Progestational agents and preterm birth: An updated review of the literature

The preterm birth rate has been increasing over time in the United States, causing a large social and individual financial burden. Though the cause of preterm birth is now known, risk factors such as a previous preterm birth and a short cervical length have been identified as possible predictors. There are many contributing social and behavioral factors that play a role was well as medical problems that occur before and during pregnancy. Though there have been prevention methods identified, such as prenatal care, tocolytic therapy and cervical cerclage, none of these methods have shown to definitively prevent preterm birth over a long period of time. 17 alpha hydroxyprogesterone has been recognized as a possible prevention method for women at high risk for preterm birth. Three out of the five studies assessed in this review showed a significant reduction in preterm birth with administration of progesterone, both for women with a previous preterm birth and with a short cervical length. Currently there is no standard of care for those at high risk for preterm birth. More large clinical trials need to be conducted to determine if this progesterone for the prevention of preterm birth is effective. ^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

A role for p53 in sensing DNA damage and triggering apoptotic responses to anticancer agents

The p53 tumor suppressor protein plays a major role in cellular responses to anticancer agents that target DNA. DNA damage triggers the accumulation of p53, resulting in the transactivation of genes, which induce cell cycle arrest to allow for repair of the damaged DNA, or signal apoptosis. The exact role that p53 plays in sensing DNA damage and the functional consequences remain to be investigated. The main goal of this project was to determine if p53 is directly involved in sensing DNA damage induced by anticancer agents and in mediating down-stream cellular responses. This was tested in two experimental models of DNA damage: (1) DNA strand termination caused by anticancer nucleoside analogs and (2) oxidative DNA damage induced by reactive oxygen species (ROS). Mobility shift assays demonstrated that p53 and DNA-PK/Ku form a complex that binds DNA containing the anticancer nucleoside analog gemcitabine monophosphate in vitro. Binding of the p53-DNA-PK/Ku complex to the analog-containing DNA inhibited DNA strand elongation. Furthermore, treatment of cells with gemcitabine resulted in the induction of apoptosis, which was associated with the accumulation of p53 protein, its phosphorylation, and nuclear localization, suggesting the activation of p53 to trigger apoptosis following gemcitabine induced DNA strand termination. The role of p53 as a DNA damage sensor was further demonstrated in response to oxidative DNA damage. Protein pull-down assays demonstrated that p53 complexes with OGG1 and APE, and binds DNA containing the oxidized DNA base 8-oxoG. Importantly, p53 enhances the activities of APE and OGG1 in excising the 8-oxoG residue as shown by functional assays in vitro. This correlated with the more rapid removal of 8-oxoG from DNA in intact cells with wild-type p53 exposed to exogenous ROS stress. Interestingly, persistent exposure to ROS resulted in the accelerated onset of apoptosis in cells with wild-type p53 when compared to isogenic cells lacking p53. Apoptosis in p53+/+ cells was associated with accumulation and phosphorylation of p53 and its nuclear localization. Taken together, these results indicate that p53 plays a key role in sensing DNA damage induced by anticancer nucleoside analogs and ROS, and in triggering down-stream apoptotic responses. This study provides new mechanistic insights into the functions of p53 in cellular responses to anticancer agents. ^