Project description:The inability of the adult human heart to regenerate lost or damaged myocardial tissue has created one of the most pressing public health dilemmas due to the devastating impact of heart failure. Our group and others have outlined several regulators of cardiomyocyte mitosis that may impact the regenerative capacity of the adult myocardium in mammals. Recently, we reported that the transcription factors Meis1 and Hoxb13 regulate postnatal cardiomyocyte cell cycle arrest, where concomitant deletion of both genes induced cardiomyocyte proliferation and myocardial regeneration following ischemic injury. These studies suggest that pharmacological targeting Meis1 and Hoxb13 transcriptional activity could be a viable path towards heart regeneration. Therefore, we performed an in-silico screen to identify FDA-approved drugs that can inhibit Meis1 and Hoxb13 transcriptional activity based on the published crystal structure of Meis1 and Hoxb13 bound to DNA. Our screen yielded several candidates based on binding profiles to either Meis1 DNA binding domain, Hoxb13 DNA binding domain, or the interface between Meis1 and Hoxb13, as well as safety and side effect profiles. Out of the shortlist of top hits, paromomycin and neomycin induced proliferation of neonatal rat ventricular myocytes in vitro and displayed dose-dependent inhibition of Meis1 and Hoxb13 transcriptional activity by luciferase assay, and disruption of DNA binding by EMSA. X-ray crystal structure revealed that both paromomycin and neomycin bind to Meis1 near the Hoxb13 interaction site where they interact with 3 out of the 5 Meis1 amino acids that participate in DNA-binding. The crystal structure also demonstrates that both drugs bind at the interface of Meis1 homodimer, therefore placing one of the Meis1 monomers in a position incompatible with DNA-binding. Importantly, administration of paromomycin-neomycin combination by intraperitoneal injection in mice induced cardiomyocyte proliferation, improved left ventricular (LV) systolic function and decreased scar formation in two models of ischemic reperfusion injury in mice. Finally, dual intravenous administration of the paromomycin-neomycin combination in Yorkshire pigs following cardiac ischemia/reperfusion injury induced cardiomyocyte proliferation, improved LV systolic function, and decreased scar formation. Collectively, we identified paromomycin and neomycin as FDA-approved drugs with therapeutic potential for induction of heart regeneration in humans.

Project description:The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

Dataset Information

Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.

Publications

Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets