Prostaglandin E2 and T cells: friends or foes?

Our understanding of the key players involved in the differential regulation of T-cell responses during inflammation, infection and auto-immunity is fundamental for designing efficient therapeutic strategies against immune diseases. With respect to this, the inhibitory role of the lipid mediator prostaglandin E2 (PGE2) in T-cell immunity has been documented since the 1970s. Studies that ensued investigating the underlying mechanisms substantiated the suppressive function of micromolar concentrations of PGE2 in T-cell activation, proliferation, differentiation and migration. However, the past decade has seen a revolution in this perspective, since nanomolar concentrations of PGE2 have been shown to potentiate Th1 and Th17 responses and aid in T-cell proliferation. The understanding of concentration-specific effects of PGE2 in other cell types, the development of mice deficient in each subtype of the PGE2 receptors (EP receptors) and the delineation of signalling pathways mediated by the EP receptors have enhanced our understanding of PGE2 as an immune-stimulator. PGE2 regulates a multitude of functions in T-cell activation and differentiation and these effects vary depending on the micro-environment of the cell, maturation and activation state of the cell, type of EP receptor involved, local concentration of PGE2 and whether it is a homeostatic or inflammatory scenario. In this review, we compartmentalize the various aspects of this complex relationship of PGE2 with T lymphocytes. Given the importance of this molecule in T-cell activation, we also address the possibility of using EP receptor antagonism as a potential therapeutic approach for some immune disorders

Population pharmacokinetics of the humanised monoclonal antibody, HuHMFG1 (AS1402), derived from a phase I study on breast cancer

International audienceBACKGROUND: HuHMFG1 (AS1402) is a humanised monoclonal antibody that has undergone a phase I trial in metastatic breast cancer. The aim of this study was to characterise the pharmacokinetics (PKs) of HuHMFG1 using a population PK model. METHOD: Data were derived from a phase I study of 26 patients receiving HuHMFG1 at doses ranging from 1 to 16 mg kg(-1). Data were analysed using NONMEM software and covariates were included. A limited sampling strategy (LSS) was developed using training and a validation data set. RESULTS: A linear two-compartment model was shown to be adequate to describe data. Covariate analysis indicated that weight was not related to clearance. An LSS was successfully developed on the basis of the model, in which one sample is collected immediately before the start of an infusion and the second is taken at the end of infusion. CONCLUSION: A two-compartment population PK model successfully describes HuHMFG1 behaviour. The model suggests using a fixed dose of HuHMFG1, which would simplify dosing. The model could be used to optimise dose level and dosing schedule if more data on the correlation between exposure and efficacy become available from future studies. The derived LSS could optimise further PK assessment of this antibody

Clinical use of biomarkers of survival in pulmonary fibrosis

<p>Abstract</p> <p>Background</p> <p>Biologic predictors or biomarkers of survival in pulmonary fibrosis with a worse prognosis, more specifically in idiopathic pulmonary fibrosis would help the clinician in deciding whether or not to treat since treatment carries a potential risk for adverse events. These decisions are made easier if accurate and objective measurements of the patients' clinical status can predict the risk of progression to death.</p> <p>Method</p> <p>A literature review is given on different biomarkers of survival in interstitial lung disease, mainly in IPF, since this disease has the worst prognosis.</p> <p>Conclusion</p> <p>Serum biomarkers, and markers measured by medical imaging as HRCT, pertechnegas, DTPA en FDG-PET are not ready for clinical use to predict mortality in different forms of ILD. A baseline FVC, a change of FVC of more than 10%, and change in 6MWD are clinically helpful predictors of survival.</p