A phase I pharmacokinetic and pharmacodynamic study of BGC9331 and carboplatin in relapsed gynaecological malignancies

Metformin, at concentrations corresponding to the treatment of diabetes, potentiates the cytotoxic effects of carboplatin in cultures of ovarian cancer cells

The use of the type 2 diabetics drug metformin has been correlated with enhanced progression-free survival in ovarian cancer. The literature has speculated that this enhancement is due to the high concentration of metformin directly causing cancer cell death. However, this explanation does not fit with clinical data reporting that the women exposed to constant micromolar concentrations of metformin, as present in the treatment of diabetes, respond better to chemotherapy. Herein, our aim was to examine whether micromolar concentrations of metformin alone could bring about cancer cell death and whether micromolar metformin could increase the cytotoxic effect of commonly used chemotherapies in A2780 and SKOV3 cell lines and primary cultured cancer cells isolated from the peritoneal fluid of patients with advanced ovarian cancer. Our results in cell lines demonstrate that no significant loss of viability or change in cell cycle was observed with micromolar metformin alone; however, we observed cytotoxicity with micromolar metformin in combination with chemotherapy at concentrations where the chemotherapy alone produced no loss in viability. We demonstrate that previous exposure and maintenance of metformin in conjunction with carboplatin produces a synergistic enhancement in cytotoxicity of A2780 and SKOV3 cells (55% and 43%, respectively). Furthermore, in 5 (44%) of the 11 ovarian cancer primary cultures, micromolar metformin improved the cytotoxic response to carboplatin but not paclitaxel or doxorubicin. In conclusion, we present data that support the need for a clinical study to evaluate the adjuvant maintenance or prescription of currently approved doses of metformin during the chemotherapeutic treatment of ovarian cancer.

A hierarchical Bayesian design for phase I trials of novel combinations of cancer therapeutic agents

We propose a hierarchical model for the probability of dose-limiting toxicity (DLT) for combinations of doses of two therapeutic agents. We apply this model to an adaptive Bayesian trial algorithm whose goal is to identify combinations with DLT rates close to a prespecified target rate. We describe methods for generating prior distributions for the parameters in our model from a basic set of information elicited from clinical investigators. We survey the performance of our algorithm in a series of simulations of a hypothetical trial that examines combinations of four doses of two agents. We also compare the performance of our approach to two existing methods and assess the sensitivity of our approach to the chosen prior distribution.