Abstract
Background:
Ibudilast has shown beneficial effects on several imaging outcomes in progressive multiple sclerosis (MS). Slowly enlarging lesions are a proposed imaging biomarker of compartmentalized inflammation within chronic active lesions.
Objective:
To assess the treatment effect of ibudilast on slowly enlarging lesion volumes over 96 weeks from a phase II clinical trial of ibudilast (Secondary and Primary Progressive Ibudilast NeuroNEXT Trial in Multiple Sclerosis [SPRINT-MS]).
Methods:
In total, 255 participants with progressive MS from 28 sites were randomized to oral ibudilast or placebo. Participants with at least four analyzable magnetic resonance imaging (MRI) were included. Slowly enlarging lesions were quantified using Jacobian determinant maps. A linear model was used to assess the effect of ibudilast. Magnetization transfer ratio within slowly enlarging lesions was assessed to determine the effect of ibudilast on tissue integrity.
Results:
In total, 195 participants were included in this analysis. Ibudilast significantly decreased slowly enlarging lesion volume (23%, p = 0.003). Ibudilast also reduced magnetization transfer ratio change in slowly enlarging lesions: 0.22%/year, p = 0.04.
Conclusion:
Ibudilast showed a significant effect on baseline volume of lesions that were slowly enlarging and magnetization transfer ratio in slowly enlarging lesions. The results support the use of slowly enlarging lesions for assessment of compartmentalized inflammation represented by chronic active lesions and provide further support for the neuroprotective effects of ibudilast in progressive MS.
Keywords: Progressive multiple sclerosis, slowly enlarging lesion, ibudilast, magnetic resonance imaging, chronic active lesion
Introduction
Multiple sclerosis (MS) is a chronic inflammatory disease characterized by focal demyelinating lesions and neurodegeneration. Numerous efficacious disease-modifying therapies have been approved for reducing the number of relapses.1 Despite the effective suppression of relapses and acute inflammatory activity, many patients experience clinical progression of disease.2 While several promising imaging markers have been developed and employed in previous clinical trials including brain atrophy and T1 hypointense lesions, one potential mechanism of such disease progression is the compartmentalized inflammation within chronic active lesions,3–5 which occur behind an intact blood–brain barrier, where currently available anti-inflammatory treatments have limited effects.1
MS lesions are traditionally classified by pathologic evaluation as one of the three lesion types:6 (1) active lesions: identified by abundant inflammatory cells with ongoing demyelination, (2) chronic inactive lesions: identified by absence of inflammatory cells with complete demyelination often with astrogliosis, and (3) chronic active lesions: inflammatory cells are present at the rim or edge of lesions, resembling the active lesions while the core of the lesions have the appearance of chronic inactive lesions. Chronic active lesions are sometimes called smoldering lesions and are thought to slowly expand and have been associated with gradual worsening of disability.7,8
Several in vivo imaging methods have been proposed to detect chronic active lesions, and these include slowly enlarging lesions (SELs), paramagnetic rim lesions, and positron emission tomography of activated microglia.9 Paramagnetic rim lesions require T2*-weighted images and are detected as a local disturbance in the phase of magnetic resonance signal,3,10–13 which is believed to arise from iron particles in phagocytic cells that engulf myelin debris, which contain high iron content. On the contrary, a recently developed method to image chronic active lesions is termed SELs and detects local growth of T2 lesions.12,14 This method does not require acquisition of specific scans such as T2*-weighted images, which are uncommon in either MS trials and routine clinical practice, but rather uses conventional T2 (or fluid-attenuated inversion recovery [FLAIR]) and structural T1-weighted scans. It is hypothesized that the expansion of active and destructive rim in the chronic active lesions can be detected using the quantitative analysis of individual lesions. Previous studies have found that SELs have more severe tissue damage than non-SEL T2 lesions, and in a clinical trial, treatment with ocrelizumab was found to improve normalized T1 intensity in SELs, supporting relevance of SELs as a target of treatment.12,14 Indeed, several small pathological studies have shown associations with chronic active lesions.5,15
Anti-inflammatory and immunomodulatory agents have been approved for relapsing forms of MS, are approved for only active forms of secondary progressive multiple sclerosis (SPMS), and only one disease-modifying agent is approved in primary progressive multiple sclerosis (PPMS). Therefore, there is a significant unmet need for disease-modifying therapies to slow, stop, or revert progression of disability in progressive forms of MS.
SPRINT-MS was a multicenter, phase II clinical trial of ibudilast in participants with progressive MS.16,17 The study demonstrated benefit of ibudilast on the primary endpoint, change in whole brain atrophy measured by the brain parenchymal fraction (BPF). The study’s MRI did not include T2*-weighted images suited for paramagnetic rim lesions, but scans allow for analysis of SELs. We hypothesize that the SEL volume would be reduced in the ibudilast-treated subjects compared with placebo in SPRINT-MS and have clinical relevance as measured through disability progression. Therefore, in this study, we evaluated (1) the effect of ibudilast on SEL volume, (2) clinical association of SEL volumes between participants with and without confirmed disability progression, (3) the imaging and clinical characteristics between participants with large and small volumes of SELs, and (4) tissue damage in lesions quantified by magnetization transfer imaging.
Methods
This study was approved by the local institutional review board (IRB 23-466). Informed consent was obtained from all participants for the SPRINT-MS study.
Participants
SPRINT-MS was a phase II, multicenter, placebo-controlled randomized clinical trial of oral ibudilast (up to 100 mg daily) for 96 weeks. The detailed protocol as well as primary and secondary results have been published.16,17 In total, 255 participants were randomized in a 1:1 ratio at 28 sites. Participants underwent magnetic resonance imaging (MRI) at baseline and weeks 24, 48, 72, and 96. Imaging including structural T1-weighted three-dimensional (3D) spoiled gradient-recalled echo 1 mm3 isotropic scans, proton density (PD)-weighted, T2-weighted turbo spin echo or fast spin echo images, and 3D spoiled gradient-recalled echo with selective excitation magnetization transfer imaging (with and without magnetization transfer pulse). All MRIs were conducted on 3T systems from Siemens (Trio/Prisma or Skyra) or General Electric (GE, version 12X or higher). Scanner’s default settings were used for magnetization transfer pulses. Due to the longitudinal nature of SEL analysis, we included only participants with at least four analyzable MRIs in this study. Therefore, we excluded the patients who had inconsistent scanners and who had three or fewer MRIs.
Image analysis
The core study quantified total T2 lesion volume (T2LV), whole brain atrophy, and cortical atrophy as well as mean magnetization transfer ratio (MTR) in normal-appearing brain tissue, normal-appearing gray matter, and whole brain as previously reported.16 For the SEL analysis, we implemented in-house version of SEL algorithm.12 The images were linearly co-registered using minctracc (https://github.com/BIC-MNI/minc-toolkit-v2) between the contrasts (T1-weighted and T2-weighted), within each time point. The T1-weighted and T2-weighted images were simultaneously and nonlinearly registered using Advanced Normalization Tools (ANTS) between baseline and follow-up scans in halfway space. The determinant of Jacobian matrix of nonlinear field was numerically calculated for each voxel. Then, the determinant maps from baseline to each follow-up time point were analyzed within the baseline T2 lesion mask for a local expansion using initial threshold on the Jacobian determinant value (Jacobian Expansion, JE1 ⩾ 12.5%/year), connected component analysis, second threshold of 4%/year (JE2), and minimal size 10 voxels, for constancy over time, and finally concentricity, resulting in the SEL mask.12 SEL status was obtained for subregions and not necessarily as whole discrete lesions (Figure 1). The total volume of the SEL was calculated as the number of SEL voxels times voxel volumes.
Figure 1.
An example of classification of slowly enlarging lesion. Within the baseline T2 lesion (green), the local voxel expansion is calculated between the baseline (a) and follow-up (c) time points, and slowly enlarging lesions are identified in red (d): (a) baseline T2w; (b) baseline T2 lesion; (c) follow-up T2w; and (d) slowly enlarging lesion.
Images with and without magnetization transfer pulse were registered to each other and to the baseline scan. MTR was calculated using the standard equation: MTR = 100% × (MToff − MTon)/ MToff. Mean MTR in baseline T2 lesion, SEL, and non-SEL (T2 lesion that is not SEL) were calculated within the corresponding mask for each time point.
Statistical analysis
Due to the skewness of the distribution of lesion volumes, both T2 lesions and SEL volumes were transformed using Johnson transformation.18 A descriptive analysis was performed on the baseline measures. For cross-sectional clinical correlation, SEL volumes were correlated with Expanded Disability Status Scale (EDSS), timed 25-foot walk test (T25FWT), 9-hole peg test (9HPT), and symbol digit modalities test (SDMT) using Spearman’s rank correlation at the last visit from the participants in the placebo group.
The main outcome of interest was the treatment effect, which was calculated by a linear model with a covariate adjustment for baseline T2LV. We investigated the treatment effect by MS disease course (PPMS vs. SPMS) as a covariate in a linear model and stratified by disease course. Participants were grouped into quartiles by SEL volume, and clinical and imaging measures at baseline and measures of on-study change in the first (smallest SEL volume) and fourth (largest SEL volume) quartiles were compared by t-tests or Mann–Whitney U-tests. A time-to-event analysis was performed with confirmed progression of disability, which was defined as an increase in the EDSS of at least 1.0 point from baseline (or an increase of ⩾0.5 points for patients with a baseline EDSS score of >5.0) that was maintained for at least 20 weeks. Specifically, log-rank tests and Cox proportional hazard models adjusted for baseline T2 lesion volume were used. The analysis was stratified by the MS type and quartile groups. Spatial distribution of SELs and T2LVs were visualized and compared using voxel-based morphometry with FMRIB Software Library (FSL) v6.0 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki). The extent of tissue damage in SEL and non-SEL was assessed using MTR. Since MTR values differed significantly between the vendors,19 vendor-based adjustment was performed using a linear model and baseline MTR in T2 lesions; thus, we subtracted 6.2% from the GE’s MTR values. The adjusted baseline MTR was compared between the SEL and non-SEL using paired t-tests. Ibudilast’s treatment effect on MTR in SEL was evaluated using the serial MTR values in SEL and mixed-effect model with a random subject effect. As this was a post hoc exploratory analysis, no adjustment for multiplicity was applied.
Results
Of the 255 enrolled participants, 195 had four or five analyzable MRIs; 26 patients had major scanner changes; 34 patients had fewer than four scans. Table 1 summarizes the baseline demographic, clinical, and imaging characteristics of the studied population.
Table 1.
Baseline demographic, clinical, and imaging characteristics of the studied population. No measure was statistically significant between ibudilast and placebo groups. Mean and SD unless indicated otherwise.
| Total | Ibudilast | Placebo | PPMS | SPMS | Disability progressiona | No disability progressiona | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Count | 195 | 97 | 98 | 107 | 88 | 48 | 147 |
| Age, years | 55.9 (7.0) | 55.0 (7.4) | 56.9 (6.4) | 55.6 (6.8) | 56.4 (7.1) | 56.4 (6.4) | 55.8 (7.1) |
| Female, n (%) | 112 (57) | 53 (55) | 59 (60) | 49 (46) | 63 (72) | 30 (63) | 82 (56) |
| PPMS, n (%) | 107 (55) | 53 (55) | 54 (55) | 28 (58) | 79 (54) | ||
| EDSS, median (IQR) | 6.0 (4.0, 6.0) | 6.0 (4.5, 6.0) | 6.0 (4.0, 6.0) | 6.0 (4.0, 6.0) | 6.0 (5.5, 6.5) | 5.5 (4.0, 6.0) | 6.0 (4.5, 6.0) |
| T25FWT, median (IQR), seconds | 9.1 (6.4, 14.6) | 8.7 (6.4, 14.5) | 9.3 (6.5, 14.9) | 7.5 (6.1, 11.4) | 12.1 (8.0, 20.9) | 7.9 (6.4, 11.4) | 9.5 (6.4, 16.4) |
| 9HPT, median (IQR), seconds | 29.9 (25.6, 38.0) | 28.3 (24.8, 37.7) | 30.1 (26.5, 38.3) | 28.2 (25.4, 33.8) | 31.1 (25.8, 43.4) | 28.4 (24.8, 32.5) | 30.1 (25.9, 40.4) |
| SDMT, #correct, median (IQR) | 40.0 (32.0, 50.0) | 42.0 (34.0, 51.0) | 38.0 (32.0, 48.0) | 40.0 (32.0, 50.0) | 40.0 (34.0, 49.3) | 39.5 (32.0, 48.3) | 40.0 (33.0, 50.0) |
| T2LV, mL | 10.3 (10.9) | 9.9 (10.2) | 10.6 (11.6) | 8.5 (10.5) | 12.5 (11.0) | 10.8 (10.7) | 10.1 (11.0) |
| T1LV, mL | 3.2 (4.3) | 3.2 (4.6) | 3.2 (4.1) | 2.4 (3.6) | 4.2 (5.0) | 3.5 (4.5) | 3.1 (4.3) |
| BPF | 0.804 (0.028) | 0.806 (0.026) | 0.802 (0.030) | 0.809 (0.025) | 0.799 (0.030) | 0.800 (0.032) | 0.806 (0.026) |
| GMF | 0.459 (0.019) | 0.459 (0.018) | 0.459 (0.020) | 0.458 (0.019) | 0.460 (0.018) | 0.456 (0.017) | 0.460 (0.020) |
| Cortical thickness (mm) | 3.42 (0.21) | 3.43 (0.2) | 3.41 (0.22) | 3.44 (0.2) | 3.39 (0.22) | 3.37 (0.22) | 3.44 (0.21) |
| Normalized MTR in normal-appearing brain tissue | 0.279 (0.285) | 0.282 (0.248) | 0.277 (0.318) | 0.324 (0.257) | 0.225 (0.308) | 0.284 (0.263) | 0.278 (0.292) |
| Normalized MTR in normal-appearing gray matter | −0.301 (0.282) | −0.302 (0.242) | −0.300 (0.319) | −0.258 (0.240) | −0.353 (0.320) | −0.314 (0.251) | −0.297 (0.292) |
| Normalized MTR in whole brain | 0.113 (0.267) | 0.111 (0.235) | 0.116 (0.297) | 0.156 (0.237) | 0.061 (0.293) | 0.124 (0.236) | 0.110 (0.277) |
PPMS: primary progressive multiple sclerosis; SPMS: secondary progressive multiple sclerosis; EDSS: Expanded Disability Status Scale; IQR: interquartile range; T25FWT: timed 25-foot walk test; 9HPT: 9-hole peg test; SDMT: symbol digit modalities test; BPF: brain parenchymal fraction; GMF: gray matter fraction; MTR: magnetization transfer ratio; T2LV: T2 lesion volume; T1LV: T1 lesion volume.
Disability progression was defined as an increase in the EDSS of at least 1.0 point from baseline (or an increase of ⩾0.5 points for patients with a baseline EDSS score of >5.0) that was sustained for at least 20 weeks.
Figure 1 shows an example of SELs overlaid on T2-weighted image. The median SEL volume was 0.50 mL (interquartile range (IQR) = 0.22, 1.22) in ibudilast group and 0.75 (0.29, 1.66) mL in the placebo group (p = 0.03). The median SEL count was 5 (IQR = 2, 11) in ibudilast group and 6 (IQR = 3, 14) for placebo group (p = 0.2 using Mann–Whitney U-test). SEL volume correlated with T25FWT, 9HPT, and SDMT (p < 0.03 for each) but not with EDSS (p = 0.3) (Figure 2). The mean (standard deviation (SD)) proportions of SEL was 5.3 (4.6)% in ibudilast and 6.8 (6.3)% for placebo groups (p = 0.17, Mann–Whitney U-test).
Figure 2.
(a) Cross-sectional clinical correlations of slowly enlarging lesion volume as a Spearman rank correlation matrix (top) with significance levels: *p < 0.05, **p < 0.005, and ***p < 0.001. (b) The bottom scatter plots show individual data points in blue for secondary progressive multiple sclerosis and orange for primary progressive multiple sclerosis. EDSS: Expanded Disability Status Scale; T25FWT: timed 25-foot walk test; 9HPT: 9-hole peg test; SDMT: symbol digit modalities test.
The lambdas for Johnson transformation were 0.216 for SELs and −0.00576 for T2LV. After Johnson transformation and with baseline T2LV as a covariate, ibudilast was associated with a smaller SEL volume (transformed coefficient = −1.16, 95% confidence interval = (−1.93, −0.40), p = 0.003) (Figure 3). Sensitivity analysis showed significant effect without the transformation (p = 0.03). In contrast, there was no treatment effect on changes in T2LV and T1LV (p > 0.3) as well as the cumulative new T2 lesion counts (p = 0.95, Mann–Whitney U-test) and new T1 lesion counts (p = 0.25, Mann–Whitney U-test).
Figure 3.
Bar graphs of T2 lesion volume-adjusted slowly enlarging lesion volumes for placebo and ibudilast groups for (a) the total group, (b) PPMS, and (c) SPMS (left to right). There was 23%, 21%, and 19% reduction for total multiple sclerosis, PPMS, and SPMS with p = 0.003, 0.02, and 0.07, respectively. PPMS: primary progressive multiple sclerosis; SPMS: secondary progressive multiple sclerosis.
There was no significant difference between PPMS (n = 107) and SPMS (n = 88) in terms of the treatment effect as an interaction variable. However, when stratified, the model with PPMS showed treatment effect (21%, p = 0.02) with a comparable but nonsignificant trend for SPMS (19%, p = 0.07) (Figure 3(b) and (c)).
When participants were divided into quartiles based on the SEL volumes, those in the highest quartile of SEL compared with the lowest showed worse baseline measures in EDSS, 9HPT, T25FWT, SDMT, T2LV, T1LV, BPF, cortical thickness, MTR in normal-appearing brain tissue, MTR in normal-appearing gray matter, and MTR in whole brain (Table 2).
Table 2.
Quartile analysis for baseline measures (mean and SD).
| Total | Q1 | Q2 | Q3 | Q4 | p between Q1 and Q4 | |
|---|---|---|---|---|---|---|
|
| ||||||
| Count | 195 | 49 | 48 | 49 | 49 | |
| SEL volume, mm3 | 1123.4 (1451.7) | 101.0 (73.3) | 415.1 (88.5) | 976.9 (277.1) | 3004.0 (1771.9) | <0.001 |
| SEL proportion of T2, % | 6.1 (5.5) | 1.2 (0.7) | 3.3 (0.5) | 6.1 (1.3) | 13.6 (5.6) | <0.001 |
| Age, years | 55.9 (7.0) | 56.0 (6.0) | 57.3 (6.7) | 54.3 (8.3) | 56.2 (6.6) | 0.8a |
| Female, n (%) | 112 (57) | 29 (59) | 22 (46) | 30 (63) | 30 (61) | 0.9b |
| Ibudilast assignment, n (%) | 97 (50) | 27 (55) | 26 (54) | 25 (52) | 19 (39) | 0.2b |
| PPMS, n (%) | 107 (55) | 36 (73) | 27 (56) | 23 (48) | 20 (41) | 0.004b |
| EDSS, median (IQR) | 6.0 (4.0, 6.0) | 5.5 (4.0, 6.0) | 6.0 (4.4, 6.0) | 6.0 (4.0, 6.0) | 6.0 (4.5, 6.5) | 0.037a |
| T25FWT, seconds | 15.0 (18.7) | 11.6 (10.1) | 14.5 (16.5) | 13.9 (18.1) | 20.1 (26.3) | 0.037a |
| 9HPT, seconds | 34.5 (16.3) | 30.8 (11.6) | 35.2 (16.1) | 31.7 (16.2) | 40.5 (19.3) | 0.003a |
| SDMT, #correct | 41.2 (13.4) | 45.2 (12.7) | 41.0 (12.1) | 42.9 (11.9) | 36.1 (15.4) | 0.002a |
| T2LV, mL | 10.3 (10.9) | 1.8 (1.7) | 4.6 (3.5) | 12.6 (7.8) | 22.2 (12.1) | <0.001a |
| T1LV, mL | 3.2 (4.3) | 0.6 (0.8) | 1.1 (1.0) | 4.0 (3.9) | 7.2 (5.6) | <0.001a |
| BPF | 0.804 (0.028) | 0.816 (0.023) | 0.810 (0.025) | 0.798 (0.027) | 0.794 (0.030) | <0.001a |
| GMF | 0.459 (0.019) | 0.464 (0.019) | 0.459 (0.016) | 0.456 (0.018) | 0.457 (0.022) | 0.069a |
| Cortical thickness (mm) | 3.42 (0.21) | 3.55 (0.16) | 3.44 (0.19) | 3.38 (0.21) | 3.30 (0.22) | <0.001a |
| Normalized MTR in normal-appearing brain tissue | 0.279 (0.285) | 0.379 (0.249) | 0.341 (0.328) | 0.215 (0.242) | 0.178 (0.271) | <0.001a |
| Normalized MTR in normal-appearing gray matter | −0.301 (0.282) | −0.207 (0.237) | −0.254 (0.312) | −0.372 (0.270) | −0.375 (0.277) | 0.002a |
| Normalized MTR in whole brain | 0.113 (0.267) | 0.196 (0.235) | 0.174 (0.316) | 0.057 (0.223) | 0.022 (0.250) | 0.001a |
Q1: first quartile; Q2: second quartile; Q3: third quartile; Q4: fourth quartile; PPMS: primary progressive multiple sclerosis; EDSS: Expanded Disability Status Scale; IQR: interquartile range; T25FWT: timed 25-foot walk test; 9HPT: 9-hole peg test; SDMT: symbol digit modalities test; BPF: brain parenchymal fraction; GMF: gray matter fraction; MTR: magnetization transfer ratio; SEL: slowly enlarging lesion; T2LV: T2 lesion volume; T1LV: T1 lesion volume.
p values from t-tests.
p values from Mann–Whitney U-tests.
Longitudinal changes were assessed using the same quartile grouping (Table 3), which showed differences in T2LV change at week 96, cumulative new T2 lesion counts, T1LV change, and change in T25FWT. The time-to-event analysis did not show association between SEL and clinical progression status, although the small proportion of subjects with progression limited this analysis power.
Table 3.
Quartile analysis for longitudinal change (mean and SD).
| Total | Q1 | Q2 | Q3 | Q4 | p between Q1 and Q4 | |
|---|---|---|---|---|---|---|
|
| ||||||
| T2LV change, mL | 0.485 (1.600) | 0.197 (0.769) | 0.182 (0.659) | 0.473 (2.308) | 1.098 (1.914) | 0.004a |
| Cumulative new T2 lesion count | 2.82 (8.68) | 0.61 (2.10) | 2.04 (6.12) | 3.48 (7.73) | 5.06 (13.90) | 0.03b |
| T1LV change, mL | 0.341 (0.794) | 0.030 (0.171) | 0.116 (0.249) | 0.398 (0.619) | 0.834 (1.297) | <0.001a |
| New T1 lesion count at week 96 | 1.1 (2.9) | 0.4 (1.6) | 0.8 (2.5) | 1.3 (2.4) | 2.0 (4.3) | 0.42b |
| Annualized change in BPF, per year | −0.001 (0.003) | −0.001 (0.002) | −0.001 (0.004) | −0.001 (0.003) | −0.001 (0.004) | 0.51a |
| Annualized change in cortical thickness, mL/year | −0.007 (0.029) | −0.002 (0.024) | −0.004 (0.031) | −0.011 (0.024) | −0.012 (0.035) | 0.12a |
| T25FWT change, seconds | 3.9 (17.5) | 0.4 (8.1) | 5.0 (15.9) | 0.9 (5.3) | 9.3 (29.0) | 0.04a |
| 9HPT change, seconds | 1.5 (9.0) | 0.8 (9.0) | 0.1 (8.7) | 1.2 (8.4) | 3.8 (9.9) | 0.12a |
| SDMT change, #correct | 0.2 (10.1) | 2.6 (11.4) | 0.7 (11.6) | −1.3 (8.1) | −0.3 (7.0) | 0.15a |
Q1: first quartile; Q2: second quartile; Q3: third quartile; Q4: fourth quartile; BPF: brain parenchymal fraction; T25FWT: timed 25-foot walk test; 9HPT: 9-hole peg test; SDMT: symbol digit modalities test; MTR: magnetization transfer ratio; T2LV: T2 lesion volume; T1LV: T1 lesion volume.
p values from t-tests.
p values from Mann–Whitney U-tests.
The voxel-based analysis showed a difference in SEL volume in the ibudilast group compared with the placebo group in anterior periventricular areas in the frontal lobe (Figure 4).
Figure 4.
Probabilistic distribution of T2 lesions (top, a–c) and slowly enlarging lesions (middle, d–f) blurred with 3 mm full-width half max kernel, with different color range (color bars). The left panels show that from the entire study population (a and d), the middle panels were from the ibudilast group (b and e), and the right panels are from the placebo group. After threshold-free cluster enhancement, two small regions in left anterior periventricular white matter showed higher slowly enlarging lesion volumes in the placebo compared with ibudilast group (g). No brain region showed statistically significant difference between ibudilast and placebo groups in T2 lesion (not shown). The area that showed statistical difference in SEL did not show difference in T2 lesion density between ibudilast (29.9 (standard deviation = 19.1)%) versus placebo (33.2 (20.3), p = 0.28).
The extent of tissue damage was assessed by MTR. At baseline, mean (SD) MTR in SEL was lower than non-SEL by 0.61 (2.06) (p < 0.001, paired t-test). The linear mixed-effect model showed ibudilast significantly reduced MTR loss in SEL over time: coefficient for ibudilast versus placebo = 0.22 (95% confidence interval = (0.014, 0.425), p = 0.036) (Figure 5). The baseline MTR values within SEL were not significantly different (p = 0.09). In contrast, the MTR in T2 lesions and non-SEL did not show differences between ibudilast and placebo groups (p > 0.2).
Figure 5.
Adjusted magnetization transfer ratio in slowly enlarging lesion over the duration of study. The blue squares and lines represent placebo, and orange circles and lines represent ibudilast group. The thick lines show the mean, and filled areas are the 95% confidence intervals. The upper right inset shows the same data on an enlarged y-axis. The change in MTR was significantly different between the ibudilast and placebo groups.
Discussion
Over 96 weeks, we found a treatment effect of ibudilast on SEL volume and MTR change in SEL. This observation contrasts with the absence of effect on change in T2 lesions.20 Taken together with previous MRI analysis from this trial, the results suggest that ibudilast’s mechanism of action is likely to be different from conventional anti-inflammatory therapies in MS. The results are consistent with ibudilast suppressing compartmentalized, chronic active inflammation and inhibition of microglial activation.21,22
Ibudilast is a selective phosphodiesterase inhibitor that preferentially inhibits phosphodiesterase-3 (PDE-3), PDE-4, PDE-10, and PDE-11 as well as macrophage migration inhibitory factor.23,24 PDEs control the signal of cyclic adenosine monophosphate, which plays an important role in regulation of activated macrophages in inflammatory response.25 Therefore, ibudilast is expected to have an effect on the activity of microglia and macrophages. A pathologic study that investigated the transcriptional profiles of microglia from the edge of chronic active lesion showed that a PDE inhibitor such as ibudilast may have effect on microglia activities.26 Ibudilast also crosses the blood–brain barrier and thus is possible to have an effect on the compartmentalized inflammation that is associated with chronic active lesions. These studies provide a mechanistic framework that supports our finding on the effect of ibudilast on SELs. Furthermore, we showed that ibudilast slowed the decline in MTR in SEL. The effect on MTR was small (0.22/year) compared with the random effect’s SD of 4.4 (data not shown). However, this result suggests secondary effects on myelin content and tissue integrity, similar to what has been observed for the thalamus in that same trial.19 Paramagnetic rim lesions are another imaging marker that assess a different aspect of chronic active lesions. Both paramagnetic rim lesions and SEL rely on indirect measurements of the different features of chronic active lesions: iron particles in microglia through the magnetic field disturbance and lesion size change through demyelination at the lesion edge. Paramagnetic rim lesions require T2*-weighted images and are detected as a local disturbance in the phase of magnetic resonance signal, which is believed to arise from iron particles in phagocytic cells that engulf myelin debris, which contain high iron content. The total overlap between SELs and paramagnetic rim lesions is small, with only a very small number of SEL lesions also showing paramagnetic rim lesions (<10%), and only about half of paramagnetic rim lesions are also SELs.27,28 In vivo imaging studies have shown higher counts of SELs suggesting SELs may have greater sensitivity and may be associated with the presence of paramagnetic rim lesions.27,28 However, no large study has compared SELs and paramagnetic rim lesions in terms of their sensitivity and specificity with pathologically confirmed chronic active lesions. Nonetheless, patients with both types of lesions have similarly poorer clinical outcomes and prognosis.27,28 While there are no clear guidelines for evaluation of progressive MS therapies, such measures reflecting chronic active lesion are suitable outcomes in progressive MS trials.
While SELs were not associated with disability progression in the survival analysis (Supplemental Figure S1), there were several important clinical cross-sectional correlations between SEL volume (T25FWT, 9HPT, and SDMT; Figure 2) and quartile groups (EDSS, T25FWT, 9HPT, and SDMT; Table 2). These findings support the clinical relevance of SELs. Future studies to define clinically meaningful SEL volumes or counts instead of quartiles may help interpret trial outcomes.
Since its inception,12 quantification of SELs has been applied in many studies including clinical trials of ocrelizumab in relapsing–remitting multiple sclerosis (RRMS) and PPMS,12,14 opicinumab in RRMS,28,29 natalizumab in SPMS,30 fingolimod in PPMS,31 multi-arm amiloride, riluzole, and fluoxetine in SPMS32 as well as observational studies.27,33–39 Many studies show evidence of clinical relevance in terms of treatment effects14,30,31 and the association to progression of disability14,27,30,32,33,36,38 as well as severity of tissue damage by other advanced imaging methods such as T1-weighted hypointensity, magnetization transfer imaging, diffusion tensor imaging, and myelin water fraction.12,28,29,33–35,37–39 Our MTR analysis results are in line with those studies, indicating more severe tissue damage at baseline and ongoing damage in SELs.12,28,29,33–35,37–39 As a marker of chronic active lesions, SELs still require pathologic confirmation. Small pathologic studies have shown the associations between lesion enlargement and chronic active status,4,15 and larger study using in vivo MRIs and postmortem brain tissues can clarify the sensitivity and specificity of SELs for detection of chronic activity. Similarly, whether chronic active lesions enlarge constantly over in the order of months or years require confirmation by noninvasive in vivo imaging technologies such as positron emission tomography for activation of microglia or infiltrating macrophages.
Limitations of this study include a lack of susceptibility-weighted images or T2*-weighted images to assert iron-laden lesions, relatively short follow-up period of 2 years to observe significant progression of disease, and a lack of postcontrast images to confirm whether the baseline T2 lesions are pre-existing and not acute. The effect on SELs should be interpreted with caution as the SEL effect is reported as the volume of lesion tissue at baseline that showed constant and concentric expansion over the study period. We do not have the SEL volume predating the study start, but given randomization we consider these values to be similar in both treatment arms. The analysis methods for SELs including the optimal follow-up period and assessment of frequency are not standardized, and robust methods for defining and quantifying SELs are still in development. Methodological variations exist among image analysis centers on definitions of enlargement, concentricity, and constancy, and their generalizability to different studies. Publicly available software may accelerate further research into the appropriate ways to apply these methods using routine clinical MRIs or in clinical trials for efficacy assessment.
In conclusion, we found a significant treatment of ibudilast on SELs, suggesting ibudilast reduced chronic active lesions and had an effect on compartmentalized inflammation. The effect of ibudilast on SELs was observed despite there being no effect on new T2 lesion development and thus reflecting a mechanism outside peripherally mediated inflammation. Ibudilast’s effect on SELs in conjunction with the effect seen on brain atrophy make this therapeutic attractive for a phase III trial in progressive MS. The measurement of SELs is a relatively straightforward approach and may be useful marker of chronic active lesions, which could be applied as a clinical trial outcome measure in progressive MS and applied to retrospective data in prior trials or observational studies.
Supplementary Material
Acknowledgements
The authors thank the persons with MS who participated in the trial and their families.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: SPRINT-MS was supported by grants from the National Institute of Neurologic Disorders and Stroke (NINDS) (U01NS082329) and the National Multiple Sclerosis Society (RG 4778-A-6) and by MediciNova through a contract with the NIH. The NeuroNEXT Network is supported by the NINDS (Central Coordinating Center, U01NS077179; Data Coordinating Center, U01NS077352; and individual grants to each trial site). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. There was no specific funding for this project.
Footnotes
Declaration of Conflicting Interests
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: K.N.: personal licensing fee from Biogen and fee for scientific advisory meeting from INmune Bio, received research support from Department of Defense, National Institutes of Health, and Patient Centered Outcomes Research Institute. B.T.: nothing to disclose. J.B.: nothing to disclose. J.A.C.: personal compensation for consulting for Astoria, Bristol Myers Squibb, Convelo, EMD Serono, FiND Therapeutics, INMune, and Sandoz; and serving as an Editor of Multiple Sclerosis Journal. R.J.F.: personal consulting fees from AB Science, Biogen, Bristol Myers Squibb, EMD Serono, Genentech, Genzyme, Greenwich Biosciences, Immunic, INmune Bio, Janssen, Lily, Novartis, Sanofi, Siemens, and TG Therapeutics; Advisory committees for AB Science, Biogen, Immunic, Janssen, Novartis, and Sanofi; Clinical trial contract and research grant funding from Biogen, Novartis, and Sanofi. D.O.: research support from the National Institutes of Health, National Multiple Sclerosis Society, Patient Centered Outcomes Research Institute, Race to Erase MS Foundation, Genentech, Genzyme, and Novartis. Consulting fees from Biogen Idec, Bristol Myers Squibb, Genentech/Roche, Genzyme, Janssen, Novartis, Merck, and Pipeline Therapeutics.
Supplemental material
Supplemental material for this article is available online.
Contributor Information
Kunio Nakamura, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Bhaskar Thoomukuntla, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
James Bena, Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Jeffrey A Cohen, Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
Robert J Fox, Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
Daniel Ontaneda, Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
References
- 1.Absinta M, Lassmann H and Trapp BD. Mechanisms underlying progression in multiple sclerosis. Curr Opin Neurol 2020; 33: 277–285. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Kappos L, Wolinsky JS, Giovannoni G, et al. Contribution of relapse-independent progression vs. relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurol 2020; 77: 1132–1140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Absinta M, Sati P, Gaitán MI, et al. Seven-tesla phase imaging of acute multiple sclerosis lesions: A new window into the inflammatory process. Ann Neurol 2013; 74(5): 669–678. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Absinta M, Sati P, Masuzzo F, et al. Association of chronic active multiple sclerosis lesions with disability in vivo. JAMA Neurol 2019; 76: 1474–1483. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Absinta M, Sati P, Schindler M, et al. Persistent 7-tesla phase rim predicts poor outcome in new multiple sclerosis patient lesions. J Clin Invest 2016; 126: 2597–2609. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Trapp BD, Peterson J, Ransohoff RM, et al. Axonal transection in the lesions of multiple sclerosis. N Engl J Med 1998; 338: 278–285. [DOI] [PubMed] [Google Scholar]
- 7.Frischer JM, Bramow S, Dal-Bianco A, et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain 2009; 132(Pt 5): 1175–1189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Frischer JM, Weigand SD, Guo Y, et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol 2015; 78(5): 710–721. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bagnato F, Sati P, Hemond C, et al. Imaging chronic active lesions in multiple sclerosis: A consensus statement from the North America imaging in multiple sclerosis cooperative. Neurology 2023; 100: 3295. [Google Scholar]
- 10.Dal-Bianco A, Grabner G, Kronnerwetter C, et al. Slow expansion of multiple sclerosis iron rim lesions: Pathology and 7 T magnetic resonance imaging. Acta Neuropathol 2017; 133(1): 25–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Dal-Bianco A, Grabner G, Kronnerwetter C, et al. Long-term evolution of multiple sclerosis iron rim lesions in 7 T MRI. Brain 2021; 144: 833–847. [DOI] [PubMed] [Google Scholar]
- 12.Elliott C, Wolinsky JS, Hauser SL, et al. Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions. Mult Scler 2019; 25(14): 1915–1925. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kaunzner UW, Kang Y, Zhang S, et al. Quantitative susceptibility mapping identifies inflammation in a subset of chronic multiple sclerosis lesions. Brain 2019; 142: 133–145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Elliott C, Belachew S, Wolinsky JS, et al. Chronic white matter lesion activity predicts clinical progression in primary progressive multiple sclerosis. Brain 2019; 142: 2787–2799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Zheng Y, Trapp BD, Ontaneda D, et al. Histological analysis of slowly expanding lesions in multiple sclerosis: Case report. In: Proceedings of the ECTRIMS-ACTRIMS: MS virtual2020, 11–13 September 2020, p. P0584. [Google Scholar]
- 16.Fox RJ, Coffey CS, Conwit R, et al. Phase 2 trial of ibudilast in progressive multiple sclerosis. N Engl J Med 2018; 379: 846–855. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Fox RJ, Coffey CS, Cudkowicz ME, et al. Design, rationale, and baseline characteristics of the randomized double-blind phase II clinical trial of ibudilast in progressive multiple sclerosis. Contemp Clin Trials 2016; 50: 166–177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Riani M, Atkinson AC and Corbellini A. Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression. Stat Method Appl 2023; 32: 75–102. [Google Scholar]
- 19.Nakamura K, Zheng Y, Mahajan KR, et al. Effect of ibudilast on thalamic magnetization transfer ratio and volume in progressive multiple sclerosis. Mult Scler 2023; 29(10): 1257–1265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Naismith RT, Bermel RA, Coffey CS, et al. Effects of ibudilast on MRI measures in the Phase 2 SPRINT-MS study. Neurology 2021; 96: e491–e500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Fujita M, Tamano R, Yoneda S, et al. Ibudilast produces anti-allodynic effects at the persistent phase of peripheral or central neuropathic pain in rats: Different inhibitory mechanism on spinal microglia from minocycline and propentofylline. Eur J Pharmacol 2018; 833: 263–274. [DOI] [PubMed] [Google Scholar]
- 22.Mizuno T, Kurotani T, Komatsu Y, et al. Neuroprotective role of phosphodiesterase inhibitor ibudilast on neuronal cell death induced by activated microglia. Neuropharmacology 2004; 46(3): 404–411. [DOI] [PubMed] [Google Scholar]
- 23.Gibson LC, Hastings SF, McPhee I, et al. The inhibitory profile of Ibudilast against the human phosphodiesterase enzyme family. Eur J Pharmacol 2006; 538: 39–42. [DOI] [PubMed] [Google Scholar]
- 24.Cho Y, Crichlow GV, Vermeire JJ, et al. Allosteric inhibition of macrophage migration inhibitory factor revealed by ibudilast. Proc Natl Acad Sci U S A 2010; 107: 11313–11318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hertz AL, Bender AT, Smith KC, et al. Elevated cyclic AMP and PDE4 inhibition induce chemokine expression in human monocyte-derived macrophages. Proc Natl Acad Sci U S A 2009; 106: 21978–21983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Absinta M, Maric D, Gharagozloo M, et al. A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis. Nature 2021; 597(7878): 709–714. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Calvi A, Clarke MA, Prados F, et al. Relationship between paramagnetic rim lesions and slowly expanding lesions in multiple sclerosis. Mult Scler 2023; 29: 352–362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Elliott C, Rudko DA, Arnold DL, et al. Lesion-level correspondence and longitudinal properties of paramagnetic rim and slowly expanding lesions in multiple sclerosis. Mult Scler 2023; 29(6): 680–690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Elliott C, Arnold DL, Chen H, et al. Patterning chronic active demyelination in slowly expanding/evolving white matter MS lesions. AJNR Am J Neuroradiol 2020; 41(9): 1584–1591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Beynon V, George IC, Elliott C, et al. Chronic lesion activity and disability progression in secondary progressive multiple sclerosis. BMJ Neurol Open 2022; 4(1): e000240. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Calvi A, Mendelsohn Z, Hamed W, et al. Treatment reduces the incidence of newly appearing multiple sclerosis lesions evolving into chronic active, slowly expanding lesions: A retrospective analysis. Eur J Neurol 2024; 31: e16092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Calvi A, Carrasco FP, Tur C, et al. Association of slowly expanding lesions on MRI with disability in people with secondary progressive multiple sclerosis. Neurology 2022; 98: e1783–e1793. [DOI] [PubMed] [Google Scholar]
- 33.Calvi A, Tur C, Chard D, et al. Slowly expanding lesions relate to persisting black-holes and clinical outcomes in relapse-onset multiple sclerosis. Neuroimage Clin 2022; 35: 103048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Klistorner A, Wang C, Yiannikas C, et al. Evidence of progressive tissue loss in the core of chronic MS lesions: A longitudinal DTI study. Neuroimage Clin 2018; 17: 1028–1035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Klistorner S, Barnett MH, Yiannikas C, et al. Expansion of chronic MS lesions is associated with an increase of radial diffusivity in periplaque white matter. Mult Scler 2022; 28: 697–706. [DOI] [PubMed] [Google Scholar]
- 36.Klistorner S, Barnett MH, Yiannikas C, et al. Expansion of chronic lesions is linked to disease progression in relapsing-remitting multiple sclerosis patients. Mult Scler 2021; 27(10): 1533–1542. [DOI] [PubMed] [Google Scholar]
- 37.Huerta M, Conway DS, Planchon SM, et al. Myelin content measures of slowly enlarging lesions using 7T MRI (P241). In: Proceedings of the ACTRIMS 2022 forum, West Palm Beach, FL, 24–26 February 2022. [Google Scholar]
- 38.Preziosa P, Pagani E, Meani A, et al. Slowly expanding lesions predict 9-year multiple sclerosis disease progression. Neurol Neuroimmunol Neuroinflamm 2022; 9(2): e1139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Preziosa P, Pagani E, Moiola L, et al. Occurrence and microstructural features of slowly expanding lesions on fingolimod or natalizumab treatment in multiple sclerosis. Mult Scler 2021; 27: 1520–1532. [DOI] [PubMed] [Google Scholar]
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