Metformin Downregulates the Insulin/IGF-I Signaling Pathway and Inhibits Different Uterine Serous Carcinoma (USC) Cells Proliferation and Migration in P53-Dependent or -Independent Manners.

Metformin Downregulates the Insulin/IGF-I Signaling Pathway and Inhibits Different Uterine Serous Carcinoma (USC) Cells Proliferation and Migration in p53-Dependent or -Independent Manners.

PLoS One. 2013; 8(4): e61537
Sarfstein R, Friedman Y, Attias-Geva Z, Fishman A, Bruchim I, Werner H

Accumulating epidemiological evidence shows that obesity is associated with an increased risk of several types of adult cancers, including endometrial cancer. Chronic hyperinsulinemia, a typical hallmark of diabetes, is one of the leading factors responsible for the obesity-cancer connection. Numerous cellular and circulating factors are involved in the biochemical chain of events leading from hyperinsulinemia and insulin resistance to increased cancer risk and, eventually, tumor development. Metformin is an oral anti-diabetic drug of the biguanide family used for treatment of type 2 diabetes. Recently, metformin was shown to exhibit anti-proliferative effects in ovarian and Type I endometrial cancer, although the mechanisms responsible for this non-classical metformin action remain unclear. The insulin-like growth factors (IGFs) play a prominent role in cancer biology and their mechanisms of action are tightly interconnected with the insulin signaling pathways. Given the cross-talk between the insulin and IGF signaling pathways, the aim of this study was to examine the hypothesis that the anti-proliferative actions of metformin in uterine serous carcinoma (USC) are potentially mediated via suppression of the IGF-I receptor (IGF-IR) pathway. Our results show that metformin interacts with the IGF pathway, and induces apoptosis and inhibition of proliferation and migration of USC cell lines with both wild type and mutant p53. Taken together, our results suggest that metformin therapy could be a novel and attractive therapeutic approach for human USC, a highly aggressive variant of endometrial cancer. HubMed – drug

Pharmacointeraction network models predict unknown drug-drug interactions.

PLoS One. 2013; 8(4): e61468
Cami A, Manzi S, Arnold A, Reis BY

Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage – a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) – a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting “contraindicated” DDIs (AUROC?=?0.92) and less effective for “minor” DDIs (AUROC?=?0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions. HubMed – drug

Bevacizumab in high-grade gliomas: a review of its uses, toxicity assessment, and future treatment challenges.

Onco Targets Ther. 2013; 6: 371-89
Rahmathulla G, Hovey EJ, Hashemi-Sadraei N, Ahluwalia MS

High-grade gliomas continue to have dismal prognosis despite advances made in understanding the molecular genetics, signaling pathways, cytoskeletal dynamics, and the role of stem cells in gliomagenesis. Conventional treatment approaches, including surgery, radiotherapy, and cytotoxic chemotherapy, have been used with limited success. Therapeutic advances using molecular targeted therapy, immunotherapy, and others such as dietary treatments have not been able to halt tumor progression and disease-related death. High-grade gliomas (World Health Organization grades III/IV) are histologically characterized by cellular and nuclear atypia, neoangiogenesis, and necrosis. The expression of vascular endothelial growth factor, a molecular mediator, plays a key role in vascular proliferation and tumor survival. Targeting vascular endothelial growth factor has demonstrated promising results, with improved quality of life and progression-free survival. Bevacizumab, a humanized monoclonal antibody to vascular endothelial growth factor, is approved by the Food and Drug Administration as a single agent in recurrent glioblastoma and is associated with manageable toxicity. This review discusses the efficacy, practical aspects, and response assessment challenges with the use of bevacizumab in the treatment of high-grade gliomas. HubMed – drug