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  • Open Access
  • Open Peer Review

Evaluation of neurological changes in secondary progressive multiple sclerosis patients treated with immune modulator MIS416: results from a feasibility study

  • 1Email author,
  • 2,
  • 3 and
  • 4
Pilot and Feasibility Studies20173:60

https://doi.org/10.1186/s40814-017-0201-4

  • Received: 10 May 2017
  • Accepted: 27 October 2017
  • Published:
Open Peer Review reports

Abstract

Background

While disease progression can be readily monitored in early stage relapsing multiple sclerosis (MS), it is more challenging for secondary progressive multiple sclerosis (SPMS). This advanced stage of disease has distinct pathophysiology due to compartmentalization of neuroinflammatory activity within the central nervous system, resulting in increased incidence and severity of cognitive dysfunction. The shift in the dominant disease pathways is underscored by the failure of relapsing therapies to benefit SPMS patients, highlighting the need for novel treatment strategies and clinical trial endpoints that are well-aligned with potential benefits. The Expanded Disability Status Scale (EDSS) is widely used but is weighted towards ambulatory ability, lacking sensitivity to other aspects of neurological impairment experienced in more severely disabled SPMS patients, so may not effectively capture their clinical status.

To investigate the feasibility of an alternative clinical trial endpoint model for a phase 2B trial of an immune modulator for SPMS, the potential for treatment efficacy-based patient-centered outcomes was assessed within the context of a before and after, 12-week clinical trial of safety and tolerability.

Methods

Patients treated with MIS416 for 12 weeks were evaluated for clinical status at baseline and end of dosing, using the established Multiple Sclerosis Functional Composite, Short Form Health Survey, and Expanded Disability Status Scale. Responder status was determined for eight outcome measures based on minimally important change, defined using published studies. To evaluate the patients’ immune response to MIS416, blood plasma samples collected at baseline and pre- and 24-h post doses 1–4 were analyzed using multiplex cytokine quantification assays.

Results

Using a combination of patient-centered outcomes, MIS416 treatment was associated with improved clinical status for 10/11 patients: eight patients showed improvement on two to five outcome measures, five of which also showed improvement by EDSS. Multi-dimensional scaling analysis of MIS416-induced factors quantified in individual patients, revealed immune response patterns which had a strong concordance with the extent of the patients’ clinical response.

Conclusions

The data support the feasibility of using patient-centered outcomes as additional clinical trial endpoints, for determining the efficacy of disease-modifying therapies, in secondary progressive multiple sclerosis patients.

Trial registration

ClinicalTrial.gov, NCT01191996

Keywords

  • Secondary progressive multiple sclerosis
  • MIS416
  • Immune modulator
  • Myeloid cells
  • Neurological improvement
  • Performance related outcome
  • Patient-reported outcome
  • Plasma immune biomarker

Background

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) [1], resulting in the occurrence of inflammatory lesions (plaques) anywhere within the white matter of the CNS, most commonly the periventricular regions, optic nerves, brain stem, cerebellum, and spinal cord [1, 2]. The symptoms and impairments experienced by people with MS vary and depend on the affected areas of the CNS [3, 4]. MS affects three times as many women as men and is typically diagnosed between the ages of 15 and 40 years [5]. It is estimated that there are 2.3 million individuals with MS worldwide [6]. The highest prevalence of MS is found in Canada, USA, and other northern European countries with rates ranging from 50 to 240 per 100,000 population [6, 7]. The most common type of MS is relapse-remitting MS (RRMS) [8], affecting 75–85% of patients, and characterized by clear and well-defined relapses followed by complete or partial remissions [8]. About 50% of individuals diagnosed with RRMS will develop secondary progressive MS (SPMS) after 10–15 years [8]. SPMS is characterized by steady worsening of the condition, with or without attacks and remissions [8]. Primary progressive MS (PPMS) affects about 10% and is characterized by continuous and slow worsening of symptoms from the onset of the condition, with no clear relapses or remissions [8]. The least common and most severe type of MS is progressive-relapsing MS (PRMS), which occurs in about 5% of the persons diagnosed with MS [8]. This type is characterized by steady worsening of the condition with clear attacks and without remissions [8].

MIS416 is a myeloid-targeted immune response modifier currently in a phase 2B randomized, double-blind, placebo-controlled trial to study the efficacy and safety in the treatment of subjects with secondary progressive multiple sclerosis (SPMS) (http://clinicalTrials.gov identifier NCT02228213). The data from a clinical trial to determine safety and tolerability has been previously published [9]. In contrast to relapse remitting MS (RRMS) therapies that target the peripheral “outside-in” adaptive immune biology, MIS416 has been developed to modulate the secondary progressive stage of MS, which is considered to reflect self-perpetuating innate inflammation that has become contained within the central nervous system (CNS) [10]. This shift in disease pathophysiology is emphasized by the failure of RRMS therapies to alter disease progression in SPMS patients [11]. Early pre-clinical and human compassionate use studies have demonstrated the ability of MIS416 to enhance several myeloid-directed anti-inflammatory pathways that can potentially access the CNS, leading to inhibition of this compartmentalized neuroinflammation and the promotion of endogenous CNS repair pathways [12, 13]. Accordingly, to fully evaluate the clinical potential of MIS416, measures of CNS function that are sensitive to SPMS-associated chronic neuroinflammatory status are desirable.

Currently, the Expanded Disability Status Scale (EDSS) is the only regulatory-approved measure of change in disease activity and burden in MS patients [14]. However, reported limitations of the EDSS [14], such as lack of sensitivity to cognitive and arm function, mean that for patients with advanced disease, improvements in clinical status may not be captured by EDSS, which is biased towards the patient’s walking ability [15]. Given the recognized limitations of the EDSS, there is increased focus on the refinement and validation of patient-reported outcomes (PROs) and performance-related outcomes (PerfOs), to account for the full range of MS symptoms [16, 17]. This shifting ground with respect to the important outcomes of MS therapy and the need to include patient-centered outcomes [18] raises uncertainty about the feasibility of including outcomes beyond the EDSS in trials to determine the treatment effect of MIS416 in SPMS patients. A recent paper by the Multiple Sclerosis Outcome Assessments Consortium (MSOAC) [19] has outlined the need for better outcome measures for MS. In preparation for a shift in the recommended outcomes for MS, this study aimed to investigate the feasibility of an alternative clinical trial endpoint model. Accordingly, we conducted a preliminary analysis of PRO and PerfO secondary endpoint measures from the dose-confirmation phase of a trial on MIS416-treated SPMS patients, to provide data for assessment of response on the patient-centered outcomes. As peripheral blood immune biomarkers associated with MIS416 mechanism of action are known [9, 20], patient samples collected immediately prior to and 24 h after MIS416 administration, for doses 1–4, were analyzed to determine any relationship between the pattern of the patient’s immune response to MIS416 and their change in clinical status, as indicated by these PerfOs and PROs. The overall aim was to support the feasibility of using patient-centered outcomes to measure response to MIS416 treatment in SPMS patients in a larger, placebo-controlled efficacy study.

Methods

Clinical trial design

A single-center, open-label, non-randomized, dose-escalation study was conducted in two phases: a dose-escalation (DE) phase, to evaluate the safety, tolerability, and maximum tolerated dose (MTD) of MIS416 administered intravenously, once weekly for four doses; and a dose-confirmation (DC) phase, comprising a single cohort treated at or below the MTD of MIS416, dosed once weekly for up to 12 doses [9]. The study was conducted in accordance with the Declaration of Helsinki [21] and was approved by the Upper South A Health and Disability Ethics Committee (URA/10/01/011). Written informed consent for blood sample collection and analysis was obtained from all study participants prior to the trial. The methods we are reporting follow the recommendations from the new CONSORT extension to randomized pilot and feasibility trials [22].

Patients

Study participants were recruited from Christchurch, New Zealand, where the clinical trial site was located. All patients provided informed consent prior to screening. Exclusion criteria included treatment with any immunomodulatory therapy in the previous 6 months or vaccine/corticosteroid in previous 60 days, as well as any diseases that might impact on the patients’ diagnosis and evaluation of MS. Altogether, 34 patients (20 females) 18 years or older were enrolled, 19 in the DE and 15 in the DC phase. All had a diagnosis of MS based on McDonald’s criteria [23], either primary or secondary progressive MS, evidence of worsening clinical status over the previous 2 years, and EDSS scores of 2.5–7.0 at screening. Enrollment in the DC phase was limited to patients with SPMS, to support a planned phase 2 trial in this more homogeneous population. The feasibility study to investigate patient-centered outcomes was conducted on the DC cohort only.

Patient clinical status and measures

The clinical status of patients who completed the DC phase (MIS416 weekly for 12 weeks) was assessed before and after completion of the study using the EDSS, Multiple Sclerosis Functional Composite (MSFC) [24], Fatigue Severity Scale (FSS) [25], and the Short Form Health Survey-36 (SF-36) [26].

Calculation of change and responder status on performance-rated outcome measures and patient-reported outcome measures (patient-centered outcomes)

Minimal important change (MIC) was used to define a positive patient responder status as estimated from published studies. For EDSS, MIC was 0.5 [27]. From the MFSC [24], MIC values were determined for the following performance-rated outcomes (PerfOs): gait speed (GS) 0.10 m/s [28]; Paced Auditory Serial Addition Test (PASAT) 9 (based on changes greater than estimates of the practice effect) [29, 30]; Nine-Hole Peg Test (NHPT) 20% [29]. For the SF-36, four subscales that were most closely related to the biological action of the investigational drug were included as outcomes. These were physical function to reflect the everyday impact of improved gait speed or walking; mental health to reflect both a primary effect or a secondary effect from walking better; vitality for fatigue as it is the most distressing symptom of people with MS and not at all captured by the EDSS; and general health as improved walking, mood, and fatigue will impact this outcome and it is an important patient-centered outcome [31]. The other four subscales, pain and the three role subscales, were not included, as pain is often either neuropathic in origin or musculo-skeletal secondary to abnormal walking pattern, and the role variables are downstream outcomes from improved mobility and mental health.

For these subscales, a change in 10 points on a 0–100 scale for each score was considered a MIC [32] (equivalent to 1/2 standard deviation (SD) as SD is approximately 20 in adults > 55 years from a large nominative sample) [30]. The FSS was originally included in the test battery, but we did not want to include two PROs which both measured fatigue, and as the other PROs came from the SF-36, to add homogeneity, we included only SF-36 PROs. In addition, the items of the FSS (n = 9) do not measure severity of fatigue but rather causes, consequences, and impact on daily life.

Each patient was classified as a responder (1) or non-responder (0) on the EDSS and the three PerfOs and four PROs from the SF-36. Response across all measures was summarized using the total number of measures with an observed response. Response pattern was used to rank patients based on total number of responses with priority given to responses on PerfO. Patients were then classified into three groups based on the distribution of ranks: high, medium, and low responder.

Quantification of plasma MIS416 immune biomarkers analysis

For quantification of immune factors in patient plasma, heparin anti-coagulated peripheral blood was collected pre-treatment (baseline), as well as pre- and 24-h post MIS416 administration for doses 1–4. Following immediate processing of blood, plasma was isolated and stored at − 80 °C until analysis. The immune factors selected were based on their capacity to reflect different aspects of MIS416-mediated immune activation, as determined in a pilot study conducted on plasma from patients who completed the DE phase of the trial: type I/II interferon signaling (IP-10, MIG, MIP-1α, neopterin, IFN-γ); pro-inflammatory mediators (IL-6, IL-11, GCSF, IL-12p40, PGE2); cell migration (fractalkine, rantes, MCP-1); anti-inflammatory mediators (TGF-β, soluble TNFR1, IL-10, IL-1RA); cell adhesion (CD62E, ICAM1, VCAM1); and the growth factor, VEGF. The concentrations (pg/mL) of these cytokines and chemokines were determined using a custom cytokine bead array matrix (Becton Dickinson CBA Flex Sets™) or by ELISA (BD Biosciences) according to the manufacturer’s instructions.

Statistical analysis

Patient-centered outcomes

Each patient was classified as a responder or non-responder on the EDSS and the three PerfOs and four PROs from the SF-36. Response across all measures was summarized using the total number of measures with an observed response. Participants were ranked in order of total responses across PerfOs and PROs. The probability of observing response patterns of the magnitude determined was estimated by referring to the binomial distribution and assuming a spontaneous response rate to be rare (≤ 3/10), given the population under study.

Immunological factors

To evaluate the extent to which each patient’s immune system was altered in response to MIS416 therapy, the maximum recorded plasma concentration for each immune factor across dose 1–4 time points was used for further analysis. The data were first normalized (subtracted the mean and divided by the standard deviation) so that all were equally weighted, then the resemblance between each pair of patients was determined. This was calculated as the sum (over all immune factors) of the squared differences between patients. Thus, for each pair (i, j) of patients,
$$ \mathrm{Resemblance}\;\left(i,j\right)={\sum}_{21}{\left({V}_i-{V}_j\right)}^2 $$
where V i and V j are the values for patients i and j respectively. Once the resemblances were calculated, a cluster algorithm was used to generate a multi-dimensional scaling analysis plot to illustrate the differences between patients. SIMPER (similarity percentages) analysis was used to determine which immunological factors mostly accounted for the differences between patients [33, 34]. These factors were used subsequently to compare the patient immune responses with the patient-centered clinical response.

Statistical analysis of immune response biomarkers in patients grouped by clinical responder status

Biomarker levels for patients grouped according to their clinical responder status (high, medium, or low) were compared using a two-way ANOVA followed by Holm-Sidak’s multiple comparison post-test (PRISM software version 7.0; GraphPad). A value of P < 0.05 was taken as significant.

Results

Of the targeted 15 patients enrolled in the DC phase, 11 subjects completed all 12 weekly doses. Two subjects withdrew for personal reasons and 2 subjects were withdrawn due to an adverse event which occurred early on in dosing schedule. Adverse events were headache (n = 2) and pain in extremity (in the arm or the leg) (n = 1).

The distribution of clinical status measures prior to MIS416 treatment of the patients who completed the DC phase [9] are summarized in Table 1.
Table 1

Distribution of the clinical characteristics of the patient sample group (DC cohort; n = 11) for age, EDSS, PerfOs, and PROs prior to MIS416 treatment

 

Median

Minimum

Maximum

Age (years) (n = 11)

53

46

60

EDSS

6

4

7

aGait speed (m/s)

0.68

0.26

1.19

NHPT (s)

28.5

19.8

152.3

bPASAT (errors)

37

23

60

cSF-36 [Norm]/100

 General health [75]

46.1

23.7

65.1

 Physical function [82]

23.1

19.3

40.3

 Bodily pain [75]

42.6

30.6

62

 Role physical [81]

30.2

2.5

39.2

 Role emotional [88]

45.7

17.9

56.2

 Social function [88]

37.3

17.2

57.3

 Vitality [68]

40.7

25.9

52.6

 Mental health [80]

50.9

29.9

61.3

a n = 10

bMaximum score possible = 60

cHigher = better health

Patient change and responder status on EDSS, PerfOs, and PROs

The amount of change on the EDSS and the three PerfO measures in absolute terms, or percent change from baseline for the NHPT, as well as how each patient was classified on responder status for each of the measures is shown in Table 2.
Table 2

Change and responder status (RS) on EDSS and PerfOs

Patient ID

EDSS

Gait speed

PASAT

NHPT

Change

RS

Change

RS

Change

RS

% change

RS

DC01

3.5

1

0.10

1

9

1

− 3.48

0

DC02

0

0

− 0.05

0

− 14

0

1.91

0

DC03

− 0.5

0

− 0.17

0

− 2

0

− 4.81

0

DC05

1

1

0.10

1

0

0

1.40

0

DC06

0

0

0.035

0

1

0

− 7.30

0

DC07

0.5

1

0.14

1

6

0

− 5.03

0

DC09

0

0

0.07

0

15

1

− 1.44

0

DC10

0.5

1

0.14

1

− 4

0

− 10.26

0

DC11a

0

0

.

.

11

1

− 13.79

0

DC12

0.5

1

− 0.10

0

24

1

42.42

0

DC14

0

0

0.04

0

0

0

7.53

0

aNon-ambulatory patient hence no gait speed measures

Five patients were responders on EDSS and 4 of those also responded on gait speed. There were 4 responders on PASAT and 0 on NHPT. In all, 7/11 patients were responders on at least one of these measures.

Table 3 shows the amount of change in absolute terms for the four PROs. Of these, there were 6 responders on vitality; 3, 2, and 1 patients responded on general health, mental health, and physical functioning respectively, with a total of 8/11 patients responding on at least one PRO.
Table 3

Change and responder status (RS) on PROs

Patient ID

Physical function

General health

Vitality

Mental health

Change

RS

Change

RS

Change

RS

% change

RS

DC01

17.2

1

7.1

0

14.9

1

7.8

0

DC02

0

0

0

0

14.9

1

2.6

0

DC03

0

0

12.9

1

− 2.9

0

2.6

0

DC05

9.6

0

− 2.4

0

11.9

1

10.5

1

DC06

5.3

0

− 9.6

0

11.9

1

10.5

1

DC07

3.8

0

− 7.1

0

− 3

0

− 5.2

0

DC09

9.6

0

14.3

1

2.9

0

5.2

0

DC10

− 1.9

0

9.5

0

14.9

1

7.9

0

DC11

7.6

0

14.3

1

11.9

1

5.3

0

DC12

0

0

1.4

0

− 8.9

0

− 23.6

0

DC14

0

0

− 2.4

0

0

0

− 2.6

0

For EDSS/PerfOs the probability of observing 7/11 responders, even assuming a spontaneous response as high as 30% in the absence of intervention is 0.022. The probability of observing 8/11 responders on PRO is 0.004 (http://stattrek.com/online-calculator/binomial.aspx).

Table 4 shows how each patient was classified as a responder ranked in order by total number of responding outcomes. While the ranking of patients was based on total number of responses, priority was given to responses on PerfO. All but one patient (DC14) showed a response on at least one variable. The patient ranking was further classified as high, medium, and low responder based on ≥ 3, 2, and ≤ 1 total number of responses accordingly (Table 4).
Table 4

Number of responses on PerfOs and PROs, responder rank, and classification of responder status

Patient ID

PerfO

PRO

Total

Rank

Classification

DC01

3

2

5

1

High

DC05

2

2

4

2

High

DC10

2

1

3

3

High

DC11

1

2

3

4

High

DC12

2

0

2

5.5

Medium

DC07

2

0

2

5.5

Medium

DC09

1

1

2

7

Medium

DC06

0

2

2

8

Medium

DC02

0

1

1

9.5

Low

DC03

0

1

1

9.5

Low

DC14

0

0

0

11

Low

Concordance of clinical responder status ranking with MIS416 pharmacodynamic immune response

MIS416 is composed of immune stimulatory ligands for innate receptors, toll-like receptor 9 (TLR-9), and nucleotide-binding oligomerization domain-containing protein 2 (NOD-2) [20]. As a result of immune crosstalk under the influence of TLR-9-dependant type I interferon signaling and NOD-2-dependant NFκB signaling, regulatory and anti-inflammatory immune activity can be established [12, 13, 35, 36]. Analysis of the patients’ immune response demonstrated that immune proteins associated with these pathways including the regulatory immune factors IFN-γ [37] and IL-10 [38] were transiently increased in patients’ plasma following MIS416 administration (maximal responses measured for each patient/immune factor are summarized in Table 5).
Table 5

Maximum value of immune factors measured in sequential peripheral blood plasma samples collected at 24-h and 7-day post doses 1, 2, 3, or 4

 

Patient ID

Immune factor (pg/mL)

DC

DC

DC

DC

DC

DC

DC

DC

DC

DC

DC

1

2

3

5

6

7

9

10

11

12

14

CD62E

18,503

33,350

16,905

21,510

18,458

37,502

19,729

20,984

27,616

18,471

8447

Fractalkine

531

907

421

458

434

350

492

308

397

143

373

GCSF

28

24

29

25

26

30

24

27

25

21

26

ICAM1 x104

70

74

51

38

64

145

59

48

66

21

40

IFN-γ

178

281

147

74

73

192

76

48

52

0

122

IL-10

11

19

12

8

7

27

10

7

11

5

29

IL11

322

423

2540

3194

19

0

179

808

168

1522

60

IL12p40

339

321

182

291

292

185

60

183

65

136

177

IL1RA

6693

7696

5797

6315

3939

18,299

18,709

8070

6858

2721

4965

IL6

36

47

181

22

32

87

81

19

23

11

140

IP10

3640

5371

6089

2812

3068

7689

4240

1646

2704

1234

1395

MCP-1

163

722

889

432

330

296

385

187

255

704

465

MIG

9766

111,584

13,706

24,823

5444

18,058

16,966

7520

2780

1514

2989

MIP1-α

4

9

6

5

8

4

5

5

5

6

9

Neopterin

5237

3378

3486

2244

4221

3927

3850

3728

3891

2060

2658

PGE2

182

219

157

160

192

212

343

398

296

207

32

RANTES

255,145

174,684

189,866

231,938

166,300

257,508

353,761

229,399

265,178

167,513

119,622

TGF-β

6034

5973

3394

3872

4699

6542

6291

11,851

8124

8236

5047

TNF-R

4850

5230

5060

4560

7870

3680

2970

2450

1930

5210

2510

VCAM-1

972,534

761,399

970,295

1,047,583

1,300,968

889,864

672,495

1,056,576

467,492

936,152

828,607

VEGF

116

67

28

37

76

45

144

88

80

81

9

The multidimensional analysis of the maximal response (normalized) for each immunological parameter is presented as a non-metric multidimensional scaling plot (Fig. 1).
Fig. 1
Fig. 1

Multidimensional scaling DS plot illustrating similarities between patients based on their immune response to MIS416. The maximum level of immune factors detected in patient plasma following MIS416 treatment (Table 5) was used to compare each patient’s overall immune response to MIS416 with the group. Clusters of patients with similar immune responses are outlined as a group. The patient responder status based on clinical improvement as determined in this study (Table 4) is indicated

The clustering pattern for the 11 patients based on similarities of their immune response pattern to MIS416 showed a major cluster defined by 5 patients, and a small cluster of 2 patients, with 4 patients showing no clustering. This grouping of patients based on their immune response demonstrated a high degree of overlap with the grouping of patients based on their clinical response ranking. High clinical responders were defined by having demonstrated at least three responses on PerfO and PRO, medium responders had two responses, and low responders had 0–1 (Table 4). Out of the major immunological cluster comprising 5 patients, 3 of these were also the highest responders on patient-centered outcomes and the other 2 were medium responders. Of the small immunological cluster of 2 patients, both of these were the lowest responders on the patient-centered outcomes.

Based on SIMPER analysis of the maximum observed value of each immune factor, those which provided the most information in discriminating patient immune responses to MIS416 were IFN-γ, MCP-1, MIG, MIP-1α, IL-6, and IL-10. To further examine the nature of these differences, the pattern of induction of immune factors by MIS416 by the high, medium, and low clinical responder groups were compared (Fig. 2).
Fig. 2
Fig. 2

MIS416 pharmacodynamic immune response of patients grouped according to change in clinical status. Immune proteins most important for discriminating differential immune responses to MIS416: (IFN-γ (a), MIG (b), MCP-1 (c), MIP-1α (d), IL-6 (e), and IL-10 (f)) were quantified at 24-hour (24 Hr) and 7 days (7 D) post doses (PD) 1–4 and values were compared between the patient groups defined as high responder (HR), medium responder (MR) or low responder (LR) based on their extent of clinical response. The patient ID numbers comprising each responder group are shown in parenthesis. Data shown are the mean values (pg/mL) ±SD. Statistical significance was determined by two-way ANOVA followed by Holm-Sidak’s multiple comparison: *p< 0.05;**p< 0.005; ***p< 0.0001

The maximum levels of all immune factors, apart from IL-10, were detected at 24-h post-dose 1, and the responses were attenuated following subsequent doses. While this pharmacodynamic pattern of response was consistent for all patients, there were clear quantitative differences in the immune response to MIS416. Notably, low clinical responders had higher levels of IFN-γ and IFN-inducible proteins MCP-1, MIG, and MIP-1α than patients that were classified as high clinical responders. Furthermore, there were also higher levels of the NFκB-dependant cytokines IL-6 and IL-10 [38, 39] in patients that were classified as low responders. Patients classified as medium responders aligned more closely with either the high- or the low-responder groups or for IL-6 and IL-10, they were midway between the two.

Discussion

Clinical-patient-reported outcomes

Of the 11 patients assessed here, 5 were classified as responders on the EDSS and of these, 4 were classified concordantly on gait speed, 2 on the PASAT, with no responders determined based on the NHPT. On any one of these 4 PerfOs, 7 people would have been classified as responders with 2 people showing a response uniquely on PASAT. For the PROs, only 1 of the EDSS responders showed a response on physical function (SF-36 PF) and this was the one person with a large EDSS response (DC01: EDSS response + 3.5). This may indicate only large changes on the EDSS will translate to a meaningful improvement in function from the patient’s perspective. Other EDSS responders also showed response on vitality (n = 3) and mental health (n = 1). However, 5 people who were not EDSS responders showed response on one or more of the PROs. In an unblinded trial, PROs may change because patients wish to please the investigators and report more favorably on these outcomes. However, only four of the patients (DC02, DC03, DC06, DC11) did the response prevalence on PROs exceed that of the PerfOs suggesting that self-report bias may not have been responsible for the response pattern observed here. PROs are also affected by a phenomenon called response shift [18], in which patients recalibrate their responses over time based on a change in perspective. Again, the concordance between the PerfO and PRO response, or the more frequent PerfO response (n = 7), does not support recalibration as the sole mechanism affecting the PRO response, suggesting that true change occurred.

Patient immune response

The observation that repeated dosing of MIS416 resulted in lower concentrations of biomarkers than were determined after dose 1 reflects desensitization of MIS416-stimulated immune pathways. Typically, this pattern of response is associated with repeat exposure to therapeutics or ligands that engage IFN and NFκB signaling and their respective negative feedback pathways [35, 4042]. This intrinsic regulatory mechanism is geared to control the host inflammatory response and is central to the maintenance of immune and tissue homeostasis [43]. That MIS416 showed activation of negative feedback pathways in patients is further evidence that NOD-2 and TLR-9 pathway activation by MIS416 occurred within the limits of immune homeostasis, which is important from both a safety and therapeutic standpoint.

Immune-clinical relationship—patient-reported outcomes

Analysis of the immunological response of the patients to MIS416 therapy supported their ranking based on the extent of their clinical improvement. While all patients responded immunologically in the same manner to MIS416, the patients that showed less clinical improvement had higher levels of MIS416 plasma biomarkers than those who were ranked as high responders. In particular, there were significantly more NFκB-dependant cytokines produced in these patients, although the significance (p value) of these differences should be treated with caution due to the small sample size. Such MIS416 hyper-responsiveness may be due to NFκB gene and pathway mutations described in MS patients which are associated with higher constitutive NFκB activity and greater sensitivity to NFκB-activating agents [44, 45].

Conclusions

This study was limited by the low number of patients as well as the lack of a placebo control group. Notwithstanding these limitations, the results support the feasibility of pursuing these associations in a larger, placebo-controlled trial of MIS416 in SPMS patients and the evaluation of clinical status using a wider portfolio of patient-centered outcomes. Furthermore, in the context of such a study, concomitant analysis of the patient’s immune response to MIS416 may provide additional insight into the significance of any change in clinical status measures, in particular those which are sensitive to change in inflammatory activity within the CNS.

Abbreviations

CD62E: 

E selectin

CNS: 

Central nervous system

DC: 

Dose confirmation

DE: 

Dose escalation

EDSS: 

Expanded Disability Status Scale

ELISA: 

Enzyme-linked immunosorbent assay

FSS: 

Fatigue Severity Scale

GCSF: 

Granulocyte colony-stimulating factor

ICAM1: 

Intracellular adhesion molecule 1

IFN-γ: 

Interferon gamma

IL: 

Interleukin

IL-1RA: 

Interleukin 1 receptor antagonist

IP-10: 

IFN-γ-inducible protein of 10 kDa

MCP-1: 

Monocyte chemotactic protein-1

MIC: 

Minimal important change

MIG: 

Monokine induced by γ-interferon

MIP-1α: 

Macrophage inflammatory protein

MSFC: 

Multiple Sclerosis Functional Composite

MTD: 

Maximum tolerated dose

NFκB: 

Nuclear factor kappaB

NHPT: 

Nine-Hole Peg Test

NOD-2: 

Nucleotide-binding oligomerization domain-containing protein 2

PASAT: 

Paced Auditory Serial Addition Test

PerfO: 

Performance-related outcome

PGE2: 

Prostaglandin E2

PRO: 

Patient-reported outcome

RRMS: 

Relapse-remitting multiple sclerosis

SF-36: 

Short Form Health Survey-36

soluble TNFR1: 

Soluble tumor necrosis factor receptors 1

SPMS: 

Secondary progressive multiple sclerosis

TGF-β: 

Transforming growth factor beta

TLR-9: 

Toll-like receptor 9

VCAM1: 

Vascular cell adhesion molecule 1

VEGF: 

Vascular endothelial growth factor

Declarations

Acknowledgements

The authors gratefully acknowledge Rebecca Girvan and Victoria Pearson (Innate Immunotherapeutics), for technical assistance with the analysis of human plasma pharmacodynamics immune markers.

Funding

Innate Immunotherapeutics funded the clinical trial, collection of data, and laboratory determinations.

Availability of data and materials

By reasonable request, the quantitative datasets analyzed in this study are available from the corresponding author.

Authors’ contributions

GW conceived the idea for the feasibility study, provided the raw data, worked with the other three authors to integrate the individual data sets meaningfully, and wrote the manuscript. NM developed and implemented the method for the pilot analysis of patient clinical status data, interpreted these results, and contributed to manuscript writing. DS was responsible for developing and implementing the method for analysis of the patients’ immune response. ALF worked with DS on the analysis and interpretation of data that related to the patient’s immune response. All authors read and approved the submitted manuscript.

Ethics approval and consent to participate

The study was conducted in New Zealand and was approved by the Upper South A Health and Disability Ethics Committee (URA/10/01/011). Written informed consent for blood sample collection, analysis, and disclosure of anonymized data was obtained from all study participants prior to the trial.

Consent for publication

Not applicable.

Competing interests

Gill A Webster is an employee of Innate Immunotherapeutics. All other authors received no financial support in relation to their contribution to this work and have no financial relationship with any other organization or individuals that may have an interest in this work.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Innate Immunotherapeutics Ltd, Auckland, New Zealand
(2)
University of Otago, Wellington, New Zealand
(3)
Victoria University, Wellington, New Zealand
(4)
McGill University, Montreal, Canada

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