ProTox 3.0: a webserver for the prediction of toxicity of chemicals.

Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.

SuperNatural 3.0-a database of natural products and natural product-based derivatives.

Natural products (NPs) are single chemical compounds, substances or mixtures produced by a living organism - found in nature. Evolutionarily, NPs have been used as healing agents since thousands of years and still today continue to be the most important source of new potential therapeutic preparations. Natural products have played a key role in modern drug discovery for several diseases. Furthermore, following consumers' increasing demand for natural food ingredients, many efforts have been made to discover natural low-calorie sweeteners in recent years. SuperNatural 3.0 is a freely available database of natural products and derivatives. The updated version contains 449 058 natural compounds along with their structural and physicochemical information. Additionally, information on pathways, mechanism of action, toxicity, vendor information if available, drug-like chemical space prediction for several diseases as antiviral, antibacterial, antimalarial, anticancer, and target specific cells like the central nervous system (CNS) are also provided for the natural compounds. The updated version of the database also provides a valuable pool of natural compounds in which potential highly sweet compounds are expected to be found. The possible taste profile of the natural compounds was predicted using our published VirtualTaste models. The SuperNatural 3.0 database is freely available via http://bioinf-applied.charite.de/supernatural_3, without any login or registration.

SuperTCM: A biocultural database combining biological pathways and historical linguistic data of Chinese Materia Medica for drug development.

AIM OF THE STUDY: Botanicals used in Traditional Chinese Medicine (TCM) are a rich source for drug discovery and provide models for multi-component drug development. To facilitate the studies of the actions of TCM drugs and expand their applications, a comprehensive database is urgently required. METHODS: One online resource connects all the relevant data from multiple scientific sources and languages. Drug information from published TCM databases and the official Chinese Pharmacopoeia as well as specialized meta-websites such as Kew's Medicinal Plant Names Service was integrated on a higher level. RESULTS: Our database, SuperTCM, covers the aspects of TCM derived from medicinal plants, encompassing pharmacological recipes up to chemical compounds. It provides the information for 6516 TCM drugs (or "herbs") with 5372 botanical species, 55,772 active ingredients against 543 targets in 254 KEGG pathways associated with 8634 diseases. SuperTCM is freely available at http://tcm.charite.de/supertcm.

SuperCYPsPred-a web server for the prediction of cytochrome activity.

Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug-drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs-published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/). The web server does not require log in or registration and is free to use.

Pain-Prescription Differences - An Analysis of 500,000 Discharge Summaries.

BACKGROUND: Pain-relief prescriptions have led to an alarming increase in drug-related abuse. OBJECTIVE: In this study, we estimate the pain reliever prescription rates at a major German academic hospital center and compare with the nationwide trends from Germany and prescription reports from the USA. METHODS: We analysed >500,000 discharge summaries from Charité, encompassing the years 2006 to 2015, and extracted the medications and diagnoses from each discharge summary. Prescription reports from the USA and Germany were collected and compared with the trends at Charité to identify the frequently prescribed pain relievers and their world-wide utilization trends. The average costs of pain therapy were also calculated and compared between the three regions. RESULTS: Metamizole (dipyrone), a non-opioid analgesic, was the most commonly prescribed pain reliever at Charité (59%) and in Germany (23%) while oxycodone (29%), a semi-synthetic opioid, was most commonly ordered in the USA. Surprisingly, metamizole was prescribed to nearly 20% of all patients at Charité, a drug that has been banned for safety reasons (agranulocytosis) in most developed countries including Canada, United Kingdom, and USA. A large number of prospective cases with high risk for agranulocytosis and other side effects were found. The average cost of pain therapy greatly varied between the USA (125.3 EUR) and Charité (17.2 EUR). CONCLUSION: The choice of pain relievers varies regionally and is often in disagreement with approved indications and regulatory guidelines. A pronounced East-West gradient was observed with metamizole use and the opposite with prescription opioids.

CardioScape mapping the cardiovascular funding landscape in Europe.

Aims: The burden of cardiovascular disease is increasing worldwide, which has to be reflected by cardiovascular (CV) research in Europe. CardioScape, a FP7 funded project initiated by the European Society of Cardiology (ESC), identified where CV research is performed, how it is funded and by whom. It could be transformed into an on-line and up-to-date resource of great relevance for researchers, funding bodies and policymakers and could be a role model for mapping CV research funding in Europe and beyond. Methods and results: Relevant funding bodies in 28 European Union (EU) countries were identified by a multistep process involving experts in each country. Projects above a funding threshold of 100 k€ during the period 2010-2012 were included using a standard questionnaire. Results were classified by experts and an adaptive text analysis software to a CV-research taxonomy, integrating existing schemes from ESC journals and congresses. An on-line query portal was set up to allow different users to interrogate the database according to their specific viewpoints. Conclusion: CV-research funding varies strongly between different nations with the EU providing 37% of total available project funding and clear geographical gradients exist. Data allow in depth comparison of funding for different research areas and led to a number of recommendations by the consortium. CardioScape can support CV research by aiding researchers, funding agencies and policy makers in their strategic decisions thus improving research quality if CardioScape strategy and technology becomes the basis of a continuously updated and expanded European wide publicly accessible database.

eGender-from e-Learning to e-Research: a web-based interactive knowledge-sharing platform for sex- and gender-specific medical education.

BACKGROUND: Sex and Gender Medicine is a novel discipline that provides equitable medical care for society and improves outcomes for both male and female patients. The integration of sex- and gender-specific knowledge into medical curricula is limited due to adequate learning material, systematic teacher training and an innovative communication strategy. We aimed at initiating an e-learning and knowledge-sharing platform for Sex and Gender Medicine, the eGender platform (http://egender.charite.de), to ensure that future doctors and health professionals will have adequate knowledge and communication skills on sex and gender differences in order to make informed decisions for their patients. METHODS: The web-based eGender knowledge-sharing platform was designed to support the blended learning pedagogical teaching concept and follows the didactic concept of constructivism. Learning materials developed by Sex and Gender Medicine experts of seven universities have been used as the basis for the new learning tools. The content of these tools is patient-centered and provides add-on information on gender-sensitive aspects of diseases. The structural part of eGender was designed and developed using the open source e-learning platform Moodle. The eGender platform comprises an English and a German version of e-learning modules: one focusing on basic knowledge and seven on specific medical disciplines. Each module consists of several courses corresponding to a disease or symptom complex. Self-organized learning has to be managed by using different learning tools, e.g., texts and audiovisual material, tools for online communication and collaborative work. RESULTS: More than 90 users from Europe registered for the eGender Medicine learning modules. The most frequently accessed module was "Gender Medicine-Basics" and the users favored discussion forums. These e-learning modules fulfill the quality criteria for higher education and are used within the elective Master Module "Gender Medicine-Basics" implemented into the accredited Master of Public Health at Charité-Berlin. CONCLUSIONS: The eGender platform is a flexible and user-friendly electronical knowledge-sharing platform providing evidence-based high-quality learning material used by a growing number of registered users. The eGender Medicine learning modules could be key in the reform of medical curricula to integrate Sex and Gender Medicine into the education of health professionals.

CancerResource--updated database of cancer-relevant proteins, mutations and interacting drugs.

Here, we present an updated version of CancerResource, freely available without registration at http://bioinformatics.charite.de/care. With upcoming information on target expression and mutations in patients' tumors, the need for systems supporting decisions on individual therapy is growing. This knowledge is based on numerous, experimentally validated drug-target interactions and supporting analyses such as measuring changes in gene expression using microarrays and HTS-efforts on cell lines. To enable a better overview about similar drug-target data and supporting information, a series of novel information connections are established and made available as described in the following. CancerResource contains about 91,000 drug-target relations, more than 2000 cancer cell lines and drug sensitivity data for about 50,000 drugs. CancerResource enables the capability of uploading external expression and mutation data and comparing them to the database's cell lines. Target genes and compounds are projected onto cancer-related pathways to get a better overview about how drug-target interactions benefit the treatment of cancer. Features like cellular fingerprints comprising of mutations, expression values and drug-sensitivity data can promote the understanding of genotype to drug sensitivity associations. Ultimately, these profiles can also be used to determine the most effective drug treatment for a cancer cell line most similar to a patient's tumor cells.

Super Natural II--a database of natural products.

Natural products play a significant role in drug discovery and development. Many topological pharmacophore patterns are common between natural products and commercial drugs. A better understanding of the specific physicochemical and structural features of natural products is important for corresponding drug development. Several encyclopedias of natural compounds have been composed, but the information remains scattered or not freely available. The first version of the Supernatural database containing ∼ 50,000 compounds was published in 2006 to face these challenges. Here we present a new, updated and expanded version of natural product database, Super Natural II (http://bioinformatics.charite.de/supernatural), comprising ∼ 326,000 molecules. It provides all corresponding 2D structures, the most important structural and physicochemical properties, the predicted toxicity class for ∼ 170,000 compounds and the vendor information for the vast majority of compounds. The new version allows a template-based search for similar compounds as well as a search for compound names, vendors, specific physical properties or any substructures. Super Natural II also provides information about the pathways associated with synthesis and degradation of the natural products, as well as their mechanism of action with respect to structurally similar drugs and their target proteins.

GenderMedDB: an interactive database of sex and gender-specific medical literature.

BACKGROUND: Searches for sex and gender-specific publications are complicated by the absence of a specific algorithm within search engines and by the lack of adequate archives to collect the retrieved results. We previously addressed this issue by initiating the first systematic archive of medical literature containing sex and/or gender-specific analyses. This initial collection has now been greatly enlarged and re-organized as a free user-friendly database with multiple functions: GenderMedDB (http://gendermeddb.charite.de). DESCRIPTION: GenderMedDB retrieves the included publications from the PubMed database. Manuscripts containing sex and/or gender-specific analysis are continuously screened and the relevant findings organized systematically into disciplines and diseases. Publications are furthermore classified by research type, subject and participant numbers. More than 11,000 abstracts are currently included in the database, after screening more than 40,000 publications. The main functions of the database include searches by publication data or content analysis based on pre-defined classifications. In addition, registrants are enabled to upload relevant publications, access descriptive publication statistics and interact in an open user forum. CONCLUSIONS: Overall, GenderMedDB offers the advantages of a discipline-specific search engine as well as the functions of a participative tool for the gender medicine community.

SuperPred: update on drug classification and target prediction.

The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound-target interactions has increased from 7000 to 665,000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de.

ProTox: a web server for the in silico prediction of rodent oral toxicity.

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes.

The Transformer database: biotransformation of xenobiotics.

As the number of prescribed drugs is constantly rising, drug-drug interactions are an important issue. The simultaneous administration of several drugs can cause severe adverse effects based on interactions with the same metabolizing enzyme(s). The Transformer database (http://bioinformatics.charite.de/transformer) contains integrated information on the three phases of biotransformation (modification, conjugation and excretion) of 3000 drugs and >350 relevant food ingredients (e.g. grapefruit juice) and herbs, which are catalyzed by 400 proteins. A total of 100,000 interactions were found through text mining and manual validation. The 3D structures of 200 relevant proteins are included. The database enables users to search for drugs with a visual display of known interactions with phase I (Cytochrome P450) and phase II enzymes, transporters, food and herbs. For each interaction, PubMed references are given. To detect mutual impairments of drugs, the drug-cocktail tool displays interactions between selected drugs. By choosing the indication for a drug, the tool offers suggestions for alternative medications to avoid metabolic conflicts. Drug interactions can also be visualized in an interactive network view. Additionally, prodrugs, including their mechanisms of activation, and further information on enzymes of biotransformation, including 3D models, can be viewed.

mVOC: a database of microbial volatiles.

Scents are well known to be emitted from flowers and animals. In nature, these volatiles are responsible for inter- and intra-organismic communication, e.g. attraction and defence. Consequently, they influence and improve the establishment of organisms and populations in ecological niches by acting as single compounds or in mixtures. Despite the known wealth of volatile organic compounds (VOCs) from species of the plant and animal kingdom, in the past, less attention has been focused on volatiles of microorganisms. Although fast and affordable sequencing methods facilitate the detection of microbial diseases, however, the analysis of signature or fingerprint volatiles will be faster and easier. Microbial VOCs (mVOCs) are presently used as marker to detect human diseases, food spoilage or moulds in houses. Furthermore, mVOCs exhibited antagonistic potential against pathogens in vitro, but their biological roles in the ecosystems remain to be investigated. Information on volatile emission from bacteria and fungi is presently scattered in the literature, and no public and up-to-date collection on mVOCs is available. To address this need, we have developed mVOC, a database available online at http://bioinformatics.charite.de/mvoc.

Polymorphic cytochrome P450 enzymes (CYPs) and their role in personalized therapy.

The cytochrome P450 (CYP) enzymes are major players in drug metabolism. More than 2,000 mutations have been described, and certain single nucleotide polymorphisms (SNPs) have been shown to have a large impact on CYP activity. Therefore, CYPs play an important role in inter-individual drug response and their genetic variability should be factored into personalized medicine. To identify the most relevant polymorphisms in human CYPs, a text mining approach was used. We investigated their frequencies in different ethnic groups, the number of drugs that are metabolized by each CYP, the impact of CYP SNPs, as well as CYP expression patterns in different tissues. The most important polymorphic CYPs were found to be 1A2, 2D6, 2C9 and 2C19. Thirty-four common allele variants in Caucasians led to altered enzyme activity. To compare the relevant Caucasian SNPs with those of other ethnicities a search in 1,000 individual genomes was undertaken. We found 199 non-synonymous SNPs with frequencies over one percent in the 1,000 genomes, many of them not described so far. With knowledge of frequent mutations and their impact on CYP activities, it may be possible to predict patient response to certain drugs, as well as adverse side effects. With improved availability of genotyping, our data may provide a resource for an understanding of the effects of specific SNPs in CYPs, enabling the selection of a more personalized treatment regimen.

SynSysNet: integration of experimental data on synaptic protein-protein interactions with drug-target relations.

We created SynSysNet, available online at http://bioinformatics.charite.de/synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction proteomics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ∼1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50 000 compounds, as well as 5000 experimentally validated protein-protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein-protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given.

Drug cocktail optimization in chemotherapy of cancer.

BACKGROUND: In general, drug metabolism has to be considered to avoid adverse effects and ineffective therapy. In particular, chemotherapeutic drug cocktails strain drug metabolizing enzymes especially the cytochrome P450 family (CYP). Furthermore, a number of important chemotherapeutic drugs such as cyclophosphamide, ifosfamide, tamoxifen or procarbazine are administered as prodrugs and have to be activated by CYP. Therefore, the genetic variability of these enzymes should be taken into account to design appropriate therapeutic regimens to avoid inadequate drug administration, toxicity and inefficiency. OBJECTIVE: The aim of this work was to find drug interactions and to avoid side effects or ineffective therapy in chemotherapy. DATA SOURCES AND METHODS: Information on drug administration in the therapy of leukemia and their drug metabolism was collected from scientific literature and various web resources. We carried out an automated textmining approach. Abstracts of PubMed were filtered for relevant articles using specific keywords. Abstracts were automatically screened for antineoplastic drugs and their synonyms in combination with a set of human CYPs in title or abstract. RESULTS: We present a comprehensive analysis of over 100 common cancer treatment regimens regarding drug-drug interactions and present alternatives avoiding CYP overload. Typical concomitant medication, e.g. antiemetics or antibiotics is a preferred subject to improvement. A webtool, which allows drug cocktail optimization was developed and is publicly available on http://bioinformatics.charite.de/chemotherapy.

Binding sites in membrane proteins--diversity, druggability and prospects.

The identification of novel drug targets is one of the major challenges in proteomics. Computational methods developed over the last decade have enhanced the process of drug design in both terms of time and quality. The main task is the design of selective compounds, which bind targets more specifically, dependent on the desired mode of action of the particular drug. This makes it necessary to create compounds, which either exhibit their functions on one single protein to exclude undesired cross-reactivity or to use the advantageous effect of less selective drugs that target numerous proteins and therefore exhibit their functions on whole protein classes. Main aspects in the assignment of interactions between ligands and putative targets involve the amino acid composition of the binding site, evolutionary conservation and similarity in sequence and structure of known targets. Similarities or differences within classified protein families can be the key to their function and give first hints to functional drug design. Hereby, binding site-based classification outnumbers sequence-based classifications since similar binding sites can also be found in more distant proteins. Membrane proteins are 'difficult targets', because of their special physicochemical characteristics and the general lack of structural information. Here, we describe recent advances in modeling methods dedicated to membrane proteins. Different descriptors of similarity between compounds and the similarity between binding sites are under development and elucidate important aspects like dynamics or entropy. The importance of computational drug design is undisputable. Nevertheless, the process of design is complicated by increasing complexity, which underlines the importance of accurate knowledge about the addressed target class(es) and particularly their binding sites. One main objective by considering named topics is to predict putative side effects and errant functions (off-target effects) of novel drugs, which requires a holistic (systems biology) view on drug-target-pathway relations. In the following, we give a brief summary about the recent discussion on drug-target interactions with emphasis on membrane proteins.

SuperTarget goes quantitative: update on drug-target interactions.

There are at least two good reasons for the on-going interest in drug-target interactions: first, drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including metabolic and signaling pathways; second, it is crucial to consider drug-target-pathway relations for the identification of novel targets for drug development. To address this on-going need, we have developed a web-based data warehouse named SuperTarget, which integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present, the updated database contains >6000 target proteins, which are annotated with >330,000 relations to 196,000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range. SuperTarget is available at http://bioinformatics.charite.de/supertarget.