Bayesian sequential meta-analysis design in evaluating cardiovascular risk in a new antidiabetic drug development program.

Recently, the Center for Drug Evaluation and Research at the Food and Drug Administration 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 phases 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 or a biologics license application. 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. We use the partial borrowing power prior to incorporate the historical survival meta-data into the Bayesian design. We examine various properties of the proposed methodology, 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. We apply the proposed methodology to the design of a hypothetical antidiabetic drug development program for evaluating cardiovascular risk.

Decitabine and suberoylanilide hydroxamic acid (SAHA) inhibit growth of ovarian cancer cell lines and xenografts while inducing expression of imprinted tumor suppressor genes, apoptosis, G2/M arrest, and autophagy.

BACKGROUND: Epigenetic therapy has had a significant impact on the management of hematologic malignancies, but its role in the treatment of ovarian cancer remains to be defined. The authors previously demonstrated that treatment of ovarian and breast cancer cells with DNA methyltransferase and histone deacetylase (HDAC) inhibitors can up-regulate the expression of imprinted tumor suppressors. In this study, demethylating agents and HDAC inhibitors were tested for their ability to induce re-expression of tumor suppressor genes, inhibiting growth of ovarian cancer cells in culture and in xenografts. METHODS: Ovarian cancer cells (Hey and SKOv3) were treated with demethylating agents (5-aza-20-deoxycytidine [DAC] or 5-azacitidine [AZA]) or with HDAC inhibitors (suberoylanilide hydroxamicacid [SAHA] or trichostatin A [TSA]) to determine their impact on cellular proliferation, cell cycle regulation, apoptosis, autophagy, and re-expression of 2 growth inhibitory imprinted tumor suppressor genes: guanosine triphosphate-binding Di-RAS-like 3 (ARHI) and paternally expressed 3 (PEG3). The in vivo activities of DAC and SAHA were assessed in a Hey xenograft model. RESULTS: The combination of DAC and SAHA produced synergistic inhibition of Hey and SKOv3 cell growth by apoptosis and cell cycle arrest. DAC induced autophagy in Hey cells that was enhanced by SAHA. Treatment with both agents induced re-expression of ARHI and PEG3 in cultured cells and in xenografts, correlating with growth inhibition. Knockdown of ARHI decreased DAC-induced autophagy. DAC and SAHA inhibited the growth of Hey xenografts and induced autophagy in vivo. CONCLUSIONS: A combination of DAC and SAHA inhibited ovarian cancer growth while inducing apoptosis, G2/M arrest, autophagy, and re-expression of imprinted tumor suppressor genes.

A Bayesian approach to dose-response assessment and synergy and its application to in vitro dose-response studies.

In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC(50)). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty.