Bayesian Meta-Experimental Design: Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes

Recent guidance from the Food and Drug Administration for the evaluation of new therapies in the treatment of type 2 diabetes (T2DM) calls for a program-wide meta-analysis of cardiovascular (CV) outcomes. In this context, we develop a new Bayesian meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for meta-analysis clinical trial design with a focus on controlling the type I error and power. We also propose the partial borrowing power prior to incorporate the historical survival meta data into the statistical design. Various properties of the proposed methodology are examined and an efficient Markov chain Monte Carlo sampling algorithm is developed to sample from the posterior distributions. In addition, we develop a simulation-based algorithm for computing various quantities, such as the power and the type I error in the Bayesian meta-analysis trial design. The proposed methodology is applied to the design of a phase 2/3 development program including a noninferiority clinical trial for CV risk assessment in T2DM studies

Landmark mediation survival analysis using longitudinal surrogate

Clinical cancer trials are designed to collect radiographic measurements of each patient’s baseline and residual tumor burden at regular intervals over the course of study. For solid tumors, the extent of reduction in tumor size following treatment is used as a measure of a drug’s antitumor activity. Statistical estimation of treatment efficacy routinely reduce the longitudinal assessment of tumor burden to a binary outcome describing the presence versus absence of an objective tumor response as defined by RECIST criteria. The objective response rate (ORR) is the predominate method for evaluating an experimental therapy in a single-arm trial. Additionally, ORR is routinely compared against a control therapy in phase III randomized controlled trials. The longitudinal assessments of tumor burden are seldom integrated into a formal statistical model, nor integrated into mediation analysis to characterize the relationships among treatment, residual tumor burden, and survival. This article presents a frameworkfor landmark mediation survival analyses devised to incorporate longitudinal assessment of tumor burden. R2 effect-size measures are developed to quantify the survival treatment mediation effects using longitudinal predictors. Analyses are demonstrated with applications to two colorectal cancer trials. Survival prediction is compared in the presence versus absence of longitudinal analysis. Simulation studies elucidate settings wherein patterns of tumor burden dynamics require longitudinal analysis

Bayesian sequential meta-analysis design in evaluating cardiovascular risk in a new antidiabetic drug development program: Bayesian Meta-experimental Design

Recently, the Center for Drug Evaluation and Research at the Food and Drug Administration (FDA) released a guidance that makes recommendations about how to demonstrate that a new antidiabetic therapy to treat Type 2 diabetes is not associated with an unacceptable increase in cardiovascular risk. One of the recommendations from the guidance is that Phase II and III trials should be appropriately designed and conducted so that a meta-analysis can be performed. In addition, the guidance implies that a sequential meta-analysis strategy could be adopted. That is, the initial meta-analysis could aim at demonstrating the upper bound of a 95% confidence interval (CI) for the estimated hazard ratio to be < 1.8 for the purpose of enabling a new drug application (NDA) or a biologics license application (BLA). Subsequently after the marketing authorization, a final meta-analysis would need to show the upper bound to be < 1.3. In this context, we develop a new Bayesian sequential meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for sequential meta-analysis clinical trial design with a focus on controlling the familywise Type I error rate and power. The partial borrowing power prior is used to incorporate the historical survival meta-data into the Bayesian design. Various properties of the proposed methodology are examined and simulation-based computational algorithms are developed to generate predictive data at various interim analyses, sample from the posterior distributions, and compute various quantities such as the power and the Type I error in the Bayesian sequential meta-analysis trial design. The proposed methodology is applied to the design of a hypothetical antidiabetic drug development program for evaluating cardiovascular risk

Clinical characteristics of patients with spinocerebellar ataxias 1, 2, 3 and 6 in the US; a prospective observational study

Background: All spinocerebellar ataxias (SCAs) are rare diseases. SCA1, 2, 3 and 6 are the four most common SCAs, all caused by expanded polyglutamine-coding CAG repeats. Their pathomechanisms are becoming increasingly clear and well-designed clinical trials will be needed. Methods: To characterize the clinical manifestations of spinocerebellar ataxia (SCA) 1, 2, 3 and 6 and their natural histories in the United States (US), we conducted a prospective multicenter study utilized a protocol identical to the European consortium study, using the Scale for the Assessment and Rating of Ataxia (SARA) score as the primary outcome, with follow-ups every 6 months up to 2 years. Results: We enrolled 345 patients (60 SCA1, 75 SCA2, 138 SCA3 and 72 SCA6) at 12 US centers. SCA6 patients had a significantly later onset, and SCA2 patients showed greater upper-body ataxia than patients with the remaining SCAs. The annual increase of SARA score was greater in SCA1 patients (mean ± SE: 1.61 ± 0.41) than in SCA2 (0.71 ± 0.31), SCA3 (0.65 ± 0.24) and SCA6 (0.87 ± 0.28) patients (p = 0.049). The functional stage also worsened faster in SCA1 than in SCA2, 3 and 6 (p = 0.002). Conclusions: The proportions of different SCA patients in US differ from those in the European consortium study, but as in the European patients, SCA1 progress faster than those with SCA2, 3 and 6. Later onset in SCA6 and greater upper body ataxia in SCA2 were noted. We conclude that progression rates of these SCAs were comparable between US and Europe cohorts, suggesting the feasibility of international collaborative clinical studies

Phosphatidylinositol 3-Kinase Mediates Bronchioalveolar Stem Cell Expansion in Mouse Models of Oncogenic K-ras-Induced Lung Cancer

Background: Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related death in Western countries. Developing more effective NSCLC therapeutics will require the elucidation of the genetic and biochemical bases for this disease. Bronchioalveolar stem cells (BASCs) are a putative cancer stem cell population in mouse models of oncogenic K-ras-induced lung adenocarcinoma, an histologic subtype of NSCLC. The signals activated by oncogenic K-ras that mediate BASC expansion have not been fully defined. Methodology/Principal Findings: We used genetic and pharmacologic approaches to modulate the activity of phosphatidylinositol 3-kinase (PI3K), a key mediator of oncogenic K-ras, in two genetic mouse models of lung adenocarcinoma. Oncogenic K-ras-induced BASC accumulation and tumor growth were blocked by treatment with a small molecule PI3K inhibitor and enhanced by inactivation of phosphatase and tensin homologue deleted from chromosome 10, a negative regulator of PI3K. Conclusions/Significance: We conclude that PI3K is a critical regulator of BASC expansion, supporting treatment strategies to target PI3K in NSCLC patients

Chasing Migration Genes: A Brain Expressed Sequence Tag Resource for Summer and Migratory Monarch Butterflies (Danaus plexippus)

North American monarch butterflies (Danaus plexippus) undergo a spectacular fall migration. In contrast to summer butterflies, migrants are juvenile hormone (JH) deficient, which leads to reproductive diapause and increased longevity. Migrants also utilize time-compensated sun compass orientation to help them navigate to their overwintering grounds. Here, we describe a brain expressed sequence tag (EST) resource to identify genes involved in migratory behaviors. A brain EST library was constructed from summer and migrating butterflies. Of 9,484 unique sequences, 6068 had positive hits with the non-redundant protein database; the EST database likely represents ∼52% of the gene-encoding potential of the monarch genome. The brain transcriptome was cataloged using Gene Ontology and compared to Drosophila. Monarch genes were well represented, including those implicated in behavior. Three genes involved in increased JH activity (allatotropin, juvenile hormone acid methyltransfersase, and takeout) were upregulated in summer butterflies, compared to migrants. The locomotion-relevant turtle gene was marginally upregulated in migrants, while the foraging and single-minded genes were not differentially regulated. Many of the genes important for the monarch circadian clock mechanism (involved in sun compass orientation) were in the EST resource, including the newly identified cryptochrome 2. The EST database also revealed a novel Na+/K+ ATPase allele predicted to be more resistant to the toxic effects of milkweed than that reported previously. Potential genetic markers were identified from 3,486 EST contigs and included 1599 double-hit single nucleotide polymorphisms (SNPs) and 98 microsatellite polymorphisms. These data provide a template of the brain transcriptome for the monarch butterfly. Our “snap-shot” analysis of the differential regulation of candidate genes between summer and migratory butterflies suggests that unbiased, comprehensive transcriptional profiling will inform the molecular basis of migration. The identified SNPs and microsatellite polymorphisms can be used as genetic markers to address questions of population and subspecies structure

Systematic Planning of Genome-Scale Experiments in Poorly Studied Species

Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms