Discovery of drug mode of action and drug repositioning from transcriptional responses

A genomic approach to predict synergistic combinations for breast cancer treatment

We leverage genomic and biochemical data to identify synergistic drug regimens for breast cancer. In order to study the mechanism of the histone deacetylase (HDAC) inhibitors valproic acid (VPA) and suberoylanilide hydroxamic acid (SAHA) in breast cancer, we generated and validated genomic profiles of drug response using a series of breast cancer cell lines sensitive to each drug. These genomic profiles were then used to model drug response in human breast tumors and show significant correlation between VPA and SAHA response profiles in multiple breast tumor data sets, highlighting their similar mechanism of action. The genes deregulated by VPA and SAHA converge on the cell cycle pathway (Bayes factor 5.21 and 5.94, respectively; P-value 10(-8.6) and 10(-9), respectively). In particular, VPA and SAHA upregulate key cyclin-dependent kinase (CDK) inhibitors. In two independent datasets, cancer cells treated with CDK inhibitors have similar gene expression profile changes to the cellular response to HDAC inhibitors. Together, these results led us to hypothesize that VPA and SAHA may interact synergistically with CDK inhibitors such as PD-033299. Experiments show that HDAC and CDK inhibitors have statistically significant synergy in both breast cancer cell lines and primary 3-dimensional cultures of cells from pleural effusions of patients. Therefore, synergistic relationships between HDAC and CDK inhibitors may provide an effective combinatorial regimen for breast cancer. Importantly, these studies provide an example of how genomic analysis of drug-response profiles can be used to design rational drug combinations for cancer treatment.

A comprehensive glossary of autophagy-related molecules and processes (2nd edition)

The study of autophagy is rapidly expanding, and our knowledge of the molecular mechanism and its connections to a wide range of physiological processes has increased substantially in the past decade. The vocabulary associated with autophagy has grown concomitantly. In fact, it is difficult for readers--even those who work in the field--to keep up with the ever-expanding terminology associated with the various autophagy-related processes. Accordingly, we have developed a comprehensive glossary of autophagy-related terms that is meant to provide a quick reference for researchers who need a brief reminder of the regulatory effects of transcription factors and chemical agents that induce or inhibit autophagy, the function of the autophagy-related proteins, and the roles of accessory components and structures that are associated with autophagy.

p73 as a pharmaceutical target for cancer therapy

About half of all human tumors contain an inactivating mutation of p53, while in the remaining tumors, the p53 pathway is frequently abrogated by alterations of other components of its signaling pathway. In humans, the p53 tumor suppressor is part of a small gene family that includes two other members, p73 and p63, structurally and functionally related to p53. Accumulating evidences indicate that all p53-family proteins function as molecular hubs of a highly interconnected signaling network that coordinates cell proliferation, differentiation and death in response to physiological inputs and oncogenic stress. Therefore, not only the p53-pathway but the entire “p53-family pathway” is a primary target for cancer drug development. In particular, the p53-related protein p73 has a crucial role in determining cellular responses to chemotherapy, and can vicariate p53 functions in triggering cell death after DNA damage in multiple experimental models. The biology and regulation of p73 is complex, since the TP73 gene incorporates both tumor-suppressive and proto-oncogenic functions. However, the p73 gene is rarely mutated in tumors, so appropriate pharmacological manipulation of the p73 pathway is a very promising approach for cancer therapy. Here we provide an overview of the principal mechanism of p73 regulation, and describe several examples of pharmacological tools that can induce p73 accumulation and function by acting on upstream p73 modulators or displacing inhibitory p73 interactors. A better understanding of how the p73 pathway works is mandatory to discover additional players intervening in this pathway and has important implications for the improvement of cancer treatment with the development of new molecules or with the reposition of currently available drugs.

Novel computational approaches to polypharmacology as a means to define responses to individual drugs

Polypharmacology, which focuses on designing therapeutics to target multiple receptors, has emerged as a new paradigm in drug discovery. Polypharmacological effects are an attribute of most, if not all, drug molecules. The efficacy and toxicity of drugs, whether designed as single- or multitarget therapeutics, result from complex interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, and environmental factors. Ultimately, to predict a drug response phenotype, it is necessary to understand the change in information flow through cellular networks resulting from dynamic drug-target interactions and the impact that this has on the complete biological system. Although such is a future objective, we review recent progress and challenges in computational techniques that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes.

GSGS: a computational approach to reconstruct signaling pathway structures from gene sets

Reconstruction of signaling pathway structures is essential to decipher complex regulatory relationships in living cells. Existing approaches often rely on unrealistic biological assumptions and do not explicitly consider signal transduction mechanisms. Signal transduction events refer to linear cascades of reactions from cell surface to nucleus and characterize a signaling pathway. We propose a novel approach, Gene Set Gibbs Sampling, to reverse engineer signaling pathway structures from gene sets related to pathways. We hypothesize that signaling pathways are structurally an ensemble of overlapping linear signal transduction events which we encode as Information Flows (IFs). We infer signaling pathway structures from gene sets, referred to as Information Flow Gene Sets (IFGSs), corresponding to these events. Thus, an IFGS only reflects which genes appear in the underlying IF but not their ordering. GSGS offers a Gibbs sampling procedure to reconstruct the underlying signaling pathway structure by sequentially inferring IFs from the overlapping IFGSs related to the pathway. In the proof-of-concept studies, our approach is shown to outperform existing network inference approaches using data generated from benchmark networks in DREAM. We perform a sensitivity analysis to assess the robustness of our approach. Finally, we implement GSGS to reconstruct signaling mechanisms in breast cancer cells.

Computational tools for polypharmacology and repurposing

Most drugs act on a multitude of targets rather than on one single target. Polypharmacology, an upcoming branch of pharmaceutical science, deals with the recognition of these off-target activities of small chemical compounds. Due to the high amount of data to be processed, application of computational methods is indispensable in this area. This review summarizes the most important in silico approaches for polypharmacology. The described methods comprise network pharmacology, machine learning techniques and chemogenomic approaches. The use of these methods for drug repurposing as a branch of drug discovery and development is discussed. Furthermore, a broad range of prospective applications is summarized to give the reader an overview of possibilities and limitations of the described techniques.

Modulatory profiling identifies mechanisms of small molecule-induced cell death

Cell death is a complex process that plays a vital role in development, homeostasis, and disease. Our understanding of and ability to control cell death is impeded by an incomplete characterization of the full range of cell death processes that occur in mammalian systems, especially in response to exogenous perturbations. We present here a general approach to address this problem, which we call modulatory profiling. Modulatory profiles are composed of the changes in potency and efficacy of lethal compounds produced by a second cell death-modulating agent in human cell lines. We show that compounds with the same characterized mechanism of action have similar modulatory profiles. Furthermore, clustering of modulatory profiles revealed relationships not evident when clustering lethal compounds based on gene expression profiles alone. Finally, modulatory profiling of compounds correctly predicted three previously uncharacterized compounds to be microtubule-destabilizing agents, classified numerous compounds that act nonspecifically, and identified compounds that cause cell death through a mechanism that is morphologically and biochemically distinct from previously established ones.

Literature mining, ontologies and information visualization for drug repurposing

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.

The role of bioinformatics in studying rheumatic and autoimmune disorders

In the past decade, the availability and abundance of individual-level molecular data, such as gene expression, proteomics and sequence data, has enabled the use of integrative computational approaches to pose and answer novel questions about disease. In this article, we discuss several examples of applications of bioinformatics techniques to study autoimmune and rheumatic disorders. We focus our discussion on how integrative techniques can be applied to analyze gene expression and genetic variation data across different diseases, and discuss the implications of such analyses. We also outline current challenges and future directions of these approaches. We show that integrative computational methods are essential for translational research and provide a powerful opportunity to improve human health by refining the current knowledge about diagnostics, therapeutics and mechanisms of disease pathogenesis.

Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.

Global network analysis of drug tolerance, mode of action and virulence in methicillin-resistant S. aureus

BACKGROUND

Staphylococcus aureus is a major human pathogen and strains resistant to existing treatments continue to emerge. Development of novel treatments is therefore important. Antimicrobial peptides represent a source of potential novel antibiotics to combat resistant bacteria such as Methicillin-Resistant Staphylococcus aureus (MRSA). A promising antimicrobial peptide is ranalexin, which has potent activity against Gram-positive bacteria, and particularly S. aureus. Understanding mode of action is a key component of drug discovery and network biology approaches enable a global, integrated view of microbial physiology, including mechanisms of antibiotic killing. We developed a systems-wide functional association network approach to integrate proteome and transcriptome profiles, enabling study of drug resistance and mode of action.

RESULTS

The functional association network was constructed by Bayesian logistic regression, providing a framework for identification of antimicrobial peptide (ranalexin) response modules from S. aureus MRSA-252 transcriptome and proteome profiling. These signatures of ranalexin treatment revealed multiple killing mechanisms, including cell wall activity. Cell wall effects were supported by gene disruption and osmotic fragility experiments. Furthermore, twenty-two novel virulence factors were inferred, while the VraRS two-component system and PhoU-mediated persister formation were implicated in MRSA tolerance to cationic antimicrobial peptides.

CONCLUSIONS

This work demonstrates a powerful integrative approach to study drug resistance and mode of action. Our findings are informative to the development of novel therapeutic strategies against Staphylococcus aureus and particularly MRSA.

DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome

Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical-protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical-protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR. When a user submits a molecule, the server will give the positive or negative association scores between the user's molecule and our library drugs based on their interaction profiles towards the targets. Users can thus predict the indications or ADRs of their molecule based on the association scores towards our library drugs. We have matched our predictions of drug-drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74%. We have also successfully predicted the connections between anti-psychotics and anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/.

Suberoylanilide hydroxamic acid (vorinostat) up-regulates progranulin transcription: rational therapeutic approach to frontotemporal dementia

Progranulin (GRN) haploinsufficiency is a frequent cause of familial frontotemporal dementia, a currently untreatable progressive neurodegenerative disease. By chemical library screening, we identified suberoylanilide hydroxamic acid (SAHA), a Food and Drug Administration-approved histone deacetylase inhibitor, as an enhancer of GRN expression. SAHA dose-dependently increased GRN mRNA and protein levels in cultured cells and restored near-normal GRN expression in haploinsufficient cells from human subjects. Although elevation of secreted progranulin levels through a post-transcriptional mechanism has recently been reported, this is, to the best of our knowledge, the first report of a small molecule enhancer of progranulin transcription. SAHA has demonstrated therapeutic potential in other neurodegenerative diseases and thus holds promise as a first generation drug for the prevention and treatment of frontotemporal dementia.

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.

Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome--clozapine-induced agranulocytosis as a case study

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs.

Idiosyncratic drug reactions (IDR) generally cannot be identified until after a drug is taken by a large population, but usually result in restricted use or withdrawal. Clozapine provides the most effective treatment for schizophrenia but its use is limited because of a life-threatening IDR, i.e., the agranulocytosis. A high impact clinical study demonstrated the necessity of moving clozapine from 3^rd line to 1^st line drug; therefore, intensive research has aimed at identifying genes responsible for clozapine-induced agranulocytosis (CIA). Olanzapine, an analog of clozapine, has much lower incidence of agranulocytosis. Based on this phenomenon, we proposed an in silico methodology termed as antithesis chemical-protein interactome (CPI), which mimics the differences in the drug-protein interactions of the two drugs across a panel of human proteins. e.g., HSPA1A was identified to be targeted by clozapine not olanzapine. Furthermore, the gene expression of the HSPA1A-related gene system was also found up-regulated after clozapine treatment. This approach can examine the system's perturbation in terms of both the off-target and the off-system's interaction with the drug, providing theoretical basis for decoding the adverse drug reactions or the new uses for old drugs.

Chemical-protein interactome and its application in off-target identification

Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.

Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping

Connectivity mapping is a recently developed technique for discovering the underlying connections between different biological states based on gene-expression similarities. The sscMap method has been shown to provide enhanced sensitivity in mapping meaningful connections leading to testable biological hypotheses and in identifying drug candidates with particular pharmacological and/or toxicological properties. Challenges remain, however, as to how to prioritise the large number of discovered connections in an unbiased manner such that the success rate of any following-up investigation can be maximised. We introduce a new concept, gene-signature perturbation, which aims to test whether an identified connection is stable enough against systematic minor changes (perturbation) to the gene-signature. We applied the perturbation method to three independent datasets obtained from the GEO database: acute myeloid leukemia (AML), cervical cancer, and breast cancer treated with letrozole. We demonstrate that the perturbation approach helps to identify meaningful biological connections which suggest the most relevant candidate drugs. In the case of AML, we found that the prevalent compounds were retinoic acids and PPAR activators. For cervical cancer, our results suggested that potential drugs are likely to involve the EGFR pathway; and with the breast cancer dataset, we identified candidates that are involved in prostaglandin inhibition. Thus the gene-signature perturbation approach added real values to the whole connectivity mapping process, allowing for increased specificity in the identification of possible therapeutic candidates.

The chemical basis of pharmacology

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