Cytopathic SARS-CoV-2 screening on VERO-E6 cells in a large-scale repurposing effort

Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the urgency for effective antiviral drugs. Despite the development of different vaccination strategies, the search for specific antiviral compounds remains crucial. Here, we combine machine learning (ML) techniques with in vitro validation to efficiently identify potential antiviral compounds. We overcome the limited amount of SARS-CoV-2 data available for ML using various techniques, supplemented with data from diverse biomedical assays, which enables end-to-end training of a deep neural network architecture. We use its predictions to identify and prioritize compounds for in vitro testing. Two top-hit compounds, PKI-179 and MTI-31, originally identified as Pi3K-mTORC1/2 pathway inhibitors, exhibit significant antiviral activity against SARS-CoV-2 at low micromolar doses. Notably, both compounds outperform the well-known mTOR inhibitor rapamycin. Furthermore, PKI-179 and MTI-31 demonstrate broad-spectrum antiviral activity against SARS-CoV-2 variants of concern and other coronaviruses. In a physiologically relevant model, both compounds show antiviral effects in primary human airway epithelial (HAE) cultures derived from healthy donors cultured in an air-liquid interface (ALI). This study highlights the potential of ML combined with in vitro testing to expedite drug discovery, emphasizing the adaptability of AI-driven approaches across different viruses, thereby contributing to pandemic preparedness.

Development and in vitro antiviral activity of ivermectin liposomes as a potential drug carrier system

This study aimed to assess and compare diverse formulations of ivermectin-loaded liposomes, employing lipid film hydration and ethanol injection methods. Three lipids (DOPC, SPC, and DSPC) were used in predetermined molar ratios. A total of 18 formulations were created, and a factorial design determined the optimal formulation based on particle size, polydispersity index (PDI), zeta potential, and encapsulation efficiency. The average mean particle size, PDI and zeta potential of the selected formulations (F1, F2, F7, F9, and F11) was, respectively, 196.40 ± 44.60 nm, 0.39 ± 0.09, and -40.24 ± 9.17. The encapsulation efficiency exceeded 80%, with a mean loading capacity of 4.00 ± 1.70%. In vitro studies included transmission electron microscopy, Fourier transform infrared spectroscopy, drug release, and antiviral activity assessments against SARS-CoV-2. The liposomal formulations demonstrated superior antiviral activity compared to free ivermectin, as indicated by lower IC50 values. The results of this study emphasize the effectiveness of ivermectin-loaded liposomes in inhibiting viral activity, highlighting their potential as promising candidates for antiviral therapy. The findings suggest that the strategic use of liposomes as drug carriers can significantly modulate and improve the antiviral properties of ivermectin, offering a novel approach to harnessing its full therapeutic potential. Collectively, these results provide a robust foundation for further exploration of ivermectin as a viral protection tool and optimization of its delivery mechanisms.

PBPK-led assessment of antimalarial drugs as candidates for Covid-19: Simulating concentrations at the site of action to inform repurposing strategies

The urgent need for safe, efficacious, and accessible drug treatments to treat coronavirus disease 2019 (COVID-19) prompted a global effort to evaluate drug repurposing opportunities. Pyronaridine and amodiaquine are both components of approved antimalarials with in vitro activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In vitro activity does not always translate to clinical efficacy across a therapeutic dose range. This study applied available, verified, physiologically based pharmacokinetic (PBPK) models for pyronaridine, amodiaquine, and its active metabolite N-desethylamodiaquine (DEAQ) to predict drug concentrations in lung tissue relative to plasma or blood in the default healthy virtual population. Lung exposures were compared to published data across the reported range of in vitro EC50 values against SARS-CoV-2. In the multicompartment permeability-limited PBPK model, the predicted total Cmax in lung mass for pyronaridine was 34.2 μM on Day 3, 30.5-fold greater than in blood (1.12 μM) and for amodiaquine was 0.530 μM, 8.83-fold greater than in plasma (0.060 μM). In the perfusion-limited PBPK model, the DEAQ predicted total Cmax on Day 3 in lung mass (30.2 μM) was 21.4-fold greater than for plasma (1.41 μM). Based on the available in vitro data, predicted drug concentrations in lung tissue for pyronaridine and DEAQ, but not amodiaquine, appeared sufficient to inhibit SARS-CoV-2 replication. Simulations indicated standard dosing regimens of pyronaridine-artesunate and artesunate-amodiaquine have potential to treat COVID-19. These findings informed repurposing strategies to select the most relevant compounds for clinical investigation in COVID-19. Clinical data for model verification may become available from ongoing clinical studies.

Drug repurposing screen to identify inhibitors of the RNA polymerase (nsp12) and helicase (nsp13) from SARS-CoV-2 replication and transcription complex

Coronaviruses contain one of the largest genomes among the RNA viruses, coding for 14-16 non-structural proteins (nsp) that are involved in proteolytic processing, genome replication and transcription, and four structural proteins that build the core of the mature virion. Due to conservation across coronaviruses, nsps form a group of promising drug targets as their inhibition directly affects viral replication and, therefore, progression of infection. A minimal but fully functional replication and transcription complex was shown to be formed by one RNA-dependent RNA polymerase (nsp12), one nsp7, two nsp8 accessory subunits, and two helicase (nsp13) enzymes. Our approach involved, targeting nsp12 and nsp13 to allow multiple starting point to interfere with virus infection progression. Here we report a combined in-vitro repurposing screening approach, identifying new and confirming reported SARS-CoV-2 nsp12 and nsp13 inhibitors.

Multiplexed multicolor antiviral assay amenable for high-throughput research

To curb viral epidemics and pandemics, antiviral drugs are needed with activity against entire genera or families of viruses. Here, we develop a cell-based multiplex antiviral assay for high-throughput screening against multiple viruses at once, as demonstrated by using three distantly related orthoflaviviruses: dengue, Japanese encephalitis and yellow fever virus. Each virus is tagged with a distinct fluorescent protein, enabling individual monitoring in cell culture through high-content imaging. Specific antisera and small-molecule inhibitors are employed to validate that multiplexing approach yields comparable inhibition profiles to single-virus infection assays. To facilitate downstream analysis, a kernel is developed to deconvolute and reduce the multidimensional quantitative data to three cartesian coordinates. The methodology is applicable to viruses from different families as exemplified by co-infections with chikungunya, parainfluenza and Bunyamwera viruses. The multiplex approach is expected to facilitate the discovery of broader-spectrum antivirals, as shown in a pilot screen of approximately 1200 drug-like small-molecules.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions

INTRODUCTION

Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community.

AREAS COVERED

In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds.

EXPERT OPINION

Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.

DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response

MOTIVATION

The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics.

RESULTS

In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Hhhzj-7/DeepCoVDR.

DHFR Inhibitors Display a Pleiotropic Anti-Viral Activity against SARS-CoV-2: Insights into the Mechanisms of Action

During the COVID-19 pandemic, drug repurposing represented an effective strategy to obtain quick answers to medical emergencies. Based on previous data on methotrexate (MTX), we evaluated the anti-viral activity of several DHFR inhibitors in two cell lines. We observed that this class of compounds showed a significant influence on the virus-induced cytopathic effect (CPE) partly attributed to the intrinsic anti-metabolic activity of these drugs, but also to a specific anti-viral function. To elucidate the molecular mechanisms, we took advantage of our EXSCALATE platform for in-silico molecular modelling and further validated the influence of these inhibitors on nsp13 and viral entry. Interestingly, pralatrexate and trimetrexate showed superior effects in counteracting the viral infection compared to other DHFR inhibitors. Our results indicate that their higher activity is due to their polypharmacological and pleiotropic profile. These compounds can thus potentially give a clinical advantage in the management of SARS-CoV-2 infection in patients already treated with this class of drugs.

Alliance of Heart and Endoderm: Multilineage Organoids to Model Co-development

Studies in animal models tracing organogenesis of the mesoderm-derived heart have emphasized the importance of signals coming from adjacent endodermal tissues in coordinating proper cardiac morphogenesis. Although in vitro models such as cardiac organoids have shown great potential to recapitulate the physiology of the human heart, they are unable to capture the complex crosstalk that takes place between the co-developing heart and endodermal organs, partly due to their distinct germ layer origins. In an effort to address this long-sought challenge, recent reports of multilineage organoids comprising both cardiac and endodermal derivatives have energized the efforts to understand how inter-organ, cross-lineage communications influence their respective morphogenesis. These co-differentiation systems have produced intriguing findings of shared signaling requirements for inducing cardiac specification together with primitive foregut, pulmonary, or intestinal lineages. Overall, these multilineage cardiac organoids offer an unprecedented window into human development that can reveal how the endoderm and heart cooperate to direct morphogenesis, patterning, and maturation. Further, through spatiotemporal reorganization, the co-emerged multilineage cells self-assemble into distinct compartments as seen in the cardiac-foregut, cardiac-intestine, and cardiopulmonary organoids and undergo cell migration and tissue reorganization to establish tissue boundaries. Looking into the future, these cardiac incorporated, multilineage organoids will inspire future strategies for improved cell sourcing for regenerative interventions and provide more effective models for disease investigation and drug testing. In this review, we will introduce the developmental context of coordinated heart and endoderm morphogenesis, discuss strategies for in vitro co-induction of cardiac and endodermal derivatives, and finally comment on the challenges and exciting new research directions enabled by this breakthrough.

Effects of Usnic Acid to Prevent Infections by Creating a Protective Barrier in an In Vitro Study

Nasal sprays are medical devices useful for preventing infection and the subsequent spread of airborne pathogens. The effectiveness of these devices depends on the activity of chosen compounds which can create a physical barrier against viral uptake as well as incorporate different substances with antiviral activity. Among antiviral compounds, UA, a dibenzofuran derived from lichens, has the mechanical ability to modify its structure by creating a branch capable of forming a protective barrier. The mechanical ability of UA to protect cells from virus infection was investigated by analyzing the branching capacity of UA, and then the protection mechanism in an in vitro model was also studied. As expected, UA at 37 °C was able to create a barrier confirming its ramification property. At the same time, UA was able to block the infection of Vero E6 and HNEpC cells by interfering with a biological interaction between cells and viruses as revealed also by the UA quantification. Therefore, UA can block virus activity through a mechanical barrier effect without altering the physiological nasal homeostasis. The findings of this research could be of great relevance in view of the growing alarm regarding the spread of airborne viral diseases.

Development of a robust and convenient dual-reporter high-throughput screening assay for SARS-CoV-2 antiviral drug discovery

Massive efforts on both vaccine development and antiviral research were launched to combat the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We contributed, amongst others, by the development of a high-throughput screening (HTS) antiviral assay against SARS-CoV-2 using a fully automated, high-containment robot system. Here, we describe the development of this novel, convenient and phenotypic dual-reporter virus-cell-based high-content imaging assay using the A549+hACE2+TMPRSS2_mCherry reporter lung carcinoma cell line and an ancestral SARS-CoV-2_Wuhan_mNeonGreen reporter virus. Briefly, by means of clonal selection, a host cell subclone was selected that (i) efficiently supports replication of the reporter virus with high expression, upon infection, of the NeonGreen fluorescent reporter protein, (ii) that is not affected by virus-induced cytopathogenic effects and, (iii) that expresses a strong fluorescent mCherry signal in the nucleus. The selected clone matched these criteria with an infection rate on average of 75% with limited cell death. The average (R)Z'-factors of the assay plates were all >0.8, which indicates a robust assay suitable for HTS purposes. A selection of reference compounds that inhibits SARS-CoV-2 replication in vitro were used to validate this novel dual-reporter assay and confirms the data reported in the literature. This assay is a convenient and powerful tool for HTS of large compound libraries against SARS-CoV-2.

A potential role for SARS-CoV-2 small viral RNAs in targeting host microRNAs and modulating gene expression

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease (COVID-19) in humans, with symptoms ranging from mild to severe, including fatality. The molecular mechanisms surrounding the effects of viral infection on the host RNA machinery remain poorly characterized. We used a comparative transcriptomics approach to investigate the effects of SARS-CoV-2 infection on the host mRNA and sRNA expression machinery in a human lung epithelial cell line (Calu-3) and an African green monkey kidney cell line (Vero-E6). Upon infection, we observed global changes in host gene expression and differential expression of dozens of host miRNAs, many with known links to viral infection and immune response. Additionally, we discovered an expanded landscape of more than a hundred SARS-CoV-2-derived small viral RNAs (svRNAs) predicted to interact with differentially expressed host mRNAs and miRNAs. svRNAs are derived from distinct regions of the viral genome and sequence signatures suggest they are produced by a non-canonical biogenesis pathway. 52 of the 67 svRNAs identified in Calu-3 cells are predicted to interact with differentially expressed miRNAs, with many svRNAs having multiple targets. Accordingly, we speculate that these svRNAs may play a role in SARS-CoV-2 propagation by modulating post-transcriptional gene regulation, and that methods for antagonizing them may have therapeutic value.

Systematic Down-Selection of Repurposed Drug Candidates for COVID-19

SARS-CoV-2 is the cause of the COVID-19 pandemic which has claimed more than 6.5 million lives worldwide, devastating the economy and overwhelming healthcare systems globally. The development of new drug molecules and vaccines has played a critical role in managing the pandemic; however, new variants of concern still pose a significant threat as the current vaccines cannot prevent all infections. This situation calls for the collaboration of biomedical scientists and healthcare workers across the world. Repurposing approved drugs is an effective way of fast-tracking new treatments for recently emerged diseases. To this end, we have assembled and curated a database consisting of 7817 compounds from the Compounds Australia Open Drug collection. We developed a set of eight filters based on indicators of efficacy and safety that were applied sequentially to down-select drugs that showed promise for drug repurposing efforts against SARS-CoV-2. Considerable effort was made to evaluate approximately 14,000 assay data points for SARS-CoV-2 FDA/TGA-approved drugs and provide an average activity score for 3539 compounds. The filtering process identified 12 FDA-approved molecules with established safety profiles that have plausible mechanisms for treating COVID-19 disease. The methodology developed in our study provides a template for prioritising drug candidates that can be repurposed for the safe, efficacious, and cost-effective treatment of COVID-19, long COVID, or any other future disease. We present our database in an easy-to-use interactive interface (CoviRx that was also developed to enable the scientific community to access to the data of over 7000 potential drugs and to implement alternative prioritisation and down-selection strategies.