The developmental pharmacology of amifostine, pharmacokinetics and measures of oxidative stress

grantor: University of TorontoUse of the broad-spectrum cytoprotective agent, amifostine, in children may allow more effective cancer treatment by selectively protecting healthy tissue from the detrimental effects of chemotherapy. This effect is mediated via the active metabolite, WR1065, whose mechanism of action may relate to modulation of glutathione levels. Experience with amifostine is limited in pediatrics; therefore, a Phase I study was used to establish pharmacokinetics in children and to evaluate the proposed mechanism of action. Similar systemic exposure to WR1065 was observed in both children and adults after correcting for dose differences, suggesting a similar degree of protection in both populations. In order to evaluate WR1065's mechanism of action, we identified and validated an improved method for glutathione sample preparation, resulting in a reliable assay that will enable us to better assess amifostine's effect on whole blood glutathione levels.M.Sc

In Vivo Antitumor and Antimetastatic Activity of Sunitinib in Preclinical Neuroblastoma Mouse Model1

Neuroblastoma (NB) is one of the most common pediatric solid tumors originating from the neural crest lineage. Despite intensive treatment protocols including megatherapy with hematopoietic stem cell transplantation, the prognosis of NB patients remains poor. More effective therapeutics are required. High vascularity has been described as a feature of aggressive, widely disseminated NB. Our previous work demonstrated the overexpression of vascular endothelial growth factor (VEGF) in NB, and we showed that an anti-VEGF receptor (VEGFR-2) antibody could induce sustained NB tumor suppression and regression. Sunitinib is a kinase inhibitor targeting platelet-derived growth factor receptors and VEGFRs and, therefore, a promising antiangiogenic agent. In this study, we investigated the antitumor activity of sunitinib and its synergistic cytotoxicity with conventional (cyclophosphamide) and novel (rapamycin) therapies. Both NB cell lines and tumor-initiating cells from patient tumor samples were used in our in vitro and in vivo models for these drug testing. We show that sunitinib inhibits tumor cell proliferation and phosphorylation of VEGFRs. It also inhibits tumor growth, angiogenesis, and metastasis in tumor xenograft models. Low-dose sunitinib (20 mg/kg) demonstrates synergistic cytotoxicity with an mTOR inhibitor, rapamycin, which is more effective than the traditional chemotherapeutic drug, cyclophosphamide. These preclinical studies provide the evidence of antitumor activity of sunitinib both in the early stage of tumor formation and in the progressive metastatic disease. These studies also provide the framework for clinical trial of sunitinib, alone and in combination with conventional and novel therapies to increase efficacy and improve patient outcome in NB

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Common variants at five new loci associated with early-onset inflammatory bowel disease.

The inflammatory bowel diseases (IBD) Crohn's disease and ulcerative colitis are common causes of morbidity in children and young adults in the western world. Here we report the results of a genome-wide association study in early-onset IBD involving 3,426 affected individuals and 11,963 genetically matched controls recruited through international collaborations in Europe and North America, thereby extending the results from a previous study of 1,011 individuals with early-onset IBD. We have identified five new regions associated with early-onset IBD susceptibility, including 16p11 near the cytokine gene IL27 (rs8049439, P = 2.41 x 10(-9)), 22q12 (rs2412973, P = 1.55 x 10(-9)), 10q22 (rs1250550, P = 5.63 x 10(-9)), 2q37 (rs4676410, P = 3.64 x 10(-8)) and 19q13.11 (rs10500264, P = 4.26 x 10(-10)). Our scan also detected associations at 23 of 32 loci previously implicated in adult-onset Crohn's disease and at 8 of 17 loci implicated in adult-onset ulcerative colitis, highlighting the close pathogenetic relationship between early- and adult-onset IBD.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe