GLUT3 inhibitor discovery through in silico ligand screening and in vivo validation in eukaryotic expression systems.

The passive transport of glucose and related hexoses in human cells is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT3 is a high-affinity glucose transporter primarily responsible for glucose entry in neurons. Changes in its expression have been implicated in neurodegenerative diseases and cancer. GLUT3 inhibitors can provide new ways to probe the pathophysiological role of GLUT3 and tackle GLUT3-dependent cancers. Through in silico screening of an ~ 8 million compounds library against the inward- and outward-facing models of GLUT3, we selected ~ 200 ligand candidates. These were tested for in vivo inhibition of GLUT3 expressed in hexose transporter-deficient yeast cells, resulting in six new GLUT3 inhibitors. Examining their specificity for GLUT1-5 revealed that the most potent GLUT3 inhibitor (G3iA, IC50 ~ 7 µM) was most selective for GLUT3, inhibiting less strongly only GLUT2 (IC50 ~ 29 µM). None of the GLUT3 inhibitors affected GLUT5, three inhibited GLUT1 with equal or twofold lower potency, and four showed comparable or two- to fivefold better inhibition of GLUT4. G3iD was a pan-Class 1 GLUT inhibitor with the highest preference for GLUT4 (IC50 ~ 3.9 µM). Given the prevalence of GLUT1 and GLUT3 overexpression in many cancers and multiple myeloma's reliance on GLUT4, these GLUT3 inhibitors may discriminately hinder glucose entry into various cancer cells, promising novel therapeutic avenues in oncology.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

How to Illuminate the Druggable Genome Using Pharos.

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

New insights in the in vitro characterisation and molecular modelling of the P-glycoprotein inhibitory promiscuity.

The presence of several binding sites for both substrates and inhibitors is yet a poorly explored thematic concerning the assessment of the drug-drug interactions risk due to interactions of multiple drugs with the human transport protein P-glycoprotein (P-gp or MDR1, gene ABCB1). In this study we measured the inhibitory behaviour of a set of known drugs towards P-gp by using three different probe substrates (digoxin, Hoechst 33,342 and rhodamine 123). A structure-based model was built to unravel the different substrates binding sites and to rationalize the cases where drugs were not inhibiting all the substrates. A separate set of experiments was used to validate the model and confirmed its suitability to either detect the substrate-dependent P-gp inhibition and to anticipate proper substrates for in vitro experiments case by case. The modelling strategy described can be applied for either design safer drugs (P-gp as antitarget) or to target specific sub-site inhibitors towards other drugs (P-gp as target).

Side-Chain Modified Ergosterol and Stigmasterol Derivatives as Liver X Receptor Agonists.

A series of stigmasterol and ergosterol derivatives, characterized by the presence of oxygenated functions at C-22 and/or C-23 positions, were designed as potential liver X receptor (LXR) agonists. The absolute configuration of the newly created chiral centers was definitively assigned for all the corresponding compounds. Among the 16 synthesized compounds, 21, 27, and 28 were found to be selective LXRα agonists, whereas 20, 22, and 25 showed good selectivity for the LXRß isoform. In particular, 25 showed the same degree of potency as 22R-HC (3) at LXRß, while it was virtually inactive at LXRα (EC50 = 14.51 µM). Interestingly, 13, 19, 20, and 25 showed to be LXR target gene-selective modulators, by strongly inducing the expression of ABCA1, while poorly or not activating the lipogenic genes SREBP1 and SCD1 or FASN, respectively.