflumilast, in order to substantiate the differences observed in the molecular and cellular effects of these compounds, and to search for other possible differentiating effects. Therefore the main aim of this thesis was to utilise cutting-edge biochemical techniques to discover whether Apremilast and Roflumilast work with different modes of action. In the first part of my thesis I used novel genetically encoded FRET based cAMP sensors targeted to different intracellular compartments, in order to monitor cAMP levels within specific microdomains of cells as a consequence of challenge with Apremilast and Roflumilast, which revealed that Apremilast and Roflumilast do regulate different pools of cAMP in cells. In the second part of my thesis I focussed on assessing whether Apremilast and Roflumilast cause differential effects on the PKA phosphorylation state of proteins in cells. I used various biochemical techniques (Western blotting, Substrate kinase arrays and Reverse Phase Protein array and found that Apremilast and Roflumilast do lead to differential PKA substrate phosphorylation. For example I found that Apremilast increases the phosphorylation of Ribosomal Protein S6 at Ser240/244 and Fyn Y530 in the S6 Ribosomal pathway of Rheumatoid Arthritis Synovial fibroblast and HEK293 cells, whereas Roflumilast does not. This data suggests that Apremilast has distinct biological effects from that of Roflumilast and could represent a new therapeutic role for Apremilast in other diseases. In the final part of my thesis, Phage display technology was employed in order to identify any novel binding motifs that associate with PDE4 and to identify sequences that were differentially regulated by the inhibitors in an attempt to find binding motifs that may exist in previously characterised signalling proteins. Petide array technology was then used to confirm binding of specific peptide sequences or motifs. Results showed that Apremilast and Roflumilast can either enhance or decrease the binding of PDE4A4 to specific peptide sequences or motifs that are found in a variety of proteins in the human proteome, most interestingly Ubiquitin-related proteins. The data from this chapter is preliminary but may be used in the discovery of novel binding partners for PDE4 or to provide a new role for PDE inhibition in disease. Therefore the work in this thesis provides a unique snapshot of the complexity of the cAMP signalling system and is the first to directly compare action of the two approved PDE4 inhibitors in a detailed way.

Biochemical and epidemiological investigations of non-steroidal anti-inflammatory drug usage and related side effects in equids

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used in equine veterinary practice. These drugs exert their effect by inhibiting cyclooxygenase (COX) enzymes, which control prostaglandin production, a major regulator of tissue perfusion. Two isoforms of COX enzymes exist: COX-1 is physiologically present in tissues, while COX-2 is up-regulated during inflammation and has been indicated as responsible for the negative effects of an inflammatory response. Evidence suggests that NSAIDs that inhibit only COX-2, preserving the physiological function of COX-1 might have a safer profile. Studies that evaluate the effect of NSAIDs on COX enzymes are all performed under experimental conditions and none uses actual clinical patients. The biochemical investigations in this work focus on describing the effect on COX enzymes activity of flunixin meglumine and phenylbutazone, two non-selective COX inhibitors and firocoxib, a COX-2 selective inhibitor, in clinical patients undergoing elective surgery. A separate epidemiological investigation was aimed at describing the impact that the findings of biochemical data have on a large population of equids. Electronic medical records (EMRs) from 454,153 equids were obtained from practices in the United Kingdom, United States of America and Canada. Information on prevalence and indications for NSAIDs use was extracted from the EMRs via a text mining technique, improved from the literature and described and validated within this Thesis. Further the prevalence of a clinical sign compatible with NSAID toxicity, such as diarrhoea, is reported along with analysis evaluating NSAID administration in light of concurrent administration of other drugs and comorbidities. This work confirms findings from experimental settings that NSAIDs firocoxib is COX-2 selective and that flunixin meglumine and phenylbutazone are non-selective COX inhibitors and therefore their administration carries a greater risk of toxicity. However the impact of this finding needs to be interpreted with caution as epidemiological data suggest that the prevalence of toxicity is in fact small and the use of these drugs at the labelled dose is quite safe.

Applied evolution: An integrated approach to studying life history traits in response to drug selection

The use of chemical control measures to reduce the impact of parasite and pest species has frequently resulted in the development of resistance. Thus, resistance management has become a key concern in human and veterinary medicine, and in agricultural production. Although it is known that factors such as gene flow between susceptible and resistant populations, drug type, application methods, and costs of resistance can affect the rate of resistance evolution, less is known about the impacts of density-dependent eco-evolutionary processes that could be altered by drug-induced mortality. The overall aim of this thesis was to take an experimental evolution approach to assess how life history traits respond to drug selection, using a free-living dioecious worm (Caenorhabditis remanei) as a model. In Chapter 2, I defined the relationship between C. remanei survival and Ivermectin dose over a range of concentrations, in order to control the intensity of selection used in the selection experiment described in Chapter 4. The dose-response data were also used to appraise curve-fitting methods, using Akaike Information Criterion (AIC) model selection to compare a series of nonlinear models. The type of model fitted to the dose response data had a significant effect on the estimates of LD50 and LD99, suggesting that failure to fit an appropriate model could give misleading estimates of resistance status. In addition, simulated data were used to establish that a potential cost of resistance could be predicted by comparing survival at the upper asymptote of dose-response curves for resistant and susceptible populations, even when differences were as low as 4%. This approach to dose-response modeling ensures that the maximum amount of useful information relating to resistance is gathered in one study. In Chapter 3, I asked how simulations could be used to inform important design choices used in selection experiments. Specifically, I focused on the effects of both within- and between-line variation on estimated power, when detecting small, medium and large effect sizes. Using mixed-effect models on simulated data, I demonstrated that commonly used designs with realistic levels of variation could be underpowered for substantial effect sizes. Thus, use of simulation-based power analysis provides an effective way to avoid under or overpowering a study designs incorporating variation due to random effects. In Chapter 4, I 3 investigated how Ivermectin dosage and changes in population density affect the rate of resistance evolution. I exposed replicate lines of C. remanei to two doses of Ivermectin (high and low) to assess relative survival of lines selected in drug-treated environments compared to untreated controls over 10 generations. Additionally, I maintained lines where mortality was imposed randomly to control for differences in density between drug treatments and to distinguish between the evolutionary consequences of drug treatment versus ecological processes affected by changes in density-dependent feedback. Intriguingly, both drug-selected and random-mortality lines showed an increase in survivorship when challenged with Ivermectin; the magnitude of this increase varied with the intensity of selection and life-history stage. The results suggest that interactions between density-dependent processes and life history may mediate evolved changes in susceptibility to control measures, which could result in misleading conclusions about the evolution of heritable resistance following drug treatment. In Chapter 5, I investigated whether the apparent changes in drug susceptibility found in Chapter 4 were related to evolved changes in life-history of C. remanei populations after selection in drug-treated and random-mortality environments. Rapid passage of lines in the drug-free environment had no effect on the measured life-history traits. In the drug-free environment, adult size and fecundity of drug-selected lines increased compared to the controls but drug selection did not affect lifespan. In the treated environment, drug-selected lines showed increased lifespan and fecundity relative to controls. Adult size of randomly culled lines responded in a similar way to drug-selected lines in the drug-free environment, but no change in fecundity or lifespan was observed in either environment. The results suggest that life histories of nematodes can respond to selection as a result of the application of control measures. Failure to take these responses into account when applying control measures could result in adverse outcomes, such as larger and more fecund parasites, as well as over-estimation of the development of genetically controlled resistance. In conclusion, my thesis shows that there may be a complex relationship between drug selection, density-dependent regulatory processes and life history of populations challenged with control measures. This relationship could have implications for how resistance is monitored and managed if life histories of parasitic species show such eco-evolutionary responses to drug application.