Recent advances in drug repositioning for the discovery of new anticancer drugs

Computational Analysis of Pathogenetic Pathways in Alzheimer’s Disease and Prediction of Potential Therapeutic Drugs

Background. Alzheimer’s disease (AD) is a chronic and progressive neurodegenerative disease which affects more than 50 million patients and represents 60–80% of all cases of dementia. Mutations in the APP gene, mostly affecting the γ-secretase site of cleavage and presenilin mutations, have been identified in inherited forms of AD. Methods. In the present study, we performed a meta-analysis of the transcriptional signatures that characterize two familial AD mutations (APP^V7171F and PSEN1^M146V) in order to characterize the common altered biomolecular pathways affected by these mutations. Next, an anti-signature perturbation analysis was performed using the AD meta-signature and the drug meta-signatures obtained from the L1000 database, using cosine similarity as distance metrics. Results. Overall, the meta-analysis identified 1479 differentially expressed genes (DEGs), 684 downregulated genes, and 795 upregulated genes. Additionally, we found 14 drugs with a significant anti-similarity to the AD signature, with the top five drugs being naftifine, moricizine, ketoconazole, perindopril, and fexofenadine. Conclusions. This study aimed to integrate the transcriptional profiles associated with common familial AD mutations in neurons in order to characterize the pathogenetic mechanisms involved in AD and to find more effective drugs for AD.

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Indication extension or repositioning of drugs can, if done well, provide a faster, cheaper, and derisked route to the approval of new therapies, creating new options to address pockets of unmet medical need for patients and offering the potential for significant commercial and clinical benefits. We look at the promises and challenges of different repositioning strategies and the disease insights and scalability that new high-resolution patient stratification methodologies can bring. This is exemplified by a systematic analysis of all development candidates and on-market drugs, which identified 477 indication extension opportunities across 30 chronic disease areas, each supported by patient stratification biomarkers. This illustrates the potential that new artificial intelligence (AI) and combinatorial analytics methods have to enhance the rate and cost of innovation across the drug discovery industry.

Targeting Metabolic Reprogramming of T-Cells for Enhanced Anti-Tumor Response

Cancer immunotherapy is an effective treatment option against cancer. One of the approaches of cancer immunotherapy is the modification of T cell-based anti-tumor immune responses. T-cells, a type of adaptive immune response cells responsible for cell-mediated immunity, have long been recognized as key regulators of immune-mediated anti-tumor immunity. T-cell activities have been reported to be suppressed or enhanced by changes in cell metabolism. Moreover, metabolic reprogramming during activation of T cells is required for the development of distinct differentiation profiles of these cells, which may allow the development of long-term cell-mediated anti-tumor immunity. However, T cells have been shown to undergo metabolic exhaustion in tumor microenvironment (TME) as it poses several obstacles to their function. Applications of several mechanistic solutions to improve the efficacy of T cell-based therapies including chimeric antigen receptor (CAR) T cell therapy are yet to be determined. Modifying the metabolic properties of these cells and employing them in cancer immunotherapy is a potential strategy for improving their anti-tumor activity and therapeutic efficacy. To give an insight, in this review paper, we endeavoured to cover metabolic reprogramming in cancer and T cells, signalling mechanisms involved in immuno-metabolic regulation, the effects of the TME on T cell metabolic fitness, and targeting metabolic reprogramming of T cells for an enhanced anti-tumor response.

Design, Synthesis, and Development of 4‐[(7‐Chloroquinoline‐4‐yl)amino]phenol as a Potential SARS‐CoV‐2 Mpro Inhibitor

A series of chloroquine analogs were designed to search for a less toxic chloroquine derivative as a potential SARS‐CoV‐2 Mpro inhibitor. Herein, an ANN‐based QSAR model was built to predict the IC₅₀ values of each analog using the experimental values of other 4‐aminoquinolines as the training set. Subsequently, molecular docking was used to evaluate each analog's binding affinity to Mpro. The analog that showed the greatest affinity and lowest IC₅₀ values was synthesized and characterized for its posterior incorporation into a polycaprolactone‐based nanoparticulate system. After characterizing the loaded nanoparticles, an in vitro drug release assay was carried out, and the cytotoxicity of the analog and loaded nanoparticles was evaluated using murine fibroblast (L929) and human lung adenocarcinoma (A549) cell lines. Results show that the synthesized analog is much less toxic than chloroquine and that the nanoparticulate system allowed for the prolonged release of the analog without evidence of adverse effects on the cell lines used; therefore, suggesting that the analog could be a potential therapeutic option for COVID‐19.

Characterization of Altered Molecular Pathways in the Entorhinal Cortex of Alzheimer’s Disease Patients and In Silico Prediction of Potential Repurposable Drugs

Alzheimer’s disease (AD) is the most common cause of dementia worldwide and is characterized by a progressive decline in cognitive functions. Accumulation of amyloid-β plaques and neurofibrillary tangles are a typical feature of AD neuropathological changes. The entorhinal cortex (EC) is the first brain area associated with pathologic changes in AD, even preceding atrophy of the hippocampus. In the current study, we have performed a meta-analysis of publicly available expression data sets of the entorhinal cortex (EC) in order to identify potential pathways underlying AD pathology. The meta-analysis identified 1915 differentially expressed genes (DEGs) between the EC from normal and AD patients. Among the downregulated DEGs, we found a significant enrichment of biological processes pertaining to the “neuronal system” (R-HSA-112316) and the “synaptic signaling” (GO:0099536), while the “regulation of protein catabolic process” (GO:00042176) and “transport of small molecules” (R-HSA-382551) resulted in enrichment among both the upregulated and downregulated DEGs. Finally, by means of an in silico pharmacology approach, we have prioritized drugs and molecules potentially able to revert the transcriptional changes associated with AD pathology. The drugs with a mostly anti-correlated signature were: efavirenz, an anti-retroviral drug; tacrolimus, a calcineurin inhibitor; and sirolimus, an mTOR inhibitor. Among the predicted drugs, those potentially able to cross the blood-brain barrier have also been identified. Overall, our study found a disease-specific set of dysfunctional biological pathways characterizing the EC in AD patients and identified a set of drugs that could in the future be exploited as potential therapeutic strategies. The approach used in the current study has some limitations, as it does not account for possible post-transcriptional events regulating the cellular phenotype, and also, much clinical information about the samples included in the meta-analysis was not available. However, despite these limitations, our study sets the basis for future investigations on the pathogenetic processes occurring in AD and proposes the repurposing of currently used drugs for the treatment of AD patients.

Use of "Repurposed" Drugs in the Treatment of Epithelial Ovarian Cancer: A Systematic Review

Epithelial ovarian cancer has poor outcomes with standard therapy and limited options for treatment of recurrent disease. This systematic review summarizes the data on the clinical use of repurposed drugs. We searched for clinical studies using "repurposed" agents for the treatment of ovarian cancer in the following databases: PubMed, clinicaltrials.gov, Clinical Trial Registry of India, European Clinical Trials Registry, and Chinese Clinical Trial Registry. We excluded reviews, preclinical studies, and non-English language studies. We assessed the quality of included studies. The following agents/class of agents were included: statins, hydroxychloroquine, metformin, itraconazole, nonsteroidal anti-inflammatory drugs, vitamin D, proton pump inhibitors, beta-blockers, and sodium valproate. Only one randomized controlled trial investigated metformin, which found no benefit of metformin. However, this had a high risk of bias (no details of randomization). Among the observational studies, 70% were of high quality (Newcastle-Ottawa scale ≥7). Clinical benefit was seen for itraconazole, beta-blockers, metformin, statins, and proton pump inhibitors. Though multiple studies aim to repurpose agents in epithelial ovarian cancer, the most published literature is observational, and none are practice-changing. Given the solid preclinical data regarding the anticancer efficacy of these agents, well-designed clinical trials are urgently required.

Drug Repurposing Strategies for Non-Cancer to Cancer Therapeutics

Global efforts invested for the prevention and treatment of cancer need to be repositioned to develop safe, effective, and economic anticancer therapeutics by adopting rational approaches of drug discovery. Drug repurposing is one of the established approaches to reposition old, clinically approved off patent noncancer drugs with known targets into newer indications. The literature review suggests key role of drug repurposing in the development of drugs intended for cancer as well as noncancer therapeutics. A wide category of noncancer drugs namely, drugs acting on CNS, anthelmintics, cardiovascular drugs, antimalarial drugs, anti-inflammatory drugs have come out with interesting outcomes during preclinical and clinical phases. In the present article a comprehensive overview of the current scenario of drug repurposing for the treatment of cancer has been focused. The details of some successful studies along with examples have been included followed by associated challenges.

Past, Present, and Future of Therapies for Pituitary Neuroendocrine Tumors: Need for Omics and Drug Repositioning Guidance

Innovation roadmaps are important, because they encourage the actors in an innovation ecosystem to creatively imagine multiple possible science future(s), while anticipating the prospects and challenges on the innovation trajectory. In this overarching context, this expert review highlights the present unmet need for therapeutic innovations for pituitary neuroendocrine tumors (PitNETs), also known as pituitary adenomas. Although there are many drugs used in practice to treat PitNETs, many of these drugs can have negative side effects and show highly variable outcomes in terms of overall recovery. Building innovation roadmaps for PitNETs' treatments can allow incorporation of systems biology approaches to bring about insights at multiple levels of cell biology, from genes to proteins to metabolites. Using the systems biology techniques, it will then be possible to offer potential therapeutic strategies for the convergence of preventive approaches and patient-centered disease treatment. Here, we first provide a comprehensive overview of the molecular subtypes of PitNETs and therapeutics for these tumors from the past to the present. We then discuss examples of clinical trials and drug repositioning studies and how multi-omics studies can help in discovery and rational development of new therapeutics for PitNETs. Finally, this expert review offers new public health and personalized medicine approaches on cases that are refractory to conventional treatment or recur despite currently used surgical and/or drug therapy.

Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing

Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical’s mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.

In vitro anticancer and antibacterial potentials of selected medicinal plants and isolation and characterization of a natural compound from Withania coagulans

In the current study, five different plants, Syzygium Cumini, Fagonia cretica, Acacia modesta, Withania coagulans, and Olea europaea aqueous extracts were prepared and applied against the anticancer and antibacterial activities. It was observed that O. Europaea extract shows the highest anticancer activity with cell viability of 21.5%. All the five plants extract was also used against the inhibition of Bacillus subtilis where O. Europaea extract shows a promising inhibitory activity of 3.2 cm followed by W. coagulans. Furthermore, W. coagulans was subjected to the process of column chromatography as a result a withanolide was isolated. The fast atom bombardment mass spectrometry (FAB-MS) and high resolution fast atom bombardment (HRFAB-MS) [M + 1] indicated molecular weight at m/z 453 and molecular formula C₂₈H₃₇O₅. The UV-Vis. spectrum shows absorbance at 210 nm suggesting the presence of conjugated system, and Fourier-transform infrared spectroscopy (FTIR) was recorded to explore the functional groups. Similarly, 1D and 2D NMR spectroscopy techniques such as ¹H, ¹³C NMR, correlation spectroscopy (COSY-45°), heteronuclear single quantum correlation (HSQC), heteronuclear multiple bond correlation (HMBC) and Nuclear Overhauser effect Spectroscopy (NOESY) techniques was carried out to determine the unknown natural product. The collective data of all these techniques established the structure of the unknown compound and recognized as a withanolide.

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 & Development (R&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.

Drug Repurposing: Deferasirox Inhibits the Anti-Apoptotic Activity of Mcl-1

Introduction

With the aim of repositioning commercially available drugs for the inhibition of the anti-apoptotic myeloid cell leukemia protein, Mcl-1, implied in various cancers, five molecules, highlighted from a published theoretical screening, were selected to experimentally validate their affinity toward Mcl-1.

Results

A detailed NMR study revealed that only two of the five tested drugs, Torsemide and Deferasirox, interacted with Mcl-1. NMR data analysis allowed the complete characterization of the binding mode of both drugs to Mcl-1, including the estimation of their affinity for Mcl-1. Biological assays evidenced that the biological activity of Torsemide was lower as compared to the Deferasirox, which was able to efficiently and selectively inhibit the anti-apoptotic activity of Mcl-1. Finally, docking and molecular dynamics led to a 3D model for the Deferasirox:Mcl-1 complex and revealed the positioning of the drug in the Mcl-1 P2/P3 pockets as well as almost all synthetic Mcl-1 inhibitors. Interestingly, contrary to known synthetic Mcl-1 inhibitors which interact through Arg263, Deferasirox, establishes a salt bridge with Lys234.

Conclusion

Deferasirox could be a potential candidate for drug repositioning as Mcl-1 inhibitor.

A network-based drug repurposing method via non-negative matrix factorization

MOTIVATION

Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs.

RESULTS

The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction.

AVAILABILITY AND IMPLEMENTATION

The program is available at https://github.com/sshaghayeghs/NMF-DR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Drugs repurposed: An advanced step towards the treatment of breast cancer and associated challenges

Breast cancer (BC) is mostly observed in women and is responsible for huge mortality in women subjects globally. Due to the continued development of drug resistance and other contributing factors, the scientific community needs to look for new alternatives, and drug repurposing is one of the best opportunities. Here we light upon the drug repurposing with a major focus on breast cancer. BC is a division of cancer known as the leading cause of death of 2.3 million women globally, with 685,000 fatalities. This number is steadily rising, necessitating the development of a treatment that can extend survival time. All available treatments for BC are very costly as well as show side effects. This unfulfilled requirement of the anti-cancer drugs ignited an enthusiasm for drug repositioning, which means finding out the anti-cancer use of already marketed drugs for other complications. With the advancement in proteomics, genomics, and computational approaches, the drug repurposing process hastens. So many drugs are repurposed for the BC, including alkylating agents, antimetabolite, anthracyclines, an aromatase inhibitor, mTOR, and many more. The drug resistance in breast cancer is rising, so reviewing how the challenges in breast cancer can be combated with drug repurposing. This paper provides the updated information on all the repurposed drugs candidates for breast cancer with the molecular mechanism responsible for their anti-tumor activity. Additionally, all the challenges that occur during the repurposing of the drugs are discussed.

Repurposing of Anticancer Stem Cell Drugs in Brain Tumors

Brain tumors in adults may be infrequent when compared with other cancer etiologies, but they remain one of the deadliest with bleak survival rates. Current treatment modalities encompass surgical resection, chemotherapy, and radiotherapy. However, increasing resistance rates are being witnessed, and this has been attributed, in part, to cancer stem cells (CSCs). CSCs are a subpopulation of cancer cells that reside within the tumor bulk and have the capacity for self-renewal and can differentiate and proliferate into multiple cell lineages. Studying those CSCs enables an increasing understanding of carcinogenesis, and targeting CSCs may overcome existing treatment resistance. One approach to weaponize new drugs is to target these CSCs through drug repurposing which entails using drugs, which are Food and Drug Administration-approved and safe for one defined disease, for a new indication. This approach serves to save both time and money that would otherwise be spent in designing a totally new therapy. In this review, we will illustrate drug repurposing strategies that have been used in brain tumors and then further elaborate on how these approaches, specifically those that target the resident CSCs, can help take the field of drug repurposing to a new level.

Drug-Repositioning Approaches Based on Medical and Life Science Databases

Drug repositioning is a drug discovery strategy in which an existing drug is utilized as a therapeutic agent for a different disease. As information regarding the safety, pharmacokinetics, and formulation of existing drugs is already available, the cost and time required for drug development is reduced. Conventional drug repositioning has been dominated by a method involving the search for candidate drugs that act on the target molecules of an organism in a diseased state through basic research. However, recently, information hosted on medical information and life science databases have been used in translational research to bridge the gap between basic research in drug repositioning and clinical application. Here, we review an example of drug repositioning wherein candidate drugs were found and their mechanisms of action against a novel therapeutic target were identified via a basic research method that combines the findings retrieved from various medical and life science databases.

Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning

Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug-protein interactions (positive samples) is much lower than that of the unknown ones (negative samples), which forms a class imbalance. Most previous methods only utilised part of the negative samples to train the prediction model, so most of the information on negative samples was neglected. Therefore, a new method must be developed to predict candidate drug-related proteins and fully utilise negative samples to improve prediction performance. We present a method based on non-negative matrix factorisation and gradient boosting decision tree (GBDT), named NGDTP, to identify the candidate drug-protein interactions. NGDTP integrates multiple kinds of protein similarities, drugs-proteins interactions, and multiple kinds of drugs similarities at different levels, including target proteins of drugs, drug-related diseases, and side effects of drugs. We propose a network representation learning method based on matrix factorisation to learn low-dimensional vector representations of drug and protein nodes. On the basis of these low-dimensional node representations, a GBDT-based prediction model was constructed and it obtains the association scores through establishing multiple decision trees for a drug-protein pairs. NGDTP is an ensemble learning model that fully utilises all the negative samples to effectively alleviate the problem of class imbalance. NGDTP achieves superior prediction performance when it is compared with several state-of-the-art methods. The experimental results indicate that NGDTP also retrieves more actual drug-protein interactions in the top part of prediction result, which drew significant attention from the biologists. In addition, case studies on 10 drugs further confirmed the ability of the NGDTP to identify potential candidate proteins for drugs.

Repurposing drugs as novel triple negative breast cancer therapeutics

BACKGROUND

Among all the types of breast cancer (BC), triple negative breast cancer (TNBC) is the most aggressive form having high metastasis and recurrence rate with limited treatment options. Conventional treatments such as chemotherapy and radiotherapy have lots of toxic side effects and also no FDA approved therapies are available till now. Repurposing of old clinically approved drugs towards various targets of TNBC is the new approach with lesser side effects and also leads to successful inexpensive drug development with less time consuming. Medicinal plants containg various phytoconstituents (flavonoids, alkaloids, phenols, essential oils, tanins, glycosides, lactones) plays very crucial role in combating various types of diseases and used in drug development process because of having lesser side effects.

OBJECTIVE

The present review focuses in summarization of various categories of repurposed drugs against multitarget of TNBC and also summarizes the phytochemical categories that targets TNBC singly or in combination with synthetic old drugs.

METHODS

Literature information was collected from various databases such as Pubmed, Web of Science, Scopus and Medline to understand and clarify the role and mechanism of repurposed synthetic drugs and phytoconstituents aginst TNBC by using keywords like "breast cancer", "repurposed drugs", "TNBC" and "phytoconstituents".

RESULTS

Various repurposed drugs and phytochemicals targeting different signaling pathways that exerts their cytotoxic activities on TNBC cells ultimately leads to apoptosis of cells and also lowers the recurrence rate and stops the metastasis process.

CONCLUSION

Inhibitory effects seen in different levels, which provides information and evidences to researchers towards drug developments process and thus further more investigations and researches need to be taken to get the better therapeutic treatment options against TNBC.

Human Protein Complex-Based Drug Signatures for Personalized Cancer Medicine

Disease signature-based drug repositioning approaches typically first identify a disease signature from gene expression profiles of disease samples to represent a particular disease. Then such a disease signature is connected with the drug-induced gene expression profiles to find potential drugs for the particular disease. In order to obtain reliable disease signatures, the size of disease samples should be large enough, which is not always a single case in practice, especially for personalized medicine. On the other hand, the sample sizes of drug-induced gene expression profiles are generally large. In this study, we propose a new drug repositioning approach (HDgS), in which the drug signature is first identified from drug-induced gene expression profiles, and then connected to the gene expression profiles of disease samples to find the potential drugs for patients. In order to take the dependencies among genes into account, the human protein complexes (HPC) are used to define the drug signature. The proposed HDgS is applied to the drug-induced gene expression profiles in LINCS and several types of cancer samples. The results indicate that the HPC-based drug signature can effectively find drug candidates for patients and that the proposed HDgS can be applied for personalized medicine with even one patient sample.

WaterMap and Molecular Dynamic Simulation-Guided Discovery of Potential PAK1 Inhibitors Using Repurposing Approaches

p21-Activated kinase 1 (PAK1) is positioned at the nexus of several oncogenic signaling pathways. Currently, there are no approved inhibitors for disabling the transfer of phosphate in the active site directly, as they are limited by lower affinity, and poor kinase selectivity. In this work, a repurposing study utilizing FDA-approved drugs from the DrugBank database was pursued with an initial selection of 27 molecules out of ∼2162 drug molecules, based on their docking energies and molecular interaction patterns. From the molecules that were considered for WaterMap analysis, seven molecules, namely, Mitoxantrone, Labetalol, Acalabrutinib, Sacubitril, Flubendazole, Trazodone, and Niraparib, ascertained the ability to overlap with high-energy hydration sites. Considering many other displaced unfavorable water molecules, only Acalabrutinib, Flubendazole, and Trazodone molecules highlighted their prominence in terms of binding affinity gains through ΔΔG that ranges between 6.44 and 2.59 kcal/mol. Even if Mitoxantrone exhibited the highest docking score and greater interaction strength, it did not comply with the WaterMap and molecular dynamics simulation results. Moreover, detailed MD simulation trajectory analyses suggested that the drug molecules Flubendazole, Niraparib, and Acalabrutinib were highly stable, observed from their RMSD values and consistent interaction pattern with Glu315, Glu345, Leu347, and Asp407 including the hydrophobic interactions maintained in the three replicates. However, the drug molecule Trazodone displayed a loss of crucial interaction with Leu347, which was essential to inhibit the kinase activity of PAK1. The molecular orbital and electrostatic potential analyses elucidated the reactivity and strong complementarity potentials of the drug molecules in the binding pocket of PAK1. Therefore, the CADD-based reposition efforts, reported in this work, helped in the successful identification of new PAK1 inhibitors that requires further investigation by in vitro analysis.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

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

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

Drug Repurposing, an Attractive Strategy in Pancreatic Cancer Treatment: Preclinical and Clinical Updates

Pancreatic cancer (PC) is one of the deadliest malignancies worldwide, since patients rarely display symptoms until an advanced and unresectable stage of the disease. Current chemotherapy options are unsatisfactory and there is an urgent need for more effective and less toxic drugs to improve the dismal PC therapy. Repurposing of non-oncology drugs in PC treatment represents a very promising therapeutic option and different compounds are currently being considered as candidates for repurposing in the treatment of this tumor. In this review, we provide an update on some of the most promising FDA-approved, non-oncology, repurposed drug candidates that show prominent clinical and preclinical data in pancreatic cancer. We also focus on proposed mechanisms of action and known molecular targets that they modulate in PC. Furthermore, we provide an explorative bioinformatic analysis, which suggests that some of the PC repurposed drug candidates have additional, unexplored, oncology-relevant targets. Finally, we discuss recent developments regarding the immunomodulatory role displayed by some of these drugs, which may expand their potential application in synergy with approved anticancer immunomodulatory agents that are mostly ineffective as single agents in PC.

In silico drug repurposing for the treatment of heart diseases using gene expression data and molecular docking techniques

Heart diseases are known as the most primary causes of mortality worldwide. Although many therapeutic approaches and medications are proposed for these diseases, the identification of novel therapeutics in fatal heart conditions is promptly demanded. Besides, the interplay between gene expression data and molecular docking provides several novel insights to discover more effective and specific drugs for the treatment of the diseases. This study aimed to discover potent therapeutic drugs in the heart diseases based on the expression profile of heart-specific genes exclusively. Initially, the heart-specific and highly expressed genes were identified by comparing the gene expression profile of different body tissues. Subsequently, the druggable-genes were identified using in silico techniques. The interaction between these druggable genes with more than 1600 FDA approved drugs was then investigated using the molecular docking simulation. By comprehensively analyzing RNA-sequencing data obtained from 949 normal tissue samples, 48 heart-specific genes were identified in both the heart development and function. Notably, of these, 24 heart-specific genes were capable to be considered as druggable genes, among which only MYBPC3, MYLK3, and SCN5A genes entered the molecular docking process due to their functions. Afterward, the pharmacokinetics properties of top 10 ligands with the highest binding affinity for these proteins were studied. Accordingly, methylergonovine, fosaprepitant, pralatrexate, daunorubicin, glecaprevir, digoxin, and venetoclax drugs were competent, in order to interact with the target proteins perfectly. It was shown that these medications can be used as specific drugs for the treatment of heart diseases after fulfilling further experiments in this regard.

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

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

Computational Drug Repurposing: Classification of the Research Opportunities and Challenges

BACKGROUND

Drug repurposing has grown significantly in recent years. Research and innovation in drug repurposing are extremely popular due to its practical and explicit advantages. However, its adoption into practice is slow because researchers and industries have to face various challenges.

OBJECTIVE

As this field, there is a lack of a comprehensive platform for systematic identification for removing development limitations. This paper deals with a comprehensive classification of challenges in drug repurposing.

METHODS

Initially, a classification of various existing repurposing models is propounded. Next, the benefits of drug repurposing are summarized. Further, a categorization for computational drug repurposing shortcomings is presented. Finally, the methods are evaluated based on their strength to addressing the drawbacks.

RESULTS

This work can offer a desirable platform for comparing the computational repurposing methods by measuring the methods in light of these challenges.

CONCLUSION

A proper comparison could prepare guidance for a genuine understanding of methods. Accordingly, this comprehension of the methods will help researchers eliminate the barriers thereby developing and improving methods. Furthermore, in this study, we conclude why despite all the benefits of drug repurposing, it is not being done anymore.

Identification of Repurpose Drugs by Computational Analysis of Disease-Gene-Drug Associations

Repurposing of marketed drugs to find new indications has become an alternative to circumvent the risk of traditional drug development by its productivity quality. Despite many approaches, computational analysis has great potential to fuel the development of all-rounder drugs to find new classes of medicine for neglected and rare disease. The genes that can explain variations in drug response associated to disease are more important and significant in drug therapeutics necessitate elucidating the relationships of a gene, drug, and disease. The proposed computational analysis facilitates the discovery of knowledge on both target and disease-based relationships from large sources of biomedical literature spread over different platforms. It uses the utility of text mining for automatic extraction of valuable aggregated biomedical entities (disease, gene, and drug) from PubMed to serves as an input to the analysis of association prediction. The top-ranked associations considered for identification of repurposing drugs and also the hidden associations identified using concurrence principle to extrapolate the new relationships. Such findings are reported as novel and contribute to the knowledge base for pharmacogenomics, would immensely support the discovery and progress of novel therapeutic pathways and patient segment biomarkers.

Repurposing existing therapeutics, its importance in oncology drug development: Kinases as a potential target

Repurposing the large arsenal of existing non‐cancer drugs is an attractive proposition to expand the clinical pipelines for cancer therapeutics. The earlier successes in repurposing resulted primarily from serendipitous findings, but more recently, drug or target‐centric systematic identification of repurposing opportunities continues to rise. Kinases are one of the most sought‐after anti‐cancer drug targets over the last three decades. There are many non‐cancer approved drugs that can inhibit kinases as “off‐targets” as well as many existing kinase inhibitors that can target new additional kinases in cancer. Identifying cancer‐associated kinase inhibitors through mining commercial drug databases or new kinase targets for existing inhibitors through comprehensive kinome profiling can offer more effective trial‐ready options to rapidly advance drugs for clinical validation. In this review, we argue that drug repurposing is an important approach in modern drug development for cancer therapeutics. We have summarized the advantages of repurposing, the rationale behind this approach together with key barriers and opportunities in cancer drug development. We have also included examples of non‐cancer drugs that inhibit kinases or are associated with kinase signalling as a basis for their anti‐cancer action.

Large-scale phenotypic drug screen identifies neuroprotectants in zebrafish and mouse models of retinitis pigmentosa

Retinitis pigmentosa (RP) and associated inherited retinal diseases (IRDs) are caused by rod photoreceptor degeneration, necessitating therapeutics promoting rod photoreceptor survival. To address this, we tested compounds for neuroprotective effects in multiple zebrafish and mouse RP models, reasoning drugs effective across species and/or independent of disease mutation may translate better clinically. We first performed a large-scale phenotypic drug screen for compounds promoting rod cell survival in a larval zebrafish model of inducible RP. We tested 2934 compounds, mostly human-approved drugs, across six concentrations, resulting in 113 compounds being identified as hits. Secondary tests of 42 high-priority hits confirmed eleven lead candidates. Leads were then evaluated in a series of mouse RP models in an effort to identify compounds effective across species and RP models, that is, potential pan-disease therapeutics. Nine of 11 leads exhibited neuroprotective effects in mouse primary photoreceptor cultures, and three promoted photoreceptor survival in mouse rd1 retinal explants. Both shared and complementary mechanisms of action were implicated across leads. Shared target tests implicated parp1-dependent cell death in our zebrafish RP model. Complementation tests revealed enhanced and additive/synergistic neuroprotective effects of paired drug combinations in mouse photoreceptor cultures and zebrafish, respectively. These results highlight the value of cross-species/multi-model phenotypic drug discovery and suggest combinatorial drug therapies may provide enhanced therapeutic benefits for RP patients.

Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

This study aimed to identify potential novel drug candidates and targets for Parkinson's disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

Premedication with pioglitazone prevents doxorubicin-induced left ventricular dysfunction in mice

BACKGROUND

Doxorubicin (DOX) is widely used as an effective chemotherapeutic agent for cancers; however, DOX induces cardiac toxicity, called DOX-induced cardiomyopathy. Although DOX-induced cardiomyopathy is known to be associated with a high cumulative dose of DOX, the mechanisms of its long-term effects have not been completely elucidated. Pioglitazone (Pio) is presently contraindicated in patients with symptomatic heart failure owing to the side effects. The concept of drug repositioning led us to hypothesize the potential effects of Pio as a premedication before DOX treatment, and to analyze this hypothesis in mice.

METHODS

First, for the hyperacute (day 1) and acute (day 7) DOX-induced dysfunction models, mice were fed a standard diet with or without 0.02% (wt/wt) Pio for 5 days before DOX treatment (15 mg/kg body weight [BW] via intraperitoneal [i.p.] administration). The following 3 treatment groups were analyzed: standard diet + vehicle (Vehicle), standard diet + DOX (DOX), and Pio + DOX. Next, for the chronic model (day 35), the mice were administrated DOX once a week for 5 weeks (5 mg/kg BW/week, i.p.).

RESULTS

In the acute phase after DOX treatment, the percent fractional shortening of the left ventricle (LV) was significantly decreased in DOX mice. This cardiac malfunction was improved in Pio + DOX mice. In the chronic phase, we observed that LV function was preserved in Pio + DOX mice.

CONCLUSIONS

Our findings may provide a new pathophysiological explanation by which Pio plays a role in the treatment of DOX-induced cardiomyopathy, but the molecular links between Pio and DOX-induced LV dysfunction remain largely elusive.

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease

BACKGROUND

Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective.

METHODS

In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes.

RESULTS

We applied our computational framework to prioritize novel putative target genes for Alzheimer's disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib).

CONCLUSIONS

Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

Unexpected beneficial effects of drugs: an analysis of cases in the Dutch spontaneous reporting system

INTRODUCTION

Drug use is inherently related to both beneficial effects on health as well as the occurrence of risks. The beneficial effects may be related to efficacy, the treatment range of a product, or even to user-friendliness of a product. However, in addition to the occurrence of adverse drug reactions, a drug can also have an unexpected beneficial effect on a patient's health, not related to the indication for which the drug was used. The aim of this article is to characterize the reports of unexpected beneficial effects of drugs in the Dutch spontaneous reporting system.

METHODS

A descriptive analysis was used to gain insight in number of reports and drug classes responsible for unexpected beneficial effects of drugs. Grouping of positive side effects into classes was done by a conventional qualitative content analysis of the cases.

RESULTS

Four hundred nine reports which described unexpected beneficial effects of drugs were included, which mentioned 451 associations between suspected drugs and unexpected beneficial effects. There were 147 drug classes on the 4th ATC level involved. Content analysis of the reports gave rise to 22 categories of unexpected beneficial effects of drugs, including one "other category". DISCUSSION AND CONCLUSION: The analysis showed a diverse spectrum of reported reactions and drugs with some categories of unexpected beneficial effects of drugs mentioned multiple times for certain drug classes on the 4th ATC level. Most of these findings are consistent with the existing literature and knowledge on the pharmacological mechanism of the drugs in question. Coding harmonization would make it possible to study these effects in international databases.

Screening of Benzimidazole-Based Anthelmintics and Their Enantiomers as Repurposed Drug Candidates in Cancer Therapy

Repurposing of approved non-antitumor drugs represents a promising and affordable strategy that may help to increase the repertoire of effective anticancer drugs. Benzimidazole-based anthelmintics are antiparasitic drugs commonly employed both in human and veterinary medicine. Benzimidazole compounds are being considered for drug repurposing due to antitumor activities displayed by some members of the family. In this study, we explored the effects of a large series of benzimidazole-based anthelmintics (and some enantiomerically pure forms of those containing a stereogenic center) on the viability of different tumor cell lines derived from paraganglioma, pancreatic and colorectal cancer. Flubendazole, parbendazole, oxibendazole, mebendazole, albendazole and fenbendazole showed the most consistent antiproliferative effects, displaying IC₅₀ values in the low micromolar range, or even in the nanomolar range. In silico evaluation of their physicochemical, pharmacokinetics and medicinal chemistry properties also provided useful information related to the chemical structures and potential of these compounds. Furthermore, in view of the potential repurposing of these drugs in cancer therapy and considering that pharmaceutically active compounds may have different mechanisms of action, we performed an in silico target prediction to assess the polypharmacology of these benzimidazoles, which highlighted previously unknown cancer-relevant molecular targets.

Harnessing Drug Repurposing for Exploration of New Diseases: An Insight to Strategies and Case Studies

BACKGROUND

Traditional drug discovery is time consuming, costly, and risky process. Owing to the large investment, excessive attrition, and declined output, drug repurposing has become a blooming approach for the identification and development of new therapeutics. The method has gained momentum in the past few years and has resulted in many excellent discoveries. Industries are resurrecting the failed and shelved drugs to save time and cost. The process accounts for approximately 30% of the new US Food and Drug Administration approved drugs and vaccines in recent years.

METHODS

A systematic literature search using appropriate keywords were made to identify articles discussing the different strategies being adopted for repurposing and various drugs that have been/are being repurposed.

RESULTS

This review aims to describe the comprehensive data about the various strategies (Blinded search, computational approaches, and experimental approaches) used for the repurposing along with success case studies (treatment for orphan diseases, neglected tropical disease, neurodegenerative diseases, and drugs for pediatric population). It also inculcates an elaborated list of more than 100 drugs that have been repositioned, approaches adopted, and their present clinical status. We have also attempted to incorporate the different databases used for computational repurposing.

CONCLUSION

The data presented is proof that drug repurposing is a prolific approach circumventing the issues poised by conventional drug discovery approaches. It is a highly promising approach and when combined with sophisticated computational tools, it also carries high precision. The review would help researches in prioritizing the drugrepositioning method much needed to flourish the drug discovery research.

Multi-Data Aspects of Protein Similarity with a Learning Technique to Identify Drug-Disease Associations

Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated with proteins, including their network topology, proteomic data, functional analysis, and druggable property. Based on the proposed PPSVs, a separate drug–disease matrix was constructed for individual to prevent characteristics from being obscured between diseases. The classification technique was employed for prediction. The results showed that more than half of the tested disease models exhibited high performance, with overall F1 scores of more than 80%. Furthermore, comparing all diseases using traditional methods in one run, we obtained an (area under the curve) AUC of 98.9%. All candidate drugs were then tested in clinical trials (p-value < 2.2 × 10−16) and were known drugs based on their functions (p-value < 0.05). An analysis revealed that, in the functional aspect, the confidence value of an interaction in the protein–protein interaction network and the functional pathway score were the best descriptors for prediction. Based on the learning processes of PPSVs with an isolated disease, the classifier exhibited high performance in predicting and identifying new potential drugs for that disease.

A comprehensive review on anticancer mechanism of bazedoxifene

Cancer is counted as a second leading cause of death among nontransmissible diseases. Identification of novel anticancer drugs is therefore necessary for the effective treatment of cancer. Conventional drug discovery is time consuming and expensive process. Unlike conventional drug discovery, drug repositioning offers a novel strategy for urgent drug discovery since it is a cost-effective and faster process. Bazedoxifene (BZA) is a synthetic selective estrogen receptor modulator, approved by the United States Food and Drug Administration for the treatment of osteoporosis in postmenopausal women. BZA is now being studied for its anticancer activity in various cancers including breast cancer, liver cancer, pancreatic cancer, colon cancer, head and neck cancer, medulloblastoma, brain cancer, and gastrointestinal cancer. Studies have reported that BZA is effective in reducing cancer progression through multiple mechanisms. BZA could effectively inhibit STAT3, PI3K/AKT, and MAPK signaling pathways and induce apoptosis. In addition to its anticancer activity as monotherapy, BZA has been shown to enhance the chemotherapeutic efficacy of clinical drugs such as paclitaxel, cisplatin, palbociclib, and oxaliplatin in multiple neoplasms. This review mainly focused on the anticancer activity, cellular targets, and anticancer mechanism of BZA, which may help the further design and conduct of research and repositioning it for oncological clinic trials.

Drug Repurposing Opportunities in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is considered one of the deadliest tumors worldwide. The diagnosis is often possible only in the latter stages of the disease, with patients already presenting an advanced or metastatic tumor. It is also one of the cancers with poorest prognosis, presenting a five-year survival rate of around 5%. Treatment of PDAC is still a major challenge, with cytotoxic chemotherapy remaining the basis of systemic therapy. However, no major advances have been made recently, and therapeutic options are limited and highly toxic. Thus, novel therapeutic options are urgently needed. Drug repurposing is a strategy for the development of novel treatments using approved or investigational drugs outside the scope of the original clinical indication. Since repurposed drugs have already completed several stages of the drug development process, a broad range of data is already available. Thus, when compared with de novo drug development, drug repurposing is time-efficient, inexpensive and has less risk of failure in future clinical trials. Several repurposing candidates have been investigated in the past years for the treatment of PDAC, as single agents or in combination with conventional chemotherapy. This review gives an overview of the main drugs that have been investigated as repurposing candidates, for the potential treatment of PDAC, in preclinical studies and clinical trials.

Auranofin reveals therapeutic anticancer potential by triggering distinct molecular cell death mechanisms and innate immunity in mutant p53 non-small cell lung cancer

Auranofin (AF) is an FDA-approved antirheumatic drug with anticancer properties that acts as a thioredoxin reductase 1 (TrxR) inhibitor. The exact mechanisms through which AF targets cancer cells remain elusive. To shed light on the mode of action, this study provides an in-depth analysis on the molecular mechanisms and immunogenicity of AF-mediated cytotoxicity in the non-small cell lung cancer (NSCLC) cell line NCI–H1299 (p53 Null) and its two isogenic derivates with mutant p53 R175H or R273H accumulation.

TrxR is highly expressed in a panel of 72 NSCLC patients, making it a valid druggable target in NSCLC for AF. The presence of mutant p53 overexpression was identified as an important sensitizer for AF in (isogenic) NSCLC cells as it was correlated with reduced thioredoxin (Trx) levels in vitro. Transcriptome analysis revealed dysregulation of genes involved in oxidative stress response, DNA damage, granzyme A (GZMA) signaling and ferroptosis. Although functionally AF appeared a potent inhibitor of GPX4 in all NCI–H1299 cell lines, the induction of lipid peroxidation and consequently ferroptosis was limited to the p53 R273H expressing cells. In the p53 R175H cells, AF mainly induced large-scale DNA damage and replication stress, leading to the induction of apoptotic cell death rather than ferroptosis. Importantly, all cell death types were immunogenic since the release of danger signals (ecto-calreticulin, ATP and HMGB1) and dendritic cell maturation occurred irrespective of (mutant) p53 expression. Finally, we show that AF sensitized cancer cells to caspase-independent natural killer cell-mediated killing by downregulation of several key targets of GZMA.

Our data provides novel insights on AF as a potent, clinically available, off-patent cancer drug by targeting mutant p53 cancer cells through distinct cell death mechanisms (apoptosis and ferroptosis). In addition, AF improves the innate immune response at both cytostatic (natural killer cell-mediated killing) and cytotoxic concentrations (dendritic cell maturation).

An Analytical Review of Computational Drug Repurposing

Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.

Pigments from Antarctic bacteria and their biotechnological applications

Pigments from microorganisms have triggered great interest in the market, mostly by their "natural" appeal, their favorable production conditions, in addition to the potential new chemical structures or naturally overproducing strains. They have been used in: food, feed, dairy, textile, pharmaceutical, and cosmetic industries. The high rate of pigment production in microorganisms recovered from Antarctica in response to selective pressures such as: high UV radiation, low temperatures, and freezing and thawing cycles makes this a unique biome which means that much of its biological heritage cannot be found elsewhere on the planet. This vast arsenal of pigmented molecules has different functions in bacteria and may exhibit different biotechnological activities, such as: extracellular sunscreens, photoprotective function, antimicrobial activity, biodegradability, etc. However, many challenges for the commercial use of these compounds have yet to be overcome, such as: the low stability of natural pigments in cosmetic formulations, the change in color when subjected to pH variations, the low yield and the high costs in their production. This review surveys the different types of natural pigments found in Antarctic bacteria, classifying them according to their chemical structure. Finally, we give an overview of the main pigments that are used commercially today.

Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research

Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.

Nelfinavir and Its Active Metabolite M8 Are Partial Agonists and Competitive Antagonists of the Human Pregnane X Receptor

The HIV protease inhibitor nelfinavir is currently being analyzed for repurposing as an anticancer drug for many different cancers because it exerts manifold off-target protein interactions, finally resulting in cancer cell death. Xenosensing pregnane X receptor (PXR), which also participates in the control of cancer cell proliferation and apoptosis, was previously shown to be activated by nelfinavir; however, the exact molecular mechanism is still unknown. The present study addresses the effects of nelfinavir and its major and pharmacologically active metabolite nelfinavir hydroxy-tert-butylamide (M8) on PXR to elucidate the underlying molecular mechanism. Molecular docking suggested direct binding to the PXR ligand-binding domain, which was confirmed experimentally by limited proteolytic digestion and competitive ligand-binding assays. Concentration-response analyses using cellular transactivation assays identified nelfinavir and M8 as partial agonists with EC₅₀ values of 0.9 and 7.3 µM and competitive antagonists of rifampin-dependent induction with IC₅₀ values of 7.5 and 25.3 µM, respectively. Antagonism exclusively resulted from binding into the PXR ligand-binding pocket. Impaired coactivator recruitment by nelfinavir as compared with the full agonist rifampin proved to be the underlying mechanism of both effects on PXR. Physiologic relevance of nelfinavir-dependent modulation of PXR activity was investigated in respectively treated primary human hepatocytes, which showed differential induction of PXR target genes and antagonism of rifampin-induced ABCB1 and CYP3A4 gene expression. In conclusion, we elucidate here the molecular mechanism of nelfinavir interaction with PXR. It is hypothesized that modulation of PXR activity may impact the anticancer effects of nelfinavir. SIGNIFICANCE STATEMENT: Nelfinavir, which is being investigated for repurposing as an anticancer medication, is shown here to directly bind to human pregnane X receptor (PXR) and thereby act as a partial agonist and competitive antagonist. Its major metabolite nelfinavir hydroxy-tert-butylamide exerts the same effects, which are based on impaired coactivator recruitment. Nelfinavir anticancer activity may involve modulation of PXR, which itself is discussed as a therapeutic target in cancer therapy and for the reversal of chemoresistance.