Evaluation of pharmacokinetics and safety of cetuximab with cisplatin/carboplatin in patients with advanced solid tumor: Result from phase II studies.

The pharmacokinetics and potential drug-drug interactions between cetuximab and cisplatin or carboplatin from two studies (JXBA and JXBB) were evaluated. These studies were multicenter, open-label phase II trials designed to evaluate the drug-drug interactions between cetuximab (400 mg m-2 initial dose) and cisplatin (JXBA; 100 mg m-2) or carboplatin (JXBB; area under the curve [AUC] = 5 mg × min mL-1) with or without 5-fluorouracil (5FU) in patients with advanced solid tumors. Concentrations of cetuximab, cisplatin and carboplatin were determined using analytical methods. The safety and tolerability of cetuximab in combination with cisplatin or carboplatin was also determined in all treated patients. The JXBA study showed that cetuximab serum concentrations were similar when cetuximab was administered alone or in combination with cisplatin. The Cmax, tmax and overall AUC for the cetuximab group (194 µg mL-1, 2.0 hour, 14 900 µg × h mL-1) and the cetuximab and cisplatin combination group (192 µg mL-1, 1.99 hour, 16 300 µg × h mL-1) were similar. The JXBB study showed that mean cetuximab serum concentrations were similar when cetuximab was administered alone or in combination with carboplatin. The Cmax, tmax and overall AUC for the cetuximab group (199 µg mL-1, 1.15 hour, 17 200 µg × h mL-1) and the cetuximab and carboplatin combination group (199 µg mL-1, 3.17 h, 16 800 µg × h mL-1) were similar. Both studies showed that the safety profile was consistent with known side effects of cetuximab, platinum-based therapies and 5-FU. There was no clinically relevant change in cetuximab pharmacokinetics when it was administered in combination with cisplatin or carboplatin.

Statistical significance for hierarchical clustering.

Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high-dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this article, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets.