An inverse docking approach for identifying new potential anti-cancer targets

Cholesterol Biosynthesis Inhibitor RO 48-8071 Suppresses Growth of Epithelial Ovarian Cancer Cells in Vitro and In Vivo

Introduction

Epithelial Ovarian Cancer (EOC) cells express enzymes in the cholesterol biosynthetic pathway, making this pathway an attractive therapeutic target for controlling ovarian cancer. Potent small molecule inhibitors of one biosynthetic enzyme, Oxidosqualene Cyclase (OSC), have been identified, and RO 48-8071 (4'-[6-(allylmethylamino)hexyloxy]-4-bromo-2'-fluorobenzophenone fumarate) (RO), has emerged as a useful chemotherapeutic agent for breast and prostate cancer.

Methods

Cell viability assays were performed to determine effects of RO 48-8071 on growth of EOC cells. Aldehyde Dehydrogenase (ALDH) assay was conducted to determine the effects of drug on reducing stem cell like properties of EOC cells. Finally, xenograft studies were performed to assess the ability of RO 48-8071 to inhibit the growth of EOC cells in vivo.

Results

We found that short-term (24-48 h) administration of pharmacological doses of RO effectively reduced the viability of drug-resistant EOC cells (SK-OV-3 and OVCAR-3), as determined with sulforhodamine B colorimetric assays. In 7-day assays, nanomolar concentrations of RO effectively inhibited the growth of EOC cells. RO also suppressed ALDH activity, a marker of stem cells. Importantly, RO significantly suppressed growth of xenografts derived from EOC cells when given to mice intraperitoneally (20-40 mg kg-1 day-1) for 27 days once tumors reached 100 mm3 (controls: 336 + 60 mm3; treated: 171 + 20 mm3) with no toxicity to the experimental animals. Mechanistically, RO induced apoptosis in tumor cells in vivo as shown with immunohistochemistry.

Conclusion

Cholesterol biosynthesis inhibitor RO 48-8071 is thus a novel and potent inhibitor of human EOC, including EOC stem cells.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

Fimbristylis aestivalis Vahl: a potential source of cyclooxygenase-2 (COX-2) inhibitors

Cyclooxygenase-2 (COX-2) is an inducible enzyme that accelerates the biosynthesis of PGs during inflammation and has emerged as an important therapeutic target for anti-inflammatory drugs. Natural compounds may serve as a source of inspiration for pharmaceutical chemists and a foundation for developing innovative COX-2 inhibitors with fewer side effects. Therefore, the objective of this study was to identify the potent COX-2 inhibitor and anti-inflammatory activity of the Fimbristylis aestivalis whole plant extract (FAWE). The plant extract was found dominant with rosmarinic acid followed by catechin hydrate, syringic acid, rutin hydrate, (-) epicatechin, quercetin, myricetin, and catechol. FAWE exhibited considerable dose-dependent analgesic efficacy in all analgesic test models. FAWE also showed promising anti-inflammatory potential in carrageenan-induced inflammations in mice. This result was corroborated by molecular docking, revealing that the aforesaid natural polyphenols adopt the same orientation as celecoxib in the COX-2 active site. On the other hand, molecular dynamics (MD) simulations were performed between the most abundant components (rosmarinic acid, catechin hydrate, and syringic acid) and COX-2. Based on hydrogen bonding, RMSD, RMSF, radius of gyration, PCA, and Gibbs free energy landscape analysis, the results demonstrated that these compounds are very stable in the active site of COX-2, indicating substantial COX-2 inhibitory activity.

Cholesterol biosynthesis inhibitor RO 48‑8071 inhibits pancreatic ductal adenocarcinoma cell viability by deactivating the JNK and ERK/MAPK signaling pathway

The morbidity and mortality of pancreatic cancer have been continuously increasing, causing seven deaths per 100,000 individuals/year. At present, effective therapies are severely lacking, thus, highlighting the importance of developing novel therapeutic approaches. The present study aimed to investigate the inhibitory roles of the 2,3-oxidosqualene cyclase inhibitor, RO 48-8071 (RO), on pancreatic ductal adenocarcinoma. RO was used to treat the pancreatic cancer cell line (PANC-1) in vitro to examine the effects of RO on cell viability, as well as to determine its potential molecular mechanism. Moreover, experiments in a xenograft model of subcutaneous tumors generated by injecting PANC-1 cells hypodermically into nude mice were performed to observe the inhibition of RO on tumor growth. It was found that RO inhibited PANC-1 cell viability when treatment was given for 24, 48 and 72 h. The in vivo study demonstrated that RO markedly inhibited subcutaneous tumor growth in nude mice. Further studies revealed that RO could induce cell cycle arrest in the G₁ phase by regulating p27, cyclin B1 and cyclin E expression to inhibit PANC-1 cell viability. Moreover, RO inactivated the JNK and ERK MAPK signaling pathway by decreasing the phosphorylation levels of JNK and ERK. Collectively, the present study demonstrated that RO served anti-pancreatic cancer roles in vitro and in vivo, which may provide new ideas and facilitate the development of novel treatment options for pancreatic cancer.

Cholesterol Metabolic Reprogramming in Cancer and Its Pharmacological Modulation as Therapeutic Strategy

Cholesterol is a ubiquitous sterol with many biological functions, which are crucial for proper cellular signaling and physiology. Indeed, cholesterol is essential in maintaining membrane physical properties, while its metabolism is involved in bile acid production and steroid hormone biosynthesis. Additionally, isoprenoids metabolites of the mevalonate pathway support protein-prenylation and dolichol, ubiquinone and the heme a biosynthesis. Cancer cells rely on cholesterol to satisfy their increased nutrient demands and to support their uncontrolled growth, thus promoting tumor development and progression. Indeed, transformed cells reprogram cholesterol metabolism either by increasing its uptake and de novo biosynthesis, or deregulating the efflux. Alternatively, tumor can efficiently accumulate cholesterol into lipid droplets and deeply modify the activity of key cholesterol homeostasis regulators. In light of these considerations, altered pathways of cholesterol metabolism might represent intriguing pharmacological targets for the development of exploitable strategies in the context of cancer therapy. Thus, this work aims to discuss the emerging evidence of in vitro and in vivo studies, as well as clinical trials, on the role of cholesterol pathways in the treatment of cancer, starting from already available cholesterol-lowering drugs (statins or fibrates), and moving towards novel potential pharmacological inhibitors or selective target modulators.

Exploration of Diosmin to Control Diabetes and Its Complications-an In Vitro and In Silico Approach

BACKGROUND

Diosmin is a flavonoid obtained from the citrus fruits of the plants. Diosmin has blood lipid lowering activities, antioxidant activity, enhances venous tone and microcirculation, protects capillaries, mainly by reducing systemic oxidative stress.

OBJECTIVE

The present study demonstrates the potential of Diosmin against the enzymes aldose reductase, α-glucosidase, and α-amylase involved in diabetes and its complications by in vitro evaluation and reverse molecular docking studies.

METHODS

The assay of aldose reductase was performed by using NADPH as starting material and DL-Glyceraldehyde as a substrate. DNS method was used for alpha amylase inhibition and in alpha glucosidase inhibitory activity p-nitrophenyl glucopyranoside (pNPG) was used as substrate. The reverse molecular docking studies was performed by using Molegro software (MVD) with grid resolution of 30 Å.

RESULTS

Diosmin shows potent inhibitory effect against aldose reductase (IC50:333.88±0.04 μg/mL), α-glucosidase (IC50:410.3±0.01 μg/mL) and α-amylase (IC50: 404.22±0.02 μg/mL) respectively. The standard drugs shows moderate inhibitory activity for enzymes. The MolDock Score of Diosmin was -224.127 against aldose reductase, -168.17 against α-glucosidase and - 176.013 against α-amylase respectively, which was much higher than standard drugs.

CONCLUSION

From the result it was concluded that diosmin was a potentially inhibitor of aldose reductase, alpha amylase and alpha glucosidase enzymes then the standard drugs and it will be helpful in the management of diabetes and its complications. This will also be benevolent to decrease the socio economical burden on the middle class family of the society.

Computational evaluation of interactions between organophosphate esters and nuclear hormone receptors

Organophosphate esters (OPEs) have gained considerable interest from many environmental chemists and toxicologists due to their frequent detection in the environment and potential adverse effects on health. Nuclear hormone receptors (NHRs) were found to mediate many of their adverse effects. However, our knowledge regarding the direct binding and interaction between OPEs and NHRs is limited. In this study, Endocrine Disruptome, an online computational tool based on the technique of inverse docking, was used to calculate the binding affinity score of 25 individual OPEs with 12 different human NHRs. Results showed that 20% of potential binding interactions between the OPEs and NHRs had medium-to-high probabilities. The accuracy, sensitivity and specificity of the predictions were 78.8, 60.0 and 80.9%, respectively. OPEs with a benzene ring were more active than those without, among which, tri-o-tolyl phosphate and tri-m-tolyl phosphate displayed the highest activities, suggesting that they might pose the greatest potential risks for interference with endocrine functions. In addition, the antagonistic conformations of androgen receptor and estrogen receptor β were found to be the two most vulnerable NHR conformations. Our findings can further the understanding about the health risk(s) of OPEs.

Discovery of Novel Potential Human Targets of Resveratrol by Inverse Molecular Docking

Resveratrol is a polyphenol known for its antioxidant and anti-inflammatory properties, which support its use as a treatment for variety of diseases. There are already known connections of resveratrol to chemoprevention of cancer because of its ability to prevent tumor initiation and inhibit tumor promotion and progression. Resveratrol is also believed to be important in cardiovascular diseases and neurological disorders, such as Alzheimer's disease. Using an inverse molecular docking approach, we sought to find new potential targets of resveratrol. Docking of resveratrol into each ProBiS predicted binding site of >38 000 protein structures from the Protein Data Bank was examined, and a number of novel potential targets into which resveratrol was docked successfully were found. These explain known actions or predict new effects of resveratrol. The results included three human proteins that are already known to bind resveratrol. A majority of proteins discovered however have no already described connections with resveratrol. We report new potential target human proteins and proteins connected with different organisms into which resveratrol can dock. Our results reveal previously unknown potential target human proteins, whose connection with cardiovascular and neurological disorders could lead to new potential treatments for variety of diseases. We believe that our research could help in future experimental studies on revestratol bioactivity in humans.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

PRIMA-1Met suppresses colorectal cancer independent of p53 by targeting MEK

PRIMA-1^Met is the methylated PRIMA-1 (p53 reactivation and induction of massive apoptosis) and could restore tumor suppressor function of mutant p53 and induce p53 dependent apoptosis in cancer cells harboring mutant p53. However, p53 independent activity of PRIMA-1^Met remains elusive. Here we reported that PRIMA-1^Met attenuated colorectal cancer cell growth irrespective of p53 status. Kinase profiling revealed that mitogen-activated or extracellular signal-related protein kinase (MEK) might be a potential target of PRIMA-1^Met. Pull-down binding and ATP competitive assay showed that PRIMA-1^Met directly bound MEK in vitro and in cells. Furthermore, the direct binding sites of PRIMA-1^Met were explored by using a computational docking model. Treatment of colorectal cancer cells with PRIMA-1^Met inhibited p53-independent phosphorylation of MEK, which in turn impaired anchorage-independent cell growth in vitro. Moreover, PRIMA-1^Met suppressed colorectal cancer growth in xenograft mouse model by inhibiting MEK1 activity.

Taken together, our findings demonstrate a novel p53-independent activity of PRIMA-1^Met to inhibit MEK and suppress colorectal cancer growth.

Docking-based inverse virtual screening: methods, applications, and challenges

Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.

Exploring Polypharmacology in Drug Design

Nowadays it is widely accepted that one compound can be able to hit several targets at once. This "magic shotgun" approach for drug development properly describes the mechanism of biomolecular recognition. The need to take into account the polypharmacology in structure-based drug design has led to the development of several computational tools. Here we present a computational protocol to identify promising compounds against several biological targets, a protocol known as inverse docking.

Design strategies of oxidosqualene cyclase inhibitors: Targeting the sterol biosynthetic pathway

Targeting the sterol biosynthesis pathway has been explored for the development of new bioactive compounds. Among the enzymes of this pathway, oxidosqualene cyclase (OSC) which catalyzes lanosterol cyclization from 2,3-oxidosqualene has emerged as an attractive target. In this work, we reviewed the most promising OSC inhibitors from different organisms and their potential for the development of new antiparasitic, antifungal, hypocholesterolemic and anticancer drugs. Different strategies have been adopted for the discovery of new OSC inhibitors, such as structural modifications of the natural substrate or the reaction intermediates, the use of the enzyme's structural information to discover compounds with novel chemotypes, modifications of known inhibitors and the use of molecular modeling techniques such as docking and virtual screening to search for new inhibitors. This review brings new perspectives on structural insights of OSC from different organisms and reveals the broad structural diversity of OSC inhibitors which may help evidence lead compounds for further investigations with various therapeutic applications.

Ligand Selectivity between the ADP-Ribosylating Toxins: An Inverse-Docking Study for Multitarget Drug Discovery

Bacterial adenosine 5'-diphosphate-ribosylating toxins are encoded by several human pathogens, such as Pseudomonas aeruginosa (exotoxin A (ETA)), Corynebacterium diphtheriae (diphtheria toxin (DT)), and Vibrio cholerae (cholix toxin (CT)). The toxins modify eukaryotic elongation factor 2, an essential human enzyme in protein synthesis, thereby causing cell death. Targeting external virulence factors, such as the above toxins, is a promising alternative for developing new antibiotics, while at the same time avoiding drug resistance. This study aims to establish a reliable computational methodology to find a "silver bullet" able to target all three toxins. Herein, we have undertaken a detailed analysis of the active sites of ETA, DT, and CT, followed by the determination of the most appropriate selection of the size of the docking sphere. Thereafter, we tested two different approaches for normalizing the docking scores and used these to verify the best target (toxin) for each ligand. The results indicate that the methodology is suitable for identifying selective as well as multitoxin inhibitors, further validating the robustness of inverse docking for target-fishing experiments.

Recent Advancements in Docking Methodologies

Nowadays molecular docking has become an important methodology in CADD (Computer-Aided Drug Design)-assisted drug discovery process. It is an important computational tool widely used to predict binding mode, binding affinity and binding free energy of a protein-ligand complex. The important factors responsible for accurate results in docking studies are correct binding site prediction, use of suitable small-molecule databases, consistent docking pose, high dock score with good MD (Molecular Dynamics), clarity whether the compound is an inhibitor or agonist, etc. However, still there are several limitations which make it difficult to obtain accurate results from docking studies. In this chapter, the main focus is on recent advancements in various aspects of molecular docking such as ligand sampling, protein flexibility, scoring functions, fragment docking, post-processing, docking into homology models and protein-protein docking.

Use of machine learning approaches for novel drug discovery

INTRODUCTION

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.

AREAS COVERED

This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.

EXPERT OPINION

Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

Cholesterol biosynthesis inhibitor RO 48-8071 suppresses growth of hormone-dependent and castration-resistant prostate cancer cells

Standard treatment for primary prostate cancer includes systemic exposure to chemotherapeutic drugs that target androgen receptor or antihormone therapy (chemical castration); however, drug-resistant cancer cells generally emerge during treatment, limiting the continued use of systemic chemotherapy. Patients are then treated with more toxic standard therapies. Therefore, there is an urgent need for novel and more effective treatments for prostate cancer. The cholesterol biosynthetic pathway is an attractive therapeutic target for treating endocrine-dependent cancers because cholesterol is an essential structural and functional component of cell membranes as well as the metabolic precursor of endogenous steroid hormones. In this study, we have examined the effects of RO 48-8071 (4′-[6-(allylmethylamino)hexyloxy]-4-bromo-2′-fluorobenzophenone fumarate; Roche Pharmaceuticals internal reference: RO0488071) (RO), which is an inhibitor of 2, 3-oxidosqualene cyclase (a key enzyme in the cholesterol biosynthetic pathway), on prostate cancer cells. Exposure of both hormone-dependent and castration-resistant human prostate cancer cells to RO reduced prostate cancer cell viability and induced apoptosis in vitro. RO treatment reduced androgen receptor protein expression in hormone-dependent prostate cancer cells and increased estrogen receptor β (ERβ) protein expression in both hormone-dependent and castration-resistant prostate cancer cell lines. Combining RO with an ERβ agonist increased its ability to reduce castration-resistant prostate cancer cell viability. In addition, RO effectively suppressed the growth of aggressive castration-resistant human prostate cancer cell xenografts in vivo without any signs of toxicity to experimental animals. Importantly, RO did not reduce the viability of normal prostate cells in vitro. Our study is the first to demonstrate that the cholesterol biosynthesis inhibitor RO effectively suppresses growth of human prostate cancer cells. Our findings suggest that cholesterol biosynthesis inhibitors such as RO, when used in combination with commonly used chemotherapeutic drugs or ERβ specific ligands, could represent a novel therapeutic approach to prevent the growth of prostate cancer tumors.

Fungal naphtho-γ-pyrones: Potent antibiotics for drug-resistant microbial pathogens

Four naphtho-γ-pyrones (fonsecinones A and C and aurasperones A and E) were identified as potential antibacterial agents against Escherichia coli, extended-spectrum β-lactamase (ESBL)-producing E. coli, Pseudomonas aeruginosa, Enterococcus faecalis, and methicillin-resistant Staphylococcus aureus (MRSA) in an in vitro antibacterial screen of 218 fungal metabolites. Fonsecinone A (2) exhibited the most potent antibacterial activity, with minimum inhibitory concentrations (MICs) of 4.26, 17.04, and 4.26 μg/mL against ESBL-producing E. coli, P. aeruginosa, and E. faecalis, respectively. The inhibitory effects of fonsecinones A (2) and C (3) against E. coli and ESBL-producing E. coli were comparable to those of amikacin. Molecular docking-based target identification of naphtho-γ-pyrones 1–8 revealed bacterial enoyl-acyl carrier protein reductase (FabI) as an antibacterial target, which was further validated by FabI affinity and inhibition assays. Fonsecinones A (2) and C (3) and aurasperones A (6) and E (7) bound FabI specifically and produced concentration-dependent inhibition effects. This work is the first report of anti-drug-resistant bacterial activities of naphtho-γ-pyrones 1–8 and their possible antibacterial mechanism of action and provides an example of the successful application of in silico methods for drug target identification and validation and the identification of new lead antibiotic compounds against drug-resistant pathogens.

SM-TF: A structural database of small molecule-transcription factor complexes

Transcription factors (TFs) are the proteins involved in the transcription process, ensuring the correct expression of specific genes. Numerous diseases arise from the dysfunction of specific TFs. In fact, over 30 TFs have been identified as therapeutic targets of about 9% of the approved drugs. In this study, we created a structural database of small molecule-transcription factor (SM-TF) complexes, available online at http://zoulab.dalton.missouri.edu/SM-TF. The 3D structures of the co-bound small molecule and the corresponding binding sites on TFs are provided in the database, serving as a valuable resource to assist structure-based drug design related to TFs. Currently, the SM-TF database contains 934 entries covering 176 TFs from a variety of species. The database is further classified into several subsets by species and organisms. The entries in the SM-TF database are linked to the UniProt database and other sequence-based TF databases. Furthermore, the druggable TFs from human and the corresponding approved drugs are linked to the DrugBank. © 2016 Wiley Periodicals, Inc.

Historeceptomic Fingerprints for Drug-Like Compounds

Most drugs exert their beneficial and adverse effects through their combined action on several different molecular targets (polypharmacology). The true molecular fingerprint of the direct action of a drug has two components: the ensemble of all the receptors upon which a drug acts and their level of expression in organs/tissues. Conversely, the fingerprint of the adverse effects of a drug may derive from its action in bystander tissues. The ensemble of targets is almost always only partially known. Here we describe an approach improving upon and integrating both components: in silico identification of a more comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Our system combines more than 300,000 experimentally determined bioactivity values from the ChEMBL database and 4.2 billion molecular docking scores. We integrated these scores with gene expression data for human receptors across a panel of human tissues to produce drug-specific tissue-receptor (historeceptomics) scores. A statistical model was designed to identify significant scores, which define an improved fingerprint representing the unique activity of any drug. These multi-dimensional historeceptomic fingerprints describe, in a novel, intuitive, and easy to interpret style, the holistic, in vivo picture of the mechanism of any drug's action. Valuable applications in drug discovery and personalized medicine, including the identification of molecular signatures for drugs with polypharmacologic modes of action, detection of tissue-specific adverse effects of drugs, matching molecular signatures of a disease to drugs, target identification for bioactive compounds with unknown receptors, and hypothesis generation for drug/compound phenotypes may be enabled by this approach. The system has been deployed at drugable.org for access through a user-friendly web site.

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

The cholesterol biosynthesis enzyme oxidosqualene cyclase is a new target to impair tumour angiogenesis and metastasis dissemination

Aberrant cholesterol homeostasis and biosynthesis has been observed in different tumour types. This paper investigates the role of the post-squalenic enzyme of cholesterol biosynthesis, oxidosqualene cyclase (OSC), in regulating tumour angiogenesis and metastasis dissemination in mouse models of cancer. We showed that Ro 48-8071, a selective inhibitor of OSC, reduced vascular density and increased pericyte coverage, with a consequent inhibition of tumour growth in a spontaneous mouse model of pancreatic tumour (RIP-Tag2) and two metastatic mouse models of human colon carcinoma (HCT116) and pancreatic adenocarcinoma (HPAF-II). Remarkably, the inhibition of OSC hampered metastasis formation in HCT116 and HPAF-II models. Ro 48-8071 induced tumour vessel normalization and enhanced the anti-tumoral and anti-metastatic effects of 5-fluorouracil (5-FU) in HCT116 mice. Ro 48-8071 exerted a strong anti-angiogenic activity by impairing endothelial cell adhesion and migration, and by blocking vessel formation in angiogenesis assays. OSC inhibition specifically interfered with the PI3K pathway. According to in vitro results, Ro 48-8071 specifically inhibited Akt phosphorylation in both cancer cells and tumour vasculature in all treated models. Thus, our results unveil a crucial role of OSC in the regulation of cancer progression and tumour angiogenesis, and indicate Ro 48-8071 as a potential novel anti-angiogenic and anti-metastatic drug.

In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

Background

Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies.

Methods

In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin.

Results

71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle– transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway.

Conclusions

The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals.

Identification of a potential anticancer target of danshensu by inverse docking

OBJECTIVE

To study potential targets of Danshensu via dual inverse docking.

METHOD

PharmMapper and idTarget servers were used as tools, and the results were checked with the molecular docking program autodock vina in PyRx 0.8.

RESULT

The disease-related target HRas was rated top, with a pharmacophore model matching well the molecular features of Danshensu. In addition, docking results indicated that the complex was also matched in terms of structure, H-bonds, and hydrophobicity.

CONCLUSION

Dual inverse docking indicates that HRas may be a potential anticancer target of Danshensu. This approach can provide useful information for studying pharmacological effects of agents of interest.

Nimbolide targets BCL2 and induces apoptosis in preclinical models of Waldenströms macroglobulinemia

Neem leaf extract (NLE) has medicinal properties, which have been attributed to its limonoid content. We identified the NLE tetranorterpenoid, nimbolide, as being the key limonoid responsible for the cytotoxicity of NLE in various preclinical models of human B-lymphocyte cancer. Of the models tested, Waldenströms macroglobulinemia (WM) cells were most sensitive to nimbolide, undergoing significant mitochondrial mediated apoptosis. Notably, nimbolide toxicity was also observed in drug-resistant (bortezomib or ibrutinib) WM cells. To identify putative targets of nimbolide, relevant in WM, we used chemoinformatics-based approaches comprised of virtual in silico screening, molecular modeling and target–ligand reverse docking. In silico analysis revealed the antiapoptotic protein BCL2 was the preferential binding partner of nimbolide. The significance of this finding was further tested in vitro in RS4;11 (BCL2-dependent) tumor cells, in which nimbolide induced significantly more apoptosis compared with BCL2 mutated (Jurkat BCL2^Ser70-Ala) cells. Lastly, intraperitoneal administration of nimbolide in WM tumor xenografted mice, significantly reduced tumor growth and IgM secretion in vivo, while modulating the expression of several proteins as seen on immunohistochemistry. Overall, our data demonstrate that nimbolide is highly active in WM cells, as well as other B-cell cancers, and engages BCL2 to exert its cytotoxic activity.

Reverse docking: a powerful tool for drug repositioning and drug rescue

Reverse or inverse docking is proving to be a powerful tool for drug repositioning and drug rescue. It involves docking a small-molecule drug/ligand in the potential binding cavities of a set of clinically relevant macromolecular targets. Detailed analyses of the binding characteristics lead to ranking of the targets according to the tightness of binding. This process can potentially identify novel molecular targets for the drug/ligand which may be relevant for its mechanism of action and/or side effect profile. Another potential application of reverse docking is during the lead discovery and optimization stages of the drug-discovery cycle. This review summarizes the state-of-the-art and future prospects of the reverse docking with particular emphasis on computational molecular design.

Curation and analysis of multitargeting agents for polypharmacological modeling

In drug discovery and development, the conventional “single drug, single target” concept has been shifted to “single drug, multiple targets” – a concept coined as polypharmacology. For studies in this emerging field, dedicated and high-quality databases of multitargeting ligands would be exceedingly beneficial. To this end, we conducted a comprehensive analysis of the structural and chemical/biological profiles of polypharmacological agents and present a Web-based database (Polypharma). All of these compounds curated herein have been cocrystallized with more than one unique protein with intensive reports of their multitargeting activities. The present study provides more insight of drug multitargeting and is particularly useful for polypharmacology modeling. This specialized curation has been made publically available at http:/imdlab.org/polypharma/

Cholesterol synthesis inhibitor RO 48-8071 suppresses transcriptional activity of human estrogen and androgen receptor

Breast cancer cells express enzymes that convert cholesterol, the synthetic precursor of steroid hormones, into estrogens and androgens, which then drive breast cancer cell proliferation. In the present study, we sought to determine whether oxidosqualene cyclase (OSC), an enzyme in the cholesterol biosynthetic pathway, may be targeted to suppress progression of breast cancer cells. In previous studies, we showed that the OSC inhibitor RO 48-8071 (RO) may be a ligand which could potentially be used to control the progression of estrogen receptor-α (ERα)-positive breast cancer cells. Herein, we showed, by real-time PCR analysis of mRNA from human breast cancer biopsies, no significant differences in OSC expression at various stages of disease, or between tumor and normal mammary cells. Since the growth of hormone-responsive tumors is ERα-dependent, we conducted experiments to determine whether RO affects ERα. Using mammalian cells engineered to express human ERα or ERβ protein, together with an ER-responsive luciferase promoter, we found that RO dose-dependently inhibited 17β-estradiol (E2)-induced ERα responsive luciferase activity (IC50 value, ~10 µM), under conditions that were non-toxic to the cells. RO was less effective against ERβ-induced luciferase activity. Androgen receptor (AR) mediated transcriptional activity was also reduced by RO. Notably, while ERα activity was reduced by atorvastatin, the HMG-CoA reductase inhibitor did not influence AR activity, showing that RO possesses broader antitumor properties. Treatment of human BT-474 breast cancer cells with RO reduced levels of estrogen-induced PR protein, confirming that RO blocks ERα activity in tumor cells. Our findings demonstrate that an important means by which RO suppresses hormone-dependent growth of breast cancer cells is through its ability to arrest the biological activity of ERα. This warrants further investigation of RO as a potential therapeutic agent for use against hormone-dependent breast cancers.

Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design

The docking methods used in structure-based virtual database screening offer the ability to quickly and cheaply estimate the affinity and binding mode of a ligand for the protein receptor of interest, such as a drug target. These methods can be used to enrich a database of compounds, so that more compounds that are subsequently experimentally tested are found to be pharmaceutically interesting. In addition, like all virtual screening methods used for drug design, structure-based virtual screening can focus on curated libraries of synthesizable compounds, helping to reduce the expense of subsequent experimental verification. In this review, we introduce the protein-ligand docking methods used for structure-based drug design and other biological applications. We discuss the fundamental challenges facing these methods and some of the current methodological topics of interest. We also discuss the main approaches for applying protein-ligand docking methods. We end with a discussion of the challenging aspects of evaluating or benchmarking the accuracy of docking methods for their improvement, and discuss future directions.

PRIMA-1Met induces myeloma cell death independent of p53 by impairing the GSH/ROS balance

The aim of this study was to assess the efficiency of p53 reactivation and induction of massive apoptosis (PRIMA-1(Met)) in inducing myeloma cell death, using 27 human myeloma cell lines (HMCLs) and 23 primary samples. Measuring the lethal dose (LD50) of HMCLs revealed that HMCLs displayed heterogeneous sensitivity, with an LD50 ranging from 4 μM to more than 200 μM. The sensitivity of HMCLs did not correlate with myeloma genomic heterogeneity or TP53 status, and PRIMA-1(Met) did not induce or increase expression of the p53 target genes CDKN1A or TNFRSF10B/DR5. However, PRIMA-1(Met) increased expression of NOXA in a p53-independent manner, and NOXA silencing decreased PRIMA1(Met)-induced cell death. PRIMA-1(Met) depleted glutathione (GSH) content and induced reactive oxygen species production. The expression of GSH synthetase correlated with PRIMA-1(Met) LD50 values, and we showed that a GSH decrease mediated by GSH synthetase silencing or by and L-buthionine sulphoximine, an irreversible inhibitor of γ-glutamylcysteine synthetase, increased PRIMA-1(Met)-induced cell death and overcame PRIMA-1(Met) resistance. PRIMA-1(Met) (10 μM) induced cell death in 65% of primary cells independent of the presence of del17p; did not increase DR5 expression, arguing against an activation of p53 pathway; and synergized with L-buthionine sulphoximine in all samples. Finally, we showed in mouse TP53(neg) JJN3-xenograft model that PRIMA-1(Met) inhibited myeloma growth and synergized with L-buthionine sulphoximine in vivo.

Cholesterol biosynthesis inhibitors as potent novel anti-cancer agents: suppression of hormone-dependent breast cancer by the oxidosqualene cyclase inhibitor RO 48-8071

In most human breast cancers, tumor cell proliferation is estrogen dependent. Although hormone-responsive tumors initially respond to anti-estrogen therapies, most of them eventually develop resistance. Our goal was to identify alternative targets that might be regulated to control breast cancer progression. Sulforhodamine B assay was used to measure the viability of cultured human breast cancer cell lines exposed to various inhibitors. Protein expression in whole-cell extracts was determined by Western blotting. BT-474 tumor xenografts in nude mice were used for in vivo studies of tumor progression. RO 48-8071 ([4'-[6-(Allylmethylamino)hexyloxy]-4-bromo-2'-fluorobenzophenone fumarate]; RO), a small-molecule inhibitor of oxidosqualene cyclase (OSC, a key enzyme in cholesterol biosynthesis), potently reduced breast cancer cell viability. In vitro exposure of estrogen receptor (ER)-positive human breast cancer cells to pharmacological levels of RO or a dose close to the IC50 for OSC (nM) reduced cell viability. Administration of RO to mice with BT-474 tumor xenografts prevented tumor growth, with no apparent toxicity. RO degraded ERα while concomitantly inducing the anti-proliferative protein ERβ. Two other cholesterol-lowering drugs, Fluvastatin and Simvastatin, were less effective in reducing breast cancer cell viability and were found not to induce ERβ. ERβ inhibition or knockdown prevented RO-dependent loss of cell viability. Importantly, RO had no effect on the viability of normal human mammary cells. RO is a potent inhibitor of hormone-dependent human breast cancer cell proliferation. The anti-tumor properties of RO appear to be in part due to an off-target effect that increases the ratio of ERβ/ERα in breast cancer cells.

Polypharmacology: drug discovery for the future

In recent years, even with remarkable scientific advancements and a significant increase of global research and development spending, drugs are frequently withdrawn from markets. This is primarily due to their side effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side effects. Polypharmacology remains one of the major challenges in drug development, and it opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.