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Motivation Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. Results We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. Availability and implementation A web tool for Gene2drug is freely available at http://gene2drug.tigem.it. An R package is under development and can be obtained from https://github.com/franapoli/gep2pep. Supplementary information Supplementary data are available at Bioinformatics online.

These are supplementary materials for the paperComputational Drug Repositioning and Elucidation of Mechanism of Action of Compounds against SARS-CoV-2 (preprint available athttps://arxiv.org/abs/2004.07697). Included files: DSEA_screend_drugs.xls Results of Drug Set Enrichment Analysis (http://dsea.tigem.it) on 10 drugs screend against SARS-CoV-2 gene2drug_ACE2TMPRSS2.xls Results of the gene2drug analysis (http://gene2drug.tigem.it) for drugs downregulating ACE2 and TMPRSS2 gene2drug_interactors.xls Results of the gene2drug analysis for drugs down-regulating human SARS-CoV-2 interactors MANTRA_COVID19_Inverse.xlsx Results of the MANTRA analysis (http://mantra.tigem.it)for drugs reverting SARS-CoV-2 induced expression profile PEPs_CMap_msigDB7-covid.zip CMap expression profiles converted to Pathway Expression Profiles using the Gep2pep package (https://bioconductor.org/packages/gep2pep/) and the MSigDB v7 (https://www.gsea-msigdb.org/gsea/msigdb). R data requiringthe gep2pep package.

We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing three-dimensional structural similarities among 5452 compounds can be used to automatically group together drugs with similar scaffolds, physicochemical parameters and mode-of-action. We compared the structural network to a network representing transcriptional similarities among a subset of 1309 drugs for which transcriptional response were available in the Connectivity Map data set. Analysis of structurally similar, but transcriptionally different drugs sharing the same MOA enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability of transcription-based approaches to drug discovery and drug repositioning. Cardiac glycosides exhibited the strongest transcriptional responses with a significant induction of pathways related to epigenetic regulation, which suggests an epigenetic mechanism of action for these drugs. Drug classes with the weakest transcriptional responses tended to induce expression of cytochrome P450 enzymes, hinting at drug-induced drug resistance. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a 'toxic' transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We found that this transcriptional signature is shared by 258 compounds and it is associated to the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Finally, we built a predictive Random Forest model of these 258 compounds based on 128 physicochemical parameters, which should help in the early identification of potentially toxic drug candidates., Systems pharmacology: the chemical-transcriptional landscape of small molecules Transcriptional responses to drug treatment can reveal mechanism of action and off-target effects thus enabling drug repositioning, but only if measured in the appropriate cells at clinically relevant concentrations. A team led by Diego di Bernardo and Diego Medina generated a network representing structural similarities among compounds to automatically group together drugs with similar scaffolds and MOA. By comparing the structural drug network with a transcriptional drug network based on similarities in transcriptional response, the team observed broad differences between the two. This observation led to the identification of a transcriptional signature related lysosomal stress and phospholipidosis, and a physicochemical model to identify such compounds. These results provide general guidelines to prevent erroneous conclusion when using transcriptional responses of small molecules for drug discovery and drug repositioning

// Diego Carrella 1 , Isabella Manni 3 , Barbara Tumaini 1 , Rosanna Dattilo 2 , Federica Papaccio 3 , Margherita Mutarelli 1 , Francesco Sirci 1 , Carla A. Amoreo 4 , Marcella Mottolese 4 , Manuela Iezzi 5 , Laura Ciolli 5 , Valentina Aria 6 , Roberta Bosotti 7 , Antonella Isacchi 7 , Fabrizio Loreni 6 , Alberto Bardelli 8,9 , Vittorio E. Avvedimento 10 , Diego di Bernardo 1,11 and Luca Cardone 3 1 Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy 2 Department of Hematology, Oncology and Molecular Medicine, Biobank Unit, Istituto Superiore di Sanita, Rome, Italy 3 Department of Research, Advanced Diagnostics, and Technological Innovations, Regina Elena National Cancer Institute, Rome, Italy 4 S.C. Anatomia Patologica, Regina Elena National Cancer Institute, Rome, Italy 5 Immuno-Oncology Laboratory, Aging Research Center, G. d’Annunzio University of Chieti, Pescara, Italy 6 Department of Biology, University of Rome Tor Vergata, Rome, Italy 7 Nerviano Medical Sciences SRL, Nerviano, Italy 8 Candiolo Cancer Institute-FPO, IRCCS, Candiolo, Torino, Italy 9 Department of Oncology, University of Torino, Candiolo, Torino, Italy 10 Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Universita degli Studi di Napoli “Federico II”, Naples, Italy 11 Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, Naples, Italy Correspondence to: Luca Cardone, email: // Diego di Bernardo, email: // Keywords : oncogenes, PI3K-dependent pathways, gene expression signatures, drugs network, FDA-approved drugs Received : January 03, 2016 Accepted : August 03, 2016 Published : August 16, 2016 Abstract The discovery of inhibitors for oncogenic signalling pathways remains a key focus in modern oncology, based on personalized and targeted therapeutics. Computational drug repurposing via the analysis of FDA-approved drug network is becoming a very effective approach to identify therapeutic opportunities in cancer and other human diseases. Given that gene expression signatures can be associated with specific oncogenic mutations, we tested whether a “reverse” oncogene-specific signature might assist in the computational repositioning of inhibitors of oncogenic pathways. As a proof of principle, we focused on oncogenic PI3K-dependent signalling, a molecular pathway frequently driving cancer progression as well as raising resistance to anticancer-targeted therapies. We show that implementation of “reverse” oncogenic PI3K-dependent transcriptional signatures combined with interrogation of drug networks identified inhibitors of PI3K-dependent signalling among FDA-approved compounds. This led to repositioning of Niclosamide (Niclo) and Pyrvinium Pamoate (PP), two anthelmintic drugs, as inhibitors of oncogenic PI3K-dependent signalling. Niclo inhibited phosphorylation of P70S6K, while PP inhibited phosphorylation of AKT and P70S6K, which are downstream targets of PI3K. Anthelmintics inhibited oncogenic PI3K-dependent gene expression and showed a cytostatic effect in vitro and in mouse mammary gland. Lastly, PP inhibited the growth of breast cancer cells harbouring PI3K mutations. Our data indicate that drug repositioning by network analysis of oncogene-specific transcriptional signatures is an efficient strategy for identifying oncogenic pathway inhibitors among FDA-approved compounds. We propose that PP and Niclo should be further investigated as potential therapeutics for the treatment of tumors or diseases carrying the constitutive activation of the PI3K/P70S6K signalling axis.

Background Mistrafficking of CFTR protein caused by F508del, the most frequent mutation in cystic fibrosis (CF), can be corrected by cell incubation at low temperature, an effect that may be mediated by altered expression of proteostasis genes. Methods To identify small molecules mimicking low temperature, we compared gene expression profiles of cells kept at 27°C with those previously generated from more than 1300 compounds. The resulting candidates were tested with a functional assay on a bronchial epithelial cell line. Results We found that anti-inflammatory glucocorticoids, such as mometasone, budesonide, and fluticasone, increased mutant CFTR function. However, this activity was not confirmed in primary bronchial epithelial cells. Actually, glucocorticoids enhanced Na + absorption, an effect that could further impair mucociliary clearance in CF airways. Conclusions Our results suggest that rescue of F508del-CFTR by low temperature cannot be easily mimicked by small molecules and that compounds with closer transcriptional and functional effects need to be found.