Abstract: Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

Abstract: BACKGROUND: Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of new medications or off-label use of medications in the critical care setting can lead to additional nephrotoxicities, often challenging to predict or detect. This study evaluates the patterns of medication utilization, their combinations, and the related associations with AKI. METHODS: We utilized correlation-based network analysis (CNA) to investigate the relationship between medications or their combinations with AKI in a large cohort of critically ill patients in a tertiary medical center between 2007 and 2018. Pairwise medication-AKI correlation analysis was performed to evaluate drug synergistic or additive effects. To investigate the inherent nephrotoxicity of medications, we further analyzed medications that were not paired with any other medications within 24 hours before or after their administration time (isolated medication analysis). RESULTS: Among 147,289 ICU admissions, we identified 244 associations among 1,555 unique medication types. In pairwise analysis, 233 significant correlations were found among 13,150,198 medication pair instances. In isolated medication analysis, ten significant AKI associations were noted. When stratified by eGFR level, substantial differences between eGFR<90 vs. eGFR>/=90 patients were observed. This highlights a need to determine eGFR as a risk factor for nephrotoxicity assessment when drug interactions are considered. CONCLUSIONS: This large-scale cohort study identified an artificial intelligence model to identify patient-agnostic relationships between medication or their pairs with AKI incidence among critically ill patients. It could be used as a continuous quality assurance tool to monitor drug-associated risk nephrotoxicity.

Abstract: Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism.

Abstract: Proper elucidation of drug-target interaction is one of the most significant steps at the early stages of the drug development research. Computer-aided drug design tools have substantial contribution to this stage. In this chapter, we specifically concentrate on the computational methods widely used to develop reversible inhibitors for monoamine oxidase (MAO) isozymes. In this context, current computational techniques in identifying the best drug candidates showing high potency are discussed. The protocols of structure-based drug design methodologies, namely, molecular docking, in silico screening, and molecular dynamics simulations, are presented. Employing case studies of safinamide binding to MAO B, we demonstrate how to use AutoDock 4.2.6 and NAMD software packages.

Abstract: Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, M(pro). The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant M(pro), with half-maximal inhibitory concentration values (IC50) in the range 0.18-18.82 muM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus M(pro) was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.

Abstract: Ischemic stroke (IS) is one of the major global causes of death and disability. Because blood clots block the neural arteries provoking ischemia and hypoxia in the brain tissue, IS results in irreversible neurological damage. Available IS treatments are currently limited. Curcumin has gained attention for many beneficial effects after IS, including neuroprotective and anti-inflammatory; however, its precise mechanism of action should be further explored. With network pharmacology, molecular docking, and molecular dynamics (MD), this study aimed to comprehensively and systematically investigate the potential targets and molecular mechanisms of curcumin on IS. We screened 1096 IS-related genes, 234 potential targets of curcumin, and 97 intersection targets. KEGG and GO enrichment analyses were performed on these intersecting targets. The findings showed that the treatment of IS using curcumin is via influencing 177 potential signaling pathways (AGE-RAGE signaling pathway, p53 signaling pathway, necroptosis, etc.) and numerous biological processes (the regulation of neuronal death, inflammatory response, etc.), and the AGE-RAGE signaling pathway had the largest degree of enrichment, indicating that it may be the core pathway. We also constructed a protein-protein interaction network and a component-target-pathway network using network pharmacology. From these, five key targets were screened: NFKB1, TP53, AKT1, STAT3, and TNF. To predict the binding conformation and intermolecular affinities of the key targets and compounds, molecular docking was used, whose results indicated that curcumin exhibited strong binding activity to the key targets. Moreover, 100 ns MD simulations further confirmed the docking findings and showed that the curcumin-protein complex could be in a stable state. In conclusion, curcumin affects multiple targets and pathways to inhibit various important pathogenic mechanisms of IS, including oxidative stress, neuronal death, and inflammatory responses. This study offers fresh perspectives on the transformation of curcumin to clinical settings and the development of IS therapeutic agents.

Abstract: Inkjet printing has the potential to advance the treatment of eye diseases by printing drugs on demand onto contact lenses for localised delivery and personalised dosing, while near-infrared (NIR) spectroscopy can further be used as a quality control method for quantifying the drug but has yet to be demonstrated with contact lenses. In this study, a glaucoma therapy drug, timolol maleate, was successfully printed onto contact lenses using a modified commercial inkjet printer. The drug-loaded ink prepared for the printer was designed to match the properties of commercial ink, whilst having maximal drug loading and avoiding ocular inflammation. This setup demonstrated personalised drug dosing by printing multiple passes. Light transmittance was found to be unaffected by drug loading on the contact lens. A novel dissolution model was built, and in vitro dissolution studies showed drug release over at least 3 h, significantly longer than eye drops. NIR was used as an external validation method to accurately quantify the drug dose. Overall, the combination of inkjet printing and NIR represent a novel method for point-of-care personalisation and quantification of drug-loaded contact lenses.

Abstract: BACKGROUND: Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics. METHODS: In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated. RESULTS: A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine. CONCLUSION: The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.

Abstract: OBJECTIVE: This study aimed to explore the molecular mechanism of Momordica grosvenori (MG) in spinal cord injury (SCI) by network pharmacology analysis. METHODS: We searched for potential active MG compounds using the TCMSP database and the BATMAN-TCM platform. The Swiss target prediction database was used to find MG-related targets and the targets of SCI from the CTD, GeneCards, and DrugBank databases. Following that, a protein-protein interaction (PPI) study was carried out. Cytoscape software was used to calculate the hub gene, and R software was used to evaluate the Gene Ontology (GO) and KEGG enrichment pathways. Finally, molecular docking between the hub protein and important compounds was performed. We verified STAT3, MAPK1, HSP90AA1, PIK3R1, PIK3CA, and RXRA potential targets by quantitative PCR. RESULTS: We obtained 293 MG-anti-SCI targets with potential therapeutic utility by intersecting 346 MG-related targets and 7214 SCI-related targets. The top 10 identified genes, ranking in descending order of value, were SRC, STAT3, MAPK1, HSP90AA1, PIK3R1, PIK3CA, RXRA, AKT1, CREBBP, and JAK2. Through enrichment analysis and literature search, 10 signaling pathways were screened out. The molecular docking of important drugs and hub targets revealed that some had a higher binding affinity. The results of quantitative PCR indicated that MAPK1, RXRA, and STAT3 were expressed differently in in vitro experiments. CONCLUSION: In conclusion, the current work indicated that MG might play an anti-SCI role via multicomponent, multitarget, and multichannel interaction, which presents a novel idea for further research into the precise mechanism of MG-anti-SCI interaction.

Abstract: BACKGROUND: Panax ginseng C. A. Mey (ginseng) is a traditional Chinese medicinal herb used for the treatment of nervous system disorders, such as Alzheimer's disease (AD). However, the pharmacological mechanisms of ginseng involved in AD have not been systematically investigated. Here, a network pharmacology approach was adopted to explore the effective components, core targets, and key pathways of ginseng against AD. METHODS: TCMSP database was used to screen the active ingredients of ginseng. Prediction of the targets of ginseng and AD-related genes was performed using online public databases. "Compound-Target," "Compound-Target-Disease," "Protein-Protein Interaction (PPI)," "Compound-Target-Pathway," and "Compound-Target-GO-Pathway" networks were constructed with Cytoscape 3.7.2 software. Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed by using the DAVID database. RESULTS: A total of 22 bioactive compounds were identified from ginseng, and 481 targets of ginseng and 763 AD-related targets were obtained from public databases. The PPI network screened out 19 hub genes of ginseng against AD. According to GO function enrichment, ginseng influenced cell proliferation, death, the nitric oxide biosynthetic process, hypoxia response, and synaptic transmission. Neuroactive ligand-receptor interaction, serotonergic synapse, calcium signaling, cAMP signaling, FoxO signaling, Ras signaling, and PI3K-AKT signaling were among the most key regulatory pathways. The compound-target-GO-route network found EGFR, MAPK1, MAPK14, AKT1, CASP3, and PRKACA as key genes, with PI3K-AKT signaling being the most important pathway for ginseng's anti-AD activity. CONCLUSION: Ginseng exerts neuroprotective effects in AD patients through multicomponent, multitarget, and multipathway modes, providing novel insight into the pharmacological and experimental research on ginseng against AD.