Recent advances in drug repurposing using machine learning

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

Predicting repurposed drugs targeting the NS3 protease of dengue virus using machine learning-based QSAR, molecular docking, and molecular dynamics simulations

Dengue fever, prevalent in Southeast Asian countries, currently lacks effective pharmaceutical interventions for virus replication control. This study employs a strategy that combines machine learning (ML)-based quantitative-structure-activity relationship (QSAR), molecular docking, and molecular dynamics simulations to discover potential inhibitors of the NS3 protease of the dengue virus. We used nine molecular fingerprints from PaDEL to extract features from the NS3 protease dataset of dengue virus type 2 in the ChEMBL database. Feature selection was achieved through the low variance threshold, F-Score, and recursive feature elimination (RFE) methods. Our investigation employed three ML models - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) - for classifier development. Our SVM model, combined with SVM-RFE, had the best accuracy (0.866) and ROC_AUC (0.964) in the testing set. We identified potent inhibitors on the basis of the optimal classifier probabilities and docking binding affinities. SHAP and LIME analyses highlighted the significant molecular fingerprints (e.g. ExtFP69, ExtFP362, ExtFP576) involved in NS3 protease inhibitory activity. Molecular dynamics simulations indicated that amphotericin B exhibited the highest binding energy of -212 kJ/mol and formed a hydrogen bond with the critical residue Ser196. This approach enhances NS3 protease inhibitor identification and expedites the discovery of dengue therapeutics.

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.

Repurposing of Antifungal Drug Flucytosine/Flucytosine Cocrystals for Anticancer Activity against Prostate Cancer Targeting Apoptosis and Inflammatory Signaling Pathways

This study aimed to repurpose the antifungal drug flucytosine (FCN) for anticancer activity together with cocrystals of nutraceutical coformers sinapic acid (SNP) and syringic acid (SYA). The cocrystal screening experiments with SNP resulted in three cocrystal hydrate forms in which two are polymorphs, namely, FCN-SNP F-I and FCN-SNP F-II, and the third one with different stoichiometry in the asymmetric unit (1:2:1 ratio of FCN:SNP:H2O, FCN-SNP F-III). Cocrystallization with SYA resulted in two hydrated cocrystal polymorphs, namely, FCN-SYA F-I and FCN-SYA F-II. All the cocrystal polymorphs were obtained concomitantly during the slow evaporation method, and one of the polymorphs of each system was produced in bulk by the slurry method. The interaction energy and lattice energies of all cocrystal polymorphs were established using solid-state DFT calculations, and the outcomes correlated with the experimental results. Further, the in vitro cytotoxic activity of the cocrystals was determined against DU145 prostate cancer and the results showed that the FCN-based cocrystals (FCN-SNP F-III and FCN-SYA F-I) have excellent growth inhibitory activity at lower concentrations compared with parent FCN molecules. The prepared cocrystals induce apoptosis by generating oxidative stress and causing nuclear damage in prostate cancer cells. The Western blot analysis also depicted that the cocrystals downregulate the inflammatory markers such as NLRP3 and caspase-1 and upregulate the intrinsic apoptosis signaling pathway marker proteins, such as Bax, p53, and caspase-3. These findings suggest that the antifungal drug FCN can be repurposed for anticancer activity.

Repurposing of anti-malarial drugs for the treatment of tuberculosis: realistic strategy or fanciful dead end?

BACKGROUND

Drug repurposing offers a strategic alternative to the development of novel compounds, leveraging the known safety and pharmacokinetic profiles of medications, such as linezolid and levofloxacin for tuberculosis (TB). Anti-malarial drugs, including quinolones and artemisinins, are already applied to other diseases and infections and could be promising for TB treatment.

METHODS

This review included studies on the activity of anti-malarial drugs, specifically quinolones and artemisinins, against Mycobacterium tuberculosis complex (MTC), summarizing results from in vitro, in vivo (animal models) studies, and clinical trials. Studies on drugs not primarily developed for TB (doxycycline, sulfonamides) and any novel developed compounds were excluded. Analysis focused on in vitro activity (minimal inhibitory concentrations), synergistic effects, pre-clinical activity, and clinical trials.

RESULTS

Nineteen studies, including one ongoing Phase 1 clinical trial, were analysed: primarily investigating quinolones like mefloquine and chloroquine, and, to a lesser extent, artemisinins. In vitro findings revealed high MIC values for anti-malarials versus standard TB drugs, suggesting a limited activity. Synergistic effects with anti-TB drugs were modest, with some synergy observed in combinations with isoniazid or pyrazinamide. In vivo animal studies showed limited activity of anti-malarials against MTC, except for one study of the combination of chloroquine with isoniazid.

CONCLUSIONS

The repurposing of anti-malarials for TB treatment is limited by high MIC values, poor synergy, and minimal in vivo effects. Concerns about potential toxicity at effective dosages and the risk of antimicrobial resistance, especially where TB and malaria overlap, further question their repurposing. These findings suggest that focusing on novel compounds might be both more beneficial and rewarding.

The interference between SARS-COV-2 and Alzheimer's disease: Potential immunological and neurobiological crosstalk from a kinase perspective reveals a delayed pandemic

Coronavirus disease 2019 (COVID-19) has infected over 700 million people, with up to 30% developing neurological manifestations, including dementias. However, there is a lack of understanding of common molecular brain markers causing Alzheimer's disease (AD). COVID-19 has etiological cofactors with AD, making patients with AD a vulnerable population at high risk of experiencing more severe symptoms and worse consequences. Both AD and COVID-19 have upregulated several shared kinases, leading to the repositioning of kinase inhibitors (KIs) for the treatment of both diseases. This review provides an overview of the interactions between the immune system and the nervous system in relation to receptor tyrosine kinases, including epidermal growth factor receptors, vascular growth factor receptors, and non-receptor tyrosine kinases such as Bruton tyrosine kinase, spleen tyrosine kinase, c-ABL, and JAK/STAT. We will discuss the promising results of kinase inhibitors in pre-clinical and clinical studies for both COVID-19 and Alzheimer's disease (AD), as well as the challenges in repositioning KIs for these diseases. Understanding the shared kinases between AD and COVID-19 could help in developing therapeutic approaches for both.

Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions

In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 [Formula: see text] of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering. Scientific contribution: The combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors

The integration of artificial intelligence (AI) into drug discovery has markedly advanced the search for effective therapeutics. In our study, we employed a comprehensive computational-experimental approach to identify potential anti-SARS-CoV-2 compounds. We developed a predictive model to assess the activities of compounds based on their structural features. This model screened a library of approximately 700,000 compounds, culminating in the selection of the top 100 candidates for experimental validation. In vitro assays on human intestinal epithelial cells (Caco-2) revealed that 19 of these compounds exhibited inhibitory activity. Notably, eight compounds demonstrated dose-dependent activity in Vero cell lines, with half-maximal effective concentration (EC50) values ranging from 1 μM to 7 μM. Furthermore, we utilized a clustering approach to pinpoint potential nucleoside analog inhibitors, leading to the discovery of two promising candidates: azathioprine and its metabolite, thioinosinic acid. Both compounds showed in vitro activity against SARS-CoV-2, with thioinosinic acid also significantly reducing viral loads in mouse lungs. These findings underscore the utility of AI in accelerating drug discovery processes.

Discovery of PLpro and Mpro Inhibitors for SARS-CoV-2

There are very few small-molecule antivirals for SARS-CoV-2 that are either currently approved (or emergency authorized) in the US or globally, including remdesivir, molnupiravir, and paxlovid. The increasing number of SARS-CoV-2 variants that have appeared since the outbreak began over three years ago raises the need for continual development of updated vaccines and orally available antivirals in order to fully protect or treat the population. The viral main protease (Mpro) and the papain-like protease (PLpro) are key for viral replication; therefore, they represent valuable targets for antiviral therapy. We herein describe an in vitro screen performed using the 2560 compounds from the Microsource Spectrum library against Mpro and PLpro in an attempt to identify additional small-molecule hits that could be repurposed for SARS-CoV-2. We subsequently identified 2 hits for Mpro and 8 hits for PLpro. One of these hits was the quaternary ammonium compound cetylpyridinium chloride with dual activity (IC50 = 2.72 ± 0.09 μM for PLpro and IC50 = 7.25 ± 0.15 μM for Mpro). A second inhibitor of PLpro was the selective estrogen receptor modulator raloxifene (IC50 = 3.28 ± 0.29 μM for PLpro and IC50 = 42.8 ± 6.7 μM for Mpro). We additionally tested several kinase inhibitors and identified olmutinib (IC50 = 0.54 ± 0.04 μM), bosutinib (IC50 = 4.23 ± 0.28 μM), crizotinib (IC50 = 3.81 ± 0.04 μM), and dacominitinib (IC50 = IC50 3.33 ± 0.06 μM) as PLpro inhibitors for the first time. In some cases, these molecules have also been tested by others for antiviral activity for this virus, or we have used Calu-3 cells infected with SARS-CoV-2. The results suggest that approved drugs can be identified with promising activity against these proteases, and in several cases we or others have validated their antiviral activity. The additional identification of known kinase inhibitors as molecules targeting PLpro may provide new repurposing opportunities or starting points for chemical optimization.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

Molecular Mechanisms of Neuroinflammation in Aging and Alzheimer's Disease Progression

Aging is the most prominent risk factor for late-onset Alzheimer's disease. Aging associates with a chronic inflammatory state both in the periphery and in the central nervous system, the evidence thereof and the mechanisms leading to chronic neuroinflammation being discussed. Nonetheless, neuroinflammation is significantly enhanced by the accumulation of amyloid beta and accelerates the progression of Alzheimer's disease through various pathways discussed in the present review. Decades of clinical trials targeting the 2 abnormal proteins in Alzheimer's disease, amyloid beta and tau, led to many failures. As such, targeting neuroinflammation via different strategies could prove a valuable therapeutic strategy, although much research is still needed to identify the appropriate time window. Active research focusing on identifying early biomarkers could help translating these novel strategies from bench to bedside.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

Multiple approaches to repurposing drugs for neuroblastoma

Neuroblastoma (NB) is the second leading extracranial solid tumor of early childhood with about two-thirds of cases presenting before the age of 5, and accounts for roughly 15 percent of all pediatric cancer fatalities in the United States. Treatments against NB are lacking, resulting in a low survival rate in high-risk patients. A repurposing approach using already approved or clinical stage compounds can be used for diseases for which the patient population is small, and the commercial market limited. We have used Bayesian machine learning, in vitro cell assays, and combination analysis to identify molecules with potential use for NB. We demonstrated that pyronaridine (SH-SY5Y IC50 1.70 µM, SK-N-AS IC50 3.45 µM), BAY 11-7082 (SH-SY5Y IC50 0.85 µM, SK-N-AS IC50 1.23 µM), niclosamide (SH-SY5Y IC50 0.87 µM, SK-N-AS IC50 2.33 µM) and fingolimod (SH-SY5Y IC50 4.71 µM, SK-N-AS IC50 6.11 µM) showed cytotoxicity against NB. As several of the molecules are approved drugs in the US or elsewhere, they may be repurposed more readily for NB treatment. Pyronaridine was also tested in combinations in SH-SY5Y cells and demonstrated an antagonistic effect with either etoposide or crizotinib. Whereas when crizotinib and etoposide were combined with each other they had a synergistic effect in these cells. We have also described several analogs of pyronaridine to explore the structure-activity relationship against cell lines. We describe multiple molecules demonstrating cytotoxicity against NB and the further evaluation of these molecules and combinations using other NB cells lines and in vivo models will be important in the future to assess translational potential.

Sonidegib Suppresses Production of Inflammatory Mediators and Cell Migration in BV2 Microglial Cells and Mice Treated with Lipopolysaccharide via JNK and NF-κB Inhibition

Our structure-based virtual screening of the FDA-approved drug library has revealed that sonidegib, a smoothened antagonist clinically used to treat basal cell carcinoma, is a potential c-Jun N-terminal kinase 3 (JNK3) inhibitor. This study investigated the binding of sonidegib to JNK3 via ¹⁹F NMR and its inhibitory effect on JNK phosphorylation in BV2 cells. Pharmacological properties of sonidegib to exert anti-inflammatory and anti-migratory effects were also characterized. We found that sonidegib bound to the ATP binding site of JNK3 and inhibited JNK phosphorylation in BV2 cells, confirming our virtual screening results. Sonidegib also inhibited the phosphorylation of MKK4 and c-Jun, the upstream and downstream signals of JNK, respectively. It reduced the lipopolysaccharide (LPS)-induced production of pro-inflammatory factors, including interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), and nitric oxide (NO), and the expression of inducible NO synthase and cyclooxygenase-2. The LPS-induced cell migration was suppressed by sonidegib. Sonidegib inhibited the LPS-induced IκBα phosphorylation, thereby blocking NF-κB nuclear translocation. Consistent with these findings, orally administered sonidegib attenuated IL-6 and TNF-α levels in the brains of LPS-treated mice. Collectively, our results indicate that sonidegib suppresses inflammation and cell migration in LPS-treated BV2 cells and mice by inhibiting JNK and NF-κB signaling. Therefore, sonidegib may be implicated for drug repurposing to alleviate neuroinflammation associated with microglial activation.

Anti-Inflammatory Effects of Spiramycin in LPS-Activated RAW 264.7 Macrophages

Drug repurposing is a simple concept with a long history, and is a paradigm shift that can significantly reduce the costs and accelerate the process of bringing a new small-molecule drug into clinical practice. We attempted to uncover a new application of spiramycin, an old medication that was classically prescribed for toxoplasmosis and various other soft-tissue infections; specifically, we initiated a study on the anti-inflammatory capacity of spiramycin. For this purpose, we used murine macrophage RAW 264.7 as a model for this experiment and investigated the anti-inflammatory effects of spiramycin by inhibiting the production of pro-inflammatory mediators and cytokines. In the present study, we demonstrated that spiramycin significantly decreased nitric oxide (NO), interleukin (IL)-1β, and IL-6 levels in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Spiramycin also inhibited the expression of NO synthase (iNOS), potentially explaining the spiramycin-induced decrease in NO production. In addition, spiramycin inhibited the phosphorylation of mitogen-activated protein kinases (MAPKs); extracellular signal-regulated kinase (ERK) and c-Jun N terminal kinase (JNK) as well as the inactivation and subsequent nuclear translocation of nuclear factor κB (NF-κB). This indicated that spiramycin attenuates macrophages’ secretion of IL-6, IL-1β, and NO, inducing iNOS expression via the inhibition of the NF-κB and MAPK signaling pathways. Finally, we tested the potential application of spiramycin as a topical material by human skin primary irritation tests. It was performed on the normal skin (upper back) of 31 volunteers to determine whether 100 μM and μM of spiramycin had irritation or sensitization potential. In these assays, spiramycin did not induce any adverse reactions. In conclusion, our results demonstrate that spiramycin can effectively attenuate the activation of macrophages, suggesting that spiramycin could be a potential candidate for drug repositioning as a topical anti-inflammatory agent.

Defending Antiviral Cationic Amphiphilic Drugs That May Cause Drug-Induced Phospholipidosis

A recent publication in Science has proposed that cationic amphiphilic drugs repurposed for COVID-19 typically use phosholipidosis as their antiviral mechanism of action in cells but will have no in vivo efficacy. On the contrary, our viewpoint, supported by additional experimental data for similar cationic amphiphilic drugs, indicates that many of these molecules have both in vitro and in vivo efficacy with no reported phospholipidosis, and therefore, this class of compounds should not be avoided but further explored, as we continue the search for broad spectrum antivirals.

Machine Learning Models Identify Inhibitors of SARS-CoV-2

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (K_d 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.