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Title: Biomarkers of response to therapy in Ankylosing Spondylitis
Author: Pathan, Ejaz Mohammed Ishaq
ISNI:       0000 0004 2752 7726
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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Ankylosing Spondylitis (AS) is a chronic inflammatory disorder of the spine which leads to progressive spinal fusion and deformity. With improvements in MRI, this condition is now being recognized earlier. The treatment of this condition so far is limited to physiotherapy, NSAIDs and anti-TNF therapy. The assessment of response to therapy is largely subjective using clinical outcome measures such as the Bath Ankylosing Spondylitis disease activity index (BASDAI). This thesis describes the search for an objective measure of response to therapy in AS. It does so by studying two separate patient cohorts- one receiving anti-TNF therapy and the other receiving a novel oral phosphodiesterase-4 inhibitor, apremilast, in a clinical trial setting. In addition to various clinical outcome measures and laboratory biomarkers, it also explores novel volumetric analysis of bone oedema lesions on MRI and its correlation to clinical indices. The results of this study indicate that apremilast improves clinical indices of response in AS and also modulates bone biomarkers. However, it may do so differently to anti-TNF agents with plasma sclerostin and RANKL: OPG possibly playing important roles in its mechanism of action. This study highlights the fact that different laboratory biomarkers may be modulated differently by different drugs. The novel volumetric analysis developed using Dynamika software showed promise with good correlation to established methods of scoring scans such as Berlin scoring. In particular, a novel biomarker, the product of the volume of the lesion and its intensity correlated well with changes in BASDAI in the anti-TNF cohort. However, there are a number of issues, notably inter-observer variability as well as time required to carry out the analysis, that need to be resolved. This could be done by developing automated regions of interest using this software on the basis of intensity of the lesions, hence providing an objective measure of response to therapy in AS.
Supervisor: Taylor, Peter ; Abraham, Sonya ; Lim, Adrian Kuok Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral