Clinical Safety and Efficacy of Pegcetacoplan in a Phase 2 Study of Patients with C3 Glomerulopathy and Other Complement-Mediated Glomerular Diseases.

Introduction: Dysregulated complement activation is likely the primary driver of disease in C3 glomerulopathy (C3G) and contributes to other complement-mediated diseases, including immunoglobulin A nephropathy (IgAN), lupus nephritis (LN), and primary membranous nephropathy (PMN). No complement inhibitors are proven to halt disease progression in these diseases. Pegcetacoplan, a targeted C3 and C3b inhibitor, may mitigate complement-mediated kidney damage in C3G and other glomerular diseases in which complement may have a pathogenic role. Methods: This open-label, phase 2, 48-week study evaluated the preliminary efficacy and safety of subcutaneous pegcetacoplan for patients with complement-mediated glomerular diseases. The primary end point was proteinuria reduction, measured as 24-hour urine protein-to-creatinine ratio. Secondary end points included remission status, changes in estimated glomerular filtration rate (eGFR), and pharmacodynamic biomarkers. Treatment-emergent adverse events (TEAEs) were monitored. Results: Efficacy results for the C3G cohort are reported herein, along with safety results for the study population. In the C3G cohort, mean proteinuria reduction from baseline to week 48 was 50.9% in the intent-to-treat (ITT) population (n = 7) and 65.4% in the per-protocol (PP) population (n = 4). Mean serum albumin normalized and mean eGFR was stable over 48 weeks. Mean serum C3 levels increased 6-fold and mean soluble C5b-9 levels decreased by 57.3% at week 48. The most common adverse events (AEs) were upper respiratory tract infection, injection site erythema, nausea, and headache. No meningitis or sepsis cases were reported, and no serious treatment-related AEs were observed. Conclusion: Pegcetacoplan may provide therapeutic benefit for C3G and has a favorable safety profile across the 4 glomerular diseases studied.

Treatment effect measures under nonproportional hazards.

In a clinical trial with a time-to-event endpoint the treatment effect can be measured in various ways. Under proportional hazards all reasonable measures (such as the hazard ratio and the difference in restricted mean survival time) are consistent in the following sense: Take any control group survival distribution such that the hazard rate remains above zero; if there is no benefit by any measure there is no benefit by all measures, and as the magnitude of treatment benefit increases by any measure it increases by all measures. Under nonproportional hazards, however, survival curves can cross, and the direction of the effect for any pair of measures can be inconsistent. In this paper we critically evaluate a variety of treatment effect measures in common use and identify flaws with them. In particular, we demonstrate that a treatment's benefit has two distinct and independent dimensions which can be measured by the difference in the survival rate at the end of follow-up and the difference in restricted mean survival time, and that commonly used measures do not adequately capture both dimensions. We demonstrate that a generalized hazard difference, which can be estimated by the difference in exposure-adjusted subject incidence rates, captures both dimensions, and that its inverse, the number of patient-years of follow-up that results in one fewer event (the NYNT), is an easily interpretable measure of the magnitude of clinical benefit.

Synthesizing studies for comparing different treatment sequences in clinical trials.

With advances in cancer treatments and improved patient survival, more patients may go through multiple lines of treatment. It is of clinical importance to choose a sequence of effective treatments (eg, lines of treatment) for individual patients with the goal of optimizing their long-term clinical outcome (eg, survival). Several important issues arise in cancer studies. First, cancer clinical trials are usually conducted by each line of treatment. For a treatment sequence, we may have first line and second line treatment data from two different studies. Second, there is typically a treatment initiation period varying from patient to patient between progression of disease and the start of the second line treatment due to administrative reasons. Additionally, the choice of the second line treatment for patients with progression of disease may depend on their characteristics. We address all these issues and develop semiparametric methods under the potential outcome framework for the estimation of the overall survival probability for a treatment sequence and for comparing different treatment sequences. We establish the large sample properties of the proposed inferential procedures. Simulation studies and an application to a colorectal clinical trial are provided.

Chronic lesion activity and disability progression in secondary progressive multiple sclerosis.

Objective: Slowly expanding lesions (SELs), a subgroup of chronic white matter lesions that gradually expand over time, have been shown to predict disability accumulation in primary progressive multiple sclerosis (MS) disease. However, the relationships between SELs, acute lesion activity (ALA), overall chronic lesion activity (CLA) and disability progression are not well understood. In this study, we examined the ASCEND phase III clinical trial, which compared natalizumab with placebo in secondary progressive MS (SPMS). Methods: Patients with complete imaging datasets between baseline and week 108 (N=600) were analysed for SEL prevalence (the number and volume of SELs), disability progression, ALA (assessed by gadolinium-enhancing lesions and new T2-hyperintense lesions) and CLA (assessed by T1-hypointense lesion volume increase within baseline T2-non-enhancing lesions identified as SELs and non-SELs). Results: CLA in both SELs and non-SELs was greater in patients with SPMS with confirmed disability progression than in those with no progression. In the complete absence of ALA at baseline and on study, SEL prevalence was significantly lower, while CLA within non-SELs remained associated with disability progression. Natalizumab decreased SEL prevalence and CLA in SELs and non-SELs compared with placebo. Conclusions: This study shows that CLA in patients with SPMS is decreased but persists in the absence of ALA and is associated with disability progression, highlighting the need for therapeutics targeting all mechanisms of CLA, including smouldering inflammation and neurodegeneration. Trial registration number: NCT01416181.

Bayesian adaptive basket trial design using model averaging.

In this article, we develop a Bayesian adaptive design methodology for oncology basket trials with binary endpoints using a Bayesian model averaging framework. Most existing methods seek to borrow information based on the degree of homogeneity of estimated response rates across all baskets. In reality, an investigational product may only demonstrate activity for a subset of baskets, and the degree of activity may vary across the subset. A key benefit of our Bayesian model averaging approach is that it explicitly accounts for the possibility that any subset of baskets may have similar activity and that some may not. Our proposed approach performs inference on the basket-specific response rates by averaging over the complete model space for the response rates, which can include thousands of models. We present results that demonstrate that this computationally feasible Bayesian approach performs favorably compared to existing state-of-the-art approaches, even when held to stringent requirements regarding false positive rates.

Biomarker threshold adaptive designs for survival endpoints.

Due to the importance of precision medicine, it is essential to identify the right patients for the right treatment. Biomarkers, which have been commonly used in clinical research as well as in clinical practice, can facilitate selection of patients with a good response to the treatment. In this paper, we describe a biomarker threshold adaptive design with survival endpoints. In the first stage, we determine subgroups for one or more biomarkers such that patients in these subgroups benefit the most from the new treatment. The analysis in this stage can be based on historical or pilot studies. In the second stage, we sample subjects from the subgroups determined in the first stage and randomly allocate them to the treatment or control group. Extensive simulation studies are conducted to examine the performance of the proposed design. Application to a real data example is provided for implementation of the first-stage algorithms.

Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information.

For clinical trials with time-to-event endpoints, predicting the accrual of the events of interest with precision is critical in determining the timing of interim and final analyses. For example, overall survival (OS) is often chosen as the primary efficacy endpoint in oncology studies, with planned interim and final analyses at a pre-specified number of deaths. Often, correlated surrogate information, such as time-to-progression (TTP) and progression-free survival, are also collected as secondary efficacy endpoints. It would be appealing to borrow strength from the surrogate information to improve the precision of the analysis time prediction. Currently available methods in the literature for predicting analysis timings do not consider utilizing the surrogate information. In this article, using OS and TTP as an example, a general parametric model for OS and TTP is proposed, with the assumption that disease progression could change the course of the overall survival. Progression-free survival, related both to OS and TTP, will be handled separately, as it can be derived from OS and TTP. The authors seek to develop a prediction procedure using a Bayesian method and provide detailed implementation strategies under certain assumptions. Simulations are performed to evaluate the performance of the proposed method. An application to a real study is also provided. Copyright © 2015 John Wiley & Sons, Ltd.

A counterfactual p-value approach for benefit-risk assessment in clinical trials.

Clinical trials generally allow various efficacy and safety outcomes to be collected for health interventions. Benefit-risk assessment is an important issue when evaluating a new drug. Currently, there is a lack of standardized and validated benefit-risk assessment approaches in drug development due to various challenges. To quantify benefits and risks, we propose a counterfactual p-value (CP) approach. Our approach considers a spectrum of weights for weighting benefit-risk values and computes the extreme probabilities of observing the weighted benefit-risk value in one treatment group as if patients were treated in the other treatment group. The proposed approach is applicable to single benefit and single risk outcome as well as multiple benefit and risk outcomes assessment. In addition, the prior information in the weight schemes relevant to the importance of outcomes can be incorporated in the approach. The proposed CPs plot is intuitive with a visualized weight pattern. The average area under CP and preferred probability over time are used for overall treatment comparison and a bootstrap approach is applied for statistical inference. We assess the proposed approach using simulated data with multiple efficacy and safety endpoints and compare its performance with a stochastic multi-criteria acceptability analysis approach.

Statistical Considerations on the Evaluation of Imbalances of Adverse Events in Randomized Clinical Trials.

Adverse events (AEs) data compose the main body of safety data in clinical trials. Medically important imbalances of AEs in large double-blind randomized controlled trials (RCTs) are signals of potential adverse drug reactions. They will be further evaluated for causality and shape the initial label that gives users necessary information on the safe use of the drug. However, causality assessment in premarketing RCTs can be challenging. This article highlights key aspects that need attention and statistical analysis approaches that could be helpful for screening and evaluation of signals generated from imbalances of AEs in moderate or large RCTs.

Choosing Appropriate Metrics to Evaluate Adverse Events in Safety Evaluation.

Safety assessment and monitoring are critical throughout the life cycle of drug development. The evaluation of safety information, specifically adverse events, from clinical trials has always been challenging for a number of reasons, such as the unexpectedness and rarity of some important adverse events, the fact that some events can recur, and the events' variability in duration and severity. To accurately characterize and communicate the risk profile of a drug, the choice of metrics is critical. However, there seems to be a lack of consistency, clear guidance, and comprehensive recommendations on choosing metrics for assessing adverse events in clinical trials. This article reviews the common metrics and provides some recommendations.

Superiority of denosumab to zoledronic acid for prevention of skeletal-related events: a combined analysis of 3 pivotal, randomised, phase 3 trials.

BACKGROUND: Patients with bone metastases from advanced cancer often experience skeletal-related events (SRE), which cause substantial pain and morbidity. Denosumab, a fully human monoclonal antibody that inhibits RANK Ligand (RANKL), is a novel bone-targeted agent with a distinct mechanism of action relative to the bisphosphonate zoledronic acid, for prevention of SRE. This pre-planned analysis evaluates the efficacy and safety of denosumab versus zoledronic acid across three pivotal studies. METHODS: Patient-level data from three identically designed, randomised, double-blind, active-controlled, phase 3 trials of patients with breast cancer, prostate cancer, other solid tumours or multiple myeloma were combined. End-points included time to first SRE, time to first and subsequent (multiple) SRE, adverse events, time to disease progression and overall survival. FINDINGS: Denosumab was superior to zoledronic acid in delaying time to first on-study SRE by a median 8.21months, reducing the risk of a first SRE by 17% (hazard ratio, 0.83 [95% confidence interval (CI): 0.76-0.90]; P<0.001). Efficacy was demonstrated for first and multiple events and across patient subgroups (prior SRE status; age). Disease progression and overall survival were similar between the treatments. In contrast to zoledronic acid, denosumab did not require monitoring or dose modification/withholding based on renal status, and was not associated with acute-phase reactions. Hypocalcaemia was more common for denosumab. Osteonecrosis of the jaw occurred at a similar rate (P=0.13). CONCLUSION: Denosumab was superior to zoledronic acid in preventing SRE with favourable safety and convenience in patients with bone metastases from advanced cancer.

The issue of multiplicity in noninferiority studies.

BACKGROUND: In a clinical study to evaluate noninferiority (NI) of an experimental drug relative to an established therapy, it is common to further test superiority of the experimental drug after NI is established. It has been shown that no multiplicity issue exists between NI and superiority tests of the primary endpoint. However, when there is an additional, or secondary, endpoint that will be tested for superiority in a hierarchical fashion to the NI testing of the primary endpoint, it is not clear whether the overall type I error rate is strictly controlled among all the tests. PURPOSE: In this article, our goal is to evaluate if the family-wise type I error rate is strictly controlled in this setting. We also evaluate a multiplicity adjustment procedure based on the Hochberg procedure. METHODS: We use the closed testing principle to evaluate the family-wise type I error rate. Some simulations are performed to appreciate the magnitude of the potential inflation of the type I error rate. RESULTS: It is demonstrated that the family-wise type I error rate is not controlled and an appropriate multiplicity adjustment procedure must take into account the NI and superiority tests of the two endpoints. When the test statistic used for superiority testing of the primary endpoint is the same as that for the NI testing, a multiplicity adjustment method using the popular Hochberg procedure is shown to be potentially conservative. LIMITATION: The assessment is based on a simplified set-up where there is only one secondary endpoint tested for superiority in addition to the primary endpoint. CONCLUSION: It is necessary to evaluate the issue of multiplicity in non-inferiority studies to assure strict control of the family-wise type I error rate.

Sarcopenia during androgen-deprivation therapy for prostate cancer.

PURPOSE: To characterize changes in lean body mass (LBM) in men with prostate cancer receiving androgen-deprivation therapy (ADT). PATIENTS AND METHODS: We prospectively evaluated LBM in a prespecified substudy of a randomized controlled trial of denosumab to prevent fractures in men receiving ADT for nonmetastatic prostate cancer. LBM was measured by total-body dual-energy x-ray absorptiometry at study baseline and at 12, 24, and 36 months. The analyses included 252 patients (132, denosumab; 120, placebo) with a baseline and at least one on-study LBM assessment. Patients were stratified by age (< 70 v ≥ 70 years) and by ADT duration (≤ 6 v > 6 months). RESULTS: Median ADT duration was 20.4 months at study baseline. Mean LBM decreased significantly from baseline, by 1.0% at month 12 (95% CI, 0.4% to 1.5%; P < .001; n = 248), by 2.1% at month 24 (95% CI, 1.5% to 2.7%; P < .001; n = 205), and by 2.4% at month 36 (95% CI, 1.6% to 3.2%; P < .001; n = 168). Men age ≥ 70 years (n = 127) had significantly greater changes in LBM at all measured time points than younger men. At 36 months, LBM decreased by 2.8% in men age ≥ 70 years and by 0.9% in younger men (P = .035). Men with ≤ 6 months of ADT at study entry (n = 36) had a greater rate of decrease in LBM compared with men who had received more than 6 months of ADT at study entry (3.7% v 2.0%; P = .0645). CONCLUSION: In men receiving ADT, LBM decreased significantly after 12, 24, and 36 months.

Predicting analysis time in event-driven clinical trials with event-reporting lag.

For a clinical trial with a time-to-event primary endpoint, the rate of accrual of the event of interest determines the timing of the analysis, upon which significant resources and strategic planning depend. It is important to be able to predict the analysis time early and accurately. Currently available methods use either parametric or nonparametric models to predict the analysis time based on accumulating information about enrollment, event, and study withdrawal rates and implicitly assume that the available data are completely reported at the time of performing the prediction. This assumption, however, may not be true when it takes a certain amount of time (i.e., event-reporting lag) for an event to be reported, in which case, the data are incomplete for prediction. Ignoring the event-reporting lag could substantially impact the accuracy of the prediction. In this paper, we describe a general parametric model to incorporate event-reporting lag into analysis time prediction. We develop a prediction procedure using a Bayesian method and provide detailed implementations for exponential distributions. Some simulations were performed to evaluate the performance of the proposed method. An application to an on-going clinical trial is also described.

Denosumab and changes in bone turnover markers during androgen deprivation therapy for prostate cancer.

Androgen deprivation therapy (ADT) for prostate cancer increases fracture risk, decreases bone mineral density, and increases bone turnover markers (BTMs) including serum type 1 C-telopeptide (sCTX), tartrate-resistant alkaline phosphatase 5b (TRAP-5b), and procollagen-1 N-terminal telopeptide (P1NP). In a prespecified exploratory analysis of a phase 3, multicenter, double-blind study, we evaluated the effects of denosumab (60 mg subcutaneously every 6 months for 3 years) versus placebo (1468 patients, 734 in each group) on BTM values. BTMs were measured at baseline, month 1, and predose at months 6, 12, 24, and 36 in the overall population. BTMs at month 1 are also reported for subgroups based on age (< 70 years versus ≥ 70 years), prior duration of ADT (≤ 6 months versus > 6 months), and baseline BTM (≤ median versus > median BTM values). Treatment with denosumab provided a rapid and sustained decrease of BTM values compared with placebo. The median change in sCTX levels at month 1 was -90% in the denosumab group and -3% in the placebo group (p < 0.0001). The median change in TRAP-5b levels at month 1 was -55% in the denosumab group and -3% in the placebo group (p < 0.0001). The maximal median change in P1NP was -64% in the denosumab group and -11% in the placebo group, (p < 0.0001). Significantly greater decreases in BTM for denosumab were also seen in subgroup analyses based on age, prior ADT treatment, and baseline BTM values. Suppression of bone turnover markers was consistent with marked increases in bone mineral density reported previously.

Estimating area under the curve and relative exposure in a pharmacokinetic study with data below quantification limit.

Area under the drug-concentration-over-time curve (AUC) is an important endpoint for many phase I/II clinical trials and laboratory assays. Drug concentrations are measured using laboratory assays with a lower limit of quantification (LLOQ). How to calculate AUC when some drug concentration data are below the LLOQ remains as a challenge. In this article, we develop a maximum likelihood method to estimate AUC and relative exposure (i.e., ratio of two AUCs) when data below LLOQ exists. We also compare the proposed method to several commonly used methods, including imputation with model-predicted values or ad hoc values (i.e., LLOQ, LLOQ/2, or zero) through a simulation study. The proposed method gives unbiased inference. Commonly used methods can provide biased estimation, especially when a large proportion of data is below LLOQ. Application to a case study is also presented.

Effects of denosumab on bone mineral density in men receiving androgen deprivation therapy for prostate cancer.

PURPOSE: In a recently completed 3-year, randomized, double-blind study, denosumab, a fully human monoclonal antibody against receptor activator of nuclear factor kappaB ligand, significantly increased bone mineral density and decreased new vertebral fractures in men receiving androgen deprivation therapy for prostate cancer. We conducted subgroup analyses to evaluate the relationships between subject characteristics and the effects of denosumab on bone mineral density at multiple skeletal sites. MATERIALS AND METHODS: A total of 1,468 subjects were randomized 1:1 to receive 60 mg subcutaneous denosumab every 6 months or placebo for 36 months. In these analyses we evaluated the effects of denosumab on bone mineral density at the lumbar spine, total hip and distal 1/3 radius (substudy of 309 subjects) during 36 months in specific subgroups according to age, duration and type of prior androgen deprivation therapy, bone mineral density T score, weight, body mass index, bone turnover marker levels and prevalent vertebral fractures. RESULTS: After 36 months denosumab significantly increased bone mineral density of the lumbar spine, total hip and distal 1/3 radius by 7.9%, 5.7% and 6.9%, respectively, compared with placebo (p <0.0001 for each comparison). Denosumab significantly increased bone mineral density to a degree similar to that observed in the overall analysis for every subgroup including older men as well as those with prevalent fractures, lower baseline bone mineral density, and higher serum C-telopeptide and tartrate-resistant alkaline phosphatase 5b. Mean increases in bone mineral density at each skeletal site were greatest for men with the highest levels of serum C-telopeptide and tartrate-resistant alkaline phosphatase 5b. CONCLUSIONS: Denosumab significantly and consistently increased bone mineral density at all skeletal sites and in every subgroup, including men at greatest risk for bone loss and fractures.

Denosumab in men receiving androgen-deprivation therapy for prostate cancer.

BACKGROUND: Androgen-deprivation therapy is well-established for treating prostate cancer but is associated with bone loss and an increased risk of fracture. We investigated the effects of denosumab, a fully human monoclonal antibody against receptor activator of nuclear factor-kappaB ligand, on bone mineral density and fractures in men receiving androgen-deprivation therapy for nonmetastatic prostate cancer. METHODS: In this double-blind, multicenter study, we randomly assigned patients to receive denosumab at a dose of 60 mg subcutaneously every 6 months or placebo (734 patients in each group). The primary end point was percent change in bone mineral density at the lumbar spine at 24 months. Key secondary end points included percent change in bone mineral densities at the femoral neck and total hip at 24 months and at all three sites at 36 months, as well as incidence of new vertebral fractures. RESULTS: At 24 months, bone mineral density of the lumbar spine had increased by 5.6% in the denosumab group as compared with a loss of 1.0% in the placebo group (P<0.001); significant differences between the two groups were seen at as early as 1 month and sustained through 36 months. Denosumab therapy was also associated with significant increases in bone mineral density at the total hip, femoral neck, and distal third of the radius at all time points. Patients who received denosumab had a decreased incidence of new vertebral fractures at 36 months (1.5%, vs. 3.9% with placebo) (relative risk, 0.38; 95% confidence interval, 0.19 to 0.78; P=0.006). Rates of adverse events were similar between the two groups. CONCLUSIONS: Denosumab was associated with increased bone mineral density at all sites and a reduction in the incidence of new vertebral fractures among men receiving androgen-deprivation therapy for nonmetastatic prostate cancer. (ClinicalTrials.gov number, NCT00089674.)

Shape-invariant modeling of circadian rhythms with random effects and smoothing spline ANOVA decompositions.

Medical studies often collect physiological and/or psychological measurements over time from multiple subjects, to study dynamics such as circadian rhythms. Under the assumption that the expected response functions of all subjects are the same after shift and scale transformations, shape-invariant models have been applied to analyze this kind of data. The shift and scale parameters provide efficient and interpretable data summaries, while the common shape function is usually modeled nonparametrically, to provide flexibility. However, due to the deterministic nature of the shift and scale parameters, potential correlations within a subject are ignored. Furthermore, the shape of the common function may depend on other factors, such as disease. In this article, we propose shape-invariant mixed effects models. A second-stage model with fixed and random effects is used to model individual shift and scale parameters. A second-stage smoothing spline ANOVA model is used to study potential covariate effects on the common shape function. We apply our methods to a real data set to investigate disease effects on circadian rhythms of cortisol, a hormone that is affected by stress. We find that patients with Cushing's syndrome lost circadian rhythms and their 24-hour means were elevated to very high levels. Patients with major depression had the same circadian shape and phases as normal subjects. However, their 24-hour mean levels were elevated and amplitudes were dampened for some patients.