Biased agonism at G protein-coupled receptors

G protein-coupled receptors (GPCRs) represent the largest family of approved therapeutic targets. Ligands stimulating these receptors specifically activate multiple signalling pathways that induce not only the desired therapeutic response, but sometimes untolerated side effects that limit their clinical use. The diversity in signalling induced by each ligand could be considered a viable path for improving this situation. Biased agonism, which offers the promise of identifying pathway-selective drugs has been proposed as a means to exploit this opportunity. However, identifying biased agonists is not an easy process and quantifying ligand bias for a given signalling pathway requires careful consideration and control of several confounding factors. To date, the molecular mechanisms of biased signalling remain unclear and known theories that constitute our understanding of the mechanisms underlying therapeutic and side effects are still being challenged, making the strategy of selecting promising potential drugs more difficult. This special issue summarizes the latest advances in the discovery and optimization of biased ligands for different GPCRs. It also focuses on identifying novel insights into the field of biased agonism, while at the same time, highlighting the conceptual and experimental limitations of that concept for drug discovery. This aims to broaden our understanding of the signalling induced by the various identified biased agonists and provide perspectives that could straighten our path towards the development of more effective and tolerable therapeutics.     

Prediction of drug combinations by integrating molecular and pharmacological data

Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.     

Assessing drug target association using semantic linked data

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.     

Synthesis and evaluation of naphthyridine compounds as antimalarial agents

Primaquine is the drug of choice for the radical cure of Plasmodium vivax malaria, but possesses serious side effects. In this study novel primaquine analogues were designed and synthesized. Lower toxicity was achieved by reducing or eliminating the tendency of forming chemically reactive and toxic intermediates and metabolites. In vitro and in vivo studies found that synthesized compounds were less toxic than the parent compound primaquine, while preserving the desired antimalarial activity. Some of these compounds possess a therapeutic index over 10 times superior to that of the commonly used antimalarial drug chloroquine. These compounds, as well as the underlying design rationale, may find usefulness in the discovery and development of new antimalarial drugs.     

Targeting the human genome-microbiome axis for drug discovery: inspirations from global systems biology and traditional Chinese medicine

Most chronic diseases impairing current human public health involve not only the human genome but also gene-environment interactions, and in the latter case the gut microbiome is an important factor. This makes the classical single drug-receptor target drug discovery paradigm much less applicable. There is widespread and increasing international interest in understanding the properties of traditional Chinese medicines (TCMs) for their potential utilization as a source of new drugs for Western markets as emerging evidence indicates that most TCM drugs are actually targeting both the host and its symbiotic microbes. In this review, we explore the challenges of and opportunities for harmonizing Eastern-Western drug discovery paradigms by focusing on emergent functions at the whole body level of humans as superorganisms. This could lead to new drug candidate compounds for chronic diseases targeting receptors outside the currently accepted "druggable genome" and shed light on current high interest issues in Western medicine such as drug-drug and drug-diet-gut microbial interactions that will be crucial in the development and delivery of future therapeutic regimes optimized for the individual patient.     

Mining small-molecule screens to repurpose drugs

Repurposing and repositioning drugs--discovering new uses for existing and experimental medicines-is an attractive strategy for rescuing stalled pharmaceutical projects, finding treatments for neglected diseases, and reducing the time, cost and risk of drug development. As this strategy emerged, academic researchers began performing high-throughput screens (HTS) of small molecules--the type of experiments once exclusively conducted in industry--and making the data from these screens available to all. Several methods can mine this data to inform repurposing and repositioning efforts. Despite these methods' limitations, it is hopeful that they will accelerate the discovery of new uses for known drugs, but this hope has not yet been realized.     

The Ubiquitination-Dependent and -Independent Functions of Cereblon in Cancer and Neurological Diseases

Cereblon (CRBN) mediates the teratogenic effect of thalidomide in zebrafish, chickens, and humans. It additionally modulates the anti-myeloma effect of the immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide. IMiDs bind to CRBN and recruit neo-substrates for their ubiquitination and proteasome-mediated degradation, which significantly expands the application of proteolysis-targeting chimeras (PROTACs) for targeted drug discovery. However, the underlying molecular mechanisms by which CRBN mediates the teratogenicity and anti-myeloma effect of IMiDs have not been fully elucidated. Furthermore, the normal physiological functions of endogenous CRBN have not been extensively studied, which prevents the thorough assessment of side effects of the CRBN ligand-based PROTACs in the treatment of cancer and neurological diseases. To advance our understanding of the diverse functions of CRBN, in this review, we will survey the ubiquitination-dependent and -independent functions of CRBN, summarize recent advances in the discovery of constitutive substrates and neo-substrates of CRBN, and explore the molecular functions of CRBN in cancer treatment and in the development of neurological diseases. We will also discuss the potential future directions toward the identification of CRBN substrates/interacting proteins and CRBN ligand-based drug discovery in the treatment of cancer and neurological diseases.     

Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps)

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60-70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the "Rule of Three" was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity.     

Risk assessment and mitigation strategies for reactive metabolites in drug discovery and development

Drug toxicity is a leading cause of attrition of candidate drugs during drug development as well as of withdrawal of drugs post-licensing due to adverse drug reactions in man. These adverse drug reactions cause a broad range of clinically severe conditions including both highly reproducible and dose dependent toxicities as well as relatively infrequent and idiosyncratic adverse events. The underlying risk factors can be split into two groups: (1) drug-related and (2) patient-related. The drug-related risk factors include metabolic factors that determine the propensity of a molecule to form toxic reactive metabolites (RMs), and the RM and non-RM mediated mechanisms which cause cell and tissue injury. Patient related risk factors may vary markedly between individuals, and encompass genetic and non-genetic processes, e.g. environmental, that influence the disposition of drugs and their metabolites, the nature of the adverse responses elicited and the resulting biological consequences. We describe a new strategy, which builds upon the strategies used currently within numerous pharmaceutical companies to avoid and minimize RM formation during drug discovery, and that is intended to reduce the likelihood that candidate drugs will cause toxicity in the human population. The new strategy addresses drug-related safety hazards, but not patient-related risk factors. A common target organ of toxicity is the liver and to decrease the likelihood that candidate drugs will cause liver toxicity (both non-idiosyncratic and idiosyncratic), we propose use of an in vitro Hepatic Liability Panel alongside in vitro methods for the detection of RMs. This will enable design and selection of compounds in discovery that have reduced propensity to cause liver toxicity. In vitro Hepatic Liability is assessed using toxicity assays that quantify: CYP 450 dependent and CYP 450 independent cell toxicity; mitochondrial impairment; and inhibition of the Bile Salt Export Pump. Prior to progression into development, a Hepatotoxicity Hazard Matrix combines data from the Hepatic Liability Panel with the Estimated RM Body Burden. The latter is defined as the level of covalent binding of radiolabelled drug to human hepatocyte proteins in vitro adjusted for the predicted human dose. We exemplify the potential value of this approach by consideration of the thiazolidinedione class of drugs.     

中药网络药理学研究中的生物信息学方法

为了更有效地治疗癌症、心血管疾病、免疫系统疾病等复杂疾病,基于分子网络的多靶点药物发现理念逐渐成为一种新的趋势,而中药整体、辨证、协同的用药观再一次引起了药物发现领域的极大兴趣。中药在治疗复杂慢性疾病方面有确切的疗效和较小的毒副作用。中药网络药理学从分子网络调控的水平上阐明中药的作用机制,为多靶点药物发现提供有益的启示和借鉴,并有可能从临床有效的中药反向开发现代多组分、多靶点新药。针对基于生物分子网络的中药药理学研究路线中的4 个步骤,介绍近年来中药网络药理学研究中相关的生物信息学方法。     

The relationship between rational drug design and drug side effects

Previous analysis of systems pharmacology has revealed a tendency of rational drug design in the pharmaceutical industry. The targets of new drugs tend to be close with the corresponding disease genes in the biological networks. However, it remains unclear whether the rational drug design introduces disadvantages, i.e. side effects. Therefore, it is important to dissect the relationship between rational drug design and drug side effects. Based on a recently released drug side effect database, SIDER, here we analyzed the relationship between drug side effects and the rational drug design. We revealed that the incidence drug side effect is significantly associated with the network distance of drug targets and diseases genes. Drugs with the distances of three or four have the smallest incidence of side effects, whereas drugs with the distances of more than four or smaller than three show significantly greater incidence of side effects. Furthermore, protein drugs and small molecule drugs show significant differences. Drugs hitting membrane targets and drugs hitting cytoplasm targets also show differences. Failure drugs because of severe side effects show smaller network distances than approved drugs. These results suggest that researchers should be prudent on rationalizing the drug design. Too small distances between drug targets and diseases genes may not always be advantageous for rational design for drug discovery.     

Functional Module Connectivity Map (FMCM): A Framework for Searching Repurposed Drug Compounds for Systems Treatment of Cancer and an Application to Colorectal Adenocarcinoma

Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.     

Drug repositioning for orphan diseases

The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism 'connecting' them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.     

Ecopharmacognosy and the responsibilities of natural product research to sustainability

The recently developed term “ecopharmacognosy” is defined as the study of sustainable, biologically active, natural resources. As a philosophical approach, it provides a conceptual framework for developing new strategies and new scientific perspectives which may improve future global food and health care product accessibility and assure beneficial outcomes. In this brief article some facets of how the precepts of ecopharmacognosy may apply in developing new medicinal products may be developed, based on sustainability and the use of integrated technologies.Although from a medicinal agent perspective, plants remain a primary source of global health care, these resources are not being pursued by major pharmaceutical companies as sources of new agents, and essentially all tropical diseases, as well as most microbially based diseases, remain outside the scope of their drug discovery programs. Countries and regions therefore must address their own drug discovery needs for “local” and some global diseases. In addition, the cost of drug importation is so high that development of local resources, i.e. traditional medicines, may be the only rational alternative approach. At the same time, network pharmacology is exploring the many diverse effects of both individual and complex natural products at the gene level, and this is offering new opportunities to rethink and restructure the core, long-standing, Western, magic bullet philosophy to drug discovery. Other ecopharmacognosy changes underway include the computer-aided design of natural product derivatives, based on molecular docking, which is providing targetable enzyme substrates, and remote sensing technologies which can assess natural materials non-invasively for critical constituents as a part of rethinking quality control strategies in the field. Furthermore, there are the hyphenated chromatographic and spectroscopic procedures to quantitatively analyze single and multicomponent plant mixtures for bioactive markers to enhance quality control and, thereby, patient care. The relationship of these evolving approaches will serve as practical examples to the philosophies of ecopharmacognosy. In summary, with respect to health care, ecopharmacognosy poses the long-term practical question for drugs, “How Green is Your Medicine?”     

Recent Advances in Drug Repositioning for the Discovery of New Anticancer Drugs

Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.     

Ginkgo biloba extract: from molecular mechanisms to the treatment of Alzhelmer's disease.

Ginkgo biloba is registered for the treatment of several diseases and disorders in Europe. In the United States, it is marketed as a dietary supplement; the French and the German agencies consider it to be effective for the treatment of several diseases, and the immense amount of clinical studies concerning Ginkgo biloba makes it worth revising the existing literature about this notable plant. A brief history of the common use of this drug will be followed by a short botanic characterization. The biochemical composition of the original drug, the leaf itself, will be described in detail together with a brief discussion of commercial extracts and the problem of studying Ginkgo biloba clinically to verify the safety and efficacy of its extracts in the treatment of disorders like Alzheimer's diseases. Aspects of molecular mechanisms modifying the efficacy of this drug will be outlined. Several agents like antioxidants, anti-inflammatory drugs, cholinergic agents, estrogens, or neurotrophic factors are in use for the treatment of this neurodegenerative disease, but none can prove fully convincing benefit. In this field, Ginkgo biloba appears as a useful and sensible supplementary medication to treat Alzheimer's disease, as it seems to be a synthesis of all the different profiles of action of the various, commonly used drugs but with less side effects.     

Genomics, systems biology and drug development for infectious diseases

Although a variety of drugs are available for many infectious diseases that predominantly affect the developing world reasons remain for continuing to search for new chemotherapeutics. First, the development of microbial resistance has made some of the most effective and inexpensive drug regimes unreliable and dangerous to use on severely ill patients. Second, many existing antimicrobial drugs show toxicity or are too expensive for countries where the per capita income is in the order of hundreds of dollars per year. In recognition of this, new publicly and privately financed drug discovery efforts have been established to identify and develop new therapies for diseases such as tuberculosis, malaria and AIDS. This in turn, has intensified the need for tools to facilitate drug identification for those microbes whose molecular biology is poorly understood, or which are difficult to grow in the laboratory. While much has been written about how functional genomics can be used to find novel protein targets for chemotherapeutics this review will concentrate on how genome-wide, systems biology approaches may be used following whole organism, cell-based screening to understand the mechanism of drug action or to identify biological targets of small molecules. Here we focus on protozoan parasites, however, many of the approaches can be applied to pathogenic bacteria or parasitic helminths, insects or disease-causing fungi.     

Polypharmacology and supercomputer-based docking: opportunities and challenges

Polypharmacology, the ability of drugs to interact with multiple targets, is a fundamental concept of interest to the pharmaceutical industry in its efforts to solve the current issues of the rise in the cost of drug development and decline in productivity. Polypharmacology has the potential to greatly benefit drug repurposing, bringing existing pharmaceuticals on the market to treat different ailments quicker and more affordably than developing new drugs, and may also facilitate the development of new, potent pharmaceuticals with reduced negative off-target effects and adverse side effects. Present day computational power, when combined with applications such as supercomputer-based virtual high-throughput screening (docking) will enable these advances on a massive chemogenomic level, potentially transforming the pharmaceutical industry. However, while the potential of supercomputing-based drug discovery is unequivocal, the technical and fundamental challenges are considerable.     

Discovery and development of SQ109: a new antitubercular drug with a novel mechanism of action

Existing drugs have limited efficacy against the rising threat of drug-resistant TB, have significant side effects, and must be given in combinations of four to six drugs for at least 6 months for drug-sensitive TB and up to 24 months for drug-resistant TB. The long treatment duration has led to increased patient noncompliance with therapy. This, in turn, drives the development of additional drug resistance in a spiral that has resulted in some forms of TB being currently untreatable by existing drugs. New antitubercular drugs in development, particularly those with mechanisms of action that are different from existing first- and second-line TB drugs, are anticipated to be effective against both drug-sensitive and drug-resistant TB. SQ109 is a new TB drug candidate with a novel mechanism of action that was safe and well tolerated in Phase I and early Phase II clinical trials. We describe herein the identification, development and characterization of SQ109 as a promising new antitubercular drug.