Abstract: BACKGROUND: Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. RESULTS: Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. CONCLUSIONS: We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

Abstract: Chagas' disease, caused by infection with the parasite Trypanosoma cruzi, is the major cause of heart failure in Latin America. Classic clinical manifestations result from the infection of heart muscle cells leading to progressive cardiomyopathy. To ameliorate disease, chemotherapy must eradicate the parasite. Current drugs are ineffective and toxic, and new therapy is a critical need. To expedite drug screening for this neglected disease, we have developed and validated a cell-based, high-throughput assay that can be used with a variety of untransfected T. cruzi isolates and host cells and that simultaneously measures efficacy against the intracellular amastigote stage and toxicity to host cells. T. cruzi-infected muscle cells were incubated in 96-well plates with test compounds. Assay plates were automatically imaged and analyzed based on size differences between the DAPI (4',6-diamidino-2-phenylindole)-stained host cell nuclei and parasite kinetoplasts. A reduction in the ratio of T. cruzi per host cell provided a quantitative measure of parasite growth inhibition, while a decrease in count of the host nuclei indicated compound toxicity. The assay was used to screen a library of clinically approved drugs and identified 55 compounds with activity against T. cruzi. The flexible assay design allows the use of various parasite strains, including clinical isolates with different biological characteristics (e.g., tissue tropism and drug sensitivity), and a broad range of host cells and may even be adapted to screen for inhibitors against other intracellular pathogens. This high-throughput assay will have an important impact in antiparasitic drug discovery.

Abstract: BACKGROUND: Gold and carbon nanoparticles absorb nonionizing radio frequency (RF) energy and release heat. Solid gold nanoparticles are delivered to cancer cells via conjugation with targeting antibodies. Here, 20-nm gold particles were conjugated to cetuximab, which is an epidermal growth factor receptor-1 (EGFR-1) antibody. METHODS: A pancreatic carcinoma cell line that highly expresses EGFR-1, Panc-1, and Cama-1, which is a breast carcinoma cell line that minimally expresses EGFR-1, were treated with 100-nmol/L cetuximab-conjugated gold nanoparticles for 3 h (n = 4). Thirty-six hours later, the dishes were placed in an RF field with a generator power of 200 W for 5 min. After another 36 h, cell injury and death were evaluated with flow cytometry. RESULTS: The targeted cell line Panc-1 had a viability of 46% +/- 12%, whereas the Cama-1 cell had a viability of 92% +/- 2% after RF field exposure (P < .008). Transmission electron microscopy showed gold nanoparticle uptake in Panc-1 cells but negligible uptake by Cama-1 cells. Nontargeted cells do not internalize a sufficient amount of antibody-conjugated gold nanoparticles to induce injury in a noninvasive RF field. CONCLUSION: This technique could be useful in cancer treatment if a cancer-specific antibody is used to localize gold nanoparticles to malignant cells.

Abstract: The biomedical literature is an important source of information about the biological activity and effects of chemicals. We present an application that extracts terms indicating biological activity of chemicals from Medline records, associates them with chemical name and stores the terms in a repository called ChemoText. We describe the construction of ChemoText and then demonstrate its utility in drug research by employing Swanson's ABC discovery paradigm. We reproduce Swanson's discovery of a connection between magnesium and migraine in a novel approach that uses only proteins as the intermediate B terms. We validate our methods by using a cutoff date and evaluate them by calculating precision and recall. In addition to magnesium, we have identified valproic acid and nitric oxide as chemicals which developed links to migraine. We hypothesize, based on protein annotations, that zinc and retinoic acid may play a role in migraine. The ChemoText repository has promise as a data source for drug discovery.

Abstract: Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug's ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs. At present, no method exists to discover these ADR-pathway associations. In this paper we introduce a computational framework for identifying a subset of these associations based on the assumption that drugs capable of modulating the same pathway may induce similar ADRs. Our model exploits multiple information resources. First, we utilize a publicly available dataset pairing drugs with their observed ADRs. Second, we identify putative protein targets for each drug using the protein structure database and in-silico virtual docking. Third, we label each protein target with its known involvement in one or more biological pathways. Finally, the relationships among these information sources are mined using multiple stages of logistic-regression while controlling for over-fitting and multiple-hypothesis testing. As proof-of-concept, we examined a dataset of 506 ADRs, 730 drugs, and 830 human protein targets. Our method yielded 185 ADR-pathway associations of which 45 were selected to undergo a manual literature review. We found 32 associations to be supported by the scientific literature.

Abstract: A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).

Abstract: Informatics and computational design methods were used to create new molecules that could potentially bind antiapoptotic proteins, thus promoting death of cancer cells. Apoptosis is a cellular process that leads to the death of damaged cells. Its malfunction can cause cancer and poor response to conventional chemotherapy. After being activated by cellular stress signals, proapoptotic proteins bind antiapoptotic proteins, thus allowing apoptosis to go forward. An excess of antiapoptotic proteins can prevent apoptosis. Designed molecules that mimic the roles of proapoptotic proteins can promote the death of cancer cells. The goal of our study was to create new putative mimetics that could simultaneously bind several antiapoptotic proteins. Five new small molecules were designed that formed stable complexes with BCL-2, BCL-XL, and MCL-1 antiapoptotic proteins. These results are novel because, to our knowledge, there are not many, if any, small molecules known to bind all three proteins. Drug-likeness studies performed on the designed molecules, as well as previous experimental and preclinical studies on similar agents, strongly suggest that the designed molecules may indeed be promising drug candidates. All five molecules showed "drug-like" properties and had overall drug-likeness scores between 81% and 96%. A single drug based on these mimetics should cost less and cause fewer side effects than a combination of drugs each aimed at a single protein. Computer-based molecular design promises to accelerate drug research by predicting potential effectiveness of designed molecules prior to laborious experiments and costly preclinical trials.

Abstract: BACKGROUND: The efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically untested and therefore do not translate into a therapeutic benefit for patients. OBJECTIVE: To present a computational method aimed to comprehensively exploit the scientific knowledge in order to foster the development of personalized cancer treatment by matching the patient's molecular profile with the available evidence on targeted therapy. METHODS: To this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for novel therapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data were manually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapy tested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochrane databases were searched. RESULTS AND CONCLUSIONS: We created a manually annotated database (Targeted Therapy Database, TTD) where the relevant data are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up for the identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatments based on the molecular profile of individual patients. In this essay we describe the principles and computational algorithms of an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal of fruitfully driving both preclinical and clinical research on anticancer targeted therapy. In the light of its theoretical nature, the prediction performance of this model must be validated before it can be implemented in the clinical setting.

Abstract: G-protein coupled receptors (GPCRs) are a major family of membrane receptors in eukaryotic cells. They play a crucial role in the communication of a cell with the environment. Ligands bind to GPCRs on the outside of the cell, activating them by causing a conformational change, and allowing them to bind to G-proteins. Through their interaction with G-proteins, several effector molecules are activated leading to many kinds of cellular and physiological responses. The great importance of GPCRs and their corresponding signal transduction pathways is indicated by the fact that they take part in many diverse disease processes and that a large part of efforts towards drug development today is focused on them. We present Human-gpDB, a database which currently holds information about 713 human GPCRs, 36 human G-proteins and 99 human effectors. The collection of information about the interactions between these molecules was done manually and the current version of Human-gpDB holds information for about 1663 connections between GPCRs and G-proteins and 1618 connections between G-proteins and effectors. Major advantages of Human-gpDB are the integration of several external data sources and the support of advanced visualization techniques. Human-gpDB is a simple, yet a powerful tool for researchers in the life sciences field as it integrates an up-to-date, carefully curated collection of human GPCRs, G-proteins, effectors and their interactions. The database may be a reference guide for medical and pharmaceutical research, especially in the areas of understanding human diseases and chemical and drug discovery. Database URLs: http://schneider.embl.de/human_gpdb; http://bioinformatics.biol.uoa.gr/human_gpdb/

Abstract: In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule's aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper.