PLoS One. 2022 Oct 24;17(10):e0276609. doi: 10.1371/journal.pone.0276609. eCollection 2022.

ABSTRACT

Drug-target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.

PMID:36279284 | DOI:10.1371/journal.pone.0276609

Development. 2022 Oct 15;149(20):dev200614. doi: 10.1242/dev.200614. Epub 2022 Oct 24.

ABSTRACT

Many esophageal diseases can arise during development or throughout life. Therefore, well-characterized in vitro models and detailed methods are essential for studying human esophageal development, homeostasis and disease. Here, we (1) create an atlas of the cell types observed in the normal adult human esophagus; (2) establish an ancestrally diverse biobank of in vitro esophagus tissue to interrogate homeostasis and injury; and (3) benchmark in vitro models using the adult human esophagus atlas. We created a single-cell RNA sequencing reference atlas using fresh adult esophagus biopsies and a continuously expanding biobank of patient-derived in vitro cultures (n=55 lines). We identify and validate several transcriptionally distinct cell classes in the native human adult esophagus, with four populations belonging to the epithelial layer, including basal, epibasal, early differentiating and terminally differentiated luminal cells. Benchmarking in vitro esophagus cultures to the in vivo reference using single-cell RNA sequencing shows that the basal stem cells are robustly maintained in vitro, and the diversity of epithelial cell types in culture is dependent on cell density. We also demonstrate that cultures can be grown in 2D or as 3D organoids, and these methods can be employed for modeling the complete epithelial layers, thereby enabling in vitro modeling of the human adult esophagus.

PMID:36278875 | DOI:10.1242/dev.200614

Front Pharmacol. 2022 Oct 5;13:962863. doi: 10.3389/fphar.2022.962863. eCollection 2022.

ABSTRACT

The novel coronavirus disease (COVID-19) caused by SARS-CoV-2 virus spreads rapidly to become a global pandemic. Researchers have been working to develop specific drugs to treat COVID-19. The main protease (Mpro) of SARS-CoV-2 virus plays a pivotal role in mediating viral replication and transcription, which makes it a potential therapeutic drug target against COVID-19. In this study, a virtual drug screening method based on the Mpro structure (Protein Data Bank ID: 6LU7) was proposed, and 8,820 compounds collected from the DrugBank database were used for molecular docking and virtual screening. A data set containing 1,545 drug molecules, derived from compounds with a low binding free energy score in the docking experiment, was established. N-1H-Indazol-5-yl-2-(6-methylpyridin-2-yl)quinazolin-4-amine, ergotamine, antrafenine, dihydroergotamine, and phthalocyanine outperformed the other compounds in binding conformation and binding free energy over the N3 inhibitor in the crystal structure. The bioactivity and ADMET properties of these five compounds were further investigated. These experimental results for five compounds suggested that they were potential therapeutics to be developed for clinical trials. To further verify the results of molecular docking, we also carried out molecular dynamics (MD) simulations on the complexes formed by the five compounds and Mpro. The five complexes showed stable affinity in terms of root mean square distance (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and hydrogen bond. It was further confirmed that the five compounds had potential inhibitory effects on SARS-CoV-2 Mpro.

PMID:36278156 | PMC:PMC9579442 | DOI:10.3389/fphar.2022.962863

Comput Struct Biotechnol J. 2022 Oct 17. doi: 10.1016/j.csbj.2022.10.017. Online ahead of print.

ABSTRACT

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

PMID:36277237 | PMC:PMC9575573 | DOI:10.1016/j.csbj.2022.10.017

Front Cell Infect Microbiol. 2022 Oct 5;12:1010771. doi: 10.3389/fcimb.2022.1010771. eCollection 2022.

ABSTRACT

Multi-drug resistant tuberculosis still remains a major public health crisis globally. With the emergence of newer active tuberculosis disease, the requirement of prolonged treatment time and adherence to therapy till its completion necessitates the search of newer therapeutics, targeting human host factors. The current work utilized statistical meta-analysis of human gene transcriptomes of active pulmonary tuberculosis disease obtained from six public datasets. The meta-analysis resulted in the identification of 2038 significantly differentially expressed genes (DEGs) in the active tuberculosis disease. The gene ontology (GO) analysis revealed that these genes were major contributors in immune responses. The pathway enrichment analyses identified from various human canonical pathways are related to other infectious diseases. In addition, the comparison of the DEGs with the tuberculosis genome wide association study (GWAS) datasets revealed the presence of few genetic variants in their proximity. The analysis of protein interaction networks (human and Mycobacterium tuberculosis) and host directed drug-target interaction network led to new candidate drug targets for drug repurposing studies. The current work sheds light on host genes and pathways enriched in active tuberculosis disease and suggest potential drug repurposing targets for host-directed therapies.

PMID:36275035 | PMC:PMC9581169 | DOI:10.3389/fcimb.2022.1010771

Acta Neuropathol Commun. 2022 Oct 23;10(1):150. doi: 10.1186/s40478-022-01463-z.

ABSTRACT

Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.

PMID:36274161 | DOI:10.1186/s40478-022-01463-z

Biomed Pharmacother. 2022 Nov;155:113794. doi: 10.1016/j.biopha.2022.113794. Epub 2022 Oct 4.

ABSTRACT

Colorectal cancer (CRC) remains a leading causes of cancer-related death in the world, mainly due to the lack of effective treatment of advanced disease. TNF-related apoptosis-inducing ligand (TRAIL)-driven cell death, a crucial event in the control of tumor growth, selectively targets malignant rather than non-transformed cells. However, the fact that cancer cells, including CRC cells, are either intrinsically resistant or acquire resistance to TRAIL, represents a major hurdle to the use of TRAIL-based strategies in the clinic. Agents able to overcome CRC cell resistance to TRAIL have thus great therapeutic potential and many researchers are making efforts to identify TRAIL sensitizers. The anthelmintic drug rafoxanide has recently emerged as a potent anti-tumor molecule for different cancer types and we recently reported that rafoxanide restrained the proliferation of CRC cells, but not of normal colonic epithelial cells, both in vitro and in a preclinical model mimicking sporadic CRC. As these findings were linked with the induction of endoplasmic reticulum stress, a phenomenon involved in the regulation of various components of the TRAIL-driven apoptotic pathway, we sought to determine whether rafoxanide could restore the sensitivity of CRC cells to TRAIL. Our data show that rafoxanide acts as a selective TRAIL sensitizer in vitro and in a syngeneic experimental model of CRC, by decreasing the levels of c-FLIP and survivin, two key molecules conferring TRAIL resistance. Collectively, our data suggest that rafoxanide could potentially be deployed as an anti-cancer drug in the combinatorial approaches aimed at overcoming CRC cell resistance to TRAIL-based therapies.

PMID:36271571 | DOI:10.1016/j.biopha.2022.113794

Biomed Pharmacother. 2022 Nov;155:113789. doi: 10.1016/j.biopha.2022.113789. Epub 2022 Oct 8.

ABSTRACT

Niclosamide is a drug used to treat parasitic infections. Recent studies have shown that niclosamide may have a wide range of clinical applications and can be used to treat cancer and other diseases. However, its application is also limited by its water solubility and safety, and drug resistance to cancer. To solve these problems, some studies have shown that niclosamide can be used in combination with chemotherapeutic drugs, targeted drugs, radiotherapy, and immunotherapy to enhance the anti-tumor effect. This review summarizes the drug combination strategies and therapeutic effect of niclosamide, to provide a reference for the combination therapy of niclosamide and wider application of antitumor drugs.

PMID:36271567 | DOI:10.1016/j.biopha.2022.113789

Biomed Pharmacother. 2022 Nov;155:113786. doi: 10.1016/j.biopha.2022.113786. Epub 2022 Sep 30.

ABSTRACT

A group of bioactive, isoprenoid pigments known as carotenoids is mostly present in fruits and vegetables. Carotenoids are essential for the prevention of physiological issues, which makes maintaining excellent health easier. They are effective functional ingredients with potent health-promoting properties that are widely present in our food and linked to a decrease in the prevalence of chronic diseases, including respiratory diseases. Respiratory infections are the primary cause of death and life-threatening conditions globally, wreaking havoc on the global health system. People rely on dietary sources of carotenoids to reduce a plethora of respiratory diseases such as chronic obstructive pulmonary disease (COPD), lung cancer, asthma, and so on. Carotenoids have received a lot of interest recently in several parts of the world due to their therapeutic potential in altering the pathogenic pathways underlying inflammatory respiratory diseases, which may improve disease control and have beneficial health benefits. This review aimed to provide a thorough understanding of the therapeutic potential of dietary carotenoids in the treatment of respiratory diseases and to identify possible candidates for novel therapeutic development.

PMID:36271564 | DOI:10.1016/j.biopha.2022.113786

J Biomed Semantics. 2022 Oct 21;13(1):25. doi: 10.1186/s13326-022-00279-z.

ABSTRACT

BACKGROUND: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020.

RESULTS: As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment.

CONCLUSION: CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

PMID:36271389 | DOI:10.1186/s13326-022-00279-z

iScience. 2022 Oct 13:105348. doi: 10.1016/j.isci.2022.105348. Online ahead of print.

ABSTRACT

SARS-CoV-2, the pathogen of COVID-19, has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multi-scale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-CoV paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 FDA-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

PMID:36267550 | PMC:PMC9556799 | DOI:10.1016/j.isci.2022.105348

Front Pharmacol. 2022 Oct 4;13:1041576. doi: 10.3389/fphar.2022.1041576. eCollection 2022.

NO ABSTRACT

PMID:36267279 | PMC:PMC9577551 | DOI:10.3389/fphar.2022.1041576

J Biomol Struct Dyn. 2022 Oct 20:1-15. doi: 10.1080/07391102.2022.2132295. Online ahead of print.

ABSTRACT

STAT3 signaling is a major intrinsic pathway for cell proliferation owing to its frequent activation in injured tissues. Various STAT3-regulated genes encode cytokines and growth factors, the receptors of which in turn activate the same STAT3 pathways, thereby regulating cell proliferation. In present study, we aimed to analyze several compounds for their wound healing and tissue repair potential by computer-aided virtual screening and Molecular dynamics (MD) simulation. Based on literature studies, a total of 36 drug molecules were selected having critical functions in wound healing and tissue repair. The pharmacological features (ADME and toxicity) of these molecules were predicted to find lead molecules among them. Further, a comparative study was performed to screen binding efficiency of STAT3 with many conventional wound healers by molecular docking. Among all, W6S, Strychnin, Prednisone and N-(6-(4-(3-(4-((4-Methylpiperazin-1-yl) methyl)-3- (trifluoromethyl)phenyl)ureido)phenoxy)pyrimidin-4-yl)cyclopropanecarboxamide showed best docking with STAT3 protein. The calculated binding energy of these molecules with STAT3 was found to be -8.9 Kca/mol for N-(6-(4-(3-(4-((4-Methylpiperazin-1-yl) methyl)-3-(trifluoromethyl) phenyl)ureido)phenoxy)pyrimidin-4-yl)cyclopropanecarboxamide, -8.7 Kcal/mol for W6S, -8.5 Kcal/mol for Strychnine and -8.4 Kcal/mol for Prednisone . The result was reconsidered for MD simulation. The simulation result showed stable binding of the ligand with STAT3 protein for 100 ns. These compounds showed better interaction potential with STAT3 was compared to known tissue repair molecules. Our data paves way for further exploration of these molecules as novel cell proliferators to be tested in various types of wound and tissue injuries.Communicated by Ramaswamy H. Sarma.

PMID:36264095 | DOI:10.1080/07391102.2022.2132295

Front Pharmacol. 2022 Oct 3;13:1007315. doi: 10.3389/fphar.2022.1007315. eCollection 2022.

ABSTRACT

Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer's disease Association estimates that 6.2 million Americans are detected with Alzheimer's disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson's disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning-based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.

PMID:36263141 | PMC:PMC9574100 | DOI:10.3389/fphar.2022.1007315

Comput Math Methods Med. 2022 Oct 10;2022:7035634. doi: 10.1155/2022/7035634. eCollection 2022.

ABSTRACT

Drug-disease correlations play an important role in revealing the mechanism of disease, finding new indications of available drugs, or drug repositioning. A variety of computational approaches were proposed to find drug-disease correlations and achieve good performances. However, these methods used a variety of network information, but integrated networks were rarely used. In addition, the role of known drug-disease association data has not been fully played. In this work, we designed a combination algorithm of random walk and supervised learning to find the drug-disease correlations. We used an integrated network to update the model and selected a gene set as the start of random walk based on the known drug-disease correlations data. The experimental results show that the proposed method can effectively find the correlation between drugs and diseases, and the prediction accuracy is 82.7%. We found that there are 8 pairs of drug-disease relationships that have not yet been reported, and 5 of them have pharmacodynamic effects on Parkinson's disease. We also found that a key linkage between Parkinson's disease and phenylhexol, a drug for the treatment of Parkinson's disease α-synuclein and tau protein, provides a useful exploration for the effectiveness of the treatment of Parkinson's disease.

PMID:36262874 | PMC:PMC9576438 | DOI:10.1155/2022/7035634

PeerJ Comput Sci. 2022 Oct 11;8:e1124. doi: 10.7717/peerj-cs.1124. eCollection 2022.

ABSTRACT

Identification of drug-target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug-drug and nine target-target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug-disease associations, and the cosine scores of drug-drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

PMID:36262151 | PMC:PMC9575853 | DOI:10.7717/peerj-cs.1124

Sci Rep. 2022 Oct 19;12(1):17438. doi: 10.1038/s41598-022-21474-z.

ABSTRACT

Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes' identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Optimizing the standard deviation such that the histogram of P values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Our contribution was that we improved these methods so as to be able to select biologically more reasonable differentially expressed genes than the state of art methods that must empirically assume negative binomial distributions and dispersion relation, which is required for the selecting more expressed genes than less expressed ones, which can be achieved by the proposed methods that do not have to assume these.

PMID:36261574 | DOI:10.1038/s41598-022-21474-z

Hist Philos Life Sci. 2022 Oct 18;44(4):47. doi: 10.1007/s40656-022-00532-9.

ABSTRACT

The current strategy of searching for an effective treatment for COVID-19 relies mainly on repurposing existing therapies developed to target other diseases. Conflicting results have emerged in regard to the efficacy of several tested compounds but later results were negative. The number of conducted and ongoing trials and the urgent need for a treatment pose the risk that false-positive results will be incorrectly interpreted as evidence for treatments' efficacy and a ground for drug approval. Our purpose is twofold. First, we show that the number of drug-repurposing trials can explain the false-positive results. Second, we assess the evidence for treatments' efficacy from the perspective of evidential pluralism and argue that considering mechanistic evidence is particularly needed in cases when the evidence from clinical trials is conflicting or of low quality. Our analysis is an application of the program of Evidence Based Medicine Plus (EBM+) to the drug repurposing trials for COVID. Our study shows that if decision-makers applied EBM+, authorizing the use of ineffective treatments would be less likely. We analyze the example of trials assessing the efficacy of hydroxychloroquine as a treatment for COVID-19 and mechanistic evidence in favor of and against its therapeutic power to draw a lesson for decision-makers and drug agencies on how excessive hypothesis testing can lead to spurious findings and how studying negative mechanistic evidence can be helpful in discriminating genuine from spurious results.

PMID:36258007 | DOI:10.1007/s40656-022-00532-9

J Mol Graph Model. 2022 Oct 1;118:108348. doi: 10.1016/j.jmgm.2022.108348. Online ahead of print.

ABSTRACT

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

PMID:36257147 | DOI:10.1016/j.jmgm.2022.108348

Antimicrob Agents Chemother. 2022 Oct 18:e0083222. doi: 10.1128/aac.00832-22. Online ahead of print.

ABSTRACT

New anti-Entamoeba histolytica multistage drugs are needed because only one drug class, nitroimidazoles, is available for treating invasive disease, and it does not effectively eradicate the infective cyst stage. Zinc ditiocarb (ZnDTC), a main metabolite of the FDA-approved drug disulfiram, was recently shown to be highly effective against the invasive trophozoite stage. In this brief report, we show that ZnDTC is active against cysts, with similar potency to first-line cysticidal drug paromomycin.

PMID:36255253 | DOI:10.1128/aac.00832-22