Browsing by Author "Bielekova, Bibiana"

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The effects of interleukin-2 on immune response regulation
(2017-02) Waters, Ryan S.; Perry, Justin S. A.; Han, SunPil; Bielekova, Bibiana; Gedeon, Tomas
The immune system has many adaptive and dynamic components that are regulated to ensure appropriate, precise and rapid response to a foreign pathogen. A delayed or inadequate immune response can lead to prolonged disease, while an excessive or under-regulated response can lead to autoimmunity. The cytokine, interleukin-2 (IL-2) and its receptor IL-2R play an important role in maintaining this balance.The IL-2 receptor transduces pSTAT5 signal through both the intermediate and high affinity receptors, which differ from each other by the presence of CD25 chain in IL-2 receptor. We present experimental data on the kinetics of pSTAT5 signalling through both of the receptors and develop a model that captures this kinetics. We then use this model to parameterize key aspects of two additional models in which we propose and study two different mechanisms by which IL-2 receptor can transduce distinct signals leading to either an activated or a non-activated cell state. We speculate that this initial state differentiation, perhaps enhanced by downstream feedbacks, may eventually lead to differential cell fates.Our result shows that non-linear dynamical models can suggest resolution of a puzzling array of seemingly contradictory experimental results on IL-2 effect on proliferation and differentiation of T-cells.
Meta-analysis of the Age-Dependent Efficacy of Multiple Sclerosis Treatments
(2017-11) Weideman, Ann Marie; Tapia-Maltos, Marco Aurelio; Johnson, Kory; Greenwood, Mark C.; Bielekova, Bibiana
Objective: To perform a meta-analysis of randomized, blinded, multiple sclerosis (MS) clinical trials, to test the hypothesis that efficacy of immunomodulatory disease-modifying therapies (DMTs) on MS disability progression is strongly dependent on age. Methods: We performed a literature search with pre-defined criteria and extracted relevant features from 38 clinical trials that assessed efficacy of DMTs on disability progression. We fit a linear regression, weighted for trial sample size, and duration, to examine the hypothesis that age has a defining effect on the therapeutic efficacy of immunomodulatory DMTs. Results: More than 28,000 MS subjects participating in trials of 13 categories of immunomodulatory drugs are included in the meta-analysis. The efficacy of immunomodulatory DMTs on MS disability strongly decreased with advancing age (R-2= 0.6757, p = 6.39e-09). Inclusion of baseline EDSS did not significantly improve the model. The regression predicts zero efficacy beyond approximately age 53 years. The comparative efficacy rank derived from the regression residuals differentiates high-and low-efficacy drugs. High-efficacy drugs outperform low-efficacy drugs in inhibiting MS disability only for patients younger than 40.5 years. Conclusion: The meta-analysis supports the notion that progressive MS is simply a later stage of the MS disease process and that age is an essential modifier of a drug efficacy. Higher efficacy treatments exert their benefit over lower efficacy treatments only during early stages of MS, and, after age 53, the model suggests that there is no predicted benefit to receiving immunomodulatory DMTs for the average MS patient.
NeurEx: digitalized neurological examination offers a novel high-resolution disability scale
(2018-10) Kosa, Peter; Barbour, Christopher; Wichman, Alison; Sandford, Mary; Greenwood, Mark C.; Bielekova, Bibiana
Objective To develop a sensitive neurological disability scale for broad utilization in clinical practice. Methods We employed advances of mobile computing to develop an iPad-based App for convenient documentation of the neurological examination into a secure, cloud-linked database. We included features present in four traditional neuroimmunological disability scales and codified their automatic computation. By combining spatial distribution of the neurological deficit with quantitative or semiquantitative rating of its severity we developed a new summary score (called NeurEx; ranging from 0 to 1349 with minimal measurable change of 0.25) and compared its performance with clinician- and App-computed traditional clinical scales. Results In the cross-sectional comparison of 906 neurological examinations, the variance between App-computed and clinician-scored disability scales was comparable to the variance between rating of the identical neurological examination by multiple sclerosis (MS)-trained clinicians. By eliminating rating ambiguity, App-computed scales achieved greater accuracy in measuring disability progression over time (n = 191 patients studied over 880.6 patient-years). The NeurEx score had no apparent ceiling effect and more than 200-fold higher sensitivity for detecting a measurable yearly disability progression (i.e., median progression slope of 8.13 relative to minimum detectable change of 0.25) than Expanded Disability Status Scale (EDSS) with a median yearly progression slope of 0.071 that is lower than the minimal measurable change on EDSS of 0.5. Interpretation NeurEx can be used as a highly sensitive outcome measure in neuroimmunology. The App can be easily modified for use in other areas of neurology and it can bridge private practice practitioners to academic centers in multicenter research studies.
New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability
(2017-11) Weideman, Ann Marie; Barbour, Christopher; Tapia-Maltos, Marco Aurelio; Tran, Tan; Jackson, Kayla; Kosa, Peter; Komori, Mika; Wichman, Alison; Johnson, Kory; Greenwood, Mark C.; Bielekova, Bibiana
The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e-06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.
Novel composite MRI scale correlates highly with disability in multiple sclerosis patients
(2015-11) Kosa, Peter; Komori, Mika; Waters, Ryan S.; Wu, Tianxia; Cortese, Irene; Ohayon, Joan; Fenton, Kaylan; Cherup, Jamie; Gedeon, Tomas; Bielekova, Bibiana
Understanding genotype-phenotype relationships or development/validation of biomarkers requires large multicenter cohorts integrated by universal quantification of crucial phenotypical traits, such as central nervous system (CNS) tissue destruction. We hypothesized that mathematical modeling-guided combination of biologically meaningful, semi-quantitative MRI elements characterized by high signal-to-noise ratio will provide such reliable, universal tool for measuring CNS tissue destruction. We retrospectively graded 15 elements in MRI scans performed in 419 untreated subjects with or without neurological diseases, while being blinded to their prospectively acquired clinical scores. We then used 305 subjects for disability-guided mathematical modeling to select and combine MRI elements that had non-redundant contributions to clinical disability, resulting in Combinatorial MRI Scale (COMRIS). We validated our model on the remaining 114 independent subjects. COMRIS requires 5-10 min per scan on average to compute and demonstrates highly significant (p<0.0001) and validation-consistent Spearman correlation coefficients (0.75, 0.76, and 0.65) for the expanded disability status scale (EDSS), Scripps neurological rating scale (SNRS), and symbol digit modality test (SDMT) measures of neurological disability, respectively. Because COMRIS is not greatly influenced by MRI scanners or protocols and can be computed even in the presence of some motion artifacts, it does not require censoring out patients and it provides comparable results across different cohorts. As such, it represents a broadly available clinical and research tool that can facilitate multicenter research studies and comparative analyses across patient cohorts and research projects.