topics / مواضيع: To uncover the genetic basis of anthracycline-induced cardiotoxicity (AIC), we recently established a genetic suppressor screening strategy in zebrafish

To uncover the genetic basis of anthracycline-induced cardiotoxicity (AIC), we recently established a genetic suppressor screening strategy in zebrafish

Abstract

To uncover the genetic basis of anthracycline-induced cardiotoxicity (AIC), we recently established a genetic suppressor screening strategy in zebrafish. Here, we report the molecular and cellular nature of GBT0419, a salutary modifier mutant that affects retinoid x receptor alpha a (rxraa). We showed that endothelial, but not myocardial or epicardial, RXRA activation confers AIC protection. We then identified isotretinoin and bexarotene, two FDA-approved RXRA agonists, which exert cardioprotective effects. The therapeutic effects of these drugs only occur when administered during early, but not late, phase of AIC or as pretreatment. Mechanistically, these spatially- and temporally-predominant benefits of RXRA activation can be ascribed to repair of damaged endothelial cell-barrier via regulating tight-junction protein Zonula occludens-1. Together, our study provides the first in vivo genetic evidence supporting RXRA as the therapeutic target for AIC, and uncovers a previously unrecognized spatiotemporally-predominant mechanism that shall inform future translational efforts.

PMID: 32064346 [PubMed - in process]

00:38

Photo

Not included, change data exporting settings to download.

256×256, 6.0 KB

00:38

In reply to this message

pubmed: ctoall&ca or conall

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers.

Front Pharmacol. 2019;10:1631

Authors: Wang Y, Huang L, Jiang S, Wang Y, Zou J, Fu H, Yang S

Abstract

Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.

PMID: 32063849 [PubMed]

00:38

Photo

Not included, change data exporting settings to download.

256×256, 6.0 KB

00:38

In reply to this message

pubmed: ctoall&ca or conall

Evaluation of the cost-effectiveness of dexrazoxane for the prevention of anthracycline-related cardiotoxicity in children with sarcoma and haematologic malignancies: a European perspective.

Cost Eff Resour Alloc. 2020;18:7

Authors: Dewilde S, Carroll K, Nivelle E, Sawyer J