Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Nanoparticles (NPs) are considered as versatile tools in various fields including medicine, electronics, and environmental science. Understanding the structural aspects of surface modifiers in nanoparticles that govern their cellular uptake is crucial for optimizing their efficacy and minimizing potential cytotoxicity. The cellular uptake is influenced by multiple factors, namely, size, shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that significantly contribute to the cellular uptake of ENMOs in multiple cell types, including pancreatic cancer cells (PaCa2), human endothelial cells (HUVEC), and human macrophage cells (U937). The best models have been identified for each cell type and analyzed to detect the structural fingerprints/features governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response.

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

MIG1, TUP1 and NRG1 mediated yeast to hyphal morphogenesis inhibition in Candida albicans by ganciclovir

Candida albicans is a polymorphic human fungal pathogen and the prime etiological agent responsible for candidiasis. The main two aspects of C. albicans virulence that have been suggested are yeast-to-hyphal (Y-H) morphological transitions and biofilm development. Anti-fungal agents targeting these virulence attributes enhances the antifungal drug development process. Repositioning with other non-fungal drugs offered a one of the new strategies and a potential alternative option to counter the urgent need for antifungal drug development. In the current study, an antiviral drug ganciclovir was screened as an antifungal agent against ATCC 90028, 10231 and clinical isolate (C1). Ganciclovir at 0.5 mg/ml concentration reduced 50% hyphal development on a silicon-based urinary catheter and was visualized using scanning electron microscopy. Ganciclovir reduced ergosterol biosynthesis in both strains and C1 isolate of C. albicans in a concentration-dependent manner. Additionally, a gene expression profile study showed that ganciclovir treatment resulted in upregulation of hyphal-specific repressors MIG1, TUP1, and NRG1 in C. albicans. Additionally, an in vivo study on the Bombyx mori silkworm model further evidenced the virulence inhibitory ability of ganciclovir (0.5 mg/ml) against C. albicans. This is the first report that explore the novel anti-morphogenic activities of ganciclovir against the pathogenic C. albicans strains, along with clinical isolates. Further, ganciclovir may be considered for therapeutic purpose after combinations with standard antifungal agents.

Glycosylation Modulation Dictates Trafficking and Interaction of SARS-CoV-2 S1 Subunit and ACE2 in Intestinal Epithelial Caco-2 Cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mainly targets the upper respiratory tract. It gains entry by interacting with the host cell receptor angiotensin-converting enzyme 2 (ACE2) via its heavily glycosylated spike glycoprotein. SARS-CoV-2 can also affect the gastrointestinal tract. Given the significant role of glycosylation in the life cycle of proteins and the multisystem target of SARS-CoV-2, the role of glycosylation in the interaction of S1 with ACE2 in Caco-2 cells was investigated after modulation of their glycosylation patterns using N-butyldeoxynojirimycin (NB-DNJ) and 1-deoxymannojirimycin (dMM), in addition to mutant CHO cells harboring mutations at different stages of glycosylation. The data show a substantial reduction in the interactions between the altered glycosylation forms of S1 and ACE2 in the presence of NB-DNJ, while varied outcomes resulted from dMM treatment. These results highlight the promising effects of NB-DNJ and its potential use as an off-label drug to treat SARS-CoV-2 infections.

Dicoumarol is an effective post-exposure prophylactic for SARS-CoV-2 Omicron infection in human airway epithelium

Repurposing existing drugs to inhibit SARS-CoV-2 infection in airway epithelial cells (AECs) is a quick way to find novel treatments for COVID-19. Computational screening has found dicoumarol (DCM), a natural anticoagulant, to be a potential SARS-CoV-2 inhibitor, but its inhibitory effects and possible working mechanisms remain unknown. Using air-liquid interface culture of primary human AECs, we demonstrated that DCM has potent antiviral activity against the infection of multiple Omicron variants (including BA.1, BQ.1 and XBB.1). Time-of-addition and drug withdrawal assays revealed that early treatment (continuously incubated after viral absorption) of DCM could markedly inhibit Omicron replication in AECs, but DCM did not affect the absorption, exocytosis and spread of viruses or directly eliminate viruses. Mechanistically, we performed single-cell sequencing analysis (a database of 77,969 cells from different airway locations from 10 healthy volunteers) and immunofluorescence staining, and showed that the expression of NAD(P)H quinone oxidoreductase 1 (NQO1), one of the known DCM targets, was predominantly localised in ciliated AECs. We further found that the NQO1 expression level was positively correlated with both the disease severity of COVID-19 patients and virus copy levels in cultured AECs. In addition, DCM treatment downregulated NQO1 expression and disrupted signalling pathways associated with SARS-CoV-2 disease outcomes (e.g., Endocytosis and COVID-19 signalling pathways) in cultured AECs. Collectively, we demonstrated that DCM is an effective post-exposure prophylactic for SARS-CoV-2 infection in the human AECs, and these findings could help physicians formulate novel treatment strategies for COVID-19.

Updates on the Biofunctionalization of Gold Nanoparticles for the Rapid and Sensitive Multiplatform Diagnosis of SARS-CoV-2 Virus and Its Proteins: From Computational Models to Validation in Human Samples

Since the outbreak of the pandemic respiratory virus SARS-CoV-2 (COVID-19), academic communities and governments/private companies have used several detection techniques based on gold nanoparticles (AuNPs). In this emergency context, colloidal AuNPs are highly valuable easy-to-synthesize biocompatible materials that can be used for different functionalization strategies and rapid viral immunodiagnosis. In this review, the latest multidisciplinary developments in the bioconjugation of AuNPs for the detection of SARS-CoV-2 virus and its proteins in (spiked) real samples are discussed for the first time, with reference to the optimal parameters provided by three approaches: one theoretical, via computational prediction, and two experimental, using dry and wet chemistry based on single/multistep protocols. Overall, to achieve high specificity and low detection limits for the target viral biomolecules, optimal running buffers for bioreagent dilutions and nanostructure washes should be validated before conducting optical, electrochemical, and acoustic biosensing investigations. Indeed, there is plenty of room for improvement in using gold nanomaterials as stable platforms for ultrasensitive and simultaneous "in vitro" detection by the untrained public of the whole SARS-CoV-2 virus, its proteins, and specific developed IgA/IgM/IgG antibodies (Ab) in bodily fluids. Hence, the lateral flow assay (LFA) approach is a quick and judicious solution to combating the pandemic. In this context, the author classifies LFAs according to four generations to guide readers in the future development of multifunctional biosensing platforms. Undoubtedly, the LFA kit market will continue to improve, adapting researchers' multidetection platforms for smartphones with easy-to-analyze results, and establishing user-friendly tools for more effective preventive and medical treatments.

Computational Screening of Approved Drugs for Inhibition of the Antibiotic Resistance Gene mecA in Methicillin-Resistant Staphylococcus aureus (MRSA) Strains

Antibiotic resistance is a critical problem that results in a high morbidity and mortality rate. The process of discovering new chemotherapy and antibiotics is challenging, expensive, and time-consuming, with only a few getting approved for clinical use. Therefore, screening already-approved drugs to combat pathogens such as bacteria that cause serious infections in humans and animals is highly encouraged. In this work, we aim to identify approved antibiotics that can inhibit the mecA antibiotic resistance gene found in methicillin-resistant Staphylococcus aureus (MRSA) strains. The MecA protein sequence was utilized to perform a BLAST search against a drug database containing 4302 approved drugs. The results revealed that 50 medications, including known antibiotics for other bacterial strains, targeted the mecA antibiotic resistance gene. In addition, a structural similarity approach was employed to identify existing antibiotics for S. aureus, followed by molecular docking. The results of the docking experiment indicated that six drugs had a high binding affinity to the mecA antibiotic resistance gene. Furthermore, using the structural similarity strategy, it was discovered that afamelanotide, an approved drug with unclear antibiotic activity, had a strong binding affinity to the MRSA-MecA protein. These findings suggest that certain already-approved drugs have potential in chemotherapy against drug-resistant pathogenic bacteria, such as MRSA.

Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = −0.008, a(R2) = 0.014, and δK = 0.021) and its prediction ability (Q2LMO = 67.89, Q2EXT = 67.75, a(Q2) = −0.068, δQ = 0.0, rm2¯ = 0.229, and Δrm2 = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.

Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach

Small molecules such as 9,10-dihydrophenanthrene derivatives have remarkable activity toward inhibition of SARS-CoV-2 3CL^pro and COVID-19 proliferation, which show a strong correlation between their structures and bioactivity. Therefore, these small compounds could be suitable for clinical pharmaceutical use against COVID-19. The objective of this study was to remodel the structures of 9,10-dihydrophenanthrene derivatives to achieve a powerful biological activity against 3CL^pro and favorable pharmacokinetic properties for drug design and discovery. Therefore, by the use of bioinformatics techniques, we developed robust 3D-QSAR models that are capable of describing the structure–activity relationship for 46 molecules based on 9,10-dihydrophenanthrene derivatives using CoMFA/SE (R² = 0.97, Q² = 0.81, R²_pred = 0.95, ^cR²_p = 0.71) and CoMSIA/SEHDA (R² = 0.94, Q² = 0.76, R²_pred = 0.91, ^cR²_p = 0.65) techniques. Accordingly, 96 lead compounds were generated based on a template molecule that showed the highest observed activity in vitro (T40, pIC₅₀ = 5.81) and predicted their activities and bioavailability in silico. The rational screening outputs of 3D-QSAR, Molecular docking, ADMET, and MM-GBSA led to the identification of 9 novel modeled molecules as potent noncovalent drugs against SARS-CoV-2-3CL^pro. Finally, by molecular dynamics simulations, the stability and structural dynamics of 3CL^pro free and complex (PDB code: 6LU7) were discussed in the presence of samples of 9,10-dihydrophenanthrene derivative in an aqueous environment. Overall, the retrosynthesis of the proposed drug compounds in this study and the evaluation of their bioactivity in vitro and in vivo may be interesting for designing and discovering a new drug effective against COVID-19.