An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. Results In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. Conclusions The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.

A computational multi-targeting approach for drug repositioning for psoriasis treatment

Background Psoriasis is an autoimmune inflammatory skin disease that affects 0.5–3% of the world’s population and current treatment options are posed with limitations. The reduced risk of failure in clinical trials for repositioned drug candidates and the time and cost-effectiveness has popularized drug reposition and computational methods in the drug research community. Results The current study attempts to reposition approved drugs for the treatment of psoriasis by docking about 2000 approved drug molecules against fifteen selected and validated anti-psoriatic targets. The docking results showed that a good number of the dataset interacted favorably with the targets as most of them had − 11.00 to − 10.00 kcal/mol binding free energies across the targets. The percentage of the dataset with binding affinity higher than the co-crystallized ligands ranged from 34.76% (JAK-3) to 0.73% (Rac-1). It was observed that 12 out of the 0.73% outperformed all the co-crystallized ligands across the 15 studied proteins. All the 12 drugs identified are currently indicated as either antiviral or anticancer drugs and are of purine and pyrimidine nuclei. This is not surprising given that there is similarity in the mechanism of the mentioned diseases. Conclusion This study, therefore, suggests that; antiviral and anticancer drugs could have anti-psoriatic effects, and molecules with purine and pyrimidine structural architecture are likely templates to consider in developing anti-psoriatic agents.

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

Understanding Viral Infection Mechanisms and Patient Symptoms for the Development of COVID-19 Therapeutics

Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become a worldwide pandemic. Symptoms range from mild fever to cough, fatigue, severe pneumonia, acute respiratory distress syndrome (ARDS), and organ failure, with a mortality rate of 2.2%. However, there are no licensed drugs or definitive treatment strategies for patients with severe COVID-19. Only antiviral or anti-inflammatory drugs are used as symptomatic treatments based on clinician experience. Basic medical researchers are also trying to develop COVID-19 therapeutics. However, there is limited systematic information about the pathogenesis of COVID-19 symptoms that cause tissue damage or death and the mechanisms by which the virus infects and replicates in cells. Here, we introduce recent knowledge of time course changes in viral titers, delayed virus clearance, and persistent systemic inflammation in patients with severe COVID-19. Based on the concept of drug reposition, we review which antiviral or anti-inflammatory drugs can effectively treat COVID-19 patients based on progressive symptoms and the mechanisms inhibiting virus infection and replication.

Chemogenomics Mapping of Potential Drugs and Targets for Treatment of Multiple Myeloma

Multiple myeloma (MM) is the second common hematological malignancy affecting about 352,000 worldwide. Some subgroups of MM patients still cannot benefit from the currently available anti-MM drugs and therefore are at high risk of death. The pathological mechanism of MM remains to be unraveled. The identification of a global gene signature for MM might lead toward development of novel diagnostics and therapeutic interventions. Here, we identified common differentially expressed genes (DEGs) shared by 30 MM microarray data sets and compared the common DEGs with those induced by genetic or chemical perturbations. We found some potential therapeutic targets for MM treatment, for example RARA, FGFR1, PML, ROR1, SLAMF7, MTDH and Daxx. as modulating them can reverse the MM-induced gene signature. Based on our analysis results, we also predicted and validated some drug reposition, such as Imatinib, Decitabine, Dexamethasone, Vincristine, Paclitaxel, as well as Bortezomib plus Bafilomycin A1 combination for MM treatment by a literature search, data mining, and in vitro bioassays. This study could provide guidance and indications for the development of MM specific diagnostic biomarkers, indication predictors and therapeutic treatment.

Study of exaptation evidence in clinical research for delivering pharmaceutical innovation

Exaptation is a process that is characterized by the evolution of characteristics, its evolution for other usages, and later coopted for their current role. In the pharmaceutical industry, innovations have high potential in terms of competitive advantage and profit but are also connected with high risks associated with costs, time, and uncertainty. To minimize risks, companies frequently choose the strategy of exaptation in terms of redeveloping compounds for use in a different disease. The intention of this research is to provide analysis and quantification (measurement) of drugs that have been exaptated through the process of drug reposition. Results indicate that on level of 6098 clinical studies Phase I, we have observed a total of 659 drugs/substances (~11%) that have been used in more than one clinical research for the same or different indication showing the level of exaptation use in clinical research.

Are Hsp90 inhibitors good candidates against Covid-19?

: Drug reposition, or repurposing, has become a promising strategy in therapeutics due to its advantages in several aspects of drug therapy. General drug development is expensive and can take more than 10 years to go through the designing, development, and necessary approval steps. However, established drugs have already overcome these steps and thus a potential candidate may be already available decreasing the risks and costs involved. Viruses invade cells, usually provoking biochemical changes, leading to tissue damage, alteration of normal physiological condition in organisms and can even result in death. Inside the cell, the virus finds the machinery necessary for its multiplication, as for instance the protein quality control system, which involves chaperones and Hsps (heat shock proteins) that, in addition to physiological functions, help in the stabilization of viral proteins. Recently, many inhibitors of Hsp90 have been developed as therapeutic strategies against diseases such as the Hsp90 inhibitors used in anticancer therapy. Several shreds of evidence indicate that these inhibitors can also be used as therapeutic strategies against viruses. Therefore, since a drug treatment for COVID-19 is urgently needed, this review aims to discuss the potential use of Hsp90 inhibitors in the treatment of this globally threatening disease.

Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition

Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus’ replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively.

Potential inhibitors of protease 3CLpro virus COVID-19: drug reposition

Pneumonia caused by the COVID-19 virus has led to quick search of drugs that would able to block the spread of this virus. A standard way of drug development is a long process. One approach that can significantly accelerate drug development is drug reposition. In this study a virtual screening of the database of approved drugs has been used for search inhibitors against 3СLpro COVID-19, the main protease of COVID-19. Molecular docking, simulation of molecular dynamics and binding energy estimation by MM-GBSA method allowed to select several compounds for further experimental testing. The most promising drugs are the HIV protease inhibitor Indinavir, the inhibitor of protease hepatitis C Telaprevir, the antiulcer drug Dalargin, and the ErB receptor tyrosine kinase inhibitor Neratinib