The nature of the GRE influences the screening for GR-activity enhancing modulators

Glucocorticoid resistance (GCR), i.e. unresponsiveness to the beneficial anti-inflammatory activities of the glucocorticoid receptor (GR), poses a serious problem in the treatment of inflammatory diseases. One possible solution to try and overcome GCR, is to identify molecules that prevent or revert GCR by hyper-stimulating the biological activity of the GR. To this purpose, we screened for compounds that potentiate the dexamethasone (Dex)induced transcriptional activity of GR. To monitor GR transcriptional activity, the screen was performed using the lung epithelial cell line A549 in which a glucocorticoid responsive element (GRE) coupled to a luciferase reporter gene construct was stably integrated. Histone deacetylase inhibitors (HDACi) such as Vorinostat and Belinostat are two broad-spectrum HDACi that strongly increased the Dex-induced luciferase expression in our screening system. In sharp contrast herewith, results from a genome-wide transcriptome analysis of Dexinduced transcripts using RNAseq, revealed that Belinostat impairs the ability of GR to transactivate target genes. The stimulatory effect of Belinostat in the luciferase screen further depends on the nature of the reporter construct. In conclusion, a profound discrepancy was observed between HDACi effects on two different synthetic promoter-luciferase reporter systems. The favorable effect of HDACi on gene expression should be evaluated with care, when considering them as potential therapeutic agents. GEO accession number GSE96649

Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening approaches

Support was kindly provided by the EU COST Action CM1406 and CA15135. KN and JV kindly acknowledge national project number 172033 supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.PostprintPeer reviewe

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.</jats:p

In silico approaches for drug repurposing in oncology: a scoping review

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology.Methods: The scoping review was conducted according to the Arksey and O’Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021.Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies.Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications

Développement de nouvelles approches protéo-chimiométriques appliquées à l'étude des interactions et de la sélectivité des inhibiteurs de kinases

The human kinome contains 518 proteins. They share a common mechanism of protein phosphorylation known to play an important role in cellular signaling pathways. Impaired kinase function is recognized to be involved in severe diseases like cancer. Due to high structural similarity between protein kinases, development of potent and selective kinase inhibitors is a challenging task. The selectivity of kinase inhibitors may lead to side effects potentially harmful. In this thesis, we first developed new selectivity metrics to determine inhibitor selectivity directly from biological inhibition data. Compared to existing metrics, the new selectivity scores can be applied on diverse inhibition data types. Second, we developed a proteometric approach in order to understand why some protein kinases are never inhibited by Type II inhibitors. The statistical model built for this purpose allowed us to identify several discriminant residues of which few of them correspond to experimentally described residues of interest. Third, using a new 3D protein kinase descriptor, we developed and validated novel proteo-chemometrics approaches to study and discover new kinase inhibitors.Le kinome humain comprend 518 protéines. Elles participent au processus de phosphorylation des protéines qui joue un rôle important dans les voies de signalisation cellulaire. Leur dérégulation est connue comme étant une cause de nombreuses maladies graves telle que les cancers. Du fait de leur grande similarité structurale des protéines kinases, il est difficile de développer des inhibiteurs qui soient à la fois efficaces et sélectifs. L’absence de sélectivité conduit le plus souvent à des effets secondaires particulièrement néfastes pour l’organisme. Au cours de cette thèse, nous avons d’abord développé de nouvelles métriques dont le but est de déterminer la sélectivité d’inhibiteurs à partir de données d’inhibition. Elles présentent l’avantage, comparées à d’autres métriques, d’être applicables sur n’importe quel type de données. Dans un deuxième temps, nous avons développé une approche protéométrique dans le but de comprendre pourquoi certaines protéines kinases ne sont jamais inhibées par des inhibiteurs de Type II. Le modèle statistique mis en place nous a permis d’identifier plusieurs résidus discriminants dont certains déjà décrits expérimentalement dans la littérature. Dans un troisième temps, nous avons développé un nouveau descripteur 3D de protéines kinases avec lequel nous avons mis en place et validé des modèles protéo-chimiométriques visant à étudier et découvrir de nouveaux inhibiteurs

Drug Repurposing

This book focuses on various aspects and applications of drug repurposing, the understanding of which is important for treating diseases. Due to the high costs and time associated with the new drug discovery process, the inclination toward drug repurposing is increasing for common as well as rare diseases. A major focus of this book is understanding the role of drug repurposing to develop drugs for infectious diseases, including antivirals, antibacterial and anticancer drugs, as well as immunotherapeutics

Computational analyses of small molecules activity from phenotypic screens

Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining momentum to discover new compounds since those assays provide an environment closer to the physiological state of the disease and allow to better anticipate off-target effects and other factors that can limit the efficacy of the drugs. However, uncovering the mechanism of action of the compounds active in those assays relies on in vitro techniques that are expensive and time- consuming. In silico approaches are therefore beneficial to prioritise mechanism of action hypotheses to be tested in such systems. In this thesis, the use of machine learning algorithms for in silico ligand-target prediction for target deconvolution in phenotypic screening datasets was investigated. A computational workflow is presented in Chapter 2, that allows to improve the coverage of mechanism of action hypotheses obtained by combining two conceptually different target prediction algorithms. These models rely on the principle that two structurally similar compounds are likely to have the same target. In Chapter 3 of this thesis, it was shown that structural similarity and the similarity in phenotypic activity are correlated, and the fraction of phenotypically similar compounds that can be expected for an increase in structural similarity was subsequently quantified. Morgan fingerprints were also found to be less sensitive to the dataset employed in these analyses than two other commonly used molecular descriptors. In Chapter 4, the mechanism of action hypotheses obtained through target prediction was compared to those obtained by extracting experimental bioactivity data of compounds active in phenotypic assays. It was then showed that the mechanism of action hypotheses generated from these two types of approach agreed where a large number of compounds were active in the phenotypic assay. When there were fewer compounds active in the phenotypic assay, target prediction complemented the use of experimental bioactivity data and allowed to uncover alternative mechanisms of action for compounds active in these assays. Finally, the in silico target prediction workflow described in Chapter 2 was applied in Chapter 5 to deconvolute the activity of compounds in a kidney cyst growth reduction assay, aimed at discovering novel therapeutic opportunities for polycystic kidney disease. A metric was developed to rank predicted targets according to the activity of the compounds driving their prediction. Gene expression data and occurrences in the literature were combined with the target predictions to further narrow down the most probable mechanisms of action of cyst growth reducing compounds in the screen. Two target predictions were proposed as a potential mechanism for the reduction of kidney cyst growth, one of which agreed with docking studies.European Research Counci