Drug repurposing: far beyond new targets for old drugs

A general prediction model for compound-protein interactions based on deep learning

Background

The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning.

Methods

To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments.

Results

Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41 μM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83 μM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM.

Conclusion

Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

Repurposing of antibacterial compounds for suppression of Mycobacterium tuberculosis dormancy reactivation by targeting resuscitation-promoting factors B

Tuberculosis infection has always been a global concern for public health, and the mortality rate has increased tremendously every year. The ability of the resuscitation Mycobacterium tuberculosis (Mtb) from the dormant state is one of the major reasons for the epidemic spread of tuberculosis infection, especially latent tuberculosis infection (LTBI). The element that encourages resuscitation, RpfB (resuscitation-promoting factors B), is mostly in charge of bringing Mtb out of slumber. This reason makes RpfB a promising target for developing tuberculosis drugs because of the effects of latent tuberculosis. Therefore, this work was executed using a computational three-level screening of the Selleckhem antibiotics database consisting of 462 antibiotics against the ligand binding region of the RpfB protein, followed by an estimation of binding free energy for ideal identification and confirmation of potential RpfB inhibitor. Subsequently, three antibiotic drug molecules, i.e., Amikacin hydrate (-66.87 kcal/mol), Isepamicin sulphate (-60.8 kcal/mol), and Bekanamycin (-46.89 kcal/mol), were selected on the basis of their binding free energy value for further computational studies in comparison to reference ligand, 4-benzoyl-2-nitrophenyl thiocyanate (NPT7). Based on the intermolecular interaction profiling, 200 ns molecular dynamic simulation (MD), post-simulation analysis and principal component analysis (PCA), the selected antibiotics showed substantial stability with the RpfB protein compared to the NPT7 inhibitor. Conclusively based on the computational results, the preferred drugs can be potent inhibitors of the RpfB protein, which can be further validated using in vivo research and in vitro enzyme inhibition to understand their therapeutic activity against tuberculosis infection.Communicated by Ramaswamy H. Sarma.

Predicting associations between drugs and G protein-coupled receptors using a multi-graph convolutional network

Developing new drugs is an expensive, time-consuming process that frequently involves safety concerns. By discovering novel uses for previously verified drugs, drug repurposing helps to bypass the time-consuming and costly process of drug development. As the largest family of proteins targeted by verified drugs, G protein-coupled receptors (GPCR) are vital to efficiently repurpose drugs by inferring their associations with drugs. Drug repurposing may be sped up by computational models that predict the strength of novel drug-GPCR pairs interaction. To this end, a number of models have been put forth. In existing methods, however, drug structure, drug-drug interactions, GPCR sequence, and subfamily information couldn't simultaneously be taken into account to detect novel drugs-GPCR relationships. In this study, based on a multi-graph convolutional network, an end-to-end deep model was developed to efficiently and precisely discover latent drug-GPCR relationships by combining data from multi-sources. We demonstrated that our model, based on multi-graph convolutional networks, outperformed rival deep learning techniques as well as non-deep learning models in terms of inferring drug-GPCR relationships. Our results indicated that integrating data from multi-sources can lead to further advancement.

Drug repurposing for neurodegenerative diseases

Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.

Antifungal and antibiofilm effect of duloxetine hydrochloride against Cryptococcus neoformans and Cryptococcus gattii

Cryptococcosis is an invasive mycosis caused mainly by Cryptococcus gattii and C. neoformans and is treated with amphotericin B (AMB), fluconazole and 5-fluorocytosine. However, antifungal resistance, limited and toxic antifungal arsenal stimulate the search for therapeutic strategies such as drug repurposing. Among the repurposed drugs studied, the selective serotonin reuptake inhibitors (SSRIs) have shown activity against Cryptococcus spp. However, little is known about the antifungal effect of duloxetine hydrochloride (DH), a selective serotonin and norepinephrine reuptake inhibitor (SSNRI), against C. neoformans and C. gattii. In this study, DH inhibited the growth of several C. neoformans and C. gattii strains at concentrations ranging from 15.62 to 62.50 µg/mL. In addition, DH exhibited fungicidal activity ranging from 15.62 to 250 µg/mL. In biofilm, DH treatment reduced Cryptococcus spp. biomass at a level comparable to AMB, with a significant reduction (85%) for C. neoformans biofilms. The metabolic activity of C. neoformans and C. gattii biofilms decreased significantly (99%) after treatment with DH. Scanning electron micrographs confirmed the anti-biofilm activity of DH, as isolated cells could be observed after treatment. In conclusion, DH showed promising antifungal activity against planktonic cells and biofilms of C. neoformans and C. gattii, opening perspectives for further studies with DH in vivo.

Repurposing of biologics and biopharmaceuticals

The field of drug repurposing is gaining attention as a way to introduce pharmaceutical agents with established safety profiles to new patient populations. This approach involves finding new applications for existing drugs through observations or deliberate efforts to understand their mechanisms of action. Recent advancements in bioinformatics and pharmacology, along with the availability of extensive data repositories and analytical techniques, have fueled the demand for novel methodologies in pharmaceutical research and development. To facilitate systematic drug repurposing, various computational methodologies have emerged, combining experimental techniques and in silico approaches. These methods have revolutionized the field of drug discovery by enabling the efficient repurposing of screens. However, establishing an ideal drug repurposing pipeline requires the integration of molecular data accessibility, analytical proficiency, experimental design expertise, and a comprehensive understanding of clinical development processes. This chapter explores the key methodologies used in systematic drug repurposing and discusses the stakeholders involved in this field. It emphasizes the importance of strategic alliances to enhance the success of repurposing existing compounds for new indications. Additionally, the chapter highlights the current benefits, considerations, and challenges faced in the repurposing process, which is pursued by both biotechnology and pharmaceutical companies. Overall, drug repurposing holds great promise in expanding the use of existing drugs and bringing them to new patient populations. With the advancements in computational methodologies and the collaboration of various stakeholders, this approach has the potential to accelerate drug development and improve patient outcomes.

Design, semi-synthesis of soft coral-derived Aspergillus sp. secondary metabolite geodin derivatives and their antibacterial activities

A series of novel ester derivatives 2 - 7, of natural product geodin 1, isolated from the soft coral-derived fungus Aspergillus sp., were designed and semi-synthesised through one step reaction with high yield. Compound 5 showed strong antifouling inhibitory activities with MIC of 4.80 μM while compound 4 showed selective inhibitory activities with MICs values 8.59 μM against Aeromonas salmonicida and Pseudomonas aeruginosa (Sea-Nine 211, MIC = 0.27 μM). Compounds 3, 4 and 6 showed potent anti-pathogenic inhibitory activities with MICs of 2.29 μM, 4.29 μM and 4.56 μM respectively against Staphylococcus aureus (Ciprofloxacin, MIC = 0.156 μM). Compound 2 showed weak inhibitory activity against A. salmonicida with MIC 18.75 μM (Sea-Nine 211, MIC = 0.27 μM) and with MICs 9.38 μM against S. aureus (ciprofloxacin, MIC = 0.156 μM). However, compound 7 showed very low antibacterial activities with MIC = >20 μM. The preliminary structure-activity relationships of compounds 2 - 7 further prove that the modification of 4-OH group of natural product geodin 1 improves the antibacterial activities such as antifouling and anti-pathogenic activities.

Anticancer Drug Discovery By Structure-Based Repositioning Approach

Despite the tremendous progress that has occurred in recent years in cell biology and oncology, in chemical, physical and computer sciences, the disease cancer has continued as the major cause of death globally. Research organizations, academic institutions and pharmaceutical companies invest huge amounts of money in the discovery and development of new anticancer drugs. Though much effort is continuing and whatever available approaches are being attempted, the success of bringing one effective drug into the market has been uncertain. To overcome problems associated with drug discovery, several approaches are being attempted. One such approach has been the use of known, approved and marketed drugs to screen these for new indications, which have gained considerable interest. This approach is known in different terms as "drug repositioning or drug repurposing." Drug repositioning refers to the structure modification of the active molecule by synthesis, in vitro/ in vivo screening and in silico computational applications where macromolecular structure-based drug design (SBDD) is employed. In this perspective, we aimed to focus on the application of repositioning or repurposing of essential drug moieties present in drugs that are already used for the treatment of some diseases such as diabetes, human immunodeficiency virus (HIV) infection and inflammation as anticancer agents. This review thus covers the available literature where molecular modeling of drugs/enzyme inhibitors through SBDD is reported for antidiabetics, anti-HIV and inflammatory diseases, which are structurally modified and screened for anticancer activity using respective cell lines.

Antifungal therapy of Candida biofilms: Past, present and future

Virtually all Candida species linked to clinical candidiasis are capable of forming highly resistant biofilms on different types of surfaces, which poses an additional significant threat and further complicates therapy of these infections. There is a scarcity of antifungal agents, and their effectiveness, particularly against biofilms, is limited. Here we provide a historical perspective on antifungal agents and therapy of Candida biofilms. As we reflect upon the past, consider the present, and look towards the future of antifungal therapy of Candida biofilms, we believe that there are reasons to remain optimistic, and that the major challenges of Candida biofilm therapy can be conquered within a reasonable timeframe.

A need for clinical trial: re-purposing the Monoamine Oxidase Inhibitors (MAO-I) for rheumatoid arthritis (RA)

There is a group of enzymes called monoamine oxidase(s) (MAOs) that help with the oxidation of amines found in both our diet and our bodies. Currently, monoamine oxidase inhibitors (MAO-Is) are utilized to manage conditions like depression, Parkinson's disease, and other psychiatric disorders. Rheumatoid arthritis (RA) is an auto-immune disease that has been linked to negative changes in mental health, such as depression. When depression co-occurs with RA, it can further worsen the outcome of the disease. Inhibiting monoamine oxidases could potentially treat RA by improving its pathological markers. Using existing pre-clinical and clinical data on safety and toxicity makes drug re-purposing advantageous. Hence, the pre-clinical validation of MAO-I's effectiveness in managing RA requires urgent regulatory intervention to commence clinical trials. Back in 1983, a clinical case report put forward the idea of repurposing MAO-I for RA treatment. Although MAO-I had been used for depression, it was observed to have a significant reduction in joint pain and stiffness. However, no significant clinical research has been conducted on this matter since then. In this commentary article, we provide a summary of the pre-clinical data that is currently available. The main focus of our discussion is on the significance of clinical trials for MAO-I, repurposing it for RA, and using it for the simultaneous management of depression and RA.

Repurposing of approved antivirals against dengue virus serotypes: an in silico and in vitro mechanistic study

Dengue is an emerging, mosquito-borne viral disease of international public health concern. Dengue is endemic in more than 100 countries across the world. However, there are no clinically approved antivirals for its cure. Drug repurposing proves to be an efficient alternative to conventional drug discovery approaches in this regard, as approved drugs with an established safety profile are tested for new indications, which circumvents several time-consuming experiments. In the present study, eight approved RNA-dependent RNA polymerase inhibitors of Hepatitis C virus were virtually screened against the Dengue virus polymerase protein, and their antiviral activity was assessed in vitro. Schrödinger software was used for in silico screening, where the compounds were passed through several hierarchical filters. Among the eight compounds, dasabuvir was finally selected for in vitro cytotoxicity and antiviral screening. Cytotoxicity profiling of dasabuvir in Vero cells revealed changes in cellular morphology, cell aggregation, and detachment at 50 μM. Based on these results, four noncytotoxic concentrations of dasabuvir (0.1, 0.25, 0.5, and 1 µM) were selected for antiviral screening against DENV-2 under three experimental conditions: pre-infection, co-infection, and post-infection treatment, by plaque reduction assay. Viral plaques were reduced significantly (p < 0.05) in the co-infection and post-infection treatment regimens; however, no reduction was observed in the pretreatment group. This indicated a possible interference of dasabuvir with NS5 RdRp, as seen from in silico interaction studies, translating into a reduction in virus plaques. Such studies reiterate the usefulness of drug repurposing as a viable strategy in antiviral drug discovery. In this drug repurposing study, dasabuvir, a known anti-hepatitis C drug, was selected through virtual screening and assessed for its anti-dengue activity.

Drug Repositioning for Refractory Benign Tumors of the Central Nervous System

Drug repositioning (DR) is the process of identifying novel therapeutic potentials for already-approved drugs and discovering new therapies for untreated diseases. DR can play an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared with the process of de novo drug discovery. Although the number of publications related to DR has rapidly increased, most therapeutic approaches were reported for malignant tumors. Surgical resection represents the definitive treatment for benign tumors of the central nervous system (BTCNS). However, treatment options remain limited for surgery-, chemotherapy- and radiation-refractory BTCNS, as well as malignant tumors. Meningioma, pituitary neuroendocrine tumor (PitNET), and schwannoma are the most common BTCNS. The treatment strategy using DR may be applied for refractory BTCNS, such as Grade 2 meningiomas, neurofibromatosis type 2-related schwannomatosis, and PitNETs with cavernous sinus invasion. In the setting of BTCNS, stable disease can provide significant benefit to the patient. DR may provide a longer duration of survival without disease progression for patients with refractory BTCNS. This article reviews the utility of DR for refractory BTCNS.

COVID-19 therapeutics: Clinical application of repurposed drugs and futuristic strategies for target-based drug discovery

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes the complicated disease COVID-19. Clinicians are continuously facing huge problems in the treatment of patients, as COVID-19-specific drugs are not available, hence the principle of drug repurposing serves as a one-and-only hope. Globally, the repurposing of many drugs is underway; few of them are already approved by the regulatory bodies for their clinical use and most of them are in different phases of clinical trials. Here in this review, our main aim is to discuss in detail the up-to-date information on the target-based pharmacological classification of repurposed drugs, the potential mechanism of actions, and the current clinical trial status of various drugs which are under repurposing since early 2020. At last, we briefly proposed the probable pharmacological and therapeutic drug targets that may be preferred as a futuristic drug discovery approach in the development of effective medicines.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Drug Repositioning as a Therapeutic Strategy against Streptococcus pneumoniae: Cell Membrane as Potential Target

A collection of repurposing drugs (Prestwick Chemical Library) containing 1200 compounds was screened to investigate the drugs' antimicrobial effects against planktonic cultures of the respiratory pathogen Streptococcus pneumoniae. After four discrimination rounds, a set of seven compounds was finally selected, namely (i) clofilium tosylate; (ii) vanoxerine; (iii) mitoxantrone dihydrochloride; (iv) amiodarone hydrochloride; (v) tamoxifen citrate; (vi) terfenadine; and (vii) clomiphene citrate (Z, E). These molecules arrested pneumococcal growth in a liquid medium and induced a decrease in bacterial viability between 90.0% and 99.9% at 25 µM concentration, with minimal inhibitory concentrations (MICs) also in the micromolar range. Moreover, all compounds but mitoxantrone caused a remarkable increase in the permeability of the bacterial membrane and share a common, minimal chemical structure consisting of an aliphatic amine linked to a phenyl moiety via a short carbon/oxygen linker. These results open new possibilities to tackle pneumococcal disease through drug repositioning and provide clues for the design of novel membrane-targeted antimicrobials with a related chemical structure.

Structure-based virtual screening of chemical libraries as potential MELK inhibitors and their therapeutic evaluation against breast cancer

New targeted therapy for triple negative breast cancer (TNBC) is an urgent need, as advanced disease responds poorly to conventional chemotherapy. Genomic and proteomic studies are currently investigating new genes and proteins as promising therapeutic targets. One of such therapeutic targets is a cell cycle regulatory kinase; Maternal Embryonic Leucine Zipper Kinase (MELK), overexpressed in TNBC and correlated with cancer development. We performed molecular docking for virtual screening of chemical libraries (phytochemicals/synthetic drugs) against MELK protein structure and identified 8 phytoconstituents (isoxanthorin, emodin, gamma-coniceine, quercetin, tenuazonic acid, isoliquiritigenin, kaempferol, and Nobiletin) and 8 synthetic drugs (tetrahydrofolic acid, alfuzosin, lansoprazole, ketorolac, ketoprofen, variolin B, orantinib, and firestein) as potential hits interacting with the active site residues of MELK based on bound poses, hydrogen bond, hydrophobic interactions and MM/GBSA binding free energies. ADME and drug-likeness prediction further identified few hits with high drug-likeness properties and were further tested for anti-tumorigenic potential. Two phytochemicals isoliquiritigenin and emodin demonstrated growth inhibitory effects on TNBC MDA-MB-231 cells while much lower effect was observed on non-tumorigenic MCF-10A mammary epithelial cells. Treatment with both molecules downregulated MELK expression, induced cell cycle arrest, accumulated DNA damage and enhanced apoptosis. The study identified isoliquiritigenin and emodin as potential MELK inhibitors and provides a basis for subsequent experimental validation and drug development against cancer.

An overview of current strategies and future prospects in drug repurposing in tuberculosis

A large number of the population faces mortality as an effect of tuberculosis (TB). The line of treatment in the management of TB faces a jolt with ever-increasing multi-drug resistance (DR) cases. Further, the drugs engaged in the treatment of TB are associated with different toxicities, such as renal and hepatic toxicity. Different combinations are sought for effective anti-tuberculosis (anti-TB) effects with a decrease in toxicity. In this regard, drug repurposing has been very promising in improving the efficacy of drugs by enhancement of bioavailability and widening the safety margin. The success in drug repurposing lies in specified binding and inhibition of a particular target in the drug molecule. Different drugs have been repurposed for various ailments like cancer, Alzheimer’s disease, acquired immunodeficiency syndrome (AIDS), hair loss, etc. Repurposing in anti-TB drugs holds great potential too. The use of whole-cell screening assays and the availability of large chemical compounds for testing against Mycobacterium tuberculosis poses a challenge in this development. The target-based discovery of sites has emerged in the form of phenotypic screening as ethionamide R (EthR) and malate synthase inhibitors are similar to pharmaceuticals. In this review, the authors have thoroughly described the drug repurposing techniques on the basis of pharmacogenomics and drug metabolism, pathogen-targeted therapy, host-directed therapy, and bioinformatics approaches for the identification of drugs. Further, the significance of repurposing of drugs elaborated on large databases has been revealed. The role of genomics and network-based methods in drug repurposing has been also discussed in this article.

Drug Repurposing: An Effective Tool in Modern Drug Discovery

Drug repurposing is using an existing drug for a new treatment that was not indicated before. It has received immense attention during the COVID-19 pandemic emergency. Drug repurposing has become the need of time to fasten the drug discovery process and find quicker solutions to the over-exerted healthcare scenario and drug needs. Drug repurposing involves identifying the drug, evaluating its efficiency using preclinical models, and proceeding to phase II clinical trials. Identification of the drug candidate can be made through computational and experimental approaches. This approach usually utilizes public databases for drugs. Data from primary and translational research, clinical trials, anecdotal reports regarding off-label uses, and other published human data information available are included. Using artificial intelligence algorithms and other bioinformatics tools, investigators systematically try to identify the interaction between drugs and protein targets. It can be combined with genetic data, clinical analysis, structure (molecular docking), pathways, signatures, targets, phenotypes, binding assays, and artificial intelligence to get an optimum outcome in repurposing. This article describes the strategies involved in drug repurposing and enlists a series of repurposed drugs and their indications.

Small molecules to perform big roles: The search for Parkinson's and Huntington's disease therapeutics

Neurological motor disorders (NMDs) such as Parkinson's disease and Huntington's disease are characterized by the accumulation and aggregation of misfolded proteins that trigger cell death of specific neuronal populations in the central nervous system. Differential neuronal loss initiates the impaired motor control and cognitive function in the affected patients. Although major advances have been carried out to understand the molecular basis of these diseases, to date there are no treatments that can prevent, cure, or significantly delay the progression of the disease. In this context, strategies such as gene editing, cellular therapy, among others, have gained attention as they effectively reduce the load of toxic protein aggregates in different models of neurodegeneration. Nevertheless, these strategies are expensive and difficult to deliver into the patients' nervous system. Thus, small molecules and natural products that reduce protein aggregation levels are highly sought after. Numerous drug discovery efforts have analyzed large libraries of synthetic compounds for the treatment of different NMDs, with a few candidates reaching clinical trials. Moreover, the recognition of new druggable targets for NMDs has allowed the discovery of new small molecules that have demonstrated their efficacy in pre-clinical studies. It is also important to recognize the contribution of natural products to the discovery of new candidates that can prevent or cure NMDs. Additionally, the repurposing of drugs for the treatment of NMDs has gained huge attention as they have already been through clinical trials confirming their safety in humans, which can accelerate the development of new treatment. In this review, we will focus on the new advances in the discovery of small molecules for the treatment of Parkinson's and Huntington's disease. We will begin by discussing the available pharmacological treatments to modulate the progression of neurodegeneration and to alleviate the motor symptoms in these diseases. Then, we will analyze those small molecules that have reached or are currently under clinical trials, including natural products and repurposed drugs.

Off-label treatments as potential accelerators in the search for the ideal antifungal treatment of cryptococcosis

Cryptococcosis is an opportunistic mycosis that mainly affects immunosuppressed patients. The treatment is a combination of three antifungal agents: amphotericin B, 5-flucytosine and fluconazole. However, these drugs have many disadvantages, such as high nephrotoxicity, marketing bans in some countries and fungal resistance. One of the solutions to find possible new drugs is pharmacological repositioning. This work presents repositioned drugs as an alternative for new antifungal therapies for cryptococcosis. All the studies here were performed in vitro or in animal models, except for sertraline, which reached phase III in humans. There is still no pharmacological repositioning approval for cryptococcosis in humans, though this review shows the potential of repurposing as a rapid approach to finding new agents to treat cryptococcosis.

Selected Statins as Dual Antiproliferative-Antiinflammatory Compounds

BACKGROUND

We hypothesized that superlative dual cytotoxicity-antiinflammtion bioefficacies of 9 selected lipophilic statins correlate to their chelation effect of 3,5-dihydroxyheptanoic acid.

METHODOLOGY

Lipophilic-acid chelating statins have been screened for in vitro duality of proliferation inhibition and NO-radical scavenging capacities.

RESULTS

Their spectrum of selectivity indices for safety in PDL fibroblasts -based 72h incubations was reported. Surprisingly despite its lack on macrophages LPS-triggered inflammation over 5-200 µM and unlike the 8 statins; cerivastatin had growth inhibition IC50 values of 40nM (SW620), 110nM (HT29), 2.9 µM (HCT116), 6µM (SW480), and most notably 38µM (<50 µM, in Caco2). Exclusively cerivastatin exerted antitumorigenesis IC50 values <50 µM in all T47D, MCF7 and PANC1 72h cultures. In statins with greater antiinflammation affinity than indomethacin's; lovastatin had cytotoxicity IC50 values <20 µM in SW620<HT29<ACT116100 µM in Caco2. Atorvastatin was found of viability reduction IC50 value <20 µM in HCT11650µM in T47D, MCF7 and PANC1. Rosuvastatin had antineoplastic IC50 values (<50 µM) in SW620<SW48050 µM in remaining colorectal, breast and pancreatic cancer cell lines. In statins with appreciable antiinflammation but reasonably lower affinity than indomethacin's and cytotoxicity IC50 values >50µM in T47D, MCF7 and PANC1; pravastatin had viability reduction IC50 values <50µM in HT2950 µM were for statins in remaining colorectal cancer cell lines, breast cancer and pancreatic cancer cell lines.

CONCLUSION

Among the rest, cerivastatin warrants further novel scaffold development to maximize efficacy and optimal molecular action mechanisms of chemotherapy/prevention.

Acetylsalicylic Acid-Primus Inter Pares in Pharmacology

Acetylsalicylic acid (ASA) is one of the first drugs to be obtained by synthesis while being the most used. It has experienced the longest lasting commercial success and is considered the most popular drug of the modern era. ASA, originally used as an anti-inflammatory medication, nowadays is predominantly used as an antiplatelet agent for prophylaxis in cardiac patients. Many studies show that the benefits of using ASA far outweigh the potential risk of side effects. With particular emphasis on the possibility of ASA repositioning for new therapies, extending the indications for use beyond the diseases from the spectrum of atherosclerotic diseases, such as cancer, requires shifting the benefit-risk ratio, although very good, even more towards safety. Interesting activities consisting not only of changing the formulation but also modifying the drug molecule seem to be an important goal of the 21st century. ASA has become a milestone in two important fields: pharmacy and medicine. For a pharmacist, ASA is a long-used drug for which individual indications are practically maintained. For a doctor, acetylsalicylic acid is primarily an antiplatelet drug that saves millions of lives of patients with coronary heart disease or after a stroke. These facts do not exempt us from improving therapeutic methods based on ASA, the main goal of which is to reduce the risk of side effects, as well as to extend effectiveness. Modified acetylsalicylic acid molecules already seem to be a promising therapeutic option.

Drug-protein interaction prediction via variational autoencoders and attention mechanisms

During the process of drug discovery, exploring drug-protein interactions (DPIs) is a key step. With the rapid development of biological data, computer-aided methods are much faster than biological experiments. Deep learning methods have become popular and are mainly used to extract the characteristics of drugs and proteins for further DPIs prediction. Since the prediction of DPIs through machine learning cannot fully extract effective features, in our work, we propose a deep learning framework that uses variational autoencoders and attention mechanisms; it utilizes convolutional neural networks (CNNs) to obtain local features and attention mechanisms to obtain important information about drugs and proteins, which is very important for predicting DPIs. Compared with some machine learning methods on the C.elegans and human datasets, our approach provides a better effect. On the BindingDB dataset, its accuracy (ACC) and area under the curve (AUC) reach 0.862 and 0.913, respectively. To verify the robustness of the model, multiclass classification tasks are performed on Davis and KIBA datasets, and the ACC values reach 0.850 and 0.841, respectively, thus further demonstrating the effectiveness of the model.

Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach

Purpose

The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs' possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations.

Methods

Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands' binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit.

Results

Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme.

Conclusion

In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.

RSDB: A rare skin disease database to link drugs with potential drug targets for rare skin diseases

Rare skin diseases include more than 800 diseases affecting more than 6.8 million patients worldwide. However, only 100 drugs have been developed for treating rare skin diseases in the past 38 years. To investigate potential treatments through drug repurposing for rare skin diseases, it is necessary to have a well-organized database to link all known disease causes, mechanisms, and related information to accelerate the process. Drug repurposing provides less expensive and faster potential options to develop treatments for known diseases. In this work, we designed and constructed a rare skin disease database (RSDB) as a disease-centered information depository to facilitate repurposing drug candidates for rare skin diseases. We collected and integrated associated genes, chemicals, and phenotypes into a network connected by pairwise relationships between different components for rare skin diseases. The RSDB covers 891 rare skin diseases defined by the Orphanet and GARD databases. The organized network for each rare skin disease comprises associated genes, phenotypes, and chemicals with the corresponding connections. The RSDB is available at https://rsdb.cmdm.tw .

Drug repurposing: An emerging strategy in alleviating skin cancer

Skin cancer is one of the most common forms of cancer. Several million people are estimated to have affected with this condition worldwide. Skin cancer generally includes melanoma and non-melanoma with the former being the most dangerous. Chemotherapy has been one of the key therapeutic strategies employed in the treatment of skin cancer, especially in advanced stages of the disease. It could be also used as an adjuvant with other treatment modalities depending on the type of skin cancer. However, there are several shortfalls associated with the use of chemotherapy such as non-selectivity, tumour resistance, life-threatening toxicities, and the exorbitant cost of medicines. Furthermore, new drug discovery is a lengthy and costly process with minimal likelihood of success. Thus, drug repurposing (DR) has emerged as a new avenue where the drug approved formerly for the treatment of one disease can be used for the treatment of another disease like cancer. This approach is greatly beneficial over the de novo approach in terms of time and cost. Moreover, there is minimal risk of failure of repurposed therapeutics in clinical trials. There are a considerable number of studies that have reported on drugs repurposed for the treatment of skin cancer. Thus, the present manuscript offers a comprehensive overview of drugs that have been investigated as repurposing candidates for the efficient treatment of skin cancers mainly melanoma and its oncogenic subtypes, and non-melanoma. The prospects of repurposing phytochemicals against skin cancer are also discussed. Furthermore, repurposed drug delivery via topical route and repurposed drugs in clinical trials are briefed. Based on the findings from the reported studies discussed in this manuscript, drug repurposing emerges to be a promising approach and thus is expected to offer efficient treatment at a reasonable cost in devitalizing skin cancer.

Chlorhexidine as a Keap1-Nrf2 inhibitor: a new target for an old drug for Parkinson's disease therapy

Oxidative stress plays a vital role in the pathophysiology of most neurodegenerative diseases such as Parkinson's disease (PD). The Keap1-Nrf2-ARE pathway, one of the internal defense mechanisms, curbs the reactive oxygen species (ROS) generated in the cellular environment. The pathway leads to the expression of antioxidant genes such as HO-1, GCLC, and NQO1, which act as cellular redox switches and protect the cellular environment. Keap1, the negative regulator of Nrf2, is a potential therapeutic target for treating age-related neurodegenerative diseases. Tecfidera (Dimethyl fumarate), used in the intervention for relapsing multiple sclerosis, is the only commercial drug known to regulate the Nrf2 function. Here, we have identified a repurposing drug, chlorhexidine (LBP125), through ligand-based pharmacophore development and screening against the DrugBank, as a potential inhibitor of the β-propeller domain of Keap1 (Keap1-DC). Chlorhexidine, an antimicrobial agent, is widely used as a mouthwash, skin cleanser, and intervening bacterial infection during childbirth. The biochemical assay confirmed a significant binding affinity of 30 µM and competitively inhibited the Nrf2 peptide interaction. Moreover, chlorhexidine also exerts cytoprotection in a neurotoxic cell model of PD through Keap1-Nrf2 disruption leading to nuclear translocation of Nrf2 and expression of downstream genes, HO-1, and NQO1. Hence, the chemical scaffold of chlorhexidine is a potential lead to develop new chemical libraries with drug-like properties for treating PD. Communicated by Ramaswamy H. Sarma.

Old drugs, new tricks: Emerging role of drug repurposing in the management of atopic dermatitis

Atopic dermatitis is a chronic recurring pruritic inflammatory skin disease manifested by increased pro-inflammatory mediators which lead to dry, thickened, cracked, scaly skin. The current treatment options for atopic dermatitis management comprise drawbacks and leave unmet effective clinical needs. So, the approach for repurposing existing drugs for atopic dermatitis management may potentially overcome these unmet needs. Diseases that share the common pathophysiological pathways with atopic dermatitis can serve as a foundation for the repurposing of drugs. Drugs used in the management of cancer, rheumatoid arthritis, and other immune-mediated diseases such as psoriasis are under investigation to know the potential in atopic dermatitis management by utilizing repurposing strategies for a novel therapeutic indication. This review mainly envisages the probable repurposing of drugs for the management of atopic dermatitis disease; the barriers and regulatory aspects involved in the repurposing of existing drugs.

Diuretics: a contemporary pharmacological classification?

Diuretics are drugs that increase the flow of urine. They are commonly used to treat edema, hypertension, and heart failure. Typically, the pharmacological group consists of five classes: thiazide diuretics, loop diuretics, potassium-sparing diuretics, osmotic diuretics, and carbonic anhydrase inhibitors. This traditional classification and the nomenclature of diuretics have not changed over the last decades, which means that it was not adapted to current pharmacological research. Modern approaches in the field of pharmacological nomenclature suggest the introduction of mechanism-based drug class designations, which is not yet reflected in the group of diuretics. Moreover, included drug classes have lost their relevance as diuretic agents. Carbonic anhydrase inhibitors, for example, are mainly used in the treatment of glaucoma. Newer agents such as vasopressin-2 receptor antagonists or SGLT2 inhibitors possess diuretic properties but are not included in the pharmacological group. This review discusses the currentness of the pharmacological classification of diuretics. We elaborate changes in the field of nomenclature, the contemporary medical use of classical diuretics, and new diuretic agents.

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.

The diaryl-imidazopyridazine anti-plasmodial compound, MMV652103, exhibits anti-breast cancer activity

Breast cancer is the most common malignancy in women worldwide and it remains a global health burden, in part, due to poor response and tolerance to current therapeutics. Drug repurposing, which seeks to identify new indications for existing and investigational drugs, has become an exciting strategy to address these challenges. Here we describe the anti-breast cancer activity of a diaryl-imidazopyridazine compound, MMV652103, which was previously identified for its anti-plasmodial activity. We demonstrate that MMV652103 potently inhibits the oncogenic PI4KB and PIK3C2G lipid kinases, is selectively cytotoxic to MCF7 and T47D estrogen receptor positive breast cancer cells and inhibits their ability to survive and migrate. The underlying mechanisms involved included the induction of reactive oxygen species and activation of the DNA damage and p38 MAPK stress signaling pathways. This was associated with a G1 cell cycle arrest and an increase in levels of the cyclin-dependent kinase inhibitor p21 and activation of apoptotic and autophagic cell death pathways. Lastly, MMV652103 significantly reduced the weight and metastases of MCF7 induced tumors in an in vivo chick embryo model and displayed a favorable safety profile. These findings position MMV652103 as a promising chemotherapeutic in the treatment of oestrogen receptor positive breast cancers.

Transcription Factor Activation Profiles (TFAP) identify compounds promoting differentiation of Acute Myeloid Leukemia cell lines

Repurposing of drugs for new therapeutic use has received considerable attention for its potential to limit time and cost of drug development. Here we present a new strategy to identify chemicals that are likely to promote a desired phenotype. We used data from the Connectivity Map (CMap) to produce a ranked list of drugs according to their potential to activate transcription factors that mediate myeloid differentiation of leukemic progenitor cells. To validate our strategy, we tested the in vitro differentiation potential of candidate compounds using the HL-60 human cell line as a myeloid differentiation model. Ten out of 22 compounds, which were ranked high in the inferred list, were confirmed to promote significant differentiation of HL-60. These compounds may be considered candidate for differentiation therapy. The method that we have developed is versatile and it can be adapted to different drug repurposing projects.

Repurposing – second life for drugs

Drug repurposing refers to finding new indications for existing drugs. The paradigm shift from traditional drug discovery to drug repurposing is driven by the fact that new drug pipelines are getting dried up because of mounting Research &amp; Development (R&amp;D) costs, long timeline for new drug development, low success rate for new molecular entities, regulatory hurdles coupled with revenue loss from patent expiry and competition from generics. Anaemic drug pipelines along with increasing demand for newer effective, cheaper, safer drugs and unmet medical needs call for new strategies of drug discovery and, drug repurposing seems to be a promising avenue for such endeavours. Drug repurposing strategies have progressed over years from simple serendipitous observations to more complex computational methods in parallel with our ever-growing knowledge on drugs, diseases, protein targets and signalling pathways but still the knowledge is far from complete. Repurposed drugs too have to face many obstacles, although lesser than new drugs, before being successful.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Repurposing approved therapeutics for new indication: Addressing unmet needs in psoriasis treatment

Psoriasis is a chronic inflammatory autoimmune condition manifested by the hyperproliferation of keratinocytes with buildup of inflammatory red patches and scales on skin surfaces. The available treatment options for the management of psoriasis have various drawbacks, and the clinical need for effective therapeutics for this disease remain unmet; therefore, the approaches of drug repurposing or drug repositioning could potentially be used for treating indications of psoriasis. The undiscovered potential of drug repurposing or repositioning compensates for the limitations and hurdles in drug discovery and drug development processes. Drugs initially approved for other indications, including anticancer, antidiabetic, antihypertensive, and anti-arthritic activities, are being investigated for their potential in psoriasis management as a new therapeutic indication by using repurposing strategies. This article envisages the potential of various therapeutics for the management of psoriasis.

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Psoriasis is an autoimmune inflammatory skin disorder with complex physiology.

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Conventional treatments for psoriasis cause severe adverse effects; therefore an unmet need remains for safer and more effective therapies for psoriasis.

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Various drugs that effectively decrease the inflammation and proliferation of skin cells can be repurposed for the management of psoriasis.

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Repurposed drugs provide various incentives to the pharmaceutical industry.

Drug repurposing for COVID-19: Approaches, challenges and promising candidates

Traditional drug development and discovery has not kept pace with threats from emerging and re-emerging diseases such as Ebola virus, MERS-CoV and more recently, SARS-CoV-2. Among other reasons, the exorbitant costs, high attrition rate and extensive periods of time from research to market approval are the primary contributing factors to the lag in recent traditional drug developmental activities. Due to these reasons, drug developers are starting to consider drug repurposing (or repositioning) as a viable alternative to the more traditional drug development process. Drug repurposing aims to find alternative uses of an approved or investigational drug outside of its original indication. The key advantages of this approach are that there is less developmental risk, and it is less time-consuming since the safety and pharmacological profile of the repurposed drug is already established. To that end, various approaches to drug repurposing are employed. Computational approaches make use of machine learning and algorithms to model disease and drug interaction, while experimental approaches involve a more traditional wet-lab experiments. This review would discuss in detail various ongoing drug repurposing strategies and approaches to combat the current COVID-19 pandemic, along with the advantages and the potential challenges.

A unified drug-target interaction prediction framework based on knowledge graph and recommendation system

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

Repurposing of the Fasciolicide Triclabendazole to Treat Infections Caused by Staphylococcus spp. and Vancomycin-Resistant Enterococci

One approach to combat the increasing incidence of multidrug-resistant (MDR) bacterial pathogens involves repurposing existing compounds with known safety and development pathways as new antibacterial classes with potentially novel mechanisms of action. Here, triclabendazole (TCBZ), a drug originally developed to treat Fasciola hepatica (liver fluke) in sheep and cattle, and later in humans, was evaluated as an antibacterial alone or in combination with sub-inhibitory concentrations of polymyxin B (PMB) against clinical isolates and reference strains of key Gram-positive and Gram-negative bacteria. We show for the first time that in vitro, TCBZ selectively kills methicillin-sensitive and methicillin-resistant Staphylococcus aureus and Staphylococcus pseudintermedius at a minimum inhibitory concentration (MIC) range of 2–4 µg/mL, and vancomycin-resistant enterococci at a MIC range of 4–8 µg/mL. TCBZ also inhibited key Gram-negative bacteria in the presence of sub-inhibitory concentrations of PMB, returning MIC₉₀ values of 1 µg/mL for Escherichia coli, 8 µg/mL for Klebsiella pneumoniae, 2 µg/mL for Acinetobacter baumannii and 4 µg/mL for Pseudomonasaeruginosa. Interestingly, TCBZ was found to be bacteriostatic against intracellular S. aureus but bactericidal against intracellular S. pseudintermedius. Additionally, TCBZ’s favourable pharmacokinetic (PK) and pharmacodynamic (PD) profile was further explored by in vivo safety and efficacy studies using a bioluminescent mouse model of S. aureus sepsis. We show that repeated four-hourly oral treatment of mice with 50 mg/kg TCBZ after systemic S. aureus challenge resulted in a significant reduction in S. aureus populations in the blood to 18 h post-infection (compared to untreated mice) but did not clear the bacterial infection from the bloodstream, consistent with in vivo bacteriostatic activity. These results indicate that additional pharmaceutical development of TCBZ may enhance its PK/PD, allowing it to be an appropriate candidate for the treatment of serious MDR bacterial pathogens.

Topological network measures for drug repositioning

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

The Interface of Therapeutics and Genomics in Cardiovascular Medicine

Pharmacogenomics has a burgeoning role in cardiovascular medicine, from warfarin dosing to antiplatelet choice, with recent developments in sequencing bringing the promise of personalised medicine ever closer to the bedside. Further scientific evidence, real-world clinical trials, and economic modelling are needed to fully realise this potential. Additionally, tools such as polygenic risk scores, and results from Mendelian randomisation analyses, are only in the early stages of clinical translation and merit further investigation. Genetically targeted rational drug design has a strong evidence base and, due to the nature of genetic data, academia, direct-to-consumer companies, healthcare systems, and industry may meet in an unprecedented manner. Data sharing navigation may prove problematic. The present manuscript addresses these issues and concludes a need for further guidance to be provided to prescribers by professional bodies to aid in the consideration of such complexities and guide translation of scientific knowledge to personalised clinical action, thereby striving to improve patient care. Additionally, technologic infrastructure equipped to handle such large complex data must be adapted to pharmacogenomics and made user friendly for prescribers and patients alike.

Repurposing Pharmaceuticals Previously Approved by Regulatory Agencies to Medically Counter Injuries Arising Either Early or Late Following Radiation Exposure

The increasing risks of radiological or nuclear attacks or associated accidents have served to renew interest in developing radiation medical countermeasures. The development of prospective countermeasures and the subsequent gain of Food and Drug Administration (FDA) approval are invariably time consuming and expensive processes, especially in terms of generating essential human data. Due to the limited resources for drug development and the need for expedited drug approval, drug developers have turned, in part, to the strategy of repurposing agents for which safety and clinical data are already available. Approval of drugs that are already in clinical use for one indication and are being repurposed for another indication is inherently faster and more cost effective than for new agents that lack regulatory approval of any sort. There are four known growth factors which have been repurposed in the recent past as radiomitigators following the FDA Animal Rule: Neupogen, Neulasta, Leukine, and Nplate. These four drugs were in clinic for several decades for other indications and were repurposed. A large number of additional agents approved by various regulatory authorities for given indications are currently under investigation for dual use for acute radiation syndrome or for delayed pathological effects of acute radiation exposure. The process of drug repurposing, however, is not without its own set of challenges and limitations.

Discovery of novel dihydroartemisinin-cinnamic hybrids inducing lung cancer cells apoptosis via inhibition of Akt/Bad signal pathway

A series of dihydroartemisinin-cinnamic acid hybrids were designed, synthesized and evaluated. Most of the tested compounds showed enhanced anti-proliferative activities than artemisinin and dihydroartemisinin, among which 16 g had the superior potency with IC₅₀ values ranging from 5.07 μM to 7.88 μM against four tested cancer cell lines. The cell cycle arrest revealed that 16 g induced A549 cell cycle arrest at G0/G1 phase via regulation of G1-related protein expression (Cdk4). Further mechanism studies reveal that 16 g induced A549 cells apoptosis via inhibiting Akt/Bad pathway. Moreover, 16 g depolarized the mitochondria membrane potentials and induced ROS generation in A549. Additionally, 16 g blocked migration of A549 cells in a concentration-dependent manner. What's more, 16 g is barely nontoxic to zebrafish embryos. Overall, the cell cycle arrest, inhibition of Akt/Bad signal pathway, ROS generation and migration blocked might explain the potent anti-proliferative activities of these compounds.

Tetracyclines, a promise for neuropsychiatric disorders: from adjunctive therapy to the discovery of new targets for rational drug design in psychiatry

Major mental disorders, such as schizophrenia, bipolar disorder, and major depressive disorder, represent the leading cause of disability worldwide. Nevertheless, the current pharmacotherapy has several limitations, and a large portion of patients do not respond appropriately to it or remain with disabling symptoms overtime. Traditionally, pharmacological interventions for psychiatric disorders modulate dysfunctional neurotransmitter systems. In the last decades, compelling evidence has advocated for chronic inflammatory mechanisms underlying these disorders. Therefore, the repurposing of anti-inflammatory agents has emerged as an attractive therapeutic tool for mental disorders. Minocycline (MINO) and doxycycline (DOXY) are semisynthetic second-generation tetracyclines with neuroprotective and anti-inflammatory properties. More recently, the most promising results obtained in clinical trials using tetracyclines for major psychiatric disorders were for schizophrenia. In a reverse translational approach, tetracyclines inhibit microglial reactivity and toxic inflammation by mechanisms related to the inhibition of nuclear factor kappa B signaling, cyclooxygenase 2, and matrix metalloproteinases. However, the molecular mechanism underlying the effects of these tetracyclines is not fully understood. Therefore, the present review sought to summarize the latest findings of MINO and DOXY use for major psychiatric disorders and present the possible targets to their molecular and behavioral effects. In conclusion, tetracyclines hold great promise as (ready-to-use) agents for being used as adjunctive therapy for human neuropsychiatric disorders. Hence, the understanding of their molecular mechanisms may contribute to the discovery of new targets for the rational drug design of novel psychoactive agents.

Inhibitory mechanism of an antifungal drug, caspofungin against amyloid β peptide aggregation: Repurposing via neuroinformatics and an experimental approach

The multifactorial neurological condition called Alzheimer's disease (AD) primarily affects elderly individuals. Despite the calamitous consequences of AD, curative strategies for a regimen to apply remain inadequate as several factors contribute to AD etiology. Drug repurposing is an advance strategy prior to drug discovery as various effective drugs perform through alteration of multiple targets, and the present "poly-pharmacology" can be a curative approach to complex disorders. AD's multifactorial behavior actively encourages the hypothesis for a drug design approach focused on drug repurposing. In this study, we discovered that an antifungal drug, Caspofungin (CAS) is a potent Aβ aggregation inhibitor that displays significantly reduced toxicity associated with AD. Drug reprofiling and REMD simulations demonstrated that CAS interacts with the β-sheet section, known as Aβ amyloid fibrils hotspot. CAS leads to destabilization of β-sheet and, conclusively, in its devaluation. Later, in vitro experiments were acquired in which the fibrillar volume was reduced for CAS-treated Aβ peptide. For the first time ever, this study has determined an antifungal agent as the Aβ amyloid aggregation's potent inhibitor. Several efficient sequence-reliant potent inhibitors can be developed in future against the amyloid aggregation for different amyloid peptide by the processing and conformational optimization of CAS.

Rimonabant potentiates the antifungal activity of amphotericin B by increasing cellular oxidative stress and cell membrane permeability

Amphotericin B (AmB) is a very effective antifungal agent, and resistance in clinical isolates is rare. However, clinical treatment with AmB is often associated with severe side effects. Reducing the administration dose of AmB by combining it with other agents is a promising strategy to minimize this toxicity. In this study, we screened a small compound library and observed that the anti-obesity drug rimonabant exhibited synergistic antifungal action with AmB against Candida species and Cryptococcus neoformans. Moreover, the combination of AmB and rimonabant exhibited synergistic or additive effects against Candida albicans biofilm formation and cell viability in preformed biofilms. The effects of this combination were further confirmed in vivo using a murine systemic infection model. Exploration of the mechanism of synergy revealed that rimonabant enhances the fungicidal activity of AmB by increasing cellular oxidative stress and cell membrane permeability. These findings provide a foundation for the possible development of AmB-rimonabant polytherapies for fungal infections.

Level up for culture models - How 3D cell culture models benefit SARS-CoV-2 research

Welcome to a new decade and a new issue of the Biomedical Journal - casting a sorrowful look onto a year that will go down in history as a tombstone etched by the COVID-19 pandemic, but also a hopeful glance into the future, now that multiple vaccination programs against the SARS-CoV-2 virus have started. This issue is dedicated to the continuous effort by researchers all around the globe to understand and counter the pathogen, as well as to be better prepared for future threats. Therefore, we learn about the advantages of complex 3D cell culture models for studying host-virus interactions, and the disease course of COVID-19 in children. Moreover, we discover how neutralising monoclonal antibodies and peptide-based vaccines against SARS-CoV-2 are developed, and the therapeutic potentials of lopinavir/ritonavir, mesenchymal stem cells, as well as plant and algae extracts. Finally, we ponder over the lessons to be learnt from SARS-CoV and MERS, and hear about differences between nucleotide-based SARS-CoV-2 detection methods.

Prediction of drug-target binding affinity using similarity-based convolutional neural network

Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in novel public affinity data available in the DT-related databases enables advanced deep learning techniques to be used to predict binding affinities. In this paper, we propose a similarity-based model that applies 2-dimensional (2D) convolutional neural network (CNN) to the outer products between column vectors of two similarity matrices for the drugs and targets to predict DT binding affinities. To our best knowledge, this is the first application of 2D CNN in similarity-based DT binding affinity prediction. The validation results on multiple public datasets show that the proposed model is an effective approach for DT binding affinity prediction and can be quite helpful in drug development process.

Paroxetine-Overview of the Molecular Mechanisms of Action

In the 21st century and especially during a pandemic, the diagnosis and treatment of depression is an essential part of the daily practice of many family doctors. It mainly affects patients in the age category 15–44 years, regardless of gender. Anxiety disorders are often diagnosed in children and adolescents. Social phobias can account for up to 13% of these diagnoses. Social anxiety manifests itself in fear of negative social assessment and humiliation, which disrupts the quality of social functioning. Treatment of the above-mentioned disorders is based on psychotherapy and pharmacotherapy. Serious side effects or mortality from antidepressant drug overdose are currently rare. Recent studies indicate that paroxetine (ATC code: N06AB), belonging to the selective serotonin reuptake inhibitors, has promising therapeutic effects and is used off-label in children and adolescents. The purpose of this review is to describe the interaction of paroxetine with several molecular targets in various points of view including the basic chemical and pharmaceutical properties. The central point of the review is focused on the pharmacodynamic analysis based on the molecular mechanism of binding paroxetine to various therapeutic targets.