New Indications For Approved Drugs Research Articles

Repurposing Therapeutic Drugs Complexed to Vanadium in Cancer.

Repurposing drugs by uncovering new indications for approved drugs accelerates the process of establishing new treatments and reduces the high costs of drug discovery and development. Metal complexes with clinically approved drugs allow further opportunities in cancer therapy-many vanadium compounds have previously shown antitumor effects, which makes vanadium a suitable metal to complex with therapeutic drugs, potentially improving their efficacy in cancer treatment. In this review, covering the last 25 years of research in the field, we identified non-oncology-approved drugs suitable as ligands to obtain different vanadium complexes. Metformin-decavanadate, vanadium-bisphosphonates, vanadyl(IV) complexes with non-steroidal anti-inflammatory drugs, and cetirizine and imidazole-based oxidovanadium(IV) complexes, each has a parent drug known to have different medicinal properties and therapeutic indications, and all showed potential as novel anticancer treatments. Nevertheless, the precise mechanisms of action for these vanadium compounds against cancer are still not fully understood.

Репозиционирование лекарств: уроки пандемии COVID-19

We overview the possibilities and limitations of drug repositioning in the context of the COVID-19 pandemic and ways to reduce the new biogenic threats in the future. Drug repositioning — identifying new indications for approved drugs — is a natural prompt response to SARS-CoV-2 / COVID-19 viral infection. The current state of the research and development of drugs for the therapy of COVID-19 using in silico and in vitro methods is considered. In conclusion, it is noted that nowadays, the creation of innovative medicines, despite the success of translational science, takes a lot of time. Therefore, in order to select the most promising pharmaceutical agents, it is essential to integrate and analyze entire available information obtained using in silico, in vitro and in vivo methods.

Identification of prophylactic drugs for oxaliplatin-induced peripheral neuropathy using big data

BackgroundDrug repositioning is a cost-effective method to identify novel disease indications for approved drugs; it requires a shorter developmental period than conventional drug discovery methods. We aimed to identify prophylactic drugs for oxaliplatin-induced peripheral neuropathy by drug repositioning using data from large-scale medical information and life science information databases. MethodsHerein, we analyzed the reported data between 2007 and 2017 retrieved from the FDA’s database of spontaneous adverse event reports (FAERS) and the LINCS database provided by the National Institute of Health. The efficacy of the drug candidates for oxaliplatin-induced peripheral neuropathy obtained from the database analysis was examined using a rat model of peripheral neuropathy. Additionally, we compared the incidence of peripheral neuropathy in patients who received oxaliplatin at the Tokushima University Hospital, Japan. The effects of statins on the animal model were examined in six-week-old male Sprague–Dawley rats and seven or eight-week-old male BALB/C mice. Retrospective medical chart review included clinical data from Tokushima University Hospital from April 2009 to March 2018. ResultsSimvastatin, indicated for dyslipidemia, significantly reduced the severity of peripheral neuropathy and oxaliplatin-induced hyperalgesia. In the nerve tissue of model rats, the mRNA expression of Gstm1 increased with statin administration. A retrospective medical chart review using clinical data revealed that the incidence of peripheral neuropathy decreased with statin use. Conclusion and relevanceThus, drug repositioning using data from large-scale basic and clinical databases enables the discovery of new indications for approved drugs with a high probability of success.

Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.

Drug repurposing improvement using a novel data integration framework based on the drug side effect

The most significant drawback of experimental methods in drug development and discovery is that they are time-consuming and costly. Researches have indicated that designing a new drug from primary stages to its delivery to the consumer market lasts between 10 and 15 years. Moreover, this process costs about 0.8–1.5 billion dollars. Drug repurposing refers to seeking new indications for approved drugs. Recently, some methods have attempted to repurpose drugs based on incorporating computational approaches. In the present research, a method has been proposed for drug repurposing with the aim of integrating diverse and heterogeneous data sources, called DRSE. The proposed method can predict drug-disease associations based on the integration of multiple data sources through a matrix factorization algorithm considering side effect features of the drugs. The experimental results confirmed that the proposed method can improve accuracy of the drug repurposing task. In addition, the AUC and AUPR criteria have been improved by 1.13 and 14.23%, respectively, compared to the state-of-the-art methods. • A framework for drug repurposing by integration of diverse and heterogeneous data sources is proposed. • It predicts drug-disease associations by a matrix factorization algorithm based on side-effect features of drugs. • It uses a Random Walk with Restart (RWR) and feature compacting technique.

Drug repositioning based on individual bi-random walks on a heterogeneous network

BackgroundTraditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for drug repositioning and most of them apply random walk on a heterogeneous network consisted with disease and drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes.ResultsIn this study, we propose a drug repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better drug repositioning performance than other related competitive methods.ConclusionsUsing individual random walk-lengths for different nodes of heterogeneous network indeed boosts the repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.

The prescribable drugs with efficacy in experimental epilepsies (PDE3) database for drug repurposing research in epilepsy.

Current antiepileptic drugs (AEDs) have several shortcomings. For example, they fail to control seizures in 30% of patients. Hence, there is a need to identify new AEDs. Drug repurposing is the discovery of new indications for approved drugs. This drug "recycling" offers the potential of significant savings in the time and cost of drug development. Many drugs licensed for other indications exhibit antiepileptic efficacy in animal models. Our aim was to create a database of "prescribable" drugs, approved for other conditions, with published evidence of efficacy in animal models of epilepsy, and to collate data that would assist in choosing the most promising candidates for drug repurposing. The database was created by the following: (1) computational literature-mining using novel software that identifies Medline abstracts containing the name of a prescribable drug, a rodent model of epilepsy, and a phrase indicating seizure reduction; then (2) crowdsourced manual curation of the identified abstracts. The final database includes 173 drugs and 500 abstracts. It is made freely available at www.liverpool.ac.uk/D3RE/PDE3. The database is reliable: 94% of the included drugs have corroborative evidence of efficacy in animal models (for example, evidence from multiple independent studies). The database includes many drugs that are appealing candidates for repurposing, as they are widely accepted by prescribers and patients-the database includes half of the 20 most commonly prescribed drugs in England-and they target many proteins involved in epilepsy but not targeted by current AEDs. It is important to note that the drugs are of potential relevance to human epilepsy-the database is highly enriched with drugs that target proteins of known causal human epilepsy genes (Fisher's exact test P-value<3×10-5 ). We present data to help prioritize the most promising candidates for repurposing from the database. The PDE3 database is an important new resource for drug repurposing research in epilepsy.

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks.

BackgroundSince traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale.ResultsIn this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.ConclusionsWe proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.

Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs)

BackgroundThe development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of ‘ad hoc’ gene expression data-sets.ResultsBy analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence.ConclusionsOur results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation.

Abstract 2040: Indication switching of the FDA-approved anticancer tyrosine kinase inhibitors to treat metabolic diseases.

Abstract Drug switching is a unique way to identify and extend new indications for approved drugs in drug discovery &amp; development. Tyrosine kinase inhibitors (TKIs) are used in biologically targeted cancer therapy and so far 12 TKIs have been approved by FDA. We hypothesize that these TKIs act on other molecular targets in addition to tyrosine kinases and may manage metabolic diseases given that there is a complex network of kinases that work together to regulate a number of important cellular processes. Employing a comprehensive docking method with our established chemical-protein interactome (CPI) and 11 FDA-approved TKIs, we have discovered301 PDB-deposited proteins corresponding to 353 ligand binding pockets among a total of 1,780 PDB-deposited human protein entries. Notably, sorafenib, dasatinib and crizotinib had a CPI binding score (ZZ_score) of -1.2903, -1.0278 and -1.5384 against histone deacetylase 7A, respectively. In addition, those TKIs achieved high ZZ scores against B-Raf, PPAR and VDR, suggesting a high binding affinity of sorafenib, dasatinib and crizotinib with these proteins. Our preliminary studies have showed that the acetylated-lysine in α-tublin and oncogenic Akt and Raf-signaling was inhibited significantly in human multiple melanoma cells by these TKIs. Interestingly, dasatinib increased VDRE and HDLR luciferase reporter activity in the human lung adenocarcinoma A-549 cells, and dasatinib and crizotinib induced autophage by activated LC3 in vitro. Furthermore, the erlotinib and sorafenib decreased glucose levels in Wistar STZ-D rats. These TKIs are predicted to act on a series of therapeutics targets associated with metabolic diseases (such as PPAR and Sirt1). Collectively, FDA-approved TKIs may be switched to become a “magic bullet” concurrently targeting tyrosine kinase, HDAC, PPAR, VDR and B-Raf, shedding a light for future anti-metabolic disorder and anticancer drug discovery &amp; development. Further validation of additional “hot targets” besides tyrosine kinase such as HDAC, B-Raf, PPAR, Sirt1, Akt and VDR, and in vivo evaluation of anti-metabolic disorder by TKIs are undergoing at our laboratory. These findings suggest that drug switching may represent a new and effective approach to expanding the application of existing drugs. Citation Format: Jiazhi Sun, Lun Yang, Minghua Li, Zhixin Wang, Amy Haynes, Qian Wang, Isaac Raplee, Jean Melchisedek, Anand Prasad, Kevin B. Sneed, Lin He, Shufeng Zhou. Indication switching of the FDA-approved anticancer tyrosine kinase inhibitors to treat metabolic diseases. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2040. doi:10.1158/1538-7445.AM2013-2040

Predicting new indications for approved drugs using a proteochemometric method.

The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching, or simple in silico ligand docking. We now describe a novel rapid computational proteochemometric method called "train, match, fit, streamline" (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology, and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3671 FDA approved drugs across 2335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84%, and 91% for agents ranked in the top 10, 20, 30, and 40, respectively, out of all 3671 drugs. Drugs ranked in the top 1-40 that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an antiparasitic with recently discovered and unexpected anticancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity and angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27 000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets.