The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use 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 the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de

Much of the information on the Cytochrome P450 enzymes (CYPs) is spread across literature and the internet. Aggregating knowledge about CYPs into one database makes the search more efficient. Text mining on 57 CYPs and drugs led to a mass of papers, which were screened manually for facts about metabolism, SNPs and their effects on drug degradation. Information was put into a database, which enables the user not only to look up a particular CYP and all metabolized drugs, but also to check tolerability of drug-cocktails and to find alternative combinations, to use metabolic pathways more efficiently. The SuperCYP database contains 1170 drugs with more than 3800 interactions including references. Approximately 2000 SNPs and mutations are listed and ordered according to their effect on expression and/or activity. SuperCYP (http://bioinformatics.charite.de/supercyp) is a comprehensive resource focused on CYPs and drug metabolism. Homology-modeled structures of the CYPs can be downloaded in PDB format and related drugs are available as MOL-files. Within the resource, CYPs can be aligned with each other, drug-cocktails can be ‘mixed’, SNPs, protein point mutations, and their effects can be viewed and corresponding PubMed IDs are given. SuperCYP is meant to be a platform and a starting point for scientists and health professionals for furthering their research.

Bacteria from the genus Streptomyces are very important for the production of natural bioactive compounds such as antibiotic, antitumour or immunosuppressant drugs. Around two-thirds of all known natural antibiotics are produced by these bacteria. An enormous quantity of crucial data related to this genus has been generated and published, but so far no freely available and comprehensive database exists. Here, we present StreptomeDB (http://www.pharmaceutical-bioinformatics.de/streptomedb/). To the best of our knowledge, this is the largest database of natural products isolated from Streptomyces. It contains >2400 unique and diverse compounds from >1900 different Streptomyces strains and substrains. In addition to names and molecular structures of the compounds, information about source organisms, references, biological role, activities and synthesis routes (e.g. polyketide synthase derived and non-ribosomal peptides derived) is included. Data can be accessed through queries on compound names, chemical structures or organisms. Extraction from the literature was performed through automatic text mining of thousands of articles from PubMed, followed by manual curation. All annotated compound structures can be downloaded from the website and applied for in silico screenings for identifying new active molecules with undiscovered properties.

BackgroundThe increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.ResultsIntegration of two public databases allowed superposition of conformers for 193 approved drugs with 5507 crystallised target-bound counterparts. The generation of 9600 drug conformers using an atomic force field was carried out to obtain an optimal coverage of the conformational space.Bioactive conformations are best described by a conformational ensemble: half of all drugs exhibit multiple active states, distributed over the entire range of the reachable energy and conformational space.A number of up to 100 conformers per drug enabled us to reproduce the bound states within a similarity threshold of 1.0 Å in 70% of all cases. This fraction rises to about 90% for smaller or average sized drugs.ConclusionSingle drugs adopt multiple bioactive conformations if they interact with different target proteins. Due to the structural diversity of binding sites they adopt conformations that are distributed over a broad conformational space and wide energy range. Since the majority of drugs is well represented by a predefined low number of conformers (up to 100) this procedure is a valuable method to compare compounds by three-dimensional features or for fast similarity searches starting with pharmacophores.The underlying 9600 generated drug conformers are downloadable from the Super Drug Web site [1]. All superpositions are visualised at the same source. Additional conformers (110,000) of 2400 classified WHO-drugs are also available.

Objectives Prescription of excessive doses is the most common prescription error, provoking dose-dependent adverse drug reactions. Clinical decision support systems (CDSS) can prevent prescription errors especially when mainly clinically relevant warnings are issued. We have built and evaluated a CDSS providing upper dose limits personalised to individual patient characteristics thus guaranteeing for specific warnings. Methods For 170 compounds, detailed information on upper dose limits (according to the drug label) was compiled. A comprehensive software-algorithm extracted relevant patient information from the electronic chart (eg, age, renal function, comedication). The CDSS was integrated into the local prescribing platform for outpatients and patients at discharge, providing immediate dosage feedback. Its impact was evaluated in a 90-day intervention study (phase 1: baseline; phase 2: intervention). Outcome measures were frequency of excessive doses before and after intervention considering potential induction of new medication errors. Moreover, predictors for alert adherence were analysed. Results In phase 1, 552 of 12 197 (4.5%) prescriptions exceeded upper dose limits. In phase 2, initially 559 warnings were triggered (4.8%, p¼0.37). Physicians were responsive to one in four warnings mostly adjusting dosages. Thus, the final prescription rate of excessive doses was reduced to 3.6%, with 20% less excessive doses compared with baseline (p<0.001). No new manifest prescription errors were induced. Physicians' alert adherence correlated with patients' age, prescribed drug class, and reason for the alert. Conclusion During the 90-day study, implementation of a highly specific algorithm-based CDSS substantially improved prescribing quality with a high acceptance rate compared with previous studies.