Glutathione <em>S</em>-transferase P1 (<em>GSTP1</em>) directly influences platinum drug chemosensitivity in ovarian tumour cell lines

BACKGROUND: Chemotherapy response in ovarian cancer patients is frequently compromised by drug resistance, possibly due to altered drug metabolism. Platinum drugs are metabolised by glutathione S-transferase P1 (GSTP1), which is abundantly, but variably expressed in ovarian tumours. We have created novel ovarian tumour cell line models to investigate the extent to which differential GSTP1 expression influences chemosensitivity. METHODS: Glutathione S-transferase P1 was stably deleted in A2780 and expression significantly reduced in cisplatin-resistant A2780DPP cells using Mission shRNA constructs, and MTT assays used to compare chemosensitivity to chemotherapy drugs used to treat ovarian cancer. Differentially expressed genes in GSTP1 knockdown cells were identified by Illumina HT-12 expression arrays and qRT–PCR analysis, and altered pathways predicted by MetaCore (GeneGo) analysis. Cell cycle changes were assessed by FACS analysis of PI-labelled cells and invasion and migration compared in quantitative Boyden chamber-based assays. RESULTS: Glutathione S-transferase P1 knockdown selectively influenced cisplatin and carboplatin chemosensitivity (2.3- and 4.83-fold change in IC(50), respectively). Cell cycle progression was unaffected, but cell invasion and migration was significantly reduced. We identified several novel GSTP1 target genes and candidate platinum chemotherapy response biomarkers. CONCLUSIONS: Glutathione S-transferase P1 has an important role in cisplatin and carboplatin metabolism in ovarian cancer cells. Inter-tumour differences in GSTP1 expression may therefore influence response to platinum-based chemotherapy in ovarian cancer patients

RAFT polymerization in bulk and emulsion

Detailed models of the RAFT polymerization in both non-segregated (bulk) and segregated (seeded emulsion) systems are presented. it is shown that satisfactory agreements between experiments and models can be achieved, and that effects such as inhibition and retardation, or the polymerization behavior at high conversions can be readily explained. in all cases the model parameter fitting has been minimized, being mostly limited to the rate coefficients of the addition/fragmentation reactions in the RAFT polymerization. Therefore, such models are believed to be invaluable tools towards a deeper understanding of the main phenomena underlying RAFT polymerization

A Hybrid Galerkin-Monte-Carlo Approach to Higher-Dimensional Population Balances in Polymerization Kinetics

Population balance models describing not only the chain-length distribution of a polymer but also additional properties like branching or composition are still difficult to solve numerically. For simulation of such systems two essentially different approaches are discussed in the literature: deterministic solvers based on rate equations and stochastic Monte-Carlo (MC) strategies based on chemical master equations. The paper presents a novel hybrid approach to polymer reaction kinetics that combines the best of these two worlds. We discuss the theoretical conditions of the algorithm, describe its numerical\ud realization, and show that, if applicable, it is more efficient than full-scale MC approaches and leads to more detailed information in additional property indices than deterministic solvers