Model selection and sample size adaptation: HYPAZ trial

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Understanding the Complexity of Porous Graphitic Carbon (PGC) Chromatography: Modulation of Mobile-Stationary Phase Interactions Overcomes Loss of Retention and Reduces Variability.

Porous graphitic carbon (PGC) is an important tool in a chromatographer's armory that retains polar compounds with mass spectrometry (MS)-compatible solvents. However, its applicability is severely limited by an unpredictable loss of retention, which can be attributed to contamination. The solutions offered fail to restore the original retention and our observations of retention time shifts of gemcitabine/metabolites on PGC are not consistent with contamination. The mobile phase affects the ionization state of analytes and the polarizable PGC surface that influences the strength of dispersive forces governing retention on the stationary phase. We hypothesized that failure to maintain the same PGC surface before and after running a gradient is a cause of the observed retention loss/variability on PGC. Herein, we optimize the choice of mobile phase solvent in a gradient program with three parts: a preparatory phase, which allows binding of analytes to column; an elution phase, which gives the required separation/peak shape; and a maintenance phase, to preserve the required retention capacity. Via liquid chromatography/tandem mass spectrometry (LC-MS/MS) analysis of gemcitabine and its metabolites extracted from tumor tissue, we demonstrate reproducible chromatography on three PGC columns of different ages. This approach simplifies use of the PGC to the same level as that of a C-18 column, removes the need for column regeneration, and minimizes run times, thus allowing PGC columns to be used to their full potential.This work was funded by the Cancer Research UK Cambridge Institute (Grant NO. C14303/A17197).This is the final version of the article. It first appeared from American Chemical Society via https://doi.org/ 10.1021/acs.analchem.6b0116

Anti-tumour effects of a specific anti-ADAM17 antibody in an ovarian cancer model in vivo.

ADAM 17 (TNF-α converting enzyme, TACE) is a potential target for cancer therapy, but the small molecule inhibitors reported to date are not specific to this ADAM family member. This membrane-bound metalloproteinase is responsible for ectodomain shedding of pathologically significant substrates including TNF-α and EGFR ligands. The aim of this study was to evaluate the pharmacokinetics, pharmacodynamics and anti-tumour efficacy of the first specific inhibitor, an anti-human ADAM17 IgG antibody, clone D1(A12). We used intraperitoneal xenografts of the human ovarian cancer cell line IGROV1-Luc in Balb/c nude mice, chosen because it was previously reported that growth of these xenografts is inhibited by knock-down of TNF-α. In vitro, 200 nM D1(A12) inhibited shedding of ADAM17 substrates TNF-α, TNFR1-α, TGF-α, amphiregulin (AREG), HB-EGF and IL-6Rα, from IGROV1-Luc cells, (4.7 nM IC(50) for TNF-α shedding). In IGROV1-Luc xenografts in vivo, D1(A12) IgG showed pharmacokinetic properties suitable for efficacy studies, with a single i.p. dose of 10 mg/kg D1(A12) sufficient to maintain IgG plasma and ascites fluid concentrations above 100 nM for more than 7 days. The plasma half life was 8.6 days. Next, an efficacy study was performed, dosing D1(A12) or anti-human TNF-α antibody infliximab at 10 mg/kg q7d, quantifying IGROV1-Luc tumour burden by bioluminescence. D1(A12) IgG showed a significant reduction in tumour growth (p = 0.005), 56% of vehicle control. Surprisingly, D1(A12) did not reduce the concentration of circulating human TNF-α, suggesting that another enzyme may compensate for inhibition of ADAM17 in vivo (but not in vitro). However, D1(A12) did show clear pharmacodynamic effects in the mice, with significant inhibition of shedding from tumour of ADAM17 substrates TNFR1-α, AREG, and TGF-α (4-15-fold reductions, p<0.0001 for all three). Thus, D1(A12) has anti-ADAM17 activity in vivo, inhibits shedding of EGFR ligands and has potential for use in EGF ligand-dependent tumours

Targeting of 5-aza-2'-deoxycytidine residues by chromatin-associated DNMT1 induces proteasomal degradation of the free enzyme.

5-Aza-2'-deoxycytidine (5-aza-dC) is a nucleoside analogue with cytotoxic and DNA demethylating effects. Here we show that 5-aza-dC induces the proteasomal degradation of free (non-chromatin bound) DNMT1 through a mechanism which is dependent on DNA synthesis and the targeting of incorporated 5-aza-dC residues by DNMT1 itself. Thus, 5-aza-dC induces Dnmt1 degradation in wild-type mouse ES cells, but not in Dnmt [3a(-/-), 3b(-/-)] mouse ES cells which express Dnmt1 but lack DNA methylation (<0.7% of CpG methylated) and contain few hemi-methylated CpG sites, these being the preferred substrates for Dnmt1. We suggest that adducts formed between DNMT1 and 5-aza-dC molecules in DNA induce a ubiquitin-E3 ligase activity which preferentially targets free DNMT1 molecules for degradation by the proteasome. The proteasome inhibitor MG132 prevents DNMT1 degradation and reduces hypomethylation induced by 5-aza-dC

Capecitabine (oral 5-fluorouracil pro-drug) treatment for colorectal carcinoma causing ischaemic chest pain

We present a case of cardiac ischaemia associated with capecitabine chemotherapy. An elderly female receiving capecitabine chemotherapy developed acute onset severe anterior chest pain associated with ischaemic changes on ECG. The pain and ECG changes failed to respond to thrombolysis and she proceeded to coronary angiogram and stenting of a thrombosed right coronary vessel. She inadvertently recommenced her capecitabine with a further episode of chest pain. On cessation of capecitabine she had no further episodes of chest pain

CHK1 Inhibition Synergizes with Gemcitabine Initially by Destabilizing the DNA Replication Apparatus.

Combining cell-cycle checkpoint kinase inhibitors with the DNA-damaging chemotherapeutic agent gemcitabine offers clinical appeal, with a mechanistic rationale based chiefly on abrogation of gemcitabine-induced G2-M checkpoint activation. However, evidence supporting this mechanistic rationale from chemosensitization studies has not been consistent. Here we report a systematic definition of how pancreatic cancer cells harboring mutant p53 respond to this combination therapy, by combining mathematical models with large-scale quantitative biologic analyses of single cells and cell populations. Notably, we uncovered a dynamic range of mechanistic effects at different ratios of gemcitabine and CHK1 inhibitors. Remarkably, effective synergy was attained even where cells exhibited an apparently functional G2-M surveillance mechanism, as exemplified by a lack of both overt premature CDK1 activation and S-phase mitotic entry. Consistent with these findings, S-G2 duration was extended in treated cells, leading to a definable set of lineage-dependent catastrophic fates. At synergistic drug concentrations, global replication stress was a distinct indicator of chemosensitization as characterized molecularly by an accumulation of S-phase cells with high levels of hyperphosphorylated RPA-loaded single-stranded DNA. In a fraction of these cells, persistent genomic damage was observed, including chromosomal fragmentation with a loss of centromeric regions that prevented proper kinetochore-microtubule attachment. Together, our results suggested a "foot-in-the-door" mechanism for drug synergy where cells were destroyed not by frank G2-M phase abrogation but rather by initiating a cumulative genotoxicity that deregulated DNA synthesis.This study was funded by Cancer Research UK via Institute Senior Group Leader funding (C14303/A17197) to DI Jodrell, and by Sentinel Oncology through an award from Innovate UK.This is the author accepted manuscript. The final version is available from American Association for Cancer Research via http://dx.doi.org/10.1158/0008-5472.CAN-14-334

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.This work has been supported by the Cancer Research UK grant C14303/A17197This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/bioinformatics/btw23

The use of error-category mapping in pharmacokinetic model analysis of dynamic contrast-enhanced MRI data.

This study introduces the use of 'error-category mapping' in the interpretation of pharmacokinetic (PK) model parameter results derived from dynamic contrast-enhanced (DCE-) MRI data. Eleven patients with metastatic renal cell carcinoma were enrolled in a multiparametric study of the treatment effects of bevacizumab. For the purposes of the present analysis, DCE-MRI data from two identical pre-treatment examinations were analysed by application of the extended Tofts model (eTM), using in turn a model arterial input function (AIF), an individually-measured AIF and a sample-average AIF. PK model parameter maps were calculated. Errors in the signal-to-gadolinium concentration ([Gd]) conversion process and the model-fitting process itself were assigned to category codes on a voxel-by-voxel basis, thereby forming a colour-coded 'error-category map' for each imaged slice. These maps were found to be repeatable between patient visits and showed that the eTM converged adequately in the majority of voxels in all the tumours studied. However, the maps also clearly indicated sub-regions of low Gd uptake and of non-convergence of the model in nearly all tumours. The non-physical condition ve ≥ 1 was the most frequently indicated error category and appeared sensitive to the form of AIF used. This simple method for visualisation of errors in DCE-MRI could be used as a routine quality-control technique and also has the potential to reveal otherwise hidden patterns of failure in PK model applications.This work was supported by GlaxoSmithKline UK, Wellcome Trust, Cambridge NIHR Biomedical Research Centre, Cambridge Experimental Cancer Medicine Centre, Cancer Research UKThis is the published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S0730725X1400321X

Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model.

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters

SPARC independent drug delivery and antitumour effects of nab-paclitaxel in genetically engineered mice.

DESIGN: Pharmacokinetic and pharmacodynamic parameters of cremophor-paclitaxel, nab-paclitaxel (human-albumin-bound paclitaxel, Abraxane) and a novel mouse-albumin-bound paclitaxel (m-nab-paclitaxel) were evaluated in genetically engineered mouse models (GEMMs) by liquid chromatography-tandem mass spectrometry (LC-MS/MS), histological and biochemical analysis. Preclinical evaluation of m-nab-paclitaxel included assessment by three-dimensional high-resolution ultrasound and molecular analysis in a novel secreted protein acidic and rich in cysteine (SPARC)-deficient GEMM of pancreatic ductal adenocarcinoma (PDA). RESULTS: nab-Paclitaxel exerted its antitumoural effects in a dose-dependent manner and was associated with less toxicity compared with cremophor-paclitaxel. SPARC nullizygosity in a GEMM of PDA, Kras(G12D);p53(flox/-);p48Cre (KPfC), resulted in desmoplastic ductal pancreas tumours with impaired collagen maturation. Paclitaxel concentrations were significantly decreased in SPARC null plasma samples and tissues when administered as low-dose m-nab-paclitaxel. At the maximally tolerated dose, SPARC deficiency did not affect the intratumoural paclitaxel concentration, stromal deposition and the immediate therapeutic response. CONCLUSIONS: nab-Paclitaxel accumulates and acts in a dose-dependent manner. The interaction of plasma SPARC and albumin-bound drugs is observed at low doses of nab-paclitaxel but is saturated at therapeutic doses in murine tumours. Thus, this study provides important information for future preclinical and clinical trials in PDA using nab-paclitaxel in combination with novel experimental and targeted agents