Potential role of Drug Repositioning Strategy (DRS) for management of tauopathy

Tauopathy is a term that has been used to represent a pathological condition in which hyperphosphorylated tau protein aggregates in neurons and glia which results in neurodegeneration, synapse loss and dysfunction and cognitive impairments. Recently, drug repositioning strategy (DRS) becomes a promising field and an alternative approach to advancing new treatments from actually developed and FDA approved drugs for an indication other than the indication it was originally intended for. This paradigm provides an advantage because the safety of the candidate compound has already been established, which abolishes the need for further preclinical safety testing and thus substantially reduces the time and cost involved in progressing of clinical trials. In the present review, we focused on correlation between tauopathy and common diseases as type 2 diabetes mellitus and the global virus COVID-19 and how tau pathology can aggravate development of these diseases in addition to how these diseases can be a risk factor for development of tauopathy. Moreover, correlation between COVID-19 and type 2 diabetes mellitus was also discussed. Therefore, repositioning of a drug in the daily clinical practice of patients to manage or prevent two or more diseases at the same time with lower side effects and drug-drug interactions is a promising idea. This review concluded the results of pre-clinical and clinical studies applied on antidiabetics, COVID-19 medications, antihypertensives, antidepressants and cholesterol lowering drugs for possible drug repositioning for management of tauopathy.

Meclozine ameliorates skeletal muscle pathology and increases muscle forces in mdx mice

We screened pre-approved drugs for the survival of the Hu5/KD3 human myogenic progenitors. We found that meclozine, an anti-histamine drug that has long been used for motion sickness, promoted the proliferation and survival of Hu5/KD3 cells. Meclozine increased expression of MyoD, but reduced expression of myosin heavy chain and suppressed myotube formation. Withdrawal of meclozine, however, resumed the ability of Hu5/KD3 cells to differentiate into myotubes. We examined the effects of meclozine on mdx mouse carrying a nonsense mutation in the dystrophin gene and modeling for Duchenne muscular dystrophy. Intragastric administration of meclozine in mdx mouse increased the body weight, the muscle mass in the lower limbs, the cross-sectional area of the paravertebral muscle, and improved exercise performances. Previous reports show that inhibition of phosphorylation of ERK1/2 improves muscle functions in mouse models for Emery-Dreifuss muscular dystrophy and cancer cachexia, as well as in mdx mice. We and others previously showed that meclozine blocks the phosphorylation of ERK1/2 in cultured cells. We currently showed that meclozine decreased phosphorylation of ERK1/2 in muscles in mdx mice but not in wild-type mice. This was likely to be one of the underlying mechanisms of the effects of meclozine on mdx mice.

From GWAS to drug screening: repurposing antipsychotics for glioblastoma

BACKGROUND

Glioblastoma is currently an incurable cancer. Genome-wide association studies have demonstrated that 41 genetic variants are associated with glioblastoma and may provide an option for drug development.

METHODS

We investigated FDA-approved antipsychotics for their potential treatment of glioblastoma based on genome-wide association studies data using a 'pathway/gene-set analysis' approach.

RESULTS

The in-silico screening led to the discovery of 12 candidate drugs. DepMap portal revealed that 42 glioma cell lines show higher sensitivities to 12 candidate drugs than to Temozolomide, the current standard treatment for glioblastoma.

CONCLUSION

In particular, cell lines showed significantly higher sensitivities to Norcyclobenzaprine and Protriptyline which were predicted to bind targets to disrupt a certain molecular function such as DNA repair, response to hormones, or DNA-templated transcription, and may lead to an effect on survival-related pathways including cell cycle arrest, response to ER stress, glucose transport, and regulation of autophagy. However, it is recommended that their mechanism of action and efficacy are further determined.

Drug Repositioning Using Temporal Trajectories of Accompanying Comorbidities in Diabetes Mellitus

BACKGROUND

Most studies of systematic drug repositioning have used drug-oriented data such as chemical structures, gene expression patterns, and adverse effect profiles. As it is often difficult to prove repositioning candidates' effectiveness in real-world clinical settings, we used patient-centered real-world data for screening repositioning candidate drugs for multiple diseases simultaneously, especially for diabetic complications.

METHODS

Using the National Health Insurance Service-National Sample Cohort (2002 to 2013), we analyzed claims data of 43,048 patients with type 2 diabetes mellitus (age ≥40 years). To find repositioning candidate disease-drug pairs, a nested case-control study was used for 29 pairs of diabetic complications and the drugs that met our criteria. To validate this study design, we conducted an external validation for a selected candidate pair using electronic health records.

RESULTS

We found 24 repositioning candidate disease-drug pairs. In the external validation study for the candidate pair cerebral infarction and glycopyrrolate, we found that glycopyrrolate was associated with decreased risk of cerebral infarction (hazard ratio, 0.10; 95% confidence interval, 0.02 to 0.44).

CONCLUSION

To reduce risks of diabetic complications, it would be possible to consider these candidate drugs instead of other drugs, given the same indications. Moreover, this methodology could be applied to diseases other than diabetes to discover their repositioning candidates, thereby offering a new approach to drug repositioning.

DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction

Drug-target interaction (DTI) prediction reduces the cost and time of drug development, and plays a vital role in drug discovery. However, most of research does not fully explore the molecular structures of drug compounds in DTI prediction. To this end, we propose a deep learning model to capture the molecular structure information of drug compounds for DTI prediction. This model utilizes a transformer network incorporating multilayer graph information, which captures the features of a drug's molecular structure so that the interactions between atoms of drug compounds can be explored more deeply. At the same time, a convolutional neural network is employed to capture the local residue information in the target sequence, and effectively extract the feature information of the target. The experiments on the DrugBank dataset showed that the proposed model outperformed previous models based on the structure of target sequences. The results indicate that the improved transformer network fuses the feature information between layers in the graph convolutional neural network and extracts the interaction data for the molecular structure. The drug repositioning experiment on COVID-19 and Alzheimer's disease demonstrated the proposed model's ability to find therapeutic drugs in drug discovery. The code of our model is available at https://github.com/zhangpl109/DeepMGT-DTI.

Evaluating the performance of drug-repurposing technologies

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

Repurposing Pomalidomide as a Neuroprotective Drug: Efficacy in an Alpha-Synuclein-Based Model of Parkinson's Disease

Marketed drugs for Parkinson's disease (PD) treat disease motor symptoms but are ineffective in stopping or slowing disease progression. In the quest of novel pharmacological approaches that may target disease progression, drug-repurposing provides a strategy to accelerate the preclinical and clinical testing of drugs already approved for other medical indications. Here, we targeted the inflammatory component of PD pathology, by testing for the first time the disease-modifying properties of the immunomodulatory imide drug (IMiD) pomalidomide in a translational rat model of PD neuropathology based on the intranigral bilateral infusion of toxic preformed oligomers of human α-synuclein (H-αSynOs). The neuroprotective effect of pomalidomide (20 mg/kg; i.p. three times/week 48 h apart) was tested in the first stage of disease progression by means of a chronic two-month administration, starting 1 month after H-αSynOs infusion, when an already ongoing neuroinflammation is observed. The intracerebral infusion of H-αSynOs induced an impairment in motor and coordination performance that was fully rescued by pomalidomide, as assessed via a battery of motor tests three months after infusion. Moreover, H-αSynOs-infused rats displayed a 40-45% cell loss within the bilateral substantia nigra, as measured by stereological counting of TH + and Nissl-stained neurons, that was largely abolished by pomalidomide. The inflammatory response to H-αSynOs infusion and the pomalidomide treatment was evaluated both in CNS affected areas and peripherally in the serum. A reactive microgliosis, measured as the volume occupied by the microglial marker Iba-1, was present in the substantia nigra three months after H-αSynOs infusion as well as after H-αSynOs plus pomalidomide treatment. However, microglia differed for their phenotype among experimental groups. After H-αSynOs infusion, microglia displayed a proinflammatory profile, producing a large amount of the proinflammatory cytokine TNF-α. In contrast, pomalidomide inhibited the TNF-α overproduction and elevated the anti-inflammatory cytokine IL-10. Moreover, the H-αSynOs infusion induced a systemic inflammation with overproduction of serum proinflammatory cytokines and chemokines, that was largely mitigated by pomalidomide. Results provide evidence of the disease modifying potential of pomalidomide in a neuropathological rodent model of PD and support the repurposing of this drug for clinical testing in PD patients.

Azathioprine with Allopurinol Is a Promising First-Line Therapy for Inflammatory Bowel Diseases

BACKGROUND

Beneficial response to first-line immunosuppressive azathioprine in patients with inflammatory bowel disease (IBD) is low due to high rates of adverse events. Co-administrating allopurinol has been shown to improve tolerability. However, data on this co-therapy as first-line treatment are scarce.

AIM

Retrospective comparison of long-term effectiveness and safety of first-line low-dose azathioprine-allopurinol co-therapy (LDAA) with first-line azathioprine monotherapy (AZAm) in patients with IBD without metabolite monitoring.

METHODS

Clinical benefit was defined as ongoing therapy without initiation of steroids, biologics or surgery. Secondary outcomes included CRP, HBI/SCCAI, steroid withdrawal and adverse events.

RESULTS

In total, 166 LDAA and 118 AZAm patients (median follow-up 25 and 27 months) were evaluated. Clinical benefit was more frequently observed in LDAA patients at 6 months (74% vs. 53%, p = 0.0003), 12 months (54% vs. 37%, p = 0.01) and in the long-term (median 36 months; 37% vs. 24%, p = 0.04). Throughout follow-up, AZAm patients were 60% more likely to fail therapy, due to a higher intolerance rate (45% vs. 26%, p = 0.001). Only 73% of the effective AZA dose was tolerated in AZAm patients, while LDAA could be initiated and maintained at its target dose. Incidence of myelotoxicity and elevated liver enzymes was similar in both cohorts, and both conditions led to LDAA withdrawal in only 2%. Increasing allopurinol from 100 to 200-300 mg/day significantly lowered liver enzymes in 5/6 LDAA patients with hepatotoxicity.

CONCLUSIONS

Our poor AZAm outcomes emphasize that optimization of azathioprine is needed. We demonstrated a long-term safe and more effective profile of first-line LDAA. This co-therapy may therefore be considered standard first-line immunosuppressive.

Drug associations as alternative and complementary therapy for neglected tropical diseases

The present paper aims to establish different treatments for neglected tropical disease by a survey on drug conjugations and possible fixed-dose combinations (FDC) used to obtain alternative, safer and more effective treatments. The source databases used were Science Direct and PubMed/Medline, in the intervals between 2015 and 2021 with the drugs key-words or diseases, like "schistosomiasis", "praziquantel", "malaria", "artesunate", "Chagas' disease", "benznidazole", "filariasis", diethylcarbamazine", "ivermectin", " albendazole". 118 works were the object of intense analysis, other articles and documents were used to increase the quality of the studies, such as consensuses for harmonizing therapeutics and historical articles. As a result, an effective NTD control can be achieved when different public health approaches are combined with interventions guided by the epidemiology of each location and the availability of appropriate measures to detect, prevent and control disease. It was also possible to verify that the FDCs promote a simplification of the therapeutic regimen, which promotes better patient compliance and enables a reduction in the development of parasitic resistance, requiring further studies aimed at resistant strains, since the combined APIs usually act by different mechanisms or at different target sites. In addition to eliminating the process of developing a new drug based on the identification and validation of active compounds, which is a complex, long process and requires a strong long-term investment, other advantages that FDCs have are related to productive gain and gain from the industrial plant, which can favor and encourage the R&D of new FDCs not only for NTDs but also for other diseases that require the use of more than one drug.

Network-Based Approaches for Drug Repositioning

With deep learning creeping up into the ranks of big data, new models based on deep learning and massive data have made great leaps forward rapidly in the field of drug repositioning. However, there is no relevant review to summarize the transformations and development process of models and their data in the field of drug repositioning. Among all the computational methods, network-based methods play an extraordinary role. In view of these circumstances, understanding and comparing existing network-based computational methods applied in drug repositioning will help us recognize the cutting-edge technologies and offer valuable information for relevant researchers. Therefore, in this review, we present an interpretation of the series of important network-based methods applied in drug repositioning, together with their comparisons and development process.

An identification of MARK inhibitors using high throughput MALDI-TOF mass spectrometry

MAP/microtubule affinity-regulating kinases (MARKs) were recently identified as potential drug targets for Alzheimer's disease (AD) due to their role in pathological hyperphosphorylation of tau protein. Hyperphosphorylated tau has decreased affinity for microtubule binding, impairing their stability and associated functions. Destabilization of microtubules in neuronal cells leads to neurodegeneration, and microtubule-unbound tau forms neurofibrillary tangles, one of the primary hallmarks of AD. Many phosphorylation sites of tau protein have been identified, but phosphorylation at Ser²⁶², which occurs in early stages of AD, plays a vital role in the pathological hyperphosphorylation of tau. It has been found that Ser²⁶² is phosphorylated by MARK4, which is currently an intensively studied target for treating Alzheimer's disease and other neurodegenerative diseases. Our present study aimed to develop a high throughput compatible assay to directly detect MARK enzymatic activity using echoacoustic transfer and MALDI-TOF mass spectrometer. We optimized the assay for all four isoforms of MARK and validated its use for identifying potential inhibitors by the screening of 1280 compounds from the LOPAC®1280 International (Library Of Pharmacologically Active Compounds). Six MARK4 inhibitors with IC₅₀ < 1 µM were identified. To demonstrate their therapeutic potential, active compounds were further tested for MARK4 selectivity and ability to cross the blood-brain barrier. Lastly, the molecular docking with the most active inhibitors to predict their interaction with MARK4 was performed.

Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data

We present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell .

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

Enriching limited information on rare diseases from heterogeneous networks for drug repositioning

Background

The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father–son information, so it is extremely difficult to predict drugs for the rare disease.

Method

In this paper, we focus on father–son information mining for the rare disease. We propose GRU-Cooperation-Attention-Network (GCAN) to predict drugs for the rare disease. We construct two heterogeneous networks for information enhancement, one network contains the father-nodes of the rare disease and the other network contains the son-nodes information. To bridge two heterogeneous networks, we set a mapping to connect them. What’s more, we use the biased random walk mechanism to collect the information smoothly from two heterogeneous networks, and employ a cooperation attention mechanism to enhance repositioning ability of the network.

Result

Comparing with traditional methods, GCAN makes full use of father–son information. The experimental results on real drug data from hospitals show that GCAN outperforms state-of-the-art machine learning methods for drug repositioning.

Conclusion

The performance of GCAN for drug repositioning is mainly limited by the insufficient scale and poor quality of the data. In future research work, we will focus on how to utilize more data such as drug molecule information and protein molecule information for the drug repositioning of the rare disease.

Drug repositioning based on gene expression data for human HER2-positive breast cancer

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer represents approximately 15-30% of all invasive breast cancers. Despite the recent advances in therapeutic practices of HER2 subtype, drug resistance and tumor recurrence still have remained as major problems. Drug discovery is a long and difficult process, so the aim of this study is to find potential new application for existing therapeutic agents. Gene expression data for breast invasive carcinoma were retrieved from The Cancer Genome Atlas (TCGA) database. The normal and tumor samples were analyzed using Linear Models for Microarray Data (LIMMA) R package in order to find the differentially expressed genes (DEGs). These genes were used as entry for the library of integrated network-based cellular signatures (LINCS) L1000CDS² software and suggested 24 repurposed drugs. According to the obtained results, some of these drugs including vorinostat, mocetinostat, alvocidib, CGP-60474, BMS-387032, AT-7519, and curcumin have significant functional similarity and structural correlation with FDA-approved breast cancer drugs. Based on the drug-target network, which consisted of the repurposed drugs and their target genes, the aforementioned drugs had the highest degrees. Moreover, the experimental approach verified curcumin as an effective therapeutic agent for HER2 positive breast cancer. Hence, our work suggested that some repurposed drugs based on gene expression data can be noticed as potential drugs for the treatment of HER2-positive breast cancer.

Current status of drug repositioning in hematology

INTRODUCTION

Drug repositioning (DR) is defined as determining new therapeutic applications for existing drugs. This approach is advantageous over de novo drug discovery in accelerating clinical development, in terms of lower costs, a shortened development period, a well-known action mechanism, a feasible dosage, and an acceptable safety profile.

AREAS COVERED

This work was aimed at reviewing agents with successful DR in hematology.

EXPERT OPINION

Thalidomide and plerixafor have been successfully repositioned for treating multiple myeloma and harvesting peripheral blood stem cells, respectively. The former was originally developed as a sedative and the latter as an anti-HIV drug. Currently, the feasibility of repositioning various agents is being explored (e.g. an anti-influenza virus drug oseltamivir for primary immune thrombocytopenia, an anti-HIV drug abacavir for adult T-cell leukemia, and a macrolide antibiotic clarithromycin for multiple myeloma). Furthermore, bosutinib for chronic myeloid leukemia or the antiplatelet drug cilostazol have been suggested to have clinical benefits for the management of amyotrophic lateral sclerosis and ischemic stroke, respectively. To promote DR, effective application of artificial intelligence or stem cell models, comprehensive database construction shared between academia and pharmaceutical companies, suitable handling of drug patents, and wide cooperation in the area of specialty are warranted.

A drug repositioning algorithm based on a deep autoencoder and adaptive fusion

BACKGROUND

Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning.

RESULTS

In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset.

CONCLUSION

The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.

Drug Repositioning With an Anticancer Effect: Contributions to Reduced Cancer Incidence in Susceptible Individuals

Certain diseases and age groups are associated with a higher incidence of cancer. Cancer prevention can be achieved using repositioned drugs that have anticancer ability, thereby reducing the incidence of cancer in susceptible individuals. This implies that the selection of repositioned drugs can have dual benefits: controlling pre-existing diseases and facilitating cancer prevention. This report outlines the rationale underlying drug repositioning for medications with an anticancer effect and discusses its advantages. We discuss repositioned drugs with anticancer effects that may contribute to cancer prevention in susceptible individuals and the general population with temporary, treatable conditions. The discussion of drug repositioning in this review should facilitate the initiation of clinical trials and lead to therapeutic application of such drugs to reduce the incidence of cancer in susceptible individuals.

Deep learning in target prediction and drug repositioning: Recent advances and challenges

Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.

Imidazoles and benzimidazoles as putative inhibitors of SARS-CoV-2 B.1.1.7 (Alpha) and P.1 (Gamma) variant spike glycoproteins: A computational approach

COVID-19 is an unprecedented pandemic threatening global health, and variants were discovered rapidly after the pandemic. The two variants, namely the SARS-CoV-2 B.1.1.7 (Alpha) and P.1 (Gamma), were formed by the mutations in the receptor binding domain of spike glycoprotein (SGP). These two variants are known to possess a high binding affinity with the angiotensin-converting enzyme 2. Amidst the rapid spread of these mutant strains, research and development of novel molecules become tedious and labour-intensive. Imidazole and benzimidazole scaffolds were selected in this study based on their unique structural features and electron-rich environment, resulting in increased affinity against a variety of therapeutic targets. In the current study, imidazole- and benzimidazole-based anti-parasitic drugs are repurposed against SARS-CoV-2 Alpha and Gamma variant spike glycoproteins using computational strategies. Out of the screened 15 molecules, flubendazole and mebendazole have exhibited promising binding features to the two receptors (PDB ID: 7NEH and 7NXC), as evidenced by their glide score and binding free energy. The results are compared with that of the two standard drugs, remdesivir and hydroxychloroquine. Flubendazole and mebendazole have become convenient treatment options against mutant lineages of SARS-CoV-2. The edge of the flubendazole was further established by its stability in MD simulation conducted for 100 ns employing GROMACS software. Further, in vitro and in vivo studies are essential to understand, if flubendazole and mebendazole indeed hold the promise to manage SARS-CoV-2 mutant stains.

SARS-CoV-2 spike protein and RNA dependent RNA polymerase as targets for drug and vaccine development: A review

The present pandemic has posed a crisis to the economy of the world and the health sector. Therefore, the race to expand research to understand some good molecular targets for vaccine and therapeutic development for SARS-CoV-2 is inevitable. The newly discovered coronavirus 2019 (COVID-19) is a positive sense, single-stranded RNA, and enveloped virus, assigned to the beta CoV genus. The virus (SARS-CoV-2) is more infectious than the previously detected coronaviruses (MERS and SARS). Findings from many studies have revealed that S protein and RdRp are good targets for drug repositioning, novel therapeutic development (antibodies and small molecule drugs), and vaccine discovery. Therapeutics such as chloroquine, convalescent plasma, monoclonal antibodies, spike binding peptides, and small molecules could alter the ability of S protein to bind to the ACE-2 receptor, and drugs such as remdesivir (targeting SARS-CoV-2 RdRp), favipir, and emetine could prevent SASR-CoV-2 RNA synthesis. The novel vaccines such as mRNA1273 (Moderna), 3LNP-mRNAs (Pfizer/BioNTech), and ChAdOx1-S (University of Oxford/Astra Zeneca) targeting S protein have proven to be effective in combating the present pandemic. Further exploration of the potential of S protein and RdRp is crucial in fighting the present pandemic.

A network representation approach for COVID-19 drug recommendation

The coronavirus disease 2019 (COVID-19) has outbreak since early December 2019, and COVID-19 has caused over 100 million cases and 2 million deaths around the world. After one year of the COVID-19 outbreak, there is no certain and approve medicine against it. Drug repositioning has become one line of scientific research that is being pursued to develop an effective drug. However, due to the lack of COVID-19 data, there is still no specific drug repositioning targeting the COVID-19. In this paper, we propose a framework for COVID-19 drug repositioning. This framework has several advantages that can be exploited: one is that a local graph aggregating representation is used across a heterogeneous network to address the data sparsity problem; another is the multi-hop neighbors of the heterogeneous graph are aggregated to recall as many COVID-19 potential drugs as possible. Our experimental results show that our COVDR framework performs significantly better than baseline methods, and the docking simulation verifies that our three potential drugs have the ability to against COVID-19 disease.

DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning

Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug-target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

Identifying FDA-approved drugs with multimodal properties against COVID-19 using a data-driven approach and a lung organoid model of SARS-CoV-2 entry

BACKGROUND

Vaccination programs have been launched worldwide to halt the spread of COVID-19. However, the identification of existing, safe compounds with combined treatment and prophylactic properties would be beneficial to individuals who are waiting to be vaccinated, particularly in less economically developed countries, where vaccine availability may be initially limited.

METHODS

We used a data-driven approach, combining results from the screening of a large transcriptomic database (L1000) and molecular docking analyses, with in vitro tests using a lung organoid model of SARS-CoV-2 entry, to identify drugs with putative multimodal properties against COVID-19.

RESULTS

Out of thousands of FDA-approved drugs considered, we observed that atorvastatin was the most promising candidate, as its effects negatively correlated with the transcriptional changes associated with infection. Atorvastatin was further predicted to bind to SARS-CoV-2's main protease and RNA-dependent RNA polymerase, and was shown to inhibit viral entry in our lung organoid model.

CONCLUSIONS

Small clinical studies reported that general statin use, and specifically, atorvastatin use, are associated with protective effects against COVID-19. Our study corroborrates these findings and supports the investigation of atorvastatin in larger clinical studies. Ultimately, our framework demonstrates one promising way to fast-track the identification of compounds for COVID-19, which could similarly be applied when tackling future pandemics.

Relation extraction from DailyMed structured product labels by optimally combining crowd, experts and machines

The effectiveness of machine learning models to provide accurate and consistent results in drug discovery and clinical decision support is strongly dependent on the quality of the data used. However, substantive amounts of open data that drive drug discovery suffer from a number of issues including inconsistent representation, inaccurate reporting, and incomplete context. For example, databases of FDA-approved drug indications used in computational drug repositioning studies do not distinguish between treatments that simply offer symptomatic relief from those that target the underlying pathology. Moreover, drug indication sources often lack proper provenance and have little overlap. Consequently, new predictions can be of poor quality as they offer little in the way of new insights. Hence, work remains to be done to establish higher quality databases of drug indications that are suitable for use in drug discovery and repositioning studies. Here, we report on the combination of weak supervision (i.e., programmatic labeling and crowdsourcing) and deep learning methods for relation extraction from DailyMed text to create a higher quality drug-disease relation dataset. The generated drug-disease relation data shows a high overlap with DrugCentral, a manually curated dataset. Using this dataset, we constructed a machine learning model to classify relations between drugs and diseases from text into four categories; treatment, symptomatic relief, contradiction, and effect, exhibiting an improvement of 15.5% with Bi-LSTM (F1 score of 71.8%) over the best performing discrete method. Access to high quality data is crucial to building accurate and reliable drug repurposing prediction models. Our work suggests how the combination of crowds, experts, and machine learning methods can go hand-in-hand to improve datasets and predictive models.

Lurasidone Sensitizes Cancer Cells to Osimertinib by Inducing Autophagy and Reduction of Survivin

BACKGROUND/AIM

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are key drugs in cancer treatment due to their minor adverse effects and outstanding anticancer effects. However, drugs for overcoming EGFR-TKI resistance are not in clinical use so far. Therefore, to overcome resistance, we focused on lurasidone, a new antipsychotic drug, due to its mild adverse effect profile from the viewpoint of drug repositioning.

MATERIALS AND METHODS

We explored the effects of lurasidone alone or in combination with EGFR-TKI on the growth of osimertinib-resistant cancer cells the anti-apoptotic marker expression such as survivin, and autophagy levels by LC-3B expression.

RESULTS

Within a non-toxic concentration range in normal cells, lurasidone and osimertinib combination therapy showed a growth-inhibitory effect in osimertinib-resistant cancer cells in vitro and in vivo. Furthermore, lurasidone decreased survivin expression and mildly induced autophagy.

CONCLUSION

Lurasidone may increase the sensitivity to osimertinib in osimertinib-resistant cancer cells in drug repurposing.

A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

Drug repositioning for anti-tuberculosis drugs: an in silico polypharmacology approach

Development of potential antitubercular molecules is a challenging task due to the rapidly emerging drug-resistant strains of Mycobacterium tuberculosis (M.tb). Structure-based approaches hold greater benefit in identifying compounds/drugs with desired polypharmacological profiles. These methods can be employed based on the knowledge of protein binding sites to identify the complementary ligands. In this study, polypharmacology guided computational drug repurposing approach was applied to identify potential antitubercular drugs. 20 important druggable protein targets in M.tb were considered from the target library of Molecular Property Diagnostic Suite-Tuberculosis (MPDS^TB- http://mpds.neist.res.in:8084 ) for virtual screening. FDA approved drugs were collected, preprocessed and docked in the active sites of the 20 M.tb targets. The top 300 drug molecules from each target (20 × 300) were filtered-in and subsequently screened for possible antitubercular and antimycobacterial activity using PASS tool. Using this approach, 34 drugs with predicted antitubercular and anti-mycobacterial activity were identified along with good binding affinity against multiple M.tb targets. Interestingly, 21 out of the 34 identified drugs are antibiotics while 4 drug molecules (nitrofural, stavudine, quinine and quinidine) are non-antibiotics showing promising predicted antitubercular activity. Most of these molecules have the similar privileged antimycobacterial drugs scaffold. Further drug likeness properties were calculated to get deeper insights to M.tb lead molecules. Interestingly, it was also observed that the drugs identified from the study are under different stages of drug discovery (i.e., in vitro, clinical trials) for the effective treatment of various diseases including cancer, degenerative diseases, dengue virus infection, tuberculosis, etc. Krasavin et al., 2017 synthesized nitrofuran analogues with appreciable MICs (22-23 µM) against M.tb H37Rv. These experiments further add to the credibility of the drugs identified in this study (TB).

SANE: A sequence combined attentive network embedding model for COVID-19 drug repositioning

The COVID-19 has now spread all over the world and causes a huge burden for public health and world economy. Drug repositioning has become a promising treatment strategy in COVID-19 crisis because it can shorten drug development process, reduce pharmaceutical costs and reposition approval drugs. Existing computational methods only focus on single information, such as drug and virus similarity or drug-virus network feature, which is not sufficient to predict potential drugs. In this paper, a sequence combined attentive network embedding model SANE is proposed for identifying drugs based on sequence features and network features. On the one hand, drug SMILES and virus sequence features are extracted by encoder-decoder in SANE as node initial embedding in drug-virus network. On the other hand, SANE obtains fields for each node by attention-based Depth-First-Search (DFS) to reduce noises and improve efficiency in representation learning and adopts a bottom-up aggregation strategy to learn node network representation from selected fields. Finally, a forward neural network is used for classifying. Experiment results show that SANE has achieved the performance with 81.98% accuracy and 0.8961 AUC value and outperformed state-of-the-art baselines. Further case study on COVID-19 indicates that SANE has a strong predictive ability since 25 of the top 40 (62.5%) drugs are verified by valuable dataset and literatures. Therefore, SANE is powerful to reposition drugs for COVID-19 and provides a new perspective for drug repositioning.

Computational repurposing of tamibarotene against triple mutant variant of SARS-CoV-2

The outbreak of the triple mutant strain of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) was more virulent and pathogenic than its original strain. The viral triple mutant strain of SARS-COV-2 is extremely adaptive and increases penetrability into the host. The triple mutant viral strain was first reported in Brazil and South Africa and then communicated to different countries responsible for the second wave of the coronavirus disease (COVID-19) global pandemic with a high mortality rate. The reported genomic mutations are responsible for the alterations in the viral functional and structural proteins, causing the ineffectiveness of the existing antiviral therapy targeting these proteins. Thus, in current research, molecular docking simulation-based virtual screening of a ligand library consisting of FDA-approved existing drugs followed by molecular dynamics simulation-based validation of leads was performed to develop a potent inhibitor molecule for the triple mutant viral strain SARS-CoV-2. Based on the safety profile, tamibarotene was selected as a safe and effective drug candidate for developing therapy against the triple mutant viral spike protein of SARS-CoV-2.

A Novel Drug Repositioning Approach Based on Integrative Multiple Similarity Measures

BACKGROUND

Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development.

METHODS

In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology similarity for drug and disease. Third, a similarity integration and adjusting process is further conducted to obtain more comprehensive drug and disease similarity measure, respectively.

RESULTS

On this basis, a Bi-random walk algorithm is implemented in the constructed heterogeneous network to rank diseases for each drug. Compared with other approaches, the proposed DR_IMSM can achieve superior performance in terms of AUC on the gold standard datasets. Case studies further confirm the practical significance of DR_IMSM.

Effects of Naodesheng tablets on amyloid beta-induced dysfunction: A traditional Chinese herbal formula with novel therapeutic potential in Alzheimer's disease revealed by systems pharmacology

Naodesheng (NDS) tablets have been widely used to treat ischemic stroke clinically. NDS relieves neurological function impairment and improve learning and memory in rats with focal cerebral ischemia, suggesting that NDS has potential for Alzheimer's disease (AD) treatment. However, there are no studies about its effective material basis and possible mechanisms. In this study, a systems pharmacology method was applied to reveal the potential molecular mechanism of NDS in the treatment of AD. First, we obtained 360 NDS candidate constituents through ADMET filter analysis. Then, 115 AD-related targets were uncovered by pharmacophore model prediction via mapping the predicted targets against AD-related proteins. In addition, compound-target and target-function networks were established to suggest potential synergistic effects among the candidate constituents. Furthermore, potential targets regulated by NDS were integrated into AD-related pathways to demonstrate the therapeutic mechanism of NDS in AD treatment. Subsequently, a validation experiment proved the therapeutic effect of NDS on cognitive dysfunction in rats with intracerebroventricular injection of Aβ. We found that administration of NDS tablets regulates β-amyloid metabolism, improves synaptic plasticity, inhibits neuroinflammation and improves learning and memory function. In conclusion, this is the first study to provide a comprehensive systems pharmacology approach to elucidate the potential therapeutic mechanism of NDS tablets for AD treatment. We suggest that the protective effects of NDS in neurodegenerative conditions could be partly attributed to its role in improving synaptic plasticity and inhibiting neuroinflammation via NF-κB signaling pathway inhibition and cAMP/PKA/CREB signaling pathway activation.

Intuitive repositioning of an anti-depressant drug in combination with tivozanib: precision medicine for breast cancer therapy

Despite the existing therapies and lack of receptors such as HER-2, estrogen receptor and progesterone receptor, triple-negative breast cancer is one of the most aggressive subtypes of breast cancer. TNBCs are known for their highly aggressive metastatic behavior and typically migrate to brain and bone for secondary site propagation. Many diseases share similar molecular pathology exposing new avenues in molecular signaling for engendering innovative therapies. Generation of newer therapies and novel drugs are time consuming associated with very high resources. In order to provide personalized or precision medicine, drug repositioning will contribute in a cost-effective manner. In our study, we have repurposed and used a neoteric combination of two drug molecules namely, fluvoxamine and tivozanib, to target triple-negative breast cancer growth and progression. Our combination regime significantly targets two diverse but significant pathways in TNBCs. Subsequent analysis on migratory, invasive, and angiogenic properties showed the significance of our repurposed drug combination. Molecular array data resulted in identifying the specific and key players participating in cancer progression when the drug combination was used. The innovative combination of fluvoxamine and tivozanib reiterates the use of drug repositioning for precision medicine and subsequent companion diagnostic development.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

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.

Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children

Identification of exact causative genes is important for in silico drug repositioning based on drug-gene-disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains three steps: network-specific core gene (NCG) identification, potential therapeutic drug repositioning, and candidate drug validation. First, through the analysis of transcriptome data for 178 brain tissues, gene network analysis identified 365 NCGs in 18 coexpression modules that were significantly correlated with ASD. Second, we evaluated two proposed drug repositioning methods. In one novel approach (dtGSEA), we used the NCGs to probe drug-gene interaction data and identified 35 candidate drugs. In another approach, we compared NCG expression patterns with drug-induced transcriptome data from the Connectivity Map database and found 46 candidate drugs. Third, we validated the candidate drugs using an in-house mental diseases and compounds knowledge graph (MCKG) that contained 7509 compounds, 505 mental diseases, and 123,890 edges. We found a total of 42 candidate drugs that were associated with mental illness, among which 10 drugs (baclofen, sulpiride, estradiol, entinostat, everolimus, fluvoxamine, curcumin, calcitriol, metronidazole, and zinc) were postulated to be associated with ASD. This study proposes a powerful network-based drug repositioning framework and also provides candidate drugs as well as potential drug targets for the subsequent development of ASD therapeutic drugs.

In-silico screening for identification of potential inhibitors against SARS-CoV-2 transmembrane serine protease 2 (TMPRSS2)

A new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a respiratory infection out broke in December 2019 in Wuhan, Hubei province, China, resulted in pandemic conditions worldwide. COVID-19 spread swiftly around the world over with an alert of an emergency for an adequate drug. Therefore, in this research, we repurposed the FDA-approved medicines to find the prominent drug used to cure the COVID infected patients. We performed homology modeling of the transmembrane serine protease 2 (TMPRSS2), responsible for the viral entry. The prediction of the transmembrane region and the Conserved Domain in TMPRSS2 protein was made for docking. 4182 FDA-approved compounds from the ZINC database were downloaded and used for the calculation of physicochemical properties. Two thousand eight hundred fifteen screened compounds were used for molecular docking against the modelled protein structure. From which top hit compounds based on binding energy were extracted. At 1st site pose, ZINC3830554 showed the highest binding energy -12.91kcal/mol by forming Salt Bridge at LYS143, Hydrogen bond at ALA8, VAL45, HIS47, SER142, ASN277, ASN359, and TRP363. The hydrophobic Interactions at PHE3, LEU4, ALA7, ALA8, ALA139, PRO197, and PHE266. In the 2nd site pose, ZINC203686879 shows the highest binding energy (-12.56 kcal/mol) and forms a hydrophobic interaction with VAL187, VAL189, HIS205, LYS301, GLN347, TRP370 and hydrogen bond was at GLY300, THR302, GLN347, SER350 residues. These hit compounds were subjected to stability checks between the protein-ligand complex through the dynamics simulation (MD), and binding free energy was calculated through the Molecular Mechanics energies combined with Poisson-Boltzmann (MM/PBSA) method. We hope that hit compounds would be an efficient inhibitor that can block the TMPRSS2 activity and resist the entry of the SARS-CoV-2 virus into targeted human cells by reducing the virus's infectivity and transmissibility.

Drug Repurposing: A Network-based Approach to Amyotrophic Lateral Sclerosis

The continuous adherence to the conventional "one target, one drug" paradigm has failed so far to provide effective therapeutic solutions for heterogeneous and multifactorial diseases as amyotrophic lateral sclerosis (ALS), a rare progressive and chronic, debilitating neurological disease for which no cure is available. The present study is aimed at finding innovative solutions and paradigms for therapy in ALS pathogenesis, by exploiting new insights from Network Medicine and drug repurposing strategies. To identify new drug-ALS disease associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the proximity of disease-associated genes to drug targets in the human interactome. We prioritized 403 SAveRUNNER-predicted drugs according to decreasing values of network similarity with ALS. Among catecholamine, dopamine, serotonin, histamine, and GABA receptor modulators, as well as angiotensin-converting enzymes, cyclooxygenase isozymes, and serotonin transporter inhibitors, we found some interesting no customary ALS drugs, including amoxapine, clomipramine, mianserin, and modafinil. Furthermore, we strengthened the SAveRUNNER predictions by a gene set enrichment analysis that confirmed modafinil as a drug with the highest score among the 121 identified drugs with a score > 0. Our results contribute to gathering further proofs of innovative solutions for therapy in ALS pathogenesis.

Prospect of Anterior Gradient 2 homodimer inhibition via repurposing FDA-approved drugs using structure-based virtual screening

Anterior Gradient 2 (AGR2) has recently been reported as a tumor biomarker in various cancers, i.e., breast, prostate and lung cancer. Predominantly, AGR2 exists as a homodimer via a dimerization domain (E60-K64); after it is self-dimerized, it helps FGF2 and VEGF to homo-dimerize and promotes the angiogenesis and the invasion of vascular endothelial cells and fibroblasts. Up till now, no small molecule has been discovered to inhibit the AGR2-AGR2 homodimer. Therefore, the present study was performed to prepare a validated 3D structure of AGR2 by homology modeling and discover a small molecule by screening the FDA-approved drugs library on AGR2 homodimer as a target protein. Thirteen different homology models of AGR2 were generated based on different templates which were narrowed down to 5 quality models sorted by their overall Z-scores. The top homology model based on PDB ID = 3PH9 was selected having the best Z-score and was further assessed by Verify-3D, ERRAT and RAMPAGE analysis. Structure-based virtual screening narrowed down the large library of FDA-approved drugs to ten potential AGR2-AGR2 homodimer inhibitors having FRED score lower than - 7.8 kcal/mol in which the top 5 drugs' binding stability was counter-validated by molecular dynamic simulation. To sum up, the present study prepared a validated 3D structure of AGR2 and, for the first time reported the discovery of 5 FDA-approved drugs to inhibit AGR2-AGR2 homodimer by using structure-based virtual screening. Moreover, the binding of the top 5 hits with AGR2 was also validated by molecular dynamic simulation. A validated 3D structure of Anterior Gradient 2 (AGR2) was prepared by homology modeling, which was used in virtual screening of FDA-approved drugs library for the discovery of prospective inhibitors of AGR2-AGR2 homodimer.

[Actual use of direct oral anticoagulants in venous thromboembolic disease]

Direct oral anticoagulants recently became the first-line choice for anticoagulation in venous thromboembolic disease. Many studies have shown its non-inferiority regarding the risk of thromboembolic recurrence compared to anti-vitamin K without increasing the risk of bleeding in the general population. However, specific populations such as patients with cancer, patients with kidney failure, patients with constitutional thrombophilia, elderly patients, or patients with extreme weight are at risk of intolerance to the use of direct oral anticoagulants. Precautions in use may be necessary as discussed in recently published guidelines about antiphospholipid syndrome. This review aims to list the main clinical trials investigating direct oral anticoagulants in venous thromboembolic disease in the general population and populations at risk, as well as to provide an update on current international and French guidelines.

Proteomics based drug repositioning applied to improve in vitro fertilization implantation: an artificial intelligence model

Embryo implantation is one of the most inefficient steps in assisted reproduction, so the identifying drugs with a potential clinical application to improve it has a strong interest. This work applies artificial intelligence and systems biology-based mathematical modeling strategies to unveil potential treatments by computationally analyzing and integrating available molecular and clinical data from patients. The mathematical models of embryo implantation computationally generated here simulate the molecular networks underneath this biological process. Once generated, these models were analyzed in order to identify potential repositioned drugs (drugs already used for other indications) able to improve embryo implantation by modulating the molecular pathways involved. Interestingly, the repositioning analysis has identified drugs considering two endpoints: (1) drugs able to modulate the activity of proteins whose role in embryo implantation is already bibliographically acknowledged, and (2) drugs that modulate key proteins in embryo implantation previously predicted through a mechanistic analysis of the mathematical models. This second approach increases the scope open for examination and potential novelty of the repositioning strategy. As a result, a list of 23 drug candidates to improve embryo implantation after IVF was identified by the mathematical models. This list includes many of the compounds already tested for this purpose, which reinforces the predictive capacity of our approach, together with novel repositioned candidates (e.g., Infliximab, Polaprezinc, and Amrinone). In conclusion, the present study exploits existing molecular and clinical information to offer new hypotheses regarding molecular mechanisms in embryo implantation and therapeutic candidates to improve it. This information will be very useful to guide future research.Abbreviations: IVF: in vitro fertilization; EI: Embryo implantation; TPMS: Therapeutic Performance Mapping System; MM: mathematical models; ANN: Artificial Neuronal Networks; TNFα: tumour necrosis factor factor-alpha; HSPs: heat shock proteins; VEGF: vascular endothelial growth factor; PPARA: peroxisome proliferator activated receptor-α PXR: pregnane X receptor; TTR: transthyretin; BED: Biological Effectors Database; MLP: multilayer perceptron.

Structural binding site comparisons reveal Crizotinib as a novel LRRK2 inhibitor

Mutations in leucine-rich repeat kinase 2 (LRRK2) are a frequent cause of autosomal dominant Parkinson’s disease (PD) and have been associated with familial and sporadic PD. Reducing the kinase activity of LRRK2 is a promising therapeutic strategy since pathogenic mutations increase the kinase activity. Several small-molecule LRRK2 inhibitors are currently under investigation for the treatment of PD. However, drug discovery and development are always accompanied by high costs and a risk of late failure. The use of already approved drugs for a new indication, which is known as drug repositioning, can reduce the cost and risk.

In this study, we applied a structure-based drug repositioning approach to identify new LRRK2 inhibitors that are already approved for a different indication. In a large-scale structure-based screening, we compared the protein–ligand interaction patterns of known LRRK2 inhibitors with protein–ligand complexes in the PDB. The screening yielded 6 drug repositioning candidates. Two of these candidates, Sunitinib and Crizotinib, demonstrated an inhibition potency (IC50) and binding affinity (K_d) in the nanomolar to micromolar range. While Sunitinib has already been known to inhibit LRRK2, Crizotinib is a novel LRRK2 binder.

Our results underscore the potential of structure-based methods for drug discovery and development. In light of the recent breakthroughs in cryo-electron microscopy and structure prediction, we believe that structure-based approaches like ours will grow in importance.

In silico drug repositioning using deep learning and comprehensive similarity measures

BACKGROUND

Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized.

METHODS

In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions.

RESULTS

The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out.

CONCLUSION

The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

Drug Repositioning for the Treatment of Hematologic Disease: Limits, Challenges and Future Perspectives

Drug repositioning is a strategy to identify new uses for approved or investigational drugs that are used off-label outside the scope of the original medical indication. In this review, we report the most relevant studies about drug repositioning in hematology, reporting the signalling pathways and molecular targets of these drugs, and describing the biological mechanisms which are responsible for their anticancer effects. Although the majority of studies on drug repositioning in hematology concern acute myeloid leukemia and multiple myeloma, numerous studies are present in the literature on the possibility of using these drugs also in other hematological diseases, such as acute lymphoblastic leukemia, chronic myeloid leukemia, and lymphomas. Numerous anti-infectious drugs and chemical entities used for the therapy of neurological or endocrine diseases, oral antidiabetics, statins and medications used to treat high blood pressure and heart failure, bisphosphonate and natural substance such as artemisin and curcumin, have found a place in the treatment of hematological diseases. Moreover, several molecules drastically reversed the resistance of the tumor cells to the chemotherapeutic drugs both in vitro and in vivo.

Anticancer drugs repurposed for Alzheimer's disease: a systematic review

Background

The relationship between cancer and dementia is triggering growing research interest. Several preclinical studies have provided the biological rationale for the repurposing of specific anticancer agents in Alzheimer’s disease (AD), and a growing number of research protocols are testing their efficacy and safety/tolerability in patients with AD.

Methods

The aim of the present systematic review was to provide an overview on the repurposing of approved anticancer drugs in clinical trials for AD by considering both ongoing and completed research protocols in all phases. In parallel, a systematic literature review was conducted on PubMed, ISI Web, and the Cochrane Library to identify published clinical studies on repurposed anticancer agents in AD.

Results

Based on a structured search on the ClinicalTrials.gov and the EudraCT databases, we identified 13 clinical trials testing 11 different approved anticancer agents (five tyrosine kinase inhibitors, two retinoid X receptor agonists, two immunomodulatory agents, one histone deacetylase inhibitor, and one monoclonal antibody) in the AD continuum. The systematic literature search led to the identification of five published studies (one phase I, three phase II, and one phase IIb/III) reporting the effects of antitumoral treatments in patients with mild cognitive impairment or AD dementia. The clinical findings and the methodological characteristics of these studies are described and discussed.

Conclusion

Anticancer agents are triggering growing interest in the context of repurposed therapies in AD. Several clinical trials are underway, and data are expected to be available in the near future. To date, data emerging from published clinical studies are controversial. The promising results emerging from preclinical studies and identified research protocols should be confirmed and extended by larger, adequately designed, and high-quality clinical trials.

Anthelmintics for drug repurposing: Opportunities and challenges

Drug repositioning is defined as a process to identify a new application for drugs. This approach is critical as it takes advantage of well-known pharmacokinetics, pharmacodynamics, and toxicity profiles of the drugs; thus, the chance of their future failure decreases, and the cost of their development and the required time for their approval are reduced. Anthelmintics, which are antiparasitic drugs, have recently demonstrated promising anticancer effects in vitro and in vivo. This literature review focuses on the potential of anthelmintics for repositioning in the treatment of cancers. It also discusses their pharmacokinetics and pharmacodynamics as antiparasitic drugs, proposed anticancer mechanisms, present development conditions, challenges in cancer therapy, and strategies to overcome these challenges.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

Examination of the antiepileptic effects of valacyclovir using kindling mice- search for novel antiepileptic agents by drug repositioning using a large medical information database

Despite the availability of more than 20 clinical antiepileptic drugs, approximately 30% of patients with epilepsy do not respond to antiepileptic drug treatment. Therefore, it is important to develop antiepileptic products that function via novel mechanisms. In the present study, we evaluated data from one of the largest global databases to identify drugs with antiepileptic effects, and subsequently attempted to understand the effect of the combination of antiepileptic drugs and valacyclovir in epileptic seizures using a kindling model. To induce kindling in mice, pentylenetetrazol at a dose of 40 mg/kg was administered once every 48 h. Valacyclovir was orally administered 30 min before antiepileptic drug injection in kindled mice, and behavioral seizures were monitored for 20 min following pentylenetetrazol administration. Additionally, c-Fos expression in the hippocampal dentate gyrus was measured in kindled mice. Valacyclovir showed inhibitory effects on pentylenetetrazol-induced kindled seizures. In addition, simultaneous use of levetiracetam and valacyclovir caused more potent inhibition of seizure activity, and neither valproic acid nor diazepam augmented the anti-seizure effect in kindled mice. Furthermore, kindled mice showed increased c-Fos levels in the dentate gyrus. The increase in c-Fos expression was significantly inhibited by the simultaneous use of levetiracetam and valacyclovir. The findings of the present study indicate that a combination of levetiracetam and valacyclovir had possible anticonvulsive effects on pentylenetetrazol-induced kindled epileptic seizures. These results suggest that valacyclovir may have an antiseizure effect in patients with epilepsy.

Acarbose presents in vitro and in vivo antileishmanial activity against Leishmania infantum and is a promising therapeutic candidate against visceral leishmaniasis

Treatment against visceral leishmaniasis (VL) is mainly hampered by drug toxicity, long treatment regimens and/or high costs. Thus, the identification of novel and low-cost antileishmanial agents is urgent. Acarbose (ACA) is a specific inhibitor of glucosidase-like proteins, which has been used for treating diabetes. In the present study, we show that this molecule also presents in vitro and in vivo specific antileishmanial activity against Leishmania infantum. Results showed an in vitro direct action against L. infantum promastigotes and amastigotes, and low toxicity to mammalian cells. In addition, in vivo experiments performed using free ACA or incorporated in a Pluronic^® F127-based polymeric micelle system called ACA/Mic proved effective for the treatment of L. infantum-infected BALB/c mice. Treated animals presented significant reductions in the parasite load in their spleens, livers, bone marrows and draining lymph nodes when compared to the controls, as well as the development of antileishmanial Th1-type humoral and cellular responses based on high levels of IFN-γ, IL-12, TNF-α, GM-CSF, nitrite and IgG2a isotype antibodies. In addition, ACA or ACA-treated animals suffered from low organ toxicity. Treatment with ACA/Mic outperformed treatments using either Miltefosine or free ACA based on parasitological and immunological evaluations performed one and 15 days post-therapy. In conclusion, data suggest that the ACA/Mic is a potential therapeutic agent against L. infantum and merits further consideration for VL treatment.

In vitro and in vivo antileishmanial activity of β-acetyl-digitoxin, a cardenolide of Digitalis lanata potentially useful to treat visceral leishmaniasis

Current treatments of visceral leishmaniasis face limitations due to drug side effects and/or high cost, along with the emergence of parasite resistance. Novel and low-cost antileishmanial agents are therefore required. We report herein the antileishmanial activity of β-acetyl-digitoxin (b-AD), a cardenolide isolated from Digitalis lanata leaves, assayed in vitro and in vivo against Leishmania infantum. Results showed direct action of b-AD against parasites, as well as efficacy for the treatment of Leishmania-infected macrophages. In vivo experiments using b-AD-containing Pluronic^® F127 polymeric micelles (b-AD/Mic) to treat L. infantum-infected mice showed that this composition reduced the parasite load in distinct organs in more significant levels. It also induced the development of anti-parasite Th1-type immunity, attested by high levels of IFN-γ, IL-12, TNF-α, GM-CSF, nitrite and specific IgG2a antibodies, in addition to low IL-4 and IL-10 contents, along with higher IFN-γ-producing CD4⁺ and CD8⁺ T-cell frequency. Furthermore, low toxicity was found in the organs of the treated animals. Comparing the therapeutic effect between the treatments, b-AD/Mic was the most effective in protecting animals against infection, when compared to the other groups including miltefosine used as a drug control. Data found 15 days after treatment were similar to those obtained one day post-therapy. In conclusion, the results obtained suggest that b-AD/Mic is a promising antileishmanial agent and deserves further studies to investigate its potential to treat visceral leishmaniasis.