Induction of Selective Blood-Tumor Barrier Permeability and Macromolecular Transport by a Biostable Kinin B1 Receptor Agonist in a Glioma Rat Model

Treatment of malignant glioma with chemotherapy is limited mostly because of delivery impediment related to the blood-brain tumor barrier (BTB). B1 receptors (B1R), inducible prototypical G-protein coupled receptors (GPCR) can regulate permeability of vessels including possibly that of brain tumors. Here, we determine the extent of BTB permeability induced by the natural and synthetic peptide B1R agonists, LysdesArg9BK (LDBK) and SarLys[dPhe8]desArg9BK (NG29), in syngeneic F98 glioma-implanted Fischer rats. Ten days after tumor inoculation, we detected the presence of B1R on tumor cells and associated vasculature. NG29 infusion increased brain distribution volume and uptake profiles of paramagnetic probes (Magnevist and Gadomer) at tumoral sites (T1-weighted imaging). These effects were blocked by B1R antagonist and non-selective cyclooxygenase inhibitors, but not by B2R antagonist and non-selective nitric oxide synthase inhibitors. Consistent with MRI data, systemic co-administration of NG29 improved brain tumor delivery of Carboplatin chemotherapy (ICP-Mass spectrometry). We also detected elevated B1R expression in clinical samples of high-grade glioma. Our results documented a novel GPCR-signaling mechanism for promoting transient BTB disruption, involving activation of B1R and ensuing production of COX metabolites. They also underlined the potential value of synthetic biostable B1R agonists as selective BTB modulators for local delivery of different sized-therapeutics at (peri)tumoral sites

Expression Profile of Genes Related to Drug Metabolism in Human Brain Tumors

Background Endogenous and exogenous compounds as well as carcinogens are metabolized and detoxified by phase I and II enzymes, the activity of which could be crucial to the inactivation and hence susceptibility to carcinogenic factors. The expression of these enzymes in human brain tumor tissue has not been investigated sufficiently. We studied the association between tumor pathology and the expression profile of seven phase I and II drug metabolizing genes (CYP1A1, CYP1B1, ALDH3A1, AOX1, GSTP1, GSTT1 and GSTM3) and some of their proteins. Methods Using qRT-PCR and western blotting analysis the gene and protein expression in a cohort of 77 tumors were investigated. The major tumor subtypes were meningioma, astrocytoma and brain metastases, -the later all adenocarcinomas from a lung primary. Results Meningeal tumors showed higher expression levels for AOX1, CYP1B1, GSTM3 and GSTP1. For AOX1, GSTM and GSTP1 this could be verified on a protein level as well. A negative correlation between the WHO degree of malignancy and the strength of expression was identified on both transcriptional and translational level for AOX1, GSTM3 and GSTP1, although the results could have been biased by the prevalence of meningiomas and glioblastomas in the inevitably bipolar distribution of the WHO grades. A correlation between the gene expression and the protein product was observed for AOX1, GSTP1 and GSTM3 in astrocytomas. Conclusions The various CNS tumors show different patterns of drug metabolizing gene expression. Our results suggest that the most important factor governing the expression of these enzymes is the histological subtype and to a far lesser extent the degree of malignancy itself

Development and external validation of the 'Global Surgical-Site Infection' (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery

Background Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models. Methods This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July–November 2014) and GlobalSurg-2 (January–July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator (‘LASSO’)) and machine learning (extreme gradient boosting (‘XGBoost’)). Final model selection was based on prognostic accuracy and clinical utility. Results There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept −0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models. Conclusion The ‘Global Surgical-Site Infection’ score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm

Development and external validation of the ‘Global Surgical-Site Infection’ (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery

Background: Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models. Methods: This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July-November 2014) and GlobalSurg-2 (January-July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator ('LASSO')) and machine learning (extreme gradient boosting ('XGBoost')). Final model selection was based on prognostic accuracy and clinical utility. Results: There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept -0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models. Conclusion: The 'Global Surgical-Site Infection' score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm