MULTIPLE SCLEROSIS

IAG’s team contributes into the design and execution of clinical trials. Specifically, in Multiple Sclerosis (MS), with the increased number of available immunotherapies, the therapeutic strategy has shifted from a “relapse-prevention” approach to a personalized provision of medical care. In turn, drug developers assign higher relevance to patient-reported outcomes and use surrogate measures that can serve as predictive markers for individual treatment response.

New standards for planning clinical trials take into account MS pathophysiology and choose to reply on endpoints that can support earlier assessment of drug efficacy or mor reliable patient stratification.

Advances in Imaging Techniques in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic autoimmune, inflammatory neurological disorder that affects the brain and spinal cord.¹ MS damages the myelin sheath around the axons of the brain and Spinal cord and causes demyelination of the CNS structure. ²

MS International federation estimates that there are around 2.8 million people in the world living with MS and this number is likely to be higher as many people with MS remain undiagnosed in some parts of the world. MS affects two to three times as many women as men, indicating the role of hormones in the disease progression.³

Patient Selection Strategies / Clinical Trials Endpoints: Timely diagnosis of MS is critical as delayed diagnosis might lead to increase in disability, worsening of disease activity and increased relapses in patients leading to lower quality of life. Brain imaging is primarily used for the MS diagnosis and measuring disease activity, however, few other biomarkers and tools including EDSS score assessment are also utilized to measure disability and assess the progression of the disease.

Magnetic Resonance Imaging (MRI): MRI has emerged as a key diagnostic for MS and plays a vital role in assessing the prognosis and response to the treatment. MRI uses magnetic field and radio waves to generate detailed brain and spinal cord images. It detects the Focal white matter (WM) lesions that are the pathological hallmark of the disease and show relation to clinical disability.

The characteristic abnormalities of MS in the brain consist of multiple white matter lesions with high signal intensity on fluid attenuation inversion recovery and proton density (PD)-weighted image (WI), and T2-WI and low signal intensity (SI) on T1-WI.⁵

1. T-1 weighted without gadolinium- may show dark areas that show areas of permanent nerve damage.
2. T-1 weighted with gadolinium- may show a bright area that show areas of active inflammation.
3. T-2 weighted- show overall disease burden or lesion load (The total number of lesions, both old and new).

There are certain limitations, though, to the traditional MRI process including a weak association with clinical status and the lack of sensitivity to other clinically relevant findings, such as grey-matter disease and diffuse damage throughout the white matter.

To overcome this, we often combine lesion-based measures with advanced MRI measures of tissue integrity, such as Proton magnetic resonance spectroscopy, diffusion imaging, and magnetization transfer imaging, using voxel-wise probability maps and spatial distribution approaches.

Expanded Disability Status Scale (EDSS) measures and quantifies the overall disability in MS patients. The EDSS provides a total score on a scale range from 0 to 10 in 0.5-unit increments that represent a higher level of disability and ‘0’ indicates normal neurological examination, while ‘10’ indicates death due to MS.^7,8

In addition to the existing imaging methods, there are newer biomarkers emerging in MS such as Neurofilaments.  Neurofilaments are cytoskeletal components of neurons that are particularly abundant in axons. Following axonal damage in the central nervous system (CNS), neurofilament proteins released into the cerebrospinal fluid (CSF) indicate axonal damage and neuronal death. Neurofilaments were first used as markers of neuronal damage in a study of 12 patients with amyotrophic lateral sclerosis (ALS). Neurofilaments were found in the CSF of 60 patients with relapsing-remitting multiple sclerosis (RRMS), suggesting these proteins could also be used as a biomarker of MS disease activity.  Neurofilament research is rapidly expanding and neurofilament levels are under investigation as markers of disease activity and progression other neurological disorders including stroke. ⁶

In clinical trials, advances in imaging and advanced imaging can improve the detection of the extent and severity of the disease.  The advanced neuroimaging techniques allow to detect MS in the early stages and provide new possibilities for understanding MS pathogenesis. Trial planning and selection of the optimal imaging strategy is critical to the success of the drug development programs.

IAG’s team has deep understanding of challenges associated with design and execution of MS trials.

We understand that optimal clinical trial design is crucial. Chosen imaging modality and associated image analysis will help to prove the efficacy of the therapy.

We will recommend the optimal imaging and help selecting the trial endpoints.  Once the trial is designed, IAG’s team will select and train the sites, assist with imaging data collection and review.

Reach out to our expert team, as you are designing and planning your trial. <imaging.experts @ ia-grp.com>

About IAG, Image Analysis Group

IAG is a unique partner to life sciences companies developing new treatment and driving the hope of the up-coming precision medicine. IAG leverages expertise in medical imaging and the power of DYNAMIKA™, our proprietary cloud-based platform, to de-risk clinical development and deliver lifesaving therapies into the hands of patients much sooner. IAG provides early drug efficacy assessments, smart patient recruitment and predictive analysis of advanced treatment manifestations, thus lowering investment risk and accelerating study outcomes.

Acting as imaging Contract Research Organization, IAG’s experts also recognize the significance of a comprehensive approach to asset development. They actively engage in co-development projects with both private and public sectors, demonstrating a commitment to cultivating collaboration and advancing healthcare solutions.

Contact our expert team: imaging.experts@ia-grp.com

Publications by IAG’s team:

1. Dehmeshki, J., Chard, D.T., Leary, S.M., Watt, H.C., Silver, N.C., Tofts, P.S., Thompson, A.J.and Miller, D.H. (2003) The normal appearing grey matter in primary progressive multiple sclerosis. A magnetisation transfer imaging study. Journal of Neurology, 250(1), pp. 67-74. ISSN (print) 0340-5354
2. Griffin, C.M., Dehmeshki, Jamshid, Chard, D.T., Parker, G.J., Barker, G.J., Thompson, A.J.and Miller, D.H. (2002) T1 histograms of normal-appearing brain tissue are abnormal in early relapsing-remitting Multiple Sclerosis. Multiple Sclerosis, 8(3), 211-216 (6). ISSN (print) 1352-4585
3. Dehmeshki, J., Barker, G.J.and Tofts, P.S. (2002) Classification of disease subgroup and correlation with disease severity using magnetic resonance imaging whole-brain histograms: application to magnetization transfer ratios and multiple sclerosis. IEEE Transactions on Medical Imaging, 21(4), pp. 320-331. ISSN (print) 0278-0062
4. Dehmeshki, J, Van Buchem, M, Bosma, G P T, Huizinga, T W Jand Tofts, P S (2002) Systematic lupus erythematosus: diagnostic application of magnetization transfer ratio histograms in patients with neuropsychiatric symptoms – initial results. Radiology, 222(3), pp. 722-728. ISSN (online) 0033-8419
5. Traboulsee, A., Dehmeshki, Jamshid, Brex, P.A., Dalton, C.M., Chard, D.T., Barker, G.J., Plant, G.T.and Miller, D.H. (2002)
6.  Normal-appearing brain tissue MTR histograms in clinically isolated syndromes suggestive of MS. Journal of Neurology, 59(1), pp. 126-128. ISSN (print) 0028-3878
7. Dehmeshki, J., Ruto, A.C., Arridge, S., Silver, N.C., Miller, D.H.and Tofts, P.S. (2001) Analysis of MTR histograms in multiple sclerosis using principal components and multiple discriminant analysis. Magnetic Resonance in Medicine, 46(3), pp. 600-609. ISSN (print) 0740-3194
8. Dehmeshki, J., Silver, N.C., Leary, S.M., Tofts, P.S., Thompson, A.J.and Miller, D.H. (2001) Magnetisation transfer ratio histogram analysis of primary progressive and other multiple sclerosis subgroups. Journal of the Neurological Sciences, 185(1), pp. 11-17. ISSN (print) 0022-510X
9. Dehmeshki, Jamshid(1999) An adaptive segmentation and 3-D visualisation of the lung. Pattern Recognition Letters, 20(9), pp. 919-926. ISSN (print) 0167-8655
10. Hatfield, F.N.and Dehmeshki, J. (1998) Automatic delineation and 3-D visualisation of the human ventricular system using probabilistic neural networks. Proceedings – SPIE the International Society for Optical Engineering, 3409, pp. 361-367. ISSN (print) 0361-0748
11. Nikravanshalmani, A., Qanadli, S., Ellis, T., Crocker, M., Ebrahimdoost, F., Karamimohammadi, M.and Dehmeshki, J. (2011) Three-dimensional semi-automatic segmentation of intracranial aneurysms in CTA. In: The 10th IEEE International Conference on Information Technology and Applications in Biomedicine ( ITAB 2010); 3-5 Nov 2010, Corfu, Greece. ISBN 9781424465590
12. Parker, G.J.and Dehmeshki, J. (2002) A level set approach to determining brain region connectivity. In: IWISPA 2000. Proceedings of the First International Workshop on Image and Signal Processing and Analysis. in conjunction with 22nd International Conference on Information Technology Interfaces; 14-15 Jun 2000, Pula, Croatia. (Proceedings of the First International Workshop on Image and Signal Processing and Analysis) ISBN 9539676924
13. Traboulsee, A., Dehmeshki, J., Barker, G.J.and Miller, D.H. (2002) Normal appearing brain tissue MTR abnormalities in patients with clinically isolated syndromes and early multiple sclerosis. In: ISRM 10th Scientific Meeting and Exhibition; 18-24 May 2002, Honolulu, USA.
14. Toffs, Paul Stephen, Steens, Sca, Dehmeshki, Jamshid, Hofman, Paul, Van Waesberghe, Jan Heinand Van Buchem, Mark (2001) Matching MTR histograms for multi-centre studies. In: ISMRM & ESMRMB Joint Annual Meeting 2001; 21 – 27 Apr 2001, Glasgow, U.K..
15. Dehmeshki, Jamshid, Bosma, G.P.Th., Rood, M.J., Huizinga, T.W.J., Van Buchem, Markand Tofts, Paul Stephen (2001) A diagnostic analysis of Magnetization Transfer Ratio (MTR) histograms in Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) patients. In: ISMRM & ESMRMB Joint Annual Meeting 2001; 21 – 27 Apr 2001, Glasgow, U.K.. (Unpublished)
16. Dehmeshki, Jamshid, Tofts, Paul Stephen, Leary, S. M., Thompson, Alan P.and Miller, David H. (2001) Improved correlation of MRT histograms with disability in primary progressive and relapsing onset Multiple Sclerosis using Principle Component Analysis. In: ISMRM & ESMRMB Joint Annual Meeting 2001; 21 – 27 Apr 2001, Glasgow, U.K.. (Unpublished)
17. Dehmeshki, J., Ruto, A.C., Parker, G.J.M., Arridge, S., Miller, D.H.and Tofts, P.S. (2000) Detection and classification of MS using magnetisation transfer ratio images. In: First International Workshop on Image and Signal Processing and Analysis; 14 – 15 June 2000, Pula, Croatia. ISBN 9539676924
18. Hatfield, Fraser N.and Dehmeshki, Jamshid (1998) Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks. In: Electronic Imaging: Processing, Printing, and Publishing in Color; 18 May 1998, Zurich, Switzerland.