Review of Drug Repositioning Approaches and Resources

Potential therapeutic use of corticosteroids as SARS CoV-2 main protease inhibitors: a computational study

The outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), represents a pandemic threat to global public health. To date, ∼530,000 people died of this disease worldwide. Presently, researchers/clinicians are adopting the drug repurposing strategy to combat this disease. It has also been observed that some repurposed anti-viral drugs may serve as potent inhibitors of SARS CoV-2 Mpro, a key component of viral replication. Apart from these anti-viral drugs, recently dexamethasone (an important corticosteroid) is effectively used to treat COVID-19 patients. However, the mechanism behind the mode of its action is not so clear. Additionally, the effect of other well-known corticosteroids to control this disease by inhibiting the proteolytic activity of Mpro is ambiguous. In this study, we have adopted computational approaches to understand these aspects. Six well-known corticosteroids (cortisone, hydrocortisone, prednisolone, methylprednisolone, betamethasone and dexamethasone) and two repurposed drugs (darunavir and lopinavir) against COVID-19 were subjected for molecular docking studies. Two of them (betamethasone and dexamethasone) were selected by comparing their binding affinities with selected repurposed drugs toward Mpro. Betamethasone and dexamethasone interacted with both the catalytic residues of Mpro (His41 and Cys145). Molecular dynamics studies further revealed that these two Mpro-corticosteroid complexes are more stable, experience less conformational fluctuations and more compact than Mpro-darunavir/lopinavir complexes. These findings were additionally validated by MM-GBSA analysis. This study provides corroboration for execution of anti-COVID-19 activity of dexamethasone. Our study also emphasizes on the use of another important corticosteroid (betamethasone) as potential therapeutic agent for COVID-19 treatment.

Utilizing drug repurposing against COVID-19 - Efficacy, limitations, and challenges

The recent outbreak of Coronavirus disease (COVID-19), first in Eastern Asia and then essentially across the world has been declared a pandemic by the WHO. COVID-19 is caused by a novel virus SARS-CoV2 (2019-nCoV), against which there is currently no vaccine available; and current antiviral therapies have failed, causing a very high mortality rate. Drug repurposing i.e. utilizing an approved drug for different indication, offers a time- and cost-efficient alternative for making new therapies available to patients. Although there are several reports presenting novel approaches to treat COVID-19, still an attentive review of previous scientific literature is essential to overcome their failure to exhibit efficacy. There is an urgent need to provide a comprehensive outlook toward utilizing drug repurposing as a tool for discovery of new therapies against COVID-19. In this article, we aim to provide a to-the-point review of current literature regarding efficacy of repurposed drugs against COVID-19 and other respiratory infections caused by coronaviruses. We have briefly discussed COVID-19 epidemiology, and then have discussed drug repurposing approaches and examples, specific to respiratory viruses. Limitations of utilization of repurposed drug molecules such as dosage regimen and associated challenges such as localized delivery in respiratory tract have also been discussed in detail.

Genomic Medicine: Lessons Learned From Monogenic and Complex Bone Disorders

Current genetic studies of monogenic and complex bone diseases have broadened our understanding of disease pathophysiology, highlighting the need for medical interventions and treatments tailored to the characteristics of patients. As genomic research progresses, novel insights into the molecular mechanisms are starting to provide support to clinical decision-making; now offering ample opportunities for disease screening, diagnosis, prognosis and treatment. Drug targets holding mechanisms with genetic support are more likely to be successful. Therefore, implementing genetic information to the drug development process and a molecular redefinition of skeletal disease can help overcoming current shortcomings in pharmaceutical research, including failed attempts and appalling costs. This review summarizes the achievements of genetic studies in the bone field and their application to clinical care, illustrating the imminent advent of the genomic medicine era.

Gabapentin Inhibits Multiple Steps in the Amyloid Beta Toxicity Cascade

Oligomeric β-amyloid peptide (Aβ) is one of the main neurotoxic agents of Alzheimer's disease (AD). Oligomers associate to neuronal membranes, forming "pore-like" structures that cause intracellular calcium and neurotransmitter dyshomeostasis, leading to synaptic failure and death. Through molecular screening targeting the C terminal region of Aβ, a region involved in the toxic properties of the peptide, we detected an FDA approved compound, gabapentin (GBP), with neuroprotective effects against Aβ toxicity. At micromolar concentrations, GBP antagonized peptide aggregation over time and reduced the Aβ absorbance plateau to 28% of control. In addition, GBP decreased Aβ association to membranes by almost half, and the effects of Aβ on intracellular calcium in hippocampal neurons were antagonized without causing effects on its own. Finally, we found that GBP was able to block the synaptotoxicity induced by Aβ in hippocampal neurons, increasing post-synaptic currents from 1.7 ± 0.9 to 4.2 ± 0.7 fC and mean relative fluorescence intensity values of SV2, a synaptic protein, from 0.7 ± 0.09 to 1.00 ± 0.08. The results show that GBP can interfere with Aβ-induced toxicity by blocking multiple steps, resulting in neuroprotection, which justifies advancing toward additional animal and human studies.

Integration of Molecular Docking and In Vitro Studies: A Powerful Approach for Drug Discovery in Breast Cancer

Molecular docking in the pharmaceutical industry is a powerful in silico approach for discovering novel therapies for unmet medical needs predicting drug–target interactions. It not only provides binding affinity between drugs and targets at the atomic level, but also elucidates the fundamental pharmacological properties of specific drugs. The purpose of this review was to illustrate newer and emergent uses of docking when combined with in vitro techniques for drug discovery in metastatic breast cancer. We grouped the selected articles into five main categories; namely, systematic repositioning of drugs, natural drugs, new synthesized molecules, combinations of drugs, and drug latentiation. We focused on new promising drugs that have a good affinity with their targets, thus inducing a favorable biological response. This review suggests that the integration of molecular docking and in vitro studies can accelerate cancer drug discovery showing a good consistency of the results between the two approaches.

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Background

It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time.

Methods

In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.

Results

The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.

Conclusion

Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

Repurposing of Benzimidazole Scaffolds for HER2 Positive Breast Cancer Therapy: An In-Silico Approach

BACKGROUND

A newer trend has been seen recently to reuse the conventional drugs with distinct indications for the newer applications to speed up the drug discovery and development based on earlier records and safety data. Most of the non-cancerous agents could afford a little or tolerable side effects in individuals. However, the repositioning of these non-cancerous agents for successful anticancer therapy is an outstanding strategy for future anti-cancer drug development. Since more diverse and selective cancer drug targets are being discovered and developed, the approved drug collections are particularly useful to quickly identify clinically advanced anticancer drugs against those targets.

OBJECTIVE

Antihelminthic drugs such as Mebendazole and Albendazole (Benzimidazole class) have been reported to exhibit cytotoxicity (or anticancer activities) against several types of cancer. Therefore, this study aims to repurpose the benzimidazole scaffold for breast cancer treatment.

METHODS

In the present study, three hydrazone analogs having a benzimidazole motif in their structural frame were synthesized. Their in-silico binding studies against HER2 receptor (PDB ID: 4LQM) and ADMET studies were carried out using Accelrys drug discovery studio 4.1. Cytotoxicity of the synthesized compounds against HER2 overexpressed MCF-7 cell lines was determined by MTT assay.

RESULTS

One of the compounds 2-[2-(2,4-dinitrophenyl)hydrazinylidene]-2,3-dihydro-1H-benzimidazole (U1) has shown good cytotoxicity when compared to the standard Lapatinib, which is a well known HER2 inhibitor.

CONCLUSIONS

Thus, the designed benzimidazole scaffold might serve as the best leads for treating breast cancer, which is additionally confirmed by performing their docking study via Accelrys discovery studio.

Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak

Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug repositioning has gained recognition as an alternative approach that explores new avenues for pre-existing commercially approved or rejected drugs to treat diseases aside from the intended ones. Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities. The greatest advantage of drug repositioning is that it breathes new life into the novel, rare, orphan, and resistant diseases, such as Cushing's syndrome, HIV infection, and pandemic outbreaks such as COVID-19. Repositioning existing drugs such as Hydroxychloroquine, Remdesivir, Ivermectin and Baricitinib shows good potential for COVID-19 treatment. This can crucially aid in resolving outbreaks in urgent times of need. This review discusses the past success in drug repositioning, the current technological advancement in the field, drug repositioning for personalised medicine and the ongoing research on newly emerging drugs under consideration for the COVID-19 treatment.

Drug Repurposing Approaches: Existing Leads For Novel Threats And Drug Targets

Drug Repurposing (DR) is an alternative to the traditional drug discovery process. It is cost and time effective, with high returns and low risk process that can tackle the increasing need for interventions for varied diseases and new outbreaks. Repurposing of old drugs for other diseases has gained a wider attention, as there have been several old drugs approved by FDA for new diseases. In the global emergency of COVID19 pandemic, this is one of the strategies implemented in repurposing of old anti-infective, anti-rheumatic and anti-thrombotic drugs. The goal of the current review is to elaborate the process of DR, its advantages, repurposed drugs for a plethora of disorders, and the evolution of related academic publications. Further, detailed are the computational approaches: literature mining and semantic inference, network-based drug repositioning, signature matching, retrospective clinical analysis, molecular docking and experimental phenotypic screening. We discuss the legal and economical potential barriers in DR, existent collaborative models and recommendations for overcoming these hurdles and leveraging the complete potential of DR in finding new indications.

Tamoxifen Downregulates the Expression of Notch1 and DLL1 Genes in MKN-45 Gastric Cancer Cells

PURPOSE

Gastric cancer is one of the most prevalent cancers worldwide and the second most common cause for cancer associated mortality. Anti-tumor effects of tamoxifen in breast cancer are well-established. However, no study has so far investigated the effects of tamoxifen on gene expression of Notch1 and DLL1 in gastric cancer cell line. The present study was conducted to explore the effects of tamoxifen, as a repurposed drug, on gene expression of Notch1 and DLL1 in MKN-45, a gastric cancer cell line.

METHODS

MKN-45 cells were cultured in DMEM/F12 medium containing 10% FBS. Cytotoxic effects of tamoxifen on these cells at various concentrations were evaluated by trypan blue exclusion assay. For gene expression analysis, the cells were first incubated with 100 μM tamoxifen followed by total RNA extraction from treated and control cells. Then, cDNA was synthesized. Quantitative real-time PCR using specific primers for Notch1 and DLL1 was performed to assess the effect of tamoxifen on the transcript of them.

RESULTS

Treatment with tamoxifen decreased viability of MKN-45 cells in a dose-dependent manner. CC₅₀ was estimated to be around 200 μM. Also, tamoxifen at the dose of 100 μM could significantly downregulate mRNA levels of both Notch1 and DLL1 genes as compared with untreated cells by 24% and 92%, respectively.

CONCLUSION

Based on these results, tamoxifen interferes with Notch signaling pathway through downregulating the expression of Notch1 and DLL1 genes and this could be regarded as a mechanism for its anti-cancer effects in this malignant disease.

The genetics of asthma and the promise of genomics-guided drug target discovery

Asthma is an inflammatory airway disease that is estimated to affect 339 million people globally. The symptoms of about 5-10% of patients with asthma are not adequately controlled with current therapy, and little success has been achieved in developing drugs that target the underlying mechanisms of asthma rather than suppressing symptoms. Over the past 3 years, well powered genetic studies of asthma have increased the number of independent asthma-associated genetic loci to 128. In this Series paper, we describe the immense progress in asthma genetics over the past 13 years and link asthma genetic variants to possible drug targets. Further studies are needed to establish the functional significance of gene variants associated with asthma in subgroups of patients and to describe the biological networks within which they function. The genomics-guided discovery of plausible drug targets for asthma could pave the way for the repurposing of existing drugs for asthma and the development of new treatments.

The New Coronavirus (SARS-CoV-2): A Comprehensive Review on Immunity and the Application of Bioinformatics and Molecular Modeling to the Discovery of Potential Anti-SARS-CoV-2 Agents

On March 11, 2020, the World Health Organization (WHO) officially declared the outbreak caused by the new coronavirus (SARS-CoV-2) a pandemic. The rapid spread of the disease surprised the scientific and medical community. Based on the latest reports, news, and scientific articles published, there is no doubt that the coronavirus has overloaded health systems globally. Practical actions against the recent emergence and rapid expansion of the SARS-CoV-2 require the development and use of tools for discovering new molecular anti-SARS-CoV-2 targets. Thus, this review presents bioinformatics and molecular modeling strategies that aim to assist in the discovery of potential anti-SARS-CoV-2 agents. Besides, we reviewed the relationship between SARS-CoV-2 and innate immunity, since understanding the structures involved in this infection can contribute to the development of new therapeutic targets. Bioinformatics is a technology that assists researchers in coping with diseases by investigating genetic sequencing and seeking structural models of potential molecular targets present in SARS-CoV2. The details provided in this review provide future points of consideration in the field of virology and medical sciences that will contribute to clarifying potential therapeutic targets for anti-SARS-CoV-2 and for understanding the molecular mechanisms responsible for the pathogenesis and virulence of SARS-CoV-2.

Application of Artificial Intelligence in COVID-19 drug repurposing

BACKGROUND AND AIM

COVID-19 outbreak has created havoc and a quick cure for the disease will be a therapeutic medicine that has usage history in patients to resolve the current pandemic. With technological advancements in Artificial Intelligence (AI) coupled with increased computational power, the AI-empowered drug repurposing can prove beneficial in the COVID-19 scenario.

METHODS

The recent literature is studied and analyzed from various sources such as Scopus, Google Scholar, PubMed, and IEEE Xplore databases. The search terms used are 'COVID-19', ' AI ', and 'Drug Repurposing'.

RESULTS

AI is implemented in the field design through the generation of the learning-prediction model and performs a quick virtual screening to accurately display the output. With a drug-repositioning strategy, AI can quickly detect drugs that can fight against emerging diseases such as COVID-19. This technology has the potential to improve the drug discovery, planning, treatment, and reported outcomes of the COVID-19 patient, being an evidence-based medical tool.

CONCLUSIONS

Thus, there are chances that the application of the AI approach in drug discovery is feasible. With prior usage experiences in patients, few of the old drugs, if shown active against SARS-CoV-2, can be readily applied to treat the COVID-19 patients. With the collaboration of AI with pharmacology, the efficiency of drug repurposing can improve significantly.

Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer

Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec. 3.80 million "cancer"-related PubMed abstracts were processed as the corpus for Word2Vec to derive vector representation of biological concepts. These vectors for drugs and diseases served as the foundation for creating similarity maps of drugs and diseases, respectively, which were then employed to find potential therapy for IBC. Three hundred and thirty-six (336) known drugs and three hundred and seventy (370) diseases were expressed as vectors in this study. Nine hundred and seventy (970) previously known drug-disease association pairs among these drugs and diseases were used as the reference set. Based on the hypothesis that similar drugs can be used against similar diseases, we have identified 18 diseases similar to IBC, with 24 corresponding known drugs proposed to be the repurposing therapy for IBC. The literature search confirmed most known drugs tested for IBC, with four of them being novel candidates. We conclude that LWAS based on the Word2Vec technology is a novel approach to drug repurposing especially useful for rare diseases.

Integrating bioassay data for improved prediction of drug-target interaction

Identifying drug targets is one of the major tasks in drug discovery. As experimental identification of targets is rather challenging, development of computational methods is necessary for efficient identification of drug-target interaction. Traditional computational method, such as docking, is based solely on the chemical structure, which is not available for most of the targets. On the other hand, bioassay data might contain information helpful for prediction of drug-target interaction. In this study, a feature enrichment method integrating bioassay and chemical structure data was developed to predict drug-target interaction. Using a large-scale benchmark on the datasets, we demonstrated that the model adopting integrated fingerprint outperformed the one using chemical fingerprint. Influence of the false positive hits in bioassays and algorithm-related factors on the model performance were also investigated. The results suggested that prediction by using integrated fingerprint was robust to false positive hits, the choice of classifiers, and different random splits of the datasets.

Screening Repurposing Libraries for Identification of Drugs with Novel Antifungal Activity

Fungal organisms are ubiquitous in nature, and progress of modern medicine is creating an expanding number of severely compromised patients susceptible to a variety of opportunistic fungal infections. These infections are difficult to diagnose and treat, leading to high mortality rates. The limited antifungal arsenal, the toxicity of current antifungal drugs, the development of resistance, and the emergence of new multidrug-resistant fungi, all highlight the urgent need for new antifungal agents. Unfortunately, the development of a novel antifungal is a rather long and expensive proposition, and no new classes of antifungal agents have reached the market in the last 2 decades. Drug repurposing, or finding new indications for old drugs, represents a promising alternative pathway to drug development that is particularly appealing within the academic environment. In the last few years, there has been a growing interest in repurposing approaches in the antifungal arena, with multiple groups of investigators having performed screenings of different repurposing libraries against different pathogenic fungi in search for drugs with previously unrecognized antifungal effects. Overall, these repurposing efforts may lead to the fast deployment of drugs with novel antifungal activity, which can rapidly bring benefits to patients, while at the same time reducing health care costs.

Interplay Between Autophagy and Wnt/β-Catenin Signaling in Cancer: Therapeutic Potential Through Drug Repositioning

The widespread dysregulation that characterizes cancer cells has been dissected and many regulation pathways common to multiple cancer types have been described in depth. Wnt/β-catenin signaling and autophagy are among these principal pathways, which contribute to tumor growth and resistance to anticancer therapies. Currently, several therapeutic strategies that target either Wnt/β-catenin signaling or autophagy are in various stages of development. Targeted therapies that block specific elements that participate in both pathways; are subject to in vitro studies as well as pre-clinical and early clinical trials. Strikingly, drugs designed for other diseases also impact these pathways, which is relevant since they are already FDA-approved and sometimes even routinely used in the clinic. The main focus of this mini-review is to highlight the importance of drug repositioning to inhibit the Wnt/β-catenin and autophagy pathways, with an emphasis on the interplay between them. The data we found strongly suggested that this field is worth further examination.

Drug Repositioning for P-Glycoprotein Mediated Co-Expression Networks in Colorectal Cancer

Colorectal cancer (CRC) is one of the most fatal types of cancers that is seen in both men and women. CRC is the third most common type of cancer worldwide. Over the years, several drugs are developed for the treatment of CRC; however, patients with advanced CRC can be resistant to some drugs. P-glycoprotein (P-gp) (also known as Multidrug Resistance 1, MDR1) is a well-identified membrane transporter protein expressed by ABCB1 gene. The high expression of MDR1 protein found in several cancer types causes chemotherapy failure owing to efflux drug molecules out of the cancer cell, decreases the drug concentration, and causes drug resistance. As same as other cancers, drug-resistant CRC is one of the major obstacles for effective therapy and novel therapeutic strategies are urgently needed. Network-based approaches can be used to determine specific biomarkers, potential drug targets, or repurposing approved drugs in drug-resistant cancers. Drug repositioning is the approach for using existing drugs for a new therapeutic purpose; it is a highly efficient and low-cost process. To improve current understanding of the MDR-1-related drug resistance in CRC, we explored gene co-expression networks around ABCB1 gene with different network sizes (50, 100, 150, 200 edges) and repurposed candidate drugs targeting the ABCB1 gene and its co-expression network by using drug repositioning approach for the treatment of CRC. The candidate drugs were also assessed by using molecular docking for determining the potential of physical interactions between the drug and MDR1 protein as a drug target. We also evaluated these four networks whether they are diagnostic or prognostic features in CRC besides biological function determined by functional enrichment analysis. Lastly, differentially expressed genes of drug-resistant (i.e., oxaliplatin, methotrexate, SN38) HT29 cell lines were found and used for repurposing drugs with reversal gene expressions. As a result, it is shown that all networks exhibited high diagnostic and prognostic performance besides the identification of various drug candidates for drug-resistant patients with CRC. All these results can shed light on the development of effective diagnosis, prognosis, and treatment strategies for drug resistance in CRC.

Endothelial activation and dysfunction in metabolic syndrome, type 2 diabetes and coronavirus disease 2019

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 infection is a serious global concern. Increased morbidity and mortality is associated with older age, male gender, cardiovascular disease, diabetes, and smoking. As COVID-19 spreads from coastal borders, both state to state and country to country, our understanding of its pathophysiology has evolved. Age and type 2 diabetes mellitus (T2DM) play especially important roles in COVID-19 progression. T2DM is an age-related disease associated with metabolic syndrome, obesity, insulin resistance (hyperinsulinemia), hyperlipidemia, hypertension, hyperglycemia, and endothelial activation and dysfunction. This review evaluates the relationships and intersection between endothelial cell activation and dysfunction in T2DM and COVID-19. COVID-19 induces multiple injuries of the terminal bronchioles and alveolar blood-gas barrier and associated ultrastructural tissue remodeling. COVID-19 may unmask multiple vulnerabilities associated with T2DM including damage to the endothelial glycocalyx and multiple end-organ macro and microvascular diseases. Unmasking existing vulnerabilities in diabetic patients with COVID-19 is important. Globally, we must come together to better understand why T2DM is associated with increased COVID-19 morbidity and mortality.

A Panel of Broad-Spectrum Antivirals in Topical Ophthalmic Medications from the Drug Repurposing Approach during and after the Coronavirus Disease 2019 Era

The coronavirus disease 2019 (COVID-19) represents a global concern of public health caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its clinical manifestations are characterized by a heterogeneous group of symptoms and pictures (ranging from asymptomatic to lethal courses). The prevalence of conjunctivitis in patients with COVID-19 is at present controversial. Although it has been reported that only 0.9% developed signs of conjunctivitis, other report indicates that up to 31.6% of hospitalized patients had conjunctivitis. Considering the widespread use of topical ophthalmic medications (e.g., eye drops) by the general population, for various reasons (e.g., artificial tears, anti-glaucoma medications, topical antibiotics, etc.), the existence of their side effects as antiviral action should be investigated in-depth because it could possibly explain the aforementioned controversial data and represent a potential antiviral treatment for SARS-CoV-2 replication/diffusion on the ocular surface. Here, we discuss and elucidate the antiviral side effect of many eye drops and ophthalmic ointments commonly used for others purposes, thus showing that these secondary effects (not to be confused with the 'adverse effects') might be of primary importance in a number of viral infections (e.g., those for which there is no validated treatment protocol), according to a drug repurposing approach. Some active ingredients or excipients described here have activity against other types of viruses, thus suggesting potential broad-spectrum applications.

Repurposing Drugs to Fight Hepatic Malaria Parasites

Malaria remains one of the most prevalent infectious diseases worldwide, primarily affecting some of the most vulnerable populations around the globe. Despite achievements in the treatment of this devastating disease, there is still an urgent need for the discovery of new drugs that tackle infection by Plasmodium parasites. However, de novo drug development is a costly and time-consuming process. An alternative strategy is to evaluate the anti-plasmodial activity of compounds that are already approved for other purposes, an approach known as drug repurposing. Here, we will review efforts to assess the anti-plasmodial activity of existing drugs, with an emphasis on the obligatory and clinically silent liver stage of infection. We will also review the current knowledge on the classes of compounds that might be therapeutically relevant against Plasmodium in the context of other communicable diseases that are prevalent in regions where malaria is endemic. Repositioning existing compounds may constitute a faster solution to the current gap of prophylactic and therapeutic drugs that act on Plasmodium parasites, overall contributing to the global effort of malaria eradication.

Drug Repositioning of the α1-Adrenergic Receptor Antagonist Naftopidil: A Potential New Anti-Cancer Drug?

Failure of conventional treatments is often observed in cancer management and this requires the development of alternative therapeutic strategies. However, new drug development is known to be a high-failure process because of the possibility of a lower efficacy than expected for the drug or appearance of non-manageable side effects. Another way to find alternative therapeutic drugs consists in identifying new applications for drugs already approved for a particular disease: a concept named "drug repurposing". In this context, several studies demonstrated the potential anti-tumour activity exerted by α1-adrenergic receptor antagonists and notably renewed interest for naftopidil as an anti-cancer drug. Naftopidil is used for benign prostatic hyperplasia management in Japan and a retrospective study brought out a reduced incidence of prostate cancer in patients that had been prescribed this drug. Further studies showed that naftopidil exerted anti-proliferative and cytotoxic effects on prostate cancer as well as several other cancer types in vitro, as well as ex vivo and in vivo. Moreover, naftopidil was demonstrated to modulate the expression of Bcl-2 family pro-apoptotic members which could be used to sensitise cancer cells to targeting therapies and to overcome resistance of cancer cells to apoptosis. For most of these anti-cancer effects, the molecular pathway is either not fully deciphered or shown to involve α1-adrenergic receptor-independent pathway, suggesting off target transduction signals. In order to improve its efficacy, naftopidil analogues were designed and shown to be effective in several studies. Thereby, naftopidil appears to display anti-cancer properties on different cancer types and could be considered as a candidate for drug repurposing although its anti-cancerous activities need to be studied more deeply in prospective randomized clinical trials.

Bilayer Mucoadhesive Buccal Film for Mucosal Ulcers Treatment: Development, Characterization, and Single Study Case

The formation of mucosal ulcers is an end result of epithelial damage, and it occurs due to some specific causes, such as trauma, aphthous stomatitis, lichen planus and lichenoid reactions, cytotoxic effects of chemotherapy and radiation, and drug-induced hypersensitivity reactions and malignant settings. This study focused on films for target drug delivery with respect to the treatment of the diseases of the oral mucosa, specifically mucositis. The results of a single clinical study as a pre-experimental design was performed and followed up to the outcome until 30 days. The polymeric film was prepared in a mucoadhesive bilayer structure: the basal layer with lidocaine HCl had a faster release than the apical layer with benzydamine HCl and N-acetyl-cysteine. Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), and SEM characterized the physical–chemical and morphological properties. The cell viability and cytotoxicity were evaluated in cell line MCF7. The transport mechanism of the solvent (swelling) and the drugs in the basal or apical layer (drug release) was explained with mathematical models. To evaluate the effect of movement inside the mouth, the folding endurance was determined. The mucoadhesive bilayer film is biologically safe and stimulates cellular proliferation. A single study in vivo demonstrated the therapeutic effect of the mucoadhesive bilayer film in buccal mucositis.

A comprehensive review on drug repositioning against coronavirus disease 2019 (COVID19)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is the reason for this ongoing pandemic infection diseases termed coronavirus disease 2019 (COVID-19) that has emerged since early December 2019 in Wuhan City, Hubei Province, China. In this century, it is the worst threat to international health and the economy. After 4 months of COVID-19 outbreak, there is no certain and approved medicine against it. In this public health emergency, it makes sense to investigate the possible effects of old drugs and find drug repositioning that is efficient, economical, and riskless process. Old drugs that may be effective are from different pharmacological categories, antimalarials, anthelmintics, anti-protozoal, anti-HIVs, anti-influenza, anti-hepacivirus, antineoplastics, neutralizing antibodies, immunoglobulins, and interferons. In vitro, in vivo, or preliminary trials of these drugs in the treatment of COVID-19 have been encouraging, leading to new research projects and trials to find the best drug/s. In this review, we discuss the possible mechanisms of these drugs against COVID-19. Also, it should be mentioned that in this manuscript, we discuss preliminary rationales; however, clinical trial evidence is needed to prove them. COVID-19 therapy must be based on expert clinical experience and published literature and guidelines from major health organizations. Moreover, herein, we describe current evidence that may be changed in the future.

Dr AFC: drug repositioning through anti-fibrosis characteristic

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. In this study, anti-fibrosis knowledge base was first built by collecting data from multiple resources. Both structural and biological profiles were then derived from the knowledge base and used for constructing machine learning models including Structural Profile Prediction Model (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data sets were employed for validation purpose and further exploration of potential repositioning drugs in wider chemical space. The resulting SPPM and BPPM models achieve area under the receiver operating characteristic curve (area under the curve) of 0.879 and 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our results also demonstrate that substantial amount of multi-targeting natural products possess notable anti-fibrosis characteristics and might serve as encouraging candidates in fibrosis treatment and drug repositioning. To leverage our methodology and findings, we developed repositioning prediction platform, drug repositioning based on anti-fibrosis characteristic that is freely accessible via https://www.biosino.org/drafc.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

A novel drug repurposing approach for non-small cell lung cancer using deep learning

Drug repurposing is an attractive and pragmatic way offering reduced risks and development time in the complicated process of drug discovery. In the past, drug repurposing has been largely accidental and serendipitous. The most successful examples so far have not involved a systematic approach. Nowadays, remarkable advances in drugs, diseases and bioinformatic knowledge are offering great opportunities for designing novel drug repurposing approach through comprehensive understanding of drug information. In this study, we introduced a novel drug repurposing approach based on transcriptomic data and chemical structures using deep learning. One strong candidate for repurposing has been identified. Pimozide is an anti-dyskinesia agent that is used for the suppression of motor and phonic tics in patients with Tourette's Disorder. However, our pipeline proposed it as a strong candidate for treating non-small cell lung cancer. The cytotoxicity of pimozide against A549 cell lines has been validated.

[The "Historic Study" SOLIDARITY-Research's Answer to the Sars-CoV-2 Pandemic]

This paper is part of Forum COVID-19: Perspectives in the Humanities and Social Sciences. The novel coronavirus (Sars-CoV-2) poses a huge challenge to the world community. Knowledge about the virus and its properties is limited, but there is a great need to base political and medical decisions on scientific knowledge. This situation is leading to a dynamization of research. A prominent example of such a development is SOLIDARITY. The epistemological dimensions of this trial, which is coordinated by the WHO, and the resulting ethical implications are discussed in this article.

Oral administration of ferulic acid or ethyl ferulate attenuates retinal damage in sodium iodate-induced retinal degeneration mice

Epidemiological studies indicate that the daily intake of antioxidants from a traditional Asian diet reduces the risk of developing age-related macular degeneration. Many of the phytochemicals that are abundant in whole grains exhibit a wide variety of biological activity such as antioxidant, anti-inflammatory, and neuroprotective effects. Ferulic acid (FA) is a phenolic acid found in vegetables and grains that has therapeutic potential for diabetes mellitus, Alzheimer's disease, and other diseases. We investigated the retinal protective effect of FA in a sodium iodate (NaIO₃)-induced model of retinal degeneration. In a human retinal pigment epithelial cell line, FA attenuated H₂O₂-induced injury and lipopolysaccharide- or 7-ketocholesterol-induced inflammation. In mice, the oral administration of FA or its analog, ethyl ferulate, attenuated the morphological and functional features of NaIO₃-induced retinal degeneration according to optical coherence tomography and electroretinography. Our results demonstrate that the oral administration of FA provides protective effects to the retina, suggesting that the intake of FA as a daily supplement or daily healthy diet containing rich vegetables and whole grains may prevent age-related macular degeneration.

Privileged Scaffold Analysis of Natural Products with Deep Learning-based Indication Prediction Model

Natural products play a vital role in the drug discovery and development process as an important source of reliable and novel lead structures. But the existing criteria for drug leads were usually developed for synthetic compounds and cannot be directly applied to identify lead scaffolds from natural products. To solve this problem, we propose a method to predict indications and identify privileged scaffolds of natural products for drug design. A deep learning model was built to predict indications for natural products. Entropy-based information metrics were used to identify the privileged scaffolds for each indication and a Privileged Scaffold Dataset (PSD) of natural products was constructed. The PSD could serve as a novel source of lead compounds and circumvent existing drug patents. This method could be generalized by replacing the training set, the prediction algorithm, and the compound set, to obtain more personalized-PSDs.

Drug rechanneling: A novel paradigm for cancer treatment

Cancer continues to be one of the leading contributors towards global disease burden. According to NIH, cancer incidence rate per year will increase to 23.6 million by 2030. Even though cancer continues to be a major proportion of the disease burden worldwide, it has the lowest clinical trial success rate amongst other diseases. Hence, there is an unmet need for novel, affordable and effective anti-neoplastic medications. As a result, a growing interest has sparkled amongst researchers towards drug repurposing. Drug repurposing follows the principle of polypharmacology, which states, "any drug with multiple targets or off targets can present several modes of action". Drug repurposing also known as drug rechanneling, or drug repositioning is an economic and reliable approach that identifies new disease treatment of already approved drugs. Repurposing guarantees expedited access of drugs to the patients as these drugs are already FDA approved and their safety and toxicity profile is completely established. Epidemiological studies have identified the decreased occurrence of oncological or non-oncological conditions in patients undergoing treatment with FDA approved drugs. Data from multiple experimental studies and clinical observations have depicted that several non-neoplastic drugs have potential anticancer activity. In this review, we have summarized the potential anti-cancer effects of anti-psychotic, anti-malarial, anti-viral and anti-emetic drugs with a brief overview on their mechanism and pathways in different cancer types. This review highlights promising evidences for the repurposing of drugs in oncology.

Repurposing Zileuton as a Depression Drug Using an AI and In Vitro Approach

Repurposing drugs to target M1 macrophages inflammatory response in depression constitutes a bright alternative for commonly used antidepressants. Depression is a significant type of mood disorder, where patients suffer from pathological disturbances associated with a proinflammatory M1 macrophage phenotype. Presently, the most commonly used antidepressants such as Zoloft and Citalopram can reduce inflammation, but suffer from dangerous side effects without offering specificity toward macrophages. We employed a new strategy for drug repurposing based on the integration of RNA-seq analysis and text mining using deep neural networks. Our system employs a Google semantic AI universal encoder to compute sentences embedding. Sentences similarity is calculated using a sorting function to identify drug compounds. Then sentence relevance is computed using a custom-built convolution differential network. Our system highlighted the NRF2 pathway as a critical drug target to reprogram M1 macrophage response toward an anti-inflammatory profile (M2). Using our approach, we were also able to predict that lipoxygenase inhibitor drug zileuton could modulate NRF2 pathway in vitro. Taken together, our results indicate that reorienting zileuton usage to modulate M1 macrophages could be a novel and safer therapeutic option for treating depression.

Repurposing of drugs: An attractive pharmacological strategy for cancer therapeutics

Human malignancies are one of the major health-related issues though out the world and anticipated to rise in the future. The development of novel drugs/agents requires a huge amount of cost and time that represents a major challenge for drug discovery. In the last three decades, the number of FDA approved drugs has dropped down and this led to increasing interest in drug reposition or repurposing. The present review focuses on recent concepts and therapeutic opportunities for the utilization of antidiabetics, antibiotics, antifungal, anti-inflammatory, antipsychotic, PDE inhibitors and estrogen receptor antagonist, Antabuse, antiparasitic and cardiovascular agents/drugs as an alternative approach against human malignancies. The repurposing of approved non-cancerous drugs is an effective strategy to develop new therapeutic options for the treatment of cancer patients at an affordable cost in clinics. In the current scenario, most of the countries throughout the globe are unable to meet the medical needs of cancer patients because of the high cost of the available cancerous drugs. Some of these drugs displayed potential anti-cancer activity in preclinic and clinical studies by regulating several key molecular mechanisms and oncogenic pathways in human malignancies. The emerging pieces of evidence indicate that repurposing of drugs is crucial to the faster and cheaper discovery of anti-cancerous drugs.

Identifying drug-target interactions based on graph convolutional network and deep neural network

Identification of new drug-target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug-protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

An overview of drug discovery and development

A new medicine will take an average of 10-15 years and more than US$2 billion before it can reach the pharmacy shelf. Traditionally, drug discovery relied on natural products as the main source of new drug entities, but was later shifted toward high-throughput synthesis and combinatorial chemistry-based development. New technologies such as ultra-high-throughput drug screening and artificial intelligence are being heavily employed to reduce the cost and the time of early drug discovery, but they remain relatively unchanged. However, are there other potentially faster and cheaper means of drug discovery? Is drug repurposing a viable alternative? In this review, we discuss the different means of drug discovery including their advantages and disadvantages.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches

A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for authentication through experiments of wet-lab which are known for their expensiveness and time consumption. Chemogenomics, an emerging research area focused on the systematic examination of the biological impact of a broad series of minute molecular-weighting ligands on a broad raiment of macromolecular target spots. Additionally, with the advancement in time, the complexity of the algorithms is increasing which may result in the entry of big data technologies like Spark in this field soon. In the presented work, we intend to offer an inclusive idea and realistic evaluation of the computational Drug Target Interaction projection approaches, to perform as a guide and reference for researchers who are carrying out work in a similar direction. Precisely, we first explain the data utilized in computational Drug Target Interaction prediction attempts like this. We then sort and explain the best and most modern techniques for the prediction of DTIs. Then, a realistic assessment is executed to show the projection performance of several illustrative approaches in various situations. Ultimately, we underline possible opportunities for additional improvement of Drug Target Interaction projection enactment and also linked study objectives.

Structure-Based Design of Antivirals against Envelope Glycoprotein of Dengue Virus

Dengue virus (DENV) presents a significant threat to global public health with more than 500,000 hospitalizations and 25,000 deaths annually. Currently, there is no clinically approved antiviral drug to treat DENV infection. The envelope (E) glycoprotein of DENV is a promising target for drug discovery as the E protein is important for viral attachment and fusion. Understanding the structure and function of DENV E protein has led to the exploration of structure-based drug discovery of antiviral compounds and peptides against DENV infections. This review summarizes the structural information of the DENV E protein with regards to DENV attachment and fusion. The information enables the development of antiviral agents through structure-based approaches. In addition, this review compares the potency of antivirals targeting the E protein with the antivirals targeting DENV multifunctional enzymes, repurposed drugs and clinically approved antiviral drugs. None of the current DENV antiviral candidates possess potency similar to the approved antiviral drugs which indicates that more efforts and resources must be invested before an effective DENV drug materializes.

New drugs are not enough‑drug repositioning in oncology: An update

Drug repositioning refers to the concept of discovering novel clinical benefits of drugs that are already known for use treating other diseases. The advantages of this are that several important drug characteristics are already established (including efficacy, pharmacokinetics, pharmacodynamics and toxicity), making the process of research for a putative drug quicker and less costly. Drug repositioning in oncology has received extensive focus. The present review summarizes the most prominent examples of drug repositioning for the treatment of cancer, taking into consideration their primary use, proposed anticancer mechanisms and current development status.

Systematic Development and Optimization of Inhalable Pirfenidone Liposomes for Non-Small Cell Lung Cancer Treatment

Non-small cell lung cancer (NSCLC) is a global disorder, treatment options for which remain limited with resistance development by cancer cells and off-target events being major roadblocks for current therapies. The discovery of new drug molecules remains time-consuming, expensive, and prone to failure in safety/efficacy studies. Drug repurposing (i.e., investigating FDA-approved drug molecules for use against new indications) provides an opportunity to shorten the drug development cycle. In this project, we propose to repurpose pirfenidone (PFD), an anti-fibrotic drug, for NSCLC treatment by encapsulation in a cationic liposomal carrier. Liposomal formulations were optimized and evaluated for their physicochemical properties, in-vitro aerosol deposition behavior, cellular internalization capability, and therapeutic potential against NSCLC cell lines in-vitro and ex-vivo. Anti-cancer activity of PFD-loaded liposomes and molecular mechanistic efficacy was determined through colony formation (1.5- to 2-fold reduction in colony growth compared to PFD treatment in H4006, A549 cell lines, respectively), cell migration, apoptosis and angiogenesis assays. Ex-vivo studies using 3D tumor spheroid models revealed superior efficacy of PFD-loaded liposomes against NSCLC, as compared to plain PFD. Hence, the potential of inhalable liposome-loaded pirfenidone in NSCLC treatment has been established in-vitro and ex-vivo, where further studies are required to determine their efficacy through in vivo preclinical studies followed by clinical studies.

Computational approaches for drug discovery against trypanosomatid-caused diseases

During three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.

Next-generation drug repurposing using human genetics and network biology

Drug repurposing has attracted increased attention, especially in the context of drug discovery rates that remain too low despite a recent wave of approvals for biological therapeutics (e.g. gene therapy). These new biological entities-based treatments have high costs that are difficult to justify for small markets that include rare diseases. Drug repurposing, involving the identification of single or combinations of existing drugs based on human genetics data and network biology approaches represents a next-generation approach that has the potential to increase the speed of drug discovery at a lower cost. This Pharmacological Perspective reviews progress and perspectives in combining human genetics, especially genome-wide association studies, with network biology to drive drug repurposing for rare and common diseases with monogenic or polygenic etiologies. Also, highlighted here are important features of this next generation approach to drug repurposing, which can be combined with machine learning methods to meet the challenges of personalized medicine.

Critical View on the Usage of Ribavirin in Already Existing Psychostimulant-Use Disorder

Substance-use disorder represents a frequently hidden non-communicable chronic disease. Patients with intravenous drug addiction are at high risk of direct exposure to a variety of viral infections and are considered to be the largest subpopulation infected with the hepatitis C virus. Ribavirin is a synthetic nucleoside analog that has been used as an integral component of hepatitis C therapy. However, ribavirin medication is quite often associated with pronounced psychiatric adverse effects. It is not well understood to what extent ribavirin per se contributes to changes in drug-related neurobehavioral disturbances, especially in the case of psychostimulant drugs, such as amphetamine. It is now well-known that repeated amphetamine usage produces psychosis in humans and behavioral sensitization in animals. On the other hand, ribavirin has an affinity for adenosine A1 receptors that antagonistically modulate the activity of dopamine D1 receptors, which play a critical role in the development of behavioral sensitization. This review will focus on the current knowledge of neurochemical/ neurobiological changes that exist in the psychostimulant drug-addicted brain itself and the antipsychotic-like efficiency of adenosine agonists. Particular attention will be paid to the potential side effects of ribavirin therapy, and the opportunities and challenges related to its application in already existing psychostimulant-use disorder.

A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases

Millions of Americans are affected by rare diseases, many of which have poor survival rates. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.

Machine learning applications in drug development

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.