Application of a convolutional neural network to the quality control of MRI defacing

Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Motivated by a large clinical trials dataset with over 235,000 MRI scans, we consider the challenge of defacing — anonymisation to remove identifying facial features. The defacing process must undergo quality control (QC) checks to ensure that the facial features have been removed and that the brain tissue is left intact. Visual QC checks are time-consuming and can cause delays in preparing data. We have developed a convolutional neural network (CNN) that can assist with the QC of the application of MRI defacing; our CNN is able to distinguish between scans that are correctly defaced and can classify defacing failures into three sub-types to facilitate parameter tuning during remedial re-defacing. Since integrating the CNN into our anonymisation pipeline, over 75,000 scans have been processed. Strict thresholds have been applied so that ambiguous classifications are referred for visual QC checks, however all scans still undergo an efficient verification check before being marked as passed. After applying the thresholds, our network is 92% accurate and can classify nearly half of the scans without the need for protracted manual checks. Our model can generalise across MRI modalities and has comparable performance when tested on an independent dataset. Even with the introduction of the verification checks, incorporation of the CNN has reduced the time spent undertaking QC checks by 42% during initial defacing, and by 35% overall. With the help of the CNN, we have been able to successfully deface 96% of the scans in the project whilst maintaining high QC standards. In a similarly sized new project, we would expect the model to reduce the time spent on manual QC checks by 125 h. Our approach is applicable to other projects with the potential to greatly improve the efficiency of imaging anonymisation pipelines

Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): age is a key contributor to presentation

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity

How patients with multiple sclerosis acquire disability

Patients with multiple sclerosis acquire disability either through relapse-associated worsening (RAW) or progression independent of relapse activity (PIRA). This study addresses the relative contribution of relapses to disability worsening over the course of the disease, how early progression begins and the extent to which multiple sclerosis therapies delay disability accumulation. Using the Novartis-Oxford multiple sclerosis (NO.MS) data pool spanning all multiple sclerosis phenotypes and paediatric multiple sclerosis, we evaluated ∼200 000 Expanded Disability Status Scale (EDSS) transitions from >27 000 patients with ≤15 years follow-up. We analysed three datasets: (i) A full analysis dataset containing all observational and randomized controlled clinical trials in which disability and relapses were assessed (n = 27 328); (ii) all phase 3 clinical trials (n = 8346); and (iii) all placebo-controlled phase 3 clinical trials (n = 4970). We determined the relative importance of RAW and PIRA, investigated the role of relapses on all-cause disability worsening using Andersen-Gill models and observed the impact of the mechanism of worsening and disease-modifying therapies on the time to reach milestone disability levels using time continuous Markov models. PIRA started early in the disease process, occurred in all phenotypes and became the principal driver of disability accumulation in the progressive phase of the disease. Relapses significantly increased the hazard of all-cause disability worsening events; following a year in which relapses occurred (versus a year without relapses), the hazard increased by 31–48% (all P  Our data confirm that relapses contribute to the accumulation of disability, primarily early in multiple sclerosis. PIRA begins in relapsing-remitting multiple sclerosis and becomes the dominant driver of disability accumulation as the disease evolves. Pre-existing disability and older age are the principal risk factors for further disability accumulation. The use of disease-modifying therapies delays disability accrual by years, with the potential to gain time being highest in the earliest stages of multiple sclerosis

Serum Neurofilament Light Trajectories and Their Relation to Subclinical Radiological Disease Activity in Relapsing Multiple Sclerosis Patients in the APLIOS Trial

Introduction: Several studies have described prognostic value of serum neurofilament light chain (sNfL) at the group level in relapsing multiple sclerosis (RMS) patients. Here, we aimed to explore the temporal association between sNfL and development of subclinical disease activity as assessed by magnetic resonance imaging (MRI) at the group level and evaluate the potential of sNfL as a biomarker for capturing subclinical disease activity in individual RMS patients. Methods: In the 12-week APLIOS study, patients (N = 284) received subcutaneous ofatumumab 20 mg. Frequent sNfL sampling (14 time points over 12 weeks) and monthly MRI scans enabled key analyses including assessment of the group-level temporal relationship of sNfL levels with on-study subclinical development of gadolinium-enhancing (Gd +)T1 lesions. Prognostic value of baseline sNfL ("high" vs. "low") level for subsequent on-study clinical relapse or Gd + T1 activity was assessed. Individual patient-level development of on-study Gd + T1 lesions wascompared across three predictors: baseline Gd + T1 lesion number, baseline sNfL ("high" vs. "low"), and time-matched sNfL. Results: In patients developing Gd + T1 lesions at week 4 (absent at baseline), sNfL levels increased during the month preceding the week-4 MRI scan and then gradually decreased back to baseline. High versus low baseline sNfL conferred increased risk of subsequent on-study clinical relapse or Gd + T1 activity (HR, 2.81; p < 0.0001) in the overall population and, notably, also in the patients without baseline Gd + T1 lesions (HR, 2.48; p = 0.0213). Individual patient trajectories revealed a marked difference in Gd + T1 lesions between patients with the ten highest vs. lowest baseline sNfL levels (119 vs. 19 lesions). Prognostic value of baseline or time-matched sNfL for on-study Gd + T1 lesions was comparable to that of the number of baseline MRI Gd + T1 lesions. Conclusions: sNfL measurement may have utility in capturing and monitoring subclinical disease activity in RMS patients. sNfL assessments could complement regular MRI scans and may provide an alternative when MRI assessment is not feasible. ClinicalTrials.gov: NCT03560739. Classification of Evidence: This study provides class I evidence that serum neurofilament light may be used as a biomarker for monitoring subclinical disease activity in relapsing multiple sclerosis patients, as shown by its elevation in the weeks preceding the development of new gadolinium-enhancing T1 lesion activity

The Development of Ofatumumab, a Fully Human Anti-CD20 Monoclonal Antibody for Practical Use in Relapsing Multiple Sclerosis Treatment.

The importance of B cells in multiple sclerosis (MS) has been demonstrated through the advent of B-cell-depleting anti-CD20 antibody therapies. Ofatumumab is the first fully human anti-CD20 monoclonal antibody (mAb) developed and tested for subcutaneous (SC) self-administration at monthly doses of 20 mg, and has been approved in the US, UK, EU, and other regions and countries worldwide for the treatment of relapsing MS. The development goal of ofatumumab was to obtain a highly efficacious anti-CD20 therapy, with a safety and tolerability profile that allows for self-administration by MS patients at home and a positive benefit-risk balance for use in the broad relapsing MS population. This development goal was enabled by the unique binding site, higher affinity to B cells, and higher potency of ofatumumab compared to previous anti-CD20 mAbs; these properties of ofatumumab facilitate rapid B-cell depletion and maintenance with a low dose at a low injection volume (20 mg/0.4 ml). The high potency in turn enables the selective targeting of B cells that reside in the lymphatic system via subcutaneous (SC) administration. Through a comprehensive dose-finding program in two phase 2 studies (one intravenous and one SC) and model simulations, it was found that safety and tolerability can be further improved, and the risk of systemic injection-related reactions (IRRs) minimized, by avoiding doses ≥ 30 mg, and by reaching initial and rapid B-cell depletion via stepwise weekly administration of ofatumumab at Weeks 0, 1, and 2 (instead of a single high dose). Once near-complete B-cell depletion is reached, it can be maintained by monthly doses of 20 mg/0.4 ml. Indeed, in phase 3 trials (ASCLEPIOS I/II), rapid and sustained near-complete B-cell depletion (largely independent of body weight, race and other factors) was observed with this dosing regimen, which resulted in superior efficacy of ofatumumab versus teriflunomide on relapse rates, disability worsening, neuronal injury (serum neurofilament light chain), and imaging outcomes. Likely due to its fully human nature, ofatumumab has a low immunogenic risk profile-only 2 of 914 patients receiving ofatumumab in ASCLEPIOS I/II developed anti-drug antibodies-and this may also underlie the infrequent IRRs (20% with ofatumumab vs. 15% with the placebo injection in the teriflunomide arm) that were mostly (99.8%) mild to moderate in severity. The overall rates of infections and serious infections in patients treated with ofatumumab were similar to those in patients treated with teriflunomide (51.6% vs. 52.7% and 2.5% vs. 1.8%, respectively). The benefit-risk profile of ofatumumab was favorable compared to teriflunomide in the broad RMS population, and also in the predefined subgroups of both recently diagnosed and/or treatment-naïve patients, as well as previously disease-modifying therapy-treated patients. Interim data from the ongoing extension study (ALITHIOS) have shown that long-term treatment with ofatumumab up to 4 years is well-tolerated in RMS patients, with no new safety risks identified. In parallel to the phase 3 trials in which SC administration was carried out with a pre-filled syringe, an autoinjector pen for more convenient self-administration of the ofatumumab 20 mg dose was developed and is available for use in clinical practice

Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis

Background Novartis and the University of Oxford’s Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. Method The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the “IL-17” project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). Results A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. Conclusions An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools

Rapid and sustained B-cell depletion with subcutaneous ofatumumab in relapsing multiple sclerosis : APLIOS, a randomized phase-2 study

Background: Ofatumumab, the first fully human anti-CD20 monoclonal antibody, is approved in several countries for relapsing multiple sclerosis (RMS). Objective: To demonstrate the bioequivalence of ofatumumab administered by an autoinjector versus a pre-filled syringe (PFS) and to explore the effect of ofatumumab on B-cell depletion. Methods: APLIOS (NCT03560739) is a 12-week, open-label, parallel-group, phase-2 study in patients with RMS receiving subcutaneous ofatumumab 20 mg every 4 weeks (q4w) (from Week 4, after initial doses on Days 1, 7, and 14). Patients were randomized 10:10:1:1 to autoinjector or PFS in the abdomen, or autoinjector or PFS in the thigh, respectively. Bioequivalence was determined by area under the curve (AUCτ) and maximum plasma concentration (Cmax) for Weeks 8-12. B-cell depletion and safety/tolerability were assessed. Results: A total of 256 patients contributed to the bioequivalence analyses (autoinjector-abdomen, n = 128; PFS-abdomen, n = 128). Abdominal ofatumumab pharmacokinetic exposure was bioequivalent for autoinjector and PFS (geometric mean AUCτ, 487.7 vs 474.1 h × µg/mL (ratio 1.03); Cmax, 1.409 vs 1.409 µg/mL (ratio 1.00)). B-cell counts (median cells/µL) depleted rapidly in all groups from 214.0 (baseline) to 2.0 (Day 14). Ofatumumab was well tolerated. Conclusion: Ofatumumab 20 mg q4w self-administered subcutaneously via autoinjector is bioequivalent to PFS administration and provides rapid B-cell depletion

Variance of the SGK1 Gene Is Associated with Insulin Secretion in Different European Populations: Results from the TUEF, EUGENE2, and METSIM Studies

HYPOTHESIS:Serum- and Glucocorticoid-inducible Kinase 1 (SGK1) is involved in the regulation of insulin secretion and may represent a candidate gene for the development of type 2 diabetes mellitus in humans. METHODS:Three independent European populations were analyzed for the association of SGK1 gene (SGK) variations and insulin secretion traits. The German TUEF project provided the screening population (N = 725), and four tagging SNPs (rs1763527, rs1743966, rs1057293, rs9402571) were investigated. EUGENE2 (N = 827) served as a replication cohort for the detected associations. Finally, the detected associations were validated in the METSIM study, providing 3798 non-diabetic and 659 diabetic (type 2) individuals. RESULTS:Carriers of the minor G allele in rs9402571 had significantly higher C-peptide levels in the 2 h OGTT (+10.8%, p = 0.04; dominant model) and higher AUC(C-Peptide)/AUC(Glc) ratios (+7.5%, p = 0.04) compared to homozygous wild type TT carriers in the screening population. As interaction analysis for BMIxrs9402571 was significant (p = 0.04) for the endpoint insulin secretion, we stratified the TUEF cohort for BMI, using a cut off point of BMI = 25. The effect on insulin secretion only remained significant in lean TUEF participants (BMI< or =25). This finding was replicated in lean EUGENE2 rs9402571 minor allele carriers, who had a significantly higher AUC(Ins)/AUC(Glc) (TT: 226+/-7, XG: 246+/-9; p = 0.019). Accordingly, the METSIM trial revealed a lower prevalence of type 2 diabetes (OR: 0.85; 95%CI: 0.71-1.01; p = 0.065, dominant model) in rs9402571 minor allele carriers. CONCLUSIONS:The rs9402571 SGK genotype associates with increased insulin secretion in lean non-diabetic TUEF/EUGENE2 participants and with lower diabetes prevalence in METSIM. Our study in three independent European populations supports the conclusion that SGK variability affects diabetes risk

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

CO2 enrichment reduces the relative contribution of latex and latex-related hydrocarbons to biomass in Euphorbia lathyris

The hypothesis that plants grown under elevated CO2 allocate more carbon to the production of latex and C-rich secondary compounds whereas nutrient addition counteracts this effect was tested. Two similar experiments were conducted in two different experimental facilities. In both facilities seedlings of Euphorbia lathyris were exposed to factorial combinations of two CO2 concentrations and two levels of nutrient availability for 2 months. The CO2 treatments and growth conditions differed substantially between these two experiments but treatment responses to elevated CO2 and fertilizer addition were remarkably similar, underlining the robustness of our findings. Elevated CO2 increased biomass to a greater extent in fertilized than in unfertilized plants and reduced the leaf biomass fraction by accelerating leaf senescence. Concentrations of non-structural carbohydrates (NSC) increased in elevated CO2. However, this apparent carbon surplus did not feed into the whole plant latex pool. The latex harvest per leaf (-25 hydrocarbons (-20 experiments P > 0.05). Fertilization reduced NSC concentrations (-25 but neither affected latex yield per leaf nor the concentration of latex-related hydrocarbons. It is concluded that latex and related hydrocarbons in CO2-enriched plants are a negligible sink for excess carbon irrespective of nutrient status and thus, vigour of growth