Plasma neurofilament light chain concentrations as a biomarker of clinical and radiologic outcomes in relapsing multiple sclerosis: Post hoc analysis of Phase 3 ozanimod trials.

BACKGROUND AND PURPOSE: We investigated plasma neurofilament light chain concentration (pNfL) as a biomarker for neuroaxonal damage and disease activity using data from Phase 3 trials of ozanimod in relapsing multiple sclerosis (RMS). METHODS: pNfL was measured before and after ozanimod 0.46 mg or 0.92 mg daily or interferon ß-1a 30 µg weekly in the randomized, double-blind SUNBEAM and RADIANCE trials. In these post hoc analyses, we investigated relationships between pNfL (at baseline and median percentage change from baseline to Month 12 [SUNBEAM] or 24 [RADIANCE]) and clinical and magnetic resonance imaging outcomes. RESULTS: Median (Q1, Q3) baseline pNfL, available in 1244 of 1346 SUNBEAM participants, was 14.70 (10.16, 23.26) pg/ml and in 1109 of 1313 RADIANCE participants was 13.35 (9.42, 20.41) pg/ml. Baseline gadolinium-enhancing (GdE) and T2 lesion counts increased and brain volume decreased with increasing baseline pNfL. Baseline pNfL was higher in those with versus without on-treatment relapse. Median percentage reduction in pNfL at 12 months in SUNBEAM (n = 1238) and 24 months in RADIANCE (n = 1088) was greater for ozanimod (20%-27%) than interferon ß-1a (13%-16%; p < 0.01). Greater pNfL reduction was associated with fewer GdE lesions, fewer new/enlarging T2 lesions per scan, less loss of brain volume, lower annualized relapse rate (ARR), and no evidence of disease activity. The following models predicted ARR: 0.5111 + 0.0116 × ΔNfL at 12 months (SUNBEAM) and 0.4079 + 0.0088 × ΔNfL at 24 months (RADIANCE). CONCLUSIONS: pNfL was associated with clinical and radiologic measures of disease and treatment effects in RMS, supporting its use as a biomarker.

Effect of the sphingosine-1-phosphate receptor modulator ozanimod on leukocyte subtypes in relapsing MS.

OBJECTIVE: To better understand ozanimod's mechanism of action (MOA), we conducted exploratory analyses from a phase 1 study to characterize ozanimod's effect on circulating leukocyte subsets in patients with relapsing multiple sclerosis. METHODS: An open-label pharmacodynamic study randomized patients to oral ozanimod hydrochloride (HCl) 0.5 (n = 13) or 1 mg/d (n = 11) for ∼12 weeks (including 7-day dose escalation). Circulating leukocyte subsets were quantified using flow cytometry (days 28, 56, and 85) and epigenetic cell counting (days 2, 5, 28, 56, and 85) and compared with baseline (day 1) using descriptive statistics. RESULTS: Ozanimod caused dose-dependent reductions in absolute lymphocyte counts. Observed by both methodologies, circulating CD19+ B- and CD3+ T-cell counts were reduced by >50% with ozanimod HCl 0.5 mg and >75% with 1 mg at day 85. Based on flow cytometry, ozanimod HCl 1 mg showed greater decreases in CD4+ than CD8+ T cells, greater decreases in both CD4+ and CD8+ central memory vs effector memory T cells, and reductions in mean CD4+ and CD8+ naive T cells by ≥90% at day 85. In the flow cytometry analysis, changes in monocytes, natural killer, and natural killer T cells were minimal. Using epigenetic cell counting, greater reductions for Th17 than T regulatory cells were determined. CONCLUSION: Ozanimod induced dose-dependent reductions in circulating B- and T-cell counts and differential effects on naive and memory CD4+ and CD8+ T cells and CD19+ B cells. Data characterized with both a novel epigenetic cell-counting method and flow cytometry support ozanimod's MOA. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov NCT02797015.

Use of acoustic emission to identify novel candidate biomarkers for knee osteoarthritis (OA).

Our objective was to determine the efficacy and feasibility of a new approach for identifying candidate biomarkers for knee osteoarthritis (OA), based on selecting promising candidates from a range of high-frequency acoustic emission (AE) measurements generated during weight-bearing knee movement. Candidate AE biomarkers identified by this approach could then be validated in larger studies for use in future clinical trials and stratified medicine applications for this common health condition. A population cohort of participants with knee pain and a Kellgren-Lawrence (KL) score between 1-4 were recruited from local NHS primary and secondary care sites. Focusing on participants' self-identified worse knee, and using our established movement protocol, sources of variation in AE measurement and associations of AE markers with other markers were explored. Using this approach we identified 4 initial candidate AE biomarkers, of which "number of hits" showed the best reproducibility, in terms of within-session, day to day, week to week, between-practitioner, and between-machine variation, at 2 different machine upper frequency settings. "Number of hits" was higher in knees with KL scores of 2 than in KL1, and also showed significant associations with pain in the contralateral knee, and with body weight. "Hits" occurred predominantly in 2 of 4 defined movement quadrants. The protocol was feasible and acceptable to all participants and professionals involved. This study demonstrates how AE measurement during simple sit-stand-sit movements can be used to generate novel candidate knee OA biomarkers. AE measurements probably reflect a composite of structural changes and joint loading factors. Refinement of the method and increasing understanding of factors contributing to AE will enable this approach to be used to generate further candidate biomarkers for validation and potential use in clinical trials.

Good Signal Detection Practices: Evidence from IMI PROTECT.

Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.

Statistical Analysis of Cumulative Serious Adverse Event Data From Development Safety Update Reports.

BACKGROUND: Monitoring safety in clinical trials by regulatory authorities and sponsors involves the clinical review, often subjectively, of large data sets of different types of safety information which may require considerable resources. METHODS: This study investigated a means of statistically guided clinical review of safety data provided in the Cumulative Table of Serious Adverse Events of a Development Safety Update Report (DSUR). A simple statistical approach that treats every adverse event as independent of all others and uses a reference prior, which avoids infinite estimates of relative risk but does not unduly influence posterior inferences, was used with fixed rules of relative risk to identify a serious adverse event preferred term as a potential risk. RESULTS: This simple model, using cumulative serious adverse event (SAE) data from 5 DSURs, identified a small group of potential risks that included some not reported by the sponsor as well as most of those reported by the sponsor in the DSUR Summary of Important Risks. CONCLUSIONS: The method provides a systematic and objective approach to analysis of cumulative SAE data that could help to identify potential risks that need further investigation by a regulatory authority or sponsor.

Predicting potential liver toxicity from phase 2 data: a case study with ximelagatran.

Ximelagatran was denied marketing approval in the USA and was withdrawn from those markets in which it had been approved, because of concerns over potential liver toxicity. A retrospective analysis of phase 2 data relating to liver toxicity is performed using the methods of extreme value modelling. The analysis reveals that the phase 2 data were predictive of the phase 3 results and, had the methods been available at the time, such analysis would have provided valuable information relating to the decision to proceed with further development of the compound.

Multivariate extreme value modelling of laboratory safety data from clinical studies.

Generally, in the interpretation of clinical safety laboratory data, it is extreme values that indicate potential safety issues. We illustrate the application of multivariate extreme value modelling to such data. Applying the methods to a clinical trial dataset, we find unexpected extremal relationships that have potentially important implications for the interpretation of such data.

Extreme value modelling of laboratory safety data from clinical studies.

Most clinical studies collect several safety-related laboratory variables. Generally, it is the extreme values of these variables that indicate potential safety issues. We illustrate the novel application of extreme value modelling to such data, with the aim of predicting the incidence of severe adverse drug reactions. By applying the methods to a clinical trial data set, we identify a dose-response relationship and use Bayesian techniques to identify a potential safety concern by making predictions from the fitted model, despite the small sample size.

Models for the analysis of C-reactive protein in statin trials.

Elevation in C-reactive protein (CRP) is an independent risk factor for cardiovascular disease progression and levels are reduced by treatment with statins. However, on-treatment CRP, given baseline CRP and treatment, is not normally distributed and outliers exist even when transformations are applied. Although classical non-parametric tests address some of these issues, they do not enable straightforward inclusion of covariate information. The aims of this study were to produce a model that improved efficiency and accuracy of analysis of CRP data. Estimation of treatment effects and identification of outliers were addressed using controlled trials of rosuvastatin. The robust statistical technique of MM-estimation was used to fit models to data in the presence of outliers and was compared with least-squares estimation. To develop the model, appropriate transformations of the response and baseline variables were selected. The model was used to investigate how on-treatment CRP related to baseline CRP and estimated treatment effects with rosuvastatin. On comparing least-squares and MM-estimation, MM-estimation was superior to least-squares estimation in that parameter estimates were more efficient and outliers were clearly identified. Relative reductions in CRP were higher at higher baseline CRP levels. There was also evidence of a dose-response relationship between CRP reductions from baseline and rosuvastatin. Several large outliers were identified, although there did not appear to be any relationships between the incidence of outliers and treatments. In conclusion, using robust estimation to model CRP data is superior to least-squares estimation and non-parametric tests in terms of efficiency, outlier identification and the ability to include covariate information.

Detecting outliers in multivariate laboratory data.

Laboratory data collected in clinical trials often include outliers, and these are often the observations of most interest. In high dimensional settings, outliers can be difficult to detect and can be masked by classical statistical methods. A method of plotting robustly scaled data in such a way as to expose outliers is described and an application is presented.

Simulating titration to goal clinical trials with statins.

Many countries have local guidelines on the management of subjects' lipid levels with and without pharmaceutical intervention. The statin class of drugs is the preferred class for reducing low density lipoprotein cholesterol (LDL-C). Different statins have different potencies and different dose ranges. It is of interest to simulate clinical trials in which subjects are titrated through the dose ranges of various statins in accordance with local guidelines, in order to estimate the proportion of subjects who reach treatment goal of LDL-C at any particular dose of any particular statin.

Comparison of rosuvastatin versus atorvastatin in patients with heterozygous familial hypercholesterolemia.

Heterozygous familial hypercholesterolemia (HFH) is a common genetic disorder that confers a significantly increased risk of early coronary artery disease. This study compared atorvastatin and rosuvastatin in reducing low-density lipoprotein (LDL) cholesterol in HFH in a global, 18-week, weighted-randomization, double-blind, parallel-group, forced-titration study. Following a 6-week diet lead-in, 623 patients were randomized to 20 mg/day of atorvastatin (n = 187) or rosuvastatin (n = 436) with forced titration at 6-week intervals to 80 mg/day. The primary end point was percentage change in LDL cholesterol from baseline to week 18. At week 18, rosuvastatin therapy produced a significantly greater reduction in LDL cholesterol than atorvastatin (-57.9% vs -50.4%; p <0.001) and a significantly greater increase in high-density lipoprotein (HDL) cholesterol (12.4% vs 2.9%; p <0.001). Rosuvastatin also produced significantly greater reductions in apolipoprotein-B and all 4 major lipid ratios, as well as a significantly greater increases in apolipoprotein A-I (all p <0.001). More patients with HFH with coronary artery disease achieved the National Cholesterol Education Program Adult Treatment Panel III goal of LDL cholesterol <100 mg/dl (<2.6 mmol/L) on rosuvastatin 40 and 80 mg than atorvastatin 80 mg (17%, 24%, and 4.5%, respectively). High-sensitivity C-reactive protein median values were reduced by 33% to 34% in both the 80-mg rosuvastatin- and atorvastatin-treated groups. Both treatments were well tolerated. Thus, in HFH, rosuvastatin force titrated from 20 to 80 mg/day produced significantly greater reductions than atorvastatin 20 to 80 mg/day in LDL cholesterol and improvements in HDL cholesterol and other lipid parameters, and enabled more patients to achieve LDL cholesterol goals.

Efficacy of rosuvastatin 10 mg in patients with the metabolic syndrome.

The constellation of risk factors known as the metabolic syndrome increases the risk of coronary artery disease at any low-density lipoprotein (LDL) cholesterol level. We performed an exploratory analysis of data from 5 trials to study the effects of rosuvastatin 10 mg on lipid levels and ratios in hypercholesterolemic patients (LDL cholesterol > or =160 mg/dL and <250 mg/dL) who met a modified National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) definition of the metabolic syndrome. Of 580 patients completing 12 weeks of treatment with rosuvastatin 10 mg, 194 (33%) met the definition of the metabolic syndrome by exhibiting > or =3 of the following: body mass index >30; triglycerides > or =150 mg/dL; high-density lipoprotein (HDL) cholesterol <40 mg/dL in men and <50 mg/dL in women; blood pressure > or =130/> or =85 mm Hg or receiving current medication for hypertension; and fasting blood glucose > or =110 mg/dL. Patients with the metabolic syndrome had higher triglyceride, non-HDL cholesterol, apolipoprotein B, and lipid ratios, and lower HDL cholesterol and apolipoprotein A-I levels, at baseline compared with patients without the metabolic syndrome. In patients with the metabolic syndrome, rosuvastatin 10 mg improved LDL cholesterol (-47%), non-HDL cholesterol (-43%), non-HDL cholesterol/HDL cholesterol ratio (-47%), apolipoprotein B (-37%), apolipoprotein B/apolipoprotein A-I ratio (-40%), triglycerides (-23%), apolipoprotein A-I (+7%), and HDL cholesterol (+10%)-in a manner similar to that in hypercholesterolemic patients who did not meet these criteria. Among patients who met the metabolic syndrome criteria and who had triglycerides > or =200 mg/dL, 64% met their ATP III non-HDL goals.

Effects of rosuvastatin and atorvastatin compared over 52 weeks of treatment in patients with hypercholesterolemia.

BACKGROUND: Despite the demonstrated benefits of low-density lipoprotein cholesterol (LDL-C) reduction in reducing the risk of coronary heart disease, many patients receiving lipid-lowering therapy fail to achieve LDL-C goals. We compared the effects of rosuvastatin and atorvastatin in reducing LDL-C and achieving LDL-C goals in patients with primary hypercholesterolemia. METHODS AND RESULTS: In this 52-week, randomized, double-blind, multicenter trial (4522IL/0026), 412 patients with LDL-C 160 to <250 mg/dL received a 5-mg dose of rosuvastatin (n = 138), a 10-mg dose of rosuvastatin (n = 134), or a 10-mg dose of atorvastatin (n = 140) for 12 weeks; during the following 40 weeks, dosages could be sequentially doubled up to 80 mg if National Cholesterol Education Program Adult Treatment Panel II (ATP-II) LDL-C goals were not achieved. At 12 weeks, 5- and 10-mg doses of rosuvastatin were associated with significantly greater LDL-C reductions than 10-mg doses of atorvastatin (46% and 50% vs 39%, both P <.001). At 12 weeks, both rosuvastatin dosages brought more patients to within ATP-II and European LDL-C goals than atorvastatin (86% and 89% vs 73% and 75%, and 86% vs 55%, respectively). At 52 weeks, compared with atorvastatin, both initial rosuvastatin treatment groups significantly reduced LDL-C (47% and 53% vs 44%, P <.05 and P <.001). Overall, more patients in the initial rosuvastatin 10-mg group achieved their ATP-II LDL-C goal than those in the initial atorvastatin 10-mg group (98% vs 87%), with 82% of patients treated with rosuvastatin achieving their goal at the 10-mg starting dosage without the need for titration, compared with 59% of patients treated with atorvastatin. Both treatments were well tolerated over 52 weeks. CONCLUSION: Compared with atorvastatin, rosuvastatin produced greater reductions in LDL-C, which may offer advantages in LDL-C goal attainment over existing lipid-lowering therapies.