Environ Sci Pollut Res Int. 2021 Sep 25. doi: 10.1007/s11356-021-16427-4. Online ahead of print.

ABSTRACT

Coronavirus disease (COVID-19) is an emerging pandemic that threatens the world since the early days of 2020. Development of vaccines or new drugs against COVID-19 comprises several stages of investigation including efficacy, safety, and approval studies. A shortcut to this delicate pathway is computational-based analysis of FDA-approved drugs against assigned molecular targets of the coronavirus. In this study, we virtually screened a library of FDA-approved drugs prescribed for different therapeutic purposes against versatile COVID-19 specific proteins which are crucial for the virus life cycle. Three antibiotics in our screening polymyxin B, bafilomycin A, and rifampicin show motivating binding stability with more than one target of the virus. Another category of tested drugs is oral antiseptics of mouth rinsing solutions that unexpectedly exhibited significant affinity to the target proteins employed by the virus for attachment and cell internalization. Other OTC drugs widely used and tested in our study are heartburn drugs and they show no significant binding. We tested also some other drugs falling under the scope of investigation regarding interference with a degree of severity of COVID-19 like angiotensin II blockers used as antihypertensive, and our study suggests a therapeutic rather than predisposing effect of these drugs against COVID-19.

PMID:34562220 | DOI:10.1007/s11356-021-16427-4

BMC Med Inform Decis Mak. 2021 Sep 24;21(Suppl 8):263. doi: 10.1186/s12911-021-01617-4.

ABSTRACT

BACKGROUND: Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials.

RESULTS: In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR CONCLUSIONS: The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

PMID:34560862 | DOI:10.1186/s12911-021-01617-4

Biomed Pharmacother. 2021 Sep 21;143:112174. doi: 10.1016/j.biopha.2021.112174. Online ahead of print.

ABSTRACT

AIM: To elucidate the potential effect of promethazine on colorectal cancer (CRC) cells and the underlying mechanism.

MATERIALS AND METHODS: Targets of the drug promethazine (PMTZ) were identified by DrugBank and comparative toxicogenomic databases (CTD), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed with STRING software. The effects of PMTZ were predicted to be associated with the PI3K/AKT pathway. Cell Counting Kit 8 (CCK-8) assays were used to evaluate the effects of different concentrations of PMTZ on the proliferation of various types of CRC cells. Flow cytometry and Western blotting analyses were used to detect the degree of CRC cell apoptosis and the expression of the apoptosis-related proteins Bcl-2, Bax and caspase-3 after PMTZ treatment. The expression levels of PI3K/AKT pathway-related proteins [PI3K, AKT, phosphorylated (P)-PI3K and p-AKT] in CRC cells treated with PMTZ were analyzed by Western blotting.

RESULTS: PMTZ inhibited the proliferation and promoted the apoptosis of CRC cells and suppressed the activation of the PI3K/AKT signaling pathway in a dose-dependent manner.

DISCUSSION AND CONCLUSIONS: PMTZ may suppress the proliferation and induce the apoptosis of CRC cells by inhibiting the PI3K/ AKT signaling pathway. This study reported, for the first time, the function of PMTZ in CRC cells and the underlying mechanism and further confirmed the potential antitumor effects of phenothiazine. The combination of bioinformatics analyses and experiments provides informative evidence for the reuse of drugs and the development of new drugs.

PMID:34560542 | DOI:10.1016/j.biopha.2021.112174

J Glob Antimicrob Resist. 2021 Sep 21:S2213-7165(21)00211-3. doi: 10.1016/j.jgar.2021.09.003. Online ahead of print.

ABSTRACT

PURPOSE: We hypothesized that one or more of the non-antibiotic candidates selected for this study would demonstrate antibiotic activity against Staphylococcus aureus.

METHODS: We determined minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) for non-antibiotic drugs (amlodipine, azelastine, ebselen, and sertraline) against five clinical Staphylococcus aureus isolates and one quality control strain using microplate alamar blue assays. Our research group selected clinical isolates obtained from nasal and wound swab cultures of patients with skin and soft tissue infections (SSTIs) who were seen at primary care clinics in the South Texas Ambulatory Research Network (STARNet).

RESULTS: Three of the non-antibiotic drugs had identical MICs for all isolates: amlodipine (64 µg/ml), azelastine (200 µg/ml), and sertraline (20 µg/ml). MICs for ebselen were 0.25 µg/ml (SA-29213, A1019, and J1019), 0.5 µg/ml (A32 and B60), and 1.0 µg/ml (B72). MBCs for amlodipine, azelastine, and sertraline, were within one dilution of their MICs, indicating bactericidal activity for all test isolates. Ebselen MICs were 1 to 2 dilutions higher in most isolates, also indicating bactericidal activity for all test isolates.

CONCLUSION: In summary, all four non-antibiotics demonstrated in vitro activity to varying degrees against S. aureus clinical isolates. Ebselen was the most potent of the four non-antibiotics tested.

PMID:34560306 | DOI:10.1016/j.jgar.2021.09.003

Br J Clin Pharmacol. 2021 Sep 23. doi: 10.1111/bcp.15090. Online ahead of print.

ABSTRACT

X-linked adrenoleukodystrophy (X-ALD) is an inherited, neurodegenerative rare disease that can result in devastating symptoms of blindness, gait disturbances, and spastic quadriparesis due to progressive demyelination. Typically, the disease progresses rapidly, causing death within the first decade of life. With limited treatments available, efforts to determine an effective therapy that can alter disease progression or mitigate symptoms have been undertaken for many years, particularly through drug repurposing. Repurposing has generally been guided through clinical experience and small trials. At this time, none of the drug candidates have been approved for use, which may be due, in part, to the lack of pharmacokinetic/pharmacodynamic (PK/PD) information on the repurposed medications in the target patient population. Greater consideration for the disease pathophysiology, drug pharmacology, and potential drug-target interactions, specifically at the site of action, would improve drug repurposing and facilitate drug development. Incorporating advanced translational and clinical pharmacological approaches in preclinical studies and early stages clinical trials will improve the success of repurposed drugs for X-ALD as well as other rare diseases.

PMID:34558098 | DOI:10.1111/bcp.15090

Cell Death Dis. 2021 Sep 23;12(10):864. doi: 10.1038/s41419-021-04151-3.

ABSTRACT

Rewiring of host cytokine networks is a key feature of inflammatory bowel diseases (IBD) such as Crohn's disease (CD). Th1-type cytokines-IFN-γ and TNF-α-occupy critical nodes within these networks and both are associated with disruption of gut epithelial barrier function. This may be due to their ability to synergistically trigger the death of intestinal epithelial cells (IECs) via largely unknown mechanisms. In this study, through unbiased kinome RNAi and drug repurposing screens we identified JAK1/2 kinases as the principal and nonredundant drivers of the synergistic killing of human IECs by IFN-γ/TNF-α. Sensitivity to IFN-γ/TNF-α-mediated synergistic IEC death was retained in primary patient-derived intestinal organoids. Dependence on JAK1/2 was confirmed using genetic loss-of-function studies and JAK inhibitors (JAKinibs). Despite the presence of biochemical features consistent with canonical TNFR1-mediated apoptosis and necroptosis, IFN-γ/TNF-α-induced IEC death was independent of RIPK1/3, ZBP1, MLKL or caspase activity. Instead, it involved sustained activation of JAK1/2-STAT1 signalling, which required a nonenzymatic scaffold function of caspase-8 (CASP8). Further modelling in gut mucosal biopsies revealed an intercorrelated induction of the lethal CASP8-JAK1/2-STAT1 module during ex vivo stimulation of T cells. Functional studies in CD-derived organoids using inhibitors of apoptosis, necroptosis and JAKinibs confirmed the causative role of JAK1/2-STAT1 in cytokine-induced death of primary IECs. Collectively, we demonstrate that TNF-α synergises with IFN-γ to kill IECs via the CASP8-JAK1/2-STAT1 module independently of canonical TNFR1 and cell death signalling. This non-canonical cell death pathway may underpin immunopathology driven by IFN-γ/TNF-α in diverse autoinflammatory diseases such as IBD, and its inhibition may contribute to the therapeutic efficacy of anti-TNFs and JAKinibs.

PMID:34556638 | DOI:10.1038/s41419-021-04151-3

Eur J Med Chem. 2021 Sep 14;226:113847. doi: 10.1016/j.ejmech.2021.113847. Online ahead of print.

ABSTRACT

Transthyretin (TTR) has a well-established role in neuroprotection in Alzheimer's Disease (AD). We have setup a drug discovery program of small-molecule compounds that act as chaperones enhancing TTR/Amyloid-beta peptide (Aβ) interactions. A combination of computational drug repurposing approaches and in vitro biological assays have resulted in a set of molecules which were then screened with our in-house validated high-throughput screening ternary test. A prioritized list of chaperones was obtained and corroborated with ITC studies. Small-molecule chaperones have been discovered, among them our lead compound Iododiflunisal (IDIF), a molecule in the discovery phase; one investigational drug (luteolin); and 3 marketed drugs (sulindac, olsalazine and flufenamic), which could be directly repurposed or repositioned for clinical use. Not all TTR tetramer stabilizers behave as chaperones in vitro. These chemically diverse chaperones will be used for validating TTR as a target in vivo, and to select one repurposed drug as a candidate to enter clinical trials as AD disease-modifying drug.

PMID:34555615 | DOI:10.1016/j.ejmech.2021.113847

Comput Biol Med. 2021 Sep 10;138:104856. doi: 10.1016/j.compbiomed.2021.104856. Online ahead of print.

ABSTRACT

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.

PMID:34555571 | DOI:10.1016/j.compbiomed.2021.104856

Br J Haematol. 2021 Sep 23. doi: 10.1111/bjh.17851. Online ahead of print.

ABSTRACT

The anti-cancer potential of dipyridamole has been suggested from experiments, but evidence from population-based studies is still lacking. We aimed to explore if dipyridamole use was related to a lower risk of lymphoid neoplasms. We identified individuals with prescription of aspirin after diagnosis of ischaemic cerebrovascular disease since 2006 by linking several Swedish registers. In these aspirin users, those with dipyridamole prescription were further identified as the study group and patients without dipyridamole were randomly selected as reference group with 1:1 ratio using a propensity score-matching approach. After a median of 6·67 years of follow-up, a total of 46 patients with dipyridamole use developed lymphoid neoplasms with an incidence rate of 0·49 per 1 000 person-years, while the rate in the matched group was 0·74 per 1 000 person-years. As compared to non-users, dipyridamole users were associated with a significantly decreased risk of lymphoid neoplasms [hazard ratio (HR) = 0·65; 95% confidence interval (CI) = 0·43-0·98]. Specifically, the reduced risk was observed for non-Hodgkin lymphomas (HR = 0·64; 95% CI = 0·42-0·94), especially B-cell lymphomas (HR = 0·56; 95% CI = 0·35-0·88). Dipyridamole use was related to a lower risk of lymphoid neoplasms, indicating a clinical potential of dipyridamole to be an adjunct anti-tumour agent against lymphoid neoplasms.

PMID:34553368 | DOI:10.1111/bjh.17851

J Cheminform. 2021 Sep 22;13(1):71. doi: 10.1186/s13321-021-00552-w.

ABSTRACT

Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug-target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

PMID:34551818 | DOI:10.1186/s13321-021-00552-w

Sci Transl Med. 2021 Sep 22;13(612):eabd5524. doi: 10.1126/scitranslmed.abd5524. Epub 2021 Sep 22.

ABSTRACT

[Figure: see text].

PMID:34550729 | DOI:10.1126/scitranslmed.abd5524

Int J Pept Res Ther. 2021 Sep 17:1-21. doi: 10.1007/s10989-021-10287-9. Online ahead of print.

ABSTRACT

Staphylococcus aureus infection is a leading cause of mortality and morbidity in community, hospital and live-stock sectors, especially with the widespread emergence of methicillin-resistant S. aureus (MRSA) strains. To identify new drug molecules to treat MRSA patients, we have undertaken to search essential proteins that are indispensable for their survival but non-homologous to human host proteins. The current study utilizes a subtractive genome and proteome approach to screen the possible therapeutic targets against S. aureus USA300. Bacterial essential genes are obtained from the DEG database and are compared to avoid cross-reactivity with human host genes. In silico analysis shows 198 proteins that may be considered as therapeutic candidates. Depending on their sub-cellular localization, proteins are grouped as either vaccine or drug targets or both. Extracellular proteins such as cell division proteins (Q2FZ91, Q2FZ95), penicillin-binding proteins (Q2FZ94, Q2FYI0) of the bacterial cell wall, phosphoglucomutase (Q2FE11) and lipoteichoic acid synthase (Q2FIS2) are considered as vaccine targets, and their epitopes have been mapped. Altogether, 53 drug targets are identified, which have shown similarity with the drug targets available in the DrugBank database. Predicted drug targets belong to the common metabolic pathways of MRSA, such as fatty acid biosynthesis, folate biosynthesis, peptidoglycan biosynthesis, ribosome, etc. Protein-protein interaction analysis emphasizing peptidoglycan biosynthesis reveals the connection between penicillin-binding proteins, mur-family proteins and FemXAB proteins. In this study, staphylococcal FemA protein (P0A0A5) is subjected to structure-based virtual screening for the drug repurposing approach. There are 20 residues missing in the crystal structure of FemA, and 12 of these residues are located at the catalytic site. The missing residues are modelled, and stereochemistry is checked. FDA approved drugs available in the DrugBank database have been used in virtual screening with FemA in search of potential repurposed molecules. This approach provides us with 10 drugs that may be used in the treatment of methicillin-resistant staphylococcal mediated diseases. AutoDock 4.2 is used for in silico screening and shows a comparable inhibition constant (Ki) for all 10 FDA-approved drugs towards FemA. Most of these drugs are used in the treatment of various cancers, migraines and leukaemia. Protein-drug interaction analysis shows that the drugs mostly interact with hydrophobic residues of FemA. Moreover, Tyr328 and Lys383 contribute largely to hydrogen bondings during interactions. All interacting amino acids that bind to the drugs are part of the active site cavity of FemA.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10989-021-10287-9.

PMID:34548853 | PMC:PMC8446483 | DOI:10.1007/s10989-021-10287-9

Recent Pat Anticancer Drug Discov. 2021;16(2):120-121. doi: 10.2174/157489281602210806102833.

NO ABSTRACT

PMID:34547996 | DOI:10.2174/157489281602210806102833

IEEE Trans Neural Netw Learn Syst. 2021 Sep 21;PP. doi: 10.1109/TNNLS.2021.3111745. Online ahead of print.

ABSTRACT

The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies have been implemented to find potential anti-COVID-19 drugs in a short span of time. Motivated by this critical global challenge, in this review, we detail approaches that have been used for drug repurposing for COVID-19 and suggest improvements to the existing deep learning (DL) approach to identify and repurpose drugs to treat this complex disease. By optimizing hyperparameter settings, deploying suitable activation functions, and designing optimization algorithms, the improved DL approach will be able to perform feature extraction from quality big data, turning the traditional DL approach, referred to as a ``black box,'' which generalizes and learns the transmitted data, into a ``glass box'' that will have the interpretability of its rationale while maintaining a high level of prediction accuracy. When adopted for drug repurposing for COVID-19, this improved approach will create a new generation of DL approaches that can establish a cause and effect relationship as to why the repurposed drugs are suitable for treating COVID-19. Its ability can also be extended to repurpose drugs for other complex diseases, develop appropriate treatment strategies for new diseases, and provide precision medical treatment to patients, thus paving the way to discover new drugs that can potentially be effective for treating COVID-19.

PMID:34546931 | DOI:10.1109/TNNLS.2021.3111745

Med Biol Eng Comput. 2021 Sep 20. doi: 10.1007/s11517-021-02436-5. Online ahead of print.

ABSTRACT

Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localization is imperative for the functional characterization of protein sequences. Diverse techniques are used on protein sequences for feature extraction. However, many times a single feature extraction technique leads to poor prediction performance. In this paper, two feature augmentations are described through sequence induced, physicochemical, and evolutionary information of the amino acid residues. While augmented features preserve the sequence-order-information and protein-residue-properties. Two bacterial protein datasets Gram-Positive (G +) and Gram-Negative (G-) are utilized for the experimental work. After performing essential preprocessing on protein datasets, two sets of feature vectors are obtained. These feature vectors are used separately to train the different individual and ensembles such as decision tree (C 4.5), k-nearest neighbor (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, gradient boosting machine (GBM), and random forest (RF) with fivefold cross-validation. Prediction results of the model demonstrate that overall accuracy reported by C4.5 is highest 99.57% on G + and 97.47% on G- datasets with known protein sequences. Similarly, for the UPS overall accuracy of G + is 85.17% with SVM and 82.45% with G- dataset using MLP.

PMID:34545514 | DOI:10.1007/s11517-021-02436-5

ArXiv. 2021 Sep 14:arXiv:2109.06377v1. Preprint.

ABSTRACT

Intercellular heterogeneity is a major obstacle to successful personalized medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potentials for personalized medicine are yet to be reached. Towards this, we propose A Single-cell Guided pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD can repurpose single drugs for each cell cluster and for multiple cell clusters at individual patient levels; it can also predict personalized drug combinations to address the intercellular heterogeneity within each patient. We tested ASGARD on three independent datasets, including advanced metastatic breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC=0.95) compared to two other single-cell pipelines (AUC 0.69 and 0.57) and two other bulk-cell-based drug repurposing methods (AUC 0.80 and 0.75). The top-ranked drugs, such as fulvestrant and neratinib for breast cancer, tretinoin and vorinostat for leukemia, and chloroquine and enalapril for severe COVID19, are either approved by FDA or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising pipeline guided by single-cell RNA-seq data, for repurposing personalized drugs and drug combinations. ASGARD is free for academic use at https://github.com/lanagarmire/ASGARD.

PMID:34545335 | PMC:PMC8452105

J Cheminform. 2021 Sep 20;13(1):70. doi: 10.1186/s13321-021-00553-9.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

PMID:34544500 | DOI:10.1186/s13321-021-00553-9

J Proteins Proteom. 2021 Sep 15:1-14. doi: 10.1007/s42485-021-00074-x. Online ahead of print.

ABSTRACT

COVID-19 [coronavirus disease 2019] has resulted in over 204,644,849 confirmed cases and over 4,323,139 deaths throughout the world as of 12 August 2021, a total of 4,428,168,759 vaccine doses have been administered. The lack of potentially effective drugs against the virus is making the situation worse and dangerous. Numerous forces are working on finding an effective treatment against the virus but it is believed that a de novo drug would take several months even if huge financial support is provided. The only solution left with is drug repurposing that would not only provide effective therapy with the already used clinical drugs, but also save time and cost of the de novo drug discovery. The initiation of the COVID-19 infection starts with the attachment of spike glycoprotein of SARS-CoV-2 to the host receptor. Hence, the inhibition of the binding of the virus to the host membrane and the entry of the viral particle into the host cell are one of the main therapeutic targets. This paper not only summarizes the structure and the mechanism of spike protein, but the main focus is on the potential covalent spike protein inhibitors.

PMID:34539131 | PMC:PMC8440732 | DOI:10.1007/s42485-021-00074-x

J Invest Dermatol. 2021 Sep 15:S0022-202X(21)02160-6. doi: 10.1016/j.jid.2021.07.170. Online ahead of print.

ABSTRACT

Expression-based systematic drug repositioning has been explored to predict novel treatments for a number of skin disorders. Here, we utilize this approach to identify, to our knowledge, previously unreported therapies for epidermolysis bullosa simplex (EBS). RNA sequencing analysis was performed on skin biopsies of acute blisters (<1 week) (n=9) and non-blistered epidermis (n=11) obtained from 11 EBS patients. Transcriptomic analysis of blistered epidermis in EBS patients revealed a set of 1276 genes dysregulated in EBS blisters. The IL-6, IL-8, and IL-10 pathways were upregulated in epidermis from EBS. Consistent with this, predicted upstream regulators included TNF-α, IL-1β, IL2, IL-6, PI3K, and mTOR. The 1276 gene EBS blister signature was integrated with molecular signatures from cell lines treated with 2423 drugs using the ConnectivityMap CLUE platform. mTOR inhibitors and PI3K inhibitors most opposed the EBS signature. To determine if mTOR inhibitors could be used clinically in EBS, we conducted an independent pilot study of 2 patients with EBS treated with topical sirolimus for painful plantar keratoderma due to chronic blistering. Both individuals experienced marked clinical improvement and notable reduction of keratoderma. In summary, a computational drug repositioning analysis successfully identified, to our knowledge, previously unreported targets in the treatment of EBS.

PMID:34536484 | DOI:10.1016/j.jid.2021.07.170

Naunyn Schmiedebergs Arch Pharmacol. 2021 Sep 18. doi: 10.1007/s00210-021-02144-9. Online ahead of print.

ABSTRACT

The term "antibiotics" is a broadly used misnomer to designate antibacterial drugs. In a recent article, we have proposed to replace, e.g., the term "antibiotics" by "antibacterial drugs", "antibiosis" by "antibacterial therapy", "antibiogram" by "antibacteriogram", and "antibiotic stewardship" by "antibacterial stewardship" (Seifert and Schirmer Trends Microbiol, 2021). In the present article, we show that many traditional terms related to antibiotics are used much more widely in the biomedical literature than the respective scientifically precise terms. This practice should be stopped. Moreover, we provide arguments to end the use of other broadly used terms in the biomedical literature such as "narrow-spectrum antibiotics" and "reserve antibiotics", "chemotherapeutics", and "tuberculostatics". Finally, we provide several examples showing that antibacterial drugs are used for non-antibacterial indications and that some non-antibacterial drugs are used for antibacterial indications now. Thus, the increasing importance of drug repurposing renders it important to drop short designations of drug classes such as "antibiotics". Rather, the term "drug" should be explicitly used, facilitating the inclusion of newly emerging indications such as antipsychotic and anti-inflammatory. This article is part of an effort to implement a new rational nomenclature of drug classes across the entire field of pharmacology.

PMID:34536087 | DOI:10.1007/s00210-021-02144-9