Distinct plasma lipids predict axonal injury and multiple sclerosis activity.

BACKGROUND: Lipids are of particular interest for the study of neuroinjury and neuroinflammation as structural lipids are major components of myelin, and a variety of lipid species modulate inflammation. In this study, we performed an in-depth lipidomics analysis to identify lipids associated with injury and disease activity. METHODS: Plasma samples were collected from paediatric-onset multiple sclerosis (MS) cases within 4 years of disease onset from 17 sites. The lipidome was measured using untargeted and targeted mass spectrometry. For cross-sectional analyses, the agreement between multiple machine learning models was used to predict neurofilament light chain (NfL) levels. In longitudinal analyses, the association between clinical (relapse count) and imaging (MRI count with ≥1 enhancing or new T2 lesion) outcomes with each metabolite was estimated using adjusted negative binomial regression. RESULTS: At sample collection, 68% of the 435 included individuals were treatment-naive, with a median disease duration of 0.8 years (IQR 0.3-1.7). For longitudinal analyses, 381 and 335 subjects had at least 1 year of clinical and imaging follow-up, respectively. In cross-sectional analyses, NfL chain levels identified structural lipids (phosphatidylcholines and phosphatidylethanolamines) as the highest-performing predictors, including external validation. In contrast, longitudinal analyses found polyunsaturated fatty acids (PUFAs) and their derivatives to be protective from subsequent disease activity (q<0.001, multiple outcomes). CONCLUSION: There are two categories of lipids associated with MS processes. First, structural lipids strongly associated with NfL levels may result from cell lysis secondary to acute inflammation. In contrast, PUFAs, especially ω-3, had a protective effect on subsequent disease activity.

Serum Biomarker Signatures of Choroid Plexus Volume Changes in Multiple Sclerosis.

Increased choroid plexus (CP) volume has been recently implicated as a potential predictor of worse multiple sclerosis (MS) outcomes. The biomarker signature of CP changes in MS are currently unknown. To determine the blood-based biomarker characteristics of the cross-sectional and longitudinal MRI-based CP changes in a heterogeneous group of people with MS (pwMS), a total of 202 pwMS (148 pwRRMS and 54 pwPMS) underwent MRI examination at baseline and at a 5-year follow-up. The CP was automatically segmented and subsequently refined manually in order to obtain a normalized CP volume. Serum samples were collected at both timepoints, and the concentration of 21 protein measures relevant to MS pathophysiology were determined using the Olink™ platform. Age-, sex-, and BMI-adjusted linear regression models explored the cross-sectional and longitudinal relationships between MRI CP outcomes and blood-based biomarkers. At baseline, there were no significant proteomic predictors of CP volume, while at follow-up, greater CP volume was significantly associated with higher neurofilament light chain levels, NfL (standardized ß = 0.373, p = 0.001), and lower osteopontin levels (standardized ß = -0.23, p = 0.02). Higher baseline GFAP and lower FLRT2 levels were associated with future 5-year CP % volume expansion (standardized ß = 0.277, p = 0.004 and standardized ß = -0.226, p = 0.014, respectively). The CP volume in pwMS is associated with inflammatory blood-based biomarkers of neuronal injury (neurofilament light chain; NfL) and glial activation such as GFAP, osteopontin, and FLRT2. The expansion of the CP may play a central role in chronic and compartmentalized inflammation and may be driven by glial changes.

Lower arterial cerebral blood flow is associated with worse neuroinflammation and immunomodulation composite proteomic scores.

BACKGROUND: Brain hypoperfusion is linked with worse physical, cognitive and MRI outcomes in multiple sclerosis (MS). Understanding the proteomic signatures related to hypoperfusion could provide insights into the pathophysiological mechanism. METHODS: 140 people with MS (pwMS; 86 clinically isolated syndrome (CIS)/relapsing-remitting (RRMS) and 54 progressive (PMS)) were included. Cerebral arterial blood flow (CABF) was determined using ultrasound Doppler measurement as the sum of blood flow in the bilateral common carotid arteries and vertebral arteries. Proteomic analysis was performed using the Multiple Sclerosis Disease Activity (MSDA) test assay panel performed on the Olink™ platform. The MSDA test measures the concentrations of 18 proteins that are age and sex-adjusted. It utilizes a stacked classifier logistic regression model to determine 4 disease pathway scores (immunomodulation, neuroinflammation, myelin biology, and neuroaxonal integrity) as well as an overall disease activity score (1 to 10). MRI measures of T2 lesion volume (LV) and whole brain volume (WBV) were derived. RESULTS: The pwMS were on average 54 years old and had an average CABF of 951 mL/min. There were no differences in CABF between CIS/RRMS vs. PMS groups. Lower CABF levels were correlated with the overall disease activity score (r = -0.26, p = 0.003) and with the neuroinflammation (r = -0.29, p = 0.001), immunomodulation (r = -0.26, p = 0.003) and neuroaxonal integrity (r = -0.23, p = 0.007) pathway scores. After age and body mass index (BMI)-adjustment, lower CABF remained associated with the neuroinflammatory (r = -0.23, p = 0.011) and immunomodulation (r = -0.20, p = 0.024) pathway scores. The relationship between CABF and the neuroinflammation pathway score remained significant after adjusting for T2-LV and WBV (p = 0.038). Individual analyses identified neurofilament light chain, CCL-20 and TNFSF13B as contributors. When compared to the highest quartile (>1133.5 mL/min), the pwMS in the lowest CABF quartile (<764 mL/min) had greater overall disease activity score (p = 0.003), neuroinflammation (p = 0.001), immunomodulation (p = 0.004) and neuroaxonal integrity pathway scores (p = 0.007). CONCLUSION: Lower cerebral arterial perfusion in MS is associated with changes in neuroinflammatory/immunomodulation pathways and their respective proteomic biomarkers. These findings may suggest a relationship between the hypoperfusion and pro-inflammatory MS changes rather than being merely an epiphenomenon subsequent to lower energy demands.

Glial cell injury and atrophied lesion volume as measures of chronic multiple sclerosis inflammation.

BACKGROUND: Atrophied lesion volume (aLV), a proposed biomarker of disability progression in multiple sclerosis (MS) and transition into progressive MS (PMS), depicts chronic periventricular white matter (WM) pathology. Meningeal infiltrates, imaged as leptomeningeal contrast enhancement (LMCE), are linked with greater cortical pathology. OBJECTIVES: To determine the relationship between serum-derived proteomic data with the development of aLV and LMCE in a heterogeneous group of people with MS (pwMS). METHODS: Proteomic and MRI data for 202 pwMS (148 clinically isolated syndrome /relapsing-remitting MS and 54 progressive MS (PMS)) were acquired at baseline and at 5.4-year follow-up. The concentrations of 21 proteins related to multiple MS pathophysiology pathways were derived using a custom-developed Proximity Extension Assay on the Olink™ platform. The accrual of aLV was determined as the volume of baseline T2-weighted lesions that were replaced by cerebrospinal fluid over the follow-up. Regression models and age-adjusted analysis of covariance (ANCOVA) were used. RESULTS: Older age (standardized beta = 0.176, p = 0.022), higher glial fibrillary acidic protein (standardized beta = 0.312, p = 0.001), and lower myelin oligodendrocyte glycoprotein levels (standardized beta = -0.271, p = 0.002) were associated with accrual of aLV over follow-up. This relationship was driven by the pwPMS population. The presence of LMCE at the follow-up visit was not predicted by any baseline proteomic biomarker nor cross-sectionally associated with any protein concentration. CONCLUSION: Proteomic markers of glial activation are associated with chronic lesional WM pathology (measured as aLV) and may be specific to the progressive MS phenotype. LMCE presence in MS does not appear to relate to proteomic measures.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis.

The multifaceted nature of multiple sclerosis requires quantitative biomarkers that can provide insights related to diverse physiological pathways. To this end, proteomic analysis of deeply-phenotyped serum samples, biological pathway modeling, and network analysis were performed to elucidate inflammatory and neurodegenerative processes, identifying sensitive biomarkers of multiple sclerosis disease activity. Here, we evaluated the concentrations of > 1400 serum proteins in 630 samples from three multiple sclerosis cohorts for association with clinical and radiographic new disease activity. Twenty proteins were associated with increased clinical and radiographic multiple sclerosis disease activity for inclusion in a custom assay panel. Serum neurofilament light chain showed the strongest univariate correlation with gadolinium lesion activity, clinical relapse status, and annualized relapse rate. Multivariate modeling outperformed univariate for all endpoints. A comprehensive biomarker panel including the twenty proteins identified in this study could serve to characterize disease activity for a patient with multiple sclerosis.

Complement Activation Is Associated With Disease Severity in Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Histopathologic studies have identified immunoglobulin (Ig) deposition and complement activation as contributors of CNS tissue damage in multiple sclerosis (MS). Intrathecal IgM synthesis is associated with higher MS disease activity and severity, and IgM is the strongest complement-activating immunoglobulin. In this study, we investigated whether complement components (CCs) and complement activation products (CAPs) are increased in persons with MS, especially in those with an intrathecal IgM synthesis, and whether they are associated with disease severity and progression. METHODS: CC and CAP levels were quantified in plasma and CSF of 112 patients with clinically isolated syndrome (CIS), 127 patients with MS (90 relapsing-remitting, 14 primary progressive, and 23 secondary progressive), 31 inflammatory neurologic disease, and 44 symptomatic controls from the Basel CSF databank study. Patients with CIS/MS were followed in the Swiss MS cohort study (median 6.3 years). Levels of CC/CAP between diagnosis groups were compared; in CIS/MS, associations of CC/CAP levels with intrathecal Ig synthesis, baseline Expanded Disability Status Scale (EDSS) scores, MS Severity Score (MSSS), and neurofilament light chain (NfL) levels were investigated by linear regression, adjusted for age, sex, and albumin quotient. RESULTS: CSF (but not plasma) levels of C3a, C4a, Ba, and Bb were increased in patients with CIS/MS, being most pronounced in those with an additional intrathecal IgM production. In CIS, doubling of C3a and C4a in CSF was associated with 0.31 (CI 0.06-0.56; p = 0.016) and 0.32 (0.02-0.62; p = 0.041) increased EDSS scores at lumbar puncture. Similarly, doubling of C3a and Ba in CIS/MS was associated with 0.61 (0.19-1.03; p < 0.01) and 0.74 (0.18-1.31; p = 0.016) increased future MSSS. In CIS/MS, CSF levels of C3a, C4a, Ba, and Bb were associated with increased CSF NfL levels, e.g., doubling of C3a was associated with an increase of 58% (Est. 1.58; CI 1.37-1.81; p < 0.0001). DISCUSSION: CNS-compartmentalized activation of the classical and alternative pathways of complement is increased in CIS/MS and associated with the presence of an intrathecal IgM production. Increased complement activation within the CSF correlates with EDSS, future MSSS, and NfL levels, supporting the concept that complement activation contributes to MS pathology and disease progression. Complement inhibition should be explored as therapeutic target to attenuate disease severity and progression in MS.

A US payer perspective health economic model assessing value of monitoring disease activity to inform discontinuation and re-initiation of DMT in multiple sclerosis.

OBJECTIVES: We evaluate the potential clinical and cost impacts of discontinuing disease-modifying therapy (DMT) in people with multiple sclerosis (PwMS) when age-related immunosenescence can reduce DMT efficacy while increasing associated risks. METHODS: A Markov model simulated clinical and cost impacts to the patient and payers when a proportion of eligible patients with relapsing remitting multiple sclerosis (RRMS) discontinue DMT. Eligibility was defined as age >55 years, an RRMS diagnosis of >5 years, and no history of relapses for 5 years. Increasing the proportion of eligible patients willing to discontinue therapy was also modeled. Clinical and cost inputs were from published literature. RESULTS: Difference in EDSS progression between eligible patients who did and did not attempt discontinuation was not significant. After 1 year of eligibility, per-patient costs were $96k lower in the cohort that attempted discontinuation; however a higher proportion of relapses were seen in this group. When the proportion of patients willing to discontinue DMT increased, clinical findings remained consistent while the average cost per patient decreased. CONCLUSION: While there are increased clinical and cost benefits as more eligible patients attempt discontinuation, the risk of relapses can increase. Timely disease monitoring is required to manage safe DMT discontinuation.

Proteomic signatures of physical, cognitive, and imaging outcomes in multiple sclerosis.

BACKGROUND: A quantitative measurement of serum proteome biomarkers that would associate with disease progression endpoints can provide risk stratification for persons with multiple sclerosis (PwMS) and supplement the clinical decision-making process. MATERIALS AND METHODS: In total, 202 PwMS were enrolled in a longitudinal study with measurements at two time points with an average follow-up time of 5.4 years. Clinical measures included the Expanded Disability Status Scale, Timed 25-foot Walk, 9-Hole Peg, and Symbol Digit Modalities Tests. Subjects underwent magnetic resonance imaging to determine the volumetric measures of the whole brain, gray matter, deep gray matter, and lateral ventricles. Serum samples were analyzed using a custom immunoassay panel on the Olink™ platform, and concentrations of 18 protein biomarkers were measured. Linear mixed-effects models and adjustment for multiple comparisons were performed. RESULTS: Subjects had a significant 55.6% increase in chemokine ligand 20 (9.7 pg/mL vs. 15.1 pg/mL, p < 0.001) and neurofilament light polypeptide (10.5 pg/mL vs. 11.5 pg/mL, p = 0.003) at the follow-up time point. Additional changes in CUB domain-containing protein 1, Contactin 2, Glial fibrillary acidic protein, Myelin oligodendrocyte glycoprotein, and Osteopontin were noted but did not survive multiple comparison correction. Worse clinical performance in the 9-HPT was associated with neurofilament light polypeptide (p = 0.001). Increases in several biomarker candidates were correlated with greater neurodegenerative changes as measured by different brain volumes. CONCLUSION: Multiple proteins, selected from a disease activity test that represent diverse biological pathways, are associated with physical, cognitive, and radiographic outcomes. Future studies should determine the utility of multiple protein assays in routine clinical care.

Association between serum multi-protein biomarker profile and real-world disability in multiple sclerosis.

Few studies examined blood biomarkers informative of patient-reported outcome (PRO) of disability in people with multiple sclerosis (MS). We examined the associations between serum multi-protein biomarker profiles and patient-reported MS disability. In this cross-sectional study (2017-2020), adults with diagnosis of MS (or precursors) from two independent clinic-based cohorts were divided into a training and test set. For predictors, we examined seven clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying therapy [DMT], and time interval between sample collection and closest PRO assessment) and 19 serum protein biomarkers potentially associated with MS disease activity endpoints identified from prior studies. We trained machine learning (ML) models (Least Absolute Shrinkage and Selection Operator regression [LASSO], Random Forest, Extreme Gradient Boosting, Support Vector Machines, stacking ensemble learning, and stacking classification) for predicting Patient Determined Disease Steps (PDDS) score as the primary endpoint and reported model performance using the held-out test set. The study included 431 participants (mean age 49 years, 81% women, 94% non-Hispanic White). For binary PDDS score, combined feature input of routine clinical factors and the 19 proteins consistently outperformed base models (comprising clinical features alone or clinical features plus one single protein at a time) in predicting severe (PDDS ≥ 4) versus mild/moderate (PDDS < 4) disability across multiple machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS = 0.91) and other metrics. For ordinal PDDS score, LASSO model comprising combined clinical factors and 19 proteins as feature input (R2PDDS = 0.31) again outperformed base models. The two best-performing LASSO models (i.e., binary and ordinal PDDS score) shared six clinical features (age, sex, race/ethnicity, disease subtype, disease duration, DMT efficacy) and nine proteins (cluster of differentiation 6, CUB-domain-containing protein 1, contactin-2, interleukin-12 subunit-beta, neurofilament light chain [NfL], protogenin, serpin family A member 9, tumor necrosis factor superfamily member 13B, versican). By comparison, LASSO models with clinical features plus one single protein at a time as feature input did not select either NfL or glial fibrillary acidic protein (GFAP) as a final feature. Forcing either NfL or GFAP as a single protein feature into models did not improve performance beyond clinical features alone. Stacking classification model using five functional pathways to represent multiple proteins as meta-features implicated those involved in neuroaxonal integrity as significant contributors to predictive performance. Thus, serum multi-protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus single protein biomarker, reaching clinically actionable performance.

Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

An 18-protein multiple sclerosis (MS) disease activity (DA) test was validated based on associations between algorithm scores and clinical/radiographic assessments (N = 614 serum samples; Train [n = 426; algorithm development] and Test [n = 188; evaluation] subsets). The multi-protein model was trained based on presence/absence of gadolinium-positive (Gd+) lesions and was also strongly associated with new/enlarging T2 lesions, and active versus stable disease (composite of radiographic and clinical evidence of DA) with improved performance (p < 0.05) compared to the neurofilament light single protein model. The odds of having ≥1 Gd+ lesions with a moderate/high DA score were 4.49 times that of a low DA score, and the odds of having ≥2 Gd+ lesions with a high DA score were 20.99 times that of a low/moderate DA score. The MSDA Test was clinically validated with improved performance compared to the top-performing single-protein model and can serve as a quantitative tool to enhance the care of MS patients.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect disease activity in multiple sclerosis.

Background/Objectives: Serum proteomic analysis of deeply-phenotyped samples, biological pathway modeling and network analysis were performed to elucidate the inflammatory and neurodegenerative processes of multiple sclerosis (MS) and identify sensitive biomarkers of MS disease activity (DA). Methods: Over 1100 serum proteins were evaluated in >600 samples from three MS cohorts to identify biomarkers of clinical and radiographic (gadolinium-enhancing lesions) new MS DA. Protein levels were analyzed and associated with presence of gadolinium-enhancing lesions, clinical relapse status (CRS), and annualized relapse rate (ARR) to create a custom assay panel. Results: Twenty proteins were associated with increased clinical and radiographic MS DA. Serum neurofilament light chain (NfL) showed the strongest univariate correlation with radiographic and clinical DA measures. Multivariate modeling significantly outperformed univariate NfL to predict gadolinium lesion activity, CRS and ARR. Discussion: These findings provide insight regarding correlations between inflammatory and neurodegenerative biomarkers and clinical and radiographic MS DA. Funding: Octave Bioscience, Inc (Menlo Park, CA).

Proteomics and relationship with axonal pathology in multiple sclerosis: 5-year diffusion tensor imaging study.

Blood-based biomarkers can be economic and easily accessible tools for monitoring and predicting disease activity in multiple sclerosis. The objective of this study was to determine the predictive value of a multivariate proteomic assay for concurrent and future microstructural/axonal brain pathology in a longitudinal study of a heterogeneous group of people with multiple sclerosis. A proteomic analysis was obtained on serum samples from 202 people with multiple sclerosis (148 relapsing-remitting and 54 progressive) at baseline and 5-year follow-up. The concentration of 21 proteins related to multiple pathways of multiple sclerosis pathophysiology was derived using Proximity Extension Assay on the Olink platform. Patients were imaged on the same 3T MRI scanner at both timepoints. Тhe rate of whole brain, white matter and grey matter atrophy over the 5-year follow-up was determined using the multi-timepoint Structural Image Evaluation, using Normalisation, of Atrophy algorithms. Lesion burden measures were also assessed. The severity of microstructural axonal brain pathology was quantified using diffusion tensor imaging. Fractional anisotropy and mean diffusivity of normal-appearing brain tissue, normal-appearing white matter, grey matter, T2 and T1 lesions were calculated. Age, sex and body mass index-adjusted step-wise regression models were used. Glial fibrillary acidic protein was the most common and highest-ranked proteomic biomarker associated with greater concurrent microstructural central nervous system alterations (P < 0.001). The rate of whole brain atrophy was associated with baseline levels of glial fibrillary acidic protein, protogenin precursor, neurofilament light chain and myelin oligodendrocyte (P < 0.009), whereas grey matter atrophy was associated with higher baseline neurofilament light chain, higher osteopontin and lower protogenin precursor levels (P < 0.016). Higher baseline glial fibrillary acidic protein level was a significant predictor of future severity of the microstructural CNS alterations as measured by normal-appearing brain tissue fractional anisotropy and mean diffusivity (standardized ß = -0.397/0.327, P < 0.001), normal-appearing white matter fractional anisotropy (standardized ß = -0.466, P < 0.0012), grey matter mean diffusivity (standardized ß = 0.346, P < 0.011) and T2 lesion mean diffusivity (standardized ß = 0.416, P < 0.001) at the 5-year follow-up. Serum levels of myelin-oligodendrocyte glycoprotein, neurofilament light chain, contactin-2 and osteopontin proteins were additionally and independently associated with worse concomitant and future axonal pathology. Higher glial fibrillary acidic protein levels were associated with future disability progression (Exp(B) = 8.65, P = 0.004). Multiple proteomic biomarkers are independently associated with greater severity of axonal brain pathology as measured by diffusion tensor imaging in multiple sclerosis. Baseline serum glial fibrillary acidic protein levels can predict future disability progression.

Analytical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

PURPOSE: To characterize and analytically validate the MSDA Test, a multi-protein, serum-based biomarker assay developed using Olink® PEA methodology. EXPERIMENTAL DESIGN: Two lots of the MSDA Test panel were manufactured and subjected to a comprehensive analytical characterization and validation protocol to detect biomarkers present in the serum of patients with multiple sclerosis (MS). Biomarker concentrations were incorporated into a final algorithm used for calculating four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score. RESULTS: Analytical characterization demonstrated that the multi-protein panel satisfied the criteria necessary for a fit-for-purpose validation considering the assay's intended clinical use. This panel met acceptability criteria for 18 biomarkers included in the final algorithm out of 21 biomarkers evaluated. VCAN was omitted based on factors outside of analytical validation; COL4A1 and GH were excluded based on imprecision and diurnal variability, respectively. Performance of the four Disease Pathway and overall Disease Activity scores met the established acceptability criteria. CONCLUSIONS AND CLINICAL RELEVANCE: Analytical validation of this multi-protein, serum-based assay is the first step in establishing its potential utility as a quantitative, minimally invasive, and scalable biomarker panel to enhance the standard of care for patients with MS.

Serum RANKL levels associate with anti- citrullinated protein antibodies in early untreated rheumatoid arthritis and are modulated following methotrexate.

INTRODUCTION: Receptor activator of nuclear factor kappa B ligand (RANKL) is a key regulator of bone metabolism. Anti-citrullinated protein antibodies (ACPA) have been suggested to cause bone destruction by osteoclast activation. We investigated the relationship between RANKL and ACPA in patients with early untreated rheumatoid arthritis (RA). METHODS: Patients with newly diagnosed untreated RA (n = 183) were analyzed at baseline and 3 months after initiating methotrexate (MTX) treatment. Serum RANKL (total RANKL), ACPA (anti-CCP2) and ACPA specificities (anti-citrullinated (cit)-vimentin, anti-cit-enolase and anti-cit-fibrinogen) were determined by enzyme-linked immunosorbent assay (ELISA). Synovial RANKL expression was evaluated by immunohistochemistry in a small group of patients (n = 15). The relationship between anti-cit-vim antibodies and bone destruction was further validated in 1116 RA patients included in the EIRA cohort. Pearson's chi-square test, Wilcoxon rank sum test, Wilcoxon signed rank test and linear regression models were used. RESULTS: Serum RANKL concentration was significantly higher (p <0.05) in ACPA-positive (median: 689 pmol/L, IQR 342-1253) compared with ACPA-negative (median: 159 pmol/L, IQR 96-243) patients and this difference was also seen for synovial RANKL expression. Serum RANKL associated with ACPA (p <0.05) and bone erosions in rheumatoid factor (RF)-negative patients (n = 59). Among ACPA specificites, anti-cit-vimentin (amino acids 60-75) was associated with higher RANKL concentration and higher prevalence of bone erosion (p <0.05). Significant reductions in both serum RANKL and ACPA levels were observed after 3 months of MTX treatment (p <0.05). CONCLUSIONS: RANKL was elevated in ACPA-positive and in anti-cit-vimentin-positive patients with early untreated RA and associated with bone erosions. These findings give further support for an early direct pathogenic link between ACPA and bone destruction in RA.

Characterization of a multiplex, 12-biomarker test for rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic inflammatory disorder that primarily involves the joints. Accurate and frequent assessment of RA disease activity is critical to optimal treatment planning. A novel algorithm has been developed to determine a multi-biomarker disease activity (MBDA) score based upon measurement of the concentrations of 12 serum biomarkers in multiplex format. Biomarker assays from several different platforms were used in feasibility studies to identify biomarkers of potential significance. These assays were adapted to a multiplex platform for training and validation of the algorithm. In this study, the analytical performance of the underlying biomarker assays and the MBDA score was evaluated. Quantification of 12 biomarkers was performed with multiplexed sandwich immunoassays in three panels. Biomarker-specific capture antibodies were bound to specific locations in each well; detection antibodies were labeled with electrochemiluminescent tags. Data were acquired with a Sector Imager 6000, and analyte concentrations were determined. Parallelism, dynamic range, cross-reactivity, and precision were established for each biomarker as well as for the MBDA score. Interference by serum proteins, heterophilic antibodies, and common RA therapies was also assessed. The individual biomarker assays had 3-4 orders of magnitude dynamic ranges, with good reproducibility across time, operators, and reagent lots; the MBDA score had a median coefficient of variation of <2% across the score range. Cross-reactivity as well as interference by serum rheumatoid factor (RF), human anti-mouse antibodies (HAMA), or common RA therapies, including disease-modifying antirheumatic drugs and biologics, was minimal. The same MBDA score was observed in different subjects despite having different biomarker profiles, supporting prior literature reports that multiple pathways contribute to RA.

Pre-analytical effects of blood sampling and handling in quantitative immunoassays for rheumatoid arthritis.

Variability in pre-analytical blood sampling and handling can significantly impact results obtained in quantitative immunoassays. Understanding the impact of these variables is critical for accurate quantification and validation of biomarker measurements. Particularly, in the design and execution of large clinical trials, even small differences in sample processing and handling can have dramatic effects in analytical reliability, results interpretation, trial management and outcome. The effects of two common blood sampling methods (serum vs. plasma) and two widely-used serum handling methods (on the clot with ambient temperature shipping, "traditional", vs. centrifuged with cold chain shipping, "protocol") on protein and autoantibody concentrations were examined. Matched serum and plasma samples were collected from 32 rheumatoid arthritis (RA) patients representing a wide range of disease activity status. Additionally, a set of matched serum samples with two sample handling methods was collected. One tube was processed per manufacturer's instructions and shipped overnight on cold packs (protocol). The matched tube, without prior centrifugation, was simultaneously shipped overnight at ambient temperatures (traditional). Upon delivery, the traditional tube was centrifuged. All samples were subsequently aliquoted and frozen prior to analysis of protein and autoantibody biomarkers. Median correlation between paired serum and plasma across all autoantibody assays was 0.99 (0.98-1.00) with a median % difference of -3.3 (-7.5 to 6.0). In contrast, observed protein biomarker concentrations were significantly affected by sample types, with median correlation of 0.99 (0.33-1.00) and a median % difference of -10 (-55 to 23). When the two serum collection/handling methods were compared, the median correlation between paired samples for autoantibodies was 0.99 (0.91-1.00) with a median difference of 4%. In contrast, significant increases were observed in protein biomarker concentrations among certain biomarkers in samples processed with the 'traditional' method. Autoantibody quantification appears robust to both sample type (plasma vs. serum) and pre-analytical sample collection/handling methods (protocol vs. traditional). In contrast, for non-antibody protein biomarker concentrations, sample type had a significant impact; plasma samples generally exhibit decreased protein biomarker concentrations relative to serum. Similarly, sample handling significantly impacted the variability of protein biomarker concentrations. When biomarker concentrations are combined algorithmically into a single test score such as a multi-biomarker disease activity test for rheumatoid arthritis (MBDA), changes in protein biomarker concentrations may result in a bias of the score. These results illustrate the importance of characterizing pre-analytical methodology, sample type, sample processing and handling procedures for clinical testing in order to ensure test accuracy.

Preclinical pharmacokinetics, pharmacodynamics, and activity of a humanized anti-CD40 antibody (SGN-40) in rodents and non-human primates.

1. Cell-surface expression of CD40 in B-cell malignancies and multiple solid tumors has raised interest in its potential use as a target for antibody-based cancer therapy. SGN-40, a humanized monoclonal anti-CD40 antibody, mediates antibody-dependent cytotoxicity and inhibits B-cell tumor growth in vitro, properties of interest for the treatment of cancers, and is currently in Phase I clinical trials for B-cell malignancies. In this study, we determined in vivo activity and pharmacokinetics properties of SGN-40. 2. Effect of SGN-40 in xenograft model of CD40-expressing B-cell lymphoma in severe-combined immune deficiency mice and its in vivo pharmacokinetics properties in normal mice, rats and cynomolgus monkeys were studied. 3. Treatment with SGN-40 significantly increased the survival of mice xenografted with human B-cell lymphoma cell line. SGN-40 exhibited nearly 100% bioavailability in mice and it cleared faster when given at a low dose. In monkeys, clearance of SGN-40 was also much faster at low dose, suggesting nonlinear pharmacokinetics in these species. In rats, however, SGN-40 clearance at all tested doses was similar, suggesting that pharmacokinetics were linear in this dose range in rats. Administration of SGN-40 to monkeys also produced marked, dose-dependent, and persistent depletion of peripheral CD20(+) B lymphocytes. 4. Data presented in this report suggest that SGN-40 is active in in vivo, and based upon interspecies scaling, SGN-40 clearance in humans is predicted to be similar to observed SGN-40 clearance in monkeys. These data suggest that SGN-40 has appropriate pharmacokinetic properties that support its clinical use.

Depletion of B cells by a humanized anti-CD20 antibody PRO70769 in Macaca fascicularis.

PRO70769 is a humanized IgG1 monoclonal antibody against the CD20 molecule that is present on normal and malignant B cells. PRO70769 is being evaluated for treatment of B-cell-mediated diseases and is in a phase 1 trial for rheumatoid arthritis. As part of the preclinical toxicology evaluation, B-cell depletion profiles and safety of PRO70769 were assessed in cynomolgus monkeys. Animals were administered drug (IV) on days 1 and 15 with 10, 50, or 100 mg/kg PRO70769 and killed 2 weeks after the second dose and after a 3-month recovery period. In a parallel study, animals were not necropsied but instead were retreated with a second cycle of PRO70769 administered under an identical regimen. PRO70769 suppressed B cells in the blood to undetectable levels and significantly reduced B cells in lymphoid tissues. Splenic B cells were depleted to a greater extent compared with lymph node B cells. A second cycle of treatment resulted in a greater extent of depletion in lymphoid tissues compared with the depletion observed after one cycle of treatment; however, residual B cells in lymphoid tissues were still detectable, even at the highest dose. The rate of B-cell recovery in peripheral blood appeared similar between one and two cycles of treatment. Upon depletion there was a change in the profile of lymph node B-cell subsets. After recovery, B-cell subsets were reconstituted to normal levels. Depletion of CD20-expressing cells and lymphoid follicular atrophy were the only treatment-related effects.