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Showing 1921 to 1924 of 5186 entries
Published in January - December 2021
Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies.
Abstract: Understanding the pharmacokinetic (PK) properties of a drug, such as clearance, is a crucial step for evaluating efficacy. The PK of therapeutic antibodies can be complex and is influenced by interactions with the target, Fc-receptors, anti-drug antibodies, and antibody intrinsic factors. A growing body of literature has linked biophysical properties of antibodies, particularly nonspecific-binding propensity, hydrophobicity and charged regions to rapid clearance in preclinical species and selected human PK studies. A clear understanding of the connection between biophysical properties and their impact on PK would allow for early selection and optimization of antibodies and reduce costly attrition during clinical trials due to sub-optimal human clearance. Due to the difficulty in obtaining large and unbiased human PK data, previous studies have focused mostly on preclinical PK. For this study, we obtained and curated the most comprehensive clinical PK dataset to date and calculated accurate estimates of linear clearance for 64 monoclonal antibodies ranging from investigational candidates in Phase 2 trials to marketed products. This allows for the first time a deep analysis of the influence of biophysical and sequence-based in silico properties directly on human clearance. We use statistical analysis and a Random Forest classifier to identify properties that have the greatest influence in our dataset. Our findings indicate that in vitro poly-specificity assay and in silico estimated isoelectric point can discriminate fast and slow clearing antibodies, extending previous observations on preclinical clearance. This provides a simple yet powerful approach to select antibodies with desirable PK during early-stage screening.
Published in January 2021
In-silico drug repurposing study: Amprenavir, enalaprilat, and plerixafor, potential drugs for destabilizing the SARS-CoV-2 S-protein-angiotensin-converting enzyme 2 complex.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that leads to coronavirus disease (COVID-19) has put public health at risk in 2020. The spike protein (SP) in SARS-CoV-2 is primarily responsible for the attachment and entry of the virus into the cell, which binds to the angiotensin-converting enzyme 2 (ACE2). Owing to the lack of an effective therapy, drug repositioning is an opportunity to search for molecules with pharmacological potential for the treatment of COVID-19. In this study, three candidates with the potential to destabilize the SP-ACE2 complex are reported. Through molecular docking, 147 drugs were evaluated and their possible binding sites in the interface region of the SP-ACE2 complex and the SP of SARS-CoV-2 were identified. The five best candidate molecules were selected for molecular dynamics studies to observe changes in interactions between SP-ACE2 and ligands with the SP-ACE2 complex. Using umbrella sampling molecular dynamics simulations, the binding energy of SP with ACE2 (-29.58 kcal/mol) without ligands, and in complex with amprenavir (-20.13 kcal/mol), enalaprilat (-23.84 kcal/mol), and plerixafor (-19.72 kcal/mol) were calculated. These drugs are potential candidates for the treatment of COVID-19 as they destabilize the SP-ACE2 complex; the binding energy of SP is decreased in the presence of these drugs and may prevent the virus from entering the cell. Plerixafor is the drug with the greatest potential to destabilize the SP-ACE2 complex, followed by amprenavir and enalaprilat; thus, these three drugs are proposed for future in vitro and in vivo evaluations.
Published in January 2021
Repurposing the Antidepressant Sertraline as SHMT Inhibitor to Suppress Serine/Glycine Synthesis-Addicted Breast Tumor Growth.
Abstract: Metabolic rewiring is a hallmark of cancer that supports tumor growth, survival, and chemotherapy resistance. Although normal cells often rely on extracellular serine and glycine supply, a significant subset of cancers becomes addicted to intracellular serine/glycine synthesis, offering an attractive drug target. Previously developed inhibitors of serine/glycine synthesis enzymes did not reach clinical trials due to unfavorable pharmacokinetic profiles, implying that further efforts to identify clinically applicable drugs targeting this pathway are required. In this study, we aimed to develop therapies that can rapidly enter the clinical practice by focusing on drug repurposing, as their safety and cost-effectiveness have been optimized before. Using a yeast model system, we repurposed two compounds, sertraline and thimerosal, for their selective toxicity against serine/glycine synthesis-addicted breast cancer and T-cell acute lymphoblastic leukemia cell lines. Isotope tracer metabolomics, computational docking, enzymatic assays, and drug-target interaction studies revealed that sertraline and thimerosal inhibit serine/glycine synthesis enzymes serine hydroxymethyltransferase and phosphoglycerate dehydrogenase, respectively. In addition, we demonstrated that sertraline's antiproliferative activity was further aggravated by mitochondrial inhibitors, such as the antimalarial artemether, by causing G1-S cell-cycle arrest. Most notably, this combination also resulted in serine-selective antitumor activity in breast cancer mouse xenografts. Collectively, this study provides molecular insights into the repurposed mode-of-action of the antidepressant sertraline and allows to delineate a hitherto unidentified group of cancers being particularly sensitive to treatment with sertraline. Furthermore, we highlight the simultaneous inhibition of serine/glycine synthesis and mitochondrial metabolism as a novel treatment strategy for serine/glycine synthesis-addicted cancers.
Published in January 2021
A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data.
Abstract: Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Real-world data, such as electronic health records and insurance claims, provide information on large cohorts of users for many drugs. Here we present an efficient and easily customized framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of real-world data. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims database. We demonstrate our framework on a coronary artery disease cohort of millions of patients. We successfully identify drugs and drug combinations that substantially improve the coronary artery disease outcomes but haven't been indicated for treating coronary artery disease, paving the way for drug repurposing.
Published in January 2021
Multi-omics study for interpretation of genome-wide association study.
Abstract: Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with complex traits, including a wide variety of diseases. Despite the successful identification of associated loci, interpreting GWAS findings remains challenging and requires other biological resources. Omics, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, are increasingly used in a broad range of research fields. Integrative analyses applying GWAS with these omics data are expected to expand our knowledge of complex traits and provide insight into the pathogenesis of complex diseases and their causative factors. Recently, associations between genetic variants and omics data have been comprehensively evaluated, providing new information on the influence of genetic variants on omics. Furthermore, recent advances in analytic methods, including single-cell technologies, have revealed previously unknown disease mechanisms. To advance our understanding of complex traits, integrative analysis using GWAS with multi-omics data is needed. In this review, I describe successful examples of integrative analyses based on omics and GWAS, discuss the limitations of current multi-omics analyses, and provide a perspective on future integrative studies.
Published in January 2021
The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions.
Abstract: The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open-access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low-throughput studies and large high-throughput datasets. BioGRID also captures protein post-translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built-in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin-proteasome system, as well as specific disease areas, such as for the SARS-CoV-2 virus that causes COVID-19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome-wide CRISPR/Cas9-based genetic screens. BioGRID-ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta-databases.
Published in January 2021
Complexes of the neurotensin receptor 1 with small-molecule ligands reveal structural determinants of full, partial, and inverse agonism.
Abstract: Neurotensin receptor 1 (NTSR1) and related G protein-coupled receptors of the ghrelin family are clinically unexploited, and several mechanistic aspects of their activation and inactivation have remained unclear. Enabled by a new crystallization design, we present five new structures: apo-state NTSR1 as well as complexes with nonpeptide inverse agonists SR48692 and SR142948A, partial agonist RTI-3a, and the novel full agonist SRI-9829, providing structural rationales on how ligands modulate NTSR1. The inverse agonists favor a large extracellular opening of helices VI and VII, undescribed so far for NTSR1, causing a constriction of the intracellular portion. In contrast, the full and partial agonists induce a binding site contraction, and their efficacy correlates with the ability to mimic the binding mode of the endogenous agonist neurotensin. Providing evidence of helical and side-chain rearrangements modulating receptor activation, our structural and functional data expand the mechanistic understanding of NTSR1 and potentially other peptidergic receptors.
Published in January 2021
Pharmacology-informed prediction of the risk posed to fish by mixtures of non-steroidal anti-inflammatory drugs (NSAIDs) in the environment.
Abstract: The presence of non-steroidal anti-inflammatory drugs (NSAIDs) in the aquatic environment has raised concern that chronic exposure to these compounds may cause adverse effects in wild fish populations. This potential scenario has led some stakeholders to advocate a stricter regulation of NSAIDs, especially diclofenac. Considering their global clinical importance for the management of pain and inflammation, any regulation that may affect patient access to NSAIDs will have considerable implications for public health. The current environmental risk assessment of NSAIDs is driven by the results of a limited number of standard toxicity tests and does not take into account mechanistic and pharmacological considerations. Here we present a pharmacology-informed framework that enables the prediction of the risk posed to fish by 25 different NSAIDs and their dynamic mixtures. Using network pharmacology approaches, we demonstrated that these 25 NSAIDs display a significant mechanistic promiscuity that could enhance the risk of target-mediated mixture effects near environmentally relevant concentrations. Integrating NSAIDs pharmacokinetic and pharmacodynamic features, we provide highly specific predictions of the adverse phenotypes associated with exposure to NSAIDs, and we developed a visual multi-scale model to guide the interpretation of the toxicological relevance of any given set of NSAIDs exposure data. Our analysis demonstrated a non-negligible risk posed to fish by NSAID mixtures in situations of high drug use and low dilution of waste-water treatment plant effluents. We anticipate that this predictive framework will support the future regulatory environmental risk assessment of NSAIDs and increase the effectiveness of ecopharmacovigilance strategies. Moreover, it can facilitate the prediction of the toxicological risk posed by mixtures via the implementation of mechanistic considerations and could be readily extended to other classes of chemicals.
Published in January 2021
Exploring the mechanism of Yizhi Tongmai decoction in the treatment of vascular dementia through network pharmacology and molecular docking.
Abstract: Background: Vascular dementia (VaD) is a degenerative cerebrovascular disease that leads to progressive decline of patients' cognitive ability and memory. Yizhi Tongmai (YZTM) decoction is an empirical prescription first formulated by Professor Guomin Si. Our previous experiments proved the effectiveness of this prescription in the treatment of VaD. In this study, we aimed to use network pharmacology and molecular docking technology to systematically explain the potential anti-VaD mechanism of YZTM. Methods: We identified the core compounds of YZTM and their potential targets through the TCMSP, BATMAN, and SwissTargetPrediction databases. Then, we identified the molecular targets of YZTM in VaD using the Online Mendelian Inheritance in Man and GeneCards databases. The common targets of YZTM and VaD were screened out, and then the pathways of these target genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery v6.8. Molecular docking was used to verify the relationship between the core compounds and proteins. Results: Through network pharmacology analysis, we discovered that the 5 core compounds in YZTM exert an anti-VaD effect. The potential mechanism of YZTM anti-VaD may be through inhibiting the NLRP3 inflammasome, TNF signaling pathway, and toll-like receptor signaling pathways. Subsequently, key compounds were docked with related proteins in the NLRP3 inflammasome (NLRP3, ASC, caspase-1, interleukin-18, and interleukin-1 beta) using molecular docking technology. The compounds were found to spontaneously bind to the proteins. Conclusions: YZTM may exert an anti-VaD effect through inhibition of the NLRP3 inflammasome. In addition, TNF signaling pathway and toll-like receptor signaling pathway may also be its underlying mechanism. The application of network pharmacology and molecular docking technology may provide a novel method for research of Chinese herbal medicine. YZTM may also provide a complementary treatment option for patients with VaD.
Published in January 2021
A Multi-Omics Analysis of PON1 Lactonase Activity in Relation to Human Health and Disease.
Abstract: Paraoxonase 1 (PON1) enzyme has antioxidative properties and is present in mammalian blood and several other body fluids. In blood, PON1 is usually integrated into the high-density lipoprotein (HDL) cholesterol. PON1 is a highly versatile enzyme displaying diverse functions such as arylesterase, lactonase, and paraoxonase, among others. PON1 activities are usually investigated with artificial substrates, for example, dihydrocoumarin and thiobutyl butyrolactone for lactonase activity. The PON1 enzyme activities measured with different substrates tend to be falsely assumed as being equivalent in the literature, although there are poor or weak correlations among the PON1 enzyme activities with different substrates. In addition, and despite our knowledge of the factors influencing PON1 paraoxonase and arylesterase activities, there is little knowledge of PON1 lactonase activity variations and attendant mechanisms. This is important considering further that the lactonase activity is the native activity of PON1. We report here a multi-omics analysis of PON1 lactonase activity. The influence of genetic variations, particularly of single nucleotide polymorphisms and epigenetic, proteomic, and lipidomic variations on PON1 lactonase activity are reviewed. In addition, the influence of various environmental, clinical, and demographic variables on PON1 lactonase activity is discussed. Finally, we examine the associations between PON1 lactonase activity and health states and common complex diseases such as atherosclerosis, dementias, obesity, and diabetes. To the best of our knowledge, this is the first multi-omics analysis of PON1 lactonase activity with an eye to future applications in basic life sciences and translational medicine and the nuances of critically interpreting PON1 function with lactones as substrates.
Showing 1921 to 1924 of 5186 entries