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Showing 1271 to 1274 of 5186 entries
Published in 2021
A review on machine learning approaches and trends in drug discovery.
Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
Published in 2021
Exploring the Biological Mechanism of Huang Yam in Treating Tumors and Preventing Antitumor Drug-Induced Cardiotoxicity Using Network Pharmacology and Molecular Docking Technology.
Abstract: Drugs for the treatment of tumors could result in cardiotoxicity and cardiovascular diseases. We aimed to explore the anticancer properties of Huang yam as well as its cardioprotective properties using network pharmacology and molecular docking technology. The cardiovascular targets of the major chemical components of Huang yam were obtained from the following databases: TCMSP, ETCM, and BATMAN-TCM. The active ingredients of Huang yam were obtained from SwissADME. The cardiovascular targets of antitumor drugs were obtained using GeneCards, OMIM, DrugBank, DisGeNET, and SwissTargetPrediction databases. The drug-disease intersection genes were used to construct a drug-compound-target network using Cytoscape 3.7.1. A protein-protein interaction network was constructed using Cytoscape's BisoGenet, and the core targets of Huang yam were screened to determine their antitumor properties and identify the cardiovascular targets based on topological parameters. Potential targets were imported into the Metascape platform for GO and KEGG analysis. The results were saved and visualized using R software. The components with higher median values in the network were molecularly docked with the core targets. The network contained 10 compounds, including daucosterol, delusive, dioxin, panthogenin-B, and 124 targets, such as TP53, RPS27A, and UBC. The GO function enrichment analysis showed that there were 478 items in total. KEGG enrichment analysis showed a total of 140 main pathways associated with abnormal transcription of cancer, PI3K-Akt signaling pathway, cell cycle, cancer pathway, ubiquitination-mediated proteolysis, and other pathways. Molecular docking results showed that daucosterol, delusive, dioxin, and panthogenin-B had the highest affinity for TP53, RPS27A, and UBC. The treatment of diseases using traditional Chinese medicine encompasses multiple active ingredients, targets, and pathways. Huang yam has the potential to treat cardiotoxicity caused by antitumor drugs.
Published in 2021
Drug Repurposing and Polypharmacology to Fight SARS-CoV-2 Through Inhibition of the Main Protease.
Abstract: The outbreak of a new coronavirus (SARS-CoV-2), which is responsible for the COVID-19 disease and is spreading rapidly around the world, urgently requires effective therapeutic treatments. In this context, drug repurposing represents a valuable strategy, as it enables accelerating the identification of drug candidates with already known safety profiles, possibly aiding in the late stages of clinical evaluation. Moreover, therapeutic treatments based on drugs with beneficial multi-target activities (polypharmacology) may show an increased antiviral activity or help to counteract severe complications concurrently affecting COVID-19 patients. In this study, we present the results of a computational drug repurposing campaign that aimed at identifying potential inhibitors of the main protease (M(pro)) of the SARS-CoV-2. The performed in silico screening allowed the identification of 22 candidates with putative SARS-CoV-2 M(pro) inhibitory activity. Interestingly, some of the identified compounds have recently entered clinical trials for COVID-19 treatment, albeit not being assayed for their SARS-CoV-2 antiviral activity. Some candidates present a polypharmacology profile that may be beneficial for COVID-19 treatment and, to the best of our knowledge, have never been considered in clinical trials. For each repurposed compound, its therapeutic relevance and potential beneficial polypharmacological effects that may arise due to its original therapeutic indication are thoroughly discussed.
Published in 2021
In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19.
Abstract: Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively. Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics. Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.
Published in 2021
Drug Response Pharmacogenetics for 200,000 UK Biobank Participants.
Abstract: Pharmacogenetics studies how genetic variation leads to variability in drug response. Guidelines for selecting the right drug and right dose for patients based on their genetics are clinically effective, but are widely unused. For some drugs, the normal clinical decision making process may lead to the optimal dose of a drug that minimizes side effects and maximizes effectiveness. Without measurements of genotype, physicians and patients may adjust dosage in a manner that reflects the underlying genetics. The emergence of genetic data linked to longitudinal clinical data in large biobanks offers an opportunity to confirm known pharmacogenetic interactions as well as discover novel associations by investigating outcomes from normal clinical practice. Here we use the UK Biobank to search for pharmacogenetic interactions among 200 drugs and 9 genes among 200,000 participants. We identify associations between pharmacogene phenotypes and drug maintenance dose as well as differential drug response phenotypes. We find support for several known drug-gene associations as well as novel pharmacogenetic interactions.
Published in December 2021
Host Defence RNases as Antiviral Agents against Enveloped Single Stranded RNA Viruses.
Abstract: Owing to the recent outbreak of Coronavirus Disease of 2019 (COVID-19), it is urgent to develop effective and safe drugs to treat the present pandemic and prevent other viral infections that might come in the future. Proteins from our own innate immune system can serve as ideal sources of novel drug candidates thanks to their safety and immune regulation versatility. Some host defense RNases equipped with antiviral activity have been reported over time. Here, we try to summarize the currently available information on human RNases that can target viral pathogens, with special focus on enveloped single-stranded RNA (ssRNA) viruses. Overall, host RNases can fight viruses by a combined multifaceted strategy, including the enzymatic target of the viral genome, recognition of virus unique patterns, immune modulation, control of stress granule formation, and induction of autophagy/apoptosis pathways. The review also includes a detailed description of representative enveloped ssRNA viruses and their strategies to interact with the host and evade immune recognition. For comparative purposes, we also provide an exhaustive revision of the currently approved or experimental antiviral drugs. Finally, we sum up the current perspectives of drug development to achieve successful eradication of viral infections.
Published in 2021
Deciphering the Pharmacological Mechanisms of Guizhi-Fuling Capsule on Primary Dysmenorrhea Through Network Pharmacology.
Abstract: Guizhi-Fuling capsule (GZFLC), originated from a classical traditional Chinese herbal formula Guizhi-Fuling Wan, has been clinically used for primary dysmenorrhea in China. Nonetheless, the underlying pharmacological mechanisms of GZFLC remain unclear. The integration of computational and experimental methods of network pharmacology might be a promising way to decipher the mechanisms. In this study, the target profiles of 51 representative compounds of GZFLC were first predicted by a high-accuracy algorithm, drugCIPHER-CS, and the network target of GZFLC was identified. Then, potential functional modules of GZFLC on primary dysmenorrhea were investigated using functional enrichment analysis. Potential bioactive compounds were recognized by hierarchical clustering analysis of GZFLC compounds and first-line anti-dysmenorrhea drugs. Furthermore, the potential anti-dysmenorrhea mechanisms of GZFLC were verified through enzyme activity assays and immunofluorescence tests. Moreover, effects of GZFLC on primary dysmenorrhea were evaluated in oxytocin-induced dysmenorrhea murine model. In the network target analysis, GZFLC may act on five functional modules of pain, inflammation, endocrine, blood circulation and energy metabolism. Integrating computational and experimental approaches, we found that GZFLC significantly inhibited the writhing response and reduced the degree of uterine lesions in oxytocin-induced dysmenorrhea murine model. Furthermore, GZFLC may partially alleviate primary dysmenorrhea by inhibiting cyclooxygenase 2 (COX2) and downregulating MAPK signaling pathway. Consequently, GZFLC presented pain relief and sustained benefits for primary dysmenorrhea. This study could provide a scientific approach for deciphering pharmacological mechanisms of herbal formulae through network pharmacology.
Published in 2021
Integrative Data Analytic Framework to Enhance Cancer Precision Medicine.
Abstract: With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data, to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms the competing methods and can identify new associations. Furthermore, it captures the underlying biology predictive of drug response. Through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problem.
Published in 2021
Naegleria fowleri: Protein structures to facilitate drug discovery for the deadly, pathogenic free-living amoeba.
Abstract: Naegleria fowleri is a pathogenic, thermophilic, free-living amoeba which causes primary amebic meningoencephalitis (PAM). Penetrating the olfactory mucosa, the brain-eating amoeba travels along the olfactory nerves, burrowing through the cribriform plate to its destination: the brain's frontal lobes. The amoeba thrives in warm, freshwater environments, with peak infection rates in the summer months and has a mortality rate of approximately 97%. A major contributor to the pathogen's high mortality is the lack of sensitivity of N. fowleri to current drug therapies, even in the face of combination-drug therapy. To enable rational drug discovery and design efforts we have pursued protein production and crystallography-based structure determination efforts for likely drug targets from N. fowleri. The genes were selected if they had homology to drug targets listed in Drug Bank or were nominated by primary investigators engaged in N. fowleri research. In 2017, 178 N. fowleri protein targets were queued to the Seattle Structural Genomics Center of Infectious Disease (SSGCID) pipeline, and to date 89 soluble recombinant proteins and 19 unique target structures have been produced. Many of the new protein structures are potential drug targets and contain structural differences compared to their human homologs, which could allow for the development of pathogen-specific inhibitors. Five of the structures were analyzed in more detail, and four of five show promise that selective inhibitors of the active site could be found. The 19 solved crystal structures build a foundation for future work in combating this devastating disease by encouraging further investigation to stimulate drug discovery for this neglected pathogen.
Published in 2021
Fibrotic expression profile analysis reveals repurposed drugs with potential anti-fibrotic mode of action.
Abstract: Fibrotic diseases cover a spectrum of systemic and organ-specific maladies that affect a large portion of the population, currently without cure. The shared characteristic these diseases feature is their uncontrollable fibrogenesis deemed responsible for the accumulated damage in the susceptible tissues. Idiopathic Pulmonary Fibrosis, an interstitial lung disease, is one of the most common and studied fibrotic diseases and still remains an active research target. In this study we highlight unique and common (i) genes, (ii) biological pathways and (iii) candidate repurposed drugs among 9 fibrotic diseases. We identify 7 biological pathways involved in all 9 fibrotic diseases as well as pathways unique to some of these diseases. Based on our Drug Repurposing results, we suggest captopril and ibuprofen that both appear to slow the progression of fibrotic diseases according to existing bibliography. We also recommend nafcillin and memantine, which haven't been studied against fibrosis yet, for further wet-lab experimentation. We also observe a group of cardiomyopathy-related pathways that are exclusively highlighted for Oral Submucous Fibrosis. We suggest digoxin to be tested against Oral Submucous Fibrosis, since we observe cardiomyopathy-related pathways implicated in Oral Submucous Fibrosis and there is bibliographic evidence that digoxin may potentially clear myocardial fibrosis. Finally, we establish that Idiopathic Pulmonary Fibrosis shares several involved genes, biological pathways and candidate inhibiting-drugs with Dupuytren's Disease, IgG4-related Disease, Systemic Sclerosis and Cystic Fibrosis. We propose that treatments for these fibrotic diseases should be jointly pursued.
Showing 1271 to 1274 of 5186 entries