In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor Modulators

Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein–drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein–ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein–ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects

A Systematic Approach to Identifying Protein-Ligand Binding Profiles on a Proteome Scale

Identification of protein-ligand interaction networks on a proteome scale is crucial to address a wide range of biological problems such as correlating molecular functions to physiological processes and designing safe and efficient therapeutics. We have developed a novel computational strategy to identify ligand binding profiles of proteins across gene families and applied it to predicting protein functions, elucidating molecular mechanisms of drug adverse effects, and repositioning safe pharmaceuticals to treat different diseases

Cheminformatic Approach for Deconvolution of Active Compounds in a Complex Mixture - phytoserms in Licorice

ABSTRACT After the validation of our in silico models by using the previous knowledge in this area the alerting phytochemicals from two Glycyrrhiza species (G. glabra and G. uralensis) were clustered. Exhaustive computational mining of licorice metabolome against selected endocrinal and metabolic targets led to the discovery of a unique class of compounds which belong to the dihydrostilbenoids (DHS) class appended with prenyl groups at various positions. To the best of our knowledge this interesting group of compounds has not been studied for their estrogenic activities or PXR activation. In addition some of the bis-prenylated DHS have been reported to be present only in G. uralensis. Another aspect of the current project was to predict the phase I primary metabolites of compounds found in both species of Glycyrrhiza and assess them with computational tools to predict their binding potential against both isoforms of hERs or drug metabolizing enzymes such as (CYP) inhibition models. Our investigations revealed estrogenic character for most of the predicted metabolites and have confirmed earlier reports of potential CYP3A4 and CYP1A2 inhibition. Compilation of such data is essential to gain a better understanding of the efficacy/safety of licorice extracts used in various botanical formularies. This approach with the involved cheminformatic tools has proven effective to yield rich information to support our understanding of traditional practices. It also can expand the role of botanical drugs for introducing new chemical entities (NCEs) and/or uncovering their liabilities at early stages. In this work we endeavored to comprehend the mechanism associated with the efficacy and safety of components reported in the licorice plant. We utilized smart screening techniques such as cheminformatics tools to reveal the high number of secondary metabolites produced by licorice which are capable of interfering with the human Estrogen Receptors (hERs) and/or PXR or other vital cytochrome P450 enzymes. The genus Glycyrrhiza encompasses several species exhibiting complex structural diversity of secondary metabolites and hence biological activities. The intricate nature of botanical remedies such as licorice rendered them obsolete for scientific research or medical industry. Understanding and finding the mechanisms of efficacy or safety for a plant-based therapy is very challenging yet it remains crucial and warranted. The licorice plant is known to have Selective Estrogen Receptor Modulatory effects (SERMs) with a spectrum of estrogenic and anti-estrogenic activities attributed to women’s health. On the contrary licorice extract was shown to induce pregnane xenobiotic receptor (PXR) which may manifest as a potential route for deleterious effects such as herb-drug interaction (HDI). While many studies attributed these divergent activities to a few classes of compounds such as liquiritigenin (a weak estrogenic SERM) or glycyrrhizin (weak PXR agonist) no attempt was made to characterize the complete set of compounds responsible for these divergent activities. A plethora of licorice components is undermined which might have the potential to be developed into novel phytoSERMS or to trigger undesirable adverse effects by altering drug metabolizing enzymes and thus pharmacokinetics. Thus we have ventured to synthesize a set of constitutional isomers of stilbenoids and DHS (archetypal of those found in licorice) with different prenylation patterns. Sixteen constitutional isomers of stilbenoids (M2-M10) and DHS (M12-M18) were successfully synthesized of which six of them (M8 M9 M14 M15 M17 and M18) were synthesized for the first time to be further tested and validated with cell-based methods for their estrogenic activities. We have unveiled a novel class of compounds which possess a strong PXR activation. These results which were in accord with the in silico prediction were observed for multiple synthesized prenylated stilbenoid and DHS by the luciferase reporter gene assay at µM concentrations. Moreover this activation was further validated by the six-fold increase in mRNA expression of Cytochrome P450 3A4 (CYP3A4) where three representative compounds (M7 M10 and M15) exceeded the activation fold of the positive control

The Chemical Basis of Pharmacology

ABSTRACT: Molecular biology now dominates pharmacology so thoroughly that it is difficult to recall that only a generation ago the field was very different. To understand drug action today, we characterize the targets through which they act and new drug leads are discovered on the basis of target structure and function. Until the mid-1980s the information often flowed in reverse: investigators began with organic molecules and sought targets, relating receptors not by sequence or structure but by their ligands. Recently, investigators have returned to this chemical view of biology, bringing to it systematic and quantitative methods of relating targets by their ligands. This has allowed the discovery of new targets for established drugs, suggested the bases for their side effects, and predicted the molecular targets underlying phenotypic screens. The bases for these new methods, some of their successes and liabilities, and new opportunities for their use are described. So dominant has the molecular biology view of pharmacology become that it is difficult to remember that even 25 years ago it was little more than an aspiration. Today we understand the activity of drugs and reagents first through the specific, clonable receptor molecules with which they interact. To understan

Mind the Gap - Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs

Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome

Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover “behind-the-scenes” aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm

Xenobiotics interfering with corticosteroid action : from adrenal steroid synthesis to peripheral receptor activity

Corticosteroids are steroid hormones synthesized by the adrenal gland and regulating a variety of physiological processes to maintain whole-body homeostasis by acting through their corresponding receptors. Although the adrenal gland is considered one of the most toxin-vulnerable organs and steroid receptor regulation is recognized to have a considerable impact on tissue- and cell-specific steroid signaling, only few studies are currently exploring and characterizing the effects of xenobiotics on corticosteroid hormone action. The first part of this thesis aimed to establish optimized steroid profile analysis in cell culture supernatants and apply it in combination with further biological assessments and molecular modeling for the identification and characterization of exogenous chemicals potentially disrupting corticosteroid hormone production. A widely used in vitro model for studying effects of chemicals on adrenal steroid hormone synthesis constitutes the human H295R adrenocarcinoma cell line. Since the OECD test guideline No. 456 based on H295R cells has several limitations, this thesis refined the H295R steroidogenesis assay by simultaneously analyzing the most important adrenal steroid metabolites using a mass spectrometry-based method. A medium control at the beginning of the experiment as well as reference compounds with known mechanisms were introduced and, additionally, gene expression analyses were performed, in order to not only detect chemical-induced disturbances but also providing initial mechanistic insights into the mode-of-action of a given chemical. The newly established improved version of the H295R steroidogenesis assay was then further evolved by activating the cells either with torcetrapib, a potent inducer of corticosteroid synthesis, or with forskolin, a general inducer of steroidogenesis, allowing to assess the inhibitory potential of various test chemicals. The modified torcetrapib-stimulated H295R assay was then used to evaluate three selected hits from an in silico screening of environmental chemical databases using ligand-based pharmacophore models of 11β-hydroxylase (CYP11B1) and aldosterone synthase (CYP11B2). This proof-of-concept for the application of pharmacophore-based virtual screening followed by biological assessment has proven suitable for assessing substances potentially interfering with corticosteroid synthesis. In another study within this thesis, the adapted version of the H295R steroidogenesis assay using forskolin-stimulated cells was applied to investigate the inhibitory effects of 19 anabolic androgenic steroids (AAS) and 3 selective androgen receptor modulators (SARMs). This enabled to group the test compounds according to their individual steroid patterns. Additionally, gene expression analysis, cell-free activity assays and molecular docking calculations contributed to providing initial mechanistic information. Besides direct effects on adrenal steroidogenesis, xenobiotic-induced alterations in circulating steroid hormone levels may arise due to altered feedback regulation or disturbed peripheral steroid metabolism. Thus, in a further part of this thesis drug-induced changes in steroid hormone levels were studied by measuring steroid profiles in human blood and urine samples. In a clinical study, plasma levels of steroid hormones and adrenocorticotropic hormone (ACTH) were analyzed in healthy volunteers administered a single dose of slow-release lisdexamfetamine (100 mg) or immediate-release D-amphetamine (40.3 mg) at equimolar doses. Importantly, lisdexamfetamine and D-amphetamine similarly enhanced the levels of glucocorticoids, androgen precursors and ACTH, suggesting an acute stimulation of the hypothalamic-pituitary-adrenal (HPA) axis. Although lisdexamfetamine showed a delayed time of increase and peak levels of plasma D-amphetamine concentrations compared to the D-amphetamine treatment, drug exposure and drug effects seemed to be comparable between the two formulations. In a clinical case study, a comprehensive analysis of blood and urinary steroid profiles was conducted in samples from two patients receiving posaconazole, an antifungal agent associated with hypertension and hypokalemia due to mineralocorticoid excess. Steroid analyses indicated interindividual differences in the mechanism of mineralocorticoid-based hypertension with preferential CYP11B inhibition in one patient and predominant inhibition of 11β-HSD2 in the second patient. These results show that steroid profiling in plasma and urine samples can not only reveal disturbances of steroid homeostasis but also provide initial mechanistic information. Together, these findings emphasize that molecular modeling combined with biological evaluation represents a valuable approach for the identification and characterization of chemicals potentially interfering with corticosteroid production and to provide initial mechanistic insights. However, in vivo investigations are unavoidable to study the impact of chemicals acting on the HPA axis. Xenobiotics may not only affect steroid hormone production, feedback regulation or pre-receptor control of corticosteroid metabolism, but may also interfere directly with the receptor and steroid signal transduction. In order to understand potential disturbances of glucocorticoid action by xenobiotics, it is important to further clarify the signaling pathways involved in glucocorticoid receptor (GR) activation. Therefore, another part of this thesis focused on the impact of the serine/threonine-specific protein phosphatase PP1α on the activity of the GR. PP1α was found to increase GR activity, and preliminary mechanistic investigations showed that levels of phosphorylated GR-Ser211 were altered and glycogen synthase kinase 3 might be involved. Hence, PP1α appeared to modulate the cellular response to glucocorticoids, implying that impairment of its activity could lead to aberrant glucocorticoid hormone action. In conclusion, these studies identified a novel GR regulating protein that enhances cortisol stimulation by controlling GR phosphorylation. A profound understanding of glucocorticoid signaling might provide the basis for developing cell models and conditions for the detection of chemicals disturbing glucocorticoid sensitivity and thereby contributing to diseases

PDID: Database of molecular-level putative protein-drug interactions in the structural human proteome

© 2015 The Author 2015. Published by Oxford University Press. All rights reserved. Motivation: Many drugs interact with numerous proteins besides their intended therapeutic targets and a substantial portion of these interactions is yet to be elucidated. Protein-Drug Interaction Database (PDID) addresses incompleteness of these data by providing access to putative protein-drug interactions that cover the entire structural human proteome. Results: PDID covers 9652 structures from 3746 proteins and houses 16 800 putative interactions generated from close to 1.1 million accurate, all-atom structure-based predictions for several dozens of popular drugs. The predictions were generated with three modern methods: ILbind, SMAP and eFindSite. They are accompanied by propensity scores that quantify likelihood of interactions and coordinates of the putative location of the binding drugs in the corresponding protein structures. PDID complements the current databases that focus on the curated interactions and the BioDrugScreen database that relies on docking to find putative interactions. Moreover, we also include experimentally curated interactions which are linked to their sources: DrugBank, BindingDB and Protein Data Bank. Our database can be used to facilitate studies related to polypharmacology of drugs including repurposing and explaining side effects of drugs. Availability and implementation: PDID database is freely available at http://biomine.ece.ualberta.ca/PDID/

Automatic Filtering and Substantiation of Drug Safety Signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions