In Silico Predictions of Drug - Drug Interactions Caused by CYP1A2, 2C9 and 3A4 Inhibition - a Comparative Study of Virtual Screening Performance

In silico analysis of the antidepressant fluoxetine and similar drugs as inhibitors of the human protein acid sphingomyelinase: a related SARS-CoV-2 inhibition pathway

Acid Sphingomyelinase (ASM) is a human phosphodiesterase that catalyzes the metabolism of sphingomyelin (SM) to ceramide and phosphocholine. ASM is involved in the plasma membrane cell repair and is associated with the lysosomal inner lipid membrane by nonbonding interactions. The disruption of those interaction would result in ASM release into the lysosomal lumen and consequent degradation of its structure. Furthermore, SARS-CoV-2 infection has been linked with ASM activation and with a ceramide domain formation in the outer leaflet of the plasma membrane that is thought to be crucial for the viral particles recognition by the host cells. In this study, we have explored in silico the behavior of fluoxetine and related drugs as potential inhibitors of ASM. Theoretically, these drugs would be able to overpass lysosomal membrane and reach the interactions that sustain ASM structure, breaking them and inhibiting the ASM. The analyses of docking data indicated that fluoxetine allocated mainly in the N-terminal saposin domain via nonbonding interactions, mostly of hydrophobic nature. Similar results were obtained for venlafaxine, citalopram, atomoxetine, nisoxetine and fluoxetine's main metabolite norfluoxetine. In conclusion, it was observed that the saposin allocation may be a good indicative of the drugs inhibition mechanism, once this domain is responsible for the binding of ASM to lysosomal membrane and some of those drugs have previously been reported to inhibit the phosphodiesterase by releasing its structure in the lysosomal lumen. Our MD data also provides some insight about natural ligand C18 sphingomyelin conformations on saposin.Communicated by Ramaswamy H. Sarma.

Acid sphingomyelinase (ASM) and COVID-19: A review of the potential use of ASM inhibitors against SARS-CoV-2

In the last 2 years, different pharmacological agents have been indicated as potential inhibitors of SARS-CoV-2 in vitro. Specifically, drugs termed as functional inhibitors of acid sphingomyelinase (FIASMAs) have proved to inhibit the SARS-CoV-2 replication using different types of cells. Those therapeutic agents share several chemical structure characteristics and some well-known representatives are fluoxetine, escitalopram, fluvoxamine, and others. Most of the FIASMAs are primarily used as effective therapeutic agents to treat different pathologies, therefore, they are natural drug candidates for repositioning strategy. In this review, we summarize the two main proposed mechanisms mediating acid sphingomyelinase (ASM) inhibition and how they can explain the inhibition of SARS-CoV-2 replication by FIASMAs. The first mechanism implies a disruption in the lysosomal pH fall as the endosome-lysosome moves toward the interior of the cell. In fact, changes in cholesterol levels in endosome-lysosome membranes, which are associated with ASM inhibition is thought to be mediated by lysosomal proton pump (ATP-ase) inactivation. The second mechanism involves the formation of an extracellular ceramide-rich domain, which is blocked by FIASMAs. The ceramide-rich domains are believed to facilitate the SARS-CoV-2 entrance into the host cells.

In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms

Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure–activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.

Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation

Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Virtual screening applications in short-chain dehydrogenase/reductase research

Several members of the short-chain dehydrogenase/reductase (SDR) enzyme family play fundamental roles in adrenal and gonadal steroidogenesis as well as in the metabolism of steroids, oxysterols, bile acids, and retinoids in peripheral tissues, thereby controlling the local activation of their cognate receptors. Some of these SDRs are considered as promising therapeutic targets, for example to treat estrogen-/androgen-dependent and corticosteroid-related diseases, whereas others are considered as anti-targets as their inhibition may lead to disturbances of endocrine functions, thereby contributing to the development and progression of diseases. Nevertheless, the physiological functions of about half of all SDR members are still unknown. In this respect, in silico tools are highly valuable in drug discovery for lead molecule identification, in toxicology screenings to facilitate the identification of hazardous chemicals, and in fundamental research for substrate identification and enzyme characterization. Regarding SDRs, computational methods have been employed for a variety of applications including drug discovery, enzyme characterization and substrate identification, as well as identification of potential endocrine disrupting chemicals (EDC). This review provides an overview of the efforts undertaken in the field of virtual screening supported identification of bioactive molecules in SDR research. In addition, it presents an outlook and addresses the opportunities and limitations of computational modeling and in vitro validation methods.

Optimized Virtual Screening Workflow for the Identification of Novel G-Quadruplex Ligands

G-quadruplexes, alternative DNA secondary structures present in telomeres, emerge as promising targets for the treatment of cancer, because they prevent telomere elongation and accordingly cell proliferation. Within this study, theoretically validated pharmacophore- and shape-based models as well as a theoretically validated docking protocol were generated and applied in parallel for virtual screening and the identification of novel G-quadruplex ligands. Top-ranked hits retrieved with all methods independently and in addition in a consensus approach were selected for biological testing. Of the 32 tested virtual hits seven selectively stabilized G-quadruplexes over duplex DNA in the fluorescence melting assay. For the five most active compounds, chemically closely related analogues were collected and subjected to in vitro analysis. Thereby, seven further novel G-quadruplex ligands could be identified. These molecules do not only represent novel scaffolds, but some of them are in addition even more potent G-quadruplex stabilizers than the established reference compound berberine. This study proposes an optimized in silico workflow for the identification of novel G-quadruplex stabilizers, which can also be applied in future studies. In addition, structurally novel and promising lead candidates with strong and selective G-quadruplex stabilizing properties are reported.

Drugs from nature targeting inflammation (DNTI): a successful Austrian interdisciplinary network project

ABSTRACT

Inflammation is part of numerous pathological conditions, which are lacking satisfying treatment and effective concepts of prevention. A national research network project, DNTI, involving scientists from six Austrian universities as well as several external partners aimed to identify and characterize natural products capable of combating inflammatory processes specifically in the cardiovascular system. The combined use of computational techniques with traditional knowledge, high-tech chemical analysis and synthesis, and a broad range of in vitro, cell-based, and in vivo pharmacological models led to the identification of a series of promising anti-inflammatory drug lead candidates. Mechanistic studies contributed to a better understanding of their mechanism of action and delivered new knowledge on the molecular level of inflammatory processes. Herein, the used approaches and selected highlights of the results of this interdisciplinary project are presented.

GRAPHICAL ABSTRACT

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.