Shifting from the single to the multitarget paradigm in drug discovery

Copper and Antibiotics: Discovery, Modes of Action, and Opportunities for Medicinal Applications

Copper is a ubiquitous element in the environment as well as living organisms, with its redox capabilities and complexation potential making it indispensable for many cellular functions. However, these same properties can be highly detrimental to prokaryotes and eukaryotes when not properly controlled, damaging many biomolecules including DNA, lipids, and proteins. To restrict free copper concentrations, all bacteria have developed mechanisms of resistance, sequestering and effluxing labile copper to minimize its deleterious effects. This weakness is actively exploited by phagocytes, which utilize a copper burst to destroy pathogens. Though administration of free copper is an unreasonable therapeutic antimicrobial itself, due to insufficient selectivity between host and pathogen, small-molecule ligands may provide an opportunity for therapeutic mimicry of the immune system. By modulating cellular entry, complex stability, resistance evasion, and target selectivity, ligand/metal coordination complexes can synergistically result in high levels of antibacterial activity. Several established therapeutic drugs, such as disulfiram and pyrithione, display remarkable copper-dependent inhibitory activity. These findings have led to development of new drug discovery techniques, using copper ions as the focal point. High-throughput screens for copper-dependent inhibitors against Mycobacterium tuberculosis and Staphylococcus aureus uncovered several new compounds, including a new class of inhibitors, the NNSNs. In this review, we highlight the microbial biology of copper, its antibacterial activities, and mechanisms to discover new inhibitors that synergize with copper.

The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing

Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reverse docking, which would be easily influenced by the properties of protein pockets, resulting in scoring bias to the proteins with certain properties. This bias would cause lots of false positives in reverse docking, interferring the identification of true targets. In this paper, we have conducted a large-scale reverse docking (5000 molecules to 100 proteins) to study the scoring bias in reverse docking by DOCK, Glide, and AutoDock Vina. And we found that there were actually some frequency hits, namely interference proteins in all three docking procedures. After analyzing the differences of pocket properties between these interference proteins and the others, we speculated that the interference proteins have larger contact area (related to the size and shape of protein pockets) with ligands (for all three docking programs) or higher hydrophobicity (for Glide), which could be the causes of scoring bias. Then we applied the score normalization method to eliminate this scoring bias, which was effective to make docking score more balanced between different proteins in the reverse docking of benchmark dataset. Later, the Astex Diver Set was utilized to validate the effect of score normalization on actual cases of reverse docking, showing that the accuracy of target prediction significantly increased by 21.5% in the reverse docking by Glide after score normalization, though there was no obvious change in the reverse docking by DOCK and AutoDock Vina. Our results demonstrate the effectiveness of score normalization to eliminate the scoring bias and improve the accuracy of target prediction in reverse docking. Moreover, the properties of protein pockets causing scoring bias to certain proteins we found here can provide the theory basis to further optimize the scoring functions of docking programs for future research.

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure-activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set [Formula: see text]; test set: [Formula: see text]; whole data set: stability [Formula: see text]). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.

Discovery of a Chemical Probe Bisamide (CCT251236): An Orally Bioavailable Efficacious Pirin Ligand from a Heat Shock Transcription Factor 1 (HSF1) Phenotypic Screen

Phenotypic screens, which focus on measuring and quantifying discrete cellular changes rather than affinity for individual recombinant proteins, have recently attracted renewed interest as an efficient strategy for drug discovery. In this article, we describe the discovery of a new chemical probe, bisamide (CCT251236), identified using an unbiased phenotypic screen to detect inhibitors of the HSF1 stress pathway. The chemical probe is orally bioavailable and displays efficacy in a human ovarian carcinoma xenograft model. By developing cell-based SAR and using chemical proteomics, we identified pirin as a high affinity molecular target, which was confirmed by SPR and crystallography.

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.

An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis

Deciphering the compound Wenxin Keli's anti-arrhythmic pharmacological mechanism by integrating network pharmacology and experimental verification methods.

Exploration and analysis of drug modes of action through feature integration

Identifying drug modes of action (MoA) is of paramount importance for having a good grasp of drug indications in clinical tests.

Meeting the Challenge: Using Cytological Profiling to Discover Chemical Probes from Traditional Chinese Medicines against Parkinson's Disease

Parkinson's disease (PD) is an incurable neurodegenerative disorder with a high prevalence rate worldwide. The fact that there are currently no proven disease-modifying treatments for PD underscores the urgency for a more comprehensive understanding of the underlying disease mechanism. Chemical probes have been proven to be powerful tools for studying biological processes. Traditional Chinese medicine (TCM) contains a huge reservoir of bioactive small molecules as potential chemical probes that may hold the key to unlocking the mystery of PD biology. The TCM-sourced chemical approach to PD biology can be advanced through the use of an emerging cytological profiling (CP) technique that allows unbiased characterization of small molecules and their cellular responses. This comprehensive technique, applied to chemical probe identification from TCM and used for studying the molecular mechanisms underlying PD, may inform future therapeutic target selection and provide a new perspective to PD drug discovery.

Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns.

Recent progress in repositioning Alzheimer's disease drugs based on a multitarget strategy

Alzheimer's disease (AD) is a serious progressive neurological disorder, characterized by impaired cognition and profound irreversible memory loss. The multifactorial nature of AD and the absence of a cure so far have stimulated medicinal chemists worldwide to follow multitarget drug-design strategies based on repositioning approved drugs. This review describes a summary of recently published works focused on tailoring new derivatives of US FDA-approved acetylcholinesterase inhibitors, in addition to huperzine (a drug approved in China), either by hybridization with other pharmacophore elements (to hit more AD targets), or by combination of two FDA-approved drugs. Besides the capacity for improving the cholinergic activity, these polyfunctional derivatives are also able to tackle other important neuroprotective properties, such as anti-β-amyloid aggregation, scavenging of radical oxygen species, modulation of redox-active metals or inhibition of monoamine oxidase, thereby resulting in potentially novel and more effective therapeutics for the treatment of AD.

A concise review on advances in development of small molecule anti-inflammatory therapeutics emphasising AMPK: An emerging target

Inflammatory diseases are complex, multi-factorial outcomes of evolutionarily conserved tissue repair processes. For decades, non-steroidal anti-inflammatory drugs and cyclooxygenase inhibitors, the primary drugs of choice for the management of inflammatory diseases, addressed individual targets in the arachidonic acid pathway. Unsatisfactory safety and efficacy profiles of the above have necessitated the development of multi-target agents to treat complex inflammatory diseases. Current anti-inflammatory therapies still fall short of clinical needs and the clinical trial results of multi-target therapeutics are anticipated. Additionally, new drug targets are emerging with improved understanding of molecular mechanisms controlling the pathophysiology of inflammation. This review presents an outline of small molecules and drug targets in anti-inflammatory therapeutics with a summary of a newly identified target AMP-activated protein kinase, which constitutes a novel therapeutic pathway in inflammatory pathology.

Saponins of Agave: Chemistry and bioactivity

The genus Agave comprises more than 400 species with geographical presence in the tropical and sub-tropical regions of the world. These plants have a rich history of folkloric use and are known for a wide spectrum of applications. Secondary metabolites of diverse chemical classes have been reported from Agave species. Owing to their pharmacological significance, the steroidal saponins of Agave have caught the attention of phytochemists, biologists and drug discovery scientists. The present review describes 141 steroidal saponins and sapogenins and covers the literature published from 1970 to 2015. It is a comprehensive and coherent presentation of the structures, methods of chemical profiling, structure elucidation and biological activities of the saponins and sapogenins reported from Agave. The article provides a perspective of the research on steroidal compounds of Agave.

Resveratrol-salicylate derivatives as selective DNMT3 inhibitors and anticancer agents

Resveratrol is a natural polyphenol with plethora of biological activities. Resveratrol has previously shown to decrease DNA-methyltransferase (DNMT) enzymes expression and to reactivate silenced tumor suppressor genes. Currently, it seems that no resveratrol analogs have been developed as DNMT inhibitors. Recently, we reported the synthesis of resveratrol-salicylate derivatives and by examining the chemical structure of these analogs, we proposed that these compounds could exhibit DNMT inhibition especially that they resembled NSC 14778, a compound we previously identified as a DNMT inhibitor by virtual screening. Indeed, using in vitro DNMT inhibition assay, some of the resveratrol-salicylate analogs we screened in this work that showed selective inhibition against DNMT3 enzymes which were greater than resveratrol. A molecular docking study revealed key binding interactions with DNMT3A and DNMT3B enzymes. In addition, the most active analog, 10 showed considerable cytotoxicity against three human cancer cells; HT-29, HepG2 and SK-BR-3, which was greater than resveratrol. Further studies are needed to understand the anticancer mechanisms of these derivatives.

Molecular Modeling and Chemoinformatics to Advance the Development of Modulators of Epigenetic Targets: A Focus on DNA Methyltransferases

In light of the emerging field of Epi-informatics, ie, computational methods applied to epigenetic research, molecular docking, and dynamics, pharmacophore and activity landscape modeling and QSAR play a key role in the development of modulators of DNA methyltransferases (DNMTs), one of the major epigenetic target families. The increased chemical information available for modulators of DNMTs has opened up the avenue to explore the epigenetic relevant chemical space (ERCS). Herein, we discuss recent progress on the identification and development of inhibitors of DNMTs as potential epi-drugs and epi-probes that have been driven by molecular modeling and chemoinformatics methods. We also survey advances on the elucidation of their structure-activity relationships and exploration of ERCS. Finally, it is illustrated how computational approaches can be applied to identify modulators of DNMTs in food chemicals.

Modeling Fetal Alcohol Spectrum Disorder: Validating an Ex Vivo Primary Hippocampal Cell Culture System

BACKGROUND

Fetal alcohol spectrum disorder (FASD) is the leading nongenetic cause of mental retardation. There are no treatments for FASD to date. Preclinical in vivo and in vitro studies could help in identifying novel drug targets as for other diseases. Here, we describe an ex vivo model that combines the physiological advantages of prenatal ethanol (EtOH) exposure in vivo with the uniformity of primary fetal hippocampal culture to characterize the effects of prenatal EtOH. The insulin signaling pathways are known to be involved in hippocampal functions. Therefore, we compared the expression of insulin signaling pathway genes between fetal hippocampi (in vivo) and primary hippocampal culture (ex vivo). The similarity of prenatal EtOH effects in these 2 paradigms would deem the ex vivo culture acceptable to screen possible treatments for FASD.

METHODS

Pregnant Sprague-Dawley rats received 1 of 3 diets: ad libitum standard laboratory chow (control-C), isocaloric pair-fed (nutritional control), and EtOH containing liquid diets from gestational day (GD) 8. Fetal male and female hippocampi were collected either on GD21 (in vivo) or on GD18 for primary culture (ex vivo). Transcript levels of Igf2, Igf2r, Insr, Grb10, Rasgrf1, and Zac1 were measured by reverse transcription quantitative polymerase chain reaction.

RESULTS

Hippocampal transcript levels differed by prenatal treatment in both males and females with sex differences observed in the expression of Igf2 and Insr. The effect of prenatal EtOH on the hippocampal expression of the insulin pathway genes was parallel in the in vivo and the ex vivo conditions.

CONCLUSIONS

The similarity of gene expression changes in response to prenatal EtOH between the in vivo and the ex vivo conditions ascertains that these effects are already set in the fetal hippocampus at GD18. This strengthens the feasibility of the ex vivo primary hippocampal culture as a tool to test and screen candidate drug targets for FASD.

DrugGenEx-Net: a novel computational platform for systems pharmacology and gene expression-based drug repurposing

Background

The targeting of disease-related proteins is important for drug discovery, and yet target-based discovery has not been fruitful. Contextualizing overall biological processes is critical to formulating successful drug-disease hypotheses. Network pharmacology helps to overcome target-based bottlenecks through systems biology analytics, such as protein-protein interaction (PPI) networks and pathway regulation.

Results

We present a systems polypharmacology platform entitled DrugGenEx-Net (DGE-NET). DGE-NET predicts empirical drug-target (DT) interactions, integrates interaction pairs into a multi-tiered network analysis, and ultimately predicts disease-specific drug polypharmacology through systems-based gene expression analysis. Incorporation of established biological network annotations for protein target-disease, −signaling pathway, −molecular function, and protein-protein interactions enhances predicted DT effects on disease pathophysiology. Over 50 drug-disease and 100 drug-pathway predictions are validated. For example, the predicted systems pharmacology of the cholesterol-lowering agent ezetimibe corroborates its potential carcinogenicity.

When disease-specific gene expression analysis is integrated, DGE-NET prioritizes known therapeutics/experimental drugs as well as their contra-indications. Proof-of-concept is established for immune-related rheumatoid arthritis and inflammatory bowel disease, as well as neuro-degenerative Alzheimer’s and Parkinson’s diseases.

Conclusions

DGE-NET is a novel computational method that predicting drug therapeutic and counter-therapeutic indications by uniquely integrating systems pharmacology with gene expression analysis. DGE-NET correctly predicts various drug-disease indications by linking the biological activity of drugs and diseases at multiple tiers of biological action, and is therefore a useful approach to identifying drug candidates for re-purposing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1065-y) contains supplementary material, which is available to authorized users.

Effective Design of Multifunctional Peptides by Combining Compatible Functions

Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf.

Most proteins and peptides in nature display multiple activities either by fusing different domains (with different activities) or by evolving multiple activities in a single domain. Understanding which activities may be combined to render multifunctional proteins remains an open question relevant to understanding the organization of living organisms and to improve the design of pharmacological peptides. To address this problem, we introduce the concept of compatible activities, that is, activities that may combine without losing any of these in a single polypeptide chain. To identify compatible activities in peptide sequences, we used a machine-learning approach and discovered that a penetrating activity should be compatible with DNA-binding and antimicrobial activities. To test if these activities may combine without any functional loss, we designed peptide sequences that harbor two independent activities (nuclear localization and pheromone) and experimentally showed that all our designed peptides display penetrability, pheromone, antimicrobial and DNA-binding activities, supporting the idea that multifunctionality may be achieved combining compatible activities.

Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry

Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery. Herein, we survey advances on key topics on PLM with emphasis on questions often raised regarding the outcomes of the property landscape studies. We also emphasize on concepts of PLM that are being extended to other experimental areas beyond drug discovery. Topics discussed in this paper include applications of PLM to further characterize protein-ligand interactions, the utility of PLM as a quantitative and descriptive approach, and the statistical validation of property cliffs.

Systems psychopharmacology: A network approach to developing novel therapies

The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multi-targeted strategies. This contrasts to the majority of drug therapies used until now, addressing more or less specifically only one target molecule. Nevertheless, quite some searches for multiple molecular targets specific for mental disorders have been undertaken. For example, genome-wide association studies have been conducted to discover new target genes of disease. Unfortunately, these attempts have not fulfilled the great hopes they have started with. Polypharmacology and network pharmacology approaches of drug treatment endeavor to abandon the one-drug one-target thinking. To this end, most approaches set out to investigate network topologies searching for modules, endowed with "important" nodes, such as "hubs" or "bottlenecks", encompassing features of disease networks, and being useful as tentative targets of drug therapies. This kind of research appears to be very promising. However, blocking or inhibiting "important" targets may easily result in destruction of network integrity. Therefore, it is suggested here to study functions of nodes with lower centrality for more subtle impact on network behavior. Targeting multiple nodes with low impact on network integrity by drugs with multiple activities ("dirty drugs") or by several drugs, simultaneously, avoids to disrupt network integrity and may reset deviant dynamics of disease. Natural products typically display multi target functions and therefore could help to identify useful biological targets. Hence, future efforts should consider to combine drug-target networks with target-disease networks using mathematical (graph theoretical) tools, which could help to develop new therapeutic strategies in long-term psychiatric disorders.

Effects of the nicotinic agonist varenicline on the performance of tasks of cognition in aged and middle-aged rhesus and pigtail monkeys

RATIONALE

Due to the rising costs of drug development especially in the field of neuropsychiatry, there is increasing interest in efforts to identify new clinical uses for existing approved drugs (i.e., drug repurposing).

OBJECTIVES

The purpose of this work was to evaluate in animals the smoking cessation agent, varenicline, a partial agonist at α4β2 and full agonist at α7 nicotinic acetylcholine receptors, for its potential as a repurposed drug for disorders of cognition.

METHODS

Oral doses of varenicline ranging from 0.01 to 0.3 mg/kg were evaluated in aged and middle-aged monkeys for effects on the following: working/short-term memory in a delayed match to sample (DMTS) task, distractibility in a distractor version of the DMTS (DMTS-D), and cognitive flexibility in a ketamine-impaired reversal learning task.

RESULTS

In dose-effect studies in the DMTS and DMTS-D tasks, varenicline was not associated with statistically significant effects on performance. However, individualized "optimal doses" were effective when repeated on a separate occasion (i.e., improving DMTS accuracy at long delays and DMTS-D accuracy at short delays by approximately 13.6 and 19.6 percentage points above baseline, respectively). In reversal learning studies, ketamine impaired accuracy and increased perseverative responding, effects that were attenuated by all three doses of varenicline that were evaluated.

CONCLUSIONS

While the effects of varenicline across the different behavioral tasks were modest, these data suggest that varenicline may have potential as a repurposed drug for disorders of cognition associated with aging (e.g., Alzheimer's disease), as well as those not necessarily associated with advanced age (e.g., schizophrenia).

Filtering promiscuous compounds in early drug discovery: is it a good idea?

The use of computational filters for excluding supposedly nonspecific and promiscuous compounds from chemical libraries is a controversial issue, because many drugs used in clinics today would never reach the market if these filters were applied. In part, this conflict could be caused by the paradigm: one-drug-one-target, even though it is widely agreed that drug action is a result of a complex network of biomolecular interactions. Therefore, the so-called pan assay interference compounds (PAINS) or promiscuous compounds could be in fact assay artifacts, false positives or, simply, bright chemical matter (BCM) composed of privileged scaffolds, as we propose here. Despite apparent promiscuity, BCM can be tailored into new and safe drugs after overcoming selectivity criteria.

The influence of 5-HT(2A) activity on a 5-HT(2C) specific in vivo assay used for early identification of multiple acting SERT and 5-HT(2C) receptor ligands

As a result of our exploratory programme aimed at elaborating dually acting compounds towards the serotonin (5-HT) transporter (SERT) and the 5-HT2C receptor a novel series of 3-amino-1-phenylpropoxy substituted diphenylureas was identified. From that collection two promising compounds (2 and 3) exhibiting highest 5-HT2C receptor affinity strongly inhibited the 5-HT2C receptor agonist 1-(3-chlorophenyl)piperazine (mCPP) induced hypomotility in mice. In further pursuance of that objective (2-aminoethyl)(benzyl)sulfamoyl diphenylureas and diphenylpiperazines have also been elaborated. Herein we report the synthesis of potent multiple-acting compounds from this new class. However, when two optimized representatives (6 and 14) possessing the desired in vitro profile were tested neither reduced the motor activity of mCPP treated animals. Comparative albeit limited in vitro structure-activity relationship (SAR) analysis and detailed in vivo studies are discussed and explanation for their intricate behaviour is proposed.

A cell-based screening assay to identify pharmaceutical compounds that enhance the regenerative quality of corneal repair

The goal of this study was to develop and validate a simple but quantitative cell-based assay to identify compounds that might be used pharmaceutically to give tissue repair a more regenerative character. The cornea was used as the model, and some specific aspects of repair in this organ were incorporated into assay design. A quantitative cell-based assay was developed based on transcriptional promoter activity of fibrotic marker genes ACT2A and TGFB2. Immortalized corneal stromal cells (HTK) or corneal epithelial cells (HCLE) were tested and compared to primary corneal stromal cells. Cells were transiently transfected with constructs containing the firefly luciferase reporter gene driven by transcriptional promoters for the selected fibrotic marker genes. A selected panel of seven chemical test compounds was used, containing three known fibrosis inhibitors: lovastatin (LOV), tyrphostin AG 1296 (6,7-dimethoxy-3-phenylquinoxaline) and SB203580 (4-(4-fluorophenyl)-2-(4-methylsulfinylphenyl)-5-(4-pyridyl)1H-imidazole), and four potential fibrosis inhibitors: 5-iodotubercidin (4-amino-5-iodo-7-(β-D-ribofuranosyl)-pyrrolo(2,3-d)pyrimidine), anisomycin, DRB (5,6-dichloro-1-β-D-ribofuranosyl-benzimidazole) and latrunculin B. Transfected cells were treated with TGFB2 in the presence or absence of one of the test compounds. To validate the assay, compounds were tested for their direct effects on gene expression in the immortalized cell lines and primary human corneal keratocytes using RT-PCR and immunohistochemistry. Three "hits" were validated LOV, SB203580 and anisomycin. This assay, which can be applied in a high throughput format to screen large libraries of uncharacterized compounds, or known compounds that might be repurposed, offers a valuable tool for identifying new treatments to address a major unmet medical need. Anisomycin has not previously been characterized as antifibrotic, thus, this is a novel finding of the study.

Halogen bonding enhances activity in a series of dual 5-HT6/D2 ligands designed in a hybrid bioisostere generation/virtual screening protocol

A hybrid bioisostere generation/virtual screening method combined with narrowing of chemical space through similarity to compounds that are active at the second target was successfully applied for the development of dual 5-HT₆/D₂ receptor ligands.

One size does not fit all: Challenging some dogmas and taboos in drug discovery

Advances in genetics, biology, chemical synthesis and computational methods have contributed to the understanding of diseases and drugs and with all of the above, there is an expectation that we are in a better position than ever before to create effective medicines for our needs. The reality is, however, disconnected from the expectation – US FDA drug approval statistics show that the number of approved drugs, especially the first-in-class drugs, is not commensurate with our improved knowledge. In this perspective, we offer our views on the possible reasons for this, focusing on small-molecule drugs and suggest some ideas for further considerations.

A chemical space odyssey of inhibitors of histone deacetylases and bromodomains

The interest in epigenetic drug and probe discovery is growing as reflected in the large amount of structure-epigenetic activity information available.

Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database

Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks.

Studies on [5,6]-Fused Bicyclic Scaffolds Derivatives as Potent Dual B-RafV600E/KDR Inhibitors Using Docking and 3D-QSAR Approaches

Research and development of multi-target inhibitors has attracted increasing attention as anticancer therapeutics. B-RafV600E synergistically works with vascular endothelial growth factor receptor 2 (KDR) to promote the occurrence and progression of cancers, and the development of dual-target drugs simultaneously against these two kinds of kinase may offer a better treatment advantage. In this paper, docking and three-dimensional quantitative structure activity relationship (3D-QSAR) studies were performed on a series of dual B-Raf/KDR inhibitors with a novel hinge-binding group, [5,6]-fused bicyclic scaffold. Docking studies revealed optimal binding conformations of these compounds interacting with both B-Raf and KDR. Based on these conformations, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were constructed, and the best CoMFA (q²=0.542, r²=0.989 for B-Raf; q²=0.768, r²=0.991 for KDR) and CoMSIA models (q²=0.519, r²=0.992 for B-Raf; q²=0.849, r²=0.993 for KDR) were generated. Further external validations confirmed their predictability, yielding satisfactory correlation coefficients (r²pred=0.764 (CoMFA), r²pred=0.841 (CoMSIA) for B-Raf, r²pred=0.912 (CoMFA), r²pred=0.846 (CoMSIA) for KDR, respectively). Through graphical analysis and comparison on docking results and 3D-QSAR contour maps, key amino acids that affect the ligand-receptor interactions were identified and structural features influencing the activities were discussed. New potent derivatives were designed, and subjected to preliminary pharmacological evaluation. The study may offer useful references for the modification and development of novel dual B-Raf/KDR inhibitors.

Multitargeting of selected prostanoid receptors provides agents with enhanced anti-inflammatory activity in macrophages

A polypharmacologic approach to prostanoid based anti-inflammatory therapeutics was undertaken in order to exploit both the anti- and proinflammatory properties attributed to the various prostanoid receptors. Multitargeting of selected prostanoid receptors yielded a prototype compound, compound 1 (AGN 211377), that antagonizes prostaglandin D2 receptors (DPs) DP1 (49) and DP2 (558), prostaglandin E2 receptors (EPs) EP1 (266) and EP4 (117), prostaglandin F2α receptor (FP) (61), and thromboxane A2 receptor (TP) (11) while sparing EP2, EP3, and prostaglandin I2 receptors (IPs); Kb values (in nanomoles) are given in parentheses. Compound 1 evoked a pronounced inhibition of cytokine/chemokine secretion from lipopolysaccharide or TNF-α stimulated primary human macrophages. These cytokine/chemokines included cluster of designation 40 receptor (CD40), epithelial-derived neutrophil-activating protein 78 (ENA-78), granulocyte colony stimulating factor (G-CSF), granulocyte macrophage colony stimulating factor (GM-CSF), IL-8, IL-18, monocyte chemotactic protein-1 (CCL2) (MCP-1), tissue plasminogen activator inhibitor (PAI-1), and regulated on activation, normal T cell expressed and secreted (RANTES). In contrast, the inhibitory effects of most antagonists selective for a single receptor were modest or absent, and selective EP2 receptor blockade increased cytokine release in some instances. Compound 1 also showed clear superiority to the cyclooxygenase inhibitors diclofenac and rofecoxib. These findings reveal that blockade of multiple prostanoid receptors, with absent antagonism of EP2 and IP, may provide more effective anti-inflammatory activity than global suppression of prostanoid synthesis or highly selective prostanoid receptor blockade. These investigations demonstrate the first working example of prostanoid receptor polypharmacology for potentially safer and more effective anti-inflammatory therapeutics by blocking multiple proinflammatory receptors while sparing those with anti-inflammatory activity.

2D and 3D similarity landscape analysis identifies PARP as a novel off-target for the drug Vatalanib

BACKGROUND

Searching for two-dimensional (2D) structural similarities is a useful tool to identify new active compounds in drug-discovery programs. However, as 2D similarity measures neglect important structural and functional features, similarity by 2D might be underestimated. In the present study, we used combined 2D and three-dimensional (3D) similarity comparisons to reveal possible new functions and/or side-effects of known bioactive compounds.

RESULTS

We utilised more than 10,000 compounds from the SuperTarget database with known inhibition values for twelve different anti-cancer targets. We performed all-against-all comparisons resulting in 2D similarity landscapes. Among the regions with low 2D similarity scores are inhibitors of vascular endothelial growth factor receptor (VEGFR) and inhibitors of poly ADP-ribose polymerase (PARP). To demonstrate that 3D landscape comparison can identify similarities, which are untraceable in 2D similarity comparisons, we analysed this region in more detail. This 3D analysis showed the unexpected structural similarity between inhibitors of VEGFR and inhibitors of PARP. Among the VEGFR inhibitors that show similarities to PARP inhibitors was Vatalanib, an oral "multi-targeted" small molecule protein kinase inhibitor being studied in phase-III clinical trials in cancer therapy. An in silico docking simulation and an in vitro HT universal colorimetric PARP assay confirmed that the VEGFR inhibitor Vatalanib exhibits off-target activity as a PARP inhibitor, broadening its mode of action.

CONCLUSION

In contrast to the 2D-similarity search, the 3D-similarity landscape comparison identifies new functions and side effects of the known VEGFR inhibitor Vatalanib.

Constellation pharmacology: a new paradigm for drug discovery

Constellation pharmacology is a cell-based high-content phenotypic-screening platform that utilizes subtype-selective pharmacological agents to elucidate the cell-specific combinations (constellations) of key signaling proteins that define specific cell types. Heterogeneous populations of native cells, in which the different individual cell types have been identified and characterized, are the foundation for this screening platform. Constellation pharmacology is useful for screening small molecules or for deconvoluting complex mixtures of biologically active natural products. This platform has been used to purify natural products and discover their molecular mechanisms. In the ongoing development of constellation pharmacology, there is a positive feedback loop between the pharmacological characterization of cell types and screening for new drug candidates. As constellation pharmacology is used to discover compounds with novel targeting-selectivity profiles, those new compounds then further help to elucidate the constellations of specific cell types, thereby increasing the content of this high-content platform.

Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity?

Bioregulatory systems medicine (BrSM) is a paradigm that aims to advance current medical practices. The basic scientific and clinical tenets of this approach embrace an interconnected picture of human health, supported largely by recent advances in systems biology and genomics, and focus on the implications of multi-scale interconnectivity for improving therapeutic approaches to disease. This article introduces the formal incorporation of these scientific and clinical elements into a cohesive theoretical model of the BrSM approach. The authors review this integrated body of knowledge and discuss how the emergent conceptual model offers the medical field a new avenue for extending the armamentarium of current treatment and healthcare, with the ultimate goal of improving population health.

Nicotinic ligands as multifunctional agents for the treatment of neuropsychiatric disorders

The challenges associated with developing more effective treatments for neurologic and psychiatric illness such as Alzheimer's disease and schizophrenia are considerable. Both the symptoms and the pathophysiology of these conditions are complex and poorly understood and the clinical presentations across different patients can be very heterogeneous. Moreover, it has become apparent that the reductionist approach to drug discovery for these illnesses that has dominated the field for decades (i.e., the development of highly selective compounds or other treatment modalities focused on a very specific pathophysiologic target) has not been widely successful. Accordingly, a variety of new strategies have emerged including the development of "multitarget-directed ligands" (MTDLs), the development and/or identification of compounds that exhibit "multifunctional" activity (e.g., pro-cognitive plus neuroprotective, pro-cognitive plus antipsychotic activity), "repurposing" strategies for existing compounds that have other clinical indications, and novel "adjunctive" treatment strategies that might enhance the efficacy of the currently available treatments. Interestingly, a variety of ligands at nicotinic acetylcholine receptors (nAChRs) appear to have the potential to fulfill one or more of these desirable properties (i.e., multifunctional, repurposing, or adjunctive treatment potential). The purpose of this review (while not all-inclusive) is to provide an overview of a variety of nAChR ligands that demonstrate potential in these categories, particularly, "multifunctional" properties. Due to their densities in the mammalian brain and the amount of literature available, the review will focus on ligands of the high affinity α4β2 nAChR and the low affinity α7 nAChR.

Synthesis and Antiproliferative Activity of Silybin Conjugates with Salinomycin and Monensin

Aiming at development of multitarget drugs for the anticancer treatment, new silybin (SIL) conjugates with salinomycin (SAL) and monensin (MON) were synthesized, in mild esterification conditions, and their antiproliferative activity was studied. The conjugates obtained exhibit anticancer activity against HepG2, LoVo and LoVo/DX cancer cell lines. Moreover, MON-SIL conjugate exhibits higher anticancer potential and better selectivity than the corresponding SAL-SIL conjugate.

Multi-target drugs to address multiple checkpoints in complex inflammatory pathologies: evolutionary cues for novel "first-in-class" anti-inflammatory drug candidates: a reviewer's perspective

Inflammation is a complex, metabolically expensive process involving multiple signaling pathways and regulatory mechanisms which have evolved over evolutionary timescale. Addressing multiple targets of inflammation holistically, in moderation, is probably a more evolutionarily viable strategy, as compared to current therapy which addresses drug targets in isolation. Polypharmacology, addressing multiple targets, is commonly used in complex ailments, suggesting the superior safety and efficacy profile of multi-target (MT) drugs. Phenotypic drug discovery, which generated successful MT and first-in-class drugs in the past, is now re-emerging. A multi-pronged approach, which modulates the evolutionarily conserved, robust and pervasive cellular mechanisms of tissue repair, with AMPK at the helm, regulating the complex metabolic/immune/redox pathways underlying inflammation, is perhaps a more viable strategy than addressing single targets in isolation. Molecules that modulate multiple molecular mechanisms of inflammation in moderation (modulating TH cells toward the anti-inflammatory phenotype, activating AMPK, stimulating Nrf2 and inhibiting NFκB) might serve as a model for a novel Darwinian "first-in-class" therapeutic category that holistically addresses immune, redox and metabolic processes associated with inflammatory repair. Such a multimodal biological activity is supported by the fact that several non-calorific pleiotropic natural products with anti-inflammatory action have been incorporated into diet (chiefly guided by the adaptive development of olfacto-gustatory preferences over evolutionary timescales) rendering such molecules, endowed with evolutionarily privileged molecular scaffolds, naturally oriented toward multiple targets.

Novel insight into drug repositioning: Methylthiouracil as a case in point

Drug repositioning refers to the development of existing drugs for new indications. These drugs may have (I) failed to show efficacy in late stage clinical trials without safety issues; (II) stalled in the development for commercial reasons; (III) passed the point of patent expiry; or (IV) are being explored in new geographic markets. Over the past decade, pressure on the pharmaceutical industry caused by the 'innovation gap' owing to rising development costs and stagnant product output have become major reasons for the growing interest in drug repositioning. Companies that offer a variety of broad platforms for identifying new indications have emerged; some have been successful in building their own pipelines of candidates with reduced risks and timelines associated with further clinical development. The business models and platforms offered by these companies will be validated if they are able to generate positive proof-of-concept clinical data for their repositioned compounds. This review describes the strategy of biomarker-guided repositioning of chemotherapeutic drugs for inflammation therapy, considering the repositioning of methylthiouracil (MTU), an antithyroid drug, as a potential anti-inflammatory reagent.

Efficacy-oriented compatibility for component-based Chinese medicine

Single-target drugs have not achieved satisfactory therapeutic effects for complex diseases involving multiple factors. Instead, innovations in recent drug research and development have revealed the emergence of compound drugs, such as cocktail therapies and "polypills", as the frontier in new drug development. A traditional Chinese medicine (TCM) prescription that is usually composed of several medicinal herbs can serve a typical representative of compound medicines. Although the traditional compatibility theory of TCM cannot be well expressed using modern scientific language nowadays, the fundamental purpose of TCM compatibility can be understood as promoting efficacy and reducing toxicity. This paper introduces the theory and methods of efficacy-oriented compatibility for developing component-based Chinese medicines.

Quantitative modeling of dose-response and drug combination based on pathway network

Background

Quantitative description of dose–response of a drug for complex systems is essential for treatment of diseases and drug discovery. Given the growth of large-scale biological data obtained by multi-level assays, computational modeling has become an important approach to understand the mechanism of drug action. However, due to complicated interactions between drugs and cellular targets, the prediction of drug efficacy is a challenge, especially for complex systems. And the biological systems can be regarded as networks, where nodes represent molecular entities (DNA, RNA, protein and small compound) and processes, edges represent the relationships between nodes. Thus we combine biological pathway-based network modeling and molecular docking to evaluate drug efficacy.

Results

Network efficiency (NE) and network flux (NF) are both global measures of the network connectivity. In this work, we used NE and NF to quantitatively evaluate the inhibitory effects of compounds against the lipopolysaccharide-induced production of prostaglandin E2. The edge values of the pathway network of this biological process were reset according to the Michaelis-Menten equation, which used the binding constant and drug concentration to determine the degree of inhibition of the target protein in the pathway. The combination of NE and NF was adopted to evaluate the inhibitory effects. The dose–response curve was sigmoid and the EC50 values of 5 compounds were in good agreement with experimental results (R² = 0.93). Moreover, we found that 2 drugs produced maximal synergism when they were combined according to the ratio between each EC50.

Conclusions

This quantitative model has the ability to predict the dose–response relationships of single drug and drug combination in the context of the pathway network of biological process. These findings are valuable for the evaluation of drug efficacy and thus provide an effective approach for pathway network-based drug discovery.

Drug repositioning and repurposing: terminology and definitions in literature

Drug repositioning and similar terms have been a trending topic in literature and represent novel drug development strategies. We analysed in a quantitative and qualitative manner how these terms were used and defined in the literature. In total, 217 articles referred to 'drug repositioning', 'drug repurposing', 'drug reprofiling', 'drug redirecting' and/or 'drug rediscovery'. Only 67 included a definition ranging from brief and general to extensive and specific. No common definition was identified. Nevertheless, four common features were found: concept, action, use and product. The different wording used for these features often leads to essential differences in meaning between definitions. In case a clear definition is needed, for example from a legal or regulatory perspective, the features can provide further guidance.