Large-scale prediction of drug-target relationships

Molecular networks in drug discovery

Despite the dramatic increase of global spending on drug discovery and development, the approval rate for new drugs is declining, due chiefly to toxicity and undesirable side effects. Simultaneously, the growth of available biomedical data in the postgenomic era has provided fresh insight into the nature of redundant and compensatory drug-target pathways. This stagnation in drug approval can be overcome by the novel concept of polypharmacology, which is built on the fundamental concept that drugs modulate multiple targets. Polypharmacology can be studied with molecular networks that integrate multidisciplinary concepts including cheminformatics, bioinformatics, and systems biology. In silico techniques such as structure- and ligand-based approaches can be employed to study molecular networks and reduce costs by predicting adverse drug reactions and toxicity in the early stage of drug development. By amalgamating strides in this informatics-driven era, designing polypharmacological drugs with molecular network technology exemplifies the next generation of therapeutics with less of-target properties and toxicity. In this review, we will first describe the challenges in drug discovery, and showcase successes using multitarget drugs toward diseases such as cancer and mood disorders. We will then focus on recent development of in silico polypharmacology predictions. Finally, our technologies in molecular network analysis will be presented.

Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank

One approach to estimating the "chemical tractability" of a candidate protein target where we know the atomic resolution structure is to examine the physical properties of potential binding sites. A number of other workers have addressed this issue. We characterize ~290,000 "pockets" from ~42,000 protein crystal structures in terms of a three parameter "pocket space": volume, buriedness, and hydrophobicity. A metric DLID (drug-like density) measures how likely a pocket is to bind a drug-like molecule. This is calculated from the count of other pockets in its local neighborhood in pocket space that contain drug-like cocrystallized ligands and the count of total pockets in the neighborhood. Surprisingly, despite being defined locally, a global trend in DLID can be predicted by a simple linear regression on log(volume), buriedness, and hydrophobicity. Two levels of simplification are necessary to relate the DLID of individual pockets to "targets": taking the best DLID per Protein Data Bank (PDB) entry (because any given crystal structure can have many pockets), and taking the median DLID over all PDB entries for the same target (because different crystal structures of the same protein can vary because of artifacts and real conformational changes). We can show that median DLIDs for targets that are detectably homologous in sequence are reasonably similar and that median DLIDs correlate with the "druggability" estimate of Cheng et al. (Nature Biotechnology 2007, 25, 71-75).

FragmentStore--a comprehensive database of fragments linking metabolites, toxic molecules and drugs

Consideration of biomolecules in terms of their molecular building blocks provides valuable new information regarding their synthesis, degradation and similarity. Here, we present the FragmentStore, a resource for the comparison of fragments found in metabolites, drugs or toxic compounds. Starting from 13,000 metabolites, 16,000 drugs and 2200 toxic compounds we generated 35,000 different building blocks (fragments), which are not only relevant to their biosynthesis and degradation but also provide important information regarding side-effects and toxicity. The FragmentStore provides a variety of search options such as 2D structure, molecular weight, rotatable bonds, etc. Various analysis tools have been implemented including the calculation of amino acid preferences of fragments' binding sites, classification of fragments based on the enzyme classification class of the enzyme(s) they bind to and small molecule library generation via a fragment-assembler tool. Using the FragmentStore, it is now possible to identify the common fragments of different classes of molecules and generate hypotheses about the effects of such intersections. For instance, the co-occurrence of fragments in different drugs may indicate similar targets and possible off-target interactions whereas the co-occurrence of fragments in a drug and a toxic compound/metabolite could be indicative of side-effects. The database is publicly available at: http://bioinformatics.charite.de/fragment_store.

Drug-induced regulation of target expression

Drug perturbations of human cells lead to complex responses upon target binding. One of the known mechanisms is a (positive or negative) feedback loop that adjusts the expression level of the respective target protein. To quantify this mechanism systems-wide in an unbiased way, drug-induced differential expression of drug target mRNA was examined in three cell lines using the Connectivity Map. To overcome various biases in this valuable resource, we have developed a computational normalization and scoring procedure that is applicable to gene expression recording upon heterogeneous drug treatments. In 1290 drug-target relations, corresponding to 466 drugs acting on 167 drug targets studied, 8% of the targets are subject to regulation at the mRNA level. We confirmed systematically that in particular G-protein coupled receptors, when serving as known targets, are regulated upon drug treatment. We further newly identified drug-induced differential regulation of Lanosterol 14-alpha demethylase, Endoplasmin, DNA topoisomerase 2-alpha and Calmodulin 1. The feedback regulation in these and other targets is likely to be relevant for the success or failure of the molecular intervention.

Role of systems pharmacology in understanding drug adverse events

Systems pharmacology involves the application of systems biology approaches, combining large-scale experimental studies with computational analyses, to the study of drugs, drug targets, and drug effects. Many of these initial studies have focused on identifying new drug targets, new uses of known drugs, and systems-level properties of existing drugs. This review focuses on systems pharmacology studies that aim to better understand drug side effects and adverse events. By studying the drugs in the context of cellular networks, these studies provide insights into adverse events caused by off-targets of drugs as well as adverse events-mediated complex network responses. This allows rapid identification of biomarkers for side effect susceptibility. In this way, systems pharmacology will lead to not only newer and more effective therapies, but safer medications with fewer side effects.

A side effect resource to capture phenotypic effects of drugs

The molecular understanding of phenotypes caused by drugs in humans is essential for elucidating mechanisms of action and for developing personalized medicines. Side effects of drugs (also known as adverse drug reactions) are an important source of human phenotypic information, but so far research on this topic has been hampered by insufficient accessibility of data. Consequently, we have developed a public, computer-readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms. It contains information on frequency in patients for one-third of the drug–side effect pairs. For 199 drugs, the side effect frequency of placebo administration could also be extracted. We illustrate the potential of SIDER with a number of analyses. The resource is freely available for academic research at http://sideeffects.embl.de.

Molecular networks for the study of TCM pharmacology

To target complex, multi-factorial diseases more effectively, there has been an emerging trend of multi-target drug development based on network biology, as well as an increasing interest in traditional Chinese medicine (TCM) that applies a more holistic treatment to diseases. Thousands of years' clinic practices in TCM have accumulated a considerable number of formulae that exhibit reliable in vivo efficacy and safety. However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. The development of network-based systems biology has provided considerable support for the understanding of the holistic, complementary and synergic essence of TCM in the context of molecular networks. This review introduces available sources and methods that could be utilized for the network-based study of TCM pharmacology, proposes a workflow for network-based TCM pharmacology study, and presents two case studies on applying these sources and methods to understand the mode of action of TCM recipes.

Building a drug-target network and its applications

BACKGROUND

One of the most recent and important developments in drug discovery is a new drug development approach of building and analyzing networks that contain relationships among drugs and targets, diseases, genes and other components. These networks and their integrations provide useful information for finding new targets as well as new drugs.

OBJECTIVE

This review article aims to review recent developments in various types of networks and suggest the future direction of these network studies for drug discovery.

METHODS

Databases and networks are integrated into a more complete network to better present the relationships among drugs, targets, genes, phenotypes and diseases. After discussing the limitations and obstacles of the recent research, we suggest several strategies to build a successful and practical drug-target network.

RESULTS/CONCLUSION

A useful, integrated network can be built from various databases and networks by resolving several issues, such as limited coverage and inconsistency. This integrated network can be completed by the prediction of missing links, biological network comparison and drug target identification. Possible applications are multi-target drug development, drug repurposing, estimation of drug effect on target perturbations in the whole system and extraction of the suitable purpose of the drug-target sub-network.

Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors

Systematic identification of protein-drug interaction networks is crucial to correlate complex modes of drug action to clinical indications. We introduce a novel computational strategy to identify protein-ligand binding profiles on a genome-wide scale and apply it to elucidating the molecular mechanisms associated with the adverse drug effects of Cholesteryl Ester Transfer Protein (CETP) inhibitors. CETP inhibitors are a new class of preventive therapies for the treatment of cardiovascular disease. However, clinical studies indicated that one CETP inhibitor, Torcetrapib, has deadly off-target effects as a result of hypertension, and hence it has been withdrawn from phase III clinical trials. We have identified a panel of off-targets for Torcetrapib and other CETP inhibitors from the human structural genome and map those targets to biological pathways via the literature. The predicted protein-ligand network is consistent with experimental results from multiple sources and reveals that the side-effect of CETP inhibitors is modulated through the combinatorial control of multiple interconnected pathways. Given that combinatorial control is a common phenomenon observed in many biological processes, our findings suggest that adverse drug effects might be minimized by fine-tuning multiple off-target interactions using single or multiple therapies. This work extends the scope of chemogenomics approaches and exemplifies the role that systems biology has in the future of drug discovery.

Both the cost to launch a new drug and the attrition rate during the late stage of the drug discovery and development process are increasing. Torcetrapib is a case in point, having been withdrawn from phase III clinical trials after 15 years of development and an estimated cost of US $800 M. Torcetrapib represents a new class of therapies for the treatment of cardiovascular disease; however, clinical studies indicated that Torcetrapib has deadly side-effects as a result of hypertension. To understand the origins of these adverse drug reactions from Torcetrapib and other related drugs undergoing clinical trials, we introduce a systematic strategy to identify off-targets in the human structural proteome and investigate the roles of these off-targets in impacting human physiology and pathology using biochemical pathway analysis. Our findings suggest that potential side-effects of a new drug can be identified at an early stage of the development cycle and be minimized by fine-tuning multiple off-target interactions. The hope is that this can reduce both the cost of drug development and the mortality rates during clinical trials.

DrugBank and its relevance to pharmacogenomics

DrugBank is a freely available web-enabled database that combines detailed drug data with comprehensive drug-target and drug-action information. It was specifically designed to facilitate in silico drug-target discovery, drug design, drug-metabolism prediction, drug-interaction prediction, and general pharmaceutical education. One of the most unique and useful components of the DrugBank database is the information it contains on drug metabolism, drug-metabolizing enzymes and drug-target polymorphisms. As pharmacogenomics is fundamentally concerned with the role of genes and genetic variation of how an individual responds to a drug, DrugBank is able to offer a convenient venue to explore pharmacogenomic questions in silico. This paper provides a brief overview on DrugBank and how it can facilitate pharmacogenomic research.

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

Computational and experimental approaches to chart the Escherichia coli cell-envelope-associated proteome and interactome

The bacterial cell-envelope consists of a complex arrangement of lipids, proteins and carbohydrates that serves as the interface between a microorganism and its environment or, with pathogens, a human host. Escherichia coli has long been investigated as a leading model system to elucidate the fundamental mechanisms underlying microbial cell-envelope biology. This includes extensive descriptions of the molecular identities, biochemical activities and evolutionary trajectories of integral transmembrane proteins, many of which play critical roles in infectious disease and antibiotic resistance. Strikingly, however, only half of the c. 1200 putative cell-envelope-related proteins of E. coli currently have experimentally attributed functions, indicating an opportunity for discovery. In this review, we summarize the state of the art of computational and proteomic approaches for determining the components of the E. coli cell-envelope proteome, as well as exploring the physical and functional interactions that underlie its biogenesis and functionality. We also provide a comprehensive comparative benchmarking analysis on the performance of different bioinformatic and proteomic methods commonly used to determine the subcellular localization of bacterial proteins.

Developmental systems biology flourishing on new technologies

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems Biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

Drug target identification using side-effect similarity

Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.

Modularity in the genetic disease-phenotype network

Similar disease phenotypes are engendered as a result of the modular nature of gene networks; thus we hypothesized that all human genetic disease phenotypes appear in similar modular styles. Network representations of phenotypes make it possible to explore this hypothesis. We investigated the modularity of a network of genetic disease phenotypes. We computationally extracted phenotype modules and found that the modularity is well correlated with a physiological classification of human diseases. We also found correlations between the modularity and functional genomics as well as its connection to drug-target associations.