Compositions and methods for treating chronic active white matter lesions / radiologically isolated syndrome

Abstract

Provided herein are methods of administering a disease-modifying antibody therapy to asymptomatic and / or early stage multiple sclerosis patients, including those with radiologically isolated syndrome, based on identification and / or co-localization of slowly expanding lesions and paramagnetic rim lesions on magnetic resonance images from such patients.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 63 / 174399, filed April 13, 2021, U.S. Provisional Application No. 63 / 320655, filed March 16, 2022, French Provisional Application No. FR2103793, filed April 13, 2021, and International Application No. PCT / US2022 / 024450, filed April 12, 2022. [Background technology]

[0002] Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system, typically characterized by focal lesions in the white matter, and commonly presents a spectrum of clinical phenotypes ranging from relapsing (RMS) and relapsing-remitting (RRMS) to slowly progressive forms. The phenotypic classification of MS into relapsing and primary progressive multiple sclerosis (PPMS) and other forms was defined in 1996 by the US National Multiple Sclerosis Society (NMSS) Advisory Committee on Clinical Trials for Multiple Sclerosis. Notably, however, the proposed distinctions were not based on fundamental, well-established, and well-understood scientific characteristics of the pathophysiology of the various forms. On the contrary, at the time they were proposed, the committee acknowledged that the phenotypic descriptions were subjective opinions of consensus among experts in the field and were not supported by objective biological findings.

[0003] As clinicians have only recently come to appreciate, the differences between these various clinical phenotypes are relative rather than absolute. For example, while some evidence suggests that primary progressive multiple sclerosis (PPMS) represents a non-inflammatory or at least less inflammatory pathological form of MS distinct from secondary progressive multiple sclerosis (SPMS), a wealth of clinical, imaging, and genetic data suggests that PPMS is part of a spectrum of progressive MS phenotypes, and analyses of natural history cohorts have revealed that both SPMS and PPMS progress at similar rates of deterioration. Lublin, Fred D., et al. Defining the Clinical Course of Multiple Sclerosis: The 2013 Revisions. Neurology. 2014 Jul 15; 83(3): 278-86. Thus, there is an increasing recognition in the field that there is considerable overlap between subtypes with respect to clinical subtypes and possible pathophysiology.

[0004] Given this new realization, as well as the inherent subjectivity of common clinical phenotyping, various pathophysiological features based on the underlying biological expression of the disease are gradually gaining attention. There is a common finding that the pathology underlying MS involves both inflammation and neurodegeneration. Lublin et al.Neurology, The 2013 clinical course descriptors for multiple sclerosis: A clarification 2020:94:1-5. doi.10.1212 / WNL.0000000000009636. The relationship between the clinical evolution of the disease and these mechanisms is complex. However, further characterization is required. Ibid.

[0005] One aspect of this concerns the prevalence and spread of white matter lesions based on magnetic resonance imaging (MRI). MRI is the most sensitive imaging technique for detecting MS lesions in vivo, and lesion burden measurements based on conventional T2-weighted MRI are widely used to monitor treatment efficacy in therapeutic trials. Notably, however, there is only a poor correlation between lesion burden on conventional MRI and clinical disability in MS patients, a phenomenon called clinical-radiological dissociation. Seewan et al.,Arch Neurol. 2009;66(5):601-609.doi:10.1001 / archneurol.2009.57. Thus, there remains a clear need for more accurate and informative radiological methods to inform appropriate treatment decisions for patients.

[0006] Individuals with radiologically isolated syndromes (RIS) have incidental MRI abnormalities suggestive of MS. Recent studies using susceptibility-based imaging have revealed that a subgroup of chronic active white matter lesions (CAWML) have a rim of paramagnetic susceptibility-related signal loss at the edge of the lesion, the paramagnetic rim sign (PRS), associated with the presence of iron inside phagocytes indicative of chronic active demyelination. Suthiphosuwan et al.,JAMA Neuro. Paramagnetic Rim Sign in Radiologically Isolated Syndrome March 9,2020 doi:10.1001 / jamaneurol.20200124. Thus, these patients may actually benefit from more aggressive disease-modifying treatment options that are usually reserved for relapsing and progressive forms of MS. Unfortunately, given the historical emphasis on subjective clinical phenotype over pathophysiology in determining and approving available treatments, the RIS patient population in particular has few treatment options and the disease is typically allowed to worsen until clinical symptoms become more evident. Tysabri® (natalizumab) is an anti-very late antigen (VLA)-4 humanized monoclonal IgG4 antibody that inhibits lymphocyte migration across the blood-brain barrier by blocking VLA-4 interaction with vascular cell adhesion molecule (VCAM)-1 and reducing inflammatory lesions. Natalizumab is a biotherapeutic approved for the treatment of relapsing and progressive forms of multiple sclerosis. In US clinical practice, natalizumab is used only in patients who have experienced at least one relapse event as determined by the clinician, whereas in Europe, natalizumab therapy requires the diagnosis of at least one relapse event in addition to the identification of at least one acute lesion (typically defined as an increase in lesion burden / size in T2 or T1 gadolinium-enhancing lesions on MRI). To date, there is no consensus on its potential use in early stage disease, given the risks associated with the treatment as well as the longstanding reliance on clinical phenotypes to drive treatment decisions. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Lublin,Fred D.,et al. Defining the Clinical Course of Multiple Sclerosis:The 2013 Revisions. Neurology.2014 Jul 15;83(3):278-86 [Non-Patent Document 2] Lublin et al.Neurology, The 2013 clinical course descriptors for multiple sclerosis: A clarification 2020:94:1-5. [Non-Patent Document 3] Seewan et al.,Arch Neurol. 2009;66(5):601-609.doi:10.1001 / archneurol.2009.57 [Non-Patent Document 4] Suthiphosuwan et al.,JAMA Neuro. Paramagnetic Rim Sign in Radiologically Isolated Syndrome March 9,2020 Summary of the Invention

[0008] The present disclosure provides methods for treating and / or reducing chronic white matter lesion activity (CWMLA), also referred to as chronic lesion activity (CLA), in patients, including those with radiologically isolated syndrome (RIS), with appropriate disease-modifying therapeutic agents, e.g., anti-VLA-4 antibodies, in patients in need thereof. In particular, the present invention demonstrates that certain symptoms of CWMLA, as visualized by certain magnetic resonance imaging techniques, correlate with disease progression, such that patients with matching radiological markers can be effectively treated with more aggressive disease-modifying antibody therapeutic agents, even in the absence of clinical symptoms (e.g., relapse) traditionally used to justify such therapy. Thus, described and exemplified herein are novel methods for treating early-stage and / or asymptomatic patients (e.g., prior to the onset of the first symptoms of relapse), e.g., RIS patients, based on the presence of novel radiological features of CWMLA.

[0009] In one aspect, the disclosure provides a method for treating radiologically isolated syndrome (RIS) in a patient in need thereof, comprising administering a therapeutically effective amount of a disease-modifying antibody therapy to the patient, wherein the patient has a CWMLA defined by at least one phase rim lesion (PRL) in at least one susceptibility-weighted magnetic resonance image (MRI). In some embodiments, the patient has a CWMLA defined by at least one slowly expanding lesion (SEL). In some embodiments, the patient has a CWMLA defined by at least one SEL detected using single-timepoint non-contrast T1 and T2 weighted MRI.

[0010] In some embodiments, the patient has CWMLA defined by at least one SEL colocalizing with at least one PRL (or vice versa), hi some embodiments, the at least one SEL is detected using single-timepoint non-contrast T1- and T2-weighted MRI.

[0011] In some embodiments, SELs are detected using a machine learning based classifier that uses unweighted T1 / T2 information from a single MRI scan to distinguish between acute and chronic MS lesions and / or between SELs and non-SELs. Advantageously, a single time point brain MRI scan can then be referenced without drug contrast for patients suspected of having RIS or previously determined to have RIS and at high risk of developing MS. The scan can then be input into a classifier algorithm that can identify and differentiate between acute and chronic lesions present in the brain scan and / or between SELs and non-SELs. Based on that identification and differentiation, an appropriate disease modifying antibody therapy can be administered that is appropriate for the particular patient and disease state.

[0012] In some embodiments, one or more features having predictive value for classification of the lesion and / or SEL as either acute or chronic are utilized. In some embodiments, the features are selected from the group including or consisting of: features that quantify the primary intensity of the core region of the lesion as it appears on a T2 weighted scan image; features that quantify the amount of signal that appears as dark gray at the periphery of the lesion as it appears on a T1 weighted scan image; features that quantify the amount of light gray signal present at the periphery and / or core of the lesion as it appears on a T1 weighted scan image; features relating to the heterogeneity present in the image; features relating to the structure of the image, such as the presence of repeating patterns; and features relating to the texture of the image.

[0013] In some embodiments, the disease modifying antibody therapy is selected from natalizumab, BIIB107, and ocrelizumab. In some embodiments, the disease modifying antibody therapy is an anti-VLA-4 antibody, e.g., natalizumab or BIIB107.

[0014] In some embodiments, the anti-VLA-4 antibody is natalizumab administered in a biphasic dosing regimen, wherein the biphasic regimen includes an induction phase comprising administration of natalizumab once a month for about 10 to about 14 months, followed by a chronic phase comprising administration of natalizumab once every 5, 6, 7, or 8 weeks. In some embodiments, the induction phase comprises administration of natalizumab once a month for a period of about 10 months, about 11 months, about 12 months, about 13 months, about 14 months, or greater than about 14 months. In some embodiments, at least one period of the biphasic dosing regimen comprises subcutaneous (SC) administration. In some embodiments, both the induction phase and the chronic phase of the biphasic dosing regimen comprise SC injections. In some embodiments, the induction phase of the biphasic dosing regimen comprises SC injections. In some embodiments, the chronic phase of the biphasic dosing regimen comprises SC injections. In some embodiments, surprisingly, the SC administration and amount of natalizumab may be consistent with IV administration. In some embodiments, the therapeutically effective amount administered during the induction phase and the chronic phase is the same, and the therapeutically effective amount is 250-450 mg (e.g., 250 mg, 300 mg, 350 mg, 400 mg, or 450 mg), more preferably about 300 mg, and even more preferably 300 mg. In some embodiments, the therapeutically effective amount administered SC during the chronic phase is 300-500 mg (e.g., 300 mg, 350 mg, 400 mg, 450 mg, or 500 mg). In some embodiments, the therapeutically effective amount is about 250 to about 450 mg (e.g., about 250 mg, about 300 mg, about 350 mg, about 400 mg, or about 450 mg), more preferably about 300 mg, and even more preferably 300 mg. In some embodiments, the therapeutically effective amount administered SC during the chronic phase is about 300 to about 500 mg (e.g., about 300 mg, about 350 mg, about 400 mg, about 450 mg, or about 500 mg), more preferably about 300 mg, and even more preferably 300 mg.

[0015] In some embodiments, the anti-VLA-4 antibody is natalizumab administered in a chronic dosing regimen, wherein the chronic dosing regimen comprises administration of natalizumab at regular intervals of every 4 weeks. In some embodiments, the chronic dosing regimen is a non-weight-based fixed dose of natalizumab. In some embodiments, the therapeutically effective amount of natalizumab is about 250-450 mg, or about 300 mg. In some embodiments, the therapeutically effective amount of natalizumab is a non-weight-based fixed dose of 300 mg. In further embodiments, the chronic dosing regimen is every 4 weeks for a period of about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, about 12 months, or more than about 12 months. In some embodiments, the chronic dosing regimen comprises SC injection. In some embodiments, the chronic dosing regimen comprises IV administration.

[0016] In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number is identified as PRL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number is identified as SEL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of a patient's SELs are co-localized with the patient's PRL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of a patient's PRLs are co-localized with the patient's SELs.

[0017] In another aspect, the invention provides a method of reducing and / or treating chronic active white matter lesions in asymptomatic and / or early stage MS patients (e.g., no relapses have been diagnosed or meet consensus diagnostic criteria, e.g., Thompson AJ et al. Lancet Neurol. 2018;17:162-173), comprising administering a therapeutically effective amount of a disease-modifying antibody therapy to the patient, wherein the patient has CWMLA defined by at least one phase rim lesion (PRL) in at least one susceptibility-weighted magnetic resonance image (MRI). In some embodiments, the patient has CWMLA defined by at least one slowly expanding lesion (SEL). In some embodiments, the patient has CWMLA defined by at least one SEL detected using single time point non-contrast T1 and T2 weighted MRI.

[0018] In some embodiments, the patient has CWMLA defined by at least one SEL colocalizing with at least one PRL (or vice versa), hi some embodiments, the at least one SEL is detected using single-timepoint non-contrast T1- and T2-weighted MRI.

[0019] In some embodiments, SELs are detected using a machine learning based classifier that uses unweighted T1 / T2 information from a single MRI scan to distinguish between acute and chronic MS lesions and / or between SELs and non-SELs. Advantageously, a single time point brain MRI scan can then be referenced without drug contrast for asymptomatic and / or early stage patients suspected of suffering from a brain disease such as MS or at risk of developing MS. The scan can then be input into a classifier algorithm that can identify and differentiate between acute and chronic lesions present in the brain scan and / or between SELs and non-SELs. Based on that identification and differentiation, an appropriate disease modifying antibody therapy can be administered that is appropriate for the particular patient and disease state.

[0020] In some embodiments, one or more features having predictive value for classification of the lesion and / or SEL as either acute or chronic are utilized. In some embodiments, the features are selected from the group including or consisting of: features that quantify the primary intensity of the core region of the lesion as it appears on a T2 weighted scan image; features that quantify the amount of signal that appears as dark gray at the periphery of the lesion as it appears on a T1 weighted scan image; features that quantify the amount of light gray signal present at the periphery and / or core of the lesion as it appears on a T1 weighted scan image; features relating to the heterogeneity present in the image; features relating to the structure of the image, such as the presence of repeating patterns; and features relating to the texture of the image.

[0021] In some embodiments, the disease modifying antibody therapy is selected from natalizumab, BIIB107, and ocrelizumab. In some embodiments, the disease modifying antibody therapy is an anti-VLA-4 antibody, e.g., natalizumab or BIIB107. In some embodiments, the anti-VLA-4 antibody is natalizumab administered in a biphasic dosing regimen, wherein the biphasic regimen includes an induction phase comprising monthly administration of natalizumab for about 10 to about 14 months, followed by a chronic phase comprising monthly administration of natalizumab every 5, 6, 7, or 8 weeks. In some embodiments, the induction phase comprises monthly administration of natalizumab for a period of about 10 months, about 11 months, about 12 months, about 13 months, about 14 months, or greater than about 14 months. In some embodiments, at least one period of the biphasic protocol comprises subcutaneous (SC) administration. In some embodiments, both the induction phase and the chronic phase of the biphasic dosing regimen include SC injections. In some embodiments, the induction phase of the biphasic dosing regimen includes SC injections. In some embodiments, the chronic phase of the biphasic dosing regimen includes SC injections. In some embodiments, surprisingly, the SC dosing and amount of natalizumab may be consistent with IV dosing. In some embodiments, the therapeutically effective amount administered during the induction phase and the chronic phase is the same, and the therapeutically effective amount is 250-450 mg (e.g., 250 mg, 300 mg, 350 mg, 400 mg, or 450 mg), more preferably about 300 mg, even more preferably 300 mg. In some embodiments, the therapeutically effective amount administered SC during the chronic phase is 300-500 mg (e.g., 300 mg, 350 mg, 400 mg, 450 mg, or 500 mg). In some embodiments, the therapeutically effective amount is about 250 to about 450 mg (e.g., about 250 mg, about 300 mg, about 350 mg, about 400 mg, or about 450 mg), more preferably about 300 mg, and even more preferably 300 mg. In some embodiments, the therapeutically effective amount administered SC during the chronic phase is about 300 to about 500 mg (e.g., about 300 mg, about 350 mg, about 400 mg, about 450 mg, or about 500 mg), more preferably about 300 mg, and even more preferably 300 mg.

[0022] In some embodiments, the anti-VLA-4 antibody is natalizumab administered in a chronic dosing regimen, wherein the chronic dosing regimen comprises administration of natalizumab at regular intervals of every 4 weeks. In some embodiments, the chronic dosing regimen is a non-weight-based fixed dose of natalizumab. In some embodiments, the therapeutically effective amount of natalizumab is about 250-450 mg, or about 300 mg. In some embodiments, the therapeutically effective amount of natalizumab is a non-weight-based fixed dose of 300 mg. In further embodiments, the chronic dosing regimen is every 4 weeks for a period of about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, about 12 months, or more than about 12 months. In some embodiments, the chronic dosing regimen comprises SC injection. In some embodiments, the chronic dosing regimen comprises IV administration.

[0023] In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number is identified as PRL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number is identified as SEL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of a patient's SELs are co-localized with the patient's PRL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of a patient's PRLs are co-localized with the patient's SELs.

[0024] In another aspect, a method is provided for reducing and / or treating chronic white matter lesion activity in asymptomatic and / or early stage MS patients (e.g., no relapse events have been diagnosed), comprising: a) identifying at least one phase rim lesion (PRL) in at least one susceptibility weighted magnetic resonance image from a patient known or suspected to have chronic active white matter lesions; b) identifying one slowly expanding lesion (SEL) in at least one T1-weighted / T2-weighted MRI from the patient; c) determining whether at least one PRL co-localizes with at least one SEL (or vice versa) in the patient; and d) initiating treatment with a disease-modifying antibody therapy if co-localized. In some embodiments, the at least one SEL is detected using single time point non-contrast T1- and T2-weighted MRI. In some embodiments, the disease-modifying antibody therapy is selected from natalizumab, BIIB107, and ocrelizumab. In some embodiments, the disease-modifying antibody therapeutic is an anti-VLA-4 antibody, such as natalizumab or BIIB107.

[0025] In some embodiments, SELs are detected using a machine learning based classifier that uses unweighted T1 / T2 information from a single MRI scan to distinguish between acute and chronic MS lesions and / or between SELs and non-SELs. Advantageously, a single time point brain MRI scan can then be referenced without drug contrast for asymptomatic and / or early stage patients suspected of suffering from a brain disease such as MS or at risk of developing MS. The scan can then be input into a classifier algorithm that can identify and differentiate between acute and chronic lesions present in the brain scan and / or between SELs and non-SELs. Based on that identification and differentiation, an appropriate disease modifying antibody therapy can be administered that is appropriate for the particular patient and disease state.

[0026] In some embodiments, one or more features having predictive value for classification of the lesion and / or SEL as either acute or chronic are utilized. In some embodiments, the features are selected from the group including or consisting of: features that quantify the primary intensity of the core region of the lesion as it appears on a T2 weighted scan image; features that quantify the amount of signal that appears as dark gray at the periphery of the lesion as it appears on a T1 weighted scan image; features that quantify the amount of light gray signal present at the periphery and / or core of the lesion as it appears on a T1 weighted scan image; features relating to the heterogeneity present in the image; features relating to the structure of the image, such as the presence of repeating patterns; and features relating to the texture of the image.

[0027] In some embodiments, the method further comprises administering to the patient a therapeutically effective amount of natalizumab in a biphasic dosing regimen, wherein the biphasic dosing regimen comprises an induction phase comprising administration of the anti-VLA-4 antibody once every 2 weeks, about once every 2 weeks, about once every 4 weeks, about once every 4 weeks, about once every 30 days, about once every 30 days, monthly, or about once a month for at least 6 months, at least 8 months, at least 10 months, or at least 12 months, followed by a chronic phase comprising administration of the anti-VLA-4 antibody once every 5-10 weeks, or once every 5, 6, 7, or 8 weeks. In some embodiments, the induction phase is 6-18 months, 8-16 months, 10-14 months, or 11 months, 12 months, or 13 months. In some embodiments, the induction phase is 12 months and the chronic phase includes administration of natalizumab every 5 weeks, about every 5 weeks, every 6 weeks, about every 6 weeks, every 7 weeks, or about every 7 weeks. In some embodiments, the induction phase is 12 months and the chronic phase includes administration of natalizumab every 6 weeks.

[0028] In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of a patient's SELs are co-localized with the patient's PRL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of a patient's PRLs are co-localized with the patient's SELs. [Brief description of the drawings]

[0029] [Figure 1] Association of CWMLA or total brain volume loss with composite disability progression in the placebo group. Changes in T1LV in (A) SEL, (B) non-SEL, and (C) CNT2 lesions from baseline to week 108 were significantly associated with composite disability progression in SPMS patients. No difference in percentage change in (D) total brain volume loss from baseline to week 108 was observed in SPMS patients with composite disability progression compared to patients without progression. Composite progression was ascertained at week 24 and end of study by one or more of EDSS, Timed 25-Foot Walk, or 9-Hole Peg Test. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. p values ​​are from the Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). CNT2=chronic non-enhanced T2; CWMLA=chronic white matter lesion activity; EDSS=Extended Disability Status Scale; SEL=slowly expanding lesions; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense disease volume.

[0030] [Diagram 2] Association of CWMLA with EDSS progression. Increases in T1LV in (A) SEL and (C) CNT2 lesions, but not in (B) non-SEL, from baseline to week 108 were associated with EDSS progression in placebo-treated SPMS patients (although consistent directional trends were observed in non-SEL). EDSS progression was ascertained at week 24 and end of study. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and by baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). CNT2=chronic non-enhanced T2; CWMLA=chronic white matter lesion activity; EDSS=expanded disability status scale; SEL=slowly expanding lesions; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense disease volume.

[0031] [Diagram 3]Association of CWMLA with 9HPT progression. Changes in T1LV in (A) SEL and (C) CNT2 lesions, but not in (B) non-SEL, from baseline to week 108 were associated with 9HPT progression in placebo-treated SPMS patients (although consistent directional trends were observed in non-SEL). 9HPT progression was confirmed at week 24 and end of study. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and by baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). 9HPT=9-Hole Peg Test; CNT2=chronic non-enhanced T2; CWMLA=chronic white matter lesion activity; EDSS=expanded disability status scale; SEL=slowly expanding lesions; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense lesion volume.

[0032] [Figure 4]Association of CWMLA with T25FW progression. Changes in T1LV from baseline to week 108 in (B) non-SEL and (C) CNT2 lesions, but not in (A) SEL, were associated with T25FW progression in placebo-treated SPMS patients (although consistent directional trends were observed in SEL). T25FW progression was identified at week 24 and end of study. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. p values ​​are from the Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and by baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). CNT2=chronic non-enhanced T2; CWMLA=chronic white matter lesion activity; EDSS=Extended Disability Status Scale; SEL=slowly expanding lesions; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense disease volume; T25FW=Timed 25-Foot Walk.

[0033] [Diagram 5]Association of CWMLA with composite disability progression in the absence of AWMLA. CWMLA in (B) non-SEL and (C) CNT2 lesions remained associated with composite disability progression in the absence of AWMLA in placebo-treated SPMS patients, but (A) SEL did not (although consistent directional trends were observed for SEL). Absence of acute lesion activity was defined as absence of baseline and post-baseline T1 gadolinium enhancement and absence of post-baseline new / enlarging T2 lesions. Composite progression was confirmed by one or more of the following: EDSS, Timed 25-Foot Walk, or 9-Hole Peg Test at 24 weeks and end of study. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box extending to the highest and lowest observations. p values ​​are from the Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). AWMLA = acute white matter lesion activity, also called acute lesion activity (ALA); CNT2 = chronic non-enhanced T2; CWMLA = chronic white matter lesion activity; EDSS = expanded disability status scale; SEL = slowly expanding lesions; SPMS = secondary progressive multiple sclerosis; T1LV = T1 hypointense lesion volume.

[0034] [Figure 6A]Frequency distributions of SEL prevalence and SEL severity with and without AWMLA are shown. The (A) number, (B) absolute volume, and (C) relative volume (percentage of baseline unenhanced T2LV) of SEL were greater in placebo-treated SPMS patients with AWMLA compared to patients without AWMLA. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. Absence of acute lesion activity was defined as the absence of Gd+T1 lesions at baseline or post-baseline, and the absence of new / enlarging T2 lesions at 24, 48, 72, 96, and 108 weeks post-baseline. Acute lesions were defined as baseline Gd+T1 lesions and post-baseline Gd+T1 lesions, and new / enlarging T2 lesions at weeks 24, 48, 72, 96, and 108. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2LV classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). AWMLA=acute white matter lesion activity; BL=baseline; EDSS=expanded disability status scale; Gd+=gadolinium enhancement; SEL=slowly expanding lesions; SPMS=secondary progressive multiple sclerosis; T2LV=T2 hyperintense lesion volume. [Figure 6B]Frequency distributions of SEL prevalence and SEL severity with and without AWMLA are shown. The (A) number, (B) absolute volume, and (C) relative volume (percentage of baseline unenhanced T2LV) of SEL were greater in placebo-treated SPMS patients with AWMLA compared to patients without AWMLA. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. Absence of acute lesion activity was defined as the absence of Gd+T1 lesions at baseline or post-baseline, and the absence of new / enlarging T2 lesions at 24, 48, 72, 96, and 108 weeks post-baseline. Acute lesions were defined as baseline Gd+T1 lesions and post-baseline Gd+T1 lesions, and new / enlarging T2 lesions at weeks 24, 48, 72, 96, and 108. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2LV classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). AWMLA=acute white matter lesion activity; BL=baseline; EDSS=expanded disability status scale; Gd+=gadolinium enhancement; SEL=slowly expanding lesions; SPMS=secondary progressive multiple sclerosis; T2LV=T2 hyperintense lesion volume. [Figure 6C]Frequency distributions of SEL prevalence and SEL severity with and without AWMLA are shown. The (A) number, (B) absolute volume, and (C) relative volume (percentage of baseline unenhanced T2LV) of SEL were greater in placebo-treated SPMS patients with AWMLA compared to those without AWMLA. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. Absence of acute lesion activity was defined as the absence of Gd+T1 lesions at baseline or post-baseline, and the absence of new / enlarging T2 lesions at 24, 48, 72, 96, and 108 weeks post-baseline. Acute lesions were defined as baseline Gd+T1 lesions and post-baseline Gd+T1 lesions, and new / enlarging T2 lesions at weeks 24, 48, 72, 96, and 108. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2LV classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). AWMLA=acute white matter lesion activity; BL=baseline; EDSS=expanded disability status scale; Gd+=gadolinium enhancement; SEL=slowly expanding lesions; SPMS=secondary progressive multiple sclerosis; T2LV=T2 hyperintense lesion volume. [Figure 6D]Frequency distribution of SEL prevalence and SEL severity with and without AWMLA. Frequency distribution of patients by range of SEL prevalence, showing that patients with AWMLA had a higher percentage of total T2 lesion burden identified as SEL compared to patients without AWMLA. In these box plot representations, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. Absence of acute lesion activity was defined as no baseline or post-baseline Gd+T1 lesions and no new / enlarging T2 lesions at 24, 48, 72, 96, and 108 weeks after baseline. Acute lesions were defined as baseline Gd+T1 lesions and post-baseline Gd+T1 lesions, and new / enlarging T2 lesions at 24, 48, 72, 96, and 108 weeks. p values ​​are from the Van Elteren test: stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2LV classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). AWMLA = acute white matter lesion activity; BL = baseline; EDSS = expanded disability status scale; Gd+ = gadolinium enhancement; SEL = slowly expanding lesions; SPMS = secondary progressive multiple sclerosis; T2LV = T2 hyperintense lesion volume.

[0035] [Figure 7]Figure 1 shows the effect of natalizumab on SEL prevalence. Natalizumab reduced the (A) number, (B) absolute volume, and (C) relative volume (percentage of baseline unenhanced T2LV) of SEL in SPMS patients. In the box plot representation, the box spans the interquartile range, the median is marked by a horizontal line within the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. p values ​​are from the Van Elteren test; stratified by baseline Expanded Disability Status Scale score (≦5.5 or ≧6.0) and baseline T2LV classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). SEL=slowly expanding lesion; SPMS=secondary progressive multiple sclerosis; T2LV=T2 hyperintense lesion volume; T2w=T2 weighted.

[0036] [Figure 8A] Figure 1 shows change in CWMLA with natalizumab vs. placebo. Natalizumab reduced CWMLA when measuring (A, B) absolute increase in T1LV in SELs and non-SELs in SPMS patients compared to placebo. Undistributed quartile confidence limits are shown. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). BL=baseline; CI=confidence interval; CWMLA=chronic white matter lesion activity; EDSS=expanded disability status scale; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense lesion volume. [Figure 8B]Figure 1 shows change in CWMLA with natalizumab vs. placebo. Natalizumab reduced CWMLA when measuring (A, B) absolute increase in T1LV in SELs and non-SELs in SPMS patients compared to placebo. Undistributed quartile confidence limits are shown. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). BL=baseline; CI=confidence interval; CWMLA=chronic white matter lesion activity; EDSS=expanded disability status scale; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense lesion volume. [Figure 8C] Changes in CWMLA with natalizumab vs. placebo. Natalizumab reduced CWMLA as measured by (C, D) percentage increase in T1LV in SELs and non-SELs in SPMS patients compared to placebo. Undistributed quartile confidence limits are shown. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). BL=baseline; CI=confidence interval; CWMLA=chronic white matter lesion activity; EDSS=expanded disability status scale; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense lesion volume. [Figure 8D]Changes in CWMLA with natalizumab vs. placebo. Natalizumab reduced CWMLA as measured by (C, D) percentage increase in T1LV in SELs and non-SELs in SPMS patients compared to placebo. Undistributed quartile confidence limits are shown. p values ​​are from Van Elteren test; stratified by baseline EDSS score (≦5.5 or ≧6.0) and baseline T2 lesion volume classification based on tertiles (≦6908.79 mm3, >6908.79-18818.49 mm3, and >18818.49 mm3). BL=baseline; CI=confidence interval; CWMLA=chronic white matter lesion activity; EDSS=expanded disability status scale; SPMS=secondary progressive multiple sclerosis; T1LV=T1 hypointense lesion volume.

[0037] [Figure 9] (A)-(E) Show overlap between SEL and PRL. (A) Schematic of screening and (B) week 72 T1w images showing SEL identified based on changes in pre-existing lesions from screening to week 72. (C) Schematic of simultaneously recorded frequency map at week 72 showing phase rim (PRL) annotation. FLAIR image at week 72 is outlined (C) showing areas within the rim (D) showing T2 lesions associated with PRL. (E) Shows voxel-by-voxel overlap between SEL and T2 lesions associated with PRL (arrows indicate overlapping voxels; also shows voxels present only in SEL and only in T2 lesions associated with PRL).

[0038] [Figure 10] Correlation between the number of SELs and the number of PRLs is shown, with the number of PRLs shown on the x-axis and the number of SELs on the y-axis.

[0039] [Figure 11] (A)-(B) show a comparison of each type of lesion. (A) The size of PRL that is colocalized with SEL and the size of PRL that is not colocalized with SEL are shown. (B) The size of SEL that is colocalized with PRL and the size of SEL that is not colocalized with SEL are shown.

[0040] [Figure 12] (A)-(B) show the evolution of tissue damage within SEL / PRL lesions. Comparison of normalized magnetization transfer ratio (nMTR) trajectories for (A) PRL with and without colocalization with SEL, and (B) SEL with and without colocalization with PRL. (A) shows non-PRL; PRL and SEL; PRL and non-SEL. (B) shows non-SEL; SEL and PRL; SEL and non-PRL. (A) and (B) are calculated as weighted means for each sample (PRL or SEL). Shaded areas represent 95% CI of the mean.

[0041] [Figure 13] (A)-(B) show the evolution of tissue damage within SEL / PRL lesions. Comparison of radial diffusion trajectories is shown. (A) PRL with and without colocalization with SEL and (B) SEL with and without colocalization with PRL are shown. (A) shows non-PRL; PRL and SEL; PRL and non-SEL. (B) shows non-SEL; SEL and PRL; SEL and non-PRL. (A) and (B) are calculated as weighted means for each sample (PRL or SEL). Shaded areas represent 95% CI of the mean.

[0042] [Figure 14] Shown are selected regions of two patches (one SEL, one non-SEL) extracted from a chronic non-enhanced MS lesion on a brain T2MRI scan.

[0043] [Figure 15] (A)-(B) show non-SEL patches extracted from baseline T2 MRI scans showing the core and peripheral regions: (A) unenhanced, (B) showing the core (solid line) and peripheral (dashed line).

[0044] [Figure 16](A)-(C) are diagrams of lesion volume matching between SEL and non-SEL patches (SEL on top, non-SEL on bottom). (A), (B), and (C) each correspond to a volume-matched pair.

[0045] [Figure 17] Prevalence of each of 15 radiological features selected to discriminate between SEL and non-SEL patches is shown. Abbreviations are as follows: t1p: T1-weighted MRI pre-contrast (i.e., features were extracted from non-contrast T1-weighted MRI images); t2w: T2-weighted MRI (i.e., features were extracted from T2-weighted MRI images); core / periphery: specifies whether the feature was computed within the core or peripheral region; glrlm: gray-level run-length matrix; glcm: gray-level co-occurrence matrix; glszm: gray-level size zone matrix. Notably, 1-dimensional statistics including mean, median, and 90th percentile of T1 intensity within the core of the patch were identified as relevant. This is consistent with previous studies reporting that SELs have a higher degree of T1 hypointensity compared to non-SELs at baseline.

[0046] [Figure 18A] Confusion matrices showing the performance of classification models for patch-level SEL vs. non-SEL discrimination based on training, validation, and independent test sets are shown. ADVANCE training set (balanced accuracy: 73.0%) is shown. [Figure 18B] Confusion matrices showing the performance of the classification model for patch-level SEL vs. non-SEL discrimination based on the training set, validation set, and an independent test set are shown. The ADVANCE validation set (balanced accuracy rate: 66.8%) is shown. [Figure 18C] Confusion matrices showing the performance of the classification model for patch-level SEL vs. non-SEL discrimination based on the training set, validation set, and an independent test set are shown for the ASCEND test set (balanced accuracy: 65.7%). [Figure 18D]Confusion matrices showing the performance of classification models for patch-level SEL vs. non-SEL discrimination based on training, validation, and independent test sets are shown for the SYNERGY test set (balanced accuracy: 68.5%).

[0047] [Figure 19A] 13 shows a confusion matrix illustrating the performance of the classification model for patch-level SEL vs. non-SEL discrimination based on the training set, validation set, and independent test set on volume-matched patches. [Figure 19B] 13 shows a confusion matrix illustrating the performance of the classification model for patch-level SEL vs. non-SEL discrimination based on the training set, validation set, and independent test set on volume-matched patches. [Figure 19C] 13 shows a confusion matrix illustrating the performance of the classification model for patch-level SEL vs. non-SEL discrimination based on the training set, validation set, and independent test set on volume-matched patches. [Figure 19D] 13 shows a confusion matrix illustrating the performance of the classification model for patch-level SEL vs. non-SEL discrimination based on the training set, validation set, and independent test set on volume-matched patches. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0048] Natalizumab, sold under the trade name TYSABRI® (BIOGEN®, MA), is an integrin receptor antagonist approved by the US Food and Drug Administration (FDA) for the treatment of multiple sclerosis and Crohn's disease. The standard FDA-approved dosing regimen is 300 milligrams (mg) administered intravenously over approximately one hour every four weeks. Within the population of patients treated with natalizumab, there is a small subpopulation of patients who develop progressive multifocal leukoencephalopathy (PML) (Plavina, T. et al. Ann Neurol 2014;76:802-12). Considerable effort has been made to identify and minimize this risk, including extensive patient monitoring and the development of alternative dosing methods. Nevertheless, because of these risks as well as costs and other considerations, the art has generally not actively promoted the use of natalizumab or other more aggressive disease-modifying antibody therapies, particularly in early stage disease, including asymptomatic patients who do not exhibit any of the clinical symptoms of MS.

[0049] The availability of magnetic resonance imaging has increased the detection rate of abnormal brain findings, even in the absence of overt symptoms. When MRI findings are similar to those seen in MS patients, but the typical physical or neurological symptoms associated with MS, e.g., relapses, are asymptomatic in the patient, this is known as a radiologically isolated syndrome (RIS). Although there is a strong association between RIS and MS, a diagnosis of RIS does not necessarily progress to an MS diagnosis. In fact, when followed up over a 2-year period, only about one-third of RIS patients develop a neurological event and are diagnosed with MS, but at the same time, one-third show new findings by MRI without symptoms, and the remaining one-third show no changes. Therefore, improved methods are needed to identify RIS patients who are likely to progress to MS, thereby allowing more effective treatment decisions to be made earlier in the disease process.

[0050] Chronic active lesions, also known as smoldering plaques, are a neuropathological hallmark of chronic inflammation in multiple sclerosis (Elliott et al.Patterning chronic active demyelination in slowly expanding / evolving white matter MS legions. AJNR Am J Neuroradiol dx.doi.org / 10.3174 / ajnr.A6742). Chronic active lesions are generally surrounded by a rim of activated microglia and / or macrophages that may contain iron or zinc. These paramagnetic rim lesions (PRLs) are considered promising pathological biomarkers of iron / zinc accumulation in chronic active lesions and are identified using susceptibility weighted imaging. They have altered morphology, sparse T and B cells in the core, and slow rates of demyelination and axonal loss. It is currently known in the art that detection of PRLs requires susceptibility weighted imaging. Haller et al., Susceptibility-weighted imaging: technical essentials and clinical neurologic applications Radiology 2021;299:3-26.

[0051] Detection of slowly expanding / evolving lesions (SELs) by conventional T1-weighted / T2-weighted brain MRI provides a surrogate readout of smoldering or chronically active plaques (Elliott et al. Slowly expanding / evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions. Mult Scler J 2019;25:1915-1925). Slowly expanding lesions have been previously described in the literature (Elliott et al. Chronic white matter lesion activity predicts clinical progression in primary progressive multiple sclerosis, Brain 2019;142:2787-2799). SELs by conventional brain MRI are contiguous regions of T2 lesions that are consistently and concentrically expanding focally as assessed by the Jacobian determinant of the nonlinear transformation between reference and follow-up scans. Generally, SELs exhibit no T1 gadolinium (Gd) enhancement, lower mean T1 signal intensity at baseline, and a gradual decrease in T1 intensity over time compared to non-SEL areas of pre-existing lesions (Elliott et al Ocrelizumab may reduce tissue damage in chronic active lesions as measured by change in T1 hypointensity of slowly evolving lesions in patients with primary progressive multiple sclerosis. Poster presented at AAN; Poster 376, April 24, 2018; Los Angeles, CA).

[0052] As described and exemplified herein, the combination of these two radiological markers provides information on disease progression in early stage MS patients, e.g., those known or suspected to have RIS, thereby allowing the identification of patients earlier in the disease process who may benefit from more aggressive disease-modifying antibody therapy, including anti-VLA-4 antibody therapy, and therefore earlier initiation of treatment.

[0053] Moreover, these same early stage patients may benefit from the use of machine learning based classifiers capable of accurately and reproducibly discriminating between acute and chronic MS lesions using unenhanced T1 / T2 information from a single MRI scan, as described in Provisional Application No. FR2103793, filed April 13, 2021, and co-pending International Application No. PCT / US2022 / 024450, the disclosures of which are expressly incorporated herein by reference. Thus, in some embodiments, identification of one or more chronic active white matter lesions and / or one or more SELs in a single unenhanced MRI scan provides information on disease progression in early stage MS patients, e.g., patients known or suspected to have RIS, thereby allowing earlier identification of patients in the disease process who may benefit from more aggressive disease-modifying antibody therapy, including anti-VLA-4 antibody therapy, and thus earlier initiation of treatment.

[0054] definition The subject presented herein is typically a male or female human subject (patient) undergoing or to undergo treatment for a particular condition. The condition may be an autoimmune condition or an inflammatory condition. In many cases, an autoimmune condition is considered an inflammatory condition and vice versa, and thus, in some embodiments, the subject has an autoimmune condition and / or an inflammatory condition. An autoimmune condition is a condition in which the subject's immune system attacks the subject's own cells / tissues. Non-limiting examples of autoimmune conditions contemplated by the present invention include radiologically isolated syndrome (RIS), and asymptomatic and / or early stage multiple sclerosis (MS) (e.g., no relapses have been diagnosed).

[0055] MS-related relapses occur without fever or infection and are not associated with environmental and systemic triggers, and refer to acute inflammation in the CNS characterized by a breach of blood-brain barrier (BBB) ​​integrity. In the radiological arena, the criteria for relapse are defined as an increase in lesion burden / size in T2 imaging or T1 gadolinium enhancement of lesions by magnetic resonance imaging (MRI) in the brain, spinal cord, or both. In the clinical arena, patients may present with "mild" symptoms, such as momentary and / or paroxysmal numbness and tingling sensations lasting for a few seconds or minutes; alternatively or additionally, more severe disease exacerbations may include, for example, ataxia, visual disturbances, diplopia, fatigue, cognitive impairment, onset of bowel / bladder dysfunction, or motor paralysis of the limbs that impedes the patient's mobility, dexterity, gait, safety, or overall functional ability. The latter symptoms lead to a higher probability of detection of a relapse.

[0056] As used herein, "about" means within 0.1% to 5% of a given value (e.g., 5%, 3%, 2%, 1%, 0.5%, 0.1% range above or below a given value). When amounts and other specified values ​​are provided herein, acceptable deviations are within the bounds of pharma- ceutically acceptable standards.

[0057] As used herein, in the specification and claims, the indefinite articles "a" and "an" should be understood to mean "at least one," unless clearly indicated to the contrary.

[0058] It should also be understood that, unless expressly indicated to the contrary, in any method claimed herein that includes two or more steps or actions, the order of the method steps or actions is not necessarily limited to the order in which the method steps or actions are recited.

[0059] In the claims and the above specification, all transitional phrases, such as "comprising," "including," "carrying," "holding," "having," "containing," "involving," "holding," "composed of," and the like, are to be understood to be open-ended, i.e., to mean inclusive without limitation. Only the transitional phrases "consisting of" and "consisting essentially of" shall be closed or semi-closed transitional phrases, respectively, as provided in the United States Patent Office Manual of Patent Examining Procedure, Section 2111.03.

[0060] The terms "about" and "substantially" preceding a numerical value mean ±10% of the recited numerical value.

[0061] When a range of values ​​is provided, each value between the upper and lower limits of the range is specifically contemplated and described herein. A "pharmaceutical effective amount" or "therapeutically effective amount," used interchangeably, is an amount sufficient to treat or at least partially arrest the symptoms of a disease and / or complications of a disease.

[0062] "Disease-modifying antibody therapeutics" contemplated herein for the treatment of CWMLA include anti-VLA-4 antibodies, e.g., natalizumab and BIIB107, as well as anti-CD20 antibodies, such as ocrelizumab. An "anti-VLA-4 antibody" is an anti-very late antigen (VLA)-4 monoclonal antibody, a humanized antibody, a human antibody, or a chimeric anti-VLA-4 monoclonal antibody. Anti-VLA-4 antibodies have been previously described in the art. These include, but are not limited to, natalizumab and BIIB107, monoclonal antibodies that target alpha-4 integrin and are currently in clinical trials (ClinicalTrials.gov no. NCT04593121). See also PCT / US2011 / 032641 and PCT / US2019 / 034962, the disclosures of which are expressly incorporated herein by reference.

[0063] radiological measurements In view of the foregoing background art, in some embodiments, the present invention teaches simultaneous or sequential acquisition of at least one combination of T1 and T2 weighted images from a patient for identification of at least one SEL and at least one susceptibility weighted magnetic resonance image, for identification of at least one PRL, and for determination of the degree of colocalization, i.e., the presence of SEL colocalized with PRL and vice versa. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's SEL colocalize with the patient's PRL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of a patient's PRL co-localizes with the patient's SEL. A more detailed description of certain embodiments of the invention is provided herein below.

[0064] In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number are identified as PRL. In some embodiments, treatment with an anti-VLA-4 therapeutic agent is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number are identified as SEL. In some embodiments, SEL is detected using single time point non-contrast T1 and T2 weighted MRI.

[0065] In various embodiments, the present invention teaches a method for generating a series / set of brain images using magnetic resonance imaging. In some embodiments, the method includes using an MRI device to apply standardized 3-Tesla, 3D isotropic multi-echo, gradient-echo MRI to identify any PRL in the patient, and T1 and T2 weighted MRI to identify any SEL. The "co-localization" of the SEL and PRL may be based on a heuristic threshold set of corresponding segmented volumes by an observer's (radiologist's) visual experience, or may rely on an automated processing pipeline that determines the exact percentage of the volume of the SEL that is co-localized with the PRL (and vice versa).

[0066] It will be readily apparent to one of ordinary skill in the art that the methods of the invention described herein can be practiced using several different types of imaging systems, including all of the types of imaging systems described in the examples and experiments described herein, as well as similar systems.

[0067] Machine Learning Taxonomy In some embodiments, the present invention utilizes machine learning based classifiers that classify MS lesions using unweighted T1 / T2 information from a single MRI scan, as described in Provisional Application No. FR2103793 and co-pending International Application No. PCT / US2022 / 024450, the disclosures of which are expressly incorporated herein by reference for all purposes. The use of these classifiers may effectively increase the sensitivity of acute MS lesion detection at a single time point, and may replicate, approximate or exceed the sensitivity of conventional detection of hyperintense lesions identified on T1-weighted scans with gadolinium enhancement and / or new hyperintense lesions on T2-weighted scans compared to previous reference scans, which may be reflected in new localization information.

[0068] In some embodiments, a suitable method for classifying brain lesions based on imaging at a single time point may include obtaining patient image data from a single time point, providing the patient image data as an input to a brain lesion classification model, generating a classification for one or more lesions identified in the patient image data, and providing the classification for the one or more lesions for display by one or more display devices, where the brain lesion classification model is trained using subject image data for a plurality of subjects, the subject image data being captured at two or more time points. In some embodiments, the patient image data from a single time point includes data from two or more image scan sequences. In some embodiments, the data from the two or more image scan sequences includes non-enhanced MRI data, where the two or more image scan sequences do not include administration of a paramagnetic contrast agent. In some embodiments, the classification for one or more lesions identified in the patient image data is selected to be either acute or chronic, or SEL or non-SEL.

[0069] In some embodiments, certain radiological features that have predictive value for classification of a lesion as either acute or chronic and / or SEL are utilized, including, for example, the following exemplary embodiments: Features that quantify the primary intensity of the core region of the lesion as it appears on T2-weighted scans. Such features correspond to acute lesions, which tend to be more intense and more uniformly hyperintense than chronic lesions, which may have fewer hyperintense voxels. A feature that quantifies the amount of signal that appears as a dark grey color around the periphery of a lesion as seen on a T1-weighted scan. A feature that quantifies the amount of bright gray signal present at the periphery and / or core of a lesion as seen on a T1-weighted scan. Features related to the inhomogeneities present in the image. For example, features can quantify the complexity of the image (which may be inhomogeneous and contain rapid changes in grey levels), the variability of grey levels with respect to a mean grey level, or the presence of homogeneous patterns in the image. A feature relating to the structure of an image, such as the presence of repeating patterns. For example, an image with more repeating patterns can be considered more "structured" than one with fewer observable intensity patterns. · Texture characteristics of an image, such as how coarse or fine the image is.

[0070] In some embodiments, the radiological features for distinguishing between SEL and non-SEL are selected from the group including or consisting of the radiological features listed in FIG. Pre-contrast T1-weighted MRI computed within the core region (extracted from the non-contrast T1-weighted MRI image), gray-level run-length matrix (quantifies successive gray levels), and run-length heterogeneity (degree of similarity of run-lengths across the image; lower values ​​indicate greater homogeneity between run-lengths within the image). Pre-contrast T1-weighted MRI computed within the core region, primary features (which describe the distribution of voxel intensities within an image region defined by a commonly used mask and a criterion), and the 90th percentile voxel within the patch. · T2-weighted MRI (extracted from T2-weighted MRI images) calculated within the surrounding area, primary feature, homogeneity (degree of sum of squares of each intensity value; degree of homogeneity of the image array; higher homogeneity means higher homogeneity or smaller range of individual intensity values) Pre-contrast T1-weighted MRI, primary features, mean value (mean grey level intensity within the patch) calculated within the core region Pre-contrast T1-weighted MRI, primary features, robust mean absolute deviation (average interval of all intensity values ​​from the mean calculated on a subset of the image array whose gray levels are between the 10th and 90th percentiles or equal to the 10th and 90th percentiles) calculated within the surrounding area Pre-contrast T1-weighted MRI, primary features, median gray level intensity within patches calculated within the core region Pre-contrast T1-weighted MRI, grey-level co-occurrence matrix, and sum of entropy (sum of neighboring intensity value differences) calculated within the core region T2-weighted MRI, linear, mean square (the square root of the average of all squared intensity values; another measure of the magnitude of the image values) calculated within the surrounding area T2-weighted MRI calculated within the surrounding area, the primary value, and the maximum gray-level intensity within the patch Pre-contrast T1-weighted MRI, grey-level size zone matrix and zone entropy (degree of uncertainty / randomness in the distribution of zone sizes and grey levels; higher values ​​indicate more heterogeneity in the texture pattern) calculated within the core region Pre-contrast T1-weighted MRI, grey-level run-length matrix, and run-length heterogeneity (degree of similarity of run-lengths across the image; lower values ​​indicate greater homogeneity between run-lengths within the image) calculated within the surrounding region Pre-contrast T1-weighted MRI, grey-level run-length matrix and run entropy (degree of uncertainty / randomness in the distribution of run-lengths and grey levels; higher values ​​indicate more heterogeneity in the texture pattern) calculated within the core region T2-weighted MRI calculated within the surrounding area, the primary value, and the asymmetry degree (degree of asymmetry of the distribution of values ​​around the mean value; can be positive or negative) T2-weighted MRI calculated within the core region, primary and mean values ​​(average grey level intensity within the patch) T2-weighted MRI calculated within the surrounding region, primary value, 90th percentile voxel within the patch

[0071] More generally, a machine learning classifier may utilize one or more machine learning systems, methods, and / or models. A machine learning model may be considered a model configured to receive an input and apply one or more of weights, biases, classifications, or analyses to the input to generate an output. The output may include, for example, a classification of the input, an analysis based on the input, a design, process, prediction, or recommendation related to the input, or any other suitable type of output. A machine learning model is generally trained using training data, e.g., empirical data and / or samples of input data, e.g., the types of training data described elsewhere herein, which are fed to the model to establish, adjust, or modify one or more aspects of the model, e.g., weights, biases, criteria, to form classifications or clusters, etc. Aspects of the machine learning model may operate on the input linearly, in parallel, via a network (e.g., a neural network), or via any suitable configuration.

[0072] A training set (e.g., subject image data captured at two or more time points) may be used as input to train a machine learning classifier, which may facilitate the selection or combination of machine learning methods with the goal of creating an optimal combination of such methods. One goal of such selection is to create an optimal subset of features to differentiate the reduced imaging biomarker space between lesion types (e.g., acute vs. chronic lesions, or SEL vs. non-SEL) or the amount or degree of progression. In one embodiment, linear and nonlinear feature-pair-class correlation tests can be used to identify features that represent the greatest variance between classifications.

[0073] This evaluation and classification can utilize an initial feature ranking as shown in Figure 17, and an initial feature selection that can, for example, identify a few features with the highest individual correlation values. In one embodiment, there can be 50 such features selected as the feature subspace. From these features, the selection method implemented can leverage sparse linear models including tree-based classifiers and least absolute shrinkage and selection operators (LASSO).

[0074] In this way, starting from a feature subspace as described above, it is possible to perform a study in which some features in the feature subspace may be reduced by removing certain features, with the least useful features being removed at each step. In this way, the ensemble classifier can be optimized at each step. This process can proceed in recursive iterations of optimization and feature removal to rank each reduced combination of radiological features in relation to a lesion classification objective (e.g., active vs. chronic, or SEL vs. non-SEL).

[0075] The result of this ensemble classification mechanism may be a selected subset of classification methods that may include linear classification methods (e.g., logistic regression, support vector machines) and / or non-linear classification methods (e.g., perceptrons, deep convolutional neural networks, or other types of neural networks) that serve to optimize the separability between the two classes (active and chronic).

[0076] In one embodiment, a pool of machine learning models may undergo hyperparameter tuning via large-scale randomized grid search, followed by k-fold cross-validation for the classification task of interest. This tuning then provides a performance benchmark from which the best performing models can be selected, e.g., the n best performing models. These models can then be combined under a stacking, or winner-take-all, or probabilistic importance sampling ensemble strategy.

[0077] The implementation of the machine learning model may include the deployment of one or more machine learning techniques, including various types of neural networks, and statistical techniques such as linear regression, logistic regression, random forests, or gradient boosted machines (GBM). Depending on the embodiment, the training of the machine learning model may be supervised or unsupervised, or both. Supervised learning may include providing training data and labels corresponding to the training data. Unsupervised training may include clustering, classification, etc. Different types of clustering, or combinations of clustering, may also be used, which may be supervised or unsupervised.

[0078] In one embodiment, the machine learning based classifier may include one or more of several types of neural networks, including convolutional neural networks (CNNs), deep or fully convolutional neural networks (DCNNs, FCNNs), deep learning neural networks (DNNs), deep belief networks (DBNs), and other neural networks commonly known to those skilled in the art.

[0079] A machine learning system that may be part of a machine learning model may include one or more processors, one or more storage devices for non-volatile non-transient storage, and one or more memory devices that are volatile memory for temporary storage, but may also include non-volatile memory for non-transient storage. Multiple machine learning methods implemented by one or more machine learning systems may be utilized as part of an ensemble classification process. In one embodiment, the processor in the machine learning system may be a graphics processing unit (GPU) or a central processing unit (CPU), which may be useful for neural network structures or other learning frameworks.

[0080] Some literature distinguishes between machine learning, deep learning, artificial intelligence, and multi-example learning in various ways, and for purposes of this description, any or all of these approaches may provide the necessary structure and functionality to achieve the objectives of one or more of the inventions described herein.

[0081] Dosing regimen The present disclosure also provides a biphasic dosing regimen for reducing pathological inflammation with natalizumab, where the dosing regimen includes an induction phase using standard interval dosing (SID) followed by a chronic phase using extended interval dosing (EID). In some embodiments, at least one treatment phase uses subcutaneous administration. In some embodiments, both treatment phases use subcutaneous administration. In some embodiments, the same dose administered during the SID phase may be administered during the EID phase, and in some embodiments, the same dose administered IV may be administered SC.

[0082] A biphasic dosing regimen contemplated herein refers to administration of natalizumab in at least two periods, for example, an induction phase and a chronic phase. In some embodiments, the induction phase includes administration of natalizumab on a SID schedule, and the chronic phase includes administration of natalizumab on an EID schedule. In some embodiments, the induction phase includes administration of natalizumab once every 2 weeks, once about every 2 weeks, once every 3 weeks, once about every 3 weeks, once every 4 weeks, once about every 4 weeks, once every 30 days, once about every 30 days, once a month, or once a month for at least 6 months, at least 8 months, at least 10 months, or at least 12 months. In some embodiments, the induction phase is 6-18 months, 8-16 months, 10-14 months, 11 months, 12 months, or 13 months. In some embodiments, the chronic phase includes administration of natalizumab once every 5-10 weeks. In some embodiments, the chronic phase comprises administration of natalizumab every 5 weeks, about every 5 weeks, about every 6 weeks, about every 6 weeks, about every 7 weeks, about every 7 weeks, about every 8 weeks, about every 8 weeks. In some embodiments, both the induction phase and the chronic phase comprise SC administration. In some embodiments, the induction phase and the chronic phase comprise SC administration alone. Surprisingly, in some embodiments, the SC administration and amount of natalizumab can be consistent with IV administration.

[0083] In some embodiments, the therapeutically effective amount administered during the induction phase and the chronic phase is the same, and the therapeutically effective amount is 250-450 mg (e.g., 250 mg, 300 mg, 350 mg, 400 mg, or 450 mg), more preferably about 300 mg, and even more preferably 300 mg. In some embodiments, the therapeutically effective amount administered SC during the chronic phase is 300-500 mg (e.g., 300 mg, 350 mg, 400 mg, 450 mg, or 500 mg). In some embodiments, the therapeutically effective amount is about 250 to about 450 mg (e.g., about 250 mg, about 300 mg, about 350 mg, about 400 mg, or about 450 mg), more preferably about 300 mg, and even more preferably 300 mg. In some embodiments, the therapeutically effective amount administered SC during the chronic phase is about 300 to about 500 mg (e.g., about 300 mg, about 350 mg, about 400 mg, about 450 mg, or about 500 mg), more preferably about 300 mg, and even more preferably 300 mg. Exemplary biphasic dosing regimens are described in U.S. Provisional Application Nos. 63 / 113,864 (filed November 14, 2020), 63 / 113,865 (filed November 14, 2020), 63 / 142,968 (filed January 28, 2021), 63 / 142,970 (filed January 28, 2021), and co-pending International Application No. PCT / US2021 / 059266, the disclosures of which are expressly incorporated herein by reference.

[0084] The present disclosure also provides a chronic dosing regimen for reducing pathological inflammation with natalizumab. In some embodiments, natalizumab is administered in a chronic dosing regimen, wherein the chronic dosing regimen comprises administration of natalizumab at regular intervals of every 4 weeks. In some embodiments, the chronic dosing regimen is a non-weight-based fixed dose of natalizumab. In some embodiments, the therapeutically effective amount of natalizumab is about 250-450 mg, more preferably about 300 mg, and even more preferably a non-weight-based fixed dose of 300 mg. In further embodiments, the chronic dosing regimen is every 4 weeks for a period of about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, about 12 months, or more than about 12 months. Exemplary chronic dosing regimens are described in WO 2003 / 072040, the disclosure of which is expressly incorporated herein by reference.

[0085] In some embodiments, the chronic administration regimen comprises SC injection. In some embodiments, the chronic administration regimen comprises IV administration. Exemplary SC administration formulations are described in WO 2008 / 157356, the disclosure of which is expressly incorporated herein by reference.

[0086] Relevant biomarkers for measuring and / or monitoring the efficacy of the therapeutic procedures provided herein include, for example, sVCAM and / or Nf-L. Without being bound by theory, saturation and / or increased occupancy by natalizumab of its target α4 integrin on the surface of circulating lymphocytes not only reduces the surface expression of α4 integrin on lymphocytes, but also reduces the serum concentration of sVCAM. Correspondingly, sVCAM provides an effective surrogate biomarker for α4 integrin receptor saturation and general immune surveillance activity. See, for example, Plavina et al., Neurology (2017) 89(15):1584-1593. In contrast, neurofilament proteins such as Nf-L are indicative of axonal injury and neuronal death and serve as effective surrogate biomarkers for ongoing disease activity, particularly in MS patients. See, e.g., Kuhle et al. Mult Scler. (2013) 19:1597-603; Varhaug et al., Front Neurol. (2019) 10:338.

[0087] These biphasic dosing regimens are provided to improve the safety of natalizumab therapy. In some embodiments, biphasic dosing regimens are provided to improve the safety of chronic natalizumab therapy. Safety can be improved by reducing the risk of adverse events, such as PML. In some cases, the biphasic regimen reduces the risk of PML, reduces the risk of inducing the production of anti-natalizumab antibodies, reduces the risk of patient sensitization to natalizumab, or reduces a combination thereof. In some cases, the biphasic regimen reduces the risk of loss of efficacy of natalizumab treatment due to the production of anti-idiotypic antibodies to natalizumab in patients.

[0088] Patients who are seropositive for anti-JCV antibodies are at particular risk for PML. In some embodiments, a PML risk subject has an anti-JCV antibody index level (e.g., mean index level) of greater than 1.5. In some embodiments, a low PML risk subject is a subject with an anti-JCV antibody index level (e.g., mean index level) of 0.9 or less. Anti-JC virus index values ​​are calculated from serum / plasma two-step ELISA antibody assays (STRATIFY JCV™ Antibody (Indexed) and reflex inhibition assays; see, e.g., Lee, P. et al. J of Clin Virol, 2013; 57(2): 141-146, incorporated herein by reference). Antibody index levels, assays for assessing index levels, and the use of such index levels and assays to determine PML risk are described, e.g., in WO2012 / 166971 and WO2014 / 193804.

[0089] If a subject tests seropositive for anti-JCV antibodies before starting natalizumab therapy, or if the subject switches from a seronegative anti-JCV antibody status to a seropositive anti-JCV antibody status during natalizumab therapy, the subject may be considered to be at high risk for PML. In some embodiments, if a subject has an anti-JCV antibody index level of more than 1.5 before starting natalizumab therapy, or if the subject switches from an anti-JCV antibody index level of 0.9 or less to an anti-JCV antibody index level of more than 1.5 during natalizumab therapy, the subject is considered to be at high risk for PML. For example, before starting natalizumab therapy, the subject may be tested for the presence or absence of anti-JCV antibodies. If the test results indicate that the subject is at low risk for PML (seronegative for anti-JCV antibodies, or has an anti-JCV antibody index level of 0.9 or less), the subject may be identified as a subject for natalizumab therapy on a SID schedule with 4-week intervals. During the course of natalizumab therapy on a SID schedule, subjects may be retested for the presence or absence of anti-JCV antibodies (e.g., monthly or every 2, 3, 4, 5, 6 months, or annually). If, upon retesting, the subject switches from seronegative to seropositive for anti-JCV antibodies, or from an anti-JCV antibody index level of 0.9 or less to an anti-JCV antibody index level of greater than 1.5, the subject may be identified as a candidate for natalizumab therapy on an EID schedule with intervals of at least 5 weeks. EXAMPLES

[0090] Example 1: Association between chronic white matter lesion activity and disability progression in SPMS patients with and without acute inflammation Objective: Slowly expanding lesions (SELs), a subgroup of white matter lesions that gradually expand over time, have been shown to predict disability accumulation in primary progressive multiple sclerosis (MS) disease. The relationships between SELs, acute white matter lesion activity (AWMLA), chronic white matter lesion activity (CWMLA), and disability progression are not well understood. In this study, we assessed CWMLA and acute lesion activity (AWMLA) in cerebral white matter in a secondary progressive MS population.

[0091] In this study, CWMLA was measured by the change in T1LV in SEL, non-SEL, and total pre-existing chronic nonenhancing T2 (CNT2) lesions from baseline to week 108. AWMLA was defined as the occurrence of either 1) gadolinium-enhancing (Gd+) T1 lesions at any time during the study up to and including week 108, or 2) any post-baseline new or enlarging T2 lesions. We examined 1) the association of T1-weighted (T1w) MRI features of CWMLA in SEL and non-SEL with confirmed disability progression, 2) the association of CWMLA in the absence of AWMLA with confirmed disability progression, and 3) the association of CWMLA with AWMLA in a placebo-treated secondary progressive MS population. In this study, we also examined the phase 3 clinical trial of ASCEND (ClinicalTrials.gov no. NCT01416181), which compared natalizumab with placebo in secondary progressive MS (SPMS).

[0092] Materials and Methods Study design, patients, and MRI The ASCEND study (ClinicalTrials.gov no. NCT01416181) was a two-part, multicenter, randomized, double-blind, placebo-controlled, phase 3 study conducted in patients with SPMS to evaluate the efficacy and safety of natalizumab. Details of the study design and results have been described in detail previously. Kapoor et al. Lancet Neurol 2018;17:405-415. Axial T1W (3D spoiled gradient echo: TR = 28-35 ms; TE = 4-11 ms; flip angle = 27°-30°; resolution 0.98 × 0.98 × 3 mm) and axial T2W (2D fast spin echo: TR = 4000-7400 ms; TE = 58-95 ms; resolution = 0.98 × 0.98 × 3 mm) were acquired at baseline, weeks 24, 48, 72, 96, and 108. The SEL analysis population was a subpopulation of the intention-to-treat population in which T1w and T2-weighted (T2w) images were available at all time points between baseline and week 108 (including weeks 24, 48, 72, and 96).

[0093] Clinical measures of disability progression Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk (T25FW), and 9-Hole Peg Test (9HPT) assessments were performed at baseline and every 12 weeks until week 108. Composite confirmed disability progression was defined as meeting one or more of the following three criteria: an increase of ≥ 1.0 point from baseline EDSS score ≤ 5.5 or an increase of ≥ 0.5 point from baseline score ≥ 6.0, an increase of ≥ 20% from baseline in T25FW time, and / or an increase of ≥ 20% from baseline in 9HPT time (either hand). Progression was confirmed at visits ≥ 6 months after the onset of possible progression and at the end of the study. To minimize the possibility of capturing disability progression due to clinical relapse, confirmed disability progression events that began or were confirmed within 74 days of the onset of clinical relapse confirmed by an independent neurological review committee were not included. Absence of acute lesion activity was defined as the absence of baseline or post-baseline T1 gadolinium-enhancing (Gd+) lesions and the absence of new or enlarging T2 hyperintense lesions after baseline.

[0094] Identifying SEL, AWMLA, and overall CWMLA The process of SEL identification has been described in detail elsewhere. Elliott et al. Brain 2019;142:2787-2799 and Elliott et al. Mult Scler 2019;25:1915-1925. Briefly, SELs are adjacent areas of pre-existing T2 lesions that have consistently and concentrically expanded locally from baseline to week 108. Prior to SEL detection, T2 lesions were identified on baseline scans using a semi-automated method, in which the fully automated segmentation of T2 lesions was then manually reviewed and corrected by one trained MRI reader. Francis SJ.In:McGill University DoN,ed.,2005. The first stage of SEL detection identifies SEL candidates as contiguous regions of ≧10 voxels of the baseline T2 lesion mask that are a) free of Gd+ and b) exhibit minimal regional volumetric expansion as determined by the Jacobian determinant of the nonlinear deformation between the baseline and week 108 scans. The second stage of SEL detection scores each SEL candidate in turn based on the concentricity and consistency of expansion over time. Considering the regional expansion between baseline and each intermediate time point (weeks 24, 48, 72, and 96) allows for the identification of SEL candidates that expand consistently and gradually over time, while measuring concentricity allows for the identification of SEL candidates that expand radially outward from the center. Each SEL candidate is assigned a SEL score, which is calculated as the sum of the average normalized measures of consistency and concentricity.

[0095] SELs were identified in patients with SPMS from the ASCEND phase 3 clinical trial (ClinicalTrials.gov no. NCT01416181). Non-SELs, defined as part of a mask of non-enhancing baseline T2 lesions not identified as SELs, were also assessed, as was the totality of non-enhancing T2 lesions. The ASCEND SEL analysis population (placebo, n=292; natalizumab, n=308) was a subpopulation of patients for whom T1w and T2w images were available at all time points from baseline to week 108. Results represent SELs with an SEL score ≥0. The distribution of SELs and non-SELs by sex, baseline EDSS, age, and disease duration is shown in Tables 1–3. [Table 1-1] [Table 1-2] [Table 2-1] [Table 2-2] [Table 3-1] [Table 3-2]

[0096] AWMLA was defined as the occurrence of either 1) a Gd+T1 lesion at any time during the study up to and including week 108, or 2) any post-baseline new or enlarging T2 lesion. Gd+T1 lesions were determined as the consensus of two complete manual identifications by two trained MRI readers, with any discrepancies adjudicated by a third, independent reader. New or enlarging T2 lesions were determined by comparing each T2 lesion mask at successive time points and automatically identifying focal areas of new T2 lesions that were not present at the previous time point and that showed minimal increase in T2-weighted intensity. These focal areas of new T2 lesions could be entirely within the NAWM (new) or adjacent to existing T2 lesions (enlarging). All automatically identified new or enlarging T2 lesions were manually reviewed and corrected as necessary.

[0097] It is important to understand that the SEL approach to detection of lesion enlargement is fundamentally different from that used in clinical trials to detect so-called new "T2 enlarging lesions," commonly reported as "new or enlarging T2 lesions." Arnold et al.Mult Scler 2021;27(11):1681-3. The detection method of "enlarging" lesions in the context of "new or enlarging T2 lesion counts" (which vary by laboratory) is essentially designed to detect new foci of acute white matter lesion activity that may not qualify as "novel" new lesions (defined as having to be surrounded by normal-appearing WM) because they are adjacent to and connected to areas of pre-existing T2 signal abnormality. CWMLA was measured by the change in T1 hypointense lesion volume (T1LV) from baseline to week 108 in SEL, non-SEL, and total pre-existing chronic nonenhancing T2 (CNT2) lesions in placebo-treated SPMS patients. T1-hypointense lesions were defined as areas of T2 lesions without gadolinium enhancement and with T1-weighted intensity less than or equal to the median gray matter T1-weighted intensity.

[0098] Whole brain atrophy was measured via the Jacobi integral. Nakamura et al. Neuroimage Clin 2014;4:10-17.

[0099] statistical analysis Statistical analysis of the SEL data was exploratory and included all patients from ASCEND who were evaluable with intact T1w and T2w scans at all time points (baseline to week 108; SEL analysis population). No imputation of missing data was performed.

[0100] Two-sample proportionality tests were applied to compare baseline Gd+T1 lesions in the two treatment groups. For CWMLA, Van Elteren tests were used to compare eventers and non-eventors, and baseline T2 hyperintense lesion volume (T2LV) classification based on progression status, baseline EDSS score (≤5.5 or ≥6.0), and tertile-based classification (≤6908.79 mm). 3 ,>6908.79~18818.49mm 3 , and >18,818.49 mm 3 The analysis of the association between AWMLA and SEL prevalence was performed based on the Van Elteren test and included AWMLA, baseline EDSS score (≤5.5 or ≥6.0), and baseline T2LV classification based on tertiles (≤6908.79 mm). 3 ,>6908.79~18818.49mm 3 , and >18818.49mm 3 Comparisons of CWMLA between placebo and natalizumab were performed based on the Van Elteren test and were stratified by treatment, baseline EDSS score (≤5.5 or ≥6.0), and baseline T2LV classification based on tertiles (≤6908.79 mm 3 ,>6908.79~18818.49mm 3 , and >18818.49mm 3 Statistical tests were two-sided and performed at the 5% significance level without adjustment for multiplicity.

[0101] result Baseline demographic and brain MRI characteristics of the SPMS analysis population Baseline demographic and brain MRI characteristics of the analysis and intent-to-treat populations available for SEL detection from the ASCEND study dataset are shown in Table 4. Age and sex were similarly distributed for the SEL analysis and intent-to-treat populations and between treatment groups. In the SEL analysis population, a higher percentage of natalizumab-treated individuals had ≥1 Gd+T1 lesion at baseline than placebo-treated individuals (28% vs. 19%), but the difference was not statistically significant. In the SEL analysis population, the natalizumab- and placebo-treated groups had similar mean T2LV at baseline (18.1 vs. 16.5 cm 3 ). Baseline mean normalized brain volumes were also similar in the two treatment groups. [Table 4]

[0102] Disability progression in SPMS was associated with increased CWMLA in the placebo group

[0103] CWMLA was measured by the change in T1LV in SEL, non-SEL, and total existing CNT2 lesions from baseline to week 108 in placebo-treated SPMS patients. Because natalizumab reduces AWMLA, analyses were restricted to the placebo group to avoid treatment effects. CWMLA was compared between patients with composite disability progression (n=143) and those who remained progression-free (n=149). SPMS patients with confirmed composite disability progression had significantly more severe CWMLA than progression-free patients when measuring T1LV changes in SEL, non-SEL, and CNT2 lesions (median increase [Q1,Q3] progressor vs. non-progressor: 100[3,524] vs. 23[0,155] mm, respectively). 3 , p=0.0023; 231[17,1090] vs. 109[-29,538]mm 3 , p = 0.0170; and 372 [26,1662] vs. 160 [-23,770] mm3 , p = 0.0026, Figure 1A-C). In contrast, rates of brain atrophy, as measured by whole brain volume change from baseline to week 108, were not significantly different between SPMS patients with and without composite confirmed disability progression (p = 0.2176, Figure 1D).

[0104] When confirmed disability progression was based on EDSS score alone, CWMLA was associated with a greater increase in T1LV than SEL (median increase [Q1, Q3] in diseased vs. non-disease patients: 142 [6,815] vs. 39 [0,258] mm 3 , p = 0.0135; Fig. 2A ) and CNT2 lesions (median increase [Q1, Q3] in affected vs. nonaffected patients: 577 [66, 2529] vs. 246 [0, 1090] mm 3 , p = 0.0375; Figure 2C) were significantly higher in incident patients than in non-progressors, with a consistent trend in the same direction in non-SELs (median increase [Q1, Q3] incident vs non-progressors: 292 [26,1302] vs 159 [-9,710] mm 3 , p = 0.1156; Figure 2B). Similarly, when confirmed disability progression was based solely on the 9HPT score, CWMLA as measured by T1LV increase was associated with a median increase in SEL (median increase [Q1,Q3] in diseased vs. non-diseased patients: 112 [0,629] vs. 37 [0,258] mm 3 , p = 0.0051; Fig. 3A ) and CNT2 lesions (median elevation [Q1,Q3] in affected vs. nonaffected subjects: 549 [66,1995] vs. 197 [0,1139] mm 3 , p = 0.0075; Figure 3C) and in non-SEL, it was numerically higher in incident patients than in non-progressors (median increase [Q1, Q3] incident vs. non-progressors: 240 [-9,1097] vs. 143 [-3,661] mm 3 , p = 0.0772; Figure 3B). Finally, when confirmed disability progression was based solely on T25FW, CWMLA when measuring T1LV gain was significantly greater in non-SELs (median increase [Q1,Q3] in diseased vs. non-diseased patients: 229 [20,1133] vs. 141 [-25,609] mm 3, p = 0.0230; Fig. 4B ) and CNT2 lesions (median increase [Q1, Q3] in diseased vs. non-diseased patients: 371 [23,1634] vs. 205 [-6,954] mm 3 , p = 0.0214; Fig. 4C), and SEL was numerically higher in incident patients than in non-progressors (median increase [Q1, Q3] incident vs non-progressors: 100 [3,426] vs 29 [0,220] mm 3 , p=0.0873; Figure 4A).

[0105] Disability progression in SPMS remained associated with increased CWMLA in the complete absence of AWMLA in the placebo group

[0106] From baseline to week 108, 95 of 292 placebo SPMS patients had no AWMLA, defined as no baseline or post-baseline Gd+ T1 lesions and no new / enlarging T2 lesions after baseline. CWMLA in SPMS patients without AWMLA was compared between patients with composite confirmed disability progression (n=40) and those who remained progression-free (n=55). Patients with composite progression developed more severe CWMLA than progression-free patients, as shown by significant differences in T1LV changes in non-SEL (p=0.0045; Fig. 5B) and CNT2 lesions (p=0.0103; Fig. 5C), and a trend in the same direction was observed for SEL (p=0.2332; Fig. 5A).

[0107] Low prevalence of SEL in placebo-treated SPMS patients without AWMLA

[0108] Placebo SPMS patients with AWMLA had higher SEL prevalence (based on baseline SEL T2w boundaries) and a higher proportion of existing baseline T2LVs identified as SELs when measuring SEL number and volume than patients without AWMLA (Figure 6A-C). The proportion of patients with at least one SEL was also higher in patients with AWMLA (89%, n=197) compared to patients without AWMLA (71%, n=95). Analysis of the difference in SEL prevalence between SPMS patients without AWMLA (n=95) and those with AWMLA at baseline only (n=28) or post-baseline AWMLA (n=169) confirmed that both baseline and post-baseline AWMLA were associated with higher SEL prevalence, but the sample size of patients with AWMLA at baseline only was too small to show significant differences in SEL number and relative volume relative to the SEL prevalence (Figure 6A and C). Analysis of the frequency distribution of patients by range of SEL prevalence also showed that 19% of patients with AWMLA had a total T2 lesion burden >20% identified as SEL, compared with only 5% of patients without AWMLA ( Fig. 6 D).

[0109] Effect of natalizumab versus placebo on SEL and CWMLA in patients with SPMS

[0110] The placebo and natalizumab arms had similar percentages of patients with SELs ≥ 1 detected from baseline to week 108 (83% vs. 79%).

[0111] Natalizumab was associated with smaller SEL values ​​(median values, 3 vs. 4; p < 0.0001; Figure 7A) and smaller absolute SEL volumes (median T2w SEL volumes at baseline, 288 vs. 561 mm 3; p < 0.0001; Figure 7B). Accordingly, the proportion of total longitudinal baseline non-enhanced T2LV identified as SEL was significantly lower in natalizumab-treated patients than in placebo-treated patients (median proportion, 2.7% vs. 5.0%; p < 0.0001; Figure 7C).

[0112] Brain tissue loss associated with CWMLA, as measured by absolute and relative T1LV accumulation, was less in natalizumab-treated patients than in placebo-treated patients in both SEL (FIGS. 8A and C) and non-SEL (FIGS. 8B and D).

[0113] Consideration SELs are a subgroup of MS chronic white matter lesions with continuous expansion and tissue destruction and are predictive of clinical progression in progressive onset MS. Elliott et al. Brain 2019;142:2787-2799, Elliott et al. Mult Scler 2019;25:1915-1925 and Elliott et al. AJNR Am J Neuroradiol 2020;41:1584-1591. In SELs, there is an accumulation of T1LV over time and changes in imaging baselines that reflect progressive microstructural organization changes, including reduced magnetization transfer ratio and increased radial diffusivity on diffusion tensor imaging. Elliott et al. AJNR Am J Neuroradiol 2020;41:1584-1591. These findings suggest a chronic demyelinating process and persistent axonal / neuronal destruction (Elliott et al. AJNR Am J Neuroradiol 2020;41:1584-1591), which has been confirmed in pathology studies of chronic active lesions (Frischer et al. Ann Neurol 2015;78:710-721 and Luchetti et al. Acta Neuropathol 2018;135:511-528). However, anti-inflammatory DMTs such as ocrelizumab (Elliott et al. Brain 2019;142:2787-2799) and natalizumab (Preziosa et al. Mult Scler 2020:1352458520969105) have been reported in recent years that may moderately affect MRI measurements of brain tissue loss in SEL.

[0114] In this study, SELs were most frequently identified in SPMS patients, and confirmed disability progression was associated with more severe CWMLA when measuring T1LV accumulation in SELs but also in non-SELs. These findings are consistent with previously reported findings for relapsing MS patients in the OPERA I and II datasets and primary progressive MS patients in the ORATORIO dataset. Elliott et al. Brain 2019;142:2787-2799 and Elliott et al. Mult Scler 2019;25:1915-1925. SPMS patients progressing in the context of natural history had higher SEL prevalence and more severe CWMLA when measuring T1LV gain in SELs, non-SELs, and total baseline non-enhancing T2 lesions. SEL prevalence was reduced in SPMS patients in the absence of AWMLA (as indicated by number and volume of SELs), but >70% of patients had ≥1 SEL. Importantly, the association between confirmed disability progression and more severe CWMLA in SPMS patients is remarkably maintained in the absence of AWMLA. A consistent finding was reported in placebo-treated primary progressive MS patients in the ORATORIO dataset. In this patient population, AWMLA, CWMLA, and whole brain atrophy were measured, and only CWMLA in SELs and non-SELs predicted confirmed disability progression over time. Elliott et al.Brain 2019;142:2787-2799. This indicates that brain tissue loss associated with CWMLA may be an important driver of AWMLA-independent disability progression in MS.

[0115] Compared with placebo treatment, natalizumab treatment significantly reduced the number and volume of SELs and the proportion of baseline non-enhancing T2 lesions identified as SELs. Natalizumab reduced CWMLA as measured by T1LV increase in both SELs and non-SELs. The significant association between AWMLA and SEL prevalence suggests that the effect of natalizumab on CWMLA in progressive MS patients may be related to its high efficacy in suppressing acute inflammation. Previous studies demonstrating the effect of natalizumab and the depletion of CD20-expressing cells further support this finding. Kappos et al. JAMA Neurol 2020;77:1132-1140, Montalban et al. N Engl J Med 2017;376:209-220, Polman et al. N Engl J Med 2006;354:899-910, Butzkueven et al. J Neurol Neurosurg Psychiatry 2014;85:1190-1197, and Hauser et al. N Engl J Med 2017;376:221-234. The effect of natalizumab on chronic active lesions has also been preliminarily demonstrated in positron emission tomography studies measuring activated microglia. Kaunzner et al. Mult Scler Relat Disord 2017;15:27-33 and Sucksdorff et al. Neurol Neuroimmunol Neuroinflamm 2019;6:e574. In these studies, natalizumab reduced uptake of PK11195, reflecting activated microglia and macrophages, in non-enhanced lesions (Kaunzner et al. Mult Scler Relat Disord 2017;15:27-33) and more specifically in the rim of chronic active lesions (Sucksdorff et al. Neurol Neuroimmunol Neuroinflamm 2019;6:e574).

[0116] It seems plausible that smoldering inflammation, which contributes to CWMLA and disability progression, may be influenced by acute inflammation in MS. Recently published longitudinal brain MRI follow-up of primary progressive MS patients treated with ocrelizumab over 6.5 years in the ORATORIO study (Wolinsky et al. Lancet Neurol 2020;10:998-1009) revealed an annual increase in T1LV of approximately 3-6% (reaching a total increase of 37% from baseline in approximately 6 years), which could be attributed to CWMLA in SELs and non-SELs, independent of AWMLA, since it was shown that there was no AWMLA in these patients from week 48 onwards.

[0117] Without wishing to be bound by any particular theory, the effects of natalizumab and / or ocrelizumab on CWMLA (in SELs and non-SELs) and / or disability progression in progressive forms of MS can be explained primarily by their ability to silence AWMLA. For example, an extended approach using Bayesian inference for primary strata estimates can be used to evaluate treatment effects in subgroups characterized by AWMLA covariate thresholds as event occurrence after randomization. Magnusson et al. Stat Med 2019;38(23):4761-4771.

[0118] Efforts are underway to further refine the histopathological terminology of MS lesions (Kuhlmann et al.Acta Neuropathol 2017;133:13-24), but understanding the natural history of chronic active lesion phenotypes remains elusive as their life span may begin decades before specimens are available. Dal-Bianco et al.Brain 2021;144:833-847 doi.org / 10.1093 / brain / awaa436. Molecular studies of the lesion rim of chronic active or mixed active / inactive lesions show a predominance of M1 polarized macrophages and activated microglia. Jackle et al.Brain 2020;143:2073-2088. Interactions of the innate and adaptive immune systems may bidirectionally influence polarization and perpetuate the disease process. Strachan et al.J Interferon Cytokine Res 2014;34:615-622. The phasic rim detected by susceptibility weighted imaging is thought to be an imaging biomarker of the iron-containing microglia / macrophage rim and is associated with disease severity and brain atrophy. Both SEL (Kaunzner et al. Brain 2019;142:133-145, Absinta et al. J Clin Invest 2016;126:2597-2609, Dal-Bianco et al. Acta Neuropathol 2017;133:25-42, and Absinta et al. JAMA Neurol 2019;76:1474-1483) and phasic rim lesions (PRL) are associated with chronic active lesions (Preziosa et al. Mult Scler 2020:1352458520969105). A recent study demonstrated partial concordance between the SEL and PRL, as a significant proportion of PRLs did not expand over time and some SELs did not develop a phase rim at all (Elliott et al. Neurology 2021;96:(15 Supplement)4101).As demonstrated herein, characterization of longitudinal tissue change characteristics within distinct MRI phenotypes of chronic active lesions can be expanded by revealing the complementarity of SEL, non-SEL, and PRL degrees of CWMLA.

[0119] In conclusion, CWMLA in SEL and non-SEL are imaging biomarkers of chronic active lesions and / or secondary axonal degeneration, which reflect progressive inflammation and are associated with clinical disability progression. This disclosure shows that the presence of AWMLA, regardless of new Gd+ lesions or new T2 hyperintense lesions, is associated with higher SEL prevalence in SPMS patients. This indirect evidence of the influence of AWMLA on CWMLA highlights the importance of limiting acute inflammation with highly effective disease-modifying therapies. However, even in the absence of AWMLA, the association of CWMLA with confirmed disability progression persists, albeit to a more limited extent, with increased T1w lesion volume seen primarily in non-SEL tissue with T2w non-enhancing lesions in diseased patients compared to non-diseases.

[0120] In this study, the onset of natalizumab's effect on CWMLA was rapid, beginning at week 24 of treatment, with a statistically significant reduction in T1w lesion volume increase in SELs and non-SELs. Collectively, these results highlight the need for continued development of therapies that not only affect AWMLA, but also more specifically target CWMLA, with the aim of interrupting both smoldering inflammatory and neurodegenerative pathways that may contribute to CWMLA.

[0121] Patients with complete imaging data sets from baseline to week 108 (N=600) were analyzed for SEL prevalence (number and volume of SELs), disability progression, AWMLA (assessed by gadolinium-enhancing lesions and new T2 hyperintense lesions), and CWMLA (assessed by T1 hypointense lesion volume increase in baseline T2 non-enhanced lesions identified as SELs and non-SELs).

[0122] Results: Both SEL and non-SEL CWMLA were higher in SPMS patients with confirmed disability progression than in those without progression. SEL prevalence was significantly lower in the complete absence of AWMLA at baseline and during the study, while non-SEL CWMLA remained associated with disability progression. Natalizumab reduced SEL prevalence and SEL and non-SEL CWMLA compared with placebo.

[0123] Example 2: MRI characteristics of phase rim lesions in chronic and recent acute MS lesions Objective: To determine the prevalence of phase rim lesions (PRL), their relationship to acute new T2 lesions, and to quantify normalized T1-weighted intensity (nT1), normalized magnetization transfer ratio (nMTR), and diffusion tensor imaging radial diffusivity (DTI-RD) in T2 lesions with (PRL+) and without (PRL-) phase rims in a relapsing multiple sclerosis (RMS) population.

[0124] Context: Chronic active lesions are a subpopulation of MS lesions that are thought to be phase rim signals on susceptibility-weighted MR images.

[0125] Design / Methods: Standardized 3-Tesla, 3D isotropic multi-echo, gradient-echo MRI was used to collect PRL data for a subpopulation of RMS patients from the AFFINITY trial [NCT03222973] (N=44) at 72 and 96 weeks of follow-up.

[0126] Results: At 72 weeks of follow-up, 27 of 44 patients (61.4%) had at least one PRL, and 11 of 44 patients (25.0%) had at least four PRL. Approximately 10% of PRLs identified at 72 weeks were derived from acute new lesions that had formed between baseline and week 72. Acute new T2 lesions that were PRL+ were larger at first detection (median size 392 vs. 52 mm3) and had lower nMTR on average at detection and in the recovery phase compared with acute new T2 lesions without rims. Chronic PRL detected within pre-existing T2 lesions at baseline showed lower nMTR at baseline and higher RD. These lesions also showed a trend towards lower nMTR and increased RD from baseline to week 72 compared with chronic lesions without phase rims.

[0127] Conclusions: These data suggest that chronic T2 lesions with a phasic rim have more severe tissue damage than chronic lesions without a rim. The minority of new T2 lesions that developed persistent phasic rims were larger in size and were associated with more severe acute tissue damage as measured by nMTR decline.

[0128] Example 3: MRI characterization of chronic MS lesions by phase rim detection and / or slow expansibility characteristics Objective: To evaluate the colocalization of phase rim lesions (PRL) and slowly expanding lesions (SEL) in patients with relapsing multiple sclerosis (RMS), and to compare normalized magnetization transfer ratio (nMTR) and diffusion tensor imaging radial diffusivity (DTI-RD) in PRL and SEL.

[0129] Context: PRL detected by susceptibility-weighted phase-imaging has been associated with chronic active MS lesions (Absinta et al. J Clin Invest 2016;126:2597-2609). SEL has been hypothesized to be a marker of chronic active MS lesions that can be assessed using only conventional MRI sequences (Elliot et al. Multiple Sclerosis Journal 2018;DOI:10.1177 / 1352458518814117). These lesion types may be relevant biomarkers as SEL is concomitant with ongoing tissue damage within the lesion that is a precursor of disability progression in progressive-onset MS (Elliott et al. Brain 2019;142:2787-2799, and Elliott et al. AJNR Am J Neuroradiol 2020;41(9):1584-1591), and patients with PRL ≥ 4 showed faster accumulation of disability (Absinta et al. JAMA Neurol 2019;76:1474-1483). However, the extent of overlap between SEL and PRL is unclear.

[0130] Design / Methods: Study design: Brain MRI data were obtained in AFFINITY [NCT03222973], a phase 2 trial of opicinumab in relapsing MS with an initial blinded placebo-controlled portion followed by an open-label extension study. Patients stable on disease-modifying therapy (DMT) (interferon, natalizumab, or dimethyl fumarate) were randomized to receive 750 mg opicinumab or placebo every 4 weeks in addition to their basal DMT.

[0131] Imaging: The imaging protocol included pre- and post-gadolinium T1-weighted scans, T2-weighted FLAIR, PD-weighted and T2-weighted spin-echo images, two spoiled gradient recalled echo images with and without MT pulses to calculate the magnetization transfer ratio (MTR), and diffusion-weighted imaging using 32 directions. In part 2 only, the protocol included a standard 3T Siemens 3D isotropic multi-echo spoiled gradient T2 *Susceptibility-weighted imaging (SWI) using a 3-D CT scan was included. PRL was detected from SWI phase images at week 72 or part 2 / day 1. Longitudinal T1- and T2-weighted images were used to detect SEL as areas of baseline T2 lesions in part 1 that enlarged consistently and concentrically from baseline to week 72.

[0132] Results: Forty-one patients enrolled in the advanced MRI substudy of AFFINITY Part 2 had SWI available at week 72. Patient characteristics are shown in Table 5 . [Table 5]

[0133] Cumulatively, more than twice as many SEL (267) were detected as PRL (119) in the 41 patients analyzed. Most SEL and PRL did not overlap (Figure 9e). 39.5% (47 / 119) of PRL were colocalized with SEL, whereas 17.2% (46 / 267) of SEL were colocalized with PRL. Moderate correlation values ​​of SEL and PRL counts were observed between patients (r=0.67) (Figure 10). Lesions with SEL / PRL colocalization were larger than SEL or PRL alone lesions. PRL colocalized with SEL was larger in size than PRL without colocalization (Figures 11A-11B and Table 6). [Table 6]

[0134] Chronic lesions detected as both PRL+ / SEL+ had the lowest normalized magnetization transfer ratio (nMTR) (Figures 12A-12B) and higher diffusion tensor imaging radial diffusivity (DTI-RD) (Figures 13A-13B) compared to PRL without SEL characteristics and SEL without associated PRL.

[0135]

[0136] Conclusions: White matter lesions defined as SEL and PRL show only partial correspondence, and only a minority of SELs are associated with the topological rim (and vice versa). Colocalized SEL and PRL may represent the most severe subpopulation of chronic active white matter lesions. Ongoing investigations of SEL and PRL may clarify MRI lesion subtypes and provide more sensitive markers of MS disease progression.

[0137] Example 4: MRI characterization of chronic MS lesions by phase rim detection and / or slow expansibility characteristics Objective: To identify low-dimensionality characteristics of radiological histological biomarker discrimination of SEL vs. non-SEL MS lesion activity and to develop an ML-based classifier to discriminate between SEL and non-SEL in chronic non-enhancing white matter MS lesions from cross-sectional T1- and T2-weighted brain MRI.

[0138] Background: Chronic active lesions may play a role in the progression biology of MS through insidious damage to myelin and axons due to chronic inflammatory processes that develop within pre-existing MS lesions (Luchetti et al. Acta Neuropathol. 2018;135(4):511-528). Some chronic active lesions may be detected as MRI-identifiable slowly expanding lesions (SELs), which are areas of consistent concentric expansion adjacent to pre-existing T2 lesions (Elliott C, et al. Mult Scler. 2019;25(14):1915-1925). Machine learning and texture analysis techniques may enable discrimination of MS lesion subtypes on conventional MRI imaging. In multiple sclerosis, disease activity is traditionally classified into two forms: relapsing MS or progressive MS. Conventional MRI provides reliable biomarkers of acute disease activity concomitant with relapsing forms of the disease, but there are relatively few established biomarkers to detect tissue conditions that characterize the progressive stages of MS.

[0139] In this context, it is generally believed that chronic active lesions, pathologically characterized by a rim of activated microglia and macrophages, may play a key role in the progressive biology of MS. Throughout the class of chronic active lesions, some may be detected as SELs. These lesions are identifiable on T1-weighted MRI as areas of consistent concentric expansion adjacent to pre-existing T2 lesions. Current methods of SEL detection therefore rely on at least three longitudinal scans acquired over a 1-2 year follow-up period. The requirement for longitudinal data results in a delayed quantification of SEL activity, which is partly relevant in the context of clinical trials for DMTs targeted at progressive MS. Thus, the objective of this example is to detect SELs in a cross-sectional setting. The algorithm presented in this example utilizes image processing techniques from the field of radiological analysis combined with machine learning. As such, this solution is designed to identify histoarchitectural biomarker characteristics associated with SEL activity and to use these characteristics to discriminate between SELs and non-SELs within the range of white matter hyperintensity.

[0140] Design / Methods: T1-weighted and T2-weighted MRI were retrospectively analyzed (ADVANCE-1, 12 relapsing-remitting MS patients; ASCEND-8, 86 secondary progressive MS patients; SYNERGY-4, 19 relapsing-remitting MS / secondary progressive MS patients). Using longitudinal MRI data over 1-2 years of follow-up, ground truth SEL was detected in each baseline scan using a Jacobian integral-based method (Elliott C, et al. Mult Scler. 2019;25(14):1915-1925). Briefly, ground truth SEL was detected in each baseline scan using a Jacobian-based method that analyzes the evolution of T1 intensity over a series of longitudinal scans.

[0141] A 15×15×15 mm cubic patch was extracted from the SEL and non-SEL tissues of each baseline scan ( FIG. 14 ). Specifically, a 15×15×15 mm cubic patch was randomly sampled from the SEL and non-SEL tissues across all available baseline scans. For each patch, texture-based radiological features were extracted separately from the core and periphery of the patch, as shown in green and red in FIG. 15B , respectively. More specifically, with reference to FIG. 15B , the “core” region includes all lesion voxels located less than 4 mm away from the central voxel of the patch. The “periphery” region includes voxels within a 3 mm outer periphery from the edge of the core region. For each patch, a set of 372 radiological features was extracted from the T1- and T2-weighted MRI data.

[0142] The discriminant value of each feature was evaluated by a feature selection algorithm. The selected features were used to benchmark a pool of ML models. The five best performing models were ensemble trained using a stacking strategy. Non-informative features were further eliminated by a recursive loop. Briefly, patients from ADVANCE were split 80:20 into training and validation sets. The training set was used as input to the feature selection and ensemble classification pipeline. The feature selection pipeline evaluated the predictive value of each feature of the 372 features using an ensemble of correlation tests that evaluate the association of each feature with the patch label in both univariate and multivariate settings. This approach ranked the radiological features from most to least useful. With the input space defined by the 50 most useful features, a pool of standard machine learning models was benchmarked via 10-fold cross-validation using a patient-level split. The five best performing models were combined under a stacking strategy.

[0143] The dimensionality of the radiological feature space was further compressed by recursively eliminating non-informative features one by one. This resulted in a compact property of only 15 radiological variables, together with an optimal classifier that could interpret this property to discriminate between SEL and non-SEL. This optimal classifier was then tested on both a validation set of patches from ADVANCE patients and an independent set of patches from ASCEND and SYNERGY patients.

[0144] To discriminate between SEL and non-SEL, we additionally trained a new classifier based on exact matching of the volumes of lesions containing SEL vs. non-SEL patches (Figures 16A-16C), which allows for robust detection of volume-independent tissue structure biomarkers.

[0145] This entire pipeline was applied twice, first based on random sampling of SEL and non-SEL patches, and secondly based on exact matching of the volume of lesions found in SEL vs. non-SEL patches. This volume matching experiment allows us to evaluate the ability of the present invention to discriminate between SEL and non-SEL based solely on histological biomarkers and in a shape-independent manner.

[0146] For clarity, we restricted both experiments to radiological features that quantify texture, but it is important to recognize that some of these features, such as entropy and energy, are volume confounded, and therefore a rigorous volume matching paradigm can ensure volume-independent classification.

[0147] Results: The 15 radiological features selected via our recursive elimination pipeline define a compact signature that discriminates between SEL and non-SEL activity. The signature primarily includes information from T1-weighted MRI signal within the core of the patch. We observed that one-dimensional statistics including mean, median, and 90th percentile of T1 intensity within the core of the patch were identified as relevant. This is consistent with previous studies that reported that SELs have a higher degree of T1 hypointensity at baseline compared to non-SELs. The 15 feature radiological signatures included 10 from T1-weighted MRI, 5 from T2-weighted MRI, 8 from the patch "core", and 7 from the patch "periphery". The prevalence of each of the 15 radiological features selected to discriminate between SEL and non-SEL patches is shown in Figure 17.

[0148] In discriminating between SEL and non-SEL patches, our optimal classification model achieved a balanced accuracy rate of 67% on the ADVANCE validation set, 66% on the ASCEND test set, and 69% on the SYNERGY test set. Importantly, we achieved high sensitivity and low specificity when testing on the ASCEND population of SPMS, while in contrast, we observed low sensitivity and high specificity when testing on the SYNERGY population (Table 7). [Table 7]

[0149] 18A-18D are confusion matrices showing the performance of a classification model for patch-level SEL vs. non-SEL discrimination based on a training set, a validation set, and an independent test set.

[0150] In the volume-balanced setting, the resulting ensemble model achieved a balanced accuracy rate of 62% on the SYNERGY and ASCEND test sets in addition to the ADVANCE validation set (Table 8). The slight drop in performance reported in this volume-balanced experiment compared to the results reported in the previous table indicates that our initial experiments are capturing the volumetric differences between SELs and non-SELs beyond tissue structure information. [Table 8]

[0151] 19A-19D are confusion matrices showing the performance of classification models for patch-level SEL vs. non-SEL discrimination based on training, validation, and independent test sets on volume-matched patches.

[0152] Conclusions: Using single-timepoint non-contrast conventional T1- and T2-weighted MRI, we developed a machine learning classifier that can discriminate between SEL and non-SEL with classification accuracies ranging from 66% to 69% for random patch sampling versus 62% for strict lesion volume matching. This implies that SEL may exhibit patterns of conventional non-enhanced MRI signal that are detectable in a cross-sectional setting using ML techniques. Single-timepoint detection of SEL may reduce the need for longitudinal analysis and allow baseline quantification of chronic MS lesion subtypes. Applications of the algorithm could include population enrichment in clinical trials and improved patient prognosis in clinical practice. Additionally, chronic active lesions detectable by paramagnetic rim identification could be incorporated into the classification algorithm. * * *

[0153] Preferred embodiments of the present application are described herein, including the best mode for carrying out the application known to the inventors. Variations of these preferred embodiments will become apparent to those of ordinary skill in the art upon reading the foregoing description. It is contemplated that those skilled in the art can employ such variations as appropriate and can carry out the application in ways other than as detailed herein. Accordingly, many embodiments of the present application include all modifications and equivalents of the subject matter of the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by this application unless otherwise indicated herein or otherwise clearly contradicted by context.

[0154] All patents, patent applications, patent application publications, and other materials, such as articles, books, specifications, publications, documents, articles, etc., referenced herein are hereby incorporated by reference in their entirety for all purposes, except for any procedural file history relating to this specification, any procedural history that is inconsistent or contradictory with this specification, or that may have limitations affecting the broadest scope of any claims now or later related to this specification. By way of example, in the event of a conflict or inconsistency between the explanation, definition, and / or use of a term associated with any of the incorporated materials and the term associated with this specification, the explanation, definition, and / or use of the term in this specification shall control.

[0155] In conclusion, it should be understood that the embodiments of the application disclosed herein are illustrative of the principles of the embodiments of the application. Other variations that can be used may be within the scope of the application. Thus, by way of example, but not of limitation, alternative configurations of the embodiments of the application may be utilized in accordance with the teachings herein. Thus, the embodiments of the application are not limited to those exactly as shown and described.

Claims

1. 1. A composition for use in the treatment of radiologically isolated syndrome in a patient in need thereof, the composition comprising a disease-modifying antibody, the composition being administered to the patient, the patient having at least one phase rim lesion (PRL) in at least one susceptibility-weighted magnetic resonance image (MRI), or the patient having at least one slowly expanding lesion (SEL) in at least one T1-weighted / T2-weighted MRI.

2. 1. A composition for use in the treatment of chronic active white matter lesions in early stage and / or asymptomatic MS patients, said composition comprising a disease modifying antibody, said composition being administered to said patient, said patient having at least one PRL in at least one susceptibility weighted MRI, or said patient having at least one SEL in at least one T1 weighted / T2 weighted MRI.

3. 2. The composition of claim 1, wherein treatment with a disease-modifying antibody is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number is identified as PRL.

4. The composition of claim 2, wherein treatment with a disease-modifying antibody is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number is identified as PRL.

5. 2. The composition of claim 1, wherein treatment with a disease-modifying antibody is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number are identified as SEL.

6. The composition of claim 2, wherein treatment with a disease-modifying antibody is initiated when at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's total T2 hyperintense lesion volume and / or number are identified as SEL.

7. The composition of claim 1, wherein the patient has at least one SEL co-localized with at least one PRL or at least one PRL co-localized with at least one SEL, and optionally, the at least one SEL is detected using single-timepoint non-contrast T1 and T2 weighted MRI.

8. The composition described in claim 2, wherein the patient has at least one SEL co-localized with at least one PRL or at least one PRL co-localized with at least one SEL, and, optionally, the at least one SEL is detected using single-time point non-contrast T1- and T2-weighted MRI.

9. The composition of claim 7, characterized in that treatment with a disease-modifying antibody is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's SELs co-localize with the patient's PRLs and / or when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's PRLs co-localize with the patient's SELs.

10. The composition of claim 8, wherein treatment with a disease-modifying antibody is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's SELs co-localize with the patient's PRLs and / or when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75% of the patient's PRLs co-localize with the patient's SELs.

11. 10. The composition according to any of the preceding claims, characterized in that the SEL is detected using a machine learning based classifier that distinguishes between acute and chronic MS lesions and / or between SEL and non-SEL using single time point non-contrast T1 and T2 weighted MRI.

12. The composition of any one of claims 1 to 10, wherein the disease modifying antibody is selected from natalizumab, BIIB107 and ocrelizumab.

13. The composition of claim 11, wherein the disease-modifying antibody is selected from natalizumab, BIIB107 and ocrelizumab.

14. The composition of claim 12 , wherein the disease-modifying antibody is natalizumab.

15. The composition described in claim 13, wherein the disease-modifying antibody is natalizumab.

16. The composition of claim 14, wherein natalizumab is administered in a biphasic dosing regimen, the biphasic regimen comprising an induction phase comprising administration of natalizumab once a month for about 10 to about 14 months, followed by a chronic phase comprising administration of natalizumab once every 5, 6, 7, or 8 weeks.

17. The composition of claim 15, wherein natalizumab is administered in a biphasic dosing regimen, the biphasic regimen including an induction phase involving administration of natalizumab once a month for about 10 to about 14 months, followed by a chronic phase involving administration of natalizumab once every 5, 6, 7, or 8 weeks.

18. 17. The composition of claim 16, wherein at least one phase of the biphasic protocol is administered subcutaneously (SC).

19. The composition of claim 17, wherein at least one period of the biphasic protocol is administered subcutaneously (SC).

20. 17. The composition of claim 16, wherein both phases of the biphasic protocol are administered SC.

21. The composition of claim 17, wherein both periods of the biphasic protocol are administered SC.

22. 1. A composition for use in a method of reducing and / or treating chronic lesion activity in asymptomatic and / or early stage MS patients (e.g., in the absence of relapse events), the composition comprising a disease-modifying antibody, the method comprising: a) identifying at least one phase lim lesion (PRL) in at least one susceptibility-weighted magnetic resonance image from a patient known or suspected of having chronic lesion activity; b) identifying at least one slowly expanding lesion (SEL) in at least one T1-weighted / T2-weighted MRI from the patient; c) determining whether the at least one PRL co-localizes with the at least one SEL in the patient (or vice versa); and d) if co-localized, initiating treatment with the disease-modifying antibody.

23. 23. The composition of claim 22, wherein the at least one SEL is detected using a machine learning based classifier that distinguishes between acute and chronic MS lesions and / or between SEL and non-SEL using single time point non-contrast T1 and T2 weighted MRI.

24. The composition of claim 22, characterized in that treatment with the disease-modifying antibody is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's SELs co-localize with the patient's PRLs and / or when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's PRLs co-localize with the patient's SELs.

25. The composition of claim 23, wherein treatment with the disease-modifying antibody is initiated when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% of the patient's SELs co-localize with the patient's PRLs and / or when at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75% of the patient's PRLs co-localize with the patient's SELs.

26. The composition of any one of claims 22 to 25, wherein the disease modifying antibody is selected from natalizumab, BIIB107 and ocrelizumab.

27. The composition of any one of claims 22 to 25, wherein the disease modifying antibody is an anti-VLA-4 antibody, such as natalizumab or BIIB107.

28. The composition of claim 27, wherein the anti-VLA-4 antibody is natalizumab, and the method further comprises administering a therapeutically effective amount of natalizumab to the patient in a biphasic dosing regimen, the biphasic dosing regimen comprising an induction phase comprising administration of the anti-VLA-4 antibody once every 2 weeks, once every 4 weeks, once every 30 days, or once a month for at least 6 months, at least 8 months, at least 10 months, or at least 12 months, followed by a chronic phase comprising administration of natalizumab once every 5 to 10 weeks.

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