Discovery of novel biomarkers and phenotypes by semantic technologies.

BACKGROUND: Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents. RESULTS: This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated. CONCLUSIONS: The reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions.

Interaction of the globular domain of human C1q with Salmonella typhimurium lipopolysaccharide.

Gram-negative bacteria can bind complement protein C1q in an antibody-independent manner and activate classical pathway via their lipopolysaccharides (LPS). Earlier studies have implicated the collagen-like region of human C1q in binding LPS. In recent years, a number of C1q target molecules, previously considered to interact with collagen-like region of C1q, have been shown to bind via the globular domain (gC1q). Here we report, using recombinant forms of the globular head regions of C1q A, B and C chains, that LPS derived from Salmonella typhimurium interact specifically with the B-chain of the gC1q domain in a calcium-dependent manner. LPS and IgG-binding sites on the gC1q domain appear to be overlapping and this interaction can be inhibited by a synthetic C1q inhibitor, suggesting common interacting mechanisms.

Manual annotation of protein interactions.

Protein interactions are the basic building blocks for assembly of pathways and networks. Almost any biologically meaningful functionality (for instance, linear signaling pathways, chains of metabolic reactions, transcription factor dimmers, protein complexes of transcriptosome, gene-disease associations) can be represented as a combination of binary relationships between "network objects" (genes, proteins, RNA species, bioactive compounds). Naturally, the assembled pathways and networks are only as good as their "weakest" link (i.e., a wrongly assigned interaction), and the errors multiply in multi-step pathways. Therefore, the utility of "systems biology" is fundamentally dependent on quality and relevance of protein interactions. The second important parameter is the sheer number of interactions assembled in the database. One needs a "critical mass" of species-specific interactions in order to build cohesive networks for a gene list, not a constellation of non-connected proteins and protein pairs. The third issue is semantic consistency between interactions of different types. Transient physical signal transduction interactions, reactions of endogenous metabolism, transcription factor-promoter binding, and kinetic drug-target interactions are all very different in nature. Yet, they have to fit well into one database format and be consistent in order to be useful in reconstruction of cellular processes.High-quality protein interactions are available in peer-reviewed "small experiment" literature and, to a much smaller extent, patents. However, it is very challenging to find the interactions, annotate with searchable (and computable) parameters, catalogue in the database format in computer readable form, and assemble into a database. There are hundreds of thousands of mammalian interactions scattered in tens of thousands of papers in a few thousands of scientific journals. There are no widely used standards for reporting the interactions in scientific texts and, therefore, text-mining tools have only limited applicability. In order to generate a meaningful database of protein interactions, one needs a well-developed technology of manual curation, equipped with computational solutions, managerial procedures, quality control, and users' feedback. Here we describe our ever-evolving annotation approach, the important annotation issues and our solutions, and the mammalian protein interactions database MetaBase which we have been working on for over 8 years.

Drug design using the example of the complement system inhibitors' development.

Undesired activation of the complement system, a part of the immune system, is a major pathogenic factor contributing to various diseases, such as ischemia-reperfusion injury, sepsis, asthma, allergic reactions, rheumatoid arthritis, Alzheimer's disease, myasthenia, multiple sclerosis and others. The history of the development of complement system inhibitors, preventing its destructive action on the body, represents the evolution of the main methods of drug design. This review illustrates the main approaches of drug design, ranging from screening and modification of natural products to structure-based ligand design, on the basis of complement inhibitors' creation. The current status of the field of complement inhibitors is also discussed.

Reconstitution of the ERG Gene Expression Network Reveals New Biomarkers and Therapeutic Targets in ERG Positive Prostate Tumors.

BACKGROUND: Despite a growing number of studies evaluating cancer of prostate (CaP) specific gene alterations, oncogenic activation of the ETS Related Gene (ERG) by gene fusions remains the most validated cancer gene alteration in CaP. Prevalent gene fusions have been described between the ERG gene and promoter upstream sequences of androgen-inducible genes, predominantly TMPRSS2 (transmembrane protease serine 2). Despite the extensive evaluations of ERG genomic rearrangements, fusion transcripts and the ERG oncoprotein, the prognostic value of ERG remains to be better understood. Using gene expression dataset from matched prostate tumor and normal epithelial cells from an 80 GeneChip experiment examining 40 tumors and their matching normal pairs in 40 patients with known ERG status, we conducted a cancer signaling-focused functional analysis of prostatic carcinoma representing moderate and aggressive cancers stratified by ERG expression. RESULTS: In the present study of matched pairs of laser capture microdissected normal epithelial cells and well-to-moderately differentiated tumor epithelial cells with known ERG gene expression status from 20 patients with localized prostate cancer, we have discovered novel ERG associated biochemical networks. CONCLUSIONS: Using causal network reconstruction methods, we have identified three major signaling pathways related to MAPK/PI3K cascade that may indeed contribute synergistically to the ERG dependent tumor development. Moreover, the key components of these pathways have potential as biomarkers and therapeutic target for ERG positive prostate tumors.

Evolving BioAssay Ontology (BAO): modularization, integration and applications.

The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.

Drug target prediction and repositioning using an integrated network-based approach.

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.

Prediction of organ toxicity endpoints by QSAR modeling based on precise chemical-histopathology annotations.

The ability to accurately predict the toxicity of drug candidates from their chemical structure is critical for guiding experimental drug discovery toward safer medicines. Under the guidance of the MetaTox consortium (Thomson Reuters, CA, USA), which comprised toxicologists from the pharmaceutical industry and government agencies, we created a comprehensive ontology of toxic pathologies for 19 organs, classifying pathology terms by pathology type and functional organ substructure. By manual annotation of full-text research articles, the ontology was populated with chemical compounds causing specific histopathologies. Annotated compound-toxicity associations defined histologically from rat and mouse experiments were used to build quantitative structure-activity relationship models predicting subcategories of liver and kidney toxicity: liver necrosis, liver relative weight gain, liver lipid accumulation, nephron injury, kidney relative weight gain, and kidney necrosis. All models were validated using two independent test sets and demonstrated overall good performance: initial validation showed 0.80-0.96 sensitivity (correctly predicted toxic compounds) and 0.85-1.00 specificity (correctly predicted non-toxic compounds). Later validation against a test set of compounds newly added to the database in the 2 years following initial model generation showed 75-87% sensitivity and 60-78% specificity. General hepatotoxicity and nephrotoxicity models were less accurate, as expected for more complex endpoints.

Quantitative knowledge-based analysis in compound safety assessment.

INTRODUCTION: Despite rapid progress in OMICs and computational technologies in compound safety assessment, drug failure rate due to toxicity is still unacceptably high. One reason for this is an inadequate interpretation of high-throughput preclinical data. Another reason is the poor mechanistic understanding of drug side effects as currently just a few compound targets are linked to specific adverse reactions. AREAS COVERED: Current performance issues with statistical analysis of OMICs data or gene/protein/compound lists are discussed, illustrating potential advantages of knowledge-based approaches in prediction of human toxicity. The authors show several examples of quantitative functional analysis, including cross-tissue toxicity predictions and integrated analysis of different types of OMICs data. They also describe novel approaches linking compound targets and associated pathways to side effects. The reader will gain an update on the recent developments in knowledge-based analysis in toxicogenomics and computational methods correlating protein targets with adverse reactions. EXPERT OPINION: Quantitative pathway analysis is a useful approach for deriving multi-variant predictive biomarkers for drug safety. However, more comprehensive studies are needed for direct comparison of performance between pathway- and gene-centric methods.

A systems biology strategy for predicting similarities and differences of drug effects: evidence for drug-specific modulation of inflammation in atherosclerosis.

BACKGROUND: Successful drug development has been hampered by a limited understanding of how to translate laboratory-based biological discoveries into safe and effective medicines. We have developed a generic method for predicting the effects of drugs on biological processes. Information derived from the chemical structure and experimental omics data from short-term efficacy studies are combined to predict the possible protein targets and cellular pathways affected by drugs. RESULTS: Validation of the method with anti-atherosclerotic compounds (fenofibrate, rosuvastatin, LXR activator T0901317) demonstrated a great conformity between the computationally predicted effects and the wet-lab biochemical effects. Comparative genome-wide pathway mapping revealed that the biological drug effects were realized largely via different pathways and mechanisms. In line with the predictions, the drugs showed differential effects on inflammatory pathways (downstream of PDGF, VEGF, IFNγ, TGFß, IL1ß, TNFα, LPS), transcriptional regulators (NFκB, C/EBP, STAT3, AP-1) and enzymes (PKCδ, AKT, PLA2), and they quenched different aspects of the inflammatory signaling cascade. Fenofibrate, the compound predicted to be most efficacious in inhibiting early processes of atherosclerosis, had the strongest effect on early lesion development. CONCLUSION: Our approach provides mechanistic rationales for the differential and common effects of drugs and may help to better understand the origins of drug actions and the design of combination therapies.

Network and pathway analysis of compound-protein interactions.

We describe an integrated system that brings together predictive chemical analyses based on compound structure, knowledge bases of chemogenomics data associating compounds to biological, pharmacological and toxicological properties, and a systems biology functional data analysis and network reconstruction approach, to provide an in silico evaluation of the possible effects of xenobiotics on biological systems. We demonstrate the combination of drug and xenobiotic metabolism prediction, quantitative structure-activity relationship (QSAR) models and structural similarity searching to generate a list of similar compounds to, and possible targets for novel compounds. These lists of compounds and proteins are mapped to functional ontologies such as gene-disease associations, biological processes, and mechanisms of toxicity, and can be used to reconstruct biological networks linking together the component nodes into biologically-meaningful clusters. Thus, an assessment of biological effects can be made early in the discovery and development process that can be used to prioritize the best compounds for additional testing or development, or to direct efforts in medicinal chemistry to improve compound activity profiles.

Selective inhibition of the interaction of C1q with immunoglobulins and the classical pathway of complement activation by steroids and triterpenoids sulfates.

Since undesirable activation of the complement system through the classical pathway is associated with tissue damage and other pathologic proinflammatory consequences at ischemia/reperfusion injury, autoimmune diseases, and rejection of allo- and xenografts, creation of selective inhibitors of the classical pathway leaving the alternative pathway intact is of great importance. Classical pathway is triggered by binding of its recognizing unit, protein C1q, to a number of targets like antibodies, pentraxins, apoptotic cells, and others. In order to obtain inhibitors blocking the first step of the classical cascade, synthesis of sulfates of steroids (Delta(5)-3beta-hydroxycholenic, Delta(5)-3beta-hydroxyetiocholenic, deoxycholic, and cholic acids) and triterpenoids (betulin, 20,29-dihydro-20,29-dichloromethylenbetulin, betulinic, ursolic, and oleanolic acids) has been performed. Testing of the compounds in classical pathway inhibition assay has displayed derivatives of triterpenoid betulin (betulin disulfate and betulinic acid sulfate) to be the most potent inhibitors. Further studies of the two compounds established that their activity to inhibit the classical pathway had been due to their capability to block the interaction of C1q with antibodies. Betulin disulfate and betulinic acid sulfate have shown weak inhibition of the alternative route of activation, what makes them promising inhibitors for the selective suppression of the classical complement pathway at the earliest possible level as well as perspective agents for blocking the interaction of C1q with its other targets.

Complement C1q-target proteins recognition is inhibited by electric moment effectors.

Classical complement pathway is an important innate immune mechanism, which is usually triggered by binding of C1q to immunoglobulins, pentraxins and other target molecules. Although the activation of the classical pathway is crucial in the host defence, its undesirable and uncontrolled activation can lead to tissue damage. Thus, understanding the molecular basis of complement activation and its inhibition are of great biomedical importance. Recently, we proposed a mechanism for target recognition and classical pathway activation by C1q, which is likely governed by calcium-controlled reorientation of macromolecular electric moment vectors. Here we sought to define the mechanism of C1q inhibition by low molecular weight disulphate compounds that bind to the globular (gC1q) domain, using experimental, computational docking and theoretical modelling approaches. Our experimental results demonstrate that betulin disulphate (B2S) and 9,9-bis(4'-hydroxyphenyl)fluorene disulphate (F2S) inhibit the interaction of C1q and its recombinant globular modules with target molecules IgG1, C-reactive protein (CRP) and long pentraxin 3 (PTX3). In most C1q-inhibitor docked complexes, there is a reduction of electric moment scalar values and similarly altered direction of electric/dipole moment vectors. This could explain the inhibitory effect by impaired electrostatic steering, lacking optimal target recognition and formation of functional complex. In the presence of the inhibitor, the tilt of gC1q domains is likely to be blocked by the altered direction of the electric moment vector. Thus, the transition from the inactive (closed) towards the active (open) conformation of C1q (i.e. the complement activation signal transmission) will be impaired and the cascade initiation disrupted. These results could serve as a starting point for the exploration of a new form of 'electric moment inhibitors/effectors'.

Inhibition of classical pathway of complement activation with negative charged derivatives of bisphenol A and bisphenol disulphates.

In order to obtain strong inhibitors of classical pathway of complement activation the low weight negative charged compounds have been investigated. On the basis of bisphenol A anionic derivatives with one or two carboxylic, sulphate and phosphate groups the critical role of negative charged groups for complement-inhibiting activity has been established. It was determined that two sulphate or phosphate groups in the molecule provide the most inhibiting effect. At the next stage a set of bisphenol disulphates of varying structures has been synthesized and investigated. Bulky hydrophobic groups (cyclohexyliden, fluorenyliden, anthronyliden) at the central part of the bisphenol molecule it was found to increase complement-inhibiting activity markedly. The replacement of the ortho-positions to the charged group by halogens or alkyl groups (allyl, propyl) increases the inhibiting effect. It was showed by ELISA that several compounds studied interact with C1q, C1r /C1s components of complement. For the set of bisphenol disulphates the QSAR equation with hydrophobic coefficient and electronic parameters has been formulated. Both hydrophobic and electrostatic interactions it was established to have a great significance for the inhibition of classical pathway of complement activation.