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Showing 751 to 754 of 5186 entries
Published on July 6, 2022
Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.
Published on July 6, 2022
Crystal Engineering of Ionic Cocrystals Sustained by the Phenol-Phenolate Supramolecular Heterosynthon.
Abstract: Although crystal engineering strategies are generally well explored in the context of multicomponent crystals (cocrystals) formed by neutral coformers (molecular cocrystals), cocrystals comprised of one or more salts (ionic cocrystals, ICCs) are understudied. We herein address the design, preparation, and structural characterization of ICCs formed by phenolic moieties, a common group in natural products and drug molecules. Organic and inorganic bases were reacted with the following phenolic coformers: phenol, resorcinol, phloroglucinol, 4-methoxyphenol, and 4-isopropylphenol. Nine ICCs were crystallized, each of them sustained by the phenol-phenolate supramolecular heterosynthon (PhOH...PhO(-)). Such ICCs are of potential utility, and there are numerous examples of phenolic compounds that are biologically active, some of which suffer from low aqueous solubility. The propensity to form ICCs sustained by the PhOH...PhO(-) supramolecular heterosynthon was evaluated through a combination of Cambridge Structural Database (CSD) mining, structural characterization of nine novel ICCs, and calculation of interaction energies. Our analysis of these 9 ICCs and the 41 relevant entries archived in the CSD revealed that phenol groups can reliably form ICCs through charge-assisted PhOH...PhO(-) interactions. This conclusion is supported by hydrogen-bond strength calculations derived from CrystalExplorer that reveal the PhOH...PhO(-) interaction to be around 3 times stronger than the phenol-phenol hydrogen bond. The PhOH...PhO(-) supramolecular heterosynthon could therefore enable crystal engineering studies of a large number of phenolic pharmaceutical and nutraceutical compounds with their conjugate bases.
Published on July 5, 2022
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.
Abstract: Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.
Published on July 5, 2022
In silico identification and in vitro antiviral validation of potential inhibitors against Chikungunya virus.
Abstract: The Chikungunya virus (CHIKV) has become endemic in the Africa, Asia and Indian subcontinent, with its continuous re-emergence causing a significant public health crisis. The unavailability of specific antivirals and vaccines against the virus has highlighted an urgent need for novel therapeutics. In the present study, we have identified small molecule inhibitors targeting the envelope proteins of the CHIKV to interfere with the fusion process, eventually inhibiting the cell entry of the virus particles. We employed high throughput computational screening of large datasets against two different binding sites in the E1-E2 dimer to identify potential candidate inhibitors. Among them, four high affinity inhibitors were selected to confirm their anti-CHIKV activity in the in vitro assay. Quercetin derivatives, Taxifolin and Rutin, binds to the E1-E2 dimer at different sites and display inhibition of CHIKV infection with EC50 values 3.6 muM and 87.67 muM, respectively. Another potential inhibitor with ID ChemDiv 8015-3006 binds at both the target sites and shows anti-CHIKV activity at EC50 = 41 muM. The results show dose-dependent inhibitory effects of Taxifolin, Rutin and ChemDiv 8015-3006 against the CHIKV with minimal cytotoxicity. In addition, molecular dynamics studies revealed the structural stability of these inhibitors at their respective binding sites in the E1-E2 protein. In conclusion, our study reports Taxifolin, Rutin and ChemDiv 8015-3006 as potential inhibitors of the CHIKV entry. Also, this study suggests a few potential candidate inhibitors which could serve as a template to design envelope protein specific CHIKV entry inhibitors.
Published on July 4, 2022
Exploring the fuzzy border between senolytics and senomorphics with chemoinformatics and systems pharmacology.
Abstract: Senescent cells accumulate within tissues during aging and secrete an array of pro-inflammatory molecules known as senescent-associated secretory phenotype (SASP), which contribute to the appearance and progression of various chronic degenerative diseases. Novel pharmacological approaches aimed at modulating or eliminating senescent cells harmful effects have recently emerged: Senolytics are molecules that selectively eliminate senescent cells, while senomorphics modulate or decrease the inflammatory response to specific SASP. So far, the physicochemical, structural, and pharmacological properties that define these two kinds of pharmacological approaches remain unclear. Therefore, the identification and correct choice of molecules, based on their physicochemical, structural, and pharmacological properties, likely to exhibit the desired senotherapeutic activity is crucial for developing effective, selective, and safe senotherapies. Here we compared the physicochemical, structural, and pharmacological properties of 84 senolytics and 79 senomorphics using a chemoinformatic and systems pharmacology approach. We found great physicochemical, structural, and pharmacological similarities between them, also reflected in their cellular responses measured through transcriptome perturbations. The identified similarities between senolytics and senomorphics might explain the dual activity of some of those molecules. These findings will help design and discover new, more effective, and highly selective senotherapeutic agents.
Published on July 1, 2022
Identifying Lethal Dependencies with HUGE Predictive Power.
Abstract: Recent functional genomic screens-such as CRISPR-Cas9 or RNAi screening-have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem is related to a large number of gene knockouts limiting the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the "HUb effect in Genetic Essentiality" (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens-Project Score, CERES score and DEMETER score-identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. Using acute myeloid leukemia, breast cancer, lung adenocarcinoma and colon adenocarcinoma as disease models, we validate that our predictions are enriched in a recent harmonized knowledge base of clinical interpretations of somatic genomic variants in cancer (AUROC > 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer. All the graphs showing lethal dependencies for the 19 tumor types analyzed can be visualized in an interactive tool.
Published on July 1, 2022
Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.
Abstract: The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.
Published in June 2022
Prediction of antischistosomal small molecules using machine learning in the era of big data.
Abstract: Schistosomiasis is a neglected tropical disease caused by helminths of the Schistosoma genus. Despite its high morbidity and socio-economic burden, therapeutics are just a handful with praziquantel being the main drug. Praziquantel is an old drug registered for human use in 1982 and has since been administered en masse for chemotherapy, risking the development of resistance, thus the need for new drugs with different mechanisms of action. This review examines the use of machine learning (ML) in this era of big data to aid in the prediction of novel antischistosomal molecules. It first discusses the challenges of drug discovery in schistosomiasis. Explanations are then offered for big data, its characteristics and then, some open databases where large biochemical data on schistosomiasis can be obtained for ML model development are examined. The concepts of artificial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis. The use of binary classification in predicting antischistosomal compounds and some algorithms that have been applied including random forest and naive Bayesian are discussed. For this review, some deep learning algorithms (deep neural networks) are proposed as novel algorithms for predicting antischistosomal molecules via binary classification. Databases specifically designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets and ontologies are thus urgently needed for developing predictive ML models. This shows the application of machine learning techniques for the discovery of novel antischistosomal small molecules via binary classification in the era of big data.
Published in June 2022
SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets.
Abstract: Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.
Published in June 2022
Alternative routes to intravenous tranexamic acid for postpartum hemorrhage: A systematic search and narrative review.
Abstract: OBJECTIVE: To review available data on tranexamic acid (TXA) plasma concentration needed to inhibit fibrinolysis and the time to achieve this concentration when giving TXA by different routes in humans. To identify ongoing trials assessing alternatives to intravenous TXA administration. METHODS: We updated two previous systematic reviews by searching MEDLINE, EMBASE, OviSP, and ISI Web of Science from database inception to July 2021. We also searched the WHO International Clinical Trials Registry Platform for ongoing trials to July 2021. Titles and abstracts were screened for relevant trials. Two reviewers independently reviewed and agreed the trials to be included. RESULTS: Plasma TXA concentrations over 10 mg/L provide near maximal inhibition of fibrinolysis, with concentrations over 5 mg/L providing partial inhibition. Oral TXA tablets take about 1 h to reach a plasma concentration of 5 mg/L in postpartum women. Studies in healthy volunteers and shocked trauma patients show that intramuscular TXA achieves a plasma level of over 10 mg/L within 15 min. One trial is ongoing to determine the pharmacokinetics of intramuscular and oral solution TXA in pregnant women. CONCLUSION: Intramuscular TXA in healthy volunteers and shocked trauma patients reaches therapeutic concentration rapidly. Oral TXA tablets take too long to reach the minimum therapeutic concentration in postpartum women.
Showing 751 to 754 of 5186 entries