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Expert analysis: New dimensions in our understanding of MS

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By Norman Putzki (MD PhD), global head development neuroscience and gene therapy at Novartis.

Over the last 30 years, remarkable progress has been made in the advancement of treating multiple sclerosis (MS), a potentially disabling disease that is most likely a consequence of complex autoimmune dysfunctions in the periphery ultimately leading to demyelination in the central nervous system (CNS)[1].

Since 1993, a series of insights has led to greater understanding of the biological mechanisms and triggers driving the inflammatory attacks on the CNS.

These translational insights have also driven the development of newer and more effective treatments in MS[2].

Despite these positive developments, not all patients are equally responsive to treatment and a considerable amount of MS patients may still eventually experience disability progression[3].

Researchers are focused on understanding the disease biology and root cause of MS disease progression more comprehensively, with the aim of ultimately halting it completely[4].

We are taking the next steps towards this new frontier: developing new medicines[5] that engage the brain in processes that can protect neuronal damage from the onset, treatments that can reset the immune system for those who have an aggressive disease course, or immune tolerisation therapies that teach the immune system not to attack the nervous system at all.

These are only some of the avenues that are being explored and that have the potential to comprehensively control MS to allow people with MS to maintain a high quality of life for longer.

Over the decades, scientists have deciphered the underlying biology of acute inflammatory flare-ups (attacks, relapses).

It is these flare-ups that characterize the early stages of MS, and what is currently classified as relapsing-remitting multiple sclerosis[6]. However, the idea of relapsing-remitting MS may be a misnomer.

Today, it is well understood that there is no true ‘remission’ in MS. Irreversible changes occur from the onset of MS, often silently, stealthily and without acute symptoms[7].

However, this early disease activity is not benign, and because it still leaves irreversible damage to the brain and spinal cord behind, it is accompanied by neuronal injury and tissue loss from the onset of MS[8].

In this disease, time is brain damage. The cumulative damage to the central nervous system acquired early in the disease is a key determining factor of the later disease course[9] as it contributes to worsening and restricts the patient’s ability to compensate for further damage.

It is also abundantly clear that anti-inflammatory treatment alone is not able to stop progression completely, and patients eventually do progress in their disease in the absence of measurable focal inflammation.

Cracking the biological code of MS disease progression has the potential for three major breakthroughs over the next few decades and could entirely redefine how we diagnose, classify and treat MS.

Firstly, a quantitative understanding of the drivers of MS disease progression is required.

Decades of research have informed us of the pathology and the radiological changes involved in MS disease progression and much progress has been made in identifying potential targets for new treatments.

However, progression appears complex[10], perhaps because we lack a quantitative understanding of the relative importance of these different processes, which prevents us from being able to focus in on the most important drivers of progression.

Are the dominant drivers of MS disease progression the same in all MS patients? If so, could drug development target these main pathways?

Or, alternatively, could there be subgroups of patients who progress due to different underlying biological changes?

If so, this may provide an opportunity to apply precision medicine based on disease subtyping and the development of specific drugs for these more homogenic subgroups of patients at every stage of their disease[11].

This leads directly to the second important breakthrough: We need a unifying understanding of MS disease progression and a pathophysiological characterisation of MS for a more targeted therapeutic approach.

Currently, drug development as well as drug access in the MS space is highly reliant on the accepted clinical classification system (phenotypes), which guides how trials are designed, what populations are included into clinical studies, and ultimately, who can use these drugs based on approved labels.

However, as a sharp limitation to the current approach, the accepted subtypes of MS are descriptions of the clinical disease trajectory, without clear delineation of the underlying disease biology between subtypes.

Decades of research have revealed that none of the four currently accepted disease phenotypes[12] (clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS)) has a biologically distinct profile.

The FDA partially addressed this conundrum by clarifying what ‘relapsing forms’ of MS means to them: CIS, RRMS, and active SPMS.

However, the phenotypical distinction of ‘inactive SPMS’ and PPMS still lacks biological distinction when it comes to disability progression.

Evidence suggests that, instead, the current subtypes represent a disease continuum[13] from patients who have a more relapsing or a more progressive disease course – but in many patients relapses and progression co-occur and similar processes seem at play throughout the spectrum of MS with only quantitative differences[14].

As our biological, data analytical and computational capabilities soar, data-driven approaches to characterise MS based on underlying disease biology will be possible.

Finally, we need a unifying way to identify, measure and predict MS disease progression, taking the totality of symptomatic and asymptomatic changes into account.

We need markers and endpoints that can be used for proof-of-concept and for efficient dose finding studies to screen and select the right therapeutic approaches and doses.

In relapsing MS, focal lesions detectable with MRI scans have provided an accurate means to select the right dose and predict the outcomes of studies that targeted the suppression of clinical relapses[15].

In the future, clinical trial endpoints will be designed towards a more sensitive and more specific detection of MS disease progression.

Potential approaches around fluid, digital and radiological biomarkers[16] may help researchers to identify CNS damage with more sensitivity, measure treatment success and side effects and prognosticate progression more reliably and may ultimately support enabling efficient proof of concept trials to screen more molecules faster.

All three objectives, understanding the quantitative importance of biological drivers of disease progression, a pathophysiological classification of MS disease subtypes, and the development of validated generalisable biomarkers that can reliably detect and/or prognosticate disease progression require analysis of ultra-large multimodal datasets[17].

These datasets need to consist of well characterised clinical disease profiles and neurological assessments collected over many years from hundreds of thousands of patients, together with the corresponding longitudinal radiological and biological information to decipher the answers to the above questions with advanced machine learning techniques.

Decades of MRI data and biological samples from thousands of MS patients in combination with high quality clinical assessments is allowing a consortium of researchers to piece together the puzzle how disability progression and cognitive declines are linked with focal and diffuse damage of the central nervous system and the pathology that causes these changes[18].

Whole brain and regional brain volume loss can be tracked over time and linked to the clinical disease course of the patients.

From these enormous databases, artificial intelligence algorithms can be trained to support diagnosing and classifying MS patients, to help with disease course prognostication and to identify the better treatment options.

It is often said that MS is a disease of ‘a thousand faces’.

However today, thanks to the availability of large data sets and advanced “Big Data” analytics, researchers and biopharmaceutical companies have a new toolbox with the potential to transform care for people living with MS.

It is incumbent upon the experts in the field together with industry partners to achieve the next frontier of MS: identifying unique diagnostic and therapeutic approaches to eventually erase disease progression altogether.

References

[1] Filippi M, Bar-Or A, Piehl F, et al. Multiple sclerosis. Nat Rev Dis Primers. 2018;4(1):43. doi:10.1038/s41572-018-0041-4

[2] De Angelis F, John NA, Brownlee WJ. Disease-modifying therapies for multiple sclerosis. BMJ. Published online November 27, 2018:k4674. doi:10.1136/bmj.k4674

[3] Lublin FD, Häring DA, Ganjgahi H, et al. How patients with multiple sclerosis acquire disability. Brain. 2022;145(9):3147-3161. doi:10.1093/brain/awac016

[4] Attfield KE, Jensen LT, Kaufmann M, Friese MA, Fugger L. The immunology of multiple sclerosis. Nat Rev Immunol. 2022;22(12):734-750. doi:10.1038/s41577-022-00718-z

[5] Yang JH, Rempe T, Whitmire N, Dunn-Pirio A, Graves JS. Therapeutic Advances in Multiple Sclerosis. Front Neurol. 2022;13:824926. doi:10.3389/fneur.2022.824926

[6] McGinley MP, Goldschmidt CH, Rae-Grant AD. Diagnosis and Treatment of Multiple Sclerosis: A Review. JAMA. 2021;325(8):765. doi:10.1001/jama.2020.26858

[7] Cagol A, Schaedelin S, Barakovic M, et al. Association of Brain Atrophy With Disease Progression Independent of Relapse Activity in Patients With Relapsing Multiple Sclerosis. JAMA Neurol. 2022;79(7):682. doi:10.1001/jamaneurol.2022.1025

[8] Pukoli D, Vécsei L. Smouldering Lesion in MS: Microglia, Lymphocytes and Pathobiochemical Mechanisms. IJMS. 2023;24(16):12631. doi:10.3390/ijms241612631

[9] Fuh-Ngwa V, Charlesworth JC, Zhou Y, et al. The association between disability progression, relapses, and treatment in early relapse onset MS: an observational, multi-centre, longitudinal cohort study. Sci Rep. 2023;13(1):11584. doi:10.1038/s41598-023-38415-z

[10] Pozzilli C, Pugliatti M, Vermersch P, et al. Diagnosis and treatment of progressive multiple sclerosis: A position paper. Euro J of Neurology. 2023;30(1):9-21. doi:10.1111/ene.15593

[11] Rotstein D, Montalban X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis. Nat Rev Neurol. 2019;15(5):287-300. doi:10.1038/s41582-019-0170-8

[12] National Multiple Sclerosis Society. Types of MS. Accessed September 27, 2023. https://www.nationalmssociety.org/What-is-MS/Types-of-MS

[13] Kuhlmann T, Moccia M, Coetzee T, et al. Multiple sclerosis progression: time for a new mechanism-driven framework. The Lancet Neurology. 2023;22(1):78-88. doi:10.1016/S1474-4422(22)00289-7

[14] Pitt D, Lo CH, Gauthier SA, et al. Toward Precision Phenotyping of Multiple Sclerosis. Neurol Neuroimmunol Neuroinflamm. 2022;9(6):e200025. doi:10.1212/NXI.0000000000200025

[15] Filippi, M. et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 18, 198–210 (2019) doi: https://doi.org/10.1016/S1474-4422(18)30451-4.

[16] Ziemssen, T., Akgün, K. & Brück, W. Molecular biomarkers in multiple sclerosis. J Neuroinflammation 16, 272 (2019). https://doi.org/10.1186/s12974-019-1674-2.

[17] Engel S, Zipp F. Preventing disease progression in multiple sclerosis—insights from large real-world cohorts. Genome Med. 2022;14(1):41. doi:10.1186/s13073-022-01044-8.

[18] Eshaghi, A., Young, A.L., Wijeratne, P.A. et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun 12, 2078 (2021). https://doi.org/10.1038/s41467-021-22265-2.

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