Combenefit: an interactive platform for the analysis and visualization of drug combinations.

Motivation: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. Results: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. Availability and Implementation: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). Contact: [email protected]. Supplementary information:Supplementary data are available at Bioinformatics online

The pharmacology and toxicology of novel thymidylate synthase inhibitors, potential new anticancer agents

SIGLEAvailable from British Library Document Supply Centre-DSC:DX190781 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Population pharmacokinetics and pharmacodynamics of paclitaxel and carboplatin in ovarian cancer patients: A study by the European Organization for Research and Treatment of Cancer-Pharmacology and Molecular Mechanisms Group and New Drug Development Group

PURPOSE: Paclitaxel and carboplatin are frequently used in advanced ovarian cancer following cytoreductive surgery. Threshold models have been used to predict paclitaxel pharmacokinetic-pharmacodynamics, whereas the time above paclitaxel plasma concentration of 0.05 to 0.2 micromol/L (t(C > 0.05-0.2)) predicts neutropenia. The objective of this study was to build a population pharmacokinetic-pharmacodynamic model of paclitaxel/carboplatin in ovarian cancer patients. EXPERIMENTAL DESIGN: One hundred thirty-nine ovarian cancer patients received paclitaxel (175 mg/m(2)) over 3 h followed by carboplatin area under the concentration-time curve 5 mg/mL*min over 30 min. Plasma concentration-time data were measured, and data were processed using nonlinear mixed-effect modeling. Semiphysiologic models with linear or sigmoidal maximum response and threshold models were adapted to the data. RESULTS: One hundred five patients had complete pharmacokinetic and toxicity data. In 34 patients with measurable disease, objective response rate was 76%. Neutrophil and thrombocyte counts were adequately described by an inhibitory linear response model. Paclitaxel t(C > 0.05) was significantly higher in patients with a complete (91.8 h) or partial (76.3 h) response compared with patients with progressive disease (31.5 h; P = 0.02 and 0.05, respectively). Patients with paclitaxel t(C > 0.05) >61.4 h (mean value) had a longer time to disease progression compared with patients with paclitaxel t(C > 0.05) > 61.4 h (89.0 versus 61.9 weeks; P = 0.05). Paclitaxel t(C < 0.05) was a good predictor for severe neutropenia (P = 0.01), whereas carboplatin exposure (C(max) and area under the concentration-time curve) was the best predictor for thrombocytopenia (P > 10(-4)). CONCLUSIONS: In this group of patients, paclitaxel t(C > 0.05) is a good predictive marker for severe neutropenia and clinical outcome, whereas carboplatin exposure is a good predictive marker for thrombocytopenia

Population pharmacokinetics and pharmacodynamics of paclitaxel and carboplatin in ovarian cancer patients: A study by the European Organization for Research and Treatment of Cancer-Pharmacology and Molecular Mechanisms Group and New Drug Development Group

PURPOSE: Paclitaxel and carboplatin are frequently used in advanced ovarian cancer following cytoreductive surgery. Threshold models have been used to predict paclitaxel pharmacokinetic-pharmacodynamics, whereas the time above paclitaxel plasma concentration of 0.05 to 0.2 micromol/L (t(C > 0.05-0.2)) predicts neutropenia. The objective of this study was to build a population pharmacokinetic-pharmacodynamic model of paclitaxel/carboplatin in ovarian cancer patients. EXPERIMENTAL DESIGN: One hundred thirty-nine ovarian cancer patients received paclitaxel (175 mg/m(2)) over 3 h followed by carboplatin area under the concentration-time curve 5 mg/mL*min over 30 min. Plasma concentration-time data were measured, and data were processed using nonlinear mixed-effect modeling. Semiphysiologic models with linear or sigmoidal maximum response and threshold models were adapted to the data. RESULTS: One hundred five patients had complete pharmacokinetic and toxicity data. In 34 patients with measurable disease, objective response rate was 76%. Neutrophil and thrombocyte counts were adequately described by an inhibitory linear response model. Paclitaxel t(C > 0.05) was significantly higher in patients with a complete (91.8 h) or partial (76.3 h) response compared with patients with progressive disease (31.5 h; P = 0.02 and 0.05, respectively). Patients with paclitaxel t(C > 0.05) >61.4 h (mean value) had a longer time to disease progression compared with patients with paclitaxel t(C > 0.05) > 61.4 h (89.0 versus 61.9 weeks; P = 0.05). Paclitaxel t(C < 0.05) was a good predictor for severe neutropenia (P = 0.01), whereas carboplatin exposure (C(max) and area under the concentration-time curve) was the best predictor for thrombocytopenia (P > 10(-4)). CONCLUSIONS: In this group of patients, paclitaxel t(C > 0.05) is a good predictive marker for severe neutropenia and clinical outcome, whereas carboplatin exposure is a good predictive marker for thrombocytopenia