Safety and Efficacy of Apitegromab in Patients With Spinal Muscular Atrophy Types 2 and 3: The Phase 2 TOPAZ Study.

BACKGROUND AND OBJECTIVES: Currently approved therapies for spinal muscular atrophy (SMA) reverse the degenerative course, leading to better functional outcome, but they do not address the impairment arising from preexisting neurodegeneration. Apitegromab, an investigational, fully human monoclonal antibody, inhibits activation of myostatin (a negative regulator of skeletal muscle growth), thereby preserving muscle mass. The phase 2 TOPAZ trial assessed the safety and efficacy of apitegromab in individuals with later-onset type 2 and type 3 SMA. METHODS: In this study, designed to investigate potential meaningful combinations of eligibility and treatment regimen for future studies, participants aged 2-21 years received IV apitegromab infusions every 4 weeks for 12 months in 1 of 3 cohorts. Cohort 1 stratified ambulatory participants aged 5-21 years into 2 arms (apitegromab 20 mg/kg alone or in combination with nusinersen); cohort 2 evaluated apitegromab 20 mg/kg combined with nusinersen in nonambulatory participants aged 5-21 years; and cohort 3 blindly evaluated 2 randomized apitegromab doses (2 and 20 mg/kg) combined with nusinersen in younger participants ≥2 years of age. The primary efficacy measure was mean change from baseline using the Hammersmith Functional Motor Scale version appropriate for each cohort. Data were analyzed using a paired t test with 2-sided 5% type 1 error for the mean change from baseline for predefined cohort-specific primary efficacy end points. RESULTS: Fifty-eight participants (mean age 9.4 years) were enrolled at 16 trial sites in the United States and Europe. Participants had been treated with nusinersen for a mean of 25.9 months before enrollment in any of the 3 trial cohorts. At month 12, the mean change from baseline in Hammersmith scale score was -0.3 points (95% CI -2.1 to 1.4) in cohort 1 (n = 23), 0.6 points (-1.4 to 2.7) in cohort 2 (n = 15), and in cohort 3 (n = 20), the mean scores were 5.3 (-1.5 to 12.2) and 7.1 (1.8 to 12.5) for the 2-mg/kg (n = 8) and 20-mg/kg (n = 9) arms, respectively. The 5 most frequently reported treatment-emergent adverse events were headache (24.1%), pyrexia (22.4%), upper respiratory tract infection (22.4%), cough (22.4%), and nasopharyngitis (20.7%). No deaths or serious adverse reactions were reported. DISCUSSION: Apitegromab led to improved motor function in participants with later-onset types 2 and 3 SMA. These results support a randomized, placebo-controlled phase 3 trial of apitegromab in participants with SMA. TRIAL REGISTRATION INFORMATION: This trial is registered with ClinicalTrials.gov (NCT03921528). CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that apitegromab improves motor function in later-onset types 2 and 3 spinal muscular atrophy.

Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation.

Children represent a large underserved population of "therapeutic orphans," as an estimated 80% of children are treated off-label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or "borrowing") of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure-response information for antiepileptic drugs to pediatrics.

Comparing oncology clinical programs by use of innovative designs and expected net present value optimization: Which adaptive approach leads to the best result?

Designing an oncology clinical program is more challenging than designing a single study. The standard approaches have been proven to be not very successful during the last decade; the failure rate of Phase 2 and Phase 3 trials in oncology remains high. Improving a development strategy by applying innovative statistical methods is one of the major objectives of a drug development process. The oncology sub-team on Adaptive Program under the Drug Information Association Adaptive Design Scientific Working Group (DIA ADSWG) evaluated hypothetical oncology programs with two competing treatments and published the work in the Therapeutic Innovation and Regulatory Science journal in January 2014. Five oncology development programs based on different Phase 2 designs, including adaptive designs and a standard two parallel arm Phase 3 design were simulated and compared in terms of the probability of clinical program success and expected net present value (eNPV). In this article, we consider eight Phase2/Phase3 development programs based on selected combinations of five Phase 2 study designs and three Phase 3 study designs. We again used the probability of program success and eNPV to compare simulated programs. For the development strategies, we considered that the eNPV showed robust improvement for each successive strategy, with the highest being for a three-arm response adaptive randomization design in Phase 2 and a group sequential design with 5 analyses in Phase 3.

Bayesian methods for the design and analysis of noninferiority trials.

The gold standard for evaluating treatment efficacy of a medical product is a placebo-controlled trial. However, when the use of placebo is considered to be unethical or impractical, a viable alternative for evaluating treatment efficacy is through a noninferiority (NI) study where a test treatment is compared to an active control treatment. The minimal objective of such a study is to determine whether the test treatment is superior to placebo. An assumption is made that if the active control treatment remains efficacious, as was observed when it was compared against placebo, then a test treatment that has comparable efficacy with the active control, within a certain range, must also be superior to placebo. Because of this assumption, the design, implementation, and analysis of NI trials present challenges for sponsors and regulators. In designing and analyzing NI trials, substantial historical data are often required on the active control treatment and placebo. Bayesian approaches provide a natural framework for synthesizing the historical data in the form of prior distributions that can effectively be used in design and analysis of a NI clinical trial. Despite a flurry of recent research activities in the area of Bayesian approaches in medical product development, there are still substantial gaps in recognition and acceptance of Bayesian approaches in NI trial design and analysis. The Bayesian Scientific Working Group of the Drug Information Association provides a coordinated effort to target the education and implementation issues on Bayesian approaches for NI trials. In this article, we provide a review of both frequentist and Bayesian approaches in NI trials, and elaborate on the implementation for two common Bayesian methods including hierarchical prior method and meta-analytic-predictive approach. Simulations are conducted to investigate the properties of the Bayesian methods, and some real clinical trial examples are presented for illustration.

Nine-year change in statistical design, profile, and success rates of Phase II oncology trials.

We investigated nine-year trends in statistical design and other features of Phase II oncology clinical trials published in 2005, 2010, and 2014 in five leading oncology journals: Cancer, Clinical Cancer Research, Journal of Clinical Oncology, Annals of Oncology, and Lancet Oncology. The features analyzed included cancer type, multicenter vs. single-institution, statistical design, primary endpoint, number of treatment arms, number of patients per treatment arm, whether or not statistical methods were well described, whether the drug was found effective based on rigorous statistical testing of the null hypothesis, and whether the drug was recommended for future studies.

Enrollment and Stopping Rules for Managing Toxicity Requiring Long Follow-Up in Phase II Oncology Trials.

Monitoring of toxicity is often conducted in Phase II trials in oncology to avoid an excessive number of toxicities if the wrong dose is chosen for Phase II. Existing stopping rules for toxicity use information from patients who have already completed follow-up. We describe a stopping rule that uses all available data to determine whether to stop for toxicity or not when follow-up for toxicity is long. We propose an enrollment rule that prescribes the maximum number of patients that may be enrolled at any given point in the trial.

Bayesian assessment of the influence and interaction conditions in multipopulation tailoring clinical trials.

Multipopulation tailoring trials provide a trial design option that supports the realization of tailored therapeutics or personalized medicine. Several recent publications have focused on statistical and clinical considerations that arise in these trials that are designed to study the overall treatment effect in a population of interest as well as one or more prospectively defined subpopulations. Millen et al. (2012) introduced the influence and interaction conditions as part of a general framework to facilitate decision making in multipopulation trials. This article provides Bayesian methods for assessing the influence and interaction conditions. The methods introduced are illustrated using case studies based on clinical trials with biomarker-driven designs.

Responses to antipsychotic therapy among patients with schizophrenia or schizoaffective disorder and either predominant or prominent negative symptoms.

Patients with schizophrenia who have predominant negative symptoms are often considered less responsive to treatment. This analysis of patients with schizophrenia or schizoaffective disorder compares changes in symptom severity between those with predominant versus merely prominent negative symptoms. Prominent negative symptoms were defined by a baseline score of ≥4 on at least 3, or ≥5 on at least 2, of the 7 Positive and Negative Syndrome Scale (PANSS) negative subscale items. Predominant negative symptoms were defined by the foregoing plus a PANSS positive score of <19, a Barnes Akathisia score of <2, a Simpson-Angus score of <4, and a Calgary Depressive Scale score of <9. Adult patients with schizophrenia (n=227) or schizoaffective disorder (n=116) received either olanzapine (10-20mg/day, n=169) or quetiapine (300-700mg/day, n=174) for up to 24weeks. Data for both medications were pooled. Of the 343 patients enrolled in the study, 34.7% met the criteria for predominant negative symptoms, the remaining 65.3% being characterized only by their prominent negative symptoms. Changes in the severity of negative symptoms in both patient types largely followed similar trajectories during treatment, as reflected both in Marder PANSS negative subscale scores and in the Scale for Assessment of Negative Symptoms total and domain scores. Patients with either predominant or prominent negative symptoms therefore appear to respond similarly to atypical antipsychotic treatment. This distinction, incorporating an evaluation of the presence of positive, affective, and extrapyramidal symptoms, may therefore not have prognostic implications for the responsiveness of patients' negative symptoms to treatment.