Structure and antagonism of the receptor complex mediated by human TSLP in allergy and asthma

The pro-inflammatory cytokine thymic stromal lymphopoietin (TSLP) is pivotal to the pathophysiology of widespread allergic diseases mediated by type 2 helper T cell (Th2) responses, including asthma and atopic dermatitis. The emergence of human TSLP as a clinical target against asthma calls for maximally harnessing its therapeutic potential via structural and mechanistic considerations. Here we employ an integrative experimental approach focusing on productive and antagonized TSLP complexes and free cytokine. We reveal how cognate receptor TSLPR allosterically activates TSLP to potentiate the recruitment of the shared interleukin 7 receptor a-chain (IL-7Ra) by leveraging the flexibility, conformational heterogeneity and electrostatics of the cytokine. We further show that the monoclonal antibody Tezepelumab partly exploits these principles to neutralize TSLP activity. Finally, we introduce a fusion protein comprising a tandem of the TSLPR and IL-7Ra extracellular domains, which harnesses the mechanistic intricacies of the TSLP-driven receptor complex to manifest high antagonistic potency

The archive of the congregation of the Immaculate Heart of Mary (CICM Scheut) 1862-1967

info:eu-repo/semantics/publishe

A Symbolic Regression Model for the Prediction of Drug Binding to Human Liver Microsomes

It is common practice in the early drug discovery process to conduct in vitro screening experiments using liver microsomes in order to obtain an initial assessment of test compound metabolic stability. Compounds which bind to liver microsomes are unavailable for interaction with the drug metabolizing enzymes. As such, assessment of the unbound fraction of compound available for biotransformation is an important factor for interpretation of in vitro experimental results and to improve prediction of the in vivo metabolic clearance. Various in silico methods have been proposed for the prediction of test compound binding to microsomes, from various simple lipophilicity-based models with moderate performance to sophisticated machine learning models which demonstrate superior performance at the cost of increased complexity and higher data requirements. In this work, we attempt to strike a middle ground by developing easily implementable equations with improved predictive performance. We employ a symbolic regression approach based on a medium-size in-house data set of fraction unbound in human liver microsomes measurements allowing the identification of novel equations with improved performance. We validate the model performance on an in-house held-out test set and an external validation set

A Symbolic Regression Model for the Prediction of Drug Binding to Human Liver Microsomes

It is common practice in the early drug discovery process to conduct in vitro screening experiments using liver microsomes in order to obtain an initial assessment of test compound metabolic stability. Compounds which bind to liver microsomes are unavailable for interaction with the drug metabolizing enzymes. As such, assessment of the unbound fraction of compound available for biotransformation is an important factor for interpretation of in vitro experimental results and to improve prediction of the in vivo metabolic clearance. Various in silico methods have been proposed for the prediction of test compound binding to microsomes, from various simple lipophilicity-based models with moderate performance to sophisticated machine learning models which demonstrate superior performance at the cost of increased complexity and higher data requirements. In this work, we attempt to strike a middle ground by developing easily implementable equations with improved predictive performance. We employ a symbolic regression approach based on a medium-size in-house data set of fraction unbound in human liver microsomes measurements allowing the identification of novel equations with improved performance. We validate the model performance on an in-house held-out test set and an external validation set

Tozasertib analogues as inhibitors of necroptotic cell death

Receptor interacting protein kinase 1 (RIPK1) plays a crucial role in tumor necrosis factor (TNF)-induced necroptosis, suggesting that this pathway might be druggable. Most inhibitors of RIPK1 are classified as either type II or type III kinase inhibitors. This opened up some interesting perspectives for the discovery of novel inhibitors that target the active site of RIPK1. Tozasertib, a type I pan-aurora kinase (AurK) inhibitor, was found to show a very high affinity for RIPK1. Because tozasertib presents the typical structural elements of a type I kinase inhibitor, the development of structural analogues of tozasertib is a good starting point for identifying novel type I RIPK1 inhibitors. In this paper, we identified interesting inhibitors of mTNF-induced necroptosis with no significant effect on AurK A and B, resulting in no nuclear abnormalities as is the case for tozasertib. Compounds 71 and 72 outperformed tozasertib in an in vivo TNF-induced systemic inflammatory response syndrome (SIRS) mouse model

Mean biomass-per-unit-effort (BPUE) of the eleven most commonly caught species from 2007–2013.

<p>BPUE is represented as the estimated weight (kg) of fishes caught-per-angler-hour, using two hooks per line. Solid lines illustrate catch rates from marine protected areas (MPA), whereas dashed lines represent values from associated reference (REF) sites. Error bars denote one standard error above and below the mean.</p